HYDROLOGICAL PATHWAYS
IN THE
PRINCE ALBERT
MODEL FOREST
J. W. Pomeroy
R. J. Granger
A. Pietroniro
J. E. Elliott
B. Toth
and
N. Hedstrom
National Hydrology Research Institute Environment Canada
Saskatoon, Saskatchewan
A Final Report Submitted to:
THE PRINCE ALBERT MODEL FOREST ASSOCIATION
June 1997
The Prince Albert Model Forest Association is financially supported by the Canadian Forest Service
through Canada’s Model Forest Program.
Copyright © 1997 by:
Prince Albert Model Forest Association Inc.
P.O. Box 2406
Prince Albert, SK
S6V 7G3
Telephone:
(306) 922-1944
Fax:
(306) 763-6456
All rights reserved. No part of this report may be reproduced in any form or by any means without
prior written permission of the copyright holders.
Distribution of this report does not necessarily signify that the contents reflect the views and policies of
the partner organizations of the Prince Albert Model Forest Association. Mention of trade names or
commercial products does not constitute recommendation or endorsement for use.
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
PREFACE
The boreal forest in western Canada is increasingly stressed by changes in land use from local
economic and recreational activities; and by changes in climate resulting from effects of human
activities on the Earth's atmosphere. As awareness of these stresses has grown, so has an interest in
preserving the boreal forest biome to sustain local economic activity while at the same time ensuring a
viable forest ecosystem with clean, adequate water supplies to support wildlife habitat. The largest
forest region in Canada, the boreal forest provides employment and subsistence, economic returns
from wood products, recreation, habitat, biological diversity, and water and soil conservation, as well
as playing a critical role in regulating the Earth's atmosphere and climate. Sound management of this
valuable resource requires a strong science base to ensure that long-term sustainability is achieved.
To develop this science base, Environment Canada established the Prince Albert Model Forest Project
at the National Hydrology Research Institute. The Project was directed to coordinate research on
hydrological processes in the boreal forest and to collaborate with groups that could apply the findings
to improved forest ecosystem and water management tools and could provide input from a broad range
of perspectives. The Project has operated with the support, encouragement and coordination of the
Prince Albert Model Forest Association and with additional significant support from the Hydrological
and Aquatic Sciences Division of NHRI the Global Energy and Water Cycle Experiment - Mackenzie
GEWEX Study, the Scientific Affairs Division of NATO and the Division of Hydrology, University of
Saskatchewan. Research from the Project has shown the importance of snow processes in governing
water supply and climate in the boreal forest, the remarkable serf-regulation of summer climate and
water supply by mature forests and the role of intact soils in storing and releasing water as part of the
forests' water management scheme. The degree to which these features are disturbed by forest
harvesting and the recovery over time have also been documented and explained. A new computer
simulation has been developed by the Project that permits the visualization of hydrological impacts of
forest clearing using “virtual clearcutting”. The key findings in this Final Report have advanced our
scientific understanding of boreal forest ecosystems and the resulting recommendations will provide a
stronger scientific basis for the management of these systems.
Dr. Fred J. Wrona
Chief, Hydrological & Aquatic Sciences Division
National Hydrology Research Institute
Environment Canada
i
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ACKNOWLEDGEMENTS
The authors would like to acknowledge the support provided for this study by the following
organizations:
Prince Albert Model Forest Association Inc., Prince Albert, Sask.
National Hydrology Research Institute, Environment Canada, Saskatoon, Sask.
Prince Albert National Park, Waskesiu Lake, Sask.
Mackenzie GEWEX Study, Global Energy and Water Cycle Experiment, Environment Canada
Division of Hydrology, University of Saskatchewan, Saskatoon, Sask.
Scientific Affairs Division, North Atlantic Treaty Organization, Brussels, Belgium.
Scientific reviews of this report by Prof. D.M. Gray, Division of Hydrology, University of
Saskatchewan and Prof J. Stein, Institut national de la recherche scientifique - eau, Université du
Québec, Sainte-Foy, Québec improved the manuscript and are thankfully acknowledged.
The report was prepared in camera-ready copy by Dell Bayne, Temple Murray and Brenda
Toth, Hydrological and Aquatic Sciences Division, NHRI.
An extra note of thanks goes to Newell Hedstrom, Dell Bayne and the other research
technicians and students who set up the experiment and diligently maintained over four years via
weekly visits in often inclement conditions.
ii
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EXECUTIVE SUMMARY
This is the final report describing a four-year National Hydrology Research Institute investigation of
hydrological processes in the southern boreal forest, the interaction of these processes with climate, vegetation
and soils, and a modelling effort to compile the results in a mathematical format that can be used for simulations
of water balance in a Geographical Information System.
Spruce and pine canopies intercept 35%-65% of snowfall causing mid-winter snowpacks under conifers
to be less than in clear-cuts or deciduous stands. A large proportion of intercepted snow evaporates over the
winter. As a result late winter snowcovers under spruce and pine canopies are 30% to 45% less than in
deciduous forest or clear-cuts. However, despite deep snow in clearings, snowmelt proceeds up to three times
faster when the forest canopy is removed because of better exposure to radiation and wind in open areas. Wetter
soils and a faster snowmelt in clear-cuts promote less efficient infiltration of snowmelt water than in mature
forests. Snowmelt runoff is thereby enhanced by removal of the forest canopy and in the PAMF the only
consistent spring runoff was produced from clear-cut areas. In a dry year, snowmelt runoff from a recent clearcut was 10 times higher than the mature and regenerating forest sites. At 15 years from replanting, a regenerating clear-cut showed signs of recovery with respect to its winter and spring hydrology, with similar snow
accumulation, snowmelt and runoff to mature forests.
Mature forest canopies intercept from 9% to 55% of rainfall. Up to 70% of this intercepted rainfall
evaporates directly from the canopy, reducing the amount reaching the soil. Because intercepted rain is
evaporated and water withdrawn via roots from the soil is transpired, mature forests manage their water
differently from clear-cuts or regenerating forests. For instance, summer evaporation from mature forest stands
is one third greater than that from clearcuts. Evaporation consumes energy and that water evaporated cannot
contribute to soil moisture or runoff to streams. As a result:
i)
Mature forests remain cooler than clearcuts in hot summer periods. The energy requirements of
evaporation cool the forest canopy by up to 15 °C from the peak temperatures found in clear-cuts.
Clear-cut surface temperatures regularly exceed the wilting point for spruce seedlings.
ii)
Soil moisture storage is greater in clear-cuts than in mature forests. However, the excess soil
moisture is stored below the rooting zone where it cannot normally contribute to plant growth.
iii)
Runoff generation by rain is greater from clear-cuts than from mature forests. The excess soil
moisture and poor interception in clear-cuts contributes to runoff generation. The rill and furrow
treatment in recent clearcuts promotes runoff generation as furrows have reduced infiltration
capacities. Mature forests direct less than 0%-10% of summer rainfall to runoff, while clearcuts and
regenerating forests direct 50%-60% and 30%-40% respectively.
At 15 years from replanting, the regenerating clear-cut showed only partial recovery with respect to its
evaporation and runoff management.
Basin runoff as measured in streamflow is greater for harvested basins than for undisturbed basins, in
both spring and summer. The Boreal Ecosystem Evapotranspiration Runoff Simulation developed in this
investigation simulates, in its present form, the summer hydrological processes in Hydrological Response Units,
which have been mapped for the PAMF area. The model is physically-based, spatially-distributed and derived
from this investigation. An initial test of the model on Beartrap Creek in Prince Albert National Park is
encouraging and a “virtual clear-cutting” of 20% of the basin (pure conifer stands) showed a decrease in
evapotranspiration and increase in soil moisture storage. The summer water deficit decreased by about 1/3 for
the “virtually-harvested” basin. This change in the water balance would increase water held in storage areas
(wetlands, lakes, groundwater) and increase the likelihood of runoff in subsequent years. The hydrological
processes and their incorporation in the model will provide a useful tool for investigations of the impact of forest
clearing on hydrological pathways and water status in boreal forests and can be used to determine the concomitant impacts of a changing climate in a physically-based manner.
iii
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PERSONNEL
Research Scientists
Drs. J. W. Pomeroy, R.J. Granger, A. Pietroniro, J. E. Elliott, G. van der Kamp, NHRI
Prof. D. M. Gray, Univ. Saskatchewan
Research Officers
B. Toth, Univ. Saskatchewan
Research Technicians
N. Hedstrom, K. Best, C. Onclin, D. Schill, D. Bucilla, R. MacKay, R. Schmidt, NHRI
D. Bayne, J. Onclin, University of Saskatchewan
F. Roberts, Montreal Lake
Students
J. Parviainen, K. Foster, K. Dion, E. Cey, E. Richardson, K. Shook
Project Management
Assigned to Dr. J. Pomeroy
Overall Management
Dr. Wally Nicholaichuk, Chief, Hydrological Sciences Division, 1993-95
Dr. Fred Wrona, Chief, Hydrological & Aquatic Sciences Division, 1995-97.
iv
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
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TABLE OF CONTENTS
Chapter
Page
PREFACE............................................................................................................................................ i
ACKNOWLEDGEMENTS................................................................................................................ ii
EXECUTIVE SUMMARY ...............................................................................................................iii
PERSONNEL .................................................................................................................................... iv
TABLE OF CONTENTS ................................................................................................................... v
LIST OF TABLES...........................................................................................................................viii
LIST OF FIGURES ......................................................................................................................... ixv
CHAPTER 1 INTRODUCTION............................................................................................................. 1
1. INTRODUCTION .......................................................................................................................... 2
1.1 Overview................................................................................................................................. 2
1.2 Purpose ................................................................................................................................... 2
1.3 Background............................................................................................................................. 2
1.3.1 Boreal forest self-regulation of hydrology and climate................................................. 3
1.3.2 Effect of disturbances to the forest ecosystem on water and climate. ........................... 4
1.3.3 Snow and winter ............................................................................................................ 4
1.3.4 Snowmelt and spring ..................................................................................................... 4
1.3.5 Evaporation and summer ............................................................................................... 5
1.3.6 Infiltration to soils.......................................................................................................... 5
1.3.7 Streamflow and Hydrological Modelling. ..................................................................... 5
1.4 Objectives ............................................................................................................................... 7
CHAPTER 2 EXPERIMENTAL PROGRAM 1993-1996 ..................................................................... 8
2. EXPERIMENTAL PROGRAMME 1993-1996............................................................................. 9
2.1 Sites and Infrastructure ........................................................................................................... 9
2.1.1 Locations........................................................................................................................ 9
2.1.2 Towers ......................................................................................................................... 15
2.2 Hydrometeorological Measurements.................................................................................... 16
2.3 Special Experiments: Protocols and Application ................................................................. 17
2.3.1 Leaf Area Index ........................................................................................................... 17
2.3.2 Snow Surveys .............................................................................................................. 18
2.3.3 Snow Chemistry........................................................................................................... 18
2.3.4 Frozen Soil Infiltration ................................................................................................ 18
2.3.5 Interception .................................................................................................................. 19
2.3.6 Soil Properties.............................................................................................................. 20
2.3.7 Infiltration Tests........................................................................................................... 21
2.3.8 Soil Moisture ............................................................................................................... 21
v
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
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TABLE OF CONTENTS (cont'd)
Chapter
Page
CHAPTER 3 HYDROLOGICAL PATHWAYS DERIVING FROM SNOWFALL........................... 22
3. HYDROLOGICAL PATHWAYS DERIVING FROM SNOWFALL........................................ 23
3.1 Snow Accumulation.............................................................................................................. 23
3.1.1 Snow in the Ecosystem: Microclimate and Energetics................................................ 23
3.1.2 Interception .................................................................................................................. 25
3.1.3 Sublimation Fluxes ...................................................................................................... 37
3.1.4 Redistribution to Landscapes and Accumulation ........................................................ 43
3.1.5 Chemistry..................................................................................................................... 44
3.2 Snow Ablation ...................................................................................................................... 47
3.2.1 Snow Melt Energetics.................................................................................................. 47
CHAPTER 4 HYDROLOGICAL PATHWAYS DERIVING FROM RAINFALL............................. 55
4.0 HYDROLOGICAL PATHWAYS DERIVING FROM RAINFALL........................................ 56
4.1 Interception ........................................................................................................................... 56
4.1.1 Interception theory....................................................................................................... 56
4.1.2 The use of the Rutter model in the PAMF................................................................... 59
4.1.3 Sub-canopy rainfall estimates and the partitioning of rainfall..................................... 61
4.2 Evapotranspiration................................................................................................................ 63
4.2.1 Theory, Microclimate and Energetics.......................................................................... 63
4.2.2 Energy and Water Management by Canopies ............................................................. 64
4.2.3 Modelling Evapotranspiration ..................................................................................... 64
4.2.4 Evapotranspiration Fluxes ........................................................................................... 68
4.2.5 Energy Partitioning...................................................................................................... 70
4.2.6 Application of Remote Sensing to Inventory Evapotranspiration and Microclimate.. 75
CHAPTER 5 HYDROLOGICAL PATHWAYS IN THE SOIL .......................................................... 77
5. HYDROLOGICAL PATHWAYS IN THE SOIL ....................................................................... 78
5.1 Soil Properties....................................................................................................................... 78
5.2 Infiltration Theory ................................................................................................................ 80
5.2.1 Frozen Soils ................................................................................................................. 80
5.2.2 Unfrozen Soils ............................................................................................................. 81
5.3 Frozen Soil Infiltration ......................................................................................................... 84
5.3.1 Soil Freezing................................................................................................................ 84
5.3.2 Infiltration .................................................................................................................... 86
5.4 Infiltration to Unfrozen Soils................................................................................................ 89
5.4.1 Soil Water Movement.................................................................................................. 89
5.4.2 Soil Water Status ......................................................................................................... 90
5.5 Water-Use in a Drought Period ............................................................................................ 98
5.6 Runoff Generation .............................................................................................................. 100
5.5.1 Spring Snowmelt Runoff ........................................................................................... 100
5.6.2 Rainfall Events........................................................................................................... 101
5.6.3 Severe Runoff Generation ......................................................................................... 102
5.6.4 Basin Runoff.............................................................................................................. 105
vi
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TABLE OF CONTENTS (cont'd)
Chapter
Page
CHAPTER 6 DISTRIBUTED HYDROLOGICAL MODELLING ................................................... 109
6. DISTRIBUTED HYDROLOGICAL MODELLING................................................................. 110
6.1 An Overview of Hydrological Modelling........................................................................... 110
6.1.1 Runoff processes........................................................................................................ 110
6.1.2 Discretization of a basin ............................................................................................ 111
6.2 Hydrological Model Development for the PAMF.............................................................. 113
6.2.1 Overview.................................................................................................................... 113
6.2.2 Modelling Framework ............................................................................................... 114
6.2.3 Development of Model Components......................................................................... 116
6.3 Simulation Results .............................................................................................................. 121
6.3.1 Model Implementation in Beartrap Creek, PANP..................................................... 121
6.3.2 Model Results for Beartrap Creek ............................................................................. 123
CHAPTER 7 IMPLICATIONS FOR MANAGEMENT OF THE FOREST - WATER
ECOSYSTEM....................................................................................................................................... 128
7. IMPLICATIONS FOR MANAGEMENT OF THE FOREST-WATER ECOSYSTEM .......... 129
7.1 Canopy and Soil Changes................................................................................................... 129
7.1.1 Spring snowmelt and runoff ...................................................................................... 129
7.1.2 Growing season soil moisture.................................................................................... 130
7.1.3 Runoff generation from rainfall................................................................................. 130
7.2 Impacts of Forest Harvesting on Basins ............................................................................. 130
CHAPTER 8 FUTURE DIRECTIONS............................................................................................... 131
8. FUTURE DIRECTIONS ............................................................................................................ 132
CHAPTER 9 REFERENCES .............................................................................................................. 133
9. REFERENCES ........................................................................................................................... 134
CHAPTER 10 LIST OF SYMBOLS................................................................................................... 143
10. LIST OF SYMBOLS................................................................................................................ 144
APPENDICES
APPENDIX A Instrumentation and Towers at Experimental Sites in the
Prince Albert Model Forest .................................................................................................................. 150
APPENDIX B Infiltration Theory ....................................................................................................... 163
APPENDIX C Data Structure for Boreal Evapotranspiration Runoff Simulation .............................. 172
APPENDIX D Maps of Hydrological Response Units for Beartrap Creek ........................................ 182
APPENDIX E Source Code for the Boreal Ecosystem Evapotranspiration Runoff Simulation......... 195
vii
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LIST OF TABLES
Table
Page
Table 2.2.1 Hydrometeorological variables measured in the Prince Albert Model Forest. ................... 17
Table 2.3.1 The standard rain gauge network installed at the Prince Albert Model Forest and the
presence of tipping bucket gauges.................................................................................................. 20
Table 3.1.1 Seasonal Sublimation Losses from Stands in the Prince Albert Model Forest as calculated
from comparative snowfall measurements. .................................................................................... 41
Table 3.1.2 Maximum Late Winter Snowcovers (snow water equivalent, mm) in the PAMF for the
three years of study. The date of occurrence has a resolution of about one week.......................... 43
Table 3.1.3. Average winter loads of the major ions in snow in meq/m2, Prince Albert Model Forest,
1995. ............................................................................................................................................... 46
Table 3.2.1 Total melt energy, melt time, net radiation less ground heat flux and proportion of melt
energy provided by turbulent transfer, April snowmelt 1996......................................................... 52
Table 4.1.1 Estimate of canopy storage (ID) and gap fraction (p) parameters for selected PAMF sites.
Correlation coefficient shown for linear fits to estimate the parameters........................................ 61
Table 4.1.2 Seasonal estimates of evapotranspiration, E (Granger Method) and free-water evaporation
from the canopy (Rutter/Granger) for May 7 to October 16, 1996. ............................................... 63
Table 4.2.1 Seasonal (May - September) evapotranspiration (mm) from the various forest cover types
for the 1994, 1995 and 1996 seasons. The ratios of the seasonal evapotranspiration to that at the
Pine site are also given. .................................................................................................................. 70
Table 4.2.2 Observed albedo values for various land cover types in the boreal forest. ......................... 71
Table 5.1.1 Percentages of sand and clay (and standard deviations) for the four forest sites. ............... 79
Table 5.1.2 Estimated bulk densities for the four forest sites................................................................. 79
Table 5.4.1 Measured final infiltration rates (infiltrometer tests) with geometric means for locations
and sites. ......................................................................................................................................... 90
Table 5.4.2. Seasonal evapotranspiration (May to September) for the four sites................................... 92
Table 5.5.1 Change in soil moisture storage (0 - 1.6 m), evapotranspiration (GD method) and subcanopy rainfall (standard rain gauges) for the drought period between August 13 and September
5, 1996. ........................................................................................................................................... 99
Table 5.6.1 Spring snowmelt runoff (mm water equivalent) estimated from a balance of snow water
equivalent and infiltration for sites in the PAMF. ........................................................................ 101
Table 5.6.2. Water balance for forest stands during the rainfall event................................................. 104
Table 5.6.3 Cumulative snowmelt and rainfall runoff for Beartrap Creek and Whitegull Creek,
expressed as mm water equivalent. .............................................................................................. 107
Table 6.2.1 PAMF GIS attributes for HRU map. ................................................................................. 115
Table B.2 Parameter values based on soil texture that can be used to estimate variables of the GreenAmpt Infiltration equation using Brooks-Corey relationships. .................................................... 169
viii
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LIST OF FIGURES
Figure
Page
Figure 1.3.1 Hydrological Pathways in the Boreal Forest in Summer and Winter .................................. 3
Figure 2.1.1 Map showing the location of NHRI Experimental Sites in the PAMF and the outline of
Beartrap Creek on a forest cover map of the area. ......................................................................... 10
Figure 2.1.2 Pine Site.............................................................................................................................. 11
Figure 2.1.3 Mixed-wood Site. ............................................................................................................... 11
Figure 2.1.4 Spruce Site.......................................................................................................................... 12
Figure 2.1.5 Cleared Site. ....................................................................................................................... 13
Figure 2.1.6 Regenerating Site. .............................................................................................................. 14
Figure 2.1.7 Burn Site............................................................................................................................. 15
Figure 2.1.8 Schematic conceptualising NHRI towers in the PAMF..................................................... 16
Figure 3.1.1 Winter snow processes in the forest canopy. ..................................................................... 25
Figure 3.1.2 Canopy snow load, weekly snowfall, interception efficiency (I/P) and air temperature for
the a) Pine and b) Spruce sites, PAMF. .......................................................................................... 29
Figure 3.1.3 Modelled weekly snow interception and that estimated from the residual of sub-canopy
and open area snowfall measurements for the a) Pine and b) Spruce site, PAMF. ........................ 33
Figure 3.1.4 Sensitivity Analysis of the Snow Interception Model:....................................................... 35
Figure 3.1.5 Interception of snowfall for the three winters of measurement at various sites in the
PAMF. ............................................................................................................................................ 36
Figure 3.1.6 Sublimation rates for a 1-mm ice sphere............................................................................ 38
Figure 3.1.7 Exposure coefficients for a l-mm snow particle and the ratio of intercepted snow mass to
the maximum intercepted snow mass in the Pine canopy. ............................................................. 40
Figure 3.1.8 Comparison of modelled and measured sublimation from the Pine canopy over 23 days in
1996. ............................................................................................................................................... 42
Figure 3.1.9 Development of snowcover in the PAMF, a) 1993-94, b) 1995-96 average values
developed from extensive snow surveys. ....................................................................................... 45
Figure 3.1.10 Snow Chemistry of the PAMF, 1995. .............................................................................. 46
Figure 3.2.1 Energy fluxes over a frozen, snowcovered lake and the Pine forest in the PAMF for two
conditions:....................................................................................................................................... 48
Figure 3.2.2 Net radiation under the canopy less ground heat flux (Q*s-QG) during the melt period,
PAMF, 1996. .................................................................................................................................. 51
Figure 3.2.3 Energy consumed by melting snow, PAMF, 1996. Energy calculated from snowcover
ablation rates and latent heat of fusion. .......................................................................................... 51
Figure 3.2.4 Proportion of melt energy supplied by turbulent transfer, 1996, PAMF. .......................... 53
Figure 4.1.1 The conceptual framework for partitioning of rainfall for a Rutter type model (adapted
from Gash and Morton, 1978). ....................................................................................................... 57
Figure 4.1.2 Determining the maximum canopy storage, ID (Leyton et al., 1967). ............................... 59
Figure 4.1.3 The determination of gap fraction, p (Leyton et al., 1967)................................................ 60
Figure 4.1.4 Net rainfall (sub-canopy) measured and estimated for May 7 to October 16, 1996. ......... 62
Figure 4.1.5 Seasonal rainfall partitioning for canopy types from May 7 to October 16, 1996............. 62
Figure 4.2.1 Comparison of the cumulative evapotranspiration as derived by the model and by direct
measurement using eddy-correlation instrumentation, amounts over the Cleared site for the period
May - September, and over the Regenerating site for the period August - September 1996. ........ 67
Figure 4.2.2 Comparison of the daily estimates of evapotranspiration over the Mixed-wood site using
the model runs for daily periods with the daily totals of the model runs using half-hour periods,
for the period May-October, 1995. ................................................................................................. 68
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LIST OF FIGURES (continued)
Figure
Page
Figure 4.2.3 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1994. ....................................................................... 69
Figure 4.2.4 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1995. ....................................................................... 69
Figure 4.2.5 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1996. ....................................................................... 70
Figure 4.2.6 Monthly energy balance for PAMF for June 1995 expressed as mm water equivalent..... 72
Figure 4.2.7 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and Cleared sites for the
period May - September 1994. ....................................................................................................... 72
Figure 4.2.8 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and Cleared sites for the
period May - September 1995. ....................................................................................................... 73
Figure 4.2.9 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and Cleared sites for the
period May - September 1996. ....................................................................................................... 73
Figure 4.2.10 Daily maximum surface temperatures observed at the Pine, Mixed-wood, Regenerating
and Cleared sites in June 1995........................................................................................................ 75
Figure 4.2.11 Transects of the Landsat-derived vegetation and thermal indices for mature aspendominated mixed-wood, Jack pine, fresh clear-cut and regenerating Jack pine stands (June 1992).
........................................................................................................................................................ 76
Figure 5.2.1 Flowchart representing the Green-Ampt Infiltration process for variable rainfall
conditions........................................................................................................................................ 83
Figure 5.3.1 Soil temperatures during snowmelt, 1994-1996. The soil temperature at 100 mm depth is
shown for the Pine and Cleared sites along with air temperatures. Air temperatures denote the
time of most active snowmelt. ........................................................................................................ 85
Figure 5.3.2 Infiltration of snowmelt water into the top metre of frozen soils 1994-1996. The solid line
denotes unlimited infiltration.......................................................................................................... 86
Figure 5.3.3 Infiltration efficiency as a function of pre-melt soil water content.................................... 88
Figure 5.3.4 Measured and modelled infiltration to frozen soils, using Eq. 5.8 and measured pre-melt
soil water status and snow water equivalent. Cleared sites are excluded....................................... 88
Figure 5.3.5 Snowmelt water infiltration to frozen soils model for boreal forest soils. Model
simulations are shown for varying pore saturation levels, θp. Because, SWE increases
exponentially with infiltration, the model should not be used for SWE greater than 100 mm. ..... 89
Figure 5.4.1 Moisture storage from 0-0.5 m depth in 1995 at the Pine, Mixed-wood, Regenerating and
Cleared sites.................................................................................................................................... 91
Figure 5.4.2 Moisture storage from 0-0.5 m depth in 1996 at the Pine, Mixed-wood, Regenerating and
Cleared sites.................................................................................................................................... 91
Figure 5.4.3 Moisture storage from 0-0.5 m depth at the Pine site in 1995 (three canopy densities
shown)............................................................................................................................................. 93
Figure 5.4.4 Moisture storage from 0-0.5 m depth at the Pine site in 1996 (three canopy densities
shown)............................................................................................................................................. 93
Figure 5.4.5 Moisture storage from 0-0.5 mm depth at the Mixed-wood site in 1995 (three cover types
shown)............................................................................................................................................. 94
Figure 5.4.6 Moisture storage from 0-0.5 mm depth at the Mixed-wood site in 1996 (three cover types
shown)............................................................................................................................................. 94
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LIST OF FIGURES (continued)
Figure
Page
Figure 5.4.7 Moisture storage from 0-0.5 m depth at the Regenerating site in 1995 (ridge and furrow
locations shown). ............................................................................................................................ 95
Figure 5.4.8 Moisture storage from 0-0.5 m depth at the Regenerating site in 1996 (ridge and furrow
locations shown). ............................................................................................................................ 96
Figure 5.4.9 Moisture storage from 0-0.5 m depth at the Cleared site in 1995 (ridge and furrow
locations shown). ............................................................................................................................ 97
Figure 5.4.10 Moisture storage from 0-0.5 m depth at the Regenerating site in 1996 (ridge and furrow
locations shown). ............................................................................................................................ 97
Fig. 5.6.1 The trend between weekly runoff and rainfall derived for the Mixed-wood, Clear-cut and
Regenerating stands from water balance estimates. ..................................................................... 102
Figure 5.6.2 Soil moisture profiles before and after an extreme event at the Pine, Mixed-wood,
Regenerating and Cleared sites on 27 July (pre-rain) and 10 August (post-rain), 1995. ............. 103
Figure 5.6.3 Rainfall and basin runoff (stream discharge) for Beartrap Creek. ................................... 106
Figure 5.6.4 Weekly soil moisture storage at the Mixed-wood site, 1995. .......................................... 107
Figure 5.6.5 Rainfall (PAMF) and basin runoff (streamflow discharge) for Whitegull Creek, east of
the PAMF...................................................................................................................................... 108
Figure 6.1.1 Basin Discretization Approaches in Hydrological Modelling. ........................................ 112
Figure 6.2.1 HRU vectors derived for Beartrap Creek......................................................................... 116
Figure 6.2.2 Schematic Representation of the Boreal Ecosystem Evaporation-Runoff Simulation. ... 117
Figure 6.3.1 Example output of BEERS integrated into the GIS environment for Week 1 (1 May) of
the 1995 hydrological year. .......................................................................................................... 122
Figure 6.3.2 Rainfall and the runoff response for Beartrap Creek - Summer 1995. ............................ 122
Figure 6.3.3 Beartrap Creek Simulation Results for Week 20. ............................................................ 124
Figure 6.3.4 Beartrap Creek Simulation Results for Week 20 under "Virtual Clear-cut" of all pure
softwood stands (Pine and Spruce)............................................................................................... 125
Figure 6.3.5 BEERS run of incremental cumulative hydrological fluxes (1 May to 30 Sept.) for
Beartrap Creek summer 1995, a) virtual clear-cut of pure softwood stands (20% of area), b)
existing undisturbed vegetation coverage..................................................................................... 126
Figure 6.3.6 Comparison of summer seasonal BEERS water balance for undisturbed and virtual clearcut of conifer stands in Beartrap Creek basin, 1995..................................................................... 127
Figure B.1 Brooks and Corey Retention Function ............................................................................... 166
xi
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CHAPTER 1
INTRODUCTION
1
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1. INTRODUCTION
1.1 Overview
The Prince Albert Model Forest Project at the National Hydrology Research Institute (NHRI) was
established in 1993 to promote Environment Canada's research on the impacts of climate and land use
change on the boreal forest. Establishment of this initiative permitted the coordination of several
research efforts at common field sites and with common objectives. NHRI selected the Prince Albert
Model Forest for hydrological research because this region is known to be extremely sensitive to
surface water and climate conditions and contains a combination of harvested, burned and unharvested
landscapes. The southern boreal forest exists on a climatic margin between the semiarid prairie, where
water shortages limit primary productivity, and the boreal/subarctic, where low temperatures limit
primary productivity, and hence often experiences stress from either water shortages or low temperalures. The supply and flow of water are critical to both terrestrial and aquatic ecosystems in the region
and as the flow of water is strongly influenced by land cover and soils. The experiment was therefore
directed toward making progress in defining the role of water in the forest ecosystem and as the
mediator between the atmosphere, hydrosphere, soils and biosphere.
1.2 Purpose
An understanding of the hydrology and climate of the southern boreal forest is necessary in order to
develop techniques to aid conservation and sustainable development of the forest, determine the
impacts of climate and land use changes on the terrestrial and aquatic ecosystems and promote the
general health of the boreal forest ecosystem. NHRI’s research programme specifically targets three of
the six indicators of sustainability in the "Santiago Declaration" on Conservation and Sustainable
Management of Temperate and Boreal Forests, signed by Canada in 1995:
1) conservation of soil and water resources,
2) maintenance and enhancement of forest ecosystem condition and productivity,
3) forest ecosystem contribution to global ecological cycles.
1.3 Background
Hydrological pathways in the boreal forest are intimately connected with the forest canopy as it
influences water storage and energy exchange with the surface. A conceptualization of hydrological
pathways in the boreal forest is shown in Fig. 1.3.1 where on the left-side winter processes of snowfall,
snow interception in the canopy, sublimation (evaporation), throughfall of snow to the ground, snow
accumulation, snow melt and infiltration of snowmelt water into frozen soils are shown. On the right
side of Fig. 1.3.1 the summer processes of rainfall, rain interception in the canopy, evaporation, drip,
surface runoff, sub-surface runoff, infiltration, redistribution of soil water. The schematic shows these
pathways in their vertical configuration, however, once water reaches the soil it moves horizontally
through the basin as surface or sub-surface runoff downhill to streams or lakes and then downstream
out of the basin. This study has made contributions to the knowledge of all hydrological pathways in
the boreal forest. An overview of the hydrological pathways and the processes of water movement
along them is provided below.
2
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Figure 1.3.1 Hydrological Pathways in the Boreal Forest in Summer and Winter
1.3.1 Boreal forest self-regulation of hydrology and climate
The boreal forest sustains itself by acting as a water, climate and nutrient regulation system,
interacting with the atmosphere and soils to produce specific conditions of water flow, availability,
nutrient stares and surface climate. The forest ecosystem has adapted to perform this regulation role
and to thrive under certain stable hydrological and climatic conditions that are commonly sustained in
the forest. This system of water and climate regulation functions at various spatial scales. The smallest
important scale to hydrology is at the individual tree scale (1m to 10m) where water and energy fluxes
are important in both horizontal and vertical directions and are strongly influenced by individual tree
canopy and rooting patterns. The medium scale (10m to 5 km) corresponds to that of the forest stand,
each stand having average water and energy states with a characteristic variance about this mean. The
horizontal fluxes of water and energy between adjacent stands can be important, particularly where
stream flow occurs, however because the region is sub-humid, the largest fluxes are vertical
3
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flows between the surface and the atmosphere or soils. The stand-scale atmospheric fluxes can vary
dramatically from stand to stand, even being of opposite direction on a given day if surface characteristics are substantially different. The largest scale (5 km to 100 km) is at the landscape level. Landscapescale hydro-climatic states and fluxes change over the season in a complex response to antecedent subsurface and surface water and temperature conditions, atmospheric inputs/losses, energy partitioning
and water storage by vegetation and soil. There is an important feedback mechanism at this scale, for
over a landscape the upwind energy and water states influence the fluxes at downwind points. Hence a
hot, dry landscape upwind will promote high evaporation downwind whilst a moist landscape upwind
will suppress evaporation downwind. The integration of water and energy fluxes and states from small
to large scales is a challenge in hydrology but is necessary to understand the hydrological and climatic
regulation role of the boreal forest ecosystem.
1.3.2 Effect of disturbances to the forest ecosystem on water and climate.
Recently cut areas and regenerating stands regulate water and climate in a vastly different
manner from undisturbed boreal forest stands due to differences in vegetation and soils on the
partitioning of incoming atmospheric energy and water flow. The aerodynamic roughness of disturbed
areas is much smaller than that of mature forests and the ground surface is exposed to wind and solar
radiation. Burnt areas partition energy and water in a different manner from cut areas, because of
standing burnt timber and minimal soil disturbance. The difference between burnt areas and harvested
areas varies with season and weather patterns. It is important to quantify at what scales the water,
energy and nutrient fluxes in disturbed areas differ from undisturbed stands and how long it takes for
these fluxes to return to "normal" as the forest regenerates. This can aid the design of forest
management practises that permit sustainable harvests and minimize the effects on ecosystem health.
1.3.3 Snow and winter
Snow, though typically 30-40% of annual precipitation in the Prince Albert Model Forest
provides a much larger portion of annual runoff to lakes and streams and ecologically-critical fluxes of
moisture and stored-nutrients to soils during spring melt. In midwinter much of the snowfall can
remain intercepted in the canopy. Sublimation (flux from snow to water vapour) of intercepted snow
can return substantial amounts of annual snowfall to the atmosphere before throughfall (release to the
ground), accumulation and melt can occur, hence snow accumulation may vary with forest cover type.
Chemical deposition in winter can produce significant loadings in the boreal forest environment which
in eastern Canada have been linked to acid snowmelt and low pH events in streams and lakes in the
spring.
1.3.4 Snowmelt and spring
Rapid snowmelt generally promotes runoff to streams whilst a slow melt can more effectively
infiltrate to soils and replenish soil moisture reserves. Snowmelt occurs when the net radiative, latent
(evaporation/condensation) and sensible (air temperature) energy flux at the snow surface exceeds
losses due to soil heat flux and snowpack internal phase change (melt/freeze). An important factor
affecting radiative and sensible energy fluxes is the presence of a forest canopy. Trees absorb
shortwave radiation and emit the energy as longwave radiation which is quite effective in melting
snow. However, the forest strongly reduces the turbulent transfer of heat to the snow surface by
reducing wind speed, and dense canopies permit only small radiative heat fluxes to reach the surface.
Both effects are influenced by stand density, land use and tree species type. Snowmelt water will
infiltrate
4
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frozen boreal forest soils unless the soils are impermeable due to high ice content or basal ice
formation. Infiltration varies inversely with fall moisture content, the wetter the soil in fall, the less
meltwater infiltrates in the spring. Because of frozen soils, and the often rapid release of stored water,
spring snowmelt in the boreal forest generates important quantities of runoff for boreal forest basins.
1.3.5 Evaporation and summer
Evaporation, transpiration and the surface microclimate are strongly linked to the growth and
survival of mature trees and seedlings, climate system feedbacks and to the potential for forest fires.
Interception of rainfall by mature forest canopies exposes water to rapid direct evaporation from the
canopy. However, if water flows to the soil as throughfall or stemflow it can either infiltrate or run off.
Infiltrated water can evaporate through transpiration (evapotranspiration) which is directly related to
the primary productivity of the forest. Most rainfall to boreal forests evaporates via direct evaporation
or transpiration. Evapotranspiration is reduced however from burnt or clear-cut sites. The study of
evaporation concentrates on the partitioning of energy and moisture fluxes by vegetation cover. By
comparing summer microclimates in mature forests, burnt forests and forests that have been harvested
the effective partitioning of incoming energy and the fate of incoming precipitation at intact and
disturbed forest stands can be assessed. This permits assessing the impact of various disturbances such
as forest fires and clear-cutting on evapotranspiration and surface heating in the forest ecosystem,
documenting the role of the forest as a manager of water and energy and identifying the microclimatic
conditions which favour the regeneration of forest stands after disturbance.
1.3.6 Infiltration to soils
Water that does not evaporate or sublimate must either infiltrate to soils or runoff to streams or
lakes. The partitioning of water between infiltration and runoff is extremely important to forest growth,
seedling survival, stream discharge and maintenance of lake levels and wetland regimes. Infiltration
must be satisfied before runoff can commence. Infiltration rates are a function of water delivery to the
top of the soil, soil physical characteristics, soil wetness, frozen or unfrozen state and the presence of
macropores. These parameters are associated with forest cover type and can be modified by clearcutting and burns. For instance compaction of soils during harvesting can reduce the number of
macropores and make the soil matrix less permeable. Permeability of soils also varies with organic
content which is strongly affected by forest vegetation. The depth of frost in soils varies with
vegetation cover and snowcover. Frost depth is greater where snowcover is shallow and near
individual conifers where shallow snow and shading of direct sunlight results in a greater heat loss
over the winter. Water flow through soils is strongly related to nutrient turnover in soils and the
resulting nutrient export from a watershed. Infiltration is also the link between surface water processes
and groundwater regimes. On hillslopes, much water movement is through soil macropores, except
where compaction has reduced these channels. Hence even if water infiltrates, some can be delivered
to water bodies by shallow sub-surface flow.
1.3.7 Streamflow and Hydrological Modelling.
Streamflow generation is a function of infiltration, soil moisture and groundwater flows and
occurs where there is excess to vertical flux and storage components in the soil/ground. In relatively
dry watersheds such as those which occur in the Prince Albert Model Forest, streamflow is generated
from only part of the watershed, termed the "contributing area". Over time, the size of the contributing
area varies with the wetness of soils, precipitation excess, sources of water (snowmelt or rain) and
5
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soil infiltration characteristics. On the northern Prairies it can be less than 5% of the actual watershed
area however it is larger in the southern forest region. Peak stream flow usually occurs just after
snowmelt when the contributing area can be relatively large. Lakes or wetlands in a watershed increase
the storage capacity and can moderate peak streamflow substantially. Streamflow investigation
includes studies of the role of groundwater outflows (baseflow) and of the flow of water into small
streams from surface soil water. By its nature, stream flow is dependant upon the process studies of
snow accumulation, snowmelt, evaporation, infiltration and runoff. These phenomena interact with
each other in complex, nonlinear manners.
Stream flow is sparsely measured on a consistent basis in the Model Forest. Fortunately, a
watershed that is representative of undisturbed forest, Beartrap Creek, is monitored by Environment
Canada. Because of the sparse network of stream gauges in the boreal forest, it is desirable to conduct
computer simulations of streamflow that can indicate flow from disturbed and natural basins.
Computer models of streamflow are developed from the results of specific studies and tested on
representative basins. Two approaches are often used in computer simulations of streamflow. The first
is termed a “lumped” model, in which average basin characteristics are lumped together and
physically-based computer algorithms are used to simulate the flow of water from the basin. This
approach provides a first approximation of basin response but does not permit detailed investigation of
small-scale effects within the basin nor does it permit prediction of water and energy balance at the
stand scale. The second approach is a physically-based, spatially-distributed model of basin hydrology.
Distributed models are noted for their ability to incorporate the spatial variation nature of basin
conditions and hydro-climatic inputs in a modelling framework. The basin is distributed in that the
land surfaces have been divided into hydrological response units (HRU) for the modelling task at hand.
These HRUs tend to correspond to forest stand and soil associations and match the "stand-scale". The
distributed model Boreal Ecosystem Evapotranspiration Runoff Simulation (BEERS) has been
developed at NHRI for the PAMF. The model is conceived as a continuous simulation daily forecasting model for application to small and medium sized watersheds. The model is modular in that
components can be added so that BEERS can take algorithms resulting from process-based, limitedsite experiments and rapidly implement them in a complete simulation environment for application
over larger regions and to hypothetical climate and forest cover scenarios.
6
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1.4 Objectives
The objectives of this study are to:
1) quantify the fluxes and states of water, water-borne nutrients and energy in the major landscape
elements of undisturbed and harvested forests,
2) specify the interaction of water and energy fluxes in landscape mosaics representing
undisturbed, disturbed and regenerating regions, and,
3) demonstrate the development of process-based, spatially-distributed models of forest hydrology
that may be used to assess the water supply and surface micro-climate impacts of climate and
vegetation change.
These objectives have been obtained through a four-year intensive field and theoretical study of
hydrological processes in the southern boreal forest within the Prince Albert Model Forest,
Saskatchewan. The material presented here represents either new theoretical approaches or novel
applications of existing concepts to the boreal forest environment. The hydrological model developed
from this study represents a notable innovation in that field and as it evolves will represent a long-term
legacy of the Model Forest study.
7
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CHAPTER 2
EXPERIMENTAL PROGRAM 1993-1996
8
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2. EXPERIMENTAL PROGRAMME 1993-1996
The NHRI experimental programme in the PAMF developed in the fall of 1993 from existing
smaller scale hydrology experiments in Prince Albert National Park and using the experience of the
BOREAS study as a guide for development of an infrastracture. The experiment was designed to be
distinctive from and to complement BOREAS in that it would have a year-round observational
programme and would consider "characteristic" stands (e.g., mixed-woods, clearcuts, burns) rather
than the large uniform stands required by BOREAS for remote sensing. An overriding goal of the
experimental program was to be environmentally sensitive in our use of technology, so rather than
bringing in power lines, solar panels were used to power all sites, few if any trees were disturbed in
site installation and vehicle access was restricted to preserve the wilderness character of sites on the
Freight Trail in PANP. This operational scheme not only preserved the sites we studied, but provided
hydrological data that was uninfluenced by our operations. The sites were visited at least once per
week to check instruments, retrieve data, conduct snow surveys, empty precipitation gauges and
measure soil moisture.
2.1 Sites and Infrastructure
Seven experimental sites were established within the Prince Albert Model Forest, in the
catchments of Beartrap Creek, Bittern Creek, Waskesiu River and Snowfield Lake. The general
locations of these sites and the Beartrap Creek catchment in the Model Forest are identified on the map
in Fig. 2.1.1. The naming of the sites was determined by the vegetative cover of that specific location:
‚
‚
‚
‚
‚
‚
‚
Pine (a mature jack pine stand)
Mixed-wood (a mixed aspen and white spruce stand)
Spruce (a dense black spruce stand)
Grass (an open grass covered clearing)
Cleared (a recent clear-cut)
Regenerating (an old clear-cut)
Burn (a recently burned black spruce stand)
The intention of this distribution of sites was to characterise the major canopy and soil types,
both natural and disturbed, across the model forest. Leaf area index as used here denotes the leaf +
stem area per unit area of ground as measured with a LICOR Plant Canopy Analyser.
2.1.1 Locations
Pine
The Pine site is within the Beartrap Creek catchment in Prince Albert National Park, at
53°52.23'N, 106°07.75'W as shown in Fig. 2.1.1. The site is a broad "bench" above Shady Lake, on
sandy soils. Forest vegetation is a mature stand of jack pine, Pinus banksiana, 16-22 m tall with a
sparse understorey of deciduous bushes and kinnikinnick (bearberry). The leaf (stem) area index of this
stand is 2.2 m2/m-2 and canopy coverage of the sky is 82%. The Pine site is shown in Fig. 2.1.2.
9
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Figure 2.1.1 Map showing the location of NHRI Experimental Sites in the PAMF and the outline of
Beartrap Creek on a forest cover map of the area.
Mixed-wood
The Mixed-wood site is within the Beartrap Creek catchment in Prince Albert National Park, at
53°53.56'N, 106°07.24'W as shown in Fig. 2.1.1. The site is a broad knoll in a rolling knob and kettle
terrain that forms part of a glacial till upland. Soils are clay-silt with boulders. Forest vegetation in this
mature stand is a mixture of aspen (Populus tremuloides) and white spruce (Picea glauca) with an
understorey of grasses and kinnikinnick (bearberry). Approximately 75% of the trees are aspen 15-26
m tall, the remainder being white spruce with heights up to 15 m. The winter leaf area index of this
stand is 0.7 m2/m-2 and canopy coverage of the sky in winter is 40%. Summer leaf area index (August)
of this stand is 2.36 m2/m-2. The Mixed-wood site is shown in Fig. 2.1.3.
10
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Figure 2.1.2 Pine Site.
Figure 2.1.3 Mixed-wood Site.
11
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Spruce
The Spruce site is within the Beartrap Creek catchment in Prince Albert National Park at 53°53'10"N,
106°07'11"W as shown in Fig. 2.1.1. It is in a lowlying bench of organic soils overlying silt, just above
the exit of Beartrap Creek from Shady Lake. Forest vegetation is an extremely dense black spruce
Picea mariana stand with an understorey of Labrador tea bushes and sphagnum moss. The trees are
10-12 m tall with an occasional pine or larch reaching 15 m. The leaf area index of this stand is 4.1
m2/m-2 and canopy coverage of the sky is 92%. The Spruce site is shown in Fig. 2.1.4.
Figure 2.1.4 Spruce Site.
12
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Grass
The Grass site is within the Beartrap Creek catchment in Prince Albert National Park, 1 km southwest
of the Spruce site. This site is in the center of a 1 km x 0.5 km clearing that had formerly served as a
refuse ground for the Park but has been rehabilitated into a rough, shrubby grassland. Soils are mixed
gravel and sand with a vegetation of 0.7-m high grasses mixed with a few bushes and small aspen trees
(up to 1.3 m high). The leaf area index is zero.
Cleared
The Cleared site is in the Bittern Creek catchment at 53°59.10'N, 105°54.79'W as shown in Fig. 2.1.1.
The initial forest of spruce with some aspen was logged after the summer of 1990, trenched and
replanted to white spruce in 1992. Presently the vegetation is dominated by grass, bushes and small
aspen trees with heights up to 1.5 m but typically less than one m tall. Much of the soil surface remains
exposed to the air. Very small spruce trees, 0.3-1.0 m tall, are growing in sheltered furrows. The soils
are chaotic because of trenching but are dominated by days with areas of silt and sand. The aspen at
this site were cut back as part of a stand management practise in mid-June, 1996. The winter leaf area
index is 0.06 with a winter canopy coverage of the sky of 4%. The Cleared site is shown in Fig. 2.1.5.
Figure 2.1.5 Cleared Site.
13
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Regenerating
The Regenerating site is an older clear-cut in the Waskesiu River catchment near Ehman Lake, at
54°02.30'N, 105°54.87'W as shown in Fig. 2.1.1. The site was logged, trenched and replanted to jack
pine (Pinus banksiana) in about 1983. The vegetation is presently dominated by jack pine at with tree
heights up to four m but typically less than three m tall. The soils are chaotic because of trenching but
are dominated by silt and sand. The furrows ripped during reforestation are no longer prominent. The
winter leaf area index is 0.8 m2/m2 with a canopy coverage of the sky of 47%, in summer the leaf area
index is 1.4 m2/m2. The Regenerating site is shown in Fig. 2.1.6.
Figure 2.1.6 Regenerating Site.
14
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Burn
The Burn site is near Snowfield Lake at 54°01'63"N, 105°27'07"W in a cutting exclusion zone. The
site was previously covered with black spruce up to 10 m tall, and burned completely in the “Monday
Fire” of June 1995. The burn in this area left no living vegetation and little organic soil besides ash. In
the year after the fire notable regrowth of deciduous vegetation has occurred. The leaf area index of the
Burn site in Sept. 1996 was 0.23 m2/m2. The Burn site is shown in Fig. 2.1.7.
Figure 2.1.7 Burn Site.
2.1.2 Towers
To provide access to the forest canopy and above, towers were constructed at all sites except
the Grass and Spruce sites. At the Pine and Mixed-wood sites, internal-staircase scaffolding towers
were built on removable helical screw anchor bases. An additional triangular tower was built at the
Pine site and is described in the interception section below. Triangular towers were built at the Cleared,
Burn and Regenerating sites. A portable "pump-up" military style tower was deployed periodically at
the Regenerating site with eddy correlation equipment. Examples of the towers are shown in Figs.
2.1.2 to 2.1.7. A general schematic of the model forest tower design is shown in Fig. 2.1.8. The
schematic outlines the structure of NHRI towers in the PAMF.
15
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Figure 2.1.8 Schematic conceptualising NHRI towers in the PAMF.
2.2 Hydrometeorological Measurements
A full listing of the type and exact deployment of various instruments used in this study is
provided in Appendix A. Approximately 390 hydrometeorological instruments have been deployed
simultaneously in the PAMF. Table 2.2.1 shows the variables measured at various sites. Most variables
are measured at multiple levels in and above the canopy and in the soils to provide a vertical structure
to the hydrometeorological conditions found at each site. Not all measurements were conducted
throughout the year with sufficient accuracy for analysis. For example, radiation and wind speed
measurements after snowfall can only be used if wind speeds are high or an operator was present to
clean the instruments. Eddy correlation equipment (sensible & latent heat flux) is very sensitive and
does not operate properly during rain or heavy snowfall, when wet with dew or when frost-covered.
During cloudy periods near to the winter solstice, poor solar charging conditions led voltage levels at
some sites to decline below that necessary to ensure accurate measurements. The large number of
delicate instruments has resulted in a certain amount of breakage and failure due to falling branches,
animals, instrument age and exposure in a harsh environment. Typically two to three instruments
needed repair or replacement in a winter month, with less in the summer months.
16
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Table 2.2.1 Hydrometeorological variables measured in the Prince Albert Model Forest.
The parameters marked with an asterisk (*) are deemed the most critical to assessing the hydrological
and climatological environment of the model forest, though all the parameters measured provide
information required in modelling the hydro-ecology of the forest.
Measured Variables
Incoming Shortwave Radiation
Outgoing Shortwave Radiation
*Net Radiation
Soil Heat Flux
*Sensible (atmospheric) Heat Flux
*Latent (evaporative) Heat Flux
Snow Temperature
*Canopy/Surface Temperature
Soil Temperature
*Air Temperature
*Relative Humidity
*Horizontal Wind Speed
Vertical Wind Speed
*Liquid Water Content of Soil
Ice Content of Soil
Ground Water Level
Snowfall
*Rainfall
Stemflow (down aspen trunks)
*Throughflow (through canopy)
*Snow Accumulation
Intercepted Snow Load
Blowing Snow Flux
Snowfall Chemistry
*Snowcover Chemistry
*LANDSAT Forest Cover
*Soil Structure/type
*Digital Topography
Pine
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Mixed
X
X
X
X
X
X
X
X
X
X
X
X
Spr
X
X
X
X
X
X
X
X
X
X
X
Locations
Reg
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Clear
X
X
X
X
X
X
X
X
X
X
X
X
Burn
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
2.3 Special Experiments: Protocols and Application
2.3.1 Leaf Area Index
"Effective" leaf area index and canopy coverage were measured at all sites in both summer and winter
using a LI-COR LAI-2000 Plant Canopy Analyser (LI-COR, 1992). Effective leaf area index is a
17
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
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useful measure of canopy structure because of its physical meaning as the cumulative horizontal area
of canopy stems and leaves over a unit area of ground. The LAI-2000 uses the optical density of the
canopy and view angle to determine effective leaf area index and canopy coverage. To prepare the
instrument for measurements, the LAI-2000 was calibrated to sky brightness at the top of the tower on
a day with uniform cloud or clear skies, while using a 90° view-limiting cap to shield measurements
from the operator and the sun. Measurements were then taken (roughly 10 randomly selected spots per
site) at ground level with the same view cap and orientation, within two minutes of the calibration
readings.
2.3.2 Snow Surveys
A snow survey network was designed to provide a spatial representation of snowcover at each
of the seven sites. A 10-point snow survey line was marked at each site and weekly measurements
made of snow depth and snow density using a ruler and ESC-30 snow density gage. The snow survey
lines cross representative terrain and vegetation and sample both near to and remote from individual
trees. Snow surveys usually commence in November and cease with the end of snowmelt in April. The
surveys are supplemented by half-hourly single point depth measurements from an ultrasonic snow
depth gauge. Techniques are described in detail by Pomeroy and Gray (1995).
2.3.3 Snow Chemistry
Snow chemistry samples were collected from the Pine, Mixed-wood, Spruce, Cleared and
Regenerating sites from snow pits located under closed and open canopies in December 1994, January
and March 1995. Snow pits were dug with a PTFE scoop rinsed in dionised water. Snow layers were
identified and their thickness and density measured. Using the scoop samples of each layer were
removed and sealed in clean polyethylene bags. The samples were kept frozen until just before
analysis. The samples were rapidly melted using a microwave oven then filtered through 0.4 µm
nucleopore filter as preparation for analysis. An Orion low pH meter with low ionic strength solution
was used to determine pH. Anion analysis (Ca, NO3-, SO42-) was conducted at NHRI on a Dionex
2010i ion chromatograph with a 100-µl injection loop and suppressed conductivity detection. Samples
were introduced by Technicon sampler into the loop after mixing nine parts sample to one part
concentration eluant. A Dionex 4270 Integrator measured peak area and a Linear 100 recorder
measured peak height of the concentration traces. Many blanks on scoops, bottles, filters and analytical
equipment were consistently below detection limits showing that no contamination of samples
occurred.
2.3.4 Frozen Soil Infiltration
Frozen soil moisture content was determined using twin-probe gamma attenuation techniques
at all sites except the Grass and Burn. The technique estimates the density of material between
radioactive source and detector using the principle that attenuation of radiation emitted by a source
follows an exponential decay whose rate depends on the density, absorptivity and length of the
intervening material. To obtain the density from a measured gamma particle count rate one must know
the intensity of the source, the extinction coefficient of ice and the sample length. In our application,
two vertical, parallel PVC access tubes placed 30 cm apart were installed in the soil. The tubes were
installed in Fall, 1993 to a depth of 160 cm, though at some sites only 95-cm depth was obtained
because of stones. At this time soil samples were taken for moisture content and property analysis.
18
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To measure a vertical profile of density, a Cesium 137 source is lowered down one tube and a
gamma particle scintillation detector is lowered simultaneously down the other tube. One-minute
gamma particle counts are taken at 2-cm depth increments, using a peak detection unit built by the
Division of Hydrology, University of Saskatchewan. A frozen soil moisture profile "reading" takes
from 45-60 min. By noting changes in density from reading to reading the change in frozen+unfrozen
soil moisture can be determined and therefore the infiltration of meltwater into frozen soils. In our
program, 12 sets of twin probes were installed, with readings taken during freeze-up, in midwinter and
before and during the snowmelt period.
2.3.5 Interception
Interception controls the precipitation delivery to the soil surface and is an important storage
mechanism for both snowfall and rainfall. Interception processes were measured using sub-canopy rain
and snowfall gauges, a weighed suspended tree and a stem-flow gauge attached to an aspen tree. Both
rate and weekly volume were measured from the 18 sub-canopy rain gauges.
Rate rainfall gauges, or tipping bucket rain gauge mechanisms have been used since the 1994
summer monitoring season. They are above the canopy at the Mixed-wood, Pine and Regenerating
sites. The Tipping bucket rain gauges provide a measure of the gross rainfall intensity.
Depth gauges or standard rain gauges are employed to provide a test or calibration of
cumulative rainfall derived from the Tipping bucket gauges and to provide better spatial representation
of precipitation and interception. Above canopy Standard rain gauges have been monitored at the
Mixed-wood, Pine and Regenerating sites for the 1994 - 1996 seasons. There are also several subcanopy Standard rain gauges at each site to show the influence of differing canopy cover within each
site. Table 2.3.1 shows the full extent of the Standard rain gauge network within the PAMF. The
Standard rain gauge at the Burn site was installed during the summer of 1995. The remaining gauges
were monitored from 1994 - 1996. All Standard rain gauge measurements were done manually during
the weekly or biweekly site visits.
To test the accuracy of interception modelling, tipping bucket mechanisms were installed in
July 1996 at select locations within the study area. One Tipping bucket was set up near the Standard
rain gauge under the aspen canopy within the Mixed-wood stand. Two Tipping buckets were at the
Pine stand, one next to the Standard rain gauge in the densest canopy and one under the weighed-tree.
To ground-truth the Tipping bucket at the weighed-tree, another Standard rain gauge was installed in
the same location.
A small trough wrapped around one aspen tree and funnelled into a carboy provided a stem
flow gauge. Any water flowing down the aspen stem was diverted into the carboy, which was emptied
weekly. The stem-flow gauge was first installed in July 1994 and the carboy size increased several
times over the next year to handle the large volume of water.
The weight of intercepted snowfall on a single tree was measured by cutting, sealing the cut
end and suspending a local tree from a cable with an in line force transducer. A triangular tower
equipped with an aluminum boom and davit system was used to suspend each cut tree within the
canopy. The base of the weighed tree was stabilized by an aluminum frame attached near the bottom of
the tower. The base was inserted in a collar with Teflon rollers that allowed for vertical movement of
the tree as snow accumulated on or ablated from the hanging tree branches. The datalogger did a fourwire bridge measurement of the intercepted mass (g). The compensated temperature range of the force
transducer was -17.8 to 65.6 °C. Tests of the repeatability of measurement of the transducer
19
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
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with the tree attached were better than 70 g. Weighed trees were installed at the Pine and Regenerating
sites in the PAMF, previously a spruce tree had been weighed over a winter at the Spruce site.
Table 2.3.1 The standard rain gauge network installed at the Prince Albert Model Forest and the
presence of tipping bucket gauges.
Site
Grass
Mixed-wood
Pine
Location
open area
open
aspen
spruce
tower top
canopy top
open canopy
mid canopy
closed canopy
tower top
hanging pine
Regenerating
open canopy
closed canopy
Spruce
nipher
open canopy
closed canopy
Cleared
nipher
thick brush
Burn
open canopy
Tipping
Bucket
yes
yes
yes
yes
yes
yes
Snowfall was measured with AES (Atmospheric Environment Service) nipher-shielded
cylinders under the canopy and in open locations. One sub-canopy snowfall measurement is made at
each forested site with open canopy references collected at the Grass and Cleared sites. The snow
collected in the cylinders is emptied weekly, melted and measured as liquid water equivalent. All
snowcover and snowfall amounts are expressed as SWE, snow water equivalent (mm) which is the
equivalent depth of water covering the ground. Nipher-shielded snowfall gauges were installed below
the canopy at the Spruce, Pine and Mixed-wood sites with open exposure Nipher gauges at the Grass
and Cleared sites.
2.3.6 Soil Properties
At each soil moisture monitoring site, the soil profile was described and classified (Canada Soil
Survey Committee, 1978). Soil cores were taken to estimate bulk density (Culley, 1993) and bulk soil
samples were taken for particle size analysis by the hydrometer method (Shedrick and Wang, 1993).
Hydrogen peroxide pretreatment was used to remove organic matter before analysis.
20
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2.3.7 Infiltration Tests
In June 1995, the rate of water infiltration was measured at the sites using a single ring
infiltrometer (Bouwer, 1986). The diameter of the ring was one m and the head of water was
maintained at 75 mm. Measurements were made for at least one hour and were continued until a
constant infiltration rate was obtained. Replicate measurements were made at each of the soil moisture
monitoring sites at the Pine and Mixed-wood sites. Only one measurement was made at the monitoring
sites in the Regenerating and Cleared.
2.3.8 Soil Moisture
Soil moisture was measured at several sub-sites within each site, to characterize the natural
variability within a forest stand.
Pine. Soil moisture was monitored under a dense canopy (closed), under a relatively-open canopy
(open) and at an intermediate location (mid).
Mixed-wood. Soil moisture was monitored under a spruce tree (spruce), under an aspen tree (aspen)
and on an open grassed area (open).
Regenerating. The furrows ripped during reforestation were no longer prominent but soil moisture
was monitored at two locations with paired ridge and furrow measurements.
Cleared. To conform with the Regenerating site soil moisture was monitored at replicate ridge and
furrow locations. The aspen at the site were cut as part of a stand management practice in mid-June
1996.
Volumetric soil water content (VWC) was measured by time domain reflectometry (TDR) to
1.6-m depth (Topp, 1993). Buriable waveguides (0.2 m long) were installed horizontally at 0.05, 0.1,
and 0.2-m depths and vertically at 0.3- to 0.5-m, 0.7- to 0.9-m and 1.4- to 1.6-m depths at each
monitoring location. Most probes were in dominantly mineral soil and the standard TDR calibration
between dielectric constant (Ka) and volumetric water content was used (Topp et al., 1980) but in the
organic layers Ka was measured and a custom calibration was used to obtain VWC (Herkelrath, 1991).
TDR readings were taken irregularly in 1994, once every two weeks in the summer of 1995 and every
week in 1996. Moisture profiles were obtained by interpolating between measured depths. Although
moisture contents were measured to 1.6-m depth most discussion of moisture storage will be confined
to the 0-0.5-m depth where most tree roots are concentrated (Van Rees, 1997).
21
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CHAPTER 3
HYDROLOGICAL PATHWAYS DERIVING FROM SNOWFALL
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3. HYDROLOGICAL PATHWAYS DERIVING FROM SNOWFALL
The knowledge of hydrological pathways deriving from snowfall and snowcover in the
subhumid, continental boreal forest of North America was deficient at the beginning of this study in
1993. To improve this knowledge, the NHRI Science Report Snowcover Accumulation, Relocation
and Management was prepared, using research and examples from studies in the PAMF (Pomeroy and
Gray, 1995). For details of the physics of snow accumulation in the boreal forest and in clearings, and
a review of findings from across Canada and elsewhere, the reader is referred to this report. This
chapter focuses on the physical and chemical properties of the snowcover in the PAMF, their variation
over the winter and spring and the contribution of the snowcover to water supplies of basins in the
PAMF.
3.1 Snow Accumulation
Snow accumulation on the ground in forested regions is not a simple function of the
accumulation of seasonal snowfall. Evergreen forest canopies can intercept and store large amounts of
snowfall for weeks at a time, exposing the snow to a very different thermal regime than would be
experienced on the ground. In deciduous forests, burns and recent clearcuts, little snow is intercepted
by the canopy and accumulation on the ground without melt more directly reflects cumulative
snowfall. Snow accumulations in the canopy and on the ground strongly influence the microclimate
and energetics of these surfaces. Intercepted snow is also transformed by the high net radiation and
strong ventilation in the canopy and substantial losses of intercepted snow result from sublimation to
water vapour. The canopy therefore has a very important role in snow accumulation, which will be
proved here.
3.1.1 Snow in the Ecosystem: Microclimate and Energetics
The accumulation of snow in the boreal forest provides an important supply of water and
inorganic nutrients for the ecosystem, a unique cover for the land and ice surface, and a habitat for life.
As a hydrological phenomenon it is therefore distinctive and a defining aspect of the boreal
environment. Snow provides a harsh habitat for life overall, yet provides a mild habitat for winter life.
It may form the primary habitat for a species such as snow algae that live their life cycles within the
snowpack or form only part of a transitory habitat as for the moose, which must walk on and feed
through snow covers in the winter. Snow covers the landscape for about half the year, insulating the
ground and reflecting much of the radiation incident upon it. Over this time it accumulates and stores
frozen water and chemicals that are released rapidly when melt occurs in spring.
The snowcover may be seen not only as the medium but also as the mediator of the boreal
forest snow ecosystem that transmits and modifies interactions between microorganism, plants,
animals, chemicals, atmosphere and soil. Snow mediates because it functions as an:
Energy Bank - snow stores and releases energy. It stores latent heat and crystal bonding forces.
The bonding forces are applied by atmospheric shear stress, drifting snow impact and the impact of
animals walking over the snow. Bond strength is supplemented in canopy and some surface snow
layers by equitemperature metamorphism where ice crystals in the snowpack assume simpler forms
over time in response to differential saturation vapour pressures along their surfaces. Bond strength is
reduced by temperature gradient metamorphism that results in crystal destruction and reformation as
larger poorly-bonded forms and is due to warmer temperatures at the base of the snowpack than at the
top. The latent heat of vaporisation is extremely large, being approximately 2.83 MJ/kg of snow. The
energy required to sublimate one kilogram of snow is therefore equivalent to
23
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that required to raise the temperature of ten kilograms of liquid water 67 °C. The latent heat of fusion
is smaller, at 333 kJ/kg of snow. The energy required to melt one kilogram of snow (already at 0 °C) is
therefore equivalent to that required to raise the temperature of one kilogram of water 79 °C. Because
of latent heat, dry snow is a large energy sink in the spring. A substantial energy input is necessary for
phase change during melt and sublimation. Just as open water bodies delay the onset of below zero
temperatures in the fall; snowcovers retard warming above zero during the spring melt period. The
intake and release of energy throughout the year makes snow a variable habitat.
Radiation Shield - snow reflects most shortwave radiation, and absorbs and re-emits most
thermal infrared radiation. Its reflectance of shortwave is a critical characteristic of the global climate
system. The proportion of shortwave (solar) radiation incident upon a snowcover and then reflected
(albedo) is high compared with soil and vegetation and varies over the winter. A fresh, continuous
snowcover has an albedo of 0.8-0.9; as a snowcover ages, becomes patchy and wet the albedo can drop
to 0.2. This reflectance can be additionally reduced in the order of 10% by insitu life forms such as
populations of red snow algae and even more by deposits of canopy litter. Bare soil and vegetation will
absorb as much as eight times the shortwave radiation as a fresh, continuous snowcover. Snow behaves
almost as a "blackbody", therefore the longwave (thermal infrared) radiation incident on a snowcover
is absorbed and re-radiated as thermal radiation. The wavelength of emission depends upon the surface
temperature of the snowcover.
Insulator - as a porous medium with a large air content, snow has a high insulation capacity and
plays an important role protecting microorganisms, plants and animals from wind and severe winter
temperatures. The thermal conductivity of a snowcover is low compared to soil surfaces and varies
with the density and liquid water content of the snowcover. A typical thermal conductivity for dry
snow with a density of 100 kg/m3 is 0.045 W/(m K), more than six times less than that for soil. This
means that snow can insulate more than six times more effectively than soil, for equivalent depths. Its
insulation can result in strong temperature gradients that through metamorphism result in depth hoar
crystals. These loosely-bonded crystals provide opportunities for animals that burrow and live in the
snowcover and can constrain large mammals that must walk on the snow cover.
Reservoir - snow is a reservoir of water and chemicals that provides habitat and food sources
for various life stages of microbes, invertebrates and small mammals. The physical properties of snow:
shortwave light transmissivity, density, bond-strength, gas content, temperature, wetness and porosity,
control biological activity within the snowpack and in turn may be influenced by the behaviour of
above-snowpack organisms.
Transport Medium - snow is moved by the wind in open environments or intercepted by
vegetation in forests. This flux is influenced by macrophyte vegetation. Snow is transformed to a
vapour because of sublimation, resulting in transport to colder surfaces or to the atmosphere. During
melt, snow moves as meltwater in preferential pathways within the snowpack to the soil or directly to
streams and lakes. Important nutrients held in the snow can be moved over the winter and delivered to
either the aquatic or terrestrial ecosystems by snowmelt water.
Snowcovers are the milieus of unique ecosystems within the boreal forest partly because of the
distinctive physical properties of snow compared with other environments on the Earth's surface. These
properties are intrinsic to the snow ecosystem and all organisms living within this ecosystem must
contend with or take advantage of them to survive and prosper.
24
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3.1.2 Interception
Interception of snowfall by vegetation plays a major role in the hydrology and winter ecology
of coniferous forests as its results in characteristic snow distributions, substantial loss of snow due to
sublimation and exerts an influence on the winter micro-climate. The hydrology of the boreal forest is
influenced by interception of snowfall in coniferous canopies and the subsequent mention, release to
the ground or sublimation of this snow. As much as 60% of cumulative snowfall may be intercepted by
the PAMF in midwinter and annual sublimation losses mount to at least 30-40% of annual snowfall for
complete coniferous canopies (Pomeroy and Schmidt, 1993). Clearing or burning of these forests
reduces interception to negligible values. Information on snow intercepted in forest canopies and
eventually sublimated is needed to predict hydrological changes associated with climate change,
reforestation, logging, fires and vegetation succession in this forest environment.
Snow interception is initially governed by the efficiency of accumulation of falling snow in the
canopy; the subsequent fate of this snow is affected by sublimation, melt, unloading of snow by
canopy branches and wind redistribution. Intercepted snow receives snow primarily from snowfall and
snow unloaded from upper branches, and less importantly receives drip from melting snow on upper
branches and vapour deposition during supersaturated atmospheric conditions. Intercepted snow then
sublimates to water vapour, becomes suspended by atmospheric turbulence (and either sublimates or
redeposits), melts and drips to the surface snowpack, or is unloaded from a branch to the surface
snowpack. Therefore, intercepted snow may reach the ground as a solid, liquid or vapour, but not all
intercepted snow reaches the ground. These processes are shown in a diagram in Fig. 3.1.1. The
importance of specific processes to snowcover development varies with climatic region, local weather
pattern, tree species and canopy density.
Figure 3.1.1 Winter snow processes in the forest canopy.
25
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The interception efficiency of a canopy is the ratio of snowfall intercepted to the total snowfall.
This efficiency is a synthesis of the collection efficiencies for individual branches that compose the
canopy. The collection efficiency is the ratio of snow retained by a branch to that incident on the
horizontal area of the branch. Early in a storm, snowflakes fall through the spaces between branches
and needles, lodging in the smallest spaces until small bridges form at narrow openings. These snow
bridges increase the collection area and therefore the efficiency with which a branch accumulates
snow; additional snow is retained on the bridges by cohesion. Cohesion of snow crystals results from
the development of a thin, liquid-like layer surrounding the crystals and its movement to inter-crystal
points of contact. Shortly after initial contact cohesion increases rapidly, the rate of the increase itself
increasing as temperatures approach freezing. Usually, snow cohesion is only important for snow at
temperatures above -3 °C. Therefore, the collection efficiency of a branch due to cohesion of snow to
snow increases with increasing frequency of intercepted snow temperature above -3 °C, other factors
being equal.
Limiting the collection efficiency of a branch are three primary factors:
i) elastic rebound of snow crystals falling onto branch elements and onto snow held by the branch.
Rebound is most pronounced below temperatures of -3 °C, declining rapidly as temperature rises from
-3 to 0 °C (Schmidt and Gluns, 1991). Rebound from the branch occurs most effectively near the
branch edge. Therefore, large branches lose proportionately less snow to rebound than do small
branches.
ii) branch bending under a load of snow. Bending decreases the horizontal area of the branch and
increases the vertical slope, by that increasing the probability that falling snow crystals will bounce off.
The degree to which a branch will bend under some given load increases with branch elasticity. Branch
elasticity at sub-freezing temperatures is related to the ice crystal content of the branch and increases
linearly with increasing temperature (Schmidt and Pomeroy, 1990).
iii) strength of the snow structure. As snow accumulates on a branch the degree to which it holds
together and to the branch is related to the bonding or strength of the interlocking snow crystals.
Intercepted snow is subject to weak temperature gradients. Therefore, its bond strength increases over
time due to equitemperature metamorphism where crystal vaporisation and condensation operate over
very small scales. As temperature increases, the rate at which the snow structure "simplifies" (reduced
number of bonds) due to metamorphism increases. Therefore, snow strength will decrease with
increasing temperature and when accumulations are large, may lead to decreased interception
efficiency (Gubler and Rychetnik, 1991). At temperatures near zero, cohesion rather than bond
strength becomes the primary factor holding intercepted snow crystals together.
During a steady snowfall to a snow-free canopy, one may observe that interception efficiency is
greatest up to and during formation of snow bridges between branches. As accumulation continues,
branch bending decreases the horizontal area of the branch and intercepted snow more rapidly than
bridging can increase it, and as interception approaches a maximum given the cohesion/bond strength
of the snow and the horizontal area of branch and intercepted snow, the sharp vertical angles of the
snow surface promote crystal rebound and erosion, rather than continued accumulation.
26
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Low temperatures promote poor cohesion, more efficient rebound of falling snow particles
from the intercepted snow surface (rebound is significantly reduced at temperatures greater than -3 °C,
less bending of branches (greatest rates of change with temperature from -12 to 0 °C) and greater snow
strength, with conflicting results for interception efficiency. It has been observed at air temperatures
above -3 °C that snowfall is intercepted efficiently due to cohesion, such highly cohesive snow being
intercepted by tree trunks and branches for short periods. Intercepted snow loads are not well-retained
at these warm temperatures however, because of low snow strength and high branch elasticity. These
conditions permit light winds to shake snow loads from the supporting branches. As the temperature
drops below -3 °C, reduced branch bending at lower temperatures and increased strength will increase
the interception efficiency with decreasing temperature, particularly for large accumulations that
require stiff branches and strong snow to remain in the canopy. Hoover and Leaf (1967) and Gubler
and Rychetnik (1991) note increased interception efficiency at lower temperatures. In assessing the
direct effects of temperature on interception efficiency, one should distinguish direct effects from other
effects correlated with temperature, such as falling snow crystal form and snow density.
High winds may induce snow redistribution from conifer branches during snowfall, reducing
the apparent interception efficiency. The lowest interception efficiencies in the measurements of
Schmidt and Gluns (1991) occurred during the highest wind speeds. More effective snow particle
rebound during high winds may be the reason for reduced interception efficiency in these cases, though
release of accumulating snow, triggered by branch vibrations in the wind may play a role as well.
One may conclude that interception efficiency generally increases with increasing size of
falling snow crystals, decreasing temperature, decreasing wind speed and decreasing density of
intercepted snow. Satterlund and Haupt (1970) and Schmidt and Glum (1991) show that these
meteorological factors are more important than is conifer species type in determining interception
efficiency for single branches or single trees.
Interception models for individual snowfall events have been developed for more temperate
forest environments than the boreal forest (Satterlund and Haupt, 1967; Strobel, 1978; Harestad and
Bunnell, 1981; McNay et al., 1988; Calder, 1990). Satterlund and Haupt (1967) developed an
interception model using single, small conifers (saplings) and found snow-free branches inefficient at
intercepting snow. Initial interception efficiency was low for snow-free branches and for heavilyloaded branches and high for moderately-loaded branches. Schmidt and Gluns (1991) confirmed this
result for single branches. Strobel (1978) found that the snow interception efficiency for a canopy (not
single branches) decreased with increasing storm snowfall. His research was conducted in Swiss
forests of varying crown density and measurements included large snowfall events. Harestad and
Bunnell (1981) developed a relationship between canopy coverage and interception efficiency,
estimated from comparative snow surveys under a forest canopy and in a nearby clearing. They
analysed the relationship between canopy cover and snow water equivalent (SWE) on the ground and
27
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found substantial differences between sites with differing canopy cover and years with differing
amounts of snowfall, in which the influence of canopy cover on maximum SWE decreased with an
increase of annual snowfall. They also found that as snowfall increased, a smaller proportion of snow
was intercepted. McNay et al. (1988) conducted snow surveys on Vancouver Island before and after
snowfall events. They proposed a linear increase in new snow depth under the forest canopy with new
snow depth in open areas, with the rate of increase controlled by the canopy crown completeness.
Calder (1990) defined a snow build-up function from snow events recorded at Aviemore, Scotland that
related the rate of snow accumulation on the canopy to the rate of precipitation. This function
displayed an asymptotic increase of canopy storage with total snowfall and predicted high interception
efficiencies for small snowfall amounts and lower efficiencies for higher cumulative snowfall amounts.
Snow interception in a cold boreal forest differs from interception in temperate forests. In cold
boreal forests, intercepted snow may be retained in the canopy for several days to a month. This limits
the use of most snow interception models, which presume the complete loss of canopy snow shortly
after each snowfall. An improved understanding of the amounts of snow intercepted by forest canopies
and sublimation of this snow is therefore necessary to predict the exchange of water and energy fluxes
with the winter atmosphere and the amount of surface snow available for melt, infiltration and runoff.
The problem that arises when extrapolating the results of the interception process from a branch or
single tree to a canopy is that the bulk properties of the canopy affecting interception may override the
factors associated with interception by a branch or single tree (Pomeroy and Gray, 1995). The purpose
of this section is to develop a snow interception model that uses physically-based processes to scale
from the branch to canopy, permits snow load in the canopy to be greater than zero at the beginning of
snowfall and is suitable for the PAMF.
Interception of snow was estimated weekly for the Pine and Spruce sites using measurements
of snowfall, one beneath the canopy and the other at the Grass site. Using a mass balance approach,
measurements of snowfall in the twinned gauges can provide the increase in intercepted snow load by
relating the change in this load to the "residual", or difference in snowfall accumulation between
clearing and sub-canopy snow gauges. The residual is the cumulation of snow interception over the
snow gauge reading interval (one week). Residual measurements were scaled from point to stand using
10-point snow surveys over 100-m lengths in the forest in weeks when no snowmelt occurred.
Snowfall measurements at the Grass site require no "up-scaling". This technique provides a stand-scale
estimate of interception and snowfall that can be used .to develop and evaluate a model of snow
interception efficiency.
Figure 3.1.2 shows the seasonal progression of snow load, snowfall, interception efficiency and
air temperature 1992-1994 at the Spruce (Fig. 3.1.2a) and Pine (Fig. 3.1.2b) stands. Snow load here is
estimated from the weight of snow on a suspended tree, scaled to a stand-scale snow intercepted load
using snow surveys. It is evident from Fig. 3.1.2 that increases in intercepted snow load always
accompany snowfall events and that the rate of decrease in snow load after a snowfall varies strongly
with air temperature. The dense black Spruce stand sustains the highest intercepted snow load, a load
maintained more than one month. Interception efficiency, I/P varies from less than 0.1 to nearly 1.0
(complete efficiency). I/P is sensitive to initial snow load and snowfall amount, but distinguishing the
effects from each other given the empirical data as shown in Fig. 3.1.2 is difficult. The effect of
temperature is also difficult to distinguish empirically because it is strongly related to snow load.
28
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Figure 3.1.2 Canopy snow load, weekly snowfall, interception efficiency (I/P)
and air temperature for the a) Pine and b) Spruce sites, PAMF.
29
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The development of the model is based upon the observed behaviour of snow interception
efficiency in Fig. 3.1.2, that snow interception efficiency decreases with snowfall and canopy snow
load and increases with canopy density. Formalizing these ideas requires definitions of some useful
variables:
Ps
I
Cp
Cc
L*
L0
Ip
= snowfall over the event of interest (mm SWE)
= intercepted snowfall over the event of interest (mm SWE)
= maximum snow-leaf contact area (ratio) per unit area of ground during snowfall,
= canopy coverage (plan area of a continuous canopy per unit area of ground),
= maximum intercepted snow capacity (load) that can be retained by the forest canopy under
current canopy structure and temperature conditions (mm SWE),
= intercepted snow load at the start of a snowfall event (mm SWE),
= canopy intercepted snow storage capacity at the beginning of the snowfall event (mm SWE).
The interception efficiency is assumed to be proportional to canopy snow storage capacity, where:
dl
dPs = -k2Ip
(3.1)
and k2 is a proportionality factor. The snow storage capacity is equal to the maximum snow storage
capacity, L*, less the load at the beginning of the incremental interception, L0,
Ip = L* - L0
(3.2)
Ip can be seen as a potential for interception and as a parameter that can be found from initial
conditions and canopy characteristics that govern L*. Integrating Eq. 3.1 provides,
I = Ip (1 – e -k2PS)
(3.3)
which is similar to the expression for interception of rainfall (Linsley et al., 1949).
To evaluate k2, consider the case of a closed canopy where snow interception is completely
efficient and dI/dPs = 1.
Following Eq. 3.1 then
k2 = -
1
IP
(3.4)
However, not all snow crystals contact the canopy in the boreal forest because these forests are porous
and have partially-open canopies, therefore completely efficient interception cannot always occur.
Here the maximum interception efficiency is equal to CP. Therefore for real canopies that are never
completely closed,
k2 = -
CP
IP
(3.5)
30
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Should the snow fall vertically, it my be presumed that Cp ≈ Cc, i.e., that over a snowfall, the area of
snow-leaf contact is approximately equal to canopy coverage. This presumes that during a snowfall all
points on the canopy at some time are intersected by the path of vertically falling snowflakes.
However, consider a snowflake with horizontal velocity u equal to the wind speed and vertical velocity
equal to the terminal fall velocity ν, falling through a gap in the canopy, x m wide (downwind) with
canopy height being hc m tall. The horizontal distance travelled by the particle whilst falling through
the canopy gap from canopy top to ground is (u hc)/ ν. For extremely conservative conditions of mean
wind speed u=0.5 m/s, canopy height hc =10 m and fall velocity v = 0.8 m/s, this leaves a horizontal
distance travelled of 6.25 m as the snowflake falls through the gap, larger than the diameter of gaps
found in the stands in this experiment but smaller than that of the gaps found in certain sparse or open
coniferous canopies. If we estimate canopy closure and snow-leaf contact area as functions of the mean
canopy gap downwind width x, the mean forested canopy downwind width, j, and the downwind
particle travel distance then, for u hc / ν ≤ x,
Therefore the canopy coverage will be reduced as downwind snowflake travel distance in the
canopy gaps increases or forested downwind length decreases. Equation 3.6 suggests that for many
open boreal conifer canopies, I>Cp>Cc.
The maximum canopy load, L* can be calculated as a function of the effective leaf area index
for snow interception LAIΩ. and the maximum snow load per unit branch area, Ss (kg/m2), where,
L* = Ss LAI Ω
(3.7)
and Ss is composed of a mean species value corrected by a function that fluctuates with snow density
as proposed by Schmidt and Gluns (1991),
(3.8)
The units for fresh snow density ( ρs ) are kg/m3. Schmidt and Gluns suggested mean values of
Ss of 6.6 and 5.9 kg/m2 for Pine and Spruce respectively. As fresh snow density is a parameter not
normally available in the meteorological record, an empirical relationship is used to relate ρs, to air
31
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temperature. The relationship was determined from measurements reported by the US Army Corps of
Engineers (1956) and Schmidt and Glum (1991). It has a coefficient of determination r2 = 0.84 and a
standard error of estimate of 9.31 kg/m3. The relationship is,
(3.9)
Where Ta is ambient air temperature (°C). It is presumed for open canopies such as the Pine
stand that the dumping factor, Ω , for extinguished light as measured with LAI by the LI-COR is
similar to that for intercepted snow, Ωs. However, it is possible that Ωs< Ω in that light can scatter to
dense branch clusters that a falling snowflake could not penetrate. The leaf area within such a cluster
would not contribute to the surface available for snow interception although it played a role in light
extinction. Therefore
LAI Ωs = c LAI Ω
(3.10)
where c is a proportionality factor between Ω for light and for snow. Relationships between Ps and c
were determined using measured interception, the previous equations and measured LAI Ω values. For
the Pine canopy the fitted relationship a coefficient of determination r2 = 0.62 with a standard error of
estimate of c = 0.13. For the Spruce canopy, r2 = 0.85 with a standard error of estimate of c = 0.06. The
fitted relationships are:
for pine:
c = 0.0296 Ps + 0.132
for spruce:
c = 0.039 Ps + 0.033
(3.11)
To operate the model to calculate snow interception I over a snowfall event, the input
parameters are:
i)
ii)
iii)
iv)
v)
vi)
vii)
viii)
ix)
x)
initial snow load, L0,
effective winter leaf area index LAIΩ,
proportionality factor c for snow to light interception
air temperature, Ta
wind speed, u
canopy coverage, Cc
canopy height, hc
mean snowflake fall velocity, ν
mean forested fetch length, j
snowfall over the period, Ps
Parameters ii, iii, vi, vii and ix are properties of the forest stand; parameters iv, v and x are
meteorological measurements. Initial snow load is entered from the previous iteration of the model,
less sublimation and snow release or set to zero at the beginning of the season.
32
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The application of the model in describing the interception of snowfall in a boreal forest
environment was tested using weekly measurements of nipher-shielded snow gauge data. Figure 3.1.3
shows the weekly comparison and that the model captures the structure of snow interception increases
with snowfall reasonably well.
Figure 3.1.3 Modelled weekly snow interception and that estimated from the residual of sub-canopy and
open area snowfall measurements for the a) Pine and b) Spruce site, PAMF.
33
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Figure 3.1.4 shows a sensitivity analysis performed on parameters of the new model. This
sensitivity test displays the range of model variables and their influence on interception. The
interception efficiency, I/P is a valuable means of analysing the model components and it is evident in
Fig. 3.1.4a) that as storage capacity increases, I/P increases, the rate of increase declining with
increasing storage capacity. Storage capacity does not affect I/P for small amounts of Ps. Conversely,
when storage capacity is high, Ps does not affect I/P. Figures 3.1.4b and c show an increase in I/P as
leaf area index and snow-leaf contact area increase. Figure 3.1.4d suggests that temperature affects I/P.
Figures 3.1.4e and f show that I/P decreases with both increasing Ps and initial snow load.
Figure 3.1.5 shows the seasonal progression of snow interception for sites in the PAMF, based
on comparative sub-canopy snowfall measurements (compared to the Grass site). Intercepted snowfall
as a percentage of seasonal snowfall is plotted against time of year. It is seen that the Spruce stand
(denser than the Pine) always has the greatest interception at 50-65% early in the season, declining to
from 35-45% by the end of the season. The Pine stand follows this pattern but with interception
reaching maximum values of only 35-40% early in the season and 30-35% by the end of the season,
The Mixed-wood stand has little interception, with a maximum of about 20% early in the season and
only 10-15% by the end of the season. The higher interception values in early to midwinter result from
cold weather and a series of light snowfall events of high interception efficiency that accumulate in the
canopy. Later in the season warmer air promotes release of some snow from the canopy. The
percentage of snow still "intercepted" by the end of the winter season is considered to have sublimated
from the canopy. Sublimation losses from intercepted snow are discussed in the next section.
34
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Figure 3.1.4 Sensitivity Analysis of the Snow Interception Model:
sensitivity of interception efficiency (I/P) calculated for various factors. a) canopy storage capacity, b) leaf
area index, c) canopy coverage, d) air temperature, e) snowfall, and f) initial canopy snow load.
35
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Figure 3.1.5 Interception of snowfall for the three winters of measurement at various sites in the PAMF.
Interception determined as a “residual" between snowfall in a clearing and that under the canopy.
36
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3.1.3 Sublimation Fluxes
Sublimation of snow involves the conversion of ice to water vapour. Most intercepted snow is
distributed over the lower 10 to 20 m of the atmosphere and its ratio of surface area to mass is large.
These features offer the potential for the turbulent transfer of large mounts of heat from the atmosphere
to the intercepted snow and concomitant sublimation. The long period of exposure of snow-covered
canopies to the atmosphere during northern winters provides ample time for sublimation to occur.
The sublimation rate for a sphere of ice is controlled by the turbulent transfer of heat to the
particle, the turbulent transfer of water vapour from the particle and the radiation exchange. The rate
that water vapour can be removed from the particle surface-layer, dm/dt, is controlled by the radius of
the sphere, re(m), the diffusivity of water vapour in the atmosphere, Dv (m2/s), (Dv = 2.06 x 10-15
(T/273)1.75) the degree of turbulent transfer of water vapour from the particle surface to air (indexed by
the Sherwood number, Sh), the water vapour density of the ambient air, ρwa (kg/m3), and the water
vapour density at the particle surface, ρwp (kg/m3). However, dm/dt is also controlled by the rate that
6
energy is delivered to the sphere as influenced by hs the latent heat of sublimation (2.838 x 10 J/kg),
λT, the thermal conductivity of the atmosphere J/(m·s·k), (λT = 0.00063T + 0.0673), Nu, the Nusselt
number, and Ta, the ambient atmospheric temperature (K). Combining energy and mass balances using
the Clausius-Clapeyron equation and including short-wave radiation absorbed by the particle, Qs.
(W/m2) provides the sublimation rate for a single snow particle, dm/dt, where,
(3.12)
in which RH is the relative humidity (%), Mw is the molecular weight of water (18.01 kg/kmole), R is
the universal gas constant (8313 J/kmole · K) and psw is the saturation density of water vapour at Taz.
The complete derivation of Eq. 3.12 is given by Pomeroy and Gray (1995).
as,
In relation to measurements, sublimation may be expressed as a rate coefficient, csubl, defined
(3.13)
where m is the mass of a single ice sphere and t is time. Figure 3.1.6a shows the effect of air
temperature and humidity on csubl of an ice particle having a radius of 500 p.m. The coefficient
increases sharply as air temperature rises towards the freezing point and declines sharply as relative
humidity increases towards 100%. Figure 3.1.6b shows the sublimation rate coefficient increasing with
increasing wind speed. For wind speeds greater than 1 m/s the increase is approximately linear.
37
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Figure 3.1.6 Sublimation rates for a 1-mm ice sphere.
(a) the effect of air temperature and relative humidity, (b) the effect of wind speed.
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Scaling sublimation rates for a single ice sphere so they apply to snow intercepted by a forest
canopy requires knowledge of the leaf area in the forest, the mass of intercepted snow in a tree (of
known leaf area) and the proportion of snow exposed to atmospheric ventilation that is available to
sublimate. Three scales are defined for the purposes of scaling up; the snow particle scale where the
particle mass is m, the individual tree scale where the mass of intercepted snow on the tree is Mt, and
the forest canopy scale where the mass of snow on the forest canopy is I. Presuming uniform
atmospheric conditions surrounding a tree, the sublimation flux from a single tree, qsubl (tree), is given
by:
(3.14)
where ce(Mt) is an exposure coefficient that corrects for the difference in the ratio of surface area to
mass between the single ice sphere and the snow intercepted by a tree and Mt is the mass of snow on a
tree. Scaling to the forest sublimation rate qsubl (forest) can be accomplished using a scaling coefficient
N, where N=I/M, and:
(3.15)
and the exposure coefficient, ce (I) is now defined to correct for the difference in the ratio of surface
area to mass between a single ice sphere and snow intercepted in a forest canopy
(3.16)
If the difference in sublimation rate coefficients for a mass of snow intercepted by a tree and a
single ice sphere is primarily due to the difference in the ratio of surface area to mass between the two,
the exposure coefficient may be determined empirically as the ratio of sublimation rate coefficient for
snow intercepted on a tree to that for a "standard" ice sphere (radius of 500 µm), i.e.,
(3.17)
Pomeroy and Schmidt (1993) showed that ce can be found as a function of dimensionless
intercepted snow mass I/I*, where I* is the maximum intercepted snow load,
(3.18)
39
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where k1 is a coefficient indexing the age (structure) of snow and F is an exponent having the value of
approximately 0.3 for small trees (1-m tall) but larger values (0.4) for snow intercepted in taller spruce
trees (12-m tall).
For identical weather conditions and mount of intercepted snow, a lower exposure coefficient
results in a lower sublimation rate. The exposure coefficient varies with several factors, which are
related to the canopy and intercepted snow structure. Overall the exposure coefficient declines as the
canopy snow load increases and with the age of the snow. Figure 3.1.7 shows the relationship between
exposure coefficient and intercepted snow load as determined from PAMF tower meteorological
measurements and the weight of the suspended trees. There is a large degree of scatter in the
relationship, but the exposure coefficient tends to decline with increasing intercepted snow load.
Figure 3.1.7 Exposure coefficients for a l-mm snow particle and the ratio of intercepted
snow mass to the maximum intercepted snow mass in the Pine canopy.
To find values for F and k1 to apply to the model forest, sublimation rates dm/dt were
calculated from the model above (Eq. 3.12). The sublimation rate from a single weighed tree (dMt/dt)
was scaled-up to the stand scale using the same techniques as used to scale up intercepted snow load in
the previous section. The exponent F was estimated from digital analysis of electronic images of snow
in the canopy and based on these estimates, given the constant value F=0.4 for the Pine canopy of
PAMF. The coefficient k1 was not found to vary with snow age nor snow loads in any consistent
manner but averaged at 0.011 for the Pine canopy of the PAMF. Using these coefficients a half-hourly
comparison of modelled and measured sublimation showed a correlation coefficient r2 of 0.59. Much
of the error is due to the exclusion of a heat storage term in the sublimation calculation. The model
does much better on an event basis as shown in Fig. 3.1.8 for three sublimation events in 1996 at the
Pine
site.
Over
the
sublimation
event,
daily
heat
storage
40
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balances to near zero. The modelled sublimation from the three events totals 7.1 mm SWE and
compares well to the 6.9 mm SWE sublimation loss estimated from comparative snowfall
measurements. The agreement suggests that the physics of sublimation have been adequately
represented by the formulation. By coupling the interception and sublimation models, seasonal
sublimation values can be calculated for the PAMF.
Table 3.1.1 shows the seasonal sublimation loss of intercepted snow. In the Mixed-wood, the
high proportion of deciduous trees reduces the winter leaf area index and therefore sublimation
remains low at from 10-15% of annual snowfall, reaching a maximum loss of 15 mm SWE in 1995-96.
The Pine stand with a leaf area index of 2.2 has a uniform loss of from 30-32% of annual snowfall,
reaching a maximum loss of 39 mm SWE in 1994-95. The Spruce stand with a leaf area index of 4.1
sustained the greatest sublimation losses at from 38-45% of annual snowfall, reaching a maximum of
52 mm SWE in 1994-95. Sites with the canopy cleared by forestry activities and burns sustained no
loss due to sublimation of intercepted snow, where losses at the Regenerating site are estimated to be
from 50% to 75% of that at the Pine.
Table 3.1.1 Seasonal Sublimation Losses from Stands in the Prince Albert Model Forest
as calculated from comparative snowfall measurements.
Loss as a percentage of Annual Snowfall
Mixed-wood
Pine
Spruce
1993-94
10
32
45
1994-95
11
32
43
1995-96
15
30
38
Loss as Annual Snow Water Equivalent (mm)
Snowfall
1993-94
8
24
34
76
1994-95
13
39
52
122
1995-96
15
29
36
97
41
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Figure 3.1.8 Comparison of modelled and measured sublimation from
the Pine canopy over 23 days in 1996.
Sublimation is expressed as mm SWE over a unit area of ground and is a canopy average.
42
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3.1.4 Redistribution to Landscapes and Accumulation
Because of the combination of snow interception and sublimation processes, snow is redistributed
before it can accumulate on the ground. Snow accumulation on the ground (SWEg) can be found from
a simple mass budget, where
(3.19)
SWEg = PS - I - E - M + TSP
and PS is snowfall, I is interception, E is evaporation from the snowpack, M is snowmelt (presuming
removal from the snowpack) and TSP is snow transport to the snowpack including release of
intercepted snow (all in mm SWE). E is generally small under forest canopies. Transport from the
snowpack is considered a small term for boreal forest stands and clearings where dense vegetation and
logging residues at least 1-m deep exist. Transport to the forest snowpack occurs as intercepted snow is
released. Released snow amounts are determined from I less sublimation and melt of intercepted snow.
Melts can occur in any month of the winter, though normally the maximum snowpack depth occurs in
March or April with the large seasonal melt immediately after the time of maximum accumulation. The
interception term includes all snow intercepted by the canopy.
The maximum snowpack SWE and the approximate time of its occurrence are shown in Table
3.1.2 for each of the forest covers. The Spruce consistently develops the shallowest snowpack, whereas
the Grass or Cleared sites develop the deepest. An interesting transition is apparent in the snowcovers
developing at the Regenerating site. For 1994 and 1995, this site developed a snowcover similar to that
of the Mixed-wood with more snow than at the Pine, however in 1996 there was less snow in the
Regenerating than in the Pine, suggesting similar amounts of overwinter snow interception and
sublimation between the sites. It is possible that an increasing canopy led the Regenerating site to
intercept more snow by 1996. However, the results are confused by an increase in small mammals at
the Regenerating site that did not occur at other studied plots. The small mammals trampled the
Regenerating snowcover, increasing its density by 30% and making snow surveys more difficult to
conduct with accuracy.
Table 3.1.2 Maximum Late Winter Snowcovers (snow water equivalent, mm) in the PAMF for the
three years of study. The date of occurrence has a resolution of about one week.
1994
1995
1996
Date
2 March
6 April
2 April
Pine
34
63
49
Spruce
34
44
27
Mixed-wood
60
86
57
Grass
66
92
83
Cleared
67
98
59
Regenerating
52
73
44
Burn
--
--
70
43
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The sequence of snowcover formation over the PAMF is shown in Fig. 3.1.9 for the 1993-94
and 1995-96 seasons. To simplify an exceedingly complex series of growth curves, the sites in the
PAMF have been grouped into coniferous (Spruce, Pine) and non-coniferous (Mixed-wood, Grass,
Cleared, Regenerating) in 1993-94 and into Spruce, Pine and Open (Mixed-wood, Grass, Cleared,
Burn) in 1995-96. Note that while the Regenerating site behaved as a non-coniferous site in the first
year of study, it behaved as did the mature Pine in the third winter of study. The substantial difference
in the snow accumulation regimes is readily apparent in comparing the cover types as is the variation
throughout the year. Identifying the time and quantity of maximum snowpack is often difficult as in
1996 when two peaks occur due to a partial melt in early March, then more snowfall and a complete
melt in April.
3.1.5 Chemistry
Snow chemistry measurements were made periodically in 1994-95 from snow pits near the
snow surveys at the Mixed-wood, Spruce, Pine, Cleared and Regenerating sites. A full suite of the
major cations and anions were measured though significant concentrations were only found for
Calcium (Ca+), Nitrate (NO3-), Ammonium (NH4+) and Sulphate (SO42-) The winter average
concentrations of Ca+, NO3- and SO42- in snow are shown in Fig. 3.1.10. These concentrations do not
indicate strong local pollutant sources; though neither do they suggest “pristine” conditions. The SO42concentrations are similar to mildly polluted snow from upland Wales (Plynlimon), northern Ontario
(Muskoka) and northern Quebec (Forest Montmorency). Though 2-3 times higher than "clean
background levels" found in other remote sites in North America (e.g., Sierra Nevada Mountains,
California), the SO42- concentrations are an order of magnitude less than those found in heavily
polluted European mountain snow such as the "black snowfalls" in the Scottish Highlands. It is quite
possible that the "Arctic Haze" sulphate aerosol extends as far south as the PAMF in midwinter. This
pollution forms in the Arctic region in midwinter from industrial emissions in Russia and eastern
Europe. The aerosol is deposited as a dust and within the snow crystals during snowfall over the winter
and can lead to elevated SO42- concentrations in snow. The higher SO42- concentrations in snow under
"rough" canopies such as the Mixed-wood, Pine and Spruce suggest that dry deposition of an aerosol
such as the Arctic Aerosol is a significant component and that less SO42- is therefore deposited to
"smooth" areas such as the Cleared site. When SO42- in snow melts it becomes sulphuric acid and
contributes to acid shock to streams and lakes. The PAMF loadings (Table 3.1.3) of SO42- are at least
five times lower than those in eastern Canada where acid shock occurs and so are not considered a
potential ecological problem.
NO3- in snow is an important source of biologically-available N in spring. The concentrations
found in the PAMF are normal for boreal forests in North America and indicative of incorporation in
snowfall rather than extensive dry deposition. As the loading of N is important for ecosystem
productivity, the concentration must be corrected for maximum snow accumulation. Corrected values
for NO3- load are shown in Table 3.1.3. The lowest N loads are in the mature conifer stands; losses are
due to sublimation of snow. The reduced sublimation in Mixed-wood and Cleared areas gives these
stands a greater N load in spring. However the low roughness of the Cleared site causes a lower N dry
deposition over the winter and limits the benefits from reduced overwinter N losses. Ca+
concentrations are normal for boreal forests in the mid-continent, the much higher levels in the Mixedwood stand will enhance the buffering capacity of these stands and their ability to reduce acid levels in
snowmelt waters.
44
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Figure 3.1.9 Development of snowcover in the PAMF, a) 1993-94, b) 1995-96 average values
developed from extensive snow surveys.
45
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Table 3.1.3. Average winter loads of the major ions in snow in meq/m2,
Prince Albert Model Forest, 1995.
Ca+
SO42-
NO3-
Mixed Wood
0.72
0.86
0.90
Pine
0.36
0.59
0.65
Spruce
0.36
0.60
0.60
Regenerating
0.22
0.51
0.65
Cleared
0.29
0.59
0.78
Figure 3.1.10 Snow Chemistry of the PAMF, 1995.
Recent findings on the importance of N loads in boreal forests (Pomeroy et al. 1997, in press)
show that boreal forest carbon uptake should increase if N was more efficiently introduced to the
boreal forest ecosystem. In winter the N loading is extremely inefficient because of cold snow
processes and interception, hence the length of winter and presence of forest cover will influence the
productivity of the boreal landscape. Should winters become less severe in the boreal forest then an
increase in nitrogen fertilization and hence an increase in summer carbon uptake should occur. This
might cause an increase in primary productivity in the southern boreal forest and promote regeneration
of replanted areas.
46
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3.2 Snow Ablation
3.2.1 Snow Melt Energetics
The energetics of melt in the boreal forest can be described by an energy balance equation for melt
with respect to the surface of a melting snowpack where,
(3.20)
and QM is the energy used in snowmelt, Q* is the net radiation over snow only, QG is the ground heat
flux, QH is the turbulent sensible heat flux, QE is the turbulent latent heat flux, QAdvection U is the
internal energy of the snowpack, t is time. Net radiation over snow under a canopy is difficult to
determine, because the sub-canopy net radiation, Q*s, is composed of snow and non-snow surfaces
where,
(3.21)
Q*s = Q*snow + Q*non-snow
Presumably, some energy added to the surface as Q*non-snow is advected to the snowpack via
turbulent transfer and counted in the term QAdvection. Conditions under the canopy that influence
snowmelt can be quite different from those over the canopy where most turbulent transfer and radiative
fluxes are measured or modelled. For instance, turbulent transfer terms have been considered
insignificant in certain considerations of sub-canopy snowmelt in northern forests (Petzold and Wilson,
1974). Net radiation is strongly affected by canopy cover and fluxes can be of differing signs and
magnitudes above and below the canopy.
Terms of the energy balance can vary significantly over various boreal forest surfaces. In data
that describes the effect of a canopy, versus an open snow surface on fluxes, Harding and Pomeroy
(1996) compared the net radiation, sensible and latent heat fluxes over a snow-covered frozen lake and
the Pine site during a pre-melt and a melt period in 1994. As shown in Fig. 3.2.1 the fluxes of heat and
net radiation differed substantially between the forested and non-forested surfaces, net radiation being
negative over open snowcovers and strongly positive over the Pine canopy. Sensible heat fluxes were
usually downwards to the snowcovered surface and to snow-covered Pine canopy but were away from
the Pine canopy when it was snow-free. Evaporation consumed 55% of the net radiation when the Pine
canopy was snow-free and the snow beneath was melting. However, the snow-covered Pine canopy
(no melt) consumed an amount of energy equivalent to 240% of the net radiation, the extra energy
contributed by a downward sensible heat flux. Net radiation is evidently the driving energy term for
melting snow conditions. However, the measurements shown in Fig. 3.2.1 do not show the effect of the
canopy in modifying the net radiation available for snowmelt beneath the canopy.
Predicting the energetics that govern fluxes of snow melt and sublimation of intercepted snow
requires an understanding of the effect of conifers (pine, spruce, firs) on the exchange of radiant
energy. In eastern Canadian forests the energetics of melt under a deciduous forest canopy (Price,
1988) and an open, subarctic forest canopy (Petzold and Wilson, 1974) have been measured and
discussed. These approaches suggested that calculations of radiant energy fluxes for canopy and
surface require knowledge of the reflection of short-wave radiation from both canopy and underlying
surface snow, and the transmission and/or absorption of radiant energy through the canopy. Petzold
47
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and Wilson (1974) suggested that canopy temperature alone was sufficient to calculate net radiation at
the snow surface whilst, Price (1988) showed that incoming solar radiation was additionally needed.
Figure 3.2.1 Energy fluxes over a frozen, snowcovered lake and the
Pine forest in the PAMF for two conditions:
melting snow (canopy is snow free) and fresh snowfall. Fluxes are daily averages measured with eddy
correlation equipment and net radiometers (after Harding and Pomeroy, 1997).
Hashimoto et al. (1994) measured and modelled downward longwave radiation in deciduous
and coniferous forests during snowmelt, finding that melt rates were increased under the coniferous
forests because of higher downward longwave radiation fluxes under the conifers. The effect of canopy
coverage on the downward emission of longwave radiation was emphasized in their model. Hardy et
al. (in press) weighted the fractional contribution of longwave radiation above the canopy to that below
the canopy using canopy coverage to drive a physically-based boreal forest snowmelt model. They
found that estimated sensible and latent heat fluxes to the sub-canopy snow surface were generally
opposite in sign and similar in magnitude and that radiation dominated the energetics of melt.
Fresh snow in open fields is typically considered to have an albedo from 0.6 to 0.9 (Winther,
1993). Snow intercepted on forest canopies differs however; Leonard and Eschner (1968) reported that
the albedo of a freshly snow-covered pine stand in New York State remained below 0.2. Harding and
Pomeroy (1996) confirmed low winter albedos for coniferous canopies in the boreal forest, suggesting
multiple scattering of light within the canopy as a possible reason for the low albedo. The
characteristics of light scattering that result in this low albedo have not been fully-examined despite
important implications for climatology and hydrology. Using GCM simulations, Bonan et al. (1992)
and Thomas and Rowntree (1992) predicted that removal of the boreal forest canopy would strongly
increase spring albedos and hence decrease spring air temperatures globally. Harding and Pomeroy
(1996) showed the strongly positive net radiation over the Pine canopy contrasted with the strongly
48
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negative net radiation over a frozen, snow-covered lake in the boreal winter. The strong correlation
between net radiation beneath the canopy and air temperature measured near the canopy has been
presumed to be the physical basis for the success of empirical temperature-index models in calculating
snowmelt rates in heavily forested catchments (Male and Gray, 1981; Price, 1988) and the source of
energy for sublimation of intercepted snow (Leonard and Eschner, 1968; Lundberg and Halldin, 1994;
Pomeroy and Gray, 1995). Price (1998) suggested that melt under deciduous canopies in eastern
Canada was dominated by net radiation and that sensible and latent heat fluxes could be neglected.
Some measurements that describe the radiant energy fluxes under boreal coniferous canopies
have been descriptive (Constabel and Lieffers, 1996) and have not tried to relate canopy structure to
the extinction of shortwave radiation or attenuation of net radiation in a physically-based manner.
Other studies have modelled solar radiation through boreal conifer canopies in an intensive physical
manner, using a combination of geometrical optics and radiative transfer models to account for
extinction by canopy elements and the influence of canopy gaps on transmittance through the canopy,
the most recent and local example being that of Ni et al. (in press). While the model developed by Ni
et al. shows the effects of canopy architecture on shortwave radiation transmission and the
heterogeneity of extinction, it does not address the influence of intercepted snow in the canopy nor is it
readily applicable using normal forest inventory measures.
Net radiation, Q* (W m-2), is the balance between short (S) and longwave (L) incoming ( ↓ )
and outgoing ( ↑ ) radiation at a surface that can be expressed as
Q* = S ↓ (1 - α) + L ↓ - σ T4
(3.22)
where σ is the Stefan-Boltzmann constant, α is albedo, and T is the surface temperature (K). Knowing
the net radiation at the snow surface below a canopy, Q*snow is necessary for calculations of melt and
evaporation at the snow surface and from Eq. 3.21 this can be found from the sub-canopy net radiation,
Q*s. Presuming little change in the high surface snowcover albedo during the pre-melt and early melt
condition, then the onset of larger Q*snow, values will be primarily driven by greater long-wave
radiation emission from the lower canopy. A useful way to describe the net radiation at the canopy
bottom (where it is normally unknown) from that at the canopy top (where techniques are available to
estimate it) is the net radiation ratio, Nr , where
Nr
=
Q*s
Q*
(3.23)
The influence of albedo and canopy extinction on Nr is shown in the following expression, where
(3.24)
α is the albedo of the surface, µ is the extinction coefficient, 1 is the path length of solar radiation
through the canopy, Cc is the canopy closure, and subscript s refers to sub-canopy conditions whereas
the lack of a subscript refers to conditions over the canopy. It is assumed in Eq. 3.24 that both upward
and downward longwave radiation are emitted from canopy surface elements of similar temperatures
that may be approximated by T. As Q* is usually slightly positive during the winter day over the
boreal forest (due to a low albedo), Nr must be positive to provide the energy necessary to
49
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drive snowmelt under evergreen canopies. Presuming that much of the shortwave radiation
extinguished within the canopy heats the canopy, increasing T, then extinction of this radiation will
increase downward longwave emissions and partially contribute to Q*s Because much of the
downward shortwave radiation that is not extinguished by the canopy is reflected by the snowcover,
the implication of Eq. 3.24 is that Nr may increase with the extinction coefficient of the canopy unless
the canopy is very sparse and large fluxes of shortwave are available to intercept the surface directly. It
is expected that the actual relationship between Nr and the extinction coefficient will depend upon
canopy density, coverage and height and must be understood to model sub-canopy radiation. Pomeroy
and Dion (1996) detail the development of a model to calculate the extinction coefficient as a function
of canopy coverage and solar angles. Their best fit equation for the Pine canopy is:
(3.25)
where eext is the extinction efficiency, LAI is leaf area index, Ω is a clumping factor for radiation and
hc is canopy height. The quantity LAIΩ is the effective leaf area index as measured by LI-COR Plant
Canopy Analysers. The extinction efficiency in the Pine canopy can be found from the solar angle
above the horizon, φ, as (Pomeroy and Dion, 1996)
(3.26)
In 1996, the melt period in the PAMF began with an isolated melt in the Cleared site from 1014 March (JD 70-74) triggered by a warm air mass with strong winds. This was followed by a general
melt period that depleted the winter snow cover from 3-18 April (JD 94-109). The general melt period
is examined in detail. Many energy regimes are found for melt under various canopy covers as shown
in Fig. 3.2.2 where cumulative sub-canopy net radiation minus ground heat flux (Q*s-QG) is graphed
against time during the melt period. The accumulation of (Q*s-QG) energy during melt varies with the
canopy, as high leaf area restricts the penetration of short wave radiation to the underlying snow. The
complex diurnal fluctuation in (Q*s-QG) reflects daily variations in Q*s, T, and variation in QG
influenced by infiltration of meltwater into frozen soils as melt progresses. The Cleared location has
notably higher (Q*s-QG) energy than the forested sites and reaches quite high cumulative energy levels
in a very short time. The forested sites have roughly similar rates of increases in (Q*s-QG), though the
Pine stand has the least (Q*s-QG) of the forested sites.
The translation of incoming energy to melt energy, QM is shown in Fig. 3.2.3 where cumulative
melt energy is graphed over time for the four cover types. The melt does not progress as steadily as
does (Q*s-QG), due to storage of internal heat in the snowpack dU/dt and contributions of latent heat,
QE and sensible heat, QH from the atmosphere. The trend for the Cleared site to melt much faster than
the forested sites matches the trend in (Q*s-QG) energy between sites. The slowest melt however,
occurs in the Regenerating stand, rather than the Pine stand as (Q*s-QG) energy would suggest. This
suggests a significant source of turbulent transfer of heat (QH-QE) to the Pine snow cover that is not
available for melting the snow cover found at the Regenerating site.
50
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Figure 3.2.2 Net radiation under the canopy less ground heat flux (Q*s-QG)
during the melt period, PAMF, 1996.
Figure 3.2.3 Energy consumed by melting snow, PAMF, 1996. Energy
calculated from snowcover ablation rates and latent heat of fusion.
51
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Figure 3.2.4 shows the proportion of "residual energy", QH+QE -dU/dt, required to supply melt
energy requirements more than the measured (Q*s-QG) flux. This residual energy is presumed to be
primarily the sum of latent and sensible heat fluxes as driven by turbulent transfer through the canopy,
(QH+QE). It is seen that for all surfaces turbulent transfer is important at the very start of melt and
contributes all the melt energy for short periods early in the melt. As melt progresses, turbulent transfer
becomes less important, depending upon the canopy cover. The Cleared site is distinctive in that
turbulent transfer becomes insignificant within the first 24 hours of melt. For the forested sites
turbulent transfer provides significant energy for several days (10 days for the Pine canopy, 5.5 days
for the Mixed-wood and 2.5 days for the Regenerating stand). There are several reasons for the decline
in contribution from turbulent transfer, the primary being the decline in snow surface albedo and snowcovered area during melt and the consequent counting of net radiation to bare patches as part of the
total net radiation. Radiant energy absorbed by bare patches can be convected to the overlying air mass
and advected to the remaining snow. While this process is actually a turbulent transfer, it would not be
detected in a crude energy balance as devised here. Similarly radiant energy absorbed by tree and bush
stems just above the snow surface would be counted as part of net radiation but actually contributing in
some part to small scale advection of turbulent heat as found in open environments by Shook et al.
(1993) and Marsh and Pomeroy (1996).
Bulk properties of energetics during the melt period are shown in Table 3.2.1 where the total
melt energy, length of time to melt the snowpack, total radiant-ground heat energy and the percentage
of melt energy contributed by turbulent transfer are shown. The largest melt energy occurred where the
SWE was greatest, the Mixed-wood. It did not occur for this period in the Cleared location because of
a previous local melt, however with consideration of this previous melt energy (7000 kJ) and the short
time, the Cleared melt was by far the fastest. The melt energy in the pine stands was lower because of
snow cover lost over the winter to sublimation of intercepted snow. As noted the Cleared site melted
rapidly (nine days if previous melt is added), completing melt in almost one third of the time of the
foresteal sites. This is because the Cleared site had the most favourable radiative environment of all
sites, with more than double the net radiation that the Pine snow cover received. The turbulent
contribution to melt energy was largest at the Pine stand and declined with leaf area at the other sites.
In all cases it was quite a small component of melt energy.
Table 3.2.1 Total melt energy, melt time, net radiation less ground heat flux and proportion of melt
energy provided by turbulent transfer, April snowmelt 1996.
Pine
Mixed-wood
Regenerating Cleared
Melt Energy (kJ)
14500
20000
14000
18000
Melt Length (days)
13
15
13
5
(Q*s-QG) (kJ)
18000
40000
30000
46000
% Turbulent Contribution
18.6
7.9
8.5
2.5
52
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Figure 3.2.4 Proportion of melt energy supplied by turbulent transfer, 1996, PAMF.
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The measurement of Q*s probably overestimated Q*snow , the actual radiative transfer to the
snow. Q*s probably provides some energy for local advection of energy from bare patches, exposed
tree trunks and plants to remaining snowpatches during melt. However it is still instructive that the
source of energy for melt in the forested environment is radiation beneath the canopy and not turbulent
transfer of sensible heat as implied by temperature-index melt models. Interestingly, in the open
environment of the Cleared site, sensible and latent heat are unimportant to driving melt, in contrast to
the situation on the prairie environment to the south where they can contribute notably to early melts
(Shook, 1995). The difference is likely due to the exposure of aspen stems throughout the melt in the
Cleared location and the later time of year for melts at this latitude compared with more frequent
March melts in a prairie environment. Exposed stems would absorb short-wave radiation and heat the
surrounding air providing a source of energy for local advection of sensible heat to snow that would be
undetected in this experimental design.
The effect of forest cover on the interception and sublimation of snow in the boreal forest is
shown to be dramatic and dependent upon species, leaf area and removal of the canopy as in clearcutting. As the canopy of conifers is removed by clear-cutting, there is a resulting large increase in
snow accumulation. Clear-cutting of mixed-wood canopies would not achieve the same increase
because of the small amounts of snow intercepted by these canopies.
The snow available affects the energetics of melt, requiring less energy from the low energy
environment under dense conifers such as the Pine canopy. Net radiation under the canopy drives most
of the melt of forest snow covers and almost the entire melt of the clear-cut snow cover. The only
significant contribution from turbulent heat terms through the canopy is found under the Pine canopy.
Removal of coniferous canopies results in a greater snow accumulation, though after 15 years
of regrowth the Regenerating stand functioned normally with respect to interception and sublimation
despite its shorter tree height. The melt from the Cleared site was markedly larger, earlier and more
rapid than any of the natural forest covers or the Regenerating stand. On a daily basis melt water
production was greatest from the Cleared location. The flashier and greater melt from the Cleared site
suggests much greater potential for runoff when boreal forests are cleared, though a recovery is found
for 15-year-old regenerated stands.
54
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CHAPTER 4
HYDROLOGICAL PATHWAYS DERIVING FROM RAINFALL
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4.0 HYDROLOGICAL PATHWAYS DERIVING FROM RAINFALL
4.1 Interception
Interception of rainfall by forest canopies plays a major role in the summer hydrological cycle
of forests by partitioning the disposition of rainwater into evaporation from the canopy and water
delivery to the soil. Because of the large difference in leaf areas, interception by shrubs and grasses
beneath forest canopies is considered negligible in comparison to interception by the canopy in this
discussion. Interception and evaporation of rainfall play important roles in the water and energy
balances of a forest ecosystem. Evaporation timing is controlled by interception as is the relationship
between evapotranspiration and primary productivity of the forest. Sub-canopy rainfall directly affects
the soil moisture budget and generation of runoff.
Early studies of rainfall partitioning relied upon simple empirical relationships between
interception and rainfall. Extensive research in Britain resulted in interception rates for closed
coniferous forests in the UK to be well known (Calder, 1990). However, lacking a knowledge about
the physical processes made it difficult to transfer this knowledge to other forests with differing
species, differing precipitation patterns and energy availability. The emergence of the work of Rutter
(Rutter et al., 1971/1972; Rutter et al., 1975; Rutter and Morton, 1977) has taken the study of
interception from empiricism to more physically-based research.
4.1.1 Interception theory
A Rutter type model partitions rainfall into interception, throughfall, drainage and canopy freewater evaporation. Figure 4.1.1 illustrates the conceptual framework of a Rutter type model. The subcanopy rainfall that reaches the ground is the sum of the free throughfall (rainfall that falls through the
canopy without interception) and that rainfall intercepted, but draining from the canopy. The free
throughfall rate is simply defined as a fraction of the rainfall intensity while the interception rate is
represented by the reciprocal.
The drainage rate depends on the cumulative depth of water stored within the canopy and the
canopy storage capacity. Once the canopy storage capacity has been reached, drainage has a
logarithmic relationship to the cumulatively stored water in the canopy, using drainage parameters.
For stored depths of intercepted rainfall between zero and the canopy storage capacity, the drainage
rate is set to zero (Rutter et al., 1971; Gash and Morton, 1978).
The depth of water stored in the canopy is charged by intercepted rainfall and discharged by
drainage and evaporation. In a typical Rutter type model the depth of water stored in the canopy not
only defines the drainage rate but also dictates the free water evaporation from the canopy. When the
depth of stored rainfall is greater than the canopy storage capacity, it is assumed that the evaporation
from the canopy is equal to a maximum rate usually defined by the potential evaporation from the
Penman Monteith equation. For values of canopy-stored rainfall less than the canopy storage depth,
canopy evaporation is deemed to continue proportionally to the potential or maximum evaporation
rate.
Rutter's model was modified in this study, by substituting a modification of Granger's method
(Granger and Gray, 1989) of estimating evapotranspiration. Granger's method of estimating
evapotranspiration yields daily estimates. It was modified for use with the Rutter model to operate
half-hourly. Evapotranspiration is assumed to be satisfied first by free water evaporation from the
canopy storage before losses due to transpiration by vegetation can occur.
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Figure 4.1.1
The conceptual framework for partitioning of rainfall for a Rutter type model
(adapted from Gash and Morton, 1978).
57
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Variables and equations used within a Rutter model
ID
canopy storage (mm)
canopy storage capacity,
drainage does not occur for cumulative depths of stored interception below this capacity
p
the free throughfall coefficient (dimensionless)
is that fraction of rainfall that falls through the canopy without being intercepted
Pr
above canopy rainfall rate (mm min-1)
a direct tipping bucket (TB) measurement
Tf
free throughfall rate (mm min-1)
Tf = p * Pr
I
interception intensity (mm min-1)
I = (1-p) * Pr
C
the rate at which the canopy stores intercepted water (mm min-1)
C = I – E - Dr
ΣC
the cumulative depth of water stored within the canopy (mm)
Dr
the rate at which drainage occurs (mm min-1)
for 0 < ΣC < ID ,
and for ΣC > ID,
Dr = 0
Dr = Ds exp (b ΣC)
b, Ds
drainage parameters defined as (Rutter and Morton, 1977)
Ds = (3.91 x 10-5) * ID (mm min-1)
b = 3.89/ ID (mm-1)
EI
free water evaporation rate from the canopy (mm min-1)
defined by Granger's method
for all values of stored rainfall
Pn
is the sub-canopy rainfall (mm)
the sum of drainage and free throughfall
Pn = Dr + Tf
The physical structure of the forest canopy controls the interception process by altering canopy
closure and leaf area. The canopy storage capacity is strongly influenced by left area and the free
throughfall coefficient is strongly influenced by canopy closure. As these factors vary with tree
species, stand age and origin, the study was conducted at sites in differing stands and at a variety of
locations within a stand under canopies of differing physical structure.
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4.1.2 The use of the Rutter model in the PAMF
The initial efforts estimating the rainfall delivery to the soil surface for PAMF (see the 1994 1995 Annual Report) relied on regression techniques that implied that rainfall falling through the
canopy (sub-canopy rainfall) has a linear relationship to the rainfall at the top of the canopy (above
canopy rainfall). A more accurate method of examining sub-canopy rainfall is to recognize that the
relationship to above-canopy rainfall may not be linear but that many factors combine to yield a
relationship between above and below canopy rainfall. The current boreal forest rainfall data set
offered an opportunity to evaluate these parameters used in interception modelling. The data set also
provided an accurate estimate of sub-canopy rainfall intensity and depth delivery to the soil surface
and therefore a technique to partition evapotranspiration from the forested stand.
The evaluation of the parameters used in the Rutter model was accomplished using the rainfall
data from the 1994, 1995 and 1996 seasons. The storage capacity of the canopy was evaluated using a
graphical method developed by Leyton et al. (1967). Figure 4.1.2 shows canopy storage (ID)
determined by examining above and sub-canopy rainfall measurements for events with rainfall depths
greater than canopy storage. For large events, evaporation is neglected. After the canopy is saturated,
the drainage from the canopy must equal the interception. The slope of the above canopy to subcanopy rainfall plot must equal unity (the above canopy rainfall must equal the net delivery to the soil)
and the offset is the value of the canopy storage.
Figure 4.1.2 Determining the maximum canopy storage, ID (Leyton et al., 1967).
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The portion of rain that falls directly through the canopy is dictated by the gap fraction, p.
Figure 4.1.3 illustrates another graphical method developed by Leyton et al. (1967) which allows for a
determination of p by plotting sub-canopy rainfall against above canopy rainfall measurements for
events less than canopy storage. Sub-canopy rainfall is the sum of drainage and throughfall. For small
events less than the storage capacity, drainage is considered negligible and total rainfall to the soil
surface is free throughfall only.
Figure 4.1.3 The determination of gap fraction, p (Leyton et al., 1967).
Tipping bucket rain gauge data (above-canopy) and sub-canopy standard rain gauge
measurements for the Mixed-wood, Pine and Regenerating sites were used to estimate maximum
canopy storage and gap fractions for each type of canopy closure found within those sites (see Table
4.1.1). This was accomplished by summing the tipping bucket data and comparing the depths with the
standard rain gauge data. The analysis of these parameters was restricted to those sites that had tipping
bucket measurements, providing above canopy rainfall rate measurements.
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Table 4.1.1 Estimate of canopy storage (ID) and gap fraction (p) parameters for selected PAMF sites.
Correlation coefficient shown for linear fits to estimate the parameters.
Location
Mixed-wood
Pine
Regenerating
Site
ID
(mm)
r2
for p
p
aspen
0.91
0.84
0.98
open
1.05
0.77
0.81
spruce
5.71
0.42
0.46
open
0.89
0.81
0.89
mid
1.67
0.63
0.78
closed
5.08
0.54
0.86
opening
0.51
0.97
0.98
canopy
2.05
0.72
0.90
The determination of the gap fraction, p, allowed the partitioning of rainfall into throughfall
and intercepted rainfall. The accumulated depth of stored rainfall was then discharged by drainage and
evaporation. The drainage to canopy storage relationship defined by Rutter was adopted in determining
the drainage portion of sub-canopy rainfall. Thus for accumulated depths of intercepted rainfall greater
than the storage capacity, ID, the drainage rate exhibited a log relationship to accumulated rainfall. For
cumulative depths of rainfall less than the storage capacity drainage rate equalled zero. The
evaporation component of the intercepted rainfall was evaluated using an adaptation of Granger's
method of estimating evapotranspiration on a half hour basis. Evapotranspiration was deemed to be
satisfied first by free water evaporation from the stored rainfall. Thus, the canopy storage was depleted
by the evapotranspiration rate established by the modified Granger method.
4.1.3 Sub-canopy rainfall estimates and the partitioning of rainfall
A comparison of sub-canopy rainfall estimated by the Rutter model and that measured for the
same period (Fig. 4.1.4) shows sub-canopy rainfall to be closely predicted by the Rutter model.
Typical estimate error ranges from -4.4 to +6.6% of measured sub-canopy rainfall. However, at the
Mixed-wood, the positioning of the standard rain gauge under the spruce canopy is within the drip line
at the branch tips, and may cause a rain gauge undermeasurement due to canopy interference. At this
gauge only 124 mm of rainfall was measured whereas the interception model provided an estimate of
181 mm of sub-canopy rainfall.
Modelling the rainfall partitioning during rainfall events involved calculating a cumulative
water balance for the depth of water stored within the canopy. An initial depth of water was defined at
the outset of every time step. The rate at which the water accumulated within the canopy was simply
the difference between the rate of intercepted rainfall (addition to canopy) and the sum of canopy
drainage and evaporation rates (discharge from the canopy). The model allowed for a
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determination of the sub-canopy rainfall as a sum of throughfall and drainage. The model partitions
intercepted rainfall into either drainage or evaporation (see Fig. 4.1.5).
Figure 4.1.4 Net rainfall (sub-canopy) measured and estimated for May 7 to October 16, 1996.
Figure 4.1.5 Seasonal rainfall partitioning for canopy types from May 7 to October 16, 1996.
62
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Table 4.1.2 presents seasonal estimates of evapotranspiration and canopy freewater evaporation
for the Mixed-wood, Pine and Regenerating sites. For the Mixed-wood sites and the densest conifer
canopy conditions it can be seen that free water evaporation from the canopy comprises more than
35% percent of seasonal evapotranspiration and thus plays a significant role in the hydrological cycle
within forested environments. This component of evapotranspiration does not contribute to primary
productivity or sap flow but does provide direct evaporative cooling:
Table 4.1.2 Seasonal estimates of evapotranspiration, E (Granger Method) and free-water evaporation
from the canopy (Rutter/Granger) for May 7 to October 16, 1996.
Note that because of changes to soil moisture storage gross rainfall need not be greater than
evapotranspiration.
Location
Site
Mixed
Pine
Regenerating
open
Total Seasonal Depths (mm)
Rainfall
E (Granger)
11.0
aspen
28.9
8.6
spruce
122.4
36.6
32.6
8.8
mid
59.6
16.0
closed
106.4
28.6
8.4
2.6
51.0
15.7
opening
334.4
Canopy Evap
% of E
36.9
open
309.1
Canopy Evap
3 13.1
372.7
3 17.4
325.0
canopy
4.2 Evapotranspiration
4.2.1 Theory, Microclimate and Energetics
Evaporation is the process by which water changes from liquid to vapour phases and is
transported from the surface to the atmosphere. The term evapotranspiration is used when living plants
are involved in bringing the water to the surface (transpiration) where it can evaporate. The following
three conditions are required for the process of evaporation to proceed:
1) a supply of water;
2) a supply of energy;
3) a transport mechanism to carry the water vapour away from the surface.
For most natural surfaces (excluding water bodies) water is either deposited at the surface
during precipitation events or brought to the surface through the root and vascular systems of living
plants. Evaporation requires a source of energy since the phase change from the liquid to the vapour
phase consumes a considerable amount of energy; approximately 2470 Joules are required to evaporate
one gram of water at 30°C. For natural surfaces the dominating source of energy is the sun, that is, the
solar radiation received at the surface. Therefore, surfaces with a lower albedo win absorb more solar
radiation and can sustain large evaporation rates, if the other conditions are also met. It is not sufficient
that the water changes phase at the surface, but it must also be carried away. If there exists no transport
mechanism to carry the water vapour away from the surface, a saturated
63
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condition is quickly established in the overlying air, and evaporation is inhibited since the air can no
longer hold increases in its vapour content. Over natural surfaces, this water vapour is carried away
from the surface by the movement of the air, the turbulent action of the surface wind. The effectiveness
of this turbulent transport mechanism increases as the wind speed increases, and is greater for rougher
surfaces.
4.2.2 Energy and Water Management by Canopies
The solar energy received by a natural surface is partitioned into three major energy
components:
i) stored energy; that is the energy stored within the soil and vegetation;
ii) sensible heat; that is the thermal energy within the overlying air, and
iii) latent energy, or the energy associated with evaporation from the surface.
Growing plants win also use a small amount of energy to drive photosynthesis; this amount is
typically less than 10% of total energy supply. The energy stored within the soil is also small, reaching
a maximum of 20% of the total energy available during spring soil thawing (Granger, 1991). Although
the energy stored within the canopy can be significant when considering periods about one hour, the
diurnal cycle of daytime heating and nighttime cooling is such that this term tends to be very small
when daily periods are considered. The major components into which the solar energy is thus
partitioned are the sensible and latent heat terms, that is, the heating of the overlying air and
evaporation. Since both these energy components are driven by similar turbulent transport
mechanisms, the major factor governing the partitioning of energy between these two components is
the availability of water for evaporation. Wetter surfaces will evaporate more and will remain cooler,
conversely, dryer surfaces will evaporate less and will be subjected to more surface heating.
This is in fact how forest canopies "control" their own microclimate. By managing the water
that they receive (by interception of snow or rain, and by directing intercepted water toward effective
storage for uptake by their roots), by controlling its availability for evaporative cooling, forest canopies
can exert a significant and effective control on their own environment. Different species have adapted
different management strategies for making use of the water end energy that they receive. For example,
invading (colonizing) deciduous species such as aspen and many shrubs initially have poorly
developed roots and must grow quickly to survive. Therefore where there is adequate water they
transpire quickly and sustain high latent heat fluxes. When drier weather occurs, their rooting systems
are unable to draw sufficient water to sustain evaporation and sensible heat becomes a dominant flux.
More mature old growth forests can adopt a more effective management strategy because of their welldeveloped roots systems and low competitive requirements.
4.2.3 Modelling Evapotranspiration
Evaporation from a surface will depend on available water at the surface, energy received by
the surface, and the efficiency of the transport mechanism. Many schemes and models have been
developed, based on one or more of these considerations, often that most closely related to the field of
interest (or knowledge) of the modeler. For example, agronomists have tended to relate
evapotranspiration to the availability of soil water, and climatologists have developed relationships that
moderate the efficiency of the turbulent transport mechanism; many scientific studies have
concentrated on the energy balance approach.
For operational purposes, a model must use readily-available information, that is, routinelyobserved meteorological variables and easily-derived physical parameters. Since soil moisture
conditions can be highly variable and difficult to monitor, most useful models of evapotranspiration
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use a combination of energy and aerodynamic considerations, with the physical parameters describing
surface roughness being related to easily-determined parameters such as vegetation height and density.
For the Prince Albert Model Forest sites, the evapotranspiration was calculated from observed
net radiation, air temperature and humidity using a method developed by Granger and Gray (1989) and
modified by Granger (1996). The model uses a general evapotranspiration equation for non-saturated
surfaces, based on a combination of energy balance and aerodynamic relationships; the method is an
extension of the well-known Penman (1948) general equation for saturated surfaces.
The general evapotranspiration equation for non-saturated surfaces, derived by Granger and
Gray (1989) is given as:
E = [∆G*(Rn-S) + γGEa]/( ∆G+γ)
(4.2.1)
where, ∆ is the slope of the saturation vapour pressure vs temperature curve; G, which is termed the
relative evaporation, is the parameter describing the deviation from saturated conditions; Rn, is the net
radiation; S is the soil heat flux; γ is the psychrometric constant and Ea, the drying power of the air, is a
hypothetical evaporation rate that would occur if the humidity gradient in the air were equivalent to the
vapour pressure deficit. Equation (4.2.1) was initially derived for estimating daily values, and as such
does not consider the changes in stored energy within the canopy. Since the evapotranspiration, E, is
required in units of mm/d, the terms Rn , S and Ea can be obtained in units of mm/d equivalent
evaporation by dividing the energy flux terms by the latent heat of vaporisation and adjusting for
appropriate time units.
The slope of the saturation vapour pressure vs temperature curve, A, is derived from the
average air temperature using:
∆ = α1 β1 e*/(T + β1)2, kPa/oC
(4.2.2)
where T is the air temperature, °C.
for T ≥ 0, α1 = 17.27 and β1 = 237.3;
for T < 0, α1 = 21.88 and β1 = 265.5.
The saturation vapour pressure, e*, at the temperature, T, and is given as:
e* = 0.611 exp [α1T/(T + β1)], kPa.
(4.2.3)
The drying power, Ea, is derived using a Dalton-type formulation:
Ea = ƒ (u)* (e* - ea),
(4.2.4)
where, e, is the actual vapour pressure of the air (kPa), and ƒ (u) depends on wind speed and surface
roughness:
ƒ (u) = a + bu
(4.2.5)
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where the wind speed, u, is given in m/s; and the constants a and b are related to the surface roughness
by:
a = 8.19 + 0.0022zo,
b = 1.16 + 0.0008zo.
(4.2.6a)
(4.2.6b)
The roughness length, zo, is given in metres. If the roughness height is not available, it can be
estimated from the mean height of the vegetation, hc, by:
zo = hc / 7.6.
(4.2.7)
The average roughness heights for the study sites at the PAMF were determined from aerodynamic
considerations and/or from eddy correlation measurements; these are:
Site
zo , m
Pine
1.40
Mixed-wood
0.95
Regenerating
0.70
Cleared
0.37
Burn
0.75
The relative evaporation, G, is obtained from a dimensionless relationship with the relative drying
power, D = Ea /( Ea + Rn - S):
G = 1/(0.793 + 0.2 e4.902D) + 0.006D.
(4.2.8)
Daily values of evapotranspiration are then obtained from equation (4.2.1). This model is called
the G-D evapotranspiration model because it is controlled by the relationship between the
dimensionless parameters, G and D (it is also sometimes called the Granger method).
The G-D model performance was evaluated by comparing the estimated daily values with those
obtained using specialized eddy-correlation equipment. This equipment, which obtains very fast
response measurements of the turbulence-induced fluctuations of wind speed, air temperature and
humidity, and does the appropriate statistical analysis based on the mechanics of atmospheric
turbulence, provides a "somewhat direct" measurement of the actual evaporation rates. These eddycorrelation sensors were deployed for selected periods at the Cleared and regenerating sites during the
summer of 1996. Figure 4.2.1 shows comparisons of the cumulative evapotranspiration mounts as
derived by the model and the eddy-correlation instruments at these two sites. For the Cleared data,
covering the entire summer period May to September, and for the regenerating site data, covering the
period August to September, the agreement between modelled and measured evapotranspiration is
remarkable. The figure does confirm the usefulness of the model for providing reliable estimates of the
daily evapotranspiration rates.
66
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Figure 4.2.1 Comparison of the cumulative evapotranspiration as derived by the model and by direct
measurement using eddy-correlation instrumentation, amounts over the Cleared site for the period May September, and over the Regenerating site for the period August - September 1996.
For inclusion within the hydrological runoff model, the G-D evapotranspiration model was
modified to calculate the rates for half-hour periods. The application of such an evaporation model to
periods shorter than one day required the three following adjustments:
I. Consideration was given to the changes in storage of energy within the forest canopy. In Eq.
4.21, the term (Rn-S) becomes (Rn -S-dU/dt), where dU/dt is the canopy energy storage term, and
is estimated as: dU/dt=mc *cp,* (T2*T1); mc is the estimated mass of the canopy per unit area, cp
is the specific heat of the canopy, and T1 and T2 are the canopy temperatures at the beginning and
end of the period.
II. The wind function, ƒ (u), (used in Eq. 4.2.4) was reformulated to represent a dynamic wind
profile. Because of the diurnal cycling of atmospheric stability, the effect of the wind speed on
the transfer function will be very different when considering hourly or daily periods. The
appropriate formulation of the wind function for short time periods (of the order of one hour) is:
ƒ (u) = 0.622 k2 ρa u / [ P ln ((z-do) / zo) 2 ],
where, k, the von Karman constant has a value of 0.4, ρa being the air density, u is the wind
speed, P is the mean atmospheric pressure at the site, z is the height at which the wind speed is
measured, do is a displacement height related to the canopy height, and zo is the roughness height.
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III. Special consideration is given to the nighttime periods when the net radiation is negative. For
those periods when the net available energy (Rn-S-dU/dt) is negative, the relationship between G
and D no longer holds. (The dimensionless parameters G and D are limited to values between 0
and 1; a negative available energy will result in D> I .) In these cases, the method developed by
Priestley and Taylor (1972) was used to estimate the evaporation (condensation) as a fraction of
the available energy; thus:
E = 1.26 ∆ (Rn -S-dU/dt)/( ∆+ γ).
Comparison of the G-D model runs using daily periods with the daily totals of the model runs
using half hour periods is presented in Fig. 4.2.2. The figure shows this comparison for the estimates at
the Mixed-wood site using the data from the 1995 summer season. The agreement between the two
approaches is very good; the mean difference between the two estimates is less than 0.01 mm/d, and
the standard deviation of the difference is 0.27 mm/d.
4.2.4 Evapotranspiration Fluxes
The G-D evapotranspiration model was used to determine the evaporative fluxes at the four
PAMF sites. The daily values were then summed to provide the season total evaporative loss from
each forest cover type. Figures 4.2.3, 4.2.4 and 4.2.5 present the cumulative evapotranspiration
amounts obtained from these sites for the 1994, 1995 and 1996 summer seasons (May to September).
In 1994, the Mixed-wood site only began operation in July; for graphical purposes, the starting point of
the cumulative curve for the Mixed-wood site was therefore set to a value approximately equivalent to
that of the Regenerating site. Figures 4.2.3 to 4.2.5 show essentially the same trend for each of the
three observation periods; the mature Pine site produced the largest evapotranspiration mounts,
followed in order by the Mixed-wood, Regenerating and Cleared sites.
Figure 4.2.2 Comparison of the daily estimates of evapotranspiration over the Mixed-wood site using the
model runs for daily periods with the daily totals of the model runs using half-hour periods,
for the period May-October, 1995.
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Figure 4.2.3 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1994.
Figure 4.2.4 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1995.
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Figure 4.2.5 Cumulative evapotranspiration mounts for the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May-September, 1996.
Table 4.2.1 provides the season totals and the ratio of the season total at each site to that at the
Pine site. The table shows that, except the Regenerating site where the ratio varied between 0.74 and
0.81, the ratio values remained relatively constant for the three seasons.
Table 4.2.1 Seasonal (May - September) evapotranspiration (mm) from the various forest cover types for
the 1994, 1995 and 1996 seasons.
The ratios of the seasonal evapotranspiration to that at the Pine site are also given.
1994
1995
1996
Site
total
ratio
total
ratio
total
ratio
Pine
390
1.0
407
1.0
371
1.0
Mixed-wood
342*
0.88*
359
0.88
322
0.87
Regenerating
315
0.81
302
0.74
280
0.76
Cleared
268
0.69
288
0.71
259
0.70
* May - June totals estimated based on the ratio to that of Pine for the period July-September.
4.2.5 Energy Partitioning
The evapotranspiration totals do not, by themselves provide a complete picture of the
effectiveness of water use by various forest covers. Knowledge of the relationship between the
partitioning of the incoming solar energy and the use of water can be important to the forest manager
concerned with the sustainable use of this resource.
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The solar radiation energy absorbed by a surface is governed by its albedo. The albedo of the
various forest cover types was determined as the ratio of the reflected and incoming short-wave
radiation measured directly at each site. The average daily albedos for the four land-cover types are
presented in Table 4.2.2; values are the means of 1994 and 1995 observations. As expected, the conifer
stands (Pine and Regenerating) show the highest rate of radiation absorption, suggesting that more
energy is available for evapotranspiration and for heating the air and the forest soil.
Table 4.2.2 Observed albedo values for various land cover types in the boreal forest.
Land cover
Albedo
Pine
Regenerating
Mixed-wood
Cleared
0.091 ± 0.005
0.129 ± 0.007
0.145 ± 0.015
0.152 ± 0.023
The daily energy balance (expressed in units of mm water equivalent) was derived for each of
the forest stands. Net radiation, Rn and soil heat S were measured directly. The evaporation, E, was
calculated from observed temperature and humidity observations using the G-D model. The sensible
heat term, H, calculated as the residual in the energy balance equation then includes both the turbulent
transfer of sensible heat above the surface and the heat storage within the canopy. On a daily basis the
canopy heat storage term tends to be small and is usually neglected.
Figure 4.2.6 shows the monthly energy balances at the four study sites for June 1995. As can be
seen, the differences in the net radiation at these sites correspond generally to the differences in albedo
(Table 4.2.2). The soil heat term is always small. For the mature stands (Pine and Mixed-wood), the
ratios of sensible to latent heat terms are comparable, with monthly Bowen ratios between 0.5 and 0.6.
However, the regenerating and Cleared sites produce much less evapotranspiration, with Bowen ratios
of 0.78 and 0.98, respectively, suggesting higher surface heating.
The partitioning of the incoming net radiation energy into the various energy components, in
particular the turbulent latent and sensible heat terms, provides a very descriptive indication of the
forest's capacity to manage its own environment. Figs. 4.2.7, 4.2.8 and 4.2.9 present the monthly
Bowen ratios (QH /QE) at the four forest sites for the period May to September, for the 1994, 1995 and
1996 seasons, respectively. These figures show:
·
The Bowen ratio at the Cleared site remains relatively constant near unity, showing that
this site does not respond to variations in the supply of energy and water, and that
environmental controls are not exerted;
·
The mature sites have lower Bowen ratios, showing that a greater portion of the
received energy is directed toward evaporation, and less to surface heating; the Mixedwood Bowen ratios are generally lower than those for the Pine stand;
·
The mature sites could draw on soil moisture reserves from snowmelt to ensure early
summer development of the required foliage. However, the Regenerating site produced
less evapotranspiration and the Cleared site was the least effective evapotranspirer.
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Figure 4.2.6 Monthly energy balance for PAMF for June 1995 expressed as mm water equivalent.
Figure 4.2.7 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May - September 1994.
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Figure 4.2.8 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May - September 1995.
Figure 4.2.9 Monthly Bowen ratios at the Pine, Mixed-wood, Regenerating and
Cleared sites for the period May - September 1996.
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All sites, except the Cleared site, tended to reduce their water use during the warm months of
July and August. All the sites showed a relative increase in evapotranspiration (smaller Bowen ratio) in
September (1994 and 1995). The amplitude of these adjustments during dryer periods is also an
indication of the forest's ability to respond to its environment. The mature forests stands, in particular
the Pine stand, show a significant reduction in the evapotranspiration during the warm, dry months. For
the Cleared site, the July and August water use remained unchanged from that of May and June,
suggesting that the immature vegetation exerts little control over the partitioning of the energy. The
change in Bowen ratio at the Regenerating site during the warm summer months is less significant than
that at the mature stands, suggesting that, although it may be well established, the young plantation
does not yet have the characteristic water use behaviour of a mature forest. One can see a progression
in the amplitude of these adjustments at the regenerating site over the three years of study. In 1994, the
Regenerating site showed a much reduced response compared with the mature stands. This was
increased in 1995, and in 1996 the Regenerating stand Bowen ratios, while consistently higher than
those of the mature stands, responded similarly to the changing environmental conditions.
The reasons for the differing evapotranspiration/energy management behaviour of regenerating
and mature forest stands are not clear but probably relate to differences in the soil and root systems.
Leaf areas of the Pine and Regenerating sites are similar. However, soil moisture withdrawal patterns
suggest that the harvested sites have much shallower root zones than do the mature stands; these sites
are also affected by the absence of an organic upper layer, and greater soil compaction. Other studies in
the PAMF Project have shown differences in nutrient status (Pennock 1995) which might also affect
primary productivity and therefore evapotranspiration.
A direct consequence of the reduced evapotranspiration in clearcuts and regenerating stands is
greater surface heating. Thus, the effect of the partitioning of energy can also be shown by examining
the response of summer surface temperatures in various stands. Figure 4.2.10 shows the daily
maximum surface temperatures observed at these sites in June 1995. The surface temperatures are
observed using infrared sensors mounted above the canopy. The figure shows that at the three
established sites, the surface temperature trends are similar, with the Mixed-wood canopy being cooler
than the conifer canopies. However, the Cleared site shows maximum surface temperatures that are an
average 10 °C higher than the other sites. These surface temperatures often exceed the critical seedling
wilting point of 35°C. This, of course, can have implications for the successful regeneration of spruce
and pine seedlings.
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Figure 4.2.10 Daily maximum surface temperatures observed at the Pine, Mixed-wood,
Regenerating and Cleared sites in June 1995.
4.2.6 Application of Remote Sensing to Inventory Evapotranspiration and Microclimate
Remote sensing can be a very useful tool for forest managers. Indices, such as the Transformed
Vegetation Index (TVI), are already available which provide a measure of the vegetative matter
present. When working with a Landsat image, this index is calculated as:
TVI = {[(TM4 -TM3)/(TM4 + TM3) + 0.5]1/2 }*100 - 60,
(4.2.9)
where TM3 and TM4 are the reflectance values observed in the thematic mapper channels three and
four, respectively. Although an index such as TVI can provide a very good estimate of the mass of
vegetation present, it does not provide a complete picture of the effectiveness of forest regeneration
since, as shown above, the forest's ability to manage the energy it receives is also a factor in its
regeneration. A second index, the Transformed Thermal Index (TTI), was therefore devised to provide
an indication of the forest stand's ability to partition the energy:
TTI = {[(TM5 –TM4)/(TM5 + TM4) + 0.5]1/2 }*100 - 40,
(4.2.10)
Larger values of TTI show surfaces subjected to greater heating. The combination of the two indices,
TVI and TTI, can provide a more comprehensive picture of forest stand regeneration. Figure 4.2.11
shows a transect of these two indices for four forest stands, mature aspen-dominated mixed-wood,
mature jack pine, a fresh clear-cut and a jack pine plantation. The two indices show an inverse
response to the surface vegetation; generally, the greater the vegetation index, the lower the thermal
index, showing the effect of vegetation on the partitioning of incoming energy. The mature aspen
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mixed-wood shows more vegetation and lower surface heating than does a mature conifer stand; this
concurs with the net radiation and surface temperature observations shown in the previous section. The
clear-cut, as expected, shows the least vegetation and a very large thermal index as can be expected for
a site where, with little vegetation for evapotranspiration, the incoming energy is used largely for
heating the surface and the air. The regenerating stand, however, provides an interesting case;
Although the vegetation index suggests that this stand has almost recovered to the level of the adjacent
mature stand, the thermal index remains higher suggesting, as shown in the previous section, that this
stand has not yet fully regained its ability to manage the water and energy that it receives.
Figure 4.2.11 Transects of the Landsat-derived vegetation and thermal indices for mature aspendominated mixed-wood, Jack pine, fresh clear-cut and regenerating Jack pine stands (June 1992).
76
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CHAPTER 5
HYDROLOGICAL PATHWAYS IN THE SOIL
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5. HYDROLOGICAL PATHWAYS IN THE SOIL
Hydrological pathways in the soil include infiltration of water to the soil, vertical water
redistribution within the soil and horizontal flow of water as sub-surface runoff. Much of the water that
does infiltrate is stored until it is withdrawn by plant roots and transpired during evapotranspiration. In
sandy soils some water percolates to the groundwater system and is stored in aquifers with slow
horizontal flow velocities. Water that does not directly infiltrate is presumed to runoff to streams.
Generally this can only occur during snowmelt or large rainfall events. Aspects of the soil-water
system that are particularly important for consideration are the relationships amongst soil texture,
water retention and infiltration characteristics of the soil. The strong association between soil type and
canopy cover means that distinctive patterns of water delivery to soil, soil water withdrawal by
evapotranspiration and infiltration characteristics develop, characterising much of the PAMF
hydrology. Mathematical characterisations of these patterns permit the development of soil hydrology
algorithms that can be used to simulate the basin response to inputs of rain and snowmelt water.
5.1 Soil Properties
At the Pine site, the soils were classified as Orthic Eutric Brunisols developed on glaciofluvial
parent material. The soils at the other sites were developed on silty wind-blown parent material. The
Mixed-wood and Regenerating site soils were both classified at Orthic Gray Luvisols while the
Cleared site soil was a Brunisolic Gray Luvisol. At both the mature forest sites (Pine and Mixed-wood)
the leaf-litter layer was about 50 mm thick. At the Regenerating site, one pair of ridge and furrow soils
had a leaf-litter layer but neither the ridge nor furrow at the other monitoring location had a significant
amount of poorly decomposed organic material. Both of the ridge locations at the Cleared site had
thick (50-100 mm) leaf-litter layers but there was no organic layer at the furrow locations.
The results of the particle size analysis are shown in Table 5.1.1. The textural classification for
the soil at Pine site is loamy sand and the other soils have sandy loam textures at the surface that grade
into sandy clay loams at depth.
The surface soil texture is quite variable at all sites except the Cleared site, where recent mixing
has resulted in a more uniform spatial distribution of soil material. Similar soil textures are found on
the Mixed-wood and Regenerating sites. The Cleared site has more sand and the Pine site is much
sandier and has less clay than the other sites.
When sampling for bulk density, the presence of stones, tree roots and compaction in the
sample cores led to unreliable data. There may be some compaction at the bottom of the furrows at the
Cleared site but this could not be proved statistically. Fortunately, soil textural information can be used
to determine bulk densities from well-known relationships. Estimates of soil bulk densities are
calculated from empirical relationships developed for Saskatchewan soils (CanSIS) and are presented
in Table 5.1.2. In undisturbed sites the upper profiles of the soils where a sandy-loam or loam soil
dominated, bulk densities ranged from 1.26 to 1.37 g/cm3.
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Table 5.1.1 Percentages of sand and clay (and standard deviations) for the four forest sites.
Pine
Mixed-wood
Regenerating
Cleared
(7.8)
(14.7)
(13.2)
(6.8)
(2.6)
(1.5)
45.2
45.6
50.9
52.9
57.1
56.7
{3.4)
(11.6)
(12.4)
(1.6)
(4.2)
(0.4)
65.1
60.7
61.4
60.9
51.6
55.7
(5.7)
(7.2)
(1.8)
(5.6)
(7.7)
(2.8)
(3.8)
(2.5)
(1.6)
(4.7)
(5.0)
(2.9)
9.2
10.0
12.1
18.1
16.9
16.1
(1.2)
(2.5)
(6.7)
(1.1)
(0.1)
(0.5)
7.2
7.7
10.4
13.1
23.3
18.0
(0.7)
(1.8)
(3.8)
(1.4)
(4.8)
(1.6)
Sand
0-0.05 m
0.05-0.1 m
0.1-0.2 m
0.3-0.5 m
0.7-0.9 m
1.4-1.6 m
79.3
75.8
69.6
64.9
73.4
93.0
(12.0)
(14.3)
(14.9)
(6.1)
(2.2)
50.1
51.0
50.9
51.8
44.6
42.6
5.7
4.2
8.4
14.3
11.4
4.0
(0.7)
(4.0)
(9.2)
(2.0)
(0.9)
8.1
6.5
10.2
18.0
24.9
23.5
Clay
0-0.05 m
0.05-0.1 m
0.1-0.2 m
0.3-0.5 m
0.7-0.9 m
1.4-1.6 m
Table 5.1.2 Estimated bulk densities for the four forest sites.
Depth Interval
(m)
0 - 0.05
0.05 - 0.10
0.10 - 0.30
0.30 - 0.50
0.70 - 0.90
1.40-1.60
Pine
Mixed Wood
Regenerating
(g/cm3)
1.37
1.33
1.37
1.44
1.56
1.79
1.26
1.27
1.32
1.42
1.53
1.72
1.25
1.27
1.33
1.42
1.54
1.73
Cleared
1.25
1.25
1.35
1.42
1.55
1.74
Harvesting of the forest in areas such as the Regenerating and Cleared sites has disturbed
approximately 51% of the landscape in similar soils in central Saskatchewan. About 49% of a clear-cut
is not rutted by machinery or skidding (Richardson et al., 1996; Wulfsohn, 1995). The bulk densities
for the disturbed portion of the harvested area increase by 5.3% to 11.3%, depending on profile depth
and harvesting method (Pennock, 1997; Richardson et al., 1996). Bulk density in the undisturbed
portion of the landscape is generally unaffected (Dr. D. Pennock, personal communication) although in
the top 150 mm bulk density may actually decrease as the logging slash and surface vegetation are
incorporated into the surface layer (Walley et al., 1996).
79
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5.2 Infiltration Theory
Infiltration is the movement of water through a soil surface into the soil profile under both
gravitational and capillary forces. The presence of frozen soils in the PAMF during the spring
snowmelt period requires that differing approaches be used to describe infiltration from snowmelt and
that from rainfall in the summer. Usually frozen soils have low infiltration capacities and unfrozen
soils have higher capacities, so runoff events are much more likely during the snowmelt period when
the water application rate can more easily exceed the infiltration rate.
5.2.1 Frozen Soils
In the western Canadian boreal forest climate, the most important factor influencing the
direction of snow meltwater to streamflow runoff is the infiltration of meltwater into frozen soils.
Because of the large mount of meltwater generally available, the flux of water either as runoff or into
soils can be the largest hydrological event of the year. Infiltration into frozen ground is controlled by
the thermal and hydrophysical properties of the soil, the soil temperature and moisture regimes and the
quantity and rate of release of water from the snowcover. The movement of water is accompanied by a
phase change that depends on soil temperature and rate of water movement. In this sense frozen soil
infiltration is distinctive from infiltration to unfrozen soils where phase change and thermal processes
are considered unimportant. If only a shallow layer of soil is frozen then the energy exchanged
between the soil and infiltrating water can thaw the layer and the soil will behave as an unfrozen soil.
Soils frozen to depths of less than 150 mm usually behave as unfrozen soils.
There are few simple formulations to predict the presence of frozen soils during the snowmelt
period. The freezing process over the winter depends on the energy balance with respect to snowcover,
the thermal conductivity of the snow, convection cells within the snowcover and the thermal
conductivity and unfrozen water content of the soil. Migration of soil water to the freezing front and
vapour transfer through porous soils will complicate the above relationships. Molnau and Bissell
(1983) proposed a completely frozen ground index (Fi) which is expressed as a function of the mean
daily air temperature, T (°C), and snow depth, ds (cm)
Fi = 0.97 Fi-1 – Te-0.23ds
(5.1)
where Fi on day i is related to that on day i-1. In the Pacific Northwest US, Fi values between 56 and
83 denoted the transition between unfrozen and frozen soils.
Comprehensive physics-based calculations of infiltration to frozen soils have been recently
developed by Tao and Gray (1994) and Zhao et al. (1997). These procedures have been used to
develop one-dimensional coupled heat and mass transfer simulations for water movement into frozen
soils. The complexity of the parameterisations (13 equations with 13 unknown variables) precludes
their present use for the PAMF area because of the difficulty in measuring the large number of input
data required and characterizing the spatial variability of this data. However, conceptual model based
on the physics of the phenomenon may be used to describe and classify infiltration to frozen soils
sufficiently well to provide a degree of prediction.
Gray et al. (1985) grouped frozen soils into three classes for the purposes of calculating
snowmelt infiltration to completely-frozen soils:
1) Unlimited. Soils with large, connected, air-filled macropores that can infiltrate all or most snow
meltwater.
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2) Limited. Soils where infiltration depends upon the SWE and water/ice content of the top 300-mm
soil layer just before snowmelt. An experimentally-derived relationship between snowmelt infiltration,
If, SWE and the pre-melt degree of pore saturation of the top 300 mm of soil, θp, is
Iƒ = 5(1 − θp) SWE0.584
(5.2)
and was developed from extensive measurements on tilled soils in the prairie region of western
Canada. Its application to the boreal forest is unknown, but the relationship was developed using a
range of soils from sand to heavy days.
3) Restricted. Soils with almost no snowmelt infiltration because of impedance caused by ice lenses at
the soil/snow interface or at shallow depths. Restricted infiltration occurs after a wet soil (in fall)
freezes, midwinter melt events cause a small mount of infiltration that refreezes in the soil as "concrete
frost” or on top of the soil as “basal ice”.
5.2.2 Unfrozen Soils
The actual rate at which moisture enters the soil profile results from several conditions during
that time. These conditions include rainfall rates, distribution of soil moisture and permeability. The
appendix provides a very brief description of Richard's equation for vertical flow through porous
which is the foundation for understanding flow through unsaturated porous media. The Green-Ampt
equation, a semi-empirical formulation of infiltration approximating Richard' s equation, is used in the
PAMF model formulation and is also described detail in Appendix B.
For an infinitely small ponding depth Richard's equation can be simplified to the Green-Ampt
formulation shown below:
(5.3)
where ƒ(t) is the instantaneous infiltration rate at time (t) expressed in mm/hr, F(t) is the cumulative
infiltration given in mm; ψƒ is the wetting front suction head expressed in mm (see Appendix B for
description); K(mm/hr) is the saturated hydraulic conductivity; ∆θ is the change in volumetric soil
moisture expressed as a volumetric ratio.
Since f(t)=dF/dt, by integrating, the Green-Ampt equation for cumulative infiltration is
obtained,
(5.4)
F(t) is implicit and can be solved if the equation parameters are known using a trial and error or
Newton-Raphson iterative approach. It can also be solved directly using numerical approximations of
the Lambert Omega function described in the next section. Green-Ampt equation parameters can be
obtained from either retention curve data or from soil texture information derived through the texture
equations derived by Rawls et al. (1982). Green-Ampt infiltration rates are not only a function of the
parameters in the equation but also a function of rainfall rates. For a constant rainfall
81
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rate, the problem is outlined by Chow et al. (1988) and is described below. The infiltration formulation
for BEERS is based on the Chow (1988) description, but it has been modified as described in the next
section.
The Chow (1988) description of infiltration from constant rainfall requires that before ponding
on the soil surface, the potential infiltration rate (which is always very high at the beginning of a
rainfall) is higher than the rainfall rate. This implies that rainfall will infiltrate at a rate lower than the
potential rate. The time required for surface pending to occur can therefore be calculated by setting
infiltration rate = rainfall rate since Fp = i · tp. Substituting into the Green-Ampt infiltration rate
equation and solving for tp provides:
(5.5)
and
(5.6)
Therefore, before the time of pending the infiltration rate is simply the rainfall rate. After the time of
pending, rainfall will infiltrate at the potential rate. The potential rate cannot be calculated directly
from the Cumulative Green-Ampt equation because k assumes pending for the entire rainfall duration.
Since pending does not necessarily occur until some time into the rainfall, the total cumulative
infiltration up to the time of pending may be less than the total cumulative potential infiltration. To
adjust for this, the cumulative infiltration equation is solved for t by setting F = i · tp and solving for a
new t’. Cumulative infiltration is then estimated by solving for potential infiltration as before, except
starting from t', not the actual time tp.
(5.7)
A similar procedure is followed for variable rates of rainfall, but the complexity increases. Here each
rainfall interval must be evaluated about whether ponding occurs throughout the interval, during the
interval or whether there is no ponding at all.
Modified Green-Ampt for the PAMF
The Green-Ampt equation described above is based on continuous or near-continuous rainfall
occurring during a single rainfall/runoff event. To apply a continuous simulation model (as with the
BEERS model), modifications to account for soil drying and no-rain periods must be made to the
Green-Ampt formulations. Another modification was a direct solution to F(t) using the Lambert
Omega function and approximation to that function. This is also described in Fig. 5.2.1.
82
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Figure 5.2.1 Flowchart representing the Green-Ampt Infiltration process
for variable rainfall conditions.
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5.3 Frozen Soil Infiltration
5.3.1 Soil Freezing
All sites possessed frozen soils at the time of melt except the Cleared site, where the upper few
tenths of a metre of soil thawed during the pre-melt period. As an example, soil temperatures at the
100-mm depth are shown for the Pine and Cleared sites along with air temperatures at both sites in Fig.
5.3.1. The Pine soils are two to eight °C colder than the Cleared soils in the pre-melt period (Julian
Day 60 or 1 March). In 1994 and 1995 the Cleared 100 mm deep mils were at -1 °C on 1 March and
therefore not completely-frozen, while in 1996 the soil reached -5 °C and may be considered frozen.
The snowcovers at the Cleared site were often over twice as deep as those under coniferous canopies
(Spruce, Pine, Regenerating) providing much more insulation to the Cleared soils. The warmer winter
soils at the Cleared site were further warmed by early partial melts. Air temperatures in Fig. 5.3.1 show
from one to three early partial thaws before temperatures consistently rose above freezing. These thaws
would not strongly affect snowcovers under forest canopies as radiation cannot make a large secondary
contribution to sensible heat there and inversions often develop under forest canopies and maintain
colder air near the snow surface. However it has been noted that the early thaws do melt the open
snowcovers found in the Cleared site, reducing snowcovered area, snow depth and permitting small
amounts of meltwater to enter the soil. As a result, by the time the primary snowmelt period occurred
at the Cleared site the 100-mm depth soil temperature had reached or exceeded 0 °C in all three years
of measurement. The Cleared soils will be discussed in the section on infiltration to frozen soils, but it
should be noted that the upper soil profile was thawed during the snowmelt period in every year of
measurement.
84
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Figure 5.3.1 Soil temperatures during snowmelt, 1994-1996. The soil temperature at 100 mm depth is
shown for the Pine and Cleared sites along with air temperatures. Air temperatures denote the time of
most active snowmelt.
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5.3.2 Infiltration
Frozen soil infiltration was calculated as the depth of water equivalent (mm) infiltrated into the
upper 1.0 m of soil (approximate sometimes) from the pre-melt period to the depletion of the
snowcover. For the purposes of comparing to infiltration snow water equivalent was the average
snowcover SWE (mm) for the site with any rain on snow or snowfall during the snowmelt added.
Because snow water often moves horizontally through the pack and shallow organic layer in soil
during snowmelt, averages for the site were used to compare with each point measurement, except
under the spruce tree in the Mixed-wood where the exceptionally small SWE found under the tree was
used. Measurements of infiltration were obtained from 10 sites over three melt seasons totalling 30
point measurements. These measurements are graphed with SWE (mm) in Fig. 5.3.2 along with a line
indicating unlimited infiltration. Unlimited conditions for infiltration occurred for some points at all
sites in Spring 1995 whereas in other years the points fall in the Limited or Restricted categories. In
1994 and 1996 points at the Regenerating and Cleared and in 1996 a point at the Mixed-wood site had
Restricted infiltration with an insignificant soil water recharge from snowmelt.
Figure 5.3.2 Infiltration of snowmelt water into the top metre of frozen soils 1994-1996.
The solid line denotes unlimited infiltration.
86
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Following Gray et al.'s (1985) hypothesis for infiltration to frozen soils, the ratio of infiltration
to SWE was found to decline with pre-melt soil water content as shown in Fig. 5.3.3. The trend
displays a scatter because of differing soil texture, rates of melt, basal ice layers and incompletelyfrozen soils in some cases (Cleared site). The Unlimited case is found for soils with less than 150 mm
of water in the top 1.0 m of soil and the Restricted case occurs for soils with more than 150 mm of
water. The Limited case occurs for soils with from 100 to 250 mm of water. In several cases the
Cleared soils behave differently from the others and have ratios of infiltration to snowmelt well above
the fiend. This is presumed to be due to their partially-frozen state at the time of most snowmelt.
An attempt was made to use the Gray et al. model for these forest soils, but it was not directly
applicable as it overestimated infiltration. This is most probably due to textural differences between
tilled prairie soils and untilled forest soils and to differing melt rates between prairie and forest
snowpacks. However a physically-based infiltration expression was developed for the non-clear-cut
forest soils using nonlinear regression techniques and forcing a form consistent with the physics of
infiltration. The expression is,
(5.8)
with a correlation coefficient of 0.90 and a standard error of estimate of 16.9 mm. If is infiltration to
the top 1.0 m of soil (mm), SWE is the snow water equivalent of the snowcover plus any precipitation
during snowmelt (mm) and θp is the degree of pore saturation (water content over voids content) of top
1.0 m of soil before snowmelt began. The form of this equation does not conform at first appearance to
the physics of the process. For instance infiltration approaches zero as θp approaches 0.74. The reason
for this is most likely due to development of impermeable basal ice at the bottom of the snowpack or in
upper soil layers when there are large amounts of water in the soil profile. The exponent to SWE is
greater than one to reflect the dual role of SWE in providing meltwater for infiltration and insulating
the soil such that incomplete freezing occurs under deep snowpacks.
The fit of Eq. 5.8 to the measurements at all sites except the Cleared is shown in Fig. 5.3.4
where modelled and measured values are plotted along with a 1:1 correspondence line for comparison.
No consistent deviation from the 1:1 fit is shown over the range of infiltration values. The form of Eq.
5.8 to predict infiltration to frozen soils as a function of SWE for various degrees of pore saturation is
in Fig. 5.3.5. At pore saturations greater than 0.74 (approximately 34% soil moisture content) the soils
become Restricted and impermeable and at pore saturations less than 0.3 (approximately 14% soil
moisture content) the soils become Unlimited and can infiltrate all snowmelt water normally found in
the PAMF. Note that the power function assigned to the SWE, does not produce realistic If values for
SWE greater than 100 mm and so is not recommended for use outside the PAMF region. The result of
Eq. 5.8 is that wetter soils or soils with less snow will undergo less infiltration during the spring
snowmelt period. Infiltration to clear-cut soils in spring will sometimes exceed this estimate.
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Figure 5.3.3 Infiltration efficiency as a function of pre-melt soil water content.
Figure 5.3.4 Measured and modelled infiltration to frozen soils, using Eq. 5.8 and measured pre-melt soil
water status and snow water equivalent. Cleared sites are excluded.
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Figure 5.3.5 Snowmelt water infiltration to frozen soils model for boreal forest soils. Model simulations
are shown for varying pore saturation levels, θp. Because, SWE increases exponentially with infiltration,
the model should not be used for SWE greater than 100 mm.
5.4 Infiltration to Unfrozen Soils
5.4.1 Soil Water Movement
The measured final infiltration rates and geometric means for the sites as determined using
infiltrometer tests are shown in Table 5.4.1. Although there were no significant differences between
sites, the mean infiltration rates were lower on the clear-cut sites. When considering the implications of
measured infiltration rates from a standard application of hydraulic head, one should remain aware that
the delivery of water to the sites from sub-canopy rainfall varies substantially because of interception
and evaporation of rainfall and that soil moisture content varies substantially because of
evapotranspiration and soil textural differences. For example, the mature forested sites (Pine, Mixedwood) have much lower seasonal sub-canopy rainfall and average soil moisture content than did the
clear-cut sites (Cleared, Regenerating). Small differences in final infiltration rates can contribute to
differences in actual rainfall and soil moisture to produce large differences in seasonal infiltration and
generation of runoff.
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Table 5.4.1 Measured final infiltration rates (infiltrometer tests)
with geometric means for locations and sites.
Final Infiltration Rates, mm h-1
Pine
Mixed-wood
Regenerating
Cleared
Closed 1
Closed 2
Mean Closed
Open 1
Open 2
Mean Open
Mid 1
Mid 2
Mean Mid
44.9
15.6
26.5
39.1
28.2
33.2
8.1
27.1
14.8
Aspen 1
Aspen 2
Mean A
Open 1
Open 2
Mean Open
Spruce 1
Spruce 2
Mean Spruce
5.1
14.2
8.5
42.5
28.8
34.9
21.6
66.6
37.9
Ridge 1
Ridge 2
Mean Ridge
Furrow 1
Furrow 2
Mean Furrow
55.5
4.7
16.2
120.
3.4
20.2
Ridge 1
Ridge 2
Mean Ridge
Furrow 1
Furrow 2
Mean Furrow
52.6
31.6
40.8
16.3
6.1
10.0
Overall Mean
23.5
Overall Mean
22.4
Overall Mean
18.1
Overall Mean
20.2
At the Cleared site, final infiltration rates in the furrows were significantly lower than those on
the ridges (p=0.05). Gravity acting on the steeply sloping sides of the furrows may cause runoff of
incident precipitation into the furrows even if the infiltration capacity of the ridge tops has not been
met. Since infiltration rates in the furrow bottoms are low and rainfall delivery is high compared with
forested sites, the greatest potential for runoff occurs along the furrows. There were no significant
differences between the ridges and furrows at the Regenerating site but there were significant
differences between sampling locations. The location with the Leaf-litter layer (location 1) had higher
infiltration rates than the location without an organic layer. At the Mixed-wood site, final infiltration
rates under the aspen trees were significantly less than at the other two monitoring sites. The difference
between aspen and other mixed-wood sites is unexpected and the reasons are unknown. There was no
statistically-significant within-site variability at the Pine site.
5.4.2 Soil Water Status
Between site variability
Stored soil moisture for the 0-0.5 m depths at the four sites is shown in Fig. 5.4.1 for 1995 and
Fig. 5.4.2 for 1996. In 1995, paired t -tests showed that moisture storage in the 0-0.5 m layer was
significantly greater (p=0.01) at the Cleared site and significantly less (p=0.05) at the Pine site than at
the other sites. The same differences were found in 1996 when they were both significant at p=0.01.
Throughout 1995 and 1996 the stored moisture in the Cleared site was consistently greater than
that on the other sites. The effect became even more pronounced after 14 June 1996 when the
regenerating aspens at the site were thinned as part of stand management. Although moisture storage in
soils at the Pine site is significantly less than the other sites, the effect is not consistent during the
measurement period. At the end of the growing season in both years the moisture stored at the Pine site
is similar to that in the Mixed-wood and Regenerating sites.
90
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Figure 5.4.1 Moisture storage from 0-0.5 m depth in 1995 at the Pine, Mixed-wood,
Regenerating and Cleared sites.
Figure 5.4.2 Moisture storage from 0-0.5 m depth in 1996 at the Pine, Mixed-wood,
Regenerating and Cleared sites.
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The low soil moisture storage in the Pine site soils is likely due to several reasons, the most
prominent being high summertime evapotranspiration rates, low mounts of snow accumulation and
therefore snowmelt (due to snow interception and sublimation) and the poor water holding capacity of
the sandy soil. Soil composition is described in Table 5.1.1 and shows that the Pine site is much lighter
in texture than the other sites. The Cleared site is also light-textured, more so than the other two sites
(Mixed-wood and Regenerating), however soil moisture storage there is not reduced. Seasonal
evapotranspiration (Table 5.4.2) was lower at the Cleared site than the other sites in both 1995 and
1996 and the lower water use appears to have contributed substantially to greater moisture storage.
Table 5.4.2. Seasonal evapotranspiration (May to September) for the four sites.
Site
Pine
Mixed-wood
Regenerating
Cleared
Seasonal Evapotranspiration, mm
1995
1996
407
359
302
288
371
322
280
259
Within site variability
Within the Pine site, moisture storage to 0.5 m was consistently less at the closed canopy (close
to tree trunks) location than at the open or mid canopy locations in both 1995 and 1996 (Fig. 5.4.3 and
5.4.4). Part of the difference is due to greater interception of precipitation by the closed canopy
(Section 4) but there is probably also some influence of variability in soil properties, preferential flow
along rooting systems and soil water withdrawals by transpiring trees. Moisture storage at the open and
mid canopy sites is similar although the mid canopy site was generally wettest in both years.
At the Mixed-wood site, the location beneath the aspen tree was consistently drier to 0.5 m than
that beneath the spruce and that in an open (away from tree trunks) location in 1995 and 1996 (Fig.
5.4.5 and 5.4.6). The aspen location had significantly lower infiltration rates (Table 5.4.1) than the
other locations that may have combined with root uptake to decrease moisture storage. Van Rees
(1997) showed that rooting density of aspen to 0.5 m depth was much greater than that of white spruce.
Since the moisture monitoring locations were directly under the tree canopies, it is likely that root
uptake was greater beneath the aspen than at either the white spruce or open locations. In both years
the spruce site was wettest after snowmelt but had comparable moisture storage to the open site for the
rest of the year. The high spring moisture content under the spruce resulted from meltwater being
channelled into the snow-free area under the spruce canopy. Standing water was observed in bare
patches under spruce trees during snowmelt in most years. The shading effect of the spruce canopy
also reduces understorey vegetation and therefore evapotranspiration, and this effect, and relocation of
snowmelt water appear to have balanced the high interception losses of precipitation to maintain a
"normal" soil moisture storage under the spruce canopy.
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Figure 5.4.3 Moisture storage from 0-0.5 m depth at the Pine site in 1995
(three canopy densities shown).
Figure 5.4.4 Moisture storage from 0-0.5 m depth at the Pine site in 1996
(three canopy densities shown).
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Figure 5.4.5 Moisture storage from 0-0.5 mm depth at the Mixed-wood site in 1995
(three cover types shown).
Figure 5.4.6 Moisture storage from 0-0.5 mm depth at the Mixed-wood site in 1996
(three cover types shown).
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There was no effect of ridge and furrow location on moisture storage to 0.5 m at the
Regenerating site in either 1995 or 1996 (Fig. 5.4.7 and 5.4.8). There were however consistent
differences between the two sampling locations from mid-June to the end of the measurement period in
each year. The differences were more pronounced in 1996. Location 1 that had developed a leaf-litter
layer and some understorey vegetation was consistently drier than the more barren Location 2 despite
the higher final infiltration rates measured at Location 1.
Figure 5.4.7 Moisture storage from 0-0.5 m depth at the Regenerating site in 1995
(ridge and furrow locations shown).
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Figure 5.4.8 Moisture storage from 0-0.5 m depth at the Regenerating site in 1996
(ridge and furrow locations shown).
At the Cleared site there was a strong effect of ridge and furrow location on soil moisture
storage to 0.5 m (Fig. 5.4.9 and 5.4.10). The furrow locations consistently stored 10 to 20 mm more
water than the ridges through 1995 and 1996. The lower moisture storage could be a result of
1. relocation by localised runoff of incident water from the ridges,
2. shading of the furrows resulting in lower evaporation from furrow soil surfaces, and
3. variable rooting patterns and vegetative growth.
The report by Van Rees (1997) to the PAMF details rooting behaviour and soil moisture withdrawals
on ridges and furrows of clear-cuts.
96
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Figure 5.4.9 Moisture storage from 0-0.5 m depth at the Cleared site in 1995
(ridge and furrow locations shown).
Figure 5.4.10 Moisture storage from 0-0.5 m depth at the Regenerating site in 1996
(ridge and furrow locations shown).
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5.5 Water-Use in a Drought Period
To identify differences in water-use between and within stands during a period of water stress, changes
in soil moisture were assessed by applying a water budget during a drought period. The water budget
can be summarised by the simplified continuity equation:
Pr - E - Dr - Ro - ∆S = 0
(5.9)
where Pr is precipitation, E is evapotranspiration, Dr is drainage to groundwater, Ro is runoff (or
horizontal flow) and ∆S is change in soil moisture storage. In a closed system, there is no drainage or
runoff and the change in soil moisture less precipitation will be a measure of water-use at that location.
When the water-use is averaged over the stand, it should equal evapotranspiration for closed systems.
Between 13 August and 5 September 1996, less than 10 mm of rain were measured at the
standard rain gauges under the forest canopy at all four sites. At the Pine and Mixed sites no significant
precipitation was received in the week before this period but rain fell at the other sites (25 mm at the
Regenerating and 10 mm at the Cleared). The change in soil moisture storage, stand evapotranspiration
and below-canopy precipitation during the common drought period are shown in Table 5.5.1. Since
this was a drought period, evaporation from water stored on the canopy was assumed to be negligible.
Evapotranspiration was greatest from the Pine site, followed by the Mixed and Regenerating and then
the Cleared. At the Pine, Mixed and Regenerating sites water was extracted to a depth of 0.9 m during
the drought period but at the Cleared site there was no decrease in moisture content below 0.5-m depth.
At the Regenerating and Cleared sites, the change in water storage was balanced by belowcanopy precipitation and stand evapotranspiration indicating closed systems. The change in soil
moisture over the period was variable within stands at both sites. At the Cleared site, there was more
water-use on the ridge, where the aspen were growing, than in the furrows, where the white spruce
were planted. There were no differences between ridges and furrows at the Regenerating site but
water-use was less at site 1 (leaf litter layer present) than site 2 (barren). At the Regenerating site,
water-use decreased in each week of the drought period suggesting that water availability may have
limited evapotranspiration and therefore primary production. The decrease in soil moisture was similar
for each week at the Cleared site.
At the mature forest sites, the average change in water storage was not balanced by
evapotranspiration and precipitation showing the system is not closed or is too complex to be balanced
with simple averages. Within-site variability is large in the mature forest (Table 5.6.1), therefore a
simple average of change in soil moisture may not be appropriate for these stands. At the Mixed site,
average water use at the three locations overestimates stand evapotranspiration. Since aspen dominates
this stand, the average water use should likely be weighted toward the low value obtained at the aspen
location. If the average is weighted to account for the aspen covering 75% of the stand, the change in
moisture storage would be 50.5 mm which nearly closes the balance. Stand evapotranspiration is likely
underestimated by average water use at the Pine site because the small change in soil moisture
measured at the closed canopy location is not representative of one-third of the site. If the dosed
canopy location is assumed to cover between 15 and 20% of the site the water balance closes.
Therefore, considerations of water balance for mature boreal forest stands must include the relative
extent of cover types within the stand, as the diversity of these stands can influence the water balance
and analysis of such.
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Within the Mixed stand, the decline in soil moisture during the drought period was greatest
beneath the spruce tree and least near the aspen tree. Rooting patterns (Van Rees, 1997) had suggested
that water use would be greatest under the aspen tree. The unexpectedly low decrease in soil moisture
near the aspen occurred because available soil water was already becoming exhausted by transpiration.
In the week before this period (August 7 to 13), soil moisture in the soil zone from the surface down to
1.6-m depth decreased by 22.8 mm at the open location, 40.0 mm at the aspen and 22.4 mm at the
spruce. These data support greater water-use at the aspen location during periods of adequate moisture
and reduced water-use during drought periods. The significantly lower soil moisture contents near the
aspen throughout 1995 and 1996 (Fig. 5.4.5 and 5.4.6) result from greater water use during adequate
moisture periods in comparison to the spruce and open sites.
Table 5.5.1 Change in soil moisture storage (0 - 1.6 m), evapotranspiration (GD method) and sub-canopy
rainfall (standard rain gauges) for the drought period between August 13 and September 5, 1996.
Sub-canopy Rainfall
(mm)
Evapotranspiration
(mm)
Change in Soil
Moisture
Pine
Open
Closed
Mid
7.1
6.6
6.7
8.0
57.5
-45.3*
-52.9
-22.8
-60.3
Mixed-wood
Open
Aspen
Spruce
7.0
4.0
9.0
8.0
51.7
-64.3
-67.8
-33.7
-77.9
Regenerating
Location 1
Location 2
3.4
51.5
-48.2
-42.5
-53.9
Cleared
Ridge
Furrow
6.8
41.3
-37.5
-41.8
-33.2
* Bold numbers are averages of the sites within the stand.
At the Pine site, available water at the closed canopy site was low at the start of the drought
period. From 7 to 13 August, before the drought, soil moisture declined 18.1 mm at the open location,
25.3 mm at the closed location and 24.8 mm at the mid location. If water was not limiting, water-use at
the closed and mid locations would likely be similar, however Table 5.5.1 suggests much reduced
water use under the closed pine canopy. High water use when moisture supplies are adequate
apparently contributes to the observation of low water contents at the closed canopy location in 1995
and 1996 (Fig. 5.4.3 and 5.4.4).
Evidently, all sites readily evapotranspire when soil moisture supplies are adequate, with the
forests transpiring more than the clear-cut areas. In times of drought however, mature forests limit their
primary production to reduce water withdrawals from the soil. In this sense the mature forests manage
their water balance. The recently cleared site showed no plant-induced limitation in its
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evapotranspiration rate during the drought, with similar water use in each week of the drought. During
the drought most water was withdrawn in the cleared site by aspen on the ridges rather than the spruce
seedlings in the furrows. The regenerating site showed a limitation to water use at the site with betterestablished organic soil and undergrowth but no limitation when the soil appeared barren.
5.6 Runoff Generation
Runoff generation considers surface runoff rather than sub-surface runoff. Here runoff occurs
where the precipitation delivery to the soil surface exceeds the infiltration rate for a length of time
sufficient for water to flow toward streams or to soils with even lower infiltration capacities. So runoff
generation is a result of precipitation, canopy interception, infiltration to soils and other factors such as
slope and location on a hillside. Two critical periods for runoff generation are the spring snowmelt
period when frozen soils inhibit infiltration and heavy rainfall periods when the water delivery rate to
the soil can exceed the infiltration capacity of the soils.
Runoff can be determined using streamflow for a basin, normalised to basin area as mm water
equivalent of runoff per day. It can also be determined at a site using a water balance of precipitation,
interception and infiltration. Two useful streamflow sites are measured by the Water Survey of Canada
(Environment Canada) with stage recorders on Beartrap Creek (within PANP) at the Narrows Road
and on Whitegull Creek (east of PAMF) and south of Narrow Hills Prov. Park, in an area subject to
clear-cutting). These two gauges are deemed most appropriate for basin-scale analysis as they are not
overly influenced by large lakes and Beartrap Creek represents an undisturbed basin whilst Whitegull
Creek represents a basin that has undergone partial clear-cutting. Before comparing the runoff response
of the basins however, runoff generation at the NHRI research sites is examined for the spring and
summer periods.
5.5.1 Spring Snowmelt Runoff
With the release by melting of from 34 to 107 mm of snow water equivalent in a few days, and
with generally frozen soils to inhibit infiltration, the snowmelt period provides an excellent opportunity
for runoff generation. When the snow water equivalent exceeds the seasonal infiltration to frozen soils
then runoff can occur. Runoff occurred at the research sites during all three observed snowmelt periods
from 1994 to 1996. However, the various land cover and soil types behaved differently, generating
variable runoff quantities. Runoff was estimated from a mass balance of snow water equivalent plus
precipitation during snowmelt less infiltration to frozen soils by the end of melt. The resulting
quantities are shown in Table 5.6.1. In every year the Pine site evidently produced the least runoff
(negligible in 1995) and that the Cleared site produced the most runoff (the only site producing
significant runoff in 1995). The differences are due to a combination of factors:
a) SWE is less under coniferous canopies and greatest in clear-cuts because of variable interception
and sublimation losses,
b) Soils are drier in springtime under forest canopies and so infiltration is greater,
c) Snowmelt is faster in Cleared sites therefore there is less time for infiltration and a "flashy"
snowmelt runoff occurs.
The increase in spring runoff when forest cover is cleared is most dramatic in years with dry soils
(1995) where an almost 10-fold increase in runoff occurs in the transition from forest to recent
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Cleared. On average over the three years snowmelt runoff at the Cleared site was six times greater than
that at the Pine site and about 3.5 times greater than that at the Mixed-wood site.
Table 5.6.1 Spring snowmelt runoff (mm water equivalent) estimated from a balance of snow water
equivalent and infiltration for sites in the PAMF.
1994
1995
1996
Pine
12
2
38
Regenerating
52
0
49
Cleared
57
24
55
Mixed-wood
45
3
50
The timing of snowmelt runoff can vary significantly between sites as well. In 1996, melt of
winter snow accumulation was completed by 9 April at the Cleared site, 16 April at the Pine and
Regenerating sites and 18 April at the Mixed-wood site. So though runoff varied no more than 17 mm
amongst these sites, the timing of runoff generation varied by over one week.
5.6.2 Rainfall Events
To exert control over the evapotranspiration regime and hence the microclimate, a forest must
manage the water that it receives. An effective water management strategy is to store water such that it
is readily available for evapotranspiration; that is, within the canopy, in the soil surface litter layer and
within the soil rooting zone. Differing tree species have different canopy rain storage characteristics
and differing strategies for routing water to the root systems. Current forestry practises remove the
canopy storage potential and disturb the soils reducing the options for water storage by regenerating
forests. Any reduction in the ability to absorb water will result in increased runoff, which removes
water from the terrestrial ecosystem and the forest's water management system. The ability of a forest
soil to recover its water storage characteristics after regeneration has begun is not only an important
indicator of the recovery of a variety of canopy and soil characteristics but is diagnostic in the
generation of large runoff events that may promote soil and stream erosion and induce changes in the
aquatic systems fed by the forest.
To compare forest water management amongst sites, runoff generation was examined from
weekly water balances at the various sites using Eq. 5.9, measured above canopy rainfall, modelled
evapotranspiration and measured changes in soil moisture from 0 to 1.5 m. The water balance
presumes that all intercepted rainfall evaporates from the canopy by the end of the week. For the
Mixed-wood, Cleared and Regenerating sites the soil moisture measurement depths were sufficiently
deep to close the water balance by presuming further drainage was negligible. At the Pine site drainage
occurred precluding a water balance. An examination of soil moisture recharge in the upper soil layers
at this site suggests however, that runoff never occurred at this sandy site. The relationships between
runoff estimated using the weekly water balance and rainfall are presented in Fig. 5.6.1.
Although there is a considerable scatter in the trends, there is considerable difference between
trends for different sites. The Cleared site consistently produces the greatest amount of runoff. This
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can also be interpreted as that the Cleared site conserves the least amount of available water for
evapotranspiration and the cooling necessary to maintain its micro-climate. For weekly rainfall greater
than l0 mm, more than half the water is lost to runoff from the Cleared site. Though runoff is not as
great at the Regenerating site, it is still a significant component of the water balance. In concurrence
with Figs. 4.2.3 through 4.2.5, enhanced runoff from the Regenerating site results in a reduction of
20%-25% of summer evapotranspiration when compared to mature stands. Figure 5.6.1 suggests that
the recovery of the water management characteristics of the Regenerating stand after about 15 years is
incomplete and that it does not yet conserve its water for evapotranspiration and micro-climate control
as does a mature stand. The mature Mixed-wood with its undisturbed soils and natural heterogeneity of
species cover manages to retain most available water for evapotranspiration and cooling, whereas the
mature Pine site retains most water for the same purposes, permitting a small amount to drain to
replenish groundwater. In this sense, the mature forest stands manage water as part of an ecosystem,
whereas this function has not recovered in the cut stands.
Figure 5.6.1 The trend between weekly runoff and rainfall derived for the Mixed-wood, Clear-cut and
Regenerating stands from water balance estimates.
Points show measurements and the lines show trends, not linear relationships that could be extrapolated
to other sites. Note that the Pine site never generated runoff for any rainfall event.
5.6.3 Severe Runoff Generation
A severe rainfall event occurred on 8 and 9 August 1995, when more than 100 mm of rainfall
was received above the canopy at all four sites (Pine, Mixed-wood, Regenerating, Cleared). Those sites
in Beartrap Creek basin received over 120 mm of rain. The maximum intensity of the rainfall was 22
mm h-1 for a 30 minute period early in the event. This value is within the range of measured final
infiltration rates (Table 5.4.1) at most sites, though higher than infiltration rates for the furrows at the
Cleared site. Hence it is a candidate event to examine the occurrence of runoff generation from rainfall.
No subsequent increase in runoff was detected in the streamflow measurements of Beartrap Creek
basin (covered with mature stands of Pine and Mixed-wood) because of this rainfall event but
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streamflow did increase in nearby basins (Whitegull Creek) containing recent clear-cuts (see next
section for basin response graphs).
The change in soil moisture at each site between 27 July and 10 August 1995 is plotted in Fig.
5.6.2. All sites gain moisture to 0.5-m depth but no increase in water storage occurs below this depth at
the Regenerating site. At the Pine site, water entered the entire profile and drained to groundwater.
Water did not infiltrate to 1.4 m at the Mixed-wood and Cleared sites and only a small increase in
moisture content was measured at the 0.7- to 0.9-m depth on the Cleared site.
Figure 5.6.2 Soil moisture profiles before and after an extreme event at the Pine, Mixed-wood,
Regenerating and Cleared sites on 27 July (pre-rain) and 10 August (post-rain), 1995.
Values are means calculated for treatments.
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Table 5.6.2 shows precipitation delivery to the soil surface, measured evapotranspiration less modelled
free-water evaporation of intercepted rainfall from the canopy, the change in soil moisture from 0 to
1.6 m and runoff calculated from the water balance (Equation 5.9). On average, runoff only occurred at
the Regenerating and Cleared sites. Runoff losses from the Cleared were almost double those from the
Regenerating. Within the Cleared site, soil moisture gains were greater on the ridges than the furrows.
Before the event, moisture contents in the furrows were greater and therefore sorptivity and untilled
pore space less. Since the measured final infiltration rate for the furrows at the Cleared site was
exceeded during the storm, runoff losses may also have contributed to the low recharge in soil
moisture.
Table 5.6.2. Water balance for forest stands during the rainfall event.
All terms are mm of water accumulated from 27 July to 10 August 1995.
Sub-canopy
Rainfall
z
y
EvapoChange in
z
transpiration Soil Moisture to
1.6 m (mm)
Runoff mm
Pine
Open
Closed
Mid
132.0
136.5
126.4
133.2
23.8
26.6
20.7
24.0
149.7 y
80.7
261.4
107.0
0
Mixed-wood
Open
Aspen
Spruce
128.0
132.3
132.3
119.3
21.7
24.0
24.7
16.4
120.3
103.1
113.3
144.6
0
Regenerating
105.3
19.0
67.4
18.9
Cleared
Ridge
Furrow
110.5
18.7
56.5
69.3
43.7
35.3
Stand evapotranspiration less evaporation of intercepted rainfall.
Bold values are averages of measurements made at site.
Antecedent moisture conditions also appear to have affected moisture gains in the Mixed-wood
stand. The greatest change in storage was found at the spruce location that received least precipitation
and had the lowest soil moisture stored to 0.5 m on July 27. At the Pine stand, the closed canopy
location gained much more water than the other locations but most of the gain occurred at the deepest
measurement. More than 40 mm of water were gained between 1.4- and 1.6-m depth suggesting that
water may have flowed preferentially to that location from directly above and laterally.
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5.6.4 Basin Runoff
The two basins examined give an interesting contrast respect to land cover and runoff
generation. Beartrap Creek is 116 km2 in area with a cover of primarily mixed-wood and pine.
Whitegull Creek is 629 km2 in area with a mixed-wood and coniferous cover that has been highly
disturbed by clear-cutting, so its proportion of mature forest cover is much less than Beartrap Creek.
Figure 5.6.3 shows rainfall measured at the Mixed-wood site and discharge (runoff) of Beartrap Creek
for 1994 and 1995. Runoff is expressed as unit discharge, as mm water equivalent per day over the
basin. Runoff was much higher in 1994 than in 1995, though a snowmelt peak is evident in 1995 that is
not evident in 1994 or possibly coincides with later spring rains. Note that the timing of the snowmelt
runoff from the basin is much later than the timing of snowmelt because of lags in the routing of water
through the partially-frozen basin. The high runoff periods in 1994 are associated with a series of large
rainfall events in June and early July, with a decline to negligible runoff in late summer and no effect
from subsequent rainfalls in August and September. In 1995 runoff declines from the snowmelt peak
and is largely unaffected by any rainfall events (which are mostly quite small). Interestingly the
extreme rainfall of early August does not affect basin runoff although it is the largest input of water to
the basin that year. The non-responsive hydrograph in 1995 can be partly explained by examining the
sequence of soil moisture storage as shown in Fig. 5.6.4 for the Mixed-wood site. Soil water (mm) in
the top 0.40 m and the top 1.50 m of the soil profile are shown fortnightly from May to the end of
October. A decline in moisture storage in the upper 1.50 m of soil from 300 mm just after snowmelt to
160 mm at the end of July shows continued depletion. Any rainfall in the depleted period would
recharge soil moisture rather than generate runoff. The extreme rainfall of early August raised the soil
moisture content to its highest value that year (320 mm), which then declined through the rest of the
year.
By comparison, Fig. 5.6.5 shows rainfall (from the Mixed-wood site) and basin runoff for
Whitegull Creek in 1994 and 1995. Differences in rainfall between Beartrap and Whitegull Creek
caused a small pan of the differing runoff behaviour in 1994 (in particular the extreme runoff in midJuly). However the pattern of runoff events responding to rainfall events rather than to peak snowmelt
runoff, compares favourably with that of Beartrap Creek in many instances. In 1995 the extreme
rainfall event caused an increase in streamflow in Whitegull Creek as opposed to the non-response
from Beartrap. The difference is believed due to the higher proportion of clear-cuts in the Whitegull
basin. Annual runoff is higher from Whitegull Creek that from Beartrap Creek and this may also
reflect differences in landcover type. To examine this difference further, Table 5.6.3 shows runoff and
precipitation for the spring period (to about 15 June) and the summer-fall period (15 June to 30
October. Evidently,
1) even with similar precipitation, the runoff response from a basin can vary markedly from year to
year and from spring to summer,
2) Whitegull Creek sustains higher runoff in all periods than does Beartrap Creek. The process and site
studies show that this is due to differing runoff generation with land cover and that cleared sites
produce more runoff during the snowmelt period and from rainfall.
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Figure 5.6.3 Rainfall and basin runoff (stream discharge) for Beartrap Creek.
Data courtesy of the Water Survey of Canada, Environment, Canada.
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Figure 5.6.4 Weekly soil moisture storage at the Mixed-wood site, 1995.
Table 5.6.3 Cumulative snowmelt and rainfall runoff for Beartrap Creek and Whitegull Creek,
expressed as mm water equivalent.
Precipitation data is rainfall from the top of the NHRI Mixed-wood tower in PANP
and snowfall from the NHRI Cleared site in the PAMF.
Snowmelt Runoff (to 15 June)
Rainfall Runoff (15 June - 30 Oct.)
Beartrap Creek
Rain+Snowfall
Runoff
Rain
Runoff
1994
124
18
277
46
1995
125
11
261
3
1994
124
24
277
68
1995
125
16
261
19
Whitegull Creek
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Figure 5.6.5 Rainfall (PAMF) and basin runoff (streamflow discharge)
for Whitegull Creek, east of the PAMF.
Data courtesy Water Survey of Canada, Environment Canada.
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CHAPTER 6
DISTRIBUTED HYDROLOGICAL MODELLING
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6. DISTRIBUTED HYDROLOGICAL MODELLING
6.1 An Overview of Hydrological Modelling
Hydrological models are attempts to represent the hydrological system from precipitation to
streamflow in mathematical form. The complexity of the models varies with the user requirements and
the data availability. Models vary from simple statistical techniques which use graphical methods for
their solution to physically-based simulations of the complex three-dimensional nature of a basin.
Chow et al. (1988) categorize hydrological models based on three decision rules:
i) Does the model include randomness?
ii) Does it include spatial variation?
iii) Does it include time variation?
6.1.1 Runoff processes
The study of storm runoff generation forms the basis from which deterministic hydrological
models are derived. Historically, runoff was often simulated by a black box approach with no attempt
to reproduce the detailed hydrological processes that generate the runoff (Chow et al., 1988). The
earliest and best-known of these approaches is the unit hydrograph (Sherman, 1932). The development
of the unit hydrograph method coincided with the development of Richards equation for unsaturated
flow and Horton's work on infiltration and the production of runoff. This era is defined by Chow
(1964) as the 'Period of Rationalization'. Hortonian flow visualizes streamflow being generated by
overland runoff when the rainfall rate exceeds the infiltration capacity of the soil. Rainfall excess or
'effective rainfall' is then seen as the driving parameter from which the streamflow is generated. This
process can be characterized by two separate elements, a loss function for estimating effective rainfall
and a time transformation of the resulting excess water (Wood et al., 1990).
Computers and computer modelling have allowed the internal processes lumped within the
black box approach to be divided into several conceptual or empirical processes. However, in early
stages in model development, system states such as soil moisture were still estimated by “black box”
techniques (Engman, 1986). Over the last two decades, research has concentrated on better
understanding of the streamflow generation process with much of the attention centred on infiltration
and on the determination of effective rainfall, or rainfall delivery to the soil. Given the diverse nature
of the infiltration process, this is not a simple task.
A more realistic approach is to model runoff generation from both surface and subsurface
sources. Kirkby (1978) discussed a more thorough evaluation of runoff generation mechanisms in
which processes other than the Hortonian, infiltration limiting mode, are examined. These mechanisms
include infiltration limited overland flow, partial area overland flow, saturation excess overland flow,
subsurface stormflow and saturated wedge flow (Wood et al., 1990). The recognition that flow may be
generated from many mechanisms has led to partial area hydrological models where the contributing
areas are primarily dependent on topographic and soil properties (Wood et al., 1990) and change
during a storm event.
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6.1.2 Discretization of a basin
With the arrival of powerful computers, current activity in hydrological modelling is toward
more physically-based models that attempt to more closely represent the observed physical processes
and conduct energy and mass balances. This requires breaking the basin down into smaller units and
such spatially-distributed models are defined by their ability to incorporate the distributed nature of
basin parameters and inputs into a modelling framework. Fully distributed models are likely to produce
the next increment of progress in streamflow modelling (Link, 1983). Many distributed or partiallydistributed models are currently under development including the SHE model (Abbott et al., 1986), the
Hydrotel model (Fortin et al., 1986), the USGS Precipitation Runoff Model (Leavesley and Stannard
1990), the SLURP model (Kite, 1995a) and the WATFLOOD flood forecasting system (Kouwen,
1988).
Various rationales for basin discretization have been used (See Fig. 6.1.1). A technique
proposed by Wood et al. (1990) requires discretization of the basin into representative elemental areas
(REA). The REA is an areal element within a basin where the hydrological properties are definable and
would not be significantly different if a smaller scale of discretization were used. This technique has
been set up within the SHE model which was classified by Wood et al. (1990) as a completely
physically-based, distributed model. SHE uses a finite difference approach to solve the combinations
of partial differential equations that describe surface and subsurface flow. The computational
complexity of the model makes calibration difficult and it has been recently found (Jain et al., 1992)
that model parameters have little relevance to measurements made in the field. Wood et al. (1990) also
reported that runoff simulation over 150 days requires a run time of 50 hours on a Cyber
supercomputer. These features suggest that this type of model has research capabilities but is limited
when it comes to practical applications.
Another method of discretization is the hydrological response unit (HRU) approach. Here a
basin is subdivided into areas that represent hydrologically-homogeneous characteristics such as land
cover, slope and aspect. Kite and Kouwen (1992) note that these computational elements may be based
on a grid system as in the Hydrotel system, on a sub-basin system as in the USGS model or based on
elevation bands as in the SRM model (Martinec et al., 1983). In these cases, the HRU will generate a
distinct hydrological response but its location within the basin is only important for routing
considerations (Donald, 1992). This differs from the REA approach where the element's location will
influence hydrological response.
Kouwen et al., (1993) described a grouped response unit (GRU) used in a grid square model.
The GRU is a grouping of all areas with a similar land cover and soils such that a grid square will
contain several distinct GRUs. Runoff generated from the different groups of GRUs is then summed
together and routed to the stream and river system (Kite and Kouwen, 1992). For example, two GRUs
with the same percentages of land cover types, rainfall and initial conditions will produce the same
amount of runoff no matter how these land cover classes are distributed. This stems from ideas in
urban hydrology where runoff from small areas can be calculated by summing runoff from both
pervious and impervious areas (Kouwen, 1988). Kite and Kouwen (1992) showed that a semidistributed basin model based on the GRU approach using land cover classifications will give better
calibration and validation statistics than the lumped version of the same daily runoff model. Similar
results were reported by Tao and Kouwen (1989) for an hourly flood forecasting model
(WATFLOOD).
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Figure 6.1.1 Basin Discretization Approaches in Hydrological Modelling.
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The SLURP model (Kite, 1995a) divides a basin into several units known as aggregated
simulation areas (ASA). The ASA is not a homogeneous area but is a group of smaller areas each of
which has known properties. For example, land cover may be measured from satellites for pixels as
small as 10 m but operating at such a pixel dimension for a macro-scale basin would be impracticable
for a hydrological model. Instead, the pixels are aggregated into areas that are more convenient for
modelling. Such ASAs do not need to be squares, rectangles or any other regularly shaped areas
(although such forms are possible subsets of the ASA) and may more usually be based on stream
network shapes. The basic requirements for an ASA are that the distributions of land covers and
elevations for elements within the ASA are known and that the ASA contributes runoff to a definable
stream channel. The latter requirement is also an operational consideration since it means that the
stream system within the basin must be at a level of detail such that each ASA contains a defined
stream connected to the basin outlet.
6.2 Hydrological Model Development for the PAMF
6.2.1 Overview
Development of a hydrological model is based on many criteria, as outlined in the previous
section, with the most important aspects being end-user needs and promotion of the understanding of
hydrological processes in the environment. Simple regression or stochastic models often provide very
reliable runoff hydrograph predictions for current conditions in the specific location of development,
but have several limitations for modern hydrological science that seeks links with terrestrial and
aquatic ecosystems under conditions of changing land use and climate. These models often have a
tenuous link to the physical processes that occur in the basin or characterise these processes in an
incorrect manner. Some specific limitations of regression, stochastic or "empirical" models are:
1) the limited physical basis does not permit the model to be run under different climatic or vegetation
scenarios with confidence,
2) the models provide unrealistic state variables such as soil moisture status, energy fluxes, surface
temperature, daily runoff or snowcover status, and
3) the models cannot be extrapolated outside their location or time of development with any
confidence.
Similarly, lumped models such as the unit hydrograph models developed in the 1970's are often
based on conceptualizations of a basin and based on basin-averaged parameters that are often
calibrated to hydrograph response. The averaged parameters have little relation to the diversity of
actual states and processes in a diverse basin such as found in the boreal forest. For instance, is it
appropriate to use an average forest leaf area of 2 when one-half of the basin is forested with a leaf
area of 4 and one-half clear-cut with a leaf area of 0? These models are usually restricted to operational
use or engineering design and are limited in their general applicability. They often cannot account for
significant changes in vegetation and/or varying climatic conditions. Thus such simple models have
limited ecological use as they do not faithfully represent hydrological processes and the interaction of
these processes with the terrestrial and atmospheric environments.
Distributed models have allowed for significant changes in model design. Again, certain model
discretizations are more appropriate for particular applications than others. The GRU and ASA
approaches described earlier have been carried out on several basins throughout Canada at the mesoscale (10s to 100s of km). In these applications, the discretized unit is large enough to allow
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excess water to be routed overland to the GRU or ASA outlet. The GRU or ASA is treated like a
lumped element and location within a grid square or sub-basin is not important, all that is important is
the overall response. Again, this works well at the macro- or meso-scale, however, hydrological effects due to spatial distribution of HRU's within a GRU or ASA will not affect the water output from
the basin.
The HRU approach allows for water balance calculations on "hydrologically-unique"
individual elements. The HRU position within a basin is also important since each HRU may
contribute water to a downstream neighbour through both surface and subsurface processes, therefore
affecting the water distribution throughout the basin. HRU approaches are reasonable for small basins
due to the computational requirements involved. The Beartrap Creek experimental basin is an example
of an appropriate basin for HRU modelling. There are 9,000 polygons (HRU's) identified within this
small basin.
6.2.2 Modelling Framework
The hydrological model developed for this study has exploited the latest advancements in
programming and GIS technologies. The GIS system used to generate physiographic inputs for the
model simulation was the Arc/Info vector-based system. All data used in our analysis were derived
directly from the PAMF GIS information. This data set was far from complete and a tremendous
amount of effort was required to merge and clean the data as received. This was a required procedure
to generate the HRU polygons necessary for model simulations. The HRU polygon attributes derived
for the PAMF area are outlined in Table 6.2.1 below. The HRU vector map is shown in Fig. 6.2.1.
Development of the HRU database used data from the Forest Inventory of Prince Albert
National Park, the Saskatchewan Environment and Resource Management Forest Inventory for the
Prince Albert Model Forest, Prince Albert Model Forest soil data as received from Central Soil
Mapping and a Prince Albert Model Forest Digital Elevation Model derived from NTS. The data
structure of the inventories is described in detail in Appendix C.
The model algorithm development was programmed using object-oriented programming in the
C++ language. Object-oriented programs have the advantage over more traditional programming
languages (such as FORTRAN or BASIC) because of their ability to generate self-contained classes
(objects). These objects are device and compiler independent and when structured properly, are
completely serf contained. This allows for easy addition or removal of model components (e.g., add a
new interception component) without having to restructure any other parts of the model. In this design
framework, the hydrological model is completely modular and new components are easily added.
The HRU model was designed to incorporate the GIS information directly, and provide output
directly to the GIS. This is achieved by exporting the Arc/Info HRU database to a PC-based Arcview
file. This file then provides the pivot point for both HRU model input and graphical GIS model output
through Arcview. The only other model inputs are the hydrological and meteorological data obtained
from the archived field measurements described earlier. The HRU vectors are shown in Fig. 6.2.1. A
complete assembly of HRU graphs for Beartrap Creek are shown in the Appendix D.
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Table 6.2.1 PAMF GIS attributes for HRU map.
Fields
GreenAmpt
Elevation
(soil type) (m ASL)
Slope
Species
Cover
type
(%)
Stand origin
Height
(year AD)
(m)
A
T loamy sand <500
<1.0
Pine Unforest
no
T
vegetation
R
<1888
I sandy loam 500 - 520 1.0 - 1.5 Spruce Clearing
B
sand
520 - 540 1.5 - 2.0 Mixed
Grass 1888 - 1908
U
silt
540- 560 2.0 - 2.5 Water
Shrub 1908 - 1938
T
E
clay
560 - 580 2.5 - 3.0 Unforest Burn
1938 - 1958
S
organic 580 - 600 3.0 - 4.0
Treed 1958 - 1968
muskeg
water
600 - 650 4.0 - 5.0
Muskeg 1968-present
unknown 650 - 700 5.0 - 7.5
Clear-cut
7.5 -10.0
Unknown
> 10.0
115
Canopy
density
Summer Fall/ Winter
Spring
no
no
vegetation vegetation
2.5 - 7.5
sparse
7.5 - 12.5
dense
12.5 - 17.5
lush
17.5 - 22.5
>22.5
LAI
(m2/m2)
range of values from
0.1 to 4.0 m2/m2
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Figure 6.2.1 HRU vectors derived for Beartrap Creek.
6.2.3 Development of Model Components
The main focus of the field component of this work has been to understanding vertical
hydrological fluxes under a wide range of forest and harvested environments. These have been
described in detail in the previous chapters. The role of hydrological modelling is to integrate this
information as computer algorithms, accounting for non-measured vertical fluxes such as infiltration
and the horizontal (or hillslope) fluxes of surface and sub-surface runoff. This section summarizes each
of the model components described earlier, focusing on their integration into an HRU modelling
framework. A schematic representation of the components of the hydrological cycle is shown in Fig.
6.2.2.
The purpose of developing the Boreal Ecosystem Evaporation-Runoff Simulation (BEERS)
was to determine the feasibility of linking the hydrological processes being studied in the Prince Albert
Model Forest in a single model, and to determine the feedback mechanisms required in such a model to
maintain an overall water balance for long-term, short time step simulations. In addition, the model is
intended to incorporate data derived from GIS analysis, and generate results that can be imported and
displayed in a 65.
BEERS was developed in C++, using the Borland C++ version 4.52 compiler, to take
advantage of the many features this language offers for data encapsulation and object-oriented design.
In BEERS the objects fall into two general categories, either a specific process or a hydrological
response unit (HRU) in which these processes operate.
In the prototype version of BEERS, three processes were considered, these being canopy
interception (the Rutter model), evapotranspiration (the Granger model) and soil infiltration (the
Green-Ampt model). The implementation of each of these processes is described below, with the
means by which they were linked in a single model. The description is based on the individual classes
developed for each process and for the HRU' s.
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Figure 6.2.2 Schematic Representation of the Boreal Ecosystem Evaporation-Runoff Simulation.
Each class consists of variables and member functions. In maintaining the notion of data
encapsulation, all class data members are private, and are called by starting public member message
functions. If data from another class are required to complete a task, the data are passed in a call to the
message functions, by that ensuring the integrity of the member data. Functions specific to a particular
class are private, since there is no need for other classes to have access to them.
The required capabilities of each class evolved during development, and there are several areas
where code was simply duplicated instead of creating a small private helper function. In a prototype
such as this, such inefficiencies should be expected, knowing that future versions can refine the
functionality and performance of each class.
The exception to the encapsulation approach concerns input and output. Time constraints
precluded development of a robust user interface, and all input and output is implemented using global
file names that are hard coded in the program. While this approach is far from ideal, it works.
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The Green-Ampt Class
The Green-Ampt infiltration model considers a saturated moisture zone (with or without
ponding on the surface) with an abrupt wetting from that moves downward at a rate proportional to the
cumulative infiltration. The initial soil moisture deficit and soil suction head are considered constant,
and there is no provision for drying of the soil during periods when there is no rain input to the soil.
Traditionally, the Green-Ampt equation has been set up by solution for the incremental soil infiltration,
and the infiltration rate at the calculated cumulative infiltration volume.
However, the Green-Ampt model is but one solution of Richard's equation for soil moisture
movement (saturated or unsaturated) subject to arbitrary initial and boundary conditions. A general
solution to Richard's equation provides an alternative applicable for the Green-Ampt model, by which
the cumulative infiltration, F, may be written
F (t) = - ψ f ∆ θ { 1 + ω (x) }
(6.1)
and the infiltration rate, f, is given by
f (t) = - Ks { ω (x) / (1 + ω (x) )}
(6.2)
where the function ω (x) (Lambert's ω function) is defined by the transcendental relationship
x = ω (x) exp (ω (x) )
(6.3)
x = - exp ( -[1 + Kst / ψ f ∆ θ ] )
(6.4)
with
and ψ f , ∆θ, Ks, and t are the soil moisture tension, moisture deficit, saturated hydraulic conductivity
and time, respectively.
The Green-Ampt class uses Eq. 6.1 to 6.4 in solving the Green-Ampt equation. The
implementation includes determination of an effective origin at time t0 such that the accumulated
rainfall matches the accumulated infiltration for times when the rainfall intensity is less than the
infiltration capacity. The algorithm is straightforward, and involves:
1. Calculate ω (x) from Eq. 6.1 where F(t) is the accumulated rainfall,
2. Calculate x from Eq. 6.3, and
3. Calculate t0 from Eq. 6.4.
Lambert's function is determined by an approximate solution and then refined using Halley (or
Richmond) iteration, simply a higher order version of Newton Raphson iteration incorporating second
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derivatives. This method is used because the second derivative of ω (x) is easily calculated with small
overhead, and offers more rapid convergence than Newton Raphson.
The same procedure to find an effective origin is used when surface ponding starts during a
time step. The only change is that f(t) is set equal to the rainfall intensity and Eq. 6.2 is used in place of
Eq. 6.1 to determine ω.
Since the rainfall intensity exceeds f(t), the time t0 remains unchanged. If, during an event, the
rainfall intensity is less than f(t), the value of t0 is updated using the above procedure with Eq. 6.1.
This method differs from the traditional approach only in that it uses the Lambert function to
determine infiltration and rates, and determines a continuously updated effective time origin that is
equivalent to the rainfall being at or above the infiltration capacity of the soil. However, the GreenAmpt class was designed to incorporate more functionality than simply infiltration.
Between or even during rain events, there are times when there is no rain. In these periods,
water will drain from the upper soil layer, and the soil will regain some infiltration capacity. To
account for this behaviour, the Green-Ampt class acts as a container for a series of water plugs. Each
plug represents some depth of saturated soil, with a soil moisture content and soil tension defined at
both the top and bottom of the plug. (Under saturated conditions, the soil tension is zero at the top of
the plug.) This construction allows the creation of multiple plugs of water moving through the soil
layer, each with its own soil moisture characteristics and movement rates.
It is this structure of the Green-Ampt class that prompted the use of the Lambert function to
achieve a solution. The Lambert function is double valued, and the lower branch of the solution
represents infiltration. The upper branch has been considered to represent capillary rise, but it can just
as easily be considered to represent soil drainage, which is the situation when rain ceases and a
saturated plug moves down through the soil. The soil above the plug will drain, increasing the soil
moisture deficit and soil suction, and by that regaining infiltration capacity at the soil surface.
Not only does the Lambert function represent a solution for soil drainage, but it also represents
the soil moisture profile under a constant surface soil moisture tension. While time constraints did not
permit implementation of this feature in the model, the Green-Ampt class contains several member
functions designed for future application of this feature of the Lambert function.
Data for the Green-Ampt parameters were determined from the soil classification contained in
the GIS database, and the specific values for a given soil type were taken from the example values
given in Chow's Applied Hydrology (1988).
The Rutter Class
The Rutter model is used to intercept precipitation in the forest canopy before it reaches the
ground. The implementation is linked to Granger's evaporation model and described by the equations
in Section 4.
Evaporation from the canopy is provisionally assumed at a constant rate of 5 mm/hr for canopy
storage > maximum holding capacity of the canopy. Otherwise, the rate is reduced by the ratio of
actual to maximum holding capacity. Values for the free throughfall factor and maximum holding
capacity of the canopy were determined based on species type contained in the GIS database
developed for the project.
As described below, evapotranspiration is calculated with the Granger model. Therefore,
having an evaporation process in the interception class appears redundant. Future development should
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consider how these processes can be isolated so each class represents only one physical process, and
the interaction between classes is achieved by feedback functions within the classes.
The Granger Class
Evapotranspiration is described by the Granger equation for evapotranspiration, given in Section 4.
The values for h and zo were extracted from the GIS database and were species dependant, while the
values for ea and Qs were extracted from the data measurements and analysis. All other variables were
calculated from equations given in the ICID Bulletin Number 2. Volume 43. 1994. Annex 1.
Parameters Used in Evapotranspiration Computations. The calculations for the class were carried out
with several small private member functions. While the calculations are simple, matching the data with
Granger's model in spreadsheet format was not possible. Some reasons for this discrepancy include
differences in the calculation of some parameters. For example, there is a Granger class function to
determine γ, the psychrometric "constant", while spreadsheet calculations applied a constant value of
the "constant". In addition, different equations were used for determining e*, depending on whether the
temperature was above freezing. Given these differences, it should be expected that the calculated
evapotranspiration rates differ, although the differences are not large.
The HRU Class
The Hydrological Response Unit (HRU) class contains member data specific to each HRU, such as the
GIS polygon ID or times for filing or displaying data. However, its main functions are to serve as a
container for the process classes described above, and to carry out the series of calculations necessary
to complete the water balance. Its definition is small, requiring some private data members, the private
definitions of the previous classes, and a few public member functions to receive input and deliver
output. At this point, there is no interaction or feedback between classes, so, for example, the Rutter
class is not instructed to "give water from the canopy", or the Green-Ampt class is not told to "dry up
some soil" when the evapotranspiration exceeds the rainfall.
Program Implementation
The program is run by the main{} module. It is "bare bone" at this point. Input is obtained from three
files, one containing the rainfall in mm/hr at each time step, one containing the Granger class data for
each time step, and one containing the HRU data. The program proceeds in the following manner:
Check for input files
Read HRU data characteristics
Construct an HRU object with input data
Read rainfall and evapotranspiration each time step
Calculate water balance
On EOF, break
Delete HRU
On EOF, break
Close all files and exit
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The program has shown that combining hydrological process modelling in an integrated model that can
generate results for a GIS format is feasible. The model was structured to maintain the GIS linkage
requirements in its input and output.
6.3 Simulation Results
6.3.1 Model Implementation in Beartrap Creek, PANP
The BEERS described in the previous sections was run for the summer period only on the
Beartrap Creek basin. A subset of the PAMF GIS assimilated data set was extracted and cut along the
basin boundaries to only include those polygons that fell within the basin. This operation reduced the
number of polygons from over 600,000 to just over 9000. The polygons are essential hydrological
response units of similar slope, aspect, elevation and cover type - as described earlier. Because this
early version of the model does not include any overland routing of surface water or subsurface routing
of groundwater, slope and aspect parameters are not used in the calculations. Similarly, since initial
soil moisture conditions were considered constant throughout the basin, elevation information was not
used. Despite these simplifications, the polygon structure remained the same since adding this
functionality is forthcoming. The framework for adding the routing and initial moisture algorithms is
built into the current structure of the model.
The current version of BEERS is only concerned with vertical water balance, ignoring any
lateral transfers of water. The vertical water balance consists of rainfall, interception, canopy
evapotranspiration, total evapotranspiration and infiltration. Net gains or losses in water are reflected
in changes in the soil water balance. All inputs and losses are estimated in mm water equivalent and
calculated for each HRU with the Beartrap GIS. Parameters included in the GIS such as soil type,
canopy type and Leaf Area Index, required for the water balance algorithms, were derived directly by
reading the ARC/INFO polygon attribute table. In this way, BEERS can interface directly with the
ARC/INFO data. Similarly, model output was written to match the existing polygon structure with the
GIS. This allowed for direct integration of BEERS results into the GIS through the Arcview program.
Results for the Week 1 simulation are shown in Fig. 6.3.1 as an example of the model output and
graphical representation in the GIS.
Model output estimates are all cumulative and expressed in mm water equivalent and include,
a) total infiltration,
b) runoff (infiltration excess),
c) total evapotranspiration,
d) canopy losses and storage,
e) canopy drip and
f) effective rainfall.
The model was run for the 1995 summer season beginning May l, 1995 and ending September
3l, 1995. No initial snowpack conditions were taken into consideration resulting in much of the early
spring runoff not being accounted for. The simulations however, are still realistic since initial soil
moisture conditions were assumed to be dose to saturated (as is normally measured after snowmelt),
simulating a reasonable draw-down curve over the duration of the summer. The rainfall hyetograph
and corresponding runoff hydrograph are shown in Fig. 6.3.2.
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Figure 6.3.1 Example output of BEERS integrated into the GIS environment
for Week 1 (1 May) of the 1995 hydrological year.
Figure 6.3.2 Rainfall and the runoff response for Beartrap Creek - Summer 1995.
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Model input parameters are standard meteorological variables available from most AES and Provincial
Forest Fire Monitoring Network stations: above-canopy rainfall (mm), temperature (°C), wind speed
(above the canopy) (m/s) and relative humidity (%), all in half hourly increments. Model simulations
were run on a half-hourly time increment. However, model output was limited to weekly estimates
because of the large number of polygons and resulting size of the output data files. The simulations
were run on Pentium and 486 based microprocessors and could simulate the entire season in about one
to four hours.
6.3.2 Model Results for Beartrap Creek
The model results are consistent with observations derived from the detailed field experiments
described earlier. The model was run for the entire 1995 summer season (as shown earlier) on Beartrap
Creek using two scenarios. The first scenario uses the existing GIS database and vegetation maps to
estimate summer water balance components. The cumulative results for week 20 at the end of the
simulation period (30 Sept.) are shown as a map in Fig. 6.3.3.
To examine the model sensitivity to changes in vegetation, a “virtual clear-cut” was completed
within Beartrap Creek in Prince Albert National Park. While the prospects of clear-cutting in the
National Park are remote, the exercise helps to display the value of the present unharvested forest in
the Park. Here, all Spruce and Pine forms (about 20% of basin area) were eliminated from the basin
and assumed to be clear-cut. The resulting map derived from the GIS for week 20 is shown in Fig.
6.3.4.
The distributed water balance results were then integrated for the entire basin. This was
accomplished by summing the area/contributions of each polygon and dividing by the total area of the
basin. This procedure provided an areally-weighted average of the water balance components. The
results are highlighted in Fig. 6.3.5 showing the sequence of water balance components over time for
both the current land cover information and with the "virtual Clear-cut".
In this calculation, rainfall, and infiltration are as derived directly from the equations listed in
this section. Canopy loss is the canopy storage loss due to evapotranspiration and as such represents a
subset of the total evapotranspiration shown and is not used in the budgeting for the net loss/gain
column. The net loss/gain refers to the change in soil moisture storage not accounted for by
evapotranspiration. A net loss simply means that the basin receives less water input than it loses
through runoff and evapotranspiration. Here it suggests a depletion of snowmelt water reserves over
the summer period.
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Figure 6.3.3 Beartrap Creek Simulation Results for Week 20.
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Figure 6.3.4 Beartrap Creek Simulation Results for Week 20 under "Virtual Clear-cut"
of all pure softwood stands (Pine and Spruce).
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Figure 6.3.5 BEERS run of incremental cumulative hydrological fluxes (1 May to 30 Sept.) for Beartrap
Creek summer 1995, a) virtual clear-cut of pure softwood stands (20% of area), b) existing undisturbed
vegetation coverage.
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Comparing the two simulations shows a distinct change in evapotranspiration, infiltration and
canopy loss. The removal of coniferous stands has strongly reduced canopy evaporation loss and the
midsummer water deficit in the basin. Again, this is consistent with field observations, giving us
confidence in these preliminary results. The differences for week 20 are plotted in Fig. 6.3.6 and show
that the virtual clear-cut increased infiltration and decreased canopy evaporative loss and
evapotranspiration, resulting in a much smaller net basin water deficit at the end of the summer. A
smaller water deficit would result in wetter soils the next spring and a higher spring snowmelt runoff
and higher summer runoff so the impacts are cumulative and sequential. This one season's simulation
represents only the beginning of basin change.
Figure 6.3.6 Comparison of summer seasonal BEERS water balance for undisturbed and
virtual clear-cut of conifer stands in Beartrap Creek basin, 1995.
Note that the model did not generate significant runoff under either scenario. With the addition
of a snowmelt component and typical soil moisture patterns along slopes, the model results should
include runoff generation and will differ from the values shown here.
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CHAPTER 7
IMPLICATIONS FOR MANAGEMENT OF THE
FOREST - WATER ECOSYSTEM
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7. IMPLICATIONS FOR MANAGEMENT OF THE FOREST-WATER ECOSYSTEM
The results of this report have strong implications for management of the forest-water
ecosystem. Boreal forests are shown to have an important self-control on their hydrological and
climatic resources as "water managers". Hydrological pathways and processes in a mature boreal forest
are unique and distinctive and help create the climate and water supply associated with these areas.
Removal of the boreal forest impairs this intrinsic water and climate management function. Significant
changes have been identified in the water and energy cycles of the PAMF region associated with
harvesting practise. These changes are due to:
1) removal of the forest canopy or changes to forest species,
2) disturbance to the soils, and
3) alteration of the areal coverage of soils and canopy types within basins.
The changes to hydrological processes associated with these three factors have been documented
quantitatively and compiled in a new hydrological model, the Boreal Ecosystem Evapotranspiration
Runoff Simulation (BEERS). Specific issues are discussed below.
7.1 Canopy and Soil Changes
Changes to the forest canopy affect the interception of snow and rain, evaporation and
transpiration, wind speeds, reflection of solar radiation and ultimately the surface energy balance and
temperature. Removal of the forest canopy is shown to dramatically influence delivery of water to soil,
microclimate and annual evapotranspiration loss. This study also shows hydrological impacts resulting
from changes to soil and rooting systems that have been noted in companion studies (Pennock et al.,
1997; van Rees, 1997), e.g., compaction of soil and toughening of terrain through furrowing reduces
infiltration capacity; disturbed rooting patterns reduce the ability of plants to remove soil water through
evapotranspiration. It also shows the effect of an intact litter/organic layer in promoting the recovery of
water management in regenerating forests.
7.1.1 Spring snowmelt and runoff
The coniferous forest canopy intercepts and sublimates a large part of winter snowfall. Sites
with a coniferous forest canopy have substantially less snow at the time of melt than do sites without a
forest canopy or with deciduous cover. However, snowmelt rates are faster and snowmelt occurs
earlier at sites without a forest canopy than at forested sites. The combination of deep snowpacks, wet
soils and rapid snowmelt promotes the greatest snowmelt runoff from clear-cut sites. Snowmelt runoff
does not consistently occur from mature forested sites but occurred every year of study from clear-cut
sites. Forest soils are completely frozen at the time of melt but clear-cut site soils partly thaw,
increasing their infiltration capacity but still permitting substantial runoff generation because of deep
Snowpacks. The Regenerating pine stand is showing signs of recovery to a "mature forest"
winter/spring hydrology at 15 years since replanting.
129
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7.1.2 Growing season soil moisture
Forest canopies intercept rainfall and permit direct evaporation of intercepted rain from the
canopy. Direct evaporation of intercepted rainwater from the canopy cools the surface but is not
associated with the primary productivity of the forest. Where this canopy is removed the rain fails
directly to the soil where it may infiltrate. Forest canopies are also associated with higher transpiration
rates and albedos, resulting in greater cooling than in cleared areas. Without this cooling effect, clearcuts experience surface temperatures well in excess of air temperature and temperatures of mature
forest canopies. Transpiration withdraws moisture from the rooting zone in the soil. The combination
of higher interception, evaporation and transpiration in forests results in drier soils than cleared areas
during the most of the growing season. However, much of the excess soil water is stored below the
rooting zone of immature species in cleared and regenerating areas and cannot be drawn upon for
transpiration and therefore primary production. In times of drought mature forests reduce their
evapotranspiration demands and runoff losses, conserving soil moisture, while clear-cuts show no such
restraint and regenerating sites show limited restraint. The regenerating site has not recovered to
"mature forest" hydrology and water management in the summertime, largely because of lower
evapotranspiration than mature forests of similar species and leaf area.
7.1.3 Runoff generation from rainfall
In clear-cuts, the combination of wetter soils after snowmelt, limited interception of rainfall and
lower evapotranspiration results in a greater water excess and higher potential for runoff than forested
sites. Regenerating sites produce runoff amounts between those of clear-cuts and mature forests. In a
dry year, the cleared sites showed an ability to generate runoff when the forest soils did not. Rapid,
efficient delivery of rainwater to the soil surface and wetter soils due to low evapotranspiration
promote runoff generation in clear-arts. The potential for runoff development in furrows is greatest,
and is due to the above factors compounded by reduced infiltration capacity.
7.2 Impacts of Forest Harvesting on Basins
Differences were noted between the runoff of Beartrap and Whitegull Creek basins with greater
runoff generation in Whitegull Creek Beartrap represents an unharvested, unburnt mixed-wood basin
while pans of the forest in Whitegull have been removed by fires and harvesting and replanted with
various stages of regrowth. Both spring snowmelt runoff and the summer runoff response to extreme
rainfalls are increased in Whitegull over Beartrap. BEERS was used in a simulated "virtual clear-cut'
of pure pine and spruce stands, comprising approximately 20% of the area of Beartrap Creek with a
resulting simulated increase in soil moisture storage and decrease in evapotranspiration. While the
simulation was only carried out for one summer, the effect of wetter soils on subsequent years would
be to increase basin runoff and streamflow in both snowmelt and summer periods. It may be surmised
that by managing the cumulative recently-cleared area in any particular basin or sub-basin then
streamflows, soil moisture levels and surface temperatures characteristic of unharvested forest may be
maintained. The age structure of replanted areas in a sub-basin should also be considered; the complete
recovery lime for summer hydrological processes has not been observed in this study, but is longer
than 15 years after replanting and longer than the time required for plantations to regenerate the leaf
area of a mature forest.
130
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CHAPTER 8
FUTURE DIRECTIONS
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8. FUTURE DIRECTIONS
The processes that influence the water balance of forest stands and clearcuts in the southern
boreal forest of western Canada have been examined, described and modelled in a physically-based
manner for the first time. The improved understanding of hydrological processes will permit a better
science basis for decision-making on the fate of the boreal forest. Further incorporation of these
processes in the Boreal Ecosystem Evapotranspiration-Runoff Simulation and other models is
necessary to provide a range of tools for investigating and assessing the effect of land use and climate
change on the forest water balance. It is expected that the improved understanding of hydrological
processes in the boreal forest will result in a host of more-realistic forest hydrological, ecological and
climatological models in the future. Future process research should concentrate on five categories of
questions,
1) effectiveness of regeneration: a full recovery of snow processes and partial recovery of summer
hydrological processes has been observed in a (now) 15 year old pine plantation; how long will it take
for complete recovery? What soil and nutrient factors influence this rate of recovery?
2) burns compared to clear-cuts: do hydrological processes in burned forests mimic those in clearcuts? Preliminary results have been obtained from a burn, but the site will have to be monitored for
several more years and compared to measurements at the clear-cut site to provide the information
necessary to confidently answer this question.
3) scaling of processes: how may hydrological processes be accurately represented at scales larger than
that at which they occur? Answering this question will be necessary to link detailed hydrological
models with global circulation models, in assessing the hydrology of large areas of the boreal forest
and in detailing how the spatial pattern of hydrological states and variables influences basin and
ecosystem response.
4) advection of water and energy: how much energy and water is transferred horizontally in the boreal
forest and what influence does this transfer have on vertical energy and water fluxes? What influence
does clear-cutting have in imposing an artificial pattern of stand shape and size beyond changing the
average water balance for a region, i.e. are there horizontal interactions imposed that may affect
ecosystems downwind and downstream?
5) hillslope hydrological processes: what is the nature of flow through the near-surface and subsurface
flowpaths that route water to the wetlands, lakes and streams in the southern boreal forest? How can
the behaviour of these flowpaths be faithfully represented in a model such that the physics of flow are
reasonable yet computations are efficient? Can the horizontal redistribution of soil water be
represented simply such that the vertical fluxes over a basin are correct in their spatial pattern and
magnitudes and the effectiveness of buffer strips can be evaluated?
6) nutrient fluxes: how does soil nutrient status influence fluxes of water and energy in the boreal
forest? How do hydrological processes and climate influence the retention and transport of critical
nutrients in soils and in runoff? What are the specific processes affected that cause the release of N
from recently clear-cut lands and can this release be controlled using buffer strips?
7) aquatic ecosystems: how do the physical and basin geochemical processes control or influence
aquatic ecosystem productivity? How sensitive is this productivity to changes in hydrological
processes that stem from changes to land cover or climate?
Such developments would permit the evaluation of fine-scale and large-scale forest
management techniques and provide a better understanding of hydrogeochemical flows in the boreal
forest as influenced by land use, forest management and climate.
132
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CHAPTER 9
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Proceedings, IUFRO Seminar Mountain, Forests and Avalanches. Davos, Switzerland, pp. 63-79.
Tao, T. and N. Kouwen. 1989. Remote sensing and fully distributed modelling for flood forecasting. J.
Water Resour. Plann. and Manage. Div., ASCE, 115 (6):809-823.
Tao, Y.-X. and D.M. Gray. 1994. Prediction of snowmelt infiltration into frozen soils. Numerical Heat
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Thomas, G. and P. R. Rowntree. 1992. The boreal forest and climate. Quarterly Journal of the Royal
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457 pp.
141
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No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
Van Genuchten, M. Th. 1980. A closed form equation for predicting the hydraulic conductivity of
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trafficking effects on site impacts. Department of Agricultural and Bioresource Engineering. University
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142
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CHAPTER 10
LIST OF SYMBOLS
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10. LIST OF SYMBOLS
Symbol
Definition
α
albedo (dimensionless)
α 1 , d1
empirical coefficients used to calculate A as a function of temperature
γ
psychrometric constant (66.7 Pa °C-1)
p
the slope of the saturation vapour pressure vs temperature curve (kPa °C-1)
g
solar angle above the horizon (radians)
l
volumetric soil moisture content (volumetric ratio)
lP
pre-melt degree of pore saturation (dimensionless)
i
fitting parameter for the Brooks - Corey relationship (dimensionless)
iT
thermal conductivity of the atmosphere (J m-1 s-1 K-1)
j
exponent in the net radiation ratio calculation (dimensionless)
ν
vertical fall velocity for a snowflake (m s-1)
νz
flow through an unsaturated medium (L t-1)
ma
density of air (kg m-3)
ms
snow density (kg m-3)
msw
saturation density of water vapour at a specific temperature (kg m-3)
mwa
water vapour density of air (kg m-3)
mwp
water vapour density of air at a particle surface (kg m-3)
n
the Stefan-Boltzmann constant (5.67 x 10-8 W m-2 K-4)
Ψ
f
effective suction at the wetting front (mm)
ω
Lambert's Omega function
Ω
clumping factor for extinguished light (dimensionless)
Ω
s
clumping factor for intercepted snow (dimensionless)
a, b
empirical coefficients used to calculate the wind function
AI
surface area of intercepted snow (m-2)
Am
surface area of an ice sphere (m-2)
144
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Symbol
ASA
Definition
aggregated simulation area
ASL
Above sea level (m)
b
empirical drainage parameter defined by Rutter and Morton 1977 (mm-1)
c
proportionality factor between the clumping factor for light and snow (dimensionless)
ce(I)
exposure coefficient that corrects for the difference in the ratio of surface area to mass
between the single ice sphere and the snow intercepted by a canopy
ce(Mt)
exposure coefficient that corrects for the difference in the ratio of surface area to mass
between the single ice sphere and the snow intercepted by a tree
cp
specific heat (J kg-1 K-1 or J kg-1 °C-1 )
csubl
rate coefficient between the rate of change of the mass of an ice sphere with time, to the
mass of the ice sphere
C
rate at which the canopy stores intercepted rainfall (mm min-1)
C(ψ)
slope of the soil water retention curve
Cc
canopy coverage, plan area of continuous canopy per unit area of ground (m2 m-2)
Cp
maximum snow-leaf contact area per unit area of ground during snowfall (m2 m-2)
OC
cumulative depth of rainfall stored within the canopy (mm)
ds
snow depth (cm)
d0
displacement height (m)
D
relative drying power (dimensionless)
Dr
drainage (depth: m or mm; rate: mm min-1)
Ds
empirical drainage parameter defined by Rutter and Morton 1977 (mm min-1)
Dv
diffusivity of water vapour in the atmosphere (m2 s-1)
e*
saturation vapour pressure (kPa)
ea
vapour pressure of the air (kPa)
eext
extinction efficiency for light through the canopy (dimensionless)
E
evaporation (depth: mm or m; rate: mm min-1)
ET
evapotranspiration (depth: mm or m; rate: mm min-1)
Ez
drying power of the air (mm)
145
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Symbol
EI
Definition
free water evaporation from the canopy (depth: m or m; rate: mm min-1)
ƒ(t)
infiltration rate (mm hr-1)
ƒ(u)
Dalton type wind function
F
an exponent having the value of approximately 0.3 for small trees
Fi
completely frozen ground index
F(t)
cumulative infiltration (mm)
G
relative evaporation, the parameter describing the deviation from saturated conditions
(dimensionless)
GRU
grouped response unit
h
elevation head (m)
hs
latent heat of sublimation (J kg-1)
hc
vegetation height (m)
H
sensible heat flux (mm)
HRU
hydrological response unit
I
infiltration rate (L t-1)
I
intercepted precipitation for a canopy (depth: m or m; rate: mm min-1)
I/P
interception efficiency (dimensionless)
ID
canopy storage capacity for intercepted rainfall (mm)
If
snowmelt infiltration (mm SWE)
Ip
canopy storage capacity for intercepted snow (mm SWE)
I*
maximum intercepted snow load (mmSWE)
j, j
downwind width of the forested canopy (m)
k
von Karman constant (0.4, dimensionless)
k1
coefficient indexing the age or structure of snow (dimensionless)
k2
is the proportionality factor between the interception efficiency of a canopy to the
remaining capacity of the canopy to accept snow (dimensionless)
K
unsaturated hydraulic conductivity (mm hr-1)
Ks
saturated hydraulic conductivity (mm hr-1)
146
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Symbol
l
Definition
thermal quality of the snowpack (dimensionless fraction)
L
intercepted snow load for the canopy (mm SWE)
L*
maximum intercepted snow capacity (load) that can be retained by the forest canopy
under current canopy structure and temperature conditions (mm SWE)
L0
intercepted snow load at the start of a snowfall event (mm SWE)
LAI
leaf area index (m2 m-2, ratio)
LAI Ω, or
LAI Ωs
L↓
effective leaf area index (m2 m-2, ratio)
m
mass of a single ice sphere (kg)
mc
estimated mass of the canopy per unit area (kg m-2)
M
snowmelt (ram SWE)
MT
mass of intercepted snow on an individual tree (kg)
Mw
molecular weight of water (18.01 kg kmole-1)
M*
maximum snow load for an individual tree (kg)
dm/dt
rate at which water vapour can be removed from an ice sphere (kg s-1)
N
ratio of intercepted snow load from individual tree to the canopy (mm kg-1)
Nr
net radiation ratio, the ratio of Q*s/Q* (dimensionless)
Nu
Nusselt number (dimensionless)
p
free throughfall coefficient for rainfall (dimensionless)
pd
precipitation from canopy drip (depth: m or m; rate: mm min-1)
pt
total precipitation (depth: m or m; rate: mm min-1)
P
air pressure (kPa)
Pb
bubbling pressure (kPa)
PFC
sub-canopy snowfall (mm SWE)
Pn
sub-canopy rainfall (depth: m or m; rate: mm min-1)
Pr, Ps
precipitation, rainfall or snowfall (depth: m or m; rate: mm min-1)
q
vertical flow through soil (L3 L-2 t-1)
incoming longwave radiation (W m-2)
147
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Symbol
qsubl
Definition
sublimation loss (mm SWE)
qtran
loss of snow due to transportation (mm SWE)
Q*
net radiation (W m-2)
Q*c
net radiation over the canopy (W m-2)
Q*s
sub canopy net radiation (W m-2)
QAdvection
small-scale advection of sensible heat from bare ground or plant stems to the snow
cover (W m-2)
QE
turbulent latent heat flux (W m-2)
QG
ground heat flux (W m-2)
QH
turbulent sensible heat flux (W m-2)
QM
energy used in snow melt (W m-2)
QS
shortwave radiation (W m-2)
re
radius of a sphere (m)
R
universal gas constant (8313 J kmole-1 K-1)
REA
representative elemental area
RH
relative humidity (percentage)
RO
runoff (m or mm)
Rn
net radiation (mm)
S
soil heat flux (mm)
Sh
Sherwood number (dimensionless)
SWE
Snow water equivalent (mm)
SWEg
snow accumulation on the ground (m or mm)
Ss
maximum snow load per unit branch area (kg m-2)
S↓
incoming solar radiation (W m-2)
pS
change in soil moisture storage (mm)
t
time
tp
time required for surface ponding
T
temperature (°C or K)
148
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Symbol
Tz
Definition
ambient air temperature (°C or K)
TS
surface temperature (°C or K)
TSP
snow transport to the snow pack (mm SWE)
Tf
free throughfall (depth: m or m; rate: mm min-1)
TM3
TM4
TM4
reflectance values observed in the thematic mapper channels 3, 4 and 5
TTI
transformed thermal index
TVI
transformed vegetation index
u
wind speed (m s-1)
U
internal energy of the snowpack or forest canopy (W m-2)
dU/dt
energy flux to internal storage, snowpack or forest canopy (W m-2) over time
x, x
downwind width of the canopy gap (m)
z
height (m)
zo
roughness length (m)
Z
depth (m)
149
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APPENDIX A
Instrumentation and Towers at Experimental Sites
in the Prince Albert Model Forest
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APPENDIX A
Instrumentation and Towers at Experimental Sites in the Prince Albert Model Forest
PINE
The Pine location is in a mature Jack pine stand 16 - 22 metres in height. This site is equipped
with 6 Campbell Scientific microloggers (Pine1 - Pine6) and is divided into 5 instrumentation levels.
The first level is from 1.6 metres below the ground surface to approximately 1.5 metres above. The
Pine site is located at 53°52'13"N, 106°07'44"W. The trail into the site is a vehicle/bike path in the
summer and ski trail in the winter. The Pine site was recording data by early November 1993 with the
addition of Pine5 and Pine6 in August and October 1995.
LEVEL ONE INSTRUMENTATION
- 2 Delta T tube solarimeters, one incoming, one outgoing; positioned on camera tripods 1 metre above
the ground (W/m2).
- 1 Delta T tube net radiometer positioned on a camera tripod 1 metre above the ground (W/m2).
- 1 Atmospheric Environment Service (ALES) snow nipher gauge 1.5 metres above the ground.
- 10 snow survey stakes placed 10 metres apart ruing north to south on the west edge of the site starting
near snow nipher.
- 3 Radiation Energy Balance Systems (REBS) soil heat flux plates positioned 5 - 7 cm below the
ground mineral surface along the cardinal axes: lwest - medium dense canopy, 2east -open canopy,
3south - dense canopy (W/m2).
- 3 University of Saskatchewan (U of S) soil temperature arrays measuring at 2.5, 5, 10, 20, 40, 80 and
160 cm placed in the ground to correspond with above soil measurements (oC).
- 3 Time Domain Reflectometry (TDR) assemblies located to correspond with the above soil
measurements at the same depths as the soil temperatures (BNC connector ends are housed together in
three plastic containers).
- 3 U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to correspond
with the above soil measurements.
- 5 Omega type E thermocouples taped with reflective tape to a stake, measuring snow temperature,
located at 1west soil measurement location and measuring at 0, 5, 15, 35 and 58 cm above the ground
(°C).
- 1 Campbell Scientific SR50 Sonic Ranging device for measuring snow depth positioned 2 metres
from the south side of the tower on aluminum mast arm, 1.5 m from ground (metres).
151
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- 3 Atmospheric Environment Service (AES) standard rain gauges 0.5 metres above the ground located
to correspond with the above soil measurements (millimetres).
- 1 Omega type E thermocouple at 2 metres height on SR50 arm used to provide reference temperature
for SR50 (°C).
All of the instrumentation at levels 2 - 5 is located on a red Steeple-Jack internal stair scaffolding.
LEVEL TWO INSTRUMENTATION
Level 2 is located in the lower canopy at approximately 4.5 metres.
- 1 RM Young horizontal anemometer with wind direction positioned 1 metre from north side of tower
on aluminum mast arm (m/s and degrees).
- 1 U of S snow particle detector positioned 1 metre from east side of tower on aluminum mast arm
(counts).
- 1 RM Young vertical anemometer on the same mast arm as the particle detector at this level (m/s).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
LEVEL THREE INSTRUMENTATION
Level 3 is located in the mid to upper canopy at approximately 12 metres.
- 1 Logitech digital camera housed in a wooden insulated and heated box on the northwest corner of
the tower taking images of tree branches to the west.
- 1 RM Young horizontal anemometer with wind direction positioned 1 metre from north side of tower
on aluminum mast arm (m/s and degrees).
- 1 RM Young vertical anemometer on the same mast arm as the former particle detector at this level
(m/s).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
LEVEL FOUR INSTRUMENTATION
Level 4 is located at the top of the canopy at approximately 18 metres.
- 1 RM Young horizontal anemometer with wind direction positioned 1 metre from north side of tower
on aluminum mast arm (m/s and degrees).
- 1 U of S snow particle detector positioned 1 metre from east side of tower on aluminum mast arm
(counts).
152
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- 1 RM Young vertical anemometer on the same mast arm as the particle detector at this level (m/s).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
LEVEL FIVE INSTRUMENTATION
Level 5 is located at the top of the tower above the forest canopy at approximately 25 metres.
- 1 Everest infrared remote thermometer positioned 1 metre from the west side of the tower on
aluminum mast arm (° C).
- 1 Weathertronics 2032 cup anemometer positioned 1 metre from the northwest corner of the tower on
aluminum mast arm (m/s).
- 1 REBS Total hemispherical radiometer with soft plastic domes positioned 1.8 metres from the south
of the tower on aluminum mast arm with 1.8 m/s ventilator (W/m2).
- 2 Kipp and Zonen solar pyranometers, one incoming one outgoing positioned 1 metre from southeast
corner of tower on aluminum mast arm (W/m2).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm from northeast corner of
tower on aluminum mast arm in a gill shield (° C and %).
- 1 Atmospheric Environment Service (AES) standard rain gauge on northwest corner of tower
(millimetres).
- 1 Texas Electronics tipping bucket rain gauge on north west corner of tower (millimetres).
LEVEL FIVE EDDY CORRELATION
- 1 Campbell Scientific Krypton Hygrometer positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 26.5 metres (specific humidity).
- 1 Solent 3 axis ultrasonic anemometer positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 26.5 metres (m/s).
- 1 Omega hypodermic thermocouple positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 26.5 metres (o C).
All instrumentation at levels 2 - 5 (except the camera) and three level 1 ground radio meters are
connected to one of two 21X microloggers (Pine1-U or Pine2-Q) located on the tower. The total
hemispherical radiometer and Eddy Correlation system are connected to data loggers at level five
(Pine5 and Pine6 respectively). All soils measurements are recorded using one 21X and multiplexer
labelled Pine3-T found on the ground at the soils location to the south of the main tower. Solar
153
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panels (18-60 Watt) with regulators are connected to 12 volt batteries providing power for the data
loggers and sensors for all sites.
HANGING TREE TOWER INSTRUMENTATION
The digital camera switch, hanging tree load cell, nipher load cell, snow depth sensor and SR50
reference temperature are connected to the 21X (Pine4-H) located at the base (level 1) of the tree
hanging tower which is approximately 15 metres tall.
- 1 Load cell positioned between a hanging 9 metre jack pine and the pulley apparatus suspending the
tree measuring the weight of the tree in grams.
- 1 Omega type-e thermocouple giving the temperature of the load cell (° C).
- 1 Load cell positioned under a nipher gauge apparatus under the hanging tree giving a weight of
unloading snow in grams.
MIXED-WOOD
The Mixed-wood site is located 53°53'33"N 106°07'06"W within a mixed wood - aspen/white
spruce stand. Approximately 75% of the trees are aspen 15-25 metres in height, the remainder being
white spruce with heights up to half that of the aspen. This site started recording ground data by the
third week of November 1993. The instruments at the Mixed-wood site are at six levels and are being
recorded using three 21X Campbell Scientific microloggers and multiplexer. The first level is from 1.6
metres below the ground surface to approximately 2 metres above. This site's power is supplied from
12 volt batteries and recharged by 18-60 Watt solar panels. An internal stair tower was installed and
tower instrumentation was operational at this site in May 1994. The Eddy Correlation system was .run
periodically until June 1995, when it was installed permanently at level six.
LEVEL ONE INSTRUMENTATION
- 1 Atmospheric Environment Service (AES) snow nipher gauge 1.5 metres above the ground.
- 10 snow survey stakes placed 10 metres apart running north to south at the west edge of the site
starting near the west soil location.
- 3 Radiation Energy Balance Systems (REBS) soil heat flux plates positioned 5 - 7 cm below the
ground surface along the cardinal axes: 1west - open canopy, 2east - spruce/aspen canopy, 3north aspen canopy (W/m2).
- 3 University of Saskatchewan (U of S) soil temperature arrays measuring at 2.5, 5, 10, 20, 40, 80 and
160 cm placed in the ground to correspond with above soil measurements (° C).
154
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- 3 Time Domain Reflectometry (TDR) assemblies located to correspond with the above soil
measurements at the same depths as the soil temperatures (BNC connector ends are housed together in
three plastic containers).
- 3 U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to correspond
with the above soil measurements.
- 2 sets of Omega type E thermocouples taped with reflective tape to a stake, measuring snow
temperature, located at 1west and 2east soil measurement locations and measuring at 0, 15, and 3 5 cm
above the ground (o C).
- 3 Atmospheric Environment Service (AES) standard rain gauges 0.5 metres above the ground located
to correspond with the above soil measurements (millimetres).
- 1 Delta T tube net radiometer positioned on a camera tripod 1 metre above the ground (W/m2).
- 1 Omega type E thermocouple at 1 metre height on stake used to provide reference temperature for
SR50 (° C).
- 1 Campbell Scientific SR50 Sonic Ranging device for measuring snow depth positioned 2 metres
from the west side of' the tower on aluminum mast arm, 2 metres from ground.
- 1 Rainfall interception gauge collects stemflow from a mature aspen tree and accumulates in a 20
litre carboy and is recorded with rain standard cylinder after rainfall events (millimetres).
All of the instrumentation at levels 2 - 6 are located on a silver internal stair scaffolding.
LEVEL TWO INSTRUMENTATION
Level 2 is located in the spruce canopy below the aspen canopy at approximately 8.2 metres.
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
- 1 Weathertronics 2032 cup anemometer positioned 1 metre from the northwest corner the tower on
aluminum mast arm (m/s).
LEVEL THREE INSTRUMENTATION
Level 3 is located above the spruce canopy and below the aspen canopy at approximately 13.6 metres.
- 1 Vaisala HMP35C temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
- 1 Weathertronics 2032 cup anemometer positioned 1 metre from the northwest corner of the tower on
aluminum mast arm (m/s)
155
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LEVEL FOUR INSTRUMENTATION
Level 4 is located within the aspen canopy at approximately 22.7 metres.
- 1 Vaisala HMP35C temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %):
- 1 Weathertronics 2032 cup anemometer positioned 1 metre from the northwest corner of the tower on
aluminum mast arm (m/s).
LEVEL FIVE INSTRUMENTATION
Level 5 is located at the top of the aspen canopy at approximately 25.5 metres.
- 1 Vaisala HMP35C temperature/relative humidity gauge positioned 20 cm from northwest corner of
tower on aluminum mounting apparatus in a gill shield (° C and %).
- 1 Atmospheric Environment Service (AES) standard rain gauge on a swing arm 2 metres from the
northwest corner of tower {millimetres).
LEVEL SIX INSTRUMENTATION
Level 6 is located at the top of the tower above the forest canopy at approximately 28 metres.
- 1 Everest infrared remote thermometer positioned 1 metre from the west side of the tower on
aluminum mast arm (° C).
- 1 Weathertronics 2032 cup anemometer positioned 1 metre from the northwest corner of the tower on
aluminum mast arm (m/s).
- 1 REBS net radiometer with soft plastic domes positioned 1.8 metres from the southeast corner of the
tower on aluminum mast arm (W/m2).
- 2 Kipp and Zonen solar pyranometers, one incoming, one outgoing positioned 2 metres to the south
from southeast corner of tower on aluminum mast arm (W/m2).
- 1 Vaisala HMP35C temperature/relative humidity gauge positioned 20 cm from northeast corner of
tower on aluminum mast in a gill shield at the level of the Eddy Correlation setup 28.2 metres (o C and
%).
- 1 Atmospheric Environment Service (AES) standard rain gauge on west side of tower (millimetres).
- 1 Texas Electronics tipping bucket rain gauge on northwest corner of tower (millimetres).
LEVEL SIX EDDY CORRELATION
- 1 Campbell Scientific Krypton Hygrometer positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 28.2 metres (specific humidity).
156
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- 1 Solent 3 axis ultrasonic anemometer positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 28.2 metres (m/s).
- 1 Omega hypodermic thermocouple positioned 1 metre from aluminum mast attached on the
northeast corner of the tower to the north at a height of 28.2 metres (° C).
All instrumentation at levels 4 - 6 and ground tube net radiometer are connected to the 21X
micrologger (Mixed2) located on the tower. The Eddy Correlation system is connected to a 21X data
logger at level 6 (Mixed3). All soils measurements, snow depth and temperature and level 2 and 3 air
temperature and relative humidity are recorded using one 21X and multiplexer labelled Mixed1 found
on the ground to the west of the main tower.
SPRUCE
The Spruce site is located at 53°53’10"North 106°07’1l"West in a black spruce stand. This site
started recording data for the PAMF in November 1993 but was previously equipped with a tower and
full complement of instrumentation and weighed spruce tree for the winters 1991-93. Data is collected
using a CR10 Campbell Scientific micrologger and power is provided using a 60 Watt solar panel and
12 volt battery.
INSTRUMENTATION
- 1 Atmospheric Environment Service (AES) snow nipher gauge 1.5 metres above the ground.
- 10 snow survey stakes placed 5 metres apart running west for five stakes and then south for 5 starting
at the west side of the site.
- 1 Radiation Energy Balance Systems (REBS) soil heat flux plate positioned 5 -7 cm below the
ground surface into the mineral soil which is about 8 - 11 cm deep at this site (W/m2).
- 1 University of Saskatchewan (U of S) soil temperature array measuring at 2.5, 5, 10, 20, 40, 80 and
160 cm placed in the ground to correspond with above soil measurement (° C).
- I Time Domain Reflectometry (TDR) assembly located to correspond with the above soil
measurement at the same depths as the soil temperatures (BNC connector ends are housed together in
plastic containers).
- 1 set of U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to
correspond with the above soil measurements.
- 1 set of Omega type E thermocouples taped with reflective tape to a stake, measuring snow
temperature, located to correspond with the above soil measurement locations and measuring at 0, 15,
and 35 cm above the ground (° C).
157
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- 3 Atmospheric Environment Service (AES) standard rain gauges, two at 0.5 metres above the ground
and one at 1.5 metres from the ground in the nipher during the summer months (millimetres).
- 1 Campbell Scientific: SR50 Sonic Ranging gauge for measuring snow depth positioned 1.5 metres
from the south side of the tripod on aluminum mast arm, 13 metres from ground (metres).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 1.5 metres from ground on a
tripod in a gill shield mounting apparatus (°C only).
OPEN CLEARING
The Open site provides a good reference precipitation measurement to compare to the other
sites located within the Beartrap Creek watershed (Spruce, Pine, Mixed-wood). The Open site is
situated at 53°53’00"North and 106°07'35"West. It has previously been instrumented for winter studies
in 1991-93.
INSTRUMENTATION
- 1 Atmospheric Environment Service (AES) snow nipher gauge 1.5 metres above the ground within
the fenced area.
- 10 snow survey stakes placed 10 metres apart running south for five stakes and then east for 5
starting at the west side of the site.
- 1 Atmospheric Environment Service (AES) standard rain gauge at 1.5 metres from the ground in the
nipher during the summer months (millimetres).
CLEARED (recent or new clearcut or "clearcut")
The Cleared site is situated at 53°58'35”N 105°55'00"W and is located off of Highway Number
2, north of Bittern Creek. The site was clearcut in 1991 and replanted in 1992. It presently consists of
aspen bushes with some pine and spruce seedlings. The Cleared site was recording data by late
November 1993. The data is recorded by 21X Campbell Scientific micrologger and multiplexer and
powered by a 12 volt battery and 60 Watt solar panel. The main tower here is at 6-m height, well above
the 2-m canopy (as of fall 1995). An Eddy Correlation system was added to this site in November 1995
on a 3 metre tripod. It is powered by a 12 volt battery and 106 Watt solar panel.
158
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INSTRUMENTATION
- 1 Atmospheric Environment Service (AES) snow nipher gauge 1.5 metres above the ground.
- 10 snow survey stakes placed 5 metres apart ruing east to west starting .at the east side of the site.
- 2 Radiation Energy Balance Systems (REBS) soil heat flux plates positioned 5 -7 cm below the
ground surface into the mineral soil, least or slope and 2west or dip (W/m2).
- 4 University of Saskatchewan (U of S) soil temperature arrays measuring at 2.5, 5, 10, 20, 40, 80, and
160 cm placed in the ground to correspond with above soil measurement, 2 of the four require manual
measurements (° C).
- 2 Time Domain Reflectometry (TDR) assemblies located to correspond with the above soil
measurements at the same depths as the soil temperatures (BNC connector ends are housed together in
two plastic containers).
- 4 sets of U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to
correspond with the above soil measurements.
- 2 sets of Omega type E thermocouples taped with reflective tape to a stake, measuring snow
temperature, located to correspond with the above soil measurement locations and measuring at 0, 15,
and 35 cm above the ground.
- 1 Everest infrared remote thermometer positioned 1 metre from the south side of the mast on
aluminum mast arm with net radiometer 4 metres from the ground (° C).
- 4 Weathertronics 2032 cup anemometers positioned 1 metre from the northeast of the mast on
aluminum mast arms at four levels from the top of the bush to the top of the tower - 2.6, 3.8, 4.9 and
6.1 metres (m/s).
- 1 REBS net radiometer with soft plastic domes positioned 1.5 metres from the south of the mast on
aluminum mast arm 3.85 metres from the ground (W/m2).
- 1 Hollis MR-3 solar pyranometer, outgoing positioned 1 metre from southeast of mast on aluminum
mast arm 3.8 metres from the ground (W/m2).
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 20 cm to northeast of mast on
aluminum mast arm 1.5 metres from the ground in a gill shield (° C and %).
- 1 U of S snow particle detector positioned 1 metre to northeast of mast on aluminum mast arm with
the lowest anemometer at the top of the bush (counts).
- 1 Campbell Scientific SR50 Sonic Ranging gauge for measuring snow depth positioned 2 metres
from the southeast side of the tower on aluminum mast arm, 2 metres from ground (metres).
- 2 Atmospheric Environment Service (ALES) standard rain gas, one 0.5 metres above the ground
located to correspond with the soil measurements being conducted in the low lying area of this site and
one in the nipher apparatus at 1.5 metres during the summer months (millimetres).
159
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THREE METRE EDDY CORRELATION INSTRUMENTATION
- 1 Campbell Scientific Krypton Hygrometer positioned on aluminum mast arm 1 metre to the
northeast of the tower at a height of 3 metres (specific humidity).
- 1 Kaijo Denke single axis ultrasonic anemometer positioned 1 metre from aluminum mast positioned
to the northeast of the tower at a height of 3 metres (m/s),
- 1 Omega hypodermic thermocouple positioned 1 metre from aluminum mast tower to the northeast
attached to the Krypton Hygrometer at a height of 3 metres (° C).
REGENERATING (old) CLEAR-CUT
The Regenerating site is situated at 54°02'05"N 105°54'40"W and is located off of Highway
Number 2. It was clearcut in 1981 and replanted to jack pine. This site was recording data by late
November 1993. An 8-m tower extends well above the 4-m canopy. The data is recorded by 2 - 21X
Campbell Scientific microloggers and 1 multiplexer and powered by a 12 volt batteries and 2 - 60 Watt
solar panels.
TOWER INSTRUMENTATION
- 10 snow survey stakes placed 5 metres apart running east to west starting at the west side of the site.
- 2 Radiation Energy Balance Systems (REBS) soil heat flux plates positioned 5 -7 cm below the
ground surface into the mineral soil, 1east and 2west (W/m2).
- 2 University of Saskatchewan (U of S) soil temperature arrays measuring at 2.5, 5, 10, 20, 40, 80 and
160 cm placed in the ground to correspond with above soil measurement (o C).
- 2 Time Domain Reflectometry (TDR) assemblies located to correspond with the above soil
measurements at the same depths as the soil temperatures (BNC connector ends are housed together in
two plastic containers).
- 2 sets of U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to
correspond with the above soil measurements.
- 2 sets of Omega type E thermocouples taped with reflective tape to a stake, measuring snow
temperature, located to correspond with the above soil measurement locations and measuring at 0, 15,
and 35 cm above the ground (° C).
- 1 Everest infrared remote thermometer positioned I metre from the west side of the mast on
aluminum mast arm 8 metres from the ground (° C).
- 4 Weathertronics 2032 cup anemometers positioned 1 metre to the northeast of the mast on aluminum
mast arms at four levels: 3.0, 4.0, 6.0 and 8.0 metres from the ground (m/s).
- 1 Middleton net radiometer with soft plastic domes positioned 2.0 metres from the south of the mast
on aluminum mast arm 8.0 metres from the ground (W/m2).
- 2 Kipp and Zonen solar pyranometers, one incoming and one outgoing positioned 1 metre from the
southeast of the mast on aluminum mast arms 8.0 and 7.0 metres from the ground respectively (W/m2).
160
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- 2 Vaisala HMP35CF temperature/relative humidity gauges positioned 20 cm to northeast of the tower
on aluminum mast arms 4.5 and 8.0 metres from the ground in gill shields (o C and %).
- 2 U of S snow particle detector positioned 1 metre to northeast of mast on aluminum mast arms 1.2
and 8.0 metres from the ground (counts).
- 2 Atmospheric Environment Service (AES) standard rain gauges 0.5 metres above the ground located
to correspond with the above soil measurements (millimetres).
- 1 Logitech digital camera housed in a wooden insulated and heated box on the northwest side of the
tower taking images of tree branches to the west.
- 1 Delta T tube net radiometer positioned on a camera tripod 1 metre above the ground (W/m2).
The second 21X has the three metre mast instruments, snow depth, hanging tree load cell,
camera, camera switch and all the soils measurements attached to it. The hanging tree apparatus is
attached to a 6.1 metre triangular tower to the northeast of the 3 metre tripod and was erected and the
first tree hung in December 1994.
THREE METRE MAST INSTRUMENTATION
- 2 RM Young horizontal anemometers with wind direction positioned 1 metre from north side of mast
on aluminum mast arms 2.4 and .95 metres from the ground (m/s and degrees).
- 2 RM Young vertical anemometers with wind direction positioned 1 metre from south side of mast
on aluminum mast arms 2.4 and .8 metres from the ground (m/s).
- 1 Texas Electronics tipping bucket rain gauge on top of tripod (millimetres).
- 1 Campbell Scientific SR50 Sonic Ranging device for measuring snow depth positioned 1.5 metres
from the southeast side of the tripod on aluminum mast arm, 1.5 metres from ground.
- 1 Vaisala HMP35CF temperature/relative humidity gauge positioned 1.2 metres from ground on a
tripod in a gill shield mounting apparatus (o C and %).
BURN
The Burn site is located 54°01'63"N 105°27'07"W at UTM 471-5986 stand 501, 502 cutting
exclusion zone. This site was erected and operational by October 1995. A 12.5 m tower extends above
the 10-m burnt spruce canopy. The burn was caused by the "Monday" fire and was complete in this
area, leaving no living vegetation and little if any organic soil. One 21X micrologger and multiplexer
collects the data and power is provided from 12 volt batteries and 10-40 Watt solar panels.
161
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TOWER AND GROUND INSTRUMENTATION
- 10 snow survey stakes placed 5 metres apart running east to west starting at the west side of the site.
- 1 Radiation Energy Balance Systems (REBS) soil heat flux plates positioned 5 -7 cm below the
ground surface into the mineral soil (W/m2).
- 1 University of Saskatchewan (U of S) soil temperature arrays measuring at 2.5, 10, 20, 40, 80 and
160 cm (o C).
- 1 Time Domain Reflectometry (TDR) assembly located to correspond with the above soil
measurement at the same depths as the soil temperatures (BNC connector ends are housed together in a
plastic container).
- 1 set of U of S twin gamma probe tubes for measuring soil moisture to 1.6 metres located to
correspond with the above soil measurements.
- 1 Everest infrared remote thermometer positioned 1 metre from the west side of the tower on
aluminum mast arm 12.2 metres from the ground (o C).
- 1 REBS net radiometer with soft plastic domes positioned 2.0 metres from the south of the mast on
aluminum mast arm 12.2 metres from the ground (W/m2).
- 2 Kipp and Zonen solar pyranometers, one incoming and one outgoing positioned 2 metres from the
southwest of' the tower on an aluminum mast arm 12.2 metres from the ground
- 2 Vaisala HMP35CF temperature/relative humidity gauges positioned 20 cm to northeast of the tower
1.5 and 12.2 metres from the ground in gill shields (° C and %).
- 1 Campbell Scientific SR50 Sonic Ranging device for measuring snow depth positioned 2 metres
from the southwest side of the tower on aluminum mast arm, 2 metres from ground (metres).
- 2 Atmospheric Environment Service (AES) standard rain gauges, one 0.5 metres above the ground
located to correspond with the soil measurement and one in the nipher apparatus at 1.5 metres during
the summer months (millimetres).
- 1 RM Young horizontal anemometer with wind direction positioned on top of tower at 12.2 metres
(m/s and degrees).
- 1 Texas Electronics tipping bucket rain gauge on north west corner of tower (millimetres).
162
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APPENDIX B
Infiltration Theory
163
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APPENDIX B
Infiltration Theory
Richard's Equation
A modified version of Darcy's law can hold true for flow through an unsaturated medium. For vertical
flow, this is expressed as:
(B.1)
If the medium is isotropic, then unsaturated hydraulic conductivity is a function of moisture content
alone. Total head can also be expressed as the sum of elevation head (h) and suction head (ψ). The
elevation head is usually taken as 0 at the surface and h increases negatively downward. Similarly,
suction head is a negative pressure since action head resists flow. Total head is simply the sum of the
two. For a homogeneous soil, flow in the vertical direction can be expressed as:
(B.2)
If we examine continuity in the z direction, from conservation of mass we know that the change in
volumetric soil moisture with time must equal the change in flow rate in the vertical direction. If q1 is
flow into a representative volume of size dx, dy and dz and q2 is the flow out then from continuity:
(B.3)
If the flow value q2 is approximated by a straight line, the flow out of the block can be expressed as the
first term in a first order Taylor series approximation to the function q(z).
(B.4)
From continuity, we can then express the change in soil moisture with time as:
(B.5)
164
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Extending this to saturated or unsaturated conditions by substituting Darcy's law, we have,
(B.6)
where K(θ) is constant for saturated conditions. The above equation can be expressed as a function of
suction head (ψ) provided soil moisture θ = f(ψ). Using the product rule for the left hand side of the
equations and simplifying, the pressure head formulation of the Richard's equation for vertical flow in
an unsaturated media is given as:
(B.7)
The two functional relationships included above (i.e. C(ψ) and K(ψ)) are described in greater detail
below. From this equation and the two functional relations, the Green-Ampt infiltration equation is
easily derived. The PAMF application uses a modified form of the Green-Ampt equation and retention
curve information derived from the literature and measured in the field. These details along with their
theoretical justifications are described in the following sections.
Retention Curves and Unsaturated Conductivity
The Brooks-Corey Relationships
One of the difficulties of Richard's equation (besides solving it) is to obtain a reasonable assessment of
the unsaturated hydraulic conductivity (Ks) and the slope of the soil water retention curve C(ψ). This
topic is the subject of many studies and theoretical models, of which one will be discussed here.
Brooks and Corey (1966) developed a model for the conductivity of unsaturated soils based on a
theoretical micro-pore hydraulics model developed by Burdine (1953). This model relates head and
unsaturated conductivity by the relationship:
(B.8)
where
(B.9)
and the subscript r refers to residual saturation moisture content and s refers to saturated moisture
content.
165
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In the above formulations, the relationship between volumetric moisture content and soil suction head
must be known. This relationship has also been established by a number of authors, most notably
Brooks and Corey (1966) and Van Genuchten (1980). The relationship between suction head and
moisture content (i.e. the retention curve) can be placed into the Burdine model and a closed form
equation for unsaturated hydraulic conductivity can be derived. A typical retention function curve from
Mein and Larson (1973) is shown in Fig. B. 1.
Figure B.1 Brooks and Corey Retention Function
166
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The Brooks and Corey model for retention curves relates soil moisture content to suction head and is
given as:
(B.10)
where α and λ axe fitting parameters. It should be noted that is also known as the inverse bubbling
pressure and the Brooks - Corey relationship is most often given in the form
(B.11)
Closed forms of the relative conductivity based on the Burdine formulation can be derived from the
retention function given above. The retention and conductivity functions presented below axe by no
means the only ones available. Another often cited relationship was derived by Van Genuchten (1980).
The Brooks-Corey retention function can be solved when used in both conductivity integrals and thus
remains the same for both the Muallem and Burdine expressions. It should be noted that Brooks and
Corey used the Burdine model in their 1966 paper.
Brooks-Corey-Burdine Conductivity Functions
Relative Saturation ( Θ )
(B.12)
Suction Bead (ψ)
(B.13)
Green-Ampt Parameters
Initial work on establishing the Green-Ampt parameters was introduced by a number of authors, most
notably Bouwer (1966), Bouwer (1969), Mein and Larson (1971), Morel-Seytoux and Khanji (1974),
Brakensiek (1977), McCuen et al. (1981), Rawls et al. (1982) and Rawls et al. (1983). These papers
have let up to generalized equations which discriminate the Green-Ampt (and Brooks-Corey)
parameters based on soil texture. Probably the most important and most difficult parameter to establish
is the saturated hydraulic conductivity. An equation using the Brooks-Corey parameters was derived
by Brutsaert (1967) and is shown below;
167
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(B.14)
Early work developed by Bouwer (1966) related the wetting front suction head which he considered as
the "critical pressure head" to be:
(B.15)
He later suggested (Bouwer, 1969) that ψf could be estimated as one half the soil water air entry
pressure (the pressure at which air enters the soil during drying or the first decrease in moisture during
drying). The air entry pressure is analogous to the bubbling pressure in the Brooks-Corey relationship.
The one half air entry pressure is referred to the "water entry pressure" by Bouwer (1969). A very
similar equation was proposed by Meil and Larson (1973). Brakensiek (1977) gave a brief report on
determining the wetting front suction head by substituting the Brooks-Corey - Kr relationship into the
integral equation of Bouwer for ψf and solving for the imbibition process.
(B.16)
which simply works out to
(B.17)
In this case n = 2 + 3λ which is the Burdine formulation of relative conductivity.
Some authors have suggested that ψf is also related to initial moisture conditions. Indeed, Aggelides
and Young (1978) demonstrated that the wetting front suction head in the Green-Ampt equation for the
same soil column differed, depending on initial moisture states in the column. Several methods which
accounted for soil initial conditions in their estimates of the Wetting front suction head were tested.
They state however,
"Thus although we have shown that ψf does vary with the initial soil water content, a
constant value for ψf may be used for all initial soil water contents to give better
infiltration predictions than are obtained with these theoretical estimates [which take
initial conditions into account]"
168
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More recently, Mcuen et al. (1981) showed that soil texture can be used to discriminate both GreenAmpt parameters and the Brooks-Corey parameters. Based on what has just been shown, it would
seem reasonable to suppose that if and b were significantly different for different soil textures, then
this significance would carry over to the Green-Ampt parameters. Their paper in 1981 examined 1085
soil water retention data sets which represented 11 soil textures. These ranged from 199 for silty loam
to 14 for sandy clay. Essentially, the Brooks-Corey relationship was regressed to all these data sets by
taking logarithms and using a linear least square regression solving for λ and ψb. Since the BrooksCorey retention function is equated to effective saturation, values of r were varied until the highest
correlation was obtained. Using multivariate analysis, they demonstrated that both the Brooks-Corey
and Green-Ampt parameter means were statistically different for the 11 texture classes.
Rawls et al. (1982, 1983) have developed parameter values based on soil texture, which can be used to
estimate Brooks and Corey parameters and all the parameters of the Green-Ampt equation, with the
only unknown being the current soil moisture stares. The equations relating Brook-Corey parameters to
Green-Ampt parameters are the equation for wetting front suction head and hydraulic conductivity
shown earlier. In this case, the parameter (a) in the saturated conductivity equation was optimised and
set to a value of 21 and K in Green-Ampt = (Ks/2) (Rawls et al. 1982). A summary of the values is
presented in Rawls et al. (1982) and is shown below in Table B.2.
Table B.2 Parameter values based on soil texture that can be used to estimate variables of the GreenAmpt Infiltration equation using Brooks-Corey relationships.
Texture Class
Sand
Loamy Sand
Sandy Loam
Loam
Silty Loam
Sandy Clay
Loam
Clay Loam
Silty Clay
Loam
Sandy Clay
Silty Clay
Clay
Θ
Effective
Porosity
θs-θr
Bubbling
Pressure
(m)
Pore Size
Distribution
λ
Saturated
Conductivity
Ks (m/s)
0.437
0.437
0.453
0.456
0.501
0.398
0.046
0.080
0.091
0.058
0.030
0.171
0.417
0.401
0.412
0.434
0.486
0.330
0.073
0.087
0.147
0.112
0.208
0.281
0.592
0.474
0.322
0.220
0.221
0.250
5.83E-05
1.70E-05
7.19E-06
3.67E-06
1.89E-06
1.19E-06
0.464
0.471
0.162
0.085
0.390
0.432
0.259
0.326
0.194
0.15 1
6.39E-07
4.17E-07
0.430
0.479
0.475
0.253
0.117
0.189
0.321
0.423
0.385
0.292
0.342
0.373
0.168
0.127
0.131
3.33E-07
2.50E-07
1.67E-07
Total
Porosity
θs
Residual
Saturation
169
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Further refinement of these values was continued by Rawls and Brakensiek (1988) to include
correction for various management practices. These effects are incorporated through statistical
equations which adjust the above parameters based on bulk density (which is affected by tillage
practice), soil crusting and soil surface canopy cover.
Green-Ampt Infiltration and Rainfall Conditions
Given that the Green-Ampt parameters can be obtained from either retention curve data or from soil
texture information, the next step is to solve the Green-Ampt equation. For an infinitely small ponding
depth, the form of the Green-Ampt equation is implicit and is defined as:
(B.18)
it must be solved by method of successive substitution or by Newton-Raphson iteration. The latter was
programmed by setting:
(B.19)
and solving for the root of that equation. If we take the derivative of the above equation and solve at xi
(in our case F) then from Newton-Raphson:
(B.20)
this is outlined in the following code adapted from Press et al. (1988).
The Green-Ampt Equation
The utility of the Green-Ampt equation as a means of estimating infiltration and it's
effectiveness for a variety of rainfall conditions has been demonstrated by Mein and Larson (1973).
Although the equation is explicit in form, it readily solved if its parameters are known. The GreenAmpt parameters have been the subject of much study through the 1970's and 1980's and have been
related to the Brooks-Corey parameters discussed previously. Both the Green-Ampt equation, its
parameter estimates and algorithms for applying the equation under constant and variable rainfall
conditions are discussed.
Deriving the Equation
The continuity equation for flow through porous media is given by the Darcy equation. For
unsaturated conditions, the hydraulic conductivity becomes a function of the soil moisture and the
governing equation is a modified form of Darcy's law derived in the previous section
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and known as the Richard's equation. The infiltration rate is the rate at which water enters the soil
surface and is given as f(t), while the cumulative infiltration rate is given as F(t). Many infiltration
equations are derived from the Richard's equation, provided a few simplifying assumptions are made.
The two most common forms are the Horton equation and the Philip equation. Another approach to
solving the infiltration equation is the Green-Ampt approach, where the physical theory is
approximated and an analytical solution is derived. In this case, the soft moisture within the wetting
from boundary is given as saturated (θs) while below the wetting front is given as an unsaturated initial
condition (θi). The cumulative infiltration is then given by
(B.21)
In order to define the depth to the wetting front as a function of time, the Darcy equation is used where
the difference in head is given by the difference between the ponding depth at the surface and the total
head below the wetting front ho + (ψ + L). For a unit cross sectional area, the infiltration rate is given
as the Darcy flux, therefore substituting the potential difference into the Darcy equation, neglecting the
ponding depth and substituting for the wetting front depth given, the following is obtained (Chow,
1988):
Applying the Green-Ampt Formulation
In order to apply the Green - Ampt equation, estimates of the various parameters (K, ψf and
porosity) are required. As seen in Brooks and Corey (1966) and Van Genuchten (1980) developed
equations for determining the hydraulic conductivity as a function of soil moisture.
Estimating Green-Ampt infiltration is not only a function of the parameters in the equation, but
also a function of rainfall rates. For a constant rainfall rate, the problem is more or less trivial if the
implicit cumulative infiltration equation is used. The process is outlined in Chow et al. (1988) and a
similar method is shown here:
Prior to ponding, the potential infiltration rate (which is always very high at the beginning of a
rainfall) is usually higher than the rainfall rate. This implies that rainfall will infiltrate at a rate actually
lower than the possible rate. The lime required for surface ponding to occur can be calculated by
setting infiltration rate = rainfall rate since Fp = i · tp. Substituting into the Green-Ampt infiltration rate
equation and solving for tp we get:
(B.22)
and
(B.23)
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Therefore, prior to ponding the infiltration rate is simply the rainfall rate. After ponding,
rainfall will infiltrate at the potential rate. The potential rote cannot be calculated directly from the
Cumulative Green-Ampt equation became it assumes ponding for the entire rainfall duration. Since
ponding does not necessarily occur until some time into the rainfall, the total cumulative infiltration up
to the ponding time may be less than the total cumulative potential infiltration. To adjust for this, we
can solve the cumulative infiltration equation for t setting F = i · tp and solving for a new t'. Cumulative
infiltration can be estimated by solving for potential infiltration as before except starting from t', not
the actual time tp.
(B.24)
A similar procedure is followed for a variable rainfall however the level of complexity increases. In
this case each rainfall interval must be evaluated as to whether ponding occurs throughout the interval,
during the interval or there is no ponding at all.
APPENDIX C
Data Structure for Boreal Evapotranspiration Runoff Simulation
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APPENDIX C
Data Structure for Boreal Evapotranspiration Runoff Simulation
The Forest inventory of Prince Albert National Park (PANP) was imported as one coverage in
ARC export format and contained the following attribute file items:
AREA, PERIMETER, P1983#, P1983-ID, PANP-ID, C1HT, C1SPEC, C1DENS,
C1PERC, C2HT, C2SPEC, C2DENS, C2COND, C2PERC, C3HT, C3SPEC, C3DENS,
C3PERC, U1HT, U1SPEC, U1DENS, U1COND, U1PERC, U2HT, U2SPEC, U2DENS,
U2PERC, U3HT, U3SPEC, U3DENS, U3COND, U3PERC, G1HT, G1SPEC, G1DENS,
G1PERC, SA1, SA2, SA3, SA12, CTOTPERC, CLASS, AGECLASS
C1COND,
C3COND,
U2COND,
G1COND,
The data structure provides three levels of vegetation cover canopy (C), understorey (U) and
ground cover (G). Each level can contain a height class (HT), species combination (SPEC), density
class (DENS), condition class (COND) and percentage of primary species in each level (PERC).
These data were derived from Forest Cover Maps (scale 1:50,000) which originated from aerial
photography taken in 1968. Originally the data were referenced to NAD27, projection unknown. The
data had been adjusted to fit NAD83 datum with a UTM, zone 13, GRS1980 projection. Although this
adjustment had been made, this was not indicated in the ARC export file. The data were in double
precision format.
Saskatchewan Environment and Resource Management forest inventory data for the Prince
Albert Model Forest (PAMF) outside of PANP is in 31 - 10xl0 km map-units received in ARC export
format with the following attribute file items:
AREA, PERIMETER, file#, file-ID, MAP, HSG-FID, FID, STND_NUM, CURR_STND,
CURR_YEAR, CURR_SPEC, DST_TYPE1, DST_TYPE2, DST_TYPE3, DST_YEAR1,
DST_YEAR2, DST_YEAR3, DST_SRC1, DST_SRC2, DST_SRC3, PRV_STND1, PRV_STND2,
PRV_STND3, PRV_YEAR1, PRV_YEAR2, PRV_YEAR3, PRV_SRC1, PRV_SRC2, PRV_SRC3,
HRV_SEAS1, HRV_SEAS2, HRV_SEAS3, HRV_SRC1, HRV_SRC2, HRV_SRC3, SILV_TRT1,
SILV_TRT2, SILV_TRT3, SILV_YR1, SILV_YR2, SILV_YR3, SILV_SRC1, SILV_SRC2,
SILV_SRC3, PLNT_SPEC, PLNT_METH, PLNT_SRC, OWNER, CZONE, SOURCE, SYR, SA
SP10, SP11, SP20, HGT, D, YOO, SP12, SP21, U1, U2, MLEVEL, YSP, R1, R2, DRAIN, TEXT,
DIST, DYR, NP, OLDST, CLASS, HSGFCT, SP_SRC
This data is from 1984 aerial photographs, mapped at a 1:12,500 scale. PAMF personnel
performed random sampling tests with selected areas of the PAMF in 1991-92 to verify that the data
were still valid. Some 'redefined' items were also added to certain map-unit coverages by a consulting
firm working for the PAMF. These attribute file items were:
MAP_FID, ADMIN, REST, PROD, SPCL, OSP, USP, MAN, RSP, SITE, DISTURB
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Originally the data were referenced to NAD27, projection unknown. The data had been
adjusted to fit NAD83 datum with a UTM, zone 13, GRS 1980 projection. Although this adjustment
had been made, this was not indicated in the ARC export file. The data were in single precision format.
PAMF soil data excluding PANP was contained in 31 coverages, originally received by PAMF
from Central Soil Mapping as 1:125,000 map sheets and was converted to digital form. The data were
single precision, UTM, zone 13, NAD83, GRS 1980.
The PAMF DEM (digital elevation model) was received in TIN (triangular irregular network)
format, originally digitized from NTS map sheets. The data were in double precision format, based on
UTM, zone 13, NAD83, GRS 1980.
Deriving Hydrologic Response Units Using Arc/info
The objective of the HRU derivation was to obtain a single coverage that contained the entire
Prince Albert Model Forest area. This coverage describes the model forest area with attributes having
hydrological significance, grouped in HRUs.
Basic Method: A coverage of the area was obtained with ARC/INFO, using the single large
coverage representing PANP and the 31 coverages within PAMF outside the National Park. To achieve
a single coverage, the attributes within each of these coverages were made consistent. These attributes
were restricted to only those relating to the modeling of hydrological responses. The attributes used to
define hydrological response related to vegetation, soils, and topography. The vegetation and soils
variables were found within the forest coverages provided by PANP and PAMF above) while the
topographic variables were available in the digital elevation model.
Selecting, defining and modifying the attributes within the single coverage (PANP) was a
relatively simple matter. However, the attribute manipulation within the coverages outside the National
Park (PAMF) required that the 31 coverages be joined to a single coverage. The result was be two
coverages, PANP and PAMF, that required consistent attribute definition. Owing to the fact that the
PANP and PAMF data sets were of different origins, these coverages required extensive data synthesis.
Once the data sets were consistent, an overall coverage was created and interfaced with the DEM.
Importing and joining the two coverage areas was accomplished in the following manner: The
PANP forestry data existed as one large coverage in an export format (.e00 extension). This coverage
was imported using ARCTOOLS. This coverage was also checked for errors (node, label and intersect
errors), cleaned, built and saved as coverage P1983, containing .PAT, .BND, .AAT and .TIC files. The
Saskatchewan Environment and Resource Management forest inventory data for the Prince Albert
Model Forest (PAMF excluding PANP) was contained in 31 coverages in export format (.e00) and was
also imported using ARCTOOLS. Each individual coverage containing node errors and label errors
were cleaned and built by removing dangling nodes, relabeling and/or deleting sliver polygons using
ARCEDIT. These 31 coverages were joined (MAPJOIN) to form the right half of the PAMF outside
the PANP. Numerous errors were cleaned-up from the joining process manually or using a rubber
sheeting technique (EDGEMATCHNG). The joining of the 31 coverages had multiple considerations:
a) by joining all 31 coverages the errors between each joined mapsheet could be easily
identified,
b) the errors were similar to the ones previously described for each individual coverage but
there were more silver polygons,
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c) due to the size of the coverage and large number of polygons the area was divided into five
strips of PAMF area and cleaned up as sections,
d) these strips were then joined to each other to derive the final cleaned coverage called
join12345, containing .PAT, .BND, .AAT and .TIC files.
Definition of hydrological variables for the HRU that were consistent in both PANP and
outside of the park was based on the following considerations. The polygon attribute tables (.PAT
extension) of the PAMF (join12345 .PAT) differed from that of the PANP (P1983.PAT) prohibiting a
direct join of the two coverages. The attributes that were to be used for modeling were reclassified
(ARCEDIT) to the same item name in each coverage with coding that was suitable for each (e.g.
C1SPEC and SP10 became SPECIES). This has been, outlined and described in detail below. Other
attribute items that were not useful were dropped from the polygon attribute table. Attribute items that
were needed for modelling but were not already available were added to these coverages (e.g. Winter
leaf area index = WLAI).
The C1HT (primary stand height) from PANP was converted to HGT (stand height) used in PAMF
forest inventory based on the following criteria:
CIHT
0 = ‘noveg’
1 = 0-5 metres
2 = 5-7
3 = 7-10
4 = 10-15
5 = 15-30
6 = 30-50
7 = >50
blanks = unkeyed
HGT integer
0 = no veg
5 = 2.5 < HGT < 7.5 metres
10 = 7.5 < HGT < 12.5
15 = 12.5 < HGT < 17.5
20 = 17.5 < HGT < 22.5
25 = HGT > 22.5
25
25
99 = unknown
The C1DENS (primary stand density) from PANP was converted to D (stand density) used in
PAMF forest inventory based on the following criteria:
C1DENS
0 = noveg
1 = sparse
2 = dense
3 = lush
blanks = unkeyed
D character
A = 10-30 % cover
B = 30-55
C = 55-80
D = >80
left as blank
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The CISPEC (primary stand species) from PANP and SP10 (primary stand species) from PAMF were
reclassified into item SPECIES based on the following criteria:
C1SPEC
PB = Pinus banksiana (Jack pine)
PM = Picea mariana (Black spruce)
PG = Picea glauca (White spruce)
LL = Larix lariciana (Tamarack, Larch)
BP = Betula papyrifera (White birch)
AB = Abies balsamea (Balsam Fir)
PT = Populus tremuloides (Aspen)
ISLAND = islands
WATER = water (lakes, ponds, etc.)
blanks = unkeyed
0 = no veg
Two species combinations
Three species combinations
SPECIES character
P = pine
S = spruce
S
M = mixed-wood
M
M
M
S
WATER
U = unforest (assigned 'clear' arbitrarily)
U = unforest (based on G1SPEC - ground species)
first species used (based on above criteria)
M
SP10
WS = White spruce
BS = Black spruce
JP = Jack pine
BF = Balsam Fir
TL = Tamarack
LP = Lodgepole pine
TA = trembling aspen
BP = black poplar
WB = white birch
WE = white elm
GA = green ash
MM = Manitoba maple
BO = Burr oak
blanks = unkeyed
SPECIES
character
S
S
P
P
M
P
M
M
M
M
M
M
M
U = unforest (based on R1-regenerating species, NP non-productive code and DIST - disturbance type)
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The UNFOREST (taken from above unforest criteria) character
T MUSKEG = treed muskeg
CLEAR = clearings
GRASS = grass and meadow areas
SHRUB = shrubs
MUSKEG = muskeg
BURN = burnt areas
CUT = clear-cut areas
? = unknown
Note: there are no polygons in the PANP area that have MUSKEG, BURN or CUT.
Leaf area index (LAI) m2/m2 values were defined for each species category based on HGT and
D, and were assigned real values from data collected within the PAMF:
WLAI = winter LAI
floating point
SLAI = summer LAI
FSLAI = fall/spring LAI
Pine: (same for all seasons)
0.5, 1.0, 2.0, 3.0 m2/m2
Spruce: (same for all seasons)
0.5, 1.0, 2.0, 3.0, 4.0
Mixed: (varies for all seasons)
1.0, 1.5, 2.0, 2.5 (SLAI)
0.5 (WLAI)
0.5, 1.0, 1.5 (FSLAI)
(derived from FSLAI = SLAI-1.0 except where
D = ‘A’ :FSLAI = 0.5 and
D = ‘B’ :FSLAI = 1.0)
Unforest:
Clear = 0.1
Grass = 0.1
Shrub = 0.1 (WLAI), 0.5 (FSLAI, SLAI)
Burn = 0.5
Cut = 0.1
Tmuskeg = 1.0
Muskeg = 0.1
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The GREENAMPT attribute was derived from TEXT (soil texture) and DRAIN (soil drainage) in the
PAMF forest inventory. Lacking soils data or attributes for the PANP required an extrapolation of a
vegetation/soil type relationship found in the PAMF to the PANP forest inventory. The GREENAMPT
attribute has the following coding:
GREENAMPT
LS = loamy sand
SL = sandy loam
S = sand
Si = silt
C = clay
ORG = organic
WATER = water
? = unknown
character
The year of origin from the PAMF attribute item YOO (year of stand origin) and C1COND
(condition class) from the PANP were combined to form ORIGIN, with the following parameters:
C1COND
0 = no veg
1 = 1-10 years (1958-1968)
2 = 10 - 30 (1938-1958)
3 = 30 - 60 (1908-1938)
4 = 60 - 80 (1888-1908)
5 = > 80 (< 1888)
5a = ready for breakup
1a,2a,3a,4a = treed muskeg
blanks = unkeyed
ORIGIN integer
0 = no veg
2 = 1958-1968
3 = 1938-1958
4 = 1908-1938
5 = 1888-1908
6 = < 1888
6
9 = unknown
9 = unknown
YOO
99 = 1986-1995 years old
98 = 1976-85
97 = 1966-75
96 = 1956-65
95 = 1946-55
94 = 1936-45
93 = 1926-35
92 = 1916-25
91 = 1906-15
90 = 1896-05
89 = 1886-95
88 = 1876-85
87 = 1866-75
86 = 1865-56
85 = 1855-46
84 = 1845-36
blanks = unkeyed
ORIGIN
1 = 1969 to present
1
1
2
3
3
4
4
4
5
5
6
6
6
6
6
0
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The final forest inventory coverage was defined using both coverage polygon attribute tables (P1983
.PAT and join12345.PAT), which contained common attribute items. The next step was to strip each
coverage of the excess and uncommon attribute items and then join (MAPJOIN in ARC) the two
coverages. The PANP coverage (P1983) was stripped of excess attributes (DROPITEM in ARCEDIT),
renamed as natpark, which contained .PAT, .BND, .AAT and .TIC flies. The PAMF coverage
(join12345) was also cleared of excess attributes, renamed as restmf, which contained .PAT, .BND,
.AAT and .TIC files. The two coverages, natpark and restmf, were joined as pamfall, containing .PAT,
.BND, .AAT and .TIC files. There were a large number of label errors between these coverages that
were dealt with in an editing session providing a common, clean coverage. The common coverage
(pamfall) can be viewed as the right half of the PAMF and the entire PANP on the left side of the
coverage. The coverage of the entire PAMF region including the PANP (pamfall) was then dissolved
according to each attribute item and a new coverage was created for each item based on this dissolve.
The DISSOLVE items were:
HGT - stand height
D - stand density
ORIGIN - year of origin
SPECIES - stand species
WLAI - winter leaf area index
SLAI - summer leaf area index
FSLAI - fail/spring leaf area index
GREENAMPT - soil properties
UNFOREST - non-species item includes understorey, type of disturbance, clearing etc.
The dissolved coverages were labeled foresth, forestd, foresto, forests, forestw, forests, forestfs,
forestg, and forestu, complete with .PAT, .BND, .AAT and .TIC files.
The Digital Elevation Model (DEM) was created using the DEM TIN file, converted to a
coverage (TINARC) with slope, aspect and surface area called pamf complete with .PAT, .BND, ,AAT
and .TIC files. FUZZY TOLERANCE levels of this DEM were reduced 0.001 because not all arcs
were to be transferred to the new coverage. Point data from this coverage (PUT in ARCEDIT) were
used to derive elevation values (TINSPOT) which were added along with the arcs from the coverage
pamf (GET in ARCEDIT) to a new coverage called elevpoint complete with .PAT, .BND, .AAT and
.TIC files. A BUILD (ARC or ARCEDIT) was always required on new coverages (elevpoint) after
each alteration. The attributes of coverage elevpoint were then changed to fit future hydrological
modelling needs. ASPECT, SLOPE and ELEVATION were given new codes which have been
outlined and are described below. The final DEM coverage was then dissolved according to each
attribute item (ASPECT, SLOPE and ELEVATION) and a new coverage was created for each item
based on the dissolve: demelev, demslope and demaspect, complete with .PAT, .BND, .AAT and .TIC
files.
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ASPECT was taken from the DEM of the PAMF as a degree value and convened to 8 classes
based on cardinal directions:
ASPECT
integer
ASPECT = -1
ASPECT >=0<=45 degrees
ASPECT >45<=90
ASPECT >90<=135
ASPECT >135<=180
ASPECT > 180<=225
ASPECT >225<=270
ASPECT >270<=315
ASPECT >3 15<=360
-1 = flat
1 = north-north-east
2 = east-north-east
3 = east-south-east
4 = south-south-east
5 = south-south-west
6 = west-south-west
7 = west-north-west
8 = north-north-west
ELEVATION was derived using point data from the DEM of the PAMF and remained as an
elevation above sea level (metres) based on the following classification:
ELEVATION
1 = <500 metres
2 = >=500<520
3 = >=520<540
4 = >=540<560
5 = >=560<580
6 = >=580<600
7 = >=600<650
8 = >=650<700
9 = >=700
integer
The DEM from the PAMF was used to calculate PERCENT_SLOPE which was broken down
into 9 classes, as listed below:
SLOPE
integer
0 = <1.0 percent (flat)
1 = >=1.0<=1 .5
2 = >1.5<=2.0
3 = >2.0<=2.5
4 = >2.5<=3.0
5 = >3.0<=4.0
6 = >4.0<=5.0
7 = >5.0<=7.5
8 = >7.5<=10.0
9=>10.0
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To overlay forest and attribute changes, each dissolved coverage from the PAMF was
individually overlaid (forest1 to 7) using UNION in ARC to create one coverage called forestall
complete with .PAT, .BND, .AAT and .TIC files. This was also done for the DEM dissolved coverages
creating coverage topo complete with .PAT, .BND, .AAT and .TIC files. Since the PAMF forest
inventory coverage included the entire PANP and not just the outline of the PAMF an IDENTITY was
performed with the DEM union coverage in order to clip only the desired area creating coverage hru
complete with .PAT, .BND, .AAT and .TIC files. This HRU coverage has several <coverage># and
<coverage>-ID items from each union process in the attribute item file that were retained for the
purposes of hydrological modelling.
Attribute coding for combined PAMF and PANP topographic and forest inventory coverages is
the final HRU product to be used in the hydrological model. The forest inventory and DEM from the
PANP and PAMF have been reduced to 16 attribute file items for the HRU. The criteria for each is
listed below with bold type representing the actual codes used for that attribute. The attribute file items
are:
AREA - area
PERIMETER- perimeter
<coverage># - internal id
<coverage>-ID - external id
HGT - stand height
D - stand density
ORIGIN - year of origin
SPECIES - stand species
WLAI - winter leaf area index
SLAI - summer leaf area index
FSLAI - fall/spring leaf area index
GREENAMPT - soil properties
UNFOREST - non-species item includes understorey, type of disturbance, clearing, etc.
ASPECT - direction slope is facing
ELEVATION - metres above sea level
SLOPE - percent gradient
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APPENDIX D
Maps of Hydrological Response Units for Beartrap Creek
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APPENDIX E
Source Code for the Boreal Ecosystem Evapotranspiration Runoff Simulation
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APPENDIX E
Bored Ecosystem Evapotranspiration Runoff Simulation
Source Code for BEERS as of April 1997
National Hydrology Research Institute
Environment Canada
Saskatoon, Saskatchewan
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Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
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Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
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Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
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Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
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Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
219
Copyright © 1997 by Prince Albert Model Forest Association Inc. Prince Albert, Saskatchewan, Canada S6V 7G3. All rights reserved.
No part of this report may be reproduced in any form or by any means without prior written permission of the copyright holders.
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