Getting water into the ground and to the channel, Gordon Gulch,
Colorado
By
Eirik Melbye Buraas
Professor David P. Dethier, Advisor
A thesis
Submitted in partial fulfillment of the requirements for the
Degree of Bachelor of Arts
With Honors in Geosciences
WILLIAMS COLLEGE
Williamstown, Massachusetts
May 19th, 2009
i
ABSTRACT
Steady-state and falling-head infiltration tests in Gordon Gulch, a forested
upland catchment in the Colorado Front Range, show that rates exceed 50 mm hr-1 in
most areas. Soil infiltration rates, measured using a double-ring infiltrometer at 42
sites in upper Gordon Gulch, correlate with field measurements of soil resistance and
with laboratory values of soil sorting, loss-on-ignition (LOI), and soil composition.
These four soil properties are significantly correlated with infiltration rates (pvalue<0.05). Rates are lower, generally < 14 mm hr-1 on dirt roads, trails and recently
logged areas. Peak discharges in channels reflect precipitation intensity and the
distributed infiltration rate determined by soil physical factors. Because neither
rainfall intensity nor peak discharge have been measured in the vicinity of Gordon
Gulch, rates had to be estimated using syntheses of existing data and simple runoff
models. Precipitation intensities for the 1-hr storm were estimated at 27 mm for the
10-year storm, 42 mm for the 100-year storm and 46 mm for the 200-year storm.
Peak discharges for the 0.93 km2 upper Gordon Gulch were modeled under
various test scenarios using the rational runoff formula and ArcHydro. Calculated
peak discharge values from the two models show both similarities and differences,
depending on the model scenario. Impervious areas and human alteration of the
Gordon Gulch environment have decreased natural infiltration rates and the models
suggest that the largest contribution to peak discharge comes from areas covered by
roads and trails, fire preventative logging and possibly from rock outcrops. Calculated
peak discharge, which ranges from 0.22 m3 s-1 for the 5-year storm to 0.80 m3 s-1 for
the 200-year storm when roads and trails are included, is a small fraction of total peak
discharges measured in the 804 km2 Boulder Creek catchment. Despite the influence
of altered land use, peak flows from upper Gordon Gulch alone do not present any
i
immediate flood danger in Boulder Canyon. However, if there was already significant
flow in Boulder Creek, thunderstorms covering a few tens of km2 of watersheds, like
upper Gordon Gulch, could represent a flash flood hazard for Boulder Creek similar to
that experienced east of Estes Park in the Big Thompson Flood of 1976, which killed
139 people.
ii
ACKNOWLEDGEMENTS
First, I would like to thank my thesis advisor, Professor David P. Dethier for
giving me the opportunity to participate in his exciting field research and giving me
excellent advice and help throughout the entire process. He was also the one who
introduced me to the field of Geosciences my freshmen year and is one of the main
reasons that I became interested in this great science. I would like to thank Professor
Markes Johnson for taking on the task of being my second reader, contributing
valuable comments and suggestions for improving my written thesis. I would thank
the National Science Foundation for providing support and making my thesis research
possible and I would thank the Boulder Creek Critical Zone project for allowing me to
contribute to their research. Next I would like to thank Professor Matthias Leopold
(Technical University of Munich) for his contribution from geophysical work and
Evey Gannaway, Ken Nelson and Miguel Rodrigues, the three KECK students, for
their work in helping me collect field data and for their great company. I would also
like to thank the entire staff of the Williams College Geosciences department for all
their support in pursuing my interests within the field of Geosciences and their
incredible teaching throughout my college career. At last I would like to thank my
family, friends and Kavitha for providing moral support and great company
throughout the entire process. I would not have made it this far without any of you.
iii
TABLE OF CONTENTS
ABSTRACT……………………………………………………………...…….…......i
ACKNOWLEDGEMENTS………………………………….……..…………....…iii
LIST OF FIGURES…………………………………………………………............vi
LIST OF TABLES………………………………………………………………….ix
LIST OF EQUATIONS………………………………………………......................x
LIST OF APPENDICES………………………………………………….................xi
Chapter 1: INTRODUCTION……………………………………………………....1
Background
Basic hydrology…………………………………………………………….....1
The Critical Zone…………………………………………………………......8
Location and topography……………………………………………………10
Geologic background………………………………………………………..13
Climate and vegetation……………………………………………………...15
Local history and land use………………………………………………….21
This study: soils, infiltration and surface-water hydrology………………….....26
Chapter 2: METHODS…………………………………………………..................32
Field
Mapping……………………………………………………….………...........32
Infiltration measurements…………………………………………………..34
Soil compaction measurements…………………………………………....38
Soil sampling…………………………………………………………………39
Geophysical measurements ………………………………………………..39
Laboratory……………………………………………………………..……………...40
Moisture content and loss on ignition (LOI)…………………………….40
iv
Grain size analysis……………………………………………………...…...41
Hydrologic calculations and models……………………………………………....43
Rainfall intensity……………………………………………………………..44
Time of concentration………………………………………………...........45
Calculations of area of impermeable surfaces……………………...…..46
Infiltration rates after fire…………………………………………………..47
Modeling of peak discharge………………………………………………..47
Chapter 3: RESULTS……………………………………………………………...51
Soil analysis…………………………………………………………………………...51
Infiltration rates………………………………………………………………………53
Infiltration rates and soil properties……………………………………………….54
Rainfall intensity………………………………………………………………….…..59
Geophysics…………………………………………………………………………….60
Calculated peak discharges, upper Gordon Gulch………………………………61
Chapter 4: DISCUSSION AND CONCLUSIONS.................................................63
Discussion
Model limitations……..……………………………………………………..63
Factors controlling infiltration rates…………………………………......65
Uncertainties in measured infiltration rates……………………………..70
Calculated peak discharges, upper Gordon Gulch………………….....71
Human impact and impact of rock outcrops on basin hydrology…….77
Flood danger………………………………………………………..………..80
Conclusions and suggestions for future work………………………………..82
REFERENCES CITED……………………………………………………………85
APPENDIX…………………………………………………………………..…......88
v
LIST OF FIGURES
Figure 1. A simplified model of the global hydrologic cycle……..…………………...1
Figure 2. Upper Gordon Gulch (outlined in thick red line) showing all tributaries.......2
Figure 3. Detailed figure of water pathways in the unsaturated zone
(Dingman, 2002)…………………………………………………………...…...4
Figure 4. Flow-pathways in the unsaturated and the saturated zone
(Dingman, 2002)……………………………………………………………......5
Figure 5. Cross-section of the Critical Zone, its materials and dominant
processes (Anderson et al. 2007)……………………………..……….………..9
Figure 6. Location of the study site; upper Gordon Gulch is shaded
red (http://www.lib.utexas.edu/maps/us2001/coloradoref2001.jpg,
http://www.boulder.doc.gov/gifs/boco_map.jpeg)............................................10
Figure 7. Overview of the upper Gordon Gulch watershed looking to the
northwest with Niwot Ridge in the background……………………………...11
Figure 8. Topographic profile of the Front Range (Bubel, 2008)……………………12
Figure 9. Extent of Pleistocene glaciations (extent of Gordon Gulch shown in red)...13
Figure 10. Geologic map of Gordon Gulch (Gable, 1980)…………………………..14
Figure 11. Elevation gradient of temperature, precipitation and vegetation for
the Front Range (Birkeland et al., 2003, modified from Veblen and Lorenz,
1991)…………………………………………………………………………..15
Figure 12. Weather station location compared to Gordon Gulch; upper Gordon
Gulch is outlined in red………………………………………………………16
Figure 13. Monthly precipitation records for C-1 from 2006-2008
(INSTAAR, 2009)…………………………………………………………….17
Figure 14. Total annual precipitation amount for C-1, May through September
2001-2008 (INSTAAR, 2009)……………………………………………...…17
Figure 15. North facing and south facing slope in the Gordon Gulch
watershed, looking from south-facing to north-facing slope……………….....20
vi
Figure 16. Frankenberger Point on the Switzerland Trail A: 1905 B: 1984
(Veblen and Lorenz, 1991………………………………………………...…..24
Figure 17. Extent of areas of fire prevention patch cutting in Gordon Gulch,
area values are for the area of the fire cuts that are within the catchment
Boundaries…………………………………………………………………….25
Figure 18. Sketch of the 2-D lateral movement of water from a single
ring infiltrometer, due to capillary forces (Dingman, 2002)………………….27
Figure 19. Relationship between clay content and soil age (Machette et al.,
1976 in Birkeland et al., 2003)………………………………………………..30
Figure 20. Swath traverses used for mapping outcrop distribution…………………..33
Figure 21. A typical “extra large” outcrop of biotite gneiss in Gordon Gulch
(Evey Gannaway, is 1.8 m tall)………………………………………..…......34
Figure 22. Infiltration sites by environment; tributaries in blue and roads in black....36
Figure 23. Double ring infiltrometer, with a 15 cm diameter inner ring, prepared
an infiltration measurement…………………………………………………...37
Figure 24. Proper use of the dynamic cone penetrometer (demonstrated by
Evey Gannaway)……………………………………………………………...39
Figure 25. Beckman Multisizer™ 3 Coulter Counter………………………………..42
Figure 26. Sand, silt and clay ternary diagram (characterization after Folk,
1954), S, sand, s, sandy, Z, silt, z, silty, M, mud, m, muddy, C,
clay, c, clayey………………………………………………………………....51
Figure 27. Average infiltration rates for different environments: error bars
show one standard deviation………………………………………………....53
Figure 28. Infiltration rate vs soil sorting. Higher Φ values represent more
poorly sorted soil (Folk, 1961)………………………………………….…….55
Figure 29. Infiltration rate vs amount of organic matter (LOI) in weight percent…..56
Figure 30. Infiltration rate vs soil weight % <62.5μm……………………………….56
Figure 31. Infiltration rate vs soil resistance from 0 to -15 cm……………………....57
vii
Figure 32. Soil resistance and infiltration rates for the different environments,
roads and trails in blue, forest soil in red and hillslope in green……………...58
Figure 33. Infiltration and soil resistance, highlighting values from fire
prevention areas (in red)…………………………………………………..…..58
Figure 34. Calculated rainfall amounts and recurrence intervals averaged
between C-1 and Sugarloaf (Table 2); one-hour rainfall amount is in blue
and 24-hour rainfall amount is in red……………………………………...….59
Figure 35. 24-hour rainfall amount for selected watersheds at different
elevations in the Colorado Front Range. Sugarloaf, C-1 and NOAA are
interpolated data sets, other values are measured data sets. Flatiron
Reservoir (1678m) is in blue, Cabin Creek (3055m) is in red, Gross
Reservoir (2430m) is in green, Evergreen (2130m) is in purple,
Sugarloaf (2052m) is in light blue, C-1 (3017m) is in orange and
NOAA estimated Gordon Gulch (2500-2700m) is in black……….………....60
Figure 36. Interpreted subsurface layering along a resistivity line along the
valley of upper Gordon Gulch (Matthias Leopold, Technical
University of Munich)………………………………………………………...61
Figure 37. Flow directions in upper Gordon Gulch………………………………….72
Figure 38. Infiltration rate distribution (IDW) in upper Gordon Gulch……………..73
Figure 39. Modeled peak discharges under different scenarios, x-axis
shows recurrence intervals in years and mm/hr for each data point…………74
Figure 40. Comparison between values of unit peak discharge in Spring
Creek, Colorado, with upper Gordon Gulch, Colorado. ArcHydro model
values incorporating roads and trails are shown in red, ArcHydro model
values incorporating a saturated stream buffer in addition to roads and trails
are in blue and data points representing different years of measured
discharge in Spring Creek are shown in black (Moody and Martin, 2001)…..76
Fig 41. Measured peak discharge for three comparable Front Range basins
plus Gordon Gulch. Bobtail Creek is in blue, Bear Creek is in red, Michigan
Creek is in green and upper Gordon Gulch is in purple……………………...77
Figure 42. Photo near 7th Street in Boulder looking east during the 1894 flood
(BASIN, 2009)…………………………………………………….…….….81
viii
LIST OF TABLES
Table 1. Environment and number of infiltration measurements……………………35
Table 2. Rainfall Intensities for different recurrence intervals (Payton and
Brendecke, 1985; Miller et al., 1973). Values for C-1 and Sugarloaf
were calculated using the Gumbel method of extreme value theory
(Equation 7)…………………………………………………………………...44
Table 3. Calculated time of concentration for the upper Gordon Gulch watershed....46
Table 4. The various model scenarios. IDW is the natural infiltration rates and
IDW fire is natural infiltration rates after a forest fire………………………..48
Table 5. Summary of soil properties………………………………………………....52
Table 6. R2 and p values for road/trail-infiltration rate correlations
compared to infiltration correlations from vegetated sites. P-values of
<0.05 represents the probability that the outcome supports the
null hypothesis……………………………………………………...………...54
Table 7. Predicted peak discharge (m3/sec) under different model scenarios……......62
ix
LIST OF EQUATIONS
Equation 1. Conservation equation (Dingman, 2002)………………………………..3
Equation 2. Rational runoff formula (Dingman, 2002)………………………….….28
Equation 3. Soil resistance (Herrick and Jones 2002)………………………………38
Equation 4. Moisture content (Racela, Williams College, 2008)…………………...40
Equation 5. Loss on ignition (Racela, Williams College, 2008)……………………41
Equation 6. Methods of moments (Folk, 1961)……………………………………..43
Equation 7. Gumbel method of extreme value theory………………………………45
Equation 8. Papadakis and Kazan equation (Loukas and Quick, 1996)…………….45
x
LIST OF APPENDICES
Appendix A. Complete table of soil analysis data…………………………………...88
Appendix B. Complete table of field measurements………………………………..89
xi
Chapter 1: INTRODUCTION
Background
Basic hydrology
The hydrologic cycle (Fig. 1) has been recognized and partially understood
since the great civilizations of Mesopotamia, the Indus Valley, Hwang Ho in China
and the Nile in Egypt, first started to irrigate their crops, about 5000-6000 B.P
(Dingman, 2002). Use of the hydrological cycle was of great importance to the
survival and wealth of these areas. Contemporary studies of the hydrological cycle
focus on water supply, hazards and a variety of topics with an environmental focus,
quantifying how the impact of land use change interacts with the hydrologic cycle.
Human alteration of forested environments has the potential for disastrous impacts,
e.g. the stripping of trees from hillsides increases the potential danger for downstream
flooding and debris.
The study of hydrology can be divided into the global hydrologic cycle and the
land (terrestrial) phase of the hydrologic cycle (Dingman, 2002).
Figure 1. A simplified model of the global hydrologic cycle
1
The terrestrial phase of the hydrologic cycle involves the processes of water
storage and movement on and beneath the earth’s surface, and the physical and
chemical interactions with the earth material (Dingman, 2002), within the Critical
Zone. Studies on basin hydrology usually focus on small research areas with welldefined boundaries, like a single catchment, which is confined by the area between
topographic divides that contains all of the tributaries to the stream, e.g. Gordon Gulch
catchment (Fig. 2). The major concepts of the global hydrologic cycle are mainly
scale-independent, but the most important factors in small catchments are the
interaction between the amount of precipitation and storage and the volume and
timing of water that gets into the stream to be measured as discharge. The magnitude
of these interactions determines the change in water storage.
Figure 2. Upper Gordon Gulch (outlined in thick red line) showing all tributaries
2
Conservation of mass applied to basin hydrology can be written as the
conservation equation (Dingman, 2002):
R=P-ET±ΔS
(eq.1)
where R=runoff,
P=precipitation,
ET=evapotranspiration and
ΔS=change in storage.
Water balance must be defined for a measured area (area of study) and for a
defined time-period (Dingman, 2002). Detailed analysis of water balance involves
quantifying the amount of water that flows down the different water-pathways in the
catchment. The value for Δ in storage is usually set at zero over a water year and can
be measured during short periods when evapotranspiration values are small compared
to water flux and the change in storage (Dingman, 2002). This allows researchers to
simplify calculations for runoff, amount of evapotranspiration and hydrologic
production. In this study I can only model the amount of water that travels to the
channel by different water-pathways, because Gordon Gulch does not yet have
hydrologic instrumentation.
Depending on the soil infiltration rate, water from precipitation either
infiltrates or ponds on the surface. Ponding on the surface occurs when the intensity of
the precipitation exceeds the infiltration rate (Hortonian overland flow) and when the
water table rises to the surface (saturation overland flow). Hortonian overland flow is
believed to be one of the main mechanisms for flash flood events (Dingman, 2002).
Because the water input rate has to exceed the hydraulic conductivity to produce
Hortonian overland flow, Hortonian overland flow occurs in undisturbed soils mainly
in areas with relatively intense rainfall and relatively fine-grained soils. This is an
3
especially important flood mechanism in the semi-arid and arid parts of the western
United States (Dingman, 2002). Hortonian overland flow is more common where soils
have been disturbed and compacted. However, it should be noted that many studies
have been done on mineral soils only, and an increase of organic material in the upper
soil horizons increases the hydraulic conductivity of the soil (Dingman, 2002). If
intense precipitation continues, water may get to channels from Hortonian overland
flow if the event continues and there is a direct flow-path for the water to reach the
channel.
Figure 3. Detailed figure of water pathways in the unsaturated zone (Dingman, 2002)
As precipitation infiltrates into the subsurface, it moves through an unsaturated
zone (Fig. 3) and a saturated zone (Fig. 4). The redistribution of infiltrated water in the
unsaturated zone can involve several processes, including exfiltration, capillary rise,
4
recharge of saturated zones, transpiration and interflow (Dingman, 2002). Interflow is
the flow of water through the pore space of the soil material and pore size has a large
impact on the infiltration rate. The size of pores that are available for water-flow is
approximately equal to grain size. The distribution of available pore space can be
determined from the grain size distribution, where pore space is thought to decrease
with depth and compaction (Dingman, 2002). Water that does not fill up pore space in
the unsaturated zone will displace water that is already stored, and could contribute to
the output of water from the catchment.
Figure 4. Flow-pathways in the unsaturated and the saturated zone (Dingman, 2002)
In the saturated zone pores between rocks and soil particles are completely
filled in contrast to the mixing between water and air in the pores of the unsaturated
zone (USGS Water Science for Schools, 2008). The saturated zone extends below the
water table, and may consist of both unconfined and confined aquifers. The natural
filling of deep aquifers is a slow process that depends on the rate of water movement
5
through the unsaturated zone, which depends on the factors discussed above. Because
of this slow rate of recharge, the draining of important ground water sources has
serious implications for water supply in the future. In a catchment, flow to the channel
includes contributions mainly from the saturated zone, except during intense rainfall
or snowmelt, when flow pathways at and near the surface may be important (Fig. 4).
The infiltration rate is determined by material properties of the soil. These
properties include the distribution of pore and particle sizes, particle density, bulk
density, porosity, degree of saturation, hydraulic conductivity and amount of
compaction (Dingman, 2002). Particle density is the weighted average density of the
mineral grains in the soil. It is usually set at 2650 kg/m3, the density of quartz
(Dingman, 2002). Bulk density is measured using volume and the dry density of the
soil calculated as the weight of the dried soil compared to the weight of the original
soil. Bulk density is usually assumed to be constant over time. The porosity of the soil
is the proportion of pore spaces available for water flow in a volume of soil. Porosity
is calculated from the particle density and the bulk density; maximum porosity may
approach 0.8 (Dingman, 2002). The degree of saturation describes the proportion of
the pores that contains water, and can be determined from the relationship between the
moisture content of the soil and the soil porosity (Dingman, 2002). Hydraulic
conductivity is the rate that water moves through a medium, and is mostly determined
from the size of the pathways available for water transport (Dingman, 2002).
The saturated hydraulic conductivity is approximately the same as the
minimum infiltration rate of the soil, and is frequently a function of grain size and
sorting. However, many factors can cause the saturated hydraulic conductivity of the
surface layers to be different from the soils underneath (Dingman, 2002). These
factors include organic surface layers, frost, swelling-drying, rain compaction,
6
inwashing of fine sediment and human modification of the soil surface (Dingman,
2002). I tried to minimize the influence of these factors by removing the organic layer
and placing the double-ring infiltrometer at the boundary between the A- and Bhorizons.
Compaction is a measure of packing and porosity of the soil, usually measured
as bulk density. Short-term changes in compaction come from external disturbance of
the soil; in Gordon Gulch compaction comes from construction of trails and roads and
use by logging machinery and smaller vehicles. A change in soil compaction can
include three separate processes: compression, compaction, and consolidation (Coder,
2000). Compression leads to loss of total pore space and an increase in capillary pore
space, and occurs mostly in wet soils (Coder, 2000). True compaction is the
translocation and resorting of soil texture and the destruction of soil aggregates
(Coder, 2000). Consolidation is the deformation of soils destroying pore space and
structure. This leads to increased soil strength as more particle-to-particle contact is
produced (Coder, 2000). Studies have shown that a change in measured penetration
resistance is correlated with a change in compaction (Herrick and Jones, 2002).
Resistance is measured as the force (N) it takes to penetrate the ground, and is
converted into J/cm. The resistance of the soil can be measured using a dynamic cone
penetrometer, which quantifies the energy required to push a probe into the soil
(Herrick and Jones, 2002). I assume that a more compacted soil is more difficult to
penetrate, because more closely packed grains are harder to divide. Thus, a larger
value of soil resistance is equivalent to greater compaction.
Measuring infiltration rates and the soil properties described above allows me
to predict water input into the Critical Zone of Gordon Gulch.
7
The Critical Zone
”We live at the dynamic interface between the solid Earth and its outer fluid
envelopes. Extending from the outer vegetation canopy to the base of active
groundwater, this interface was recently named the Critical Zone because it supports
life and is increasingly impacted by human actions” (Anderson et al., 2008). The
critical zone is where the fundamentals for life form; plants need soil, microbes live in
the soil, and megafauna walks on the surface and thrives from plants and smaller
animals which get their nutrients from the dynamic system of the critical zone (Fig. 5).
To obtain a better understanding of this critical surface environment, the
National Science Foundation funded three Critical Zone observatories, one in the
southern Sierra Nevada in California, one in the Boulder Creek watershed in Colorado
and one in the Susquehanna Shale Hills in Pennsylvania (Anderson et al., 2008).
These observatories represent three different types of environments, so investigations
into the mechanics of the critical zone can be as comprehensive as possible.
The Boulder Creek Critical Zone Observatory (CZO) includes three separate
watersheds: an alpine environment (Green Lakes valley), an upland environment
(Gordon Gulch), and Betasso watershed, which is at a lower elevation. Critical Zone
processes include the chemical and physical weathering of bedrock, transformation of
saprolite into regolith and soil formation, and interactions with precipitation, and with
water that travels through the unsaturated and saturated zone. On a larger scale, the
Critical Zone is impacted by solar radiation, which drives the hydrological cycle, and
by rock uplift driven by tectonic and isostatic forces.
8
Figure 5. Cross-section of the Critical Zone, its materials and dominant processes
(Anderson et al., 2007)
The Critical Zone weathering model (Fig. 5) in contrast to a net depositional
model, in which fresh rock is added to the top, minimizes net additions to the top, and
relies mainly on the mechanical and chemical weathering of bedrock and its effective
vertical movement until it reaches the surface as soil. There are some additions to the
top from eolian sediments and organic matter from vegetation, which mixes in with
the soil matter weathered from the bedrock. The Critical Zone can be seen as a feedthrough reactor, where solid materials enter at the lower boundary, gets crushed down
to sediments of various sizes (Fig. 5), which are eroded and transported downslope
from the surface (Anderson et al., 2008). The relationship between chemical and
9
physical weathering determines how the Critical Zone forms, and along with
biological factors, determine the thickness of the regolith.
Location and Topography
Figure 6. Location of the study site; upper Gordon Gulch is shaded red
(http://www.lib.utexas.edu/maps/us 2001/colorado ref 2001.jpg,
http://www.boulder.doc.gov/gifs/boco_map.jpeg)
My study area is the upper part of Gordon Gulch, a small catchment (2.74 km2,
upper part: 0.93 km2) about 5 km northeast of the town of Nederland, Colorado (Fig.
6). The Gordon Gulch catchment is located at an elevation ranging from 2500 m to
2700 m, in the center of the Colorado Front Range.
10
Figure 7. Overview of the upper Gordon Gulch watershed looking to the northwest
with Niwot Ridge in the background
The Front Range includes foothills and mountains that stretch about 50 km
from the Great Plains to the continental divide (Anderson et al. 2006). Gordon Gulch
watershed is located between 2500 and -2700 m, in a rolling area of low relief,
sometimes termed the sub-summit surface (Anderson et al., 2006). This rolling
surface is located between the steep topography of the Flatirons in Boulder and the
high peaks at the Continental Divide. The cross-section of the distinct topographic
features of the Front Range (Fig. 8) depicts the results of glacial erosion near the
11
continental divide and the incising of canyons between the sub-summit surface and the
Great Plains.
Profile through
Boulder Canyon
Flat Irons
Surface of low relief
East
West
Figure 8. Topographic profile of the Front Range (Bubel, 2008)
The surface of low relief includes deeply weathered remnants of a depositional
and erosional surface that began forming after Eocene time (Birkeland et al., 2003).
There is considerable uncertainty regarding the initial elevation of the low-relief
surface and how it has evolved since Eocene time. There are two different hypotheses:
the surface formed close to its present elevation or it formed at a lower elevation and
was later uplifted to its present elevation (Birkeland et al., 2003). Whichever
hypothesis is true, the surface of low relief is preserved mainly east of the glacial
limit, which makes it an ideal area to study long-term weathering at rates of 510µm/yr and transport rates (Anderson et al., 2006). The late Pleistocene glacial limit
(Madole et al., 1999) shows that glaciers headed in small cirques near the Continental
Divide and flowed down narrow, U-shaped valleys, terminating as close as 3 km from
Gordon Gulch (Fig. 9).
12
Continental Divide
Surface of low relief
Flat irons
Figure 9. Extent of Pleistocene glaciations (extent of Gordon Gulch shown in red)
Geologic Background
The composition of the bedrock that underlies the Critical Zone affects
weathering rate and the rate of regolith formation. It also helps control soil
composition, which affects the infiltration rate. The last major tectonic event in the
area of the Colorado Front Range was the Laramide Orogeny, which lasted from about
70 million years ago until about 55 million years ago; its major result was the uplift
and erosion of the Rocky Mountains. Most of the bedrock underlying the Boulder
Creek CZO consists of Precambrian rocks of igneous and metamorphic origin
(Birkeland et al., 2003). The oldest metamorphic rocks, Precambrian silimanite gneiss
and Precambrian cordierite gneiss formed more than 1.7 billion years ago (Birkeland
et al., 2003). These Precambrian metamorphic rocks were intruded by the Boulder
Creek Granodiorite 1.7 billion years ago and the Silver Plume Granite 1.4 billion
years ago. Local Tertiary intrusives, mostly monzonites and quartz monzonites make
up a minor part of the intrusive rocks in the area (Birkeland et al., 2003; Gable, 1980).
13
The Colorado Mineral Belt is related to the Tertiary intrusions and the hydrothermal
alterations broadly associated with them.
The Colorado geologic map shows that the upper Gordon Gulch catchment is
underlain mainly by Precambrian gneisses, with some outcrops of Precambrian Silver
Plume Granite, Precambrian quartz monzonite, dikes of Cretaceous quartz monzonite
and Quaternary alluvium (Fig. 10). The Quaternary alluvium is located close to the
stream, suggesting thicker deposits closer to the stream than on the hillslopes.
Subsurface information that shows this relationship will be provided by geophysical
data collected under the direction of Matthias Leopold (Technical University of
Munich).
Figure 10. Geologic map of Gordon Gulch (Gable, 1980)
14
Climate and vegetation
In general, the climate of the Front Range can be characterized as a continental
climate with wide seasonal variations in temperatures. Especially at upper elevations,
the weather shows sudden and wide shifts on a daily basis. With the elevation rising
up from the Great Plains towards the Continental Divide, there are marked changes in
mean annual temperatures, mean annual precipitation, and a strong gradient in
vegetation (Fig. 11).
Figure 11. Elevation gradient of temperature, precipitation and vegetation for the
Front Range (Birkeland et al., 2003, modified from Veblen and Lorenz, 1991)
Weather records (Fig. 12) show that most of the annual rainfall comes during
the summer months, assuming that most of the precipitation between October and
April falls as snow. Summer showers and thunderstorms form mainly east of the
Continental Divide fueled by moisture from the Gulf of Mexico and the sub-tropical
Atlantic. Intense precipitation often falls in zones only a few kilometers wide (Jarrett,
2007).
15
Figure 12. Weather station location compared to Gordon Gulch; upper Gordon Gulch
is outlined in red
Average precipitation at the C-1 weather station (Fig. 12) for May-September
from 2001-2008 was 414 mm, while the May to September precipitation in 2008 was
469 mm, which indicates that the summer of 2008 was fairly wet, with a high peak of
precipitation in August. Even though the summer of 2008 was fairly wet compared to
the average annual summer precipitation (Fig. 13), the only surface water present in
upper Gordon Gulch during my field research originated from a few springs and
seeps. After thunderstorms, evidence of surface flow could be seen on the roads.
Puddles of rainwater were present on the roads and trails until they evaporated to
dryness.
16
250
Precipitation in mm
200
150
100
50
December
October
November
September
July
2008
August
May
June
April
March
January
February
December
October
November
July
September
2007
August
May
June
April
March
January
February
December
October
November
September
July
August
May
2006
June
April
March
January
February
0
Figure 13. Monthly precipitation records for C-1 from 2006-2008 (INSTAAR, 2009)
600
Precipitation in mm
500
400
300
200
100
0
2001
2002
2003
2004
2005
2006
2007
2008
Year of measurement
Figure 14. Total annual precipitation amount for C-1, May through September 2001
2008 (INSTAAR, 2009)
Flash floods occur when the amount of precipitation or snowmelt exceeds the
ground infiltration rate and Hortonian overland flow occurs, which contributes to a
rapid rise of stream discharge. When a rapid rise of stream discharge occurs at several
places at once, the rise in each tributary may add to the total discharge in the main
17
channel, potentially producing downstream flooding. In areas mostly covered by
impermeable surfaces, less precipitation is needed to cause a flash flood in a steep
canyon such as the event that in 1976 led to the Big Thompson Flood (Jarrett, 2007).
In an intense thunderstorm in the Colorado Front Range, most of the total rainfall may
fall within 30 to 60 minutes, which makes it difficult to predict floods ahead of time
(Arkell and Richards, 1986). Flash floods can also be caused by heavy snowmelt and
research has found that for Colorado streams above 2300 m, snowmelt-produced
floods predominate over rainfall produced floods (Payton and Brendecke, 1985).
Rapid development of flash floods is why it is important to model conditions that
would be needed to cause a flash flood.
The causes of peak stream-flows in the Front Range vary with elevation.
Below 2300 m, floods are mainly caused by rainfall runoff (Jarrett, 2007). Rain-onsnowmelt runoff is relatively rare in Colorado. From studies done on 70 natural-flow
basins, rain-on-snowmelt runoff appears to be a minor contributor to most flooding
(Payton and Brendecke, 1985; Jarrett, 2007). According to these studies, flooding in
Gordon Gulch catchment, because of its elevation, is likely to be caused by heavy
snow-melt. These studies were performed on large basins, however, so the small size
of upper Gordon Gulch (0.93 km2) may make it more susceptible to flooding from a
localized heavy thunderstorm.
The Gordon Gulch catchment is mainly within the upper montane environment
(Fig. 11), and can be divided into three distinct environments. These are the aspen
(Populus tremuloides) groves near the stream, open meadows and the forested
hillslopes. The hillsides are mostly covered by Lodgepole pine (Pinus contorta), some
western Douglas fir (Pseudotsuga menziesii) and limited areas of Ponderosa pines
18
(Pinus ponderosa) and aspen. For the most part, the north facing slopes are more
densely vegetated and the south facing slopes supports more open stands (Fig. 15).
The difference between the vegetation on the north and the south facing
hillslope stems from a few different factors, most importantly the difference in
moisture between the two opposite facing hillsides, the difference in needs of water
between Lodgepole pine and Ponderosa pines, and the previous use of the area. As
Lodgepole pines require more water than competitive species (Ponderosa pine and
Douglas fir), Lodgepole thrives in the mesic environment of the North-facing slopes
compared to the xeric environment of the South-facing slopes (Lotan and Critchfield,
1990).
As these areas were almost completely stripped of trees during the late 19th
century and early 20th century, Lodgepole pines were able to take over the North
facing slopes due to their early rapid growth, ability to survive in a wide variety of soil
situations, cone serotiny, and tolerance of extremes of temperature (Lotan and
Critchfield, 1990). Despite its intolerance for shade, the Lodgepole pine usually
survives in dense vegetation up to 80-100 years before groves break up. With the
present prevention of forest fires and the fact that in the absence of forest fires the
Lodgepole pine is usually succeeded by more tolerant species, like Engelmann spruce
and subalpine fir, vegetation will likely change in the future.
Because the south-facing slopes are relatively dry the less dense vegetation is
more likely a consequence of the individual trees’ competition for water rather than
competition for sunlight. In a catchment similar to Gordon Gulch, Bennett et al.
(1996) measured a density of 1050 trees/ha on the north facing slopes compared to
473 trees/ha on the south facing slopes.
19
Figure 15. North facing and south facing slope in the Gordon Gulch watershed,
looking from south-facing to north-facing slope
The present vegetation pattern of the Gordon Gulch watershed reflects landuse change related, in part, to arrival of European settlers in the area. Land-use change
is discussed in the section below on local history and land use.
The present vegetation in the Front Range is impacted by forest fires, and
forest fires are relatively common in this area as a consequence of the climate of the
western United States, which is characterized by multiyear droughts and periods of
dry weather and episodic, intense rainstorms (Moody and Martin, 2001). The mean
wildfire recurrence interval in ponderosa pine-dominated areas in the Front Range
ranges from 20-50 years (Moody and Martin, 2001). The mean wildfire recurrence
interval calculated by Moody and Martin (2001) can be applied to the south facing
slopes of Gordon Gulch catchment, which are sparsely forested with Ponderosa pine.
20
Wildfires can highly impact the hydrology of a basin. The removal of the
rainfall-intercepting canopy and of the soil-covering organic matter, intense drying of
the soil and the formation of water-repellent soils can result in decreased infiltration
rates (Cannon, 2003). More rainfall will directly impact the ground, because of the
removed rainfall-intercepting canopy, along with the decreased infiltration rate a
wildfire leads to significantly increased Hortonian overland flow and channel
discharge (Cannon, 2003). The decrease in ground cohesion after a wildfire
accelerates erosion and increases the danger of debris flows to occur as a response to
heavy thunderstorms (Cannon, 2003). It is important to analyze impacts from
wildfires on infiltration rates when considering the possible flood danger from an
intense rainstorm. Because the vegetation in Gordon Gulch has been untouched by a
severe forest fire in decades, I can only model the possible impact of a future wildfire
on the hydrology of the Gordon Gulch catchment.
Local history and land use
The Colorado Front Range was important for humans long before the Gold
Rush and its rich wildlife was harvested by Native Americans since the end of the last
ice-age. Human interactions with the environment in the Front Range have been
varied during the time of human settlement and have had significant impacts on
wildlife, vegetation and the use of the area. All of the modifications made by humans
through time impact the environment in different ways and have led to the
environment we see today. The history of how land use evolved is important in
understanding the present hydrology of the area and how previous use of the area has
implications for present infiltration rates and possible downstream flooding.
21
The earliest inhabitants in the Front Range are believed to have arrived 11 00012 000 years B.P., determined from the dating of mammoth remains (Buccholtz, 1983
in Veblen and Lorenz, 1991). The earliest occurrence of dated projectile points is
dated to 9000 years B.P (Benedict, 2005). These points are thought to record seasonal
use of travel passages through the mountains. The first permanent settlers in the area
are thought to be the Utes and the Shoshoni tribes at about 1000 years ago (Veblen
and Lorenz, 1991). These groups mostly occupied the higher elevations. Other groups
arrived in the area in the late 18th century, amongst them the Arapahos, Comanche,
Kiowa and Cheyenne (Veblen and Lorenz, 1991).
The first recorded arrival of a European was in 1859, but there were probably
visits by Spaniards from the south and French traders from the east (Buccholtz, 1983
in Veblen and Lorenz, 1991). The finding of gold in Cherry Creek where miners
founded the town Aurora, (now a neighborhood in Denver) in 1858 began a boom of
settlers to the Front Range area. It is estimated that more than 100,000 people came to
Colorado in the span of the first two years of the Gold Rush (Veblen and Lorenz,
1991). With the discovery of gold at the mouth of Boulder Canyon, the Boulder City
Town Company was formed in February 1859 (Veblen and Lorenz, 1991).
The town of Nederland, the closest town to Gordon Gulch, started growing in
1871 when Abel Breed, the owner of the Caribou silver mine, decided to make it the
location for milling of the silver ore (Town of Nederland, 2008). Two years later the
Caribou mine was sold to the Dutch mining company Nederland, hence the town
name (Town of Nederland, 2008). However, ore grade at the Caribou silver mine
soon declined, as did silver prices and left Nederland as a ghost town. At the turn of
the century tungsten was found nearby and the Caribou mine was reopened (Town of
Nederland, 2008). This second mining boom led to a rebirth of the settlement in
22
Nederland, which lasted until the mining of tungsten declined gradually after the end
of World War II (Town of Nederland, 2008). At present, Nederland is a town with a
mixed population, ranging from people who commute to Boulder to people seeking a
peaceful life in the mountains of the Front Range. It is also a big tourist attraction
during the summer time, as the starting point for hiking in the Indian Peaks area.
With the early mining boom in the Colorado Mineral Belt, there came also a
boom in the infrastructure of the area. Railroads were built to transport the mined ore
to the larger settlements. The part of the railroad that influenced Gordon Gulch was
named the Switzerland Railroad, which ran on a narrow ridge connecting Eldora to the
main railroad to Boulder. But with the decline of mining and the introduction of
trucking, the Switzerland Railroad was dismantled in 1919 (Veblen and Lorenz,
1991).
Gordon Gulch catchment was not one of the heavily mined areas, but prospect
pits can be found practically everywhere, showing how desperate prospectors were to
make a big discovery. Despite the lack of ores, Gordon Gulch probably was heavily
impacted by the mining boom as a consequence of the heavy demand for timber
resources needed for fuel, mine timbers and town construction, which mostly stripped
the montane areas of Boulder County of trees (Veblen and Lorenz, 1991) (Fig. 16 a
and b). Today, the only logging that takes place in Gordon Gulch is patch-cutting and
thinning for fire prevention purposes, and hill-slopes are covered by a variety of
vegetation.
23
a
b
Figure 16. Frankenberger Point on the Switzerland Trail A: 1905 B: 1984 (Veblen
and Lorenz, 1991)
The fire patch cutting in Gordon Gulch impacts soil compaction, and possibly
affects the infiltration rate, as more compacted soil generally is less permeable. Soil
24
compaction is a direct consequence of the weight of the logging machinery and this
compaction may last for an unknown time span before the soil recovers to its natural
state. The active logging area of summer 2008 covers 0.28 km2 of the total area of
2.74 km2 of Gordon Gulch, while it only covers 0.06 km2 of the area of 0.93 km2 of
the upper Gordon Gulch catchment (Fig. 17).
Figure 17. Extent of areas of fire prevention patch cutting in Gordon Gulch, area
values are for the area of the fire cuts that are within the catchment boundaries
The present use of the area is mostly for recreation, including hiking trails,
biking trails and dirt-bike/ATV trails. Several 4-WD roads pass through the
watershed. Human impacts have produced new flow pathways and probably have
changed the natural compaction of the soil and the rate of infiltration.
25
This study: soils, infiltration and surface-water hydrology
To be able to determine how water gets to the channel in Gordon Gulch, I
measured the infiltration rate at 45 different sites in upper Gordon Gulch during my
field research between July 12th and August 8th. The infiltration rate is defined as the
rate at which water enters the soil (Dingman, 2002), and is affected by the rate of
water input, the saturated hydraulic conductivity of the surface, degree of saturation,
slope, soil composition and the physical and chemical properties of water (Dingman,
2002).. The different infiltration sites were categorized into different environments,
where environment is defined by vegetation and position in the landscape.
Field infiltration can be measured in several ways, including zero-tension
meter, sprinkler-plot studies and ring infiltrometers. I chose to use a double-ring
infiltrometer, which was what was available to me, and it was the most precise
measuring equipment that made it possible for me to perform infiltration experiments
with only two people. A sprinkler-plot study would be both more time consuming and
would have required more water and more complicated measurements than using a
double-ring infiltrometer. Using zero-tension meters the continuous attention needed
with the ring infiltrometer is not necessary. Less water is needed for each
measurement, which makes the zero-tension meter convenient for use in a semi-arid
area. However, zero-tension meters may underestimate the infiltration rate in high
permeability areas (Reynolds et al., 2000), like semi-arid basins in the Colorado Front
Range, which made me decide on ring-infiltrometers.
The double-ring infiltrometer is more accurate than the single-ring
infiltrometer, because it minimizes lateral movement of water into the unsaturated soil
(Fig. 18). However, there are other sources of errors such as leakage between the outer
and the inner ring, which leads to an overestimation of the real infiltration rate. The
26
infiltration rate from ring- infiltrometers is also usually overestimated as it is very
difficult to account for cracks and other channels in the surface below the
infiltrometer. Ring infiltrometers have the advantage of averaging values over a large
surface compared to a zero-tension meter. The double-ring infiltrometer I used in the
field covers a surface area of 176.71 cm2.
Figure 18. Sketch of the 2-D lateral movement of water from a single ring
infiltrometer, due to capillary forces (Dingman, 2002)
Two different methods of measurements are commonly used with the ringinfiltrometers: constant-head and falling-head. Numerical modeling suggests that
constant-head measurements give an overestimate compared to falling-head
measurements for coarser textured soils and falling-head measurements tend to
underestimate the actual infiltration rate (Gregory et al., 2005). For my study it is
probably better to have an underestimate of the infiltration rate; in practice where
possible I used both constant and falling-head methods.
The modeling of infiltration as a part of a rainfall-runoff model is challenging,
because of the spatial variability in infiltration rates. Measured hydraulic conductivity
27
is often used to represent the infiltration rate over the entire study area (Meng et al.,
2006). To be able to predict the impact of rainstorms in a catchment, peak discharge is
often modeled using a simple rainfall-runoff model. For small watersheds, the rational
runoff formula estimates the peak discharge that can be expected from a model
rainfall intensity as:
Qpk = uR*CR*ieff*Ap
(Eq. 2)
where Qpk is the peak discharge
uR is a unit conversion factor
CR is the runoff coefficient, which
depends
on watershed land use
ieff is the rainfall intensity and
Ap is the drainage area
However, this model may not be sufficient in an environment like Gordon
Gulch where materials, slopes and surface cover are heterogeneous. The rational
runoff formula also implicitly assumes that the water from peak discharge flows over
the ground surface, which may not be the case.
My infiltration measurements from different environments of Gordon Gulch
allowed me to generalize infiltration rates in the catchment into my modeling. By
incorporating my measured infiltration rates into a GIS based rainfall-runoff model
(ArcHydro) I was also able to test the effects of different intensity rainstorms more
accurately than a model that estimates the infiltration rates based on the concept of a
uniform soil (Meng et al., 2006).
Because my study seeks to understand how water from precipitation moves
through the soil and to the stream and how much rain that is needed to cause a flood in
Gordon Gulch, I modeled rainfall rates predicted for the 5 year, 10 year, 25 year, 50
year, 100 year and the 200 year storm. Using these different input scenarios allows me
to make discharge predictions during both normal and extreme conditions. One major
28
limitation with the rational runoff formula and the ArcHydro extension in ArcMap, is
that the models assume peak flows in the channel arrive over the surface, ignoring
flow in the shallow subsurface as a contributor to stream-flow. Thus, these two
approaches likely underestimate actual peak discharge.
In 1976, the Big Thompson flood destroyed over 300 houses and claimed more
than 130 lives when as much as 305 mm of rain fell in a few hours near the canyon
along Highway 34 east of Estes Park, Colorado (Jarrett, 2007). Big Thompson is
similar to the Boulder Canyon, and storms of this magnitude are not uncommon in the
Front Range area (Jarrett, 2007). It is important to consider the possible dangers of
flash flooding from Gordon Gulch and adjacent catchments that empty into Boulder
Creek. Thus, my research has practical aspects for evaluating downstream flow
hazards, which may be useful to the nearby community. Infiltration rates and flood
discharge depend on the properties of the Critical Zone.
The rate of weathering is important to regolith production and for how much
fluid gets into the regolith, where it is an important variable in determining rates of
chemical weathering. However, this is a circular problem, considering that the
composition of the soil may be important in determining the rate of infiltration, and
that grain size reflects parent material and time, which determines the degree of
weathering. For instance, soil-clay content is correlated, with the age of soil material
(Fig. 19).
29
Figure 19. Relationship between clay content and soil age (Machette et al., 1976 in
Birkeland et al., 2003)
The data in Figure 18 are based on samples from the Colorado Piedmont near
Boulder, that have weathered for less than two million years; soils in the mountains
have many of the same properties in the B-horizon (Birkeland et al., 2003). The clay
content of the soils in Gordon Gulch reflects both weathering and eolian inputs. The
approximate age of the soil, along with the percentage clay for that specific age, can
be used as an indicator for how long it takes the Critical Zone to produce a certain
percentage of clay content in the soil, which can be used as the base for estimating a
weathering rate.
No previous studies have focused on the Gordon Gulch watershed, so I hope
that my research can be used to help others working on the Critical Zone project to get
30
a better understanding of the hydrologic processes of the Gordon Gulch catchment and
their relationship to soil texture and surfaces of low impermeability.
31
Chapter 2: METHODS
Field
Mapping
To calculate the area of low permeability surfaces in the upper part of the
Gordon Gulch catchment, I needed to map the spatial distribution of these features,
which in my study area included roads, dirt-bike trails and rock outcrops. Using a
Garmin Etrex and a Garmin Rino (when communication was needed), I hiked on
every road and trail in the basin recording my position using the tracking function on
the GPS. I uploaded my data into ArcMap guided by an orthophoto of the basin as a
map base. The width of roads and trails were estimated from approximations in the
field, where an average value was assigned based on my overall field observations.
The procedure of mapping the distribution of bedrock outcrops was somewhat
more challenging, because time limitations made it impossible to cover every square
meter of the basin. To work around this problem I sampled the area using swath
traverses spaced by approximately 200 m and oriented perpendicular to the main
channel (Fig. 20). On each traverse I plotted a GPS point for every outcrop that I
could see within a distance of ~40 m on each side of the traverse line. The total area
covered by my swath traverses was 0.32 km2, measured using the measuring tool in
ArcMap. The total area of upper Gordon Gulch is 0.93 km2, so I was able to sample
about 35 percent of the catchment.
32
Figure 20. Swath traverses used for mapping outcrop distribution
I categorized outcrop size into five categories based on the estimated length of
the long axis of the outcrop: small (<5 m), medium (5 m to 15 m), large (16 m to 30
m), very large (>30 m) and “extra large” (both axes longer than 30m). I was able to
measure extra large outcrops accurately by taking several waypoints around the edge
of the outcrop, and connecting them into a polygon. The nominal error of the GPS
units that I used was between 4 m and 10 m, depending on the vegetation cover.
Sampling outcrops by swath traverses (Fig. 20), allowed me to estimate the
distribution of outcrops in the entire area, using the outcrop density in the area
covered by swath traverses. Because the distribution of outcrops in the study
catchment is not closely related to slope, rock type or landscape position, I am
33
confident that my sample yields a realistic value for the total area covered by bedrock
outcrops in upper Gordon Gulch.
Figure 21. A typical “extra large” outcrop of biotite gneiss in Gordon Gulch (Evey
Gannaway, is 1.8 m tall)
Infiltration measurements
I used my knowledge of the area from my mapping and the Gold Hill
quadrangle to decide on possible locations for infiltration tests. My goal was to sample
the different environments (Table 1) that were present in the basin in order to get a
good representation of the possible local variation in infiltration rates (Fig. 22). The
exact sample locations were determined in the field, and depended on local slope and
the distribution of rocks in the top 10 cm of the soil. By choosing an area with limited
34
rocks in the top 10 cm of the soil, I was able to minimize “leakage” around the edge of
the infiltrometer rings. From the correlation plots relating infiltration rate to
environment and physical properties I can predict an average infiltration rate for each
environment, which I can use to model runoff from different flow-paths in the
catchment at different rainstorm intensities.
Table 1. Environment and number of infiltration measurements
Infiltration Test Distribution
Environment
#Samples
Trail
3
Road
7
Meadow
5
Forest Soil
22
Open Hillslope
4
Ridge
4
Total
45
The most important soil factor that I need to quantify is infiltration rate. Water
infiltration has a high local variation that stems from variability in soil properties.
35
Figure 22. Infiltration sites by environment; tributaries in blue and roads in black
I tried to do as many infiltrations tests as possible in Gordon Gulch. In my infiltration
experiments I used a double ring infiltrometer with an inner-ring diameter of 15 cm.
To prepare a location for an infiltration measurement, I temporarily removed
the soil organic material and most of the A horizon, and pushed the infiltrometer down
to a depth of approximately 3 cm in the mineral soil (Fig. 23). At most sites the base
of the infiltrometer was near the boundary between the A-horizon and the B-horizon.
36
Figure 23. Double ring infiltrometer, with a 15 cm diameter inner ring, prepared for
an infiltration measurement
For my infiltration experiments I used both steady-state and falling-head
methods. For the steady-state measurements, I kept water-level steady at between 6
and 8 cm deep, depending on the local slope, and used a stopwatch to time how long it
took to add 1000 cm3 of water. I measured these steady-state rates three times at each
location, or until the rate of infiltration stabilized. After three tests or the stabilization
of the infiltration rate was reached, I ran a falling-head experiment, where I recorded
the time it took to lower the water-level each cm until it was dried out.
To be able to carry out a water infiltration experiment during a dry period, I
carried two six-gallon containers filled with water to the experimental sites. Except for
a small spring that took 30 min to fill up both containers, I had to haul water from the
Mountain Research Station, about a 25 minute round trip from Gordon Gulch. On
average, 12 gallons of water were enough for 2.5 infiltration measurements.
37
Soil compaction measurements
At each sample location I also tested soil compaction, using a dynamic cone
penetrometer (Herrick and Jones, 2002), which had a 2 kg weight and a 40 cm drop
height. I recorded the number of blows that it took the penetrometer tip to reach 5 cm,
10 cm and 15 cm below the ground surface. The KE for a 2 kg weight falling 40 cm is
7.84 Joules; using this constant value, my recorded number of blows and the distance
traveled into the ground, using equation 3 (Herrick and Jones, 2002), I was able to
calculate the resistance of the soil (Herrick and Jones, 2002), which reflects its degree
of compaction.
Rs =Ws/Pd
(Eq. 3)
Rs, is the soil resistance in Newtons
Ws is the work done in Joules and
Pd is the distance the penetrometer travels in the
soil in meters
I also measured compaction with the dynamic cone penetrometer in some sites
affected by logging, but compaction is a factor with high local variation, and it may
not be possible to calculate a reliable estimate of the difference in compaction from
the untouched areas of the catchment.
38
Figure 24. Proper use of the dynamic cone penetrometer (demonstrated by Evey
Gannaway)
Soil sampling
I collected small samples of the mineral soil at each infiltration-test site. These
samples were collected below the O-horizon, and consisted mainly of A and small
amounts of Bw horizon. Sample weights ranged from 0.3-0.7 kg. Samples were
collected below the surface prepared for the infiltrometer, to an approximate depth of
10 cm. By collecting to a greater depth than the infiltrometer I was able to sample the
physical properties of the upper part of material through which water infiltrates.
Geophysical measurements
Geophysical data collected by Matthias Leopold (Technical University of
Munich), with help from the entire research team, allowed me to examine the
subsurface of Gordon Gulch from a line parallel to the channel at one location.
Professor Leopold used electrical resistivity techniques to determine the depth to
bedrock and layering within the surficial materials, which helps me to extend my
39
analysis of basin hydrology. The resistivity technique consists of setting up a
surveying line where electrodes are put into the ground, and each electrode pair acts as
a transmitter/receiver combination in turn to collect data about material resistivity
from all the electrodes. Experiments have been done on the electrical resistivity on
different materials, and values determined from these experiments allow us to model
the subsurface layering of the Critical Zone.
Laboratory
A detailed analysis of my soil samples, with a strong emphasis on grain-size
analysis, is important in understanding the relationship between the physical
characteristics of the soil and measured infiltration rate.
Moisture content and loss on ignition (LOI)
The moisture content in the soil samples was determined by heating
approximately ten grams of the <2 mm fraction of each sample in a crucible in a
100˚C convection oven for at least 24 hours. During this procedure, I weighed the
crucibles and the crucibles including the sample both before and after the heating to
the nearest 0.01 g using an electronic top loading balance. These measurements made
it possible for me to calculate the field moisture content using equation 4 (J. Racela,
Williams College, 2008).
% Η2Ο = ((wet-dry)/(wet-cru))*100%
where wet = wet soil plus crucible,
dry = dry soil plus crucible, and
cru = the mass of the empty crucible
40
(Eq. 4)
To calculate the loss-on-ignition (LOI), I took the dry samples from my
moisture content calculations, and burned them in a 600˚C muffle furnace for six
hours. The high temperature and oxygen from air ashes the samples, and the organic
matter is removed in the form of gaseous CO2 and H2O. After the crucibles were taken
out of the muffle furnace they were cooled in a desiccator. I weighed the samples
before I calculated the percent LOI, using equation 5 ( J. Racela, Williams College,
2008).
%LOI =( (dry-ash)/(dry-cru))*100%
(Eq. 5)
where ash = the mass of the ash and crucible
Grain size analysis
To find the sand, silt and clay weight percent of each sample I weighed the
initial sample, before I sieved it through a sieve set consisting of a 2 mm sieve, 250
µm sieve, 125 µm sieve and 62.5 µm sieve Then I weighed the different size fractions
and calculated the weight percent of each grain size class. I chose these particular
sieves, so I could distinguish between the content of gravel, medium to coarse sand,
fine sand and silt/clay.
To determine the very fine sand, silt and clay size fractions, I analyzed my
<125 µm fractions using a Beckman MultisizerTM 3 Coulter Counter (Fig. 25), which
gives the weight percent for each grain-size.
41
Figure 25. Beckman Multisizer™ 3 Coulter Counter
To prepare a sample for the Coulter Counter, I used 0.04±0.01 grams of
sediment and mixed it with 5mL of a filtered 1.67 g/L Calgon solution in a 15mL testtube, using a small shaker table. After the sample soaked in the Calgon solution for a
few hours, I sonicated the sample for 3 minutes, before I mixed it again using the
shaker table to keep all of the sediment in suspension. The concentration chosen for
the Calgon solution and the time of sonication was determined from the study of
Chappell (1997). Than I used a calibrated pipette to measure 60 µL of the sediment
mix and put it into a beaker of 200 mL filtered Calgon 1.67 g/L solution. I ran the
Coulter Counter with a 140 µm aperture, 30 000 counts and used 50 size bins ranging
from 3.9 µm to 125 µm to display the results. After a run through the Coulter Counter,
the software produces a graph and a table of the distribution of grain sizes. I copied
the table into Microsoft Excel and calculated the percent silt and clay (< 3.9 µm) in
42
the <125 µm fraction. Because of possible error when I extracted the sediment mix
with the calibrated pipette, I ran each sample three times through the Coulter Counter
in order to get a better representation of the weight percent of the different grain sizes.
Soil sorting was calculated using the method of moments (Folk, 1961) to
calculate the statistical parameters of the entire soil size distribution.
σΦ = √(∑ [w(MΦ-D)2]/∑w
(Eq.6)
where σΦ = standard deviation,
w = weight in grams
MΦ = arithmetic mean
D = Φ class midpoint
The benefit of using this method compared to a graphical method is that every
grain in the soil sample is taken into account, instead of getting my values from a
graphical presentation of the grain-size distribution. The standard deviation of the
sample in phi, represents the spread of grain sizes in the sample and can be used as a
measurement of the sorting of the soil. Lower phi values means that the sample is
more well sorted while a high phi values means that the sample is more poorly sorted.
I used the classification of sorting in Folk (1961).
Hydrologic calculations and models
For modeling the peak discharges using the rational runoff formula and
ArcHydro, rainfall intensities were needed along with measured infiltration rates to be
able to model the basin environment under different conditions.
43
Rainfall Intensity
To estimate the rainfall intensities and return periods for Gordon Gulch, I used
the eight years of measured precipitation records available from the C-1 weather
station (INSTAAR, Colorado University, Boulder) and the Sugarloaf weather station
(Utah Climate Center, Utah State University, Logan) and compared these values to
those available in the literature (Table 2).
Table 2. Rainfall Intensities for different recurrence intervals (Payton and Brendecke,
1985; Miller et al., 1973). Values for C-1 and Sugarloaf were calculated using
the Gumbel method of extreme value theory (Equation 7)
One Hour Rainfall Intensity
Recurrence Interval in years Sugarloaf mm
C-1 mm Average mm
C-1 Payton and Brendecke mm NOAA mm
5
24
21
22.5
19
25
10
29
25
27
23
30
25
36
30
33
27
38
50
41
34
37.5
N/A
43
100
46
38
42
33
48
200
51
41
46
N/A
N/A
24-hour Rainfall Intensity
Recurrence Interval in years Sugarloaf mm
C-1 mm Average mm
C-1 Payton and Brendecke mm NOAA mm
5
64
56
60
50
61
10
78
67
72.5
60
76
25
97
81
89
74
86
50
110
91
100.5
N/A
96
100
124
102
113
96
107
200
137
110
123.5
N/A
N/A
I only used precipitation intensity data for the months of June, July and
August, the most important thunderstorm months. These months are representative of
90 % of the largest one-hour storms of the year in the Rocky Mountains and the
Southwest deserts (Arkell and Richards, 1986). Because I used the annual series
method, only the largest precipitation event was needed for each year; using this threemonth sample is representative of the largest annual precipitation intensities.
I examined the one-hour rainfall intensity, because thunderstorms in the
Colorado Front Range rarely stay over an area for more than one hour (Arkell and
44
Richards, 1986), and the highest quality rainfall intensity data available to me are in
mm/hr. The average of the rainfall intensities at C-1 and Sugarloaf weather stations
was used as a proxy for conditions at Gordon Gulch; I assume that the rainstorms
occurring at these two weather stations also occur in Gordon Gulch.
I used the largest precipitation event from each year, ranked them and
calculated the return periods for each of these storms using the simplified Gumbel
method of extreme value theory (Equation 7).
T= (n+1)/m),
(Eq. 7)
Where T = recurrence interval in years
n = number of years of record and
m = rank of event
Then I plotted these values on Gumbel paper, and used linear interpolation to
estimate rainfall intensities for return periods up to 200 years (Table 2).
Time of concentration
The time of concentration was calculated by using the Papadakis and Kazan
equation, which is derived for watersheds with areas less than 5 km2 (Loukas and
Quick, 1996).
Tc = 0.66L0.50n0.52Ss-0.31ie-0.38
Tc = time of concentration in min
L = stream distance in feet
Ss = Slope (dimensionless)
Ie = rainfall intensity in in h-1
n = Manning’s roughness
45
(Εq. 8)
Stream distance used was 1473 m (4382 feet), which is the distance along the
main channel from the top of the watershed to the outlet. Slope was measured as the
average slope along the stream, 0.08 m/m. The Manning’s roughness was set to 0.1,
reflecting a stand of heavy timber with little undergrowth (Dingman, 2002). The
rainfall intensities used for calculating the different time of concentration were the
calculated values for the 5-year to the 200-year storm.
Table 3. Calculated time of concentration for the upper Gordon Gulch watershed
Recurrence Interval (yr)
Rainfall Intensity (mm/hr)
Tc* Papadakis and Kazan (min)
5
22.50
38.90
10
27.00
36.24
25
33.00
33.53
50
37.50
32.00
100
200
42.00
46.00
30.63
29.57
*Tc = Time of concentration
The calculated time of concentration ranges from about 30 minutes for the
most intense rainfall frequency to about 40 minutes for the least intense rainfall
frequency (Table 3). Because of the range of the time of concentration going beyond
thirty minutes and the available data sets, I used the one-hour rainfall intensity as my
basis for the modeling of peak discharges. However, using the one-hour rainfall
intensity to model the discharge does not reflect the fact that with continued rainfall
the soil becomes more saturated and increases the hydraulic conductivity of the soil.
Calculations of area of impermeable surfaces
The total area covered by roads and trails in upper Gordon Gulch watershed,
was calculated in ArcMap using the measuring tool to measure the total length of the
46
roads and trails that were mapped during the time of field research. Roads were
measured to be on average approximately 4 m wide, while trails were on average
approximately 1.5 m wide.
Total area covered by rock outcrops was calculated using ArcMap to count the
number of outcrops that are exposed in the upper part of the watershed. Very large and
extra large outcrops were mapped as polygons and the area could be measured using
the measuring tool in ArcMap. For the small, medium and large outcrops lengths were
assumed to be the midpoint of outcrop classes: 2.5 m, 10 m and 22.5 m. The short axis
was assumed to be 0.75 of the long axis. This assumption is based on estimates made
in the field, and might not be entirely accurate for each of the outcrop classes,
however the calculation of total area covered by outcrops is an estimate and I believe
that this assumption give me a reasonable estimated value. Total area covered by the
mapped outcrops was then converted into an approximation for the entire basin.
Infiltration rates after fire
The infiltration rates used for calculating peak discharge after a hypothetical
forest fire in Gordon Gulch were calculated as 0.38 of the measured infiltration rates
(Martin and Moody, 2001). The ratio of 0.38 is appropriate for granitic soils, probably
a reasonable approximation for the high-grade metamorphic bedrock of Gordon
Gulch.
Modeling of peak discharge
To model peak discharges from the upper Gordon Gulch watershed I used the
Rational Runoff Formula and the ArcHydro extension in ArcMap 9.3. I used the
rational runoff formula as a guideline to compare to my results from ArcHydro. Since
47
both the rational runoff formula and ArcHydro only considers surface flow in the
calculated peak discharge, my values for calculated peak discharge assume no
contribution from subsurface flow, which will lead to an underestimate of actual peak
discharge. Peak discharges were calculated for several different scenarios (Table 4).
Table 4. The various model scenarios. IDW is the natural infiltration rates and IDW
fire is natural infiltration rates after a forest fire
Rational Runoff Formula
Coeff.
Roads
1
No
0.1
No
0.2
No
0.1
0.2
ArcMap 9.3
Infiltration rate
0
IDW
IDW
IDW Fire
Yes
Yes
Roads
No
No
Yes
No
Post Fire
No
No
No
Yes
Stream Buffer
No
No
No
No
IDW
IDW
Yes
Yes
No
No
Yes, Road Infil. Rate
Yes, 0.5 IDW Infil. Rate
For my peak discharge calculations using the rational runoff formula, I used
runoff coefficient values (Table 4) from Table 9-10 in Dingman (2002). Peak
discharges from the rational runoff formula were modeled with three different runs of
the land-use coefficient, 0.1 for forest, 0.2 for forest with a lesser infiltration rate and
1 for a completely impermeable surface. For roads and trails I used 0.825, which is
within the boundaries of values set for roads (Dingman, 2002). When incorporating
the impact of roads and trails on the peak discharge, I used the calculated total area of
roads and trails in the upper Gordon Gulch watershed and added the peak discharge
from that area to the peak discharge of the total remaining area. Rainfall intensities
48
were based on Table 2. I also calculated the peak discharge of an extreme event of 100
mm/hr to see what impact a storm of approximately the intensity of the event that
produced the Big Thompson flood in 1976 (Jarrett, 2007) would have on my model
values. All my calculations were done using square kilometers, rainfall intensities of
millimeters per hour, and a unit conversion factor of 0.278, yielding results in cubic
meters per second.
The modeling in ArcMap using the ArcHydro extension allowed me to
incorporate the infiltration measurements taken in the field in the upper Gordon Gulch
watershed during July and August 2008. To incorporate these infiltration
measurements into the modeling of discharge, I first had to set the boundaries of the
drainage area, before functions in ArcHydro could derive a flow direction raster, flow
accumulation raster and a raster showing the location of the main channel and
tributaries, from the DEM.
In addition to these rasters, a raster weighted with an infiltration rate per pixel
was needed for a calculation of peak discharge. This raster was derived from a point
distribution of the infiltration sites, using the IDW function in the Spatial Analyst
extension. Another IDW was derived for post-fire infiltration rates.
In order to incorporate the impact of the area of roads and trails in Gordon
Gulch, I converted the road and trail polyline shapefile into a raster, which could be
weighted with an infiltration rate using the raster calculator. An average infiltration
rate was assigned to the entire area covered by roads, calculated from all the
infiltration measurements from roads and trails. When the road and trail polyline
shapefile was converted into a raster, the width of roads increased to six meters. The
raster width of the roads and trails leads to a slight overestimate for the peak discharge
calculation including the impact of roads and trails.
49
To incorporate both infiltration rates for the forested areas and the areas
covered by roads and trails, the raster weighted with rainfall intensity values was
subtracted from the infiltration rate raster for each of the two environment types. The
two resulting rasters were then added together into a raster where each pixel is
weighted with a value of excess runoff. The combined calculated raster was multiplied
with the flow accumulation raster to add the influence of slope on runoff
accumulation, before the peak discharge was calculated using the weighted flow
accumulation in ArcHydro. This function use a flow direction raster and the weighted
flow accumulation raster to add up the pixel values, and the pixel value that represents
the peak discharge is the pixel at the stream outlet. Because the pixels are in square
meters and the input rainfall intensity is in millimeters per hour, the calculated peak
discharge value was divided by 1000 to convert to cubic meters per hour and by 3600
seconds to convert to cubic meters per second, the desired units of peak discharge.
The impact of snowmelt was incorporated into the ArcHydro model, by
making a buffer of the stream feature shapefile, which was converted to a raster file.
Buffer was set to be 5 m on each side of the stream. Five meters is approximately five
times the width of the channel, and this buffer is meant to represent the meadow area
next to the stream that will be more saturated because of snowmelt, and therefore have
a lower rate of infiltration. The raster file was than weighted with the appropriate
value of infiltration using the raster. The resulting peak discharges from ArcHydro
including the stream buffer show the minimum and maximum possible impact from a
higher saturated zone surrounding the channel.
50
Chapter 3: RESULTS
The first section of results presents data for physical properties of 25 vegetated
soils and 8 “soils” collected from roads and trails and examines relations between
these properties and soil infiltration rates. The second section shows probable rainfall
intensities calculated from a variety of sources and the peak discharges modeled using
the rainfall intensities and measured rates of infiltration.
Soil Analysis
The results of my grain-size analysis show that soil samples are sandy and
relatively similar, and contain 2 to 13 percent silt and small amounts of clay
(Appendix A). Most of these soils can be classified as sands, whereas a few are silty
sands (Fig. 26).
zS
cS
sZ
sC
C
Z
Figure 26. Sand, silt and clay ternary diagram (characterization after Folk, 1954), S,
sand, s, sandy, Z, silt, z, silty, M, mud, m, muddy, C, clay, c, clayey
51
Soil textures from vegetated sites are similar to those collected from roads and
trails. Soil physical properties (Table 5; Appendix A) do not show systematic
variations, but LOI values are higher in vegetated soils and measured soil resistance is
higher in areas of roads and trails (Table 4).
My size results from the Coulter Counter showed that clay (<3.9 µm)
consistently comprises <1% of the total weight percent of the sample (Fig. 26; Table
5), which is lower than predicted (Birkeland et al., 2003). Sieving was used down to
coarse silt (<62.5 µm fraction), and the weight percent of silt in the soil samples
comprises, on average, 6.91 % for vegetated soils and 4.55% for areas covered by
roads and trails (Table 5). No large variations are present between vegetated soils and
areas covered by roads and trails in grain sizes larger than silt size (Table 5). The
source of error in the finest sizes may stem from clumping of grain aggregates into
larger particles that do not go through the correct sieve.
Table 5. Summary of soil properties
Soil Property
Sorting
Resistance 0-15 cm in J/cm
Moisture Content
LOI
Sand 2mm-250 µm
Fine Sand 250-125 µm
Very fine sand 125-62.5 µm
Silt 62.5-3.9 µm
Clay < 3.9 µm
Vegetated Areas n = 25
Mean
Std
1.36
0.17
18.26
8.09
4.87
2.46
7.30
3.49
68.51
5.79
19.63
4.32
3.97
2.15
6.91
2.76
0.19
0.08
52
Roads and Trails n = 8
Mean
Std
1.30
0.12
47.43
26.32
4.47
2.47
2.66
1.69
72.90
5.48
19.04
5.41
2.92
2.41
4.55
1.65
0.16
0.07
Infiltration rates
Field infiltration rates in Gordon Gulch forests and meadows are generally
high and show substantial local variation, based on the different environments in the
watershed, slope and soil properties (Fig. 27; Appendix B). When summarized, the
average infiltration rates for the different watershed environments are similar, except
for the very low infiltration rates measured on roads and trails. Using these results the
watershed can be separated into two environments: roads+trails, and areas that are
thinly to thickly vegetated and not heavily impacted by recent human activity.
600.00
500.00
Infiltration Rate mm/hr
400.00
n= 3
n= 3
300.00
n = 21
n= 3
200.00
100.00
n= 3
n= 7
0.00
Trail
Road
Forest Soil
Open Area
Hillslope
Ridge
Meadow
Environment
Figure 27. Average infiltration rates for different environments: error bars show one
standard deviation
53
Infiltration rate and soil properties
There is significant correlation between infiltration rates and some soil
properties, particular measures of grain size, sorting, LOI and soil resistance
(compaction). Most significant relationships between infiltration rates and soil
properties depend on whether the roads and trails are included in the data set (Table
6).
Table 6. R2 and p values for road/trail-infiltration rate correlations compared to
infiltration correlations from vegetated sites. P-values of <0.05 represents the
probability that the outcome supports the null hypothesis
Vegetation, n = 22
2
r
Infiltration/sorting
Infiltration/<62.5um
Infiltration/LOI
Infiltration/compaction
*it is significant to p = 0.05
0.499
0.33
0.23
0.10
p=
<0.0001*
0.0025*
0.0228*
0.1249
Vegetation + roads, n = 32
r2
0.17
0.06
0.37
0.22
p=
0.0188*
0.156
0.0004*
0.0113*
Soil sorting exerts little control on infiltration rates for the entire data set, but
explains more than 50% of the variance in infiltration rates when the road values are
excluded. However, LOI exerts significant control on the infiltration rates when
roads/trails are included in the data set. Soil properties related to porosity and organic
matter (LOI) are altered in the roads/trails whereas in this data set soil texture is not
significantly different in roads compared to forested soils. In most analysis of internal
soil property correlations below I exclude the roads/trails samples.
54
900
y = -816.9x + 1383.2
R² = 0.4993
Infiltration Rate in mm/hr
800
700
600
500
400
300
200
100
0
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
Sorting in Φ
Figure 28. Infiltration rate vs soil sorting. Higher Φ values represent more poorly
sorted soil (Folk, 1961)
Soil sorting is as defined in Folk (1961), where higher Φ values correspond to
lower degree of sorting. The infiltration rates are dependent on soil sorting, and the
relationship has an r2 value of 0.50, and is significant at a p-value <0.0001 (Table 6).
Poor sorting yields a relatively low infiltration rate (Fig. 28), because with a more
poorly sorted soil, small grains fill in pores, contributing to low porosity and often
lower permeability.
Infiltration rates are positively related to the amount of organic material in the
soil (r2 = 0.23, p = 0.0228) (Fig. 29). The range of LOI values is large because soil
samples were collected under similar conditions but some samples included more of
the A-horizon than others. When the roads and trails are included LOI shows a
significant relationship with infiltration rate (r2 = 0.37, p = 0.0004).
55
900
y = 28.33x + 43.672
R² = 0.2332
800
Infiltration rate in mm/hr
700
600
500
400
300
200
100
0
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
Weight Percent
Figure 29. Infiltration rate vs amount of organic matter (LOI) in weight percent
900
y = -38.119x + 549.32
R² = 0.3339
800
700
Infiltration Rate in mm/hr
600
500
400
300
200
100
0
0
2
4
6
8
Weight Percent
Figure 30. Infiltration rate vs soil weight % <62.5μm
56
10
12
14
16
The infiltration rate and the percent silt and clay (Fig. 30) are negatively
correlated (r2 = 0.33, p =0.0025), as might be expected from soil sorting values. There
is no significant relationship with the inclusion of the roads and trails (r2 = 0.06, p =
0.156).
900
y = -5.093x + 326.54
R² = 0.2226
800
Infiltration Rate in mm/hr
700
600
500
400
300
200
100
0
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Soil Resistance in J/cm
Figure 31. Infiltration rate vs soil resistance from 0 to -15 cm
The dynamic cone penetrometer measurements of soil resistance in the top 15
cm of the soil (Table 4, Appendix A) show a negative relationship with infiltration
rates (r2 = 0.22, p = 0.0113). Higher soil resistance leads to lower infiltration rates
(Fig. 31). When roads and trails are excluded the relationship is not significant (r2 =
0.10, p = 0.1249). A large difference is seen in the range of resistance values for the
vegetated area compared to areas of roads and trails (Fig. 32).
57
Infiltration Rate in mm/hr
1000
100
10
1
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Soil Resistance 0-15cm in J/cm
Figure 32. Soil resistance and infiltration rates for the different environments, roads
and trails in blue, forest soil in red and hillslope in green
Soil resistance values in fire prevention areas are on average lower than
samples outside these areas, but the number of samples is small so the relationship is
not significant (Fig. 33).
900
Infiltration rate in mm/hr
800
700
600
500
400
300
200
100
0
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
Soil Resistance in J/cm
Figure 33. Infiltration and soil resistance, highlighting values from fire prevention
areas (in red)
58
Rainfall intensity
The results of my calculations and synthesis of the one hour and 24-hour
rainfall intensity events from the 5-year to the 200-year storm for upper Gordon Gulch
(Fig. 34), produces apparently reasonable values, despite the short length (8 years) of
precipitation records; values matches up well with values from the NOAA Rainfall
Intensity Atlas (Miller et al., 1973), from Payton and Brendecke(1985) (Table 2), and
from four watersheds in Colorado at a similar elevation as the upper Gordon Gulch
(Fig. 36).
The small differences among the datasets come from differences in
methodology, and coarseness of analysis. There is a lot of variability in precipitation
patterns in the Colorado Front Range, which leaves some sources of uncertainty in any
generalized precipitation map, like the NOAA Precipitation Atlas (Miller et al., 1973).
Rainfall intensity in mm
140
120
100
80
60
40
20
0
1
10
100
Recurrence interval in years
Figure 34. Calculated rainfall amounts and recurrence intervals averaged between C-1
and Sugarloaf (Table 2); one-hour rainfall amount is in blue and 24-hour
rainfall amount is in red
59
250
Rainfall Iamount in mm
200
150
100
50
0
1
10
100
1000
Recurrence interval in years
Figure 35. 24-hour rainfall amount for selected watersheds at different
elevations in the Colorado Front Range. Sugarloaf, C-1 and NOAA are
interpolated data sets, other values are measured data sets. Flatiron
Reservoir (1678m) is in blue, Cabin Creek (3055m) is in red, Gross
Reservoir (2430m) is in green, Evergreen (2130m) is in purple,
Sugarloaf (2052m) is in light blue, C-1 (3017m) is in orange and NOAA
estimated Gordon Gulch (2500-2700m) is in black
Geophysics
The geophysical data results from the upper Gordon Gulch show that on the
hillslopes bedrock is generally 2 to 3 m below the surface and covered with
colluvium. One resistivity line run in a meadow area near the channel shows colluvial
and alluvial cover to be at a depth of 2-3 m, saprolite between 3 and -8m and bedrock
below about 8 m (Fig. 36). From the subsurface data it is also possible to “see” a
buried channel that is filled with colluvium. This increased depth approximately
expands to about five meters on each side of the channel, providing a basis for my
calculation of a stream buffer of thicker alluvium that could become saturated during
snow-melt.
60
Figure 36. Interpreted subsurface layering along a resistivity line along the valley of
upper Gordon Gulch (Matthias Leopold, Technical University of Munich)
Calculated peak discharges, upper Gordon Gulch
The modeled peak discharges for various test scenarios show a wide range of
results (Table 7). From these results, it is evident that high rainfall intensities are
needed to cause significant peak discharge in the vegetated areas and much of the
peak discharge results from human alteration of the basin environment (Table 7).
61
Table 7. Predicted peak discharge (m3/sec) under different model scenarios
Model results upper Gordon Gulch
Recurrence interval
mm/hr
Impermeable
5
22.5
10
27
25
33
50
37.5
100
42
200
46
ArcHydro (m3 /sec)
4.13
4.96
6.06
6.89
7.71
8.45
3
5.82
6.98
8.53
9.70
10.86
11.89
0.22
0.33
0.48
0.60
0.70
0.80
0.63
0.76
0.92
1.05
1.18
1.29
1.21
1.45
1.77
2.01
2.25
2.46
3
0.58
0.70
0.85
0.97
1.09
1.19
3
1.16
1.40
1.71
1.94
2.17
2.38
0
0
0
0.00017
0.00265
0.0139
0.04313
0.07513
0.114
0.153
0.365
0.224
0.552
0.333
0.800
0.484
0.987
0.597
1.174
0.712
1.341
0.815
RRF* (m /sec)
Infiltration with roads
ArcHydro Infil with roads (m3 /sec)
3
RRF* 0.1 Roads, Trails (m /sec)
3
RRF* 0.2 Roads, trails (m /sec)
No roads
RRF* coeff. 0.1 (m /sec)
RRF* coeff. 0.2 (m /sec)
ArcHydro prefire (m3/sec)
ArcHydro after fire (m3 /sec)
Buffer
ArcHydro with road impact, stream buffer road infilrate
ArcHydro with road impact, stream buffer, 0.5 infilrate
*RRF-Rational Runoff Formula
62
0.00056 0.001123
Chapter 4: DISCUSSION AND CONCLUSIONS
The results show that infiltration rates are mainly controlled by a few factors;
soil sorting; LOI, soil composition and soil resistance. These factors each influence
infiltration rates by different mechanisms. Peak discharges reflect an area’s infiltration
rate, which means that soil physical factors are directly related to a basin’s peak
discharge. My model results reflect control by infiltration rates and model scenarios,
including increased areas of low infiltration rates show a significant increase in
modeled peak discharge. Model results are consistent with unit peak discharge values
from similar basins in the Front Range.
Model Limitations
Both the rational runoff formula and ArcHydro models have limitations that
cause uncertainties in the modeled values of peak discharge. The main limitation of
the rational runoff formula is the runoff coefficient, which assumes a homogenous
infiltration rate. The basin can be divided into sections where different runoff
coefficients are assigned to each area. However, as results from the impact of roads
and trails show, there is a large discrepancy in discharge estimates between the
rational runoff formula and ArcHydro. Because the runoff coefficient for the rational
runoff formula “produces” runoff from soil under any rainfall intensity, the fraction of
road area to the total area is linearly related to peak discharge; a significant proportion
of roads and trails is needed to exceed the peak discharge contribution from the
natural environment. In ArcHydro there is no runoff of significance, except from
roads and trails, before the rainfall intensity surpasses the lowest infiltration rate of
33.2 mm/hr in the natural environment, which is equivalent to the 50-year storm.
63
The ArcHydro model has some definite limitations. Modeled results for peak
discharges are only for the total peak discharge that would occur at the end of a onehour storm of specific rainfall intensity; the model does not show the development of
peak discharge with time during the rainstorm. I also assumed that pixels which
showed a negative value when the infiltration rate of each pixel was subtracted from
the infiltration value, were zero. In reality some of the runoff from an area of low
infiltration might infiltrate in a nearby area of high infiltration on its way to the main
water-pathways of the basin. This assumption could cause a possible over estimate of
peak-discharge values.
The assumption of no water in the channel before the precipitation event and
the failure to model contribution to peak discharge from sub-surface flow may lead to
a significant underestimate of peak discharge generated by ArcHydro. The additional
peak discharge added by water in the channel would be especially important in
considering the impact of a subsequent precipitation event. However as this scenario
was not modeled and field observations indicates that there is no channel flow except
following a precipitation event, the impact of additional channel flow is most likely
negligible under the specified model conditions in my experiment. The possible
contribution from subsurface flow is unknown because ArcHydro has no groundwater
component, but from model results on peak discharges for an impermeable basin
which show the amount of water that is introduced to the hydrologic system during a
precipitation event, I expect this contribution to be of significant size.
The impact of the area covered by the channel and the fire-suppression areas
were not incorporated into the model and the omission of these factors lead to a
possible underestimate of the calculated peak discharge values. The amount of water
64
flow through the sub-surface that contributes to peak discharge will also lead to an
increase in peak discharges from the calculated values.
The width of roads and trails of six meters that was used in ArcMap leads to an
overestimation of the actual area covered by roads and trails and cause an
overestimation of the calculated impact of roads and trails on peak discharge; I
measured roads as 4.5 m wide and trails as 1.5 m wide. Another source of
overestimation comes from the use of one-hour rainfall intensities. If the two-hour
rainfall intensity had been used, the rain would fall at a lower rate, decreasing the peak
discharge. The incorporation of these sources of uncertainties into the modeling would
bring the modeling closer to the natural conditions and give a better representation of
the peak discharges than I was able to. However, representative data for these
variables has to be measured in the field to make such an expansion possible.
Factors controlling infiltration rates
Soils in the upper Gordon Gulch watershed consist mostly of sand-sized
grains, with some silt and minor clay (Table 4). Comparing the percentage clay
content with the age/clay content relationship of Birkeland (2003) suggests that soil
age is <5000 years (Fig. 19). Research about soil age in the Colorado Front Range
suggests that most soil located in the surface of low relief should have an age >100 k
yr (Birkeland, 2003). On average, soils of this age have between 6- to 10 percent clay
content (Birkeland, 2003). The soil survey report from the NRCS also suggests a clay
content of 5-15 percent (NRCS, 2009). These discrepancies indicate that surface soils
in Gordon Gulch are younger than predicted or that the low amounts of clay are an
artifact of the methodology used in my Coulter Counter analysis of the soil samples.
65
Variations in the amount of silt and clay act as a control on infiltration rates
(Table 4) for soils in the vegetated area. The relationship is significant at p <0.05,
where silt concentration has an inverse relationship with infiltration rates, and higher
silt/clay content yields lower infiltration rates. This apparent relationship is as
expected, since I would predict that smaller grain sizes like silt are likely to fill in pore
space, thereby decreasing infiltration. The silt/clay infiltration rate relationship might
be artificially high, since coarse silt grains have a tendency to be present in the soil as
sand sized aggregates, which are highly permeable. The amount of soil aggregates
present is not reflected in my soil analysis, because these possible aggregates were
crushed during sieving to get a realistic representation of actual silt and clay content.
When all the samples are included, no significant relationship is seen, indicating that
the amount of silt and clay in roads and trails does not control of infiltration rates.
Grain-size sorting helps to control infiltration rates for the vegetated soils of
upper Gordon Gulch watershed (Fig. 28); the degree of sorting has phi values between
0.9 and 1.6, moderately sorted to poorly sorted (Folk, 1961). Sorting is closely related
to the amount of silt in the soil. Poor sorting yields a relatively low infiltration rate
(Fig. 28), because with a more poorly sorted soil, small grains may fill pores,
contributing to low porosity and often low permeability. Because of the uncertainty in
my clay results, there is some uncertainty about my results for soil sorting. However,
this uncertainty would not change the relationship because higher clay content in the
soils with high total silt and clay would make the poorly sorted soils even more poorly
sorted.
Despite sampling that locally included different proportions of the A horizon, I
am confident that the amount of organic matter in the soil is one of the factors that
control infiltration rates in the upper Gordon Gulch watershed. LOI measures the
66
amount of organic matter in the soil and is closely related to the amount of carbon in
the soil, providing an indirect measure of porosity. My results show that infiltration
and organic content in the soils are positively correlated (Fig. 29), with r2 = 0.23 and p
= 0.02 for the vegetated samples and r2 = 0.37 and p = 0.0004 for the entire data set.
Organic matter has a higher water-holding capacity than mineral grains,
because organic matter absorbs water like a sponge, whereas mineral soils holds the
water in pore space, or attached to clays. The low density of organic matter decreases
the bulk density of the soil and decreased density is associated with increased
aggregation, which produces more pore space (Franzluebbers, 2002). Vegetation and
organic matter in soil O and A horizons also protect against compaction by raindrop
impact, and therefore maintain water infiltration and decrease the possibility of runoff.
In vegetated areas the near surface soil consists of mostly organic material from plant
decay and since the upper Gordon Gulch watershed is almost entirely covered by
sparse to moderately thick vegetation, relatively high infiltration rates are consistent
with the sandy soils and abundance of organic matter.
My measurements did not include organic matter in the soil “O” layer, where
infiltration sometimes can be affected by hydrophobic characteristics. Soils coated by
these water repellent materials may have a lesser infiltration rate, causing the
possibility for more surface flow (Orfanus, 2008) than would be predicted by
infiltration measurements in the mineral soil. Water repellent soils may also cause
preferential flow, potentially producing overland flow and causing erosion (Orfanus,
2008).
In upper Gordon Gulch, needles from Lodgepole pines form the surface in
many areas and my field observations suggest that pine needle layers show some
hydrophobic characteristics. When water was poured onto the layer, it channeled the
67
water and infiltrated into uncovered soil nearby or the water eventually seeped
through the layer of pine needles and infiltrated into the ground. Thus, my field
observations suggest that the layer of pine needles was not sufficient to lead to a
significant retardation of infiltration rates.
Soil compaction or resistance measured by the penetrometer which quantifies
soil’s ability to resist penetration, is an important control on infiltration rates (Herrick
and Jones, 2002) and shows an inverse relationship for the entire data set; r2 = 0.22
and p = 0.0113. This relationship is not significant when only the vegetated sites are
used in the calculation. Possible sources of error in my soil resistance data could come
from overestimating the resistance of the soil when the dynamic cone penetrometer
had to shoulder aside large rocks and pebbles in the subsurface. Because penetrometer
measurements were made by hand, it was fairly easy to notice when the tip of the
penetrometer hit a rock, and if this occurred the measurement was aborted and started
again a few cm away. Error could also stem from changes in soil moisture during the
time of field measurements, because research shows that increased soil moisture
increases the number of blows required to reach a certain depth (GRI report, 2005).
Because of varying weather conditions during my time in the field, including
rainstorms, I did not include my soil moisture data in most analyses. Soil moisture
ranged from 2 to 11 % in the soil samples and it is possible that the soil resistance
measurements could have been altered by the varying soil moisture.
Increased soil compaction breaks up aggregates of silt and organic matter and
these fine grains fill in pore space between larger grains and decreases infiltration rate.
Research shows that the degree of compaction is higher in soils containing silt and
clay (GRI report, 2005). The degree of compaction is significantly greater on the
roads and trails of Gordon Gulch (Fig. 32), and is reflected in the low infiltration rates
68
for these areas. These differences are quantified by the relationship between soil
resistance and infiltration rates, and from these results it is evident that soil resistance
is the main control on infiltration rates in roads and trails, along with the amount of
organic matter. It seems likely that the extreme compaction of the roads and trails
removes the effect that a difference in sorting and soil composition would have on
infiltration rates; soil is so compacted that the pore space is filled and the variation in
these soil properties is not significant. The large difference in soil resistance between
roads and trails and the vegetated areas can be attributed to the removal of the
organic-rich soil and compaction done by vehicles on the roads and hikers, ATV’s and
bikes on the trails and demonstrates the large impact that human activity has on
infiltration rates by altering the natural environment.
Since heavy machinery compacts the ground during fire prevention logging, I
would expect to see a difference in soil resistance and potentially lower infiltration
rates in these areas of upper Gordon Gulch. A slight trend can be seen where sample
sites in the fire prevention area on average have lower infiltration rates and higher soil
compaction (Fig. 33). Because of the small number of sample sites in the fire
prevention area, I am not confident in these results; sample values in the fire
prevention area could be a result of the overall local variation seen across the upper
Gordon Gulch watershed.
Slope is thought to control infiltration rates, but there is still dispute if
the relationship is positive or negative. The relationship seems to be dependent on soil
composition and surface characteristics, in addition to the slope. My results show no
significant correlation between local slope (0 to 12°) and infiltration rate. Based on my
results and the lack of conclusive evidence from other research (Bradford and Huang,
1992, Fox et.al., 1997), I conclude that slope up to 12° is not an important controlling
69
factor on the infiltration rates in the upper Gordon Gulch Catchment. However,
steeper slopes will have an impact on how fast water gets to channels and therefore
will have an impact on the response time of peak discharge after a precipitation event.
Uncertainty in measured infiltration rates
In addition to all the measured soil properties that are controlling infiltration
rates in the upper Gordon Gulch watershed, there are unmeasured factors that may
affect the rate of water infiltration. These include macropores, pebbles and rocks in
the immediate subsurface. Tree roots or openings where roots have rotted out can
allow water to flow by opening up pore space; the same is true for the soil covering
pebbles and rocks as well. There may also be places where the soil immediately below
the surface has been hollowed out by burrowing animals or organisms or local cracks
and cavities. These factors are difficult to account for when doing infiltration rate
measurements, and may have affected measured infiltration rates. If one or more of
these conditions occur beneath an infiltration site, the measured infiltration rate may
be unusually high. However, the large diameter of the double ring infiltrometer is
likely to incorporate some of the effects that these natural pores have on infiltration.
The average value of the infiltration rates measured in upper Gordon Gulch (Fig. 27)
matches well with the rates that are estimated by the NRCS for the soil-types
(Bullwark and Catamount families) that are present in upper Gordon Gulch, ranging
from 50 to 150 mm/hr (NSRC, 2009).
To be able to accurately represent the local infiltration rates in a model, ideally
a large sample pool is necessary. In comparison to models where there were no field
measurements and infiltration rates were modeled from soil property data from soil
70
surveys, I believe that my data yield a reasonable representation of the natural
environment in upper Gordon Gulch watershed.
My infiltration measurements were all taken in July and August, and they
reflect soil conditions during the summer months. Controls on infiltration rates may
change seasonally and there can be large changes in the infiltration rates on an annual
scale, which could lead to more overland flow. The main soil property that changes
with seasons is that the ground may be frozen in the winter, and studies have shown
that, as expected, soil infiltration rate decreases significantly during the winter months
(Diamond and Shanley, 2003). However, because upper Gordon Gulch watershed is
covered by snow during the winter, the effects of the frozen ground are not likely
important. The only seasonal effect that is likely to see in upper Gordon Gulch is the
impact that a change in the water-table during the year will have on peak discharges.
Calculated peak discharges, upper Gordon Gulch
Peak discharges were modeled using two different methods, the rational runoff
formula and ArcHydro. The results from the two model approaches show both
similarities and differences in the peak discharge values depending on the model
scenario. These discrepancies in the results reflect the different nature of the two
models.
Before I was able to do the actual peak discharge modeling, I defined a basin
and, using the ArcHydro extension I derived a flow direction map for the upper
Gordon Gulch (Fig. 37). The flow direction map shows the direct flow paths that
water would take to the channel without any obstacles, like vegetation and rock
outcrops. In the modeling of peak discharge, a direct downslope flow path is assumed
for the basin.
71
Figure 37. Flow directions in upper Gordon Gulch
In addition to the flow direction raster, I derived an overall map of generalized
infiltration rates using the Inverse Distance Weighted (IDW) algorithm, allowing me
to model peak discharges (Fig. 38). The IDW averages the different infiltration rates
at each site, and an infiltration rate is assigned to each pixel in the raster.
72
Figure 38. Infiltration rate distribution (IDW) in upper Gordon Gulch
As an initial check on model performance, I found that modeled results for
peak discharges from an impermeable basin are similar from both ArcHydro and the
rational runoff formula (Table 9). These calculated values suggested that ArcHydro
produced reasonable results, where the rational runoff formula was used simply as a
reference point. Because these results matched up well, I am confident that the
methodology used in ArcHydro for modeling peak discharges is internally consistent.
For a completely undeveloped basin the two modeling techniques yield quite
different results (Fig. 39), where no runoff is generated from ArcHydro until the 50year storm. This is because the low permeability surface of the channel was not
incorporated into ArcHydro, while runoff is generated from the rational runoff
formula under all modeled rainfall intensities. The modeled values for an undeveloped
basin were used as comparison to the peak discharges that would be generated after a
forest fire and to measure the impact of impermeable surfaces.
73
Forest fires may significantly decreases infiltration rates and may lead to a
large increase in peak discharge (Fig. 39). It would lead to an increase during the 200year storm from 0.001 m3/sec to 0.15 m3/sec. This model increase in peak discharge
shows that the combination of forest fires and intense precipitation can cause a
significant increase in potential flood danger in mountainous basins. The results on the
impact of forest fires are consistent with other studies done on the impact of forest
fires and peak discharges (Martin and Moody, 2001).
2
Arcmap Road Impact, Stream Buffer Road
Infiltration rate
RRF Coeff. 0.1 with Road Impact
1.8
RRF coeff. 0.1
1.6
Arcmap Road Impact, Stream Buffer 0.5
Original Infiltration Rate
Arcmap with road impact
1.4
Peak Discharge in m3/sec
Arcmap Postfire No roads
Arcmap Prefire No roads
1.2
1
0.8
0.6
0.4
0.2
0
1
22.5
10
27
33
37
100
42
46
Figure 39. Modeled peak discharges under different scenarios, x-axis shows
recurrence intervals in years and mm/hr for each data point
Both model techniques give similar peak discharge results when infiltration
rates and road/trail impacts are incorporated (Table 9; Fig. 39). Despite these similar
results, the percentage of total peak discharge that is contributed to by roads and trails
shows a large discrepancy between the two methods. Using the rational runoff
74
formula, contributions from the roads and trails only accounts for 10%, while in using
ArcHydro they account for close to 100% (Fig. 39).
During snow melt in the spring, meltwater will infiltrate into the ground and
potentially saturate the soil, leaving less unfilled pore space. The impact of snow-melt
was modeled using a 5 m buffer of more saturated soil around the stream. My
calculated peak discharges, including a 5 m buffer around the channel where a higher
degree of saturation was assumed because of snowmelt, ranged from 0.22 m3/sec to
1.34 m3/sec (Table 9) (Fig. 39). For the 200 year storm this yields a 67.5% increase of
the peak discharge value calculated without snow melt impact. Because rain-on-snow
events are somewhat unlikely at these elevations (Payton and Brendecke, 1986) this
scenario does not represent an immediate flood danger. However if an extensive zone
of saturated soil near the channel could be a remnant from a previous precipitation
event or late snowmelt, this scenario can be used to predict the effect of a subsequent
precipitation event. However, this approach does not provide a precise representation
for the processes of snowmelt and their impact on peak discharge, but it was the most
accurate way to model snow-melt runoff in ArcHydro.
The results of peak discharges from modeling in ArcHydro are consistent with
values of unit discharge per square kilometer calculated for the nearby Colorado
catchments Spring Creek (1880m) (Fig. 40), Bear Creek (2157m), Bobtail Creek
(3179m), and Michigan River (3166m) (Fig. 41). The comparison with these four
watersheds show that the modeled results are within the range of unit peak discharges
for watersheds at similar elevations and I am confident that these peak discharges are
a relatively good prediction of what can be expected after rainstorms in Gordon
Gulch. These catchments are located within elevations where peak discharges range
from those caused only by thunderstorms to pure snowmelt.
75
Rainfall intensity used in the research by Moody and Martin (2001) was for
storm durations of 30 minutes. However, as approximately 0.80 of the one-hour
rainfall may occur in the first 30 minutes (Arkell and Richards, 1986), my calculated
peak discharges at 30-minute rainfall intensity would have been slightly lower, but the
value of mm/hr would also have been slightly lower. These two factors would shift the
data points down and to the left, and my values would still match closely with the
curves of average peak discharge measured by Moody and Martin (2001).
Figure 40. Comparison between values of unit peak discharge in Spring Creek,
Colorado, with upper Gordon Gulch, Colorado. ArcHydro model values
incorporating roads and trails are shown in red, ArcHydro model values
incorporating a saturated stream buffer in addition to roads and trails are in
blue and data points representing different years of measured discharge in
Spring Creek are shown in black (Moody and Martin, 2001)
76
0.90
Peak Discharge m 3 /sec/km 2
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
1.00
10.00
100.00
Return Periods Years
1000.00
Fig 41. Measured peak discharge for three comparable Front Range basins plus
Gordon Gulch. Bobtail Creek is in blue, Bear Creek is in red, Michigan Creek
is in green and upper Gordon Gulch is in purple
A gaging station will be installed at the bottom of the upper Gordon Gulch this
year, and the results after a rainstorm can then be directly compared to my model
results.
Human impact and impact of rock outcrops on basin hydrology
Impervious areas and human alteration of the Gordon Gulch environment
lead to a significant decrease of infiltration rates and increased peak discharge caused
by the roads and trails, fire preventative logging and some contribution from rock
outcrops in upper Gordon Gulch. Roads and trails cover at least 1.15% of the 0.93km2
upper Gordon Gulch, the area of fire-preventative logging work covers 6.45% of the
total area and rock outcrops cover 6%. Infiltration rates for roads and trails are, on
average, 10 percent of the average infiltration rates for natural environments (Fig. 27);
77
infiltration rates in the fire-preventative logging areas are on average lower than the
average infiltration rate for the vegetated areas and rock outcrops are assumed to be
impermeable.
Despite the small total area covered by roads and trails, this close-toimpervious area has a large effect on the modeled peak discharge. Results from the
rational runoff formula show a contribution from roads and trails to peak discharge
that is ten times larger than its percentage area (10% of peak discharge, 1% of total
area), while the results from ArcHydro show the contribution to be close to 100% of
the total modeled peak discharge for rainfall intensities up to the 200 year storm (Fig.
39). A rainfall intensity of >100 mm/hr is needed to cause a significant contribution to
total peak discharge from the natural environment; however most of the peak
discharge would still accumulate from the roads and trails.
The impact of the logged areas was not modeled as a separate scenario,
because of a lack of infiltration measurements in the impacted area. However, average
compaction is higher and the average infiltration rates are lower inside the logging
area than outside (Fig. 33), which corresponds with the measured inverse relationship
between soil compaction and infiltration rates (Fig. 31). Despite the lack of direct
measurements, it is reasonable to assume that the logging area would contribute a
higher percentage of total peak discharge than the untouched natural environment, as a
direct function of the lower measured infiltration rates. Logged areas also had
temporary roads and skid trails that were not included in my roads and trails
measurements. In contrast to the roads and trails, the impact of logging areas may be
over a shorter time-span, and these areas will eventually recover into their natural state
if they are not converted into permanent roads or trails. From the modeled peak
discharge results from ArcHydro it is evident that human alteration severely changes
78
the hydrology of a basin, and that human development could cause a potential flood
threat in mountainous watersheds where intense thunderstorms occur frequently.
Rock outcrops are an integral part of the basin environment in upper Gordon
Gulch, accounting for as much as 6% of the total area (0.057 km2 of 0.93 km2). As
rock outcrops have an impermeable surface and cover an area that is six times larger
than what is covered by the roads and trails, it seems that the impact on peak
discharge would be significant. Assuming that the entire surface of rock outcrops
would yield runoff flowing directly into the channel contributes approximately 35% of
the total peak discharge, as modeled by the rational runoff formula. As shown above,
the rational runoff formula strongly underestimates the impact of roads. Thus, I
assume that there will be an underestimation of the impact of rock outcrops as well.
However, little research has been done on the contribution of runoff from
small outcrops towards peak discharge. The outcrops in upper Gordon Gulch are
scattered all across the basin and are surrounded by soils with high infiltration rates,
so no runoff from outcrops is likely to flow directly into the channel. Rainfall on the
rock outcrops will most likely infiltrate into these soils and not contribute to peak
discharge. The rock outcrops in upper Gordon Gulch were also observed to have small
surrounding depressions, where some of the runoff will flow directly into the subsurface, and decrease the impact of runoff on the surrounding soil. When outcrops are
located nearby roads and trails, part of the runoff from the outcrops may contribute to
peak discharge via these water pathways. From observations of the outcrop locations
in the field, contribution by this process will most likely not have a significant impact
on peak discharge.
79
Flood danger
Gordon Gulch is one of numerous tributaries to Boulder Creek and the peak
discharge of Gordon Gulch and these other tributaries under different conditions are
important in considering the possible flood danger from Boulder Creek. The average
peak discharge of Boulder Creek from 1987 to 2008 was 25.06m3/sec (USGS RealTime Water Data for Colorado, 2009). Comparing the calculated peak discharge from
the 0.93 km2 upper Gordon Gulch, which ranged from 0.20m3/sec for the 5 year storm
to 0.80m3/sec for the 200-year storm, shows that the study catchment would cause a
negligible increase of peak discharge even for the 200 year storm event. However,
there are numerous small tributaries like upper Gordon Gulch that contribute to the
peak discharge of Boulder Creek, and the combined peak discharges from all of these
tributaries during the 100-year storm could cause a severe flood danger for Boulder.
The high intensity thunderstorms are local, covering a few tens of km2 at high
elevations (Jarrett, 2007), and rarely affect the entire drainage basin for Boulder Creek
at once; hence an unusual storm both regarding rainfall intensity and the area affected
is needed to cause severe flooding in Boulder Creek. An event like the one described
above, led to the Big Thompson Flood in 1976 (Jarrett, 2007) and the 283 m3 s-1
Boulder Creek Flood in 1894 (Fig. 42) (BASIN, 2009), the occurrences of such events
show that research evaluating possible flood dangers is important.
80
Figure 42. Photo near 7th Street in Boulder looking east during the 1894 flood
(BASIN, 2009)
81
Conclusions and suggestions for future work
The infiltration rate measurements that were taken in upper Gordon Gulch
show a large local variation in infiltration rates, and these infiltration rates are
controlled by various soil properties. From my analysis of soil samples collected in
upper Gordon Gulch and soil resistance experiments using a dynamic cone
penetrometer, the most important soil properties that influence infiltration rates are
soil sorting, silt and clay content, soil compaction and the amount of organic content.
Reflecting the clear distinction between human altered environments, like roads and
trails, and the natural environment, the soil properties that control infiltration rates
somewhat differ between these two environment types.
The strongest controls on the infiltration rate in the natural environment are
soil sorting, r2 is 0.5, amount silt and clay, r2 is 0.33, LOI, r2 is 0.23 and soil resistance,
r2 is 0.22. More poorly sorted soil yields lower infiltration rates, more silt and clay
yields lower infiltration rates, higher organic content yields a higher infiltration rate
and greater soil resistance yields lower infiltration rates.
Rate of infiltration on roads and trails are mostly controlled by soil
resistance and amount of organic content, r2 is 0.37. Soil resistance values for the
measurements on roads and trails are on average more than twice as great as the
values from the natural environment, combined with infiltration rates that are shown
to be on average 10% of infiltration rates from the vegetated environment, it is evident
that the roads and trails can be considered as a distinct human altered environment.
The amount of organic content is 1/3 the amount of that in the vegetated areas, which
confirms the relationship documented between LOI and infiltration rates.
The results for the modeled peak discharges clearly show the consequences
of different environmental impacts on upper Gordon Gulch, most importantly, human
82
alteration resulting in an increased area of impermeable surfaces, forest fires and the
impact of a saturated zone buffering the stream-channel.
The impact of two consecutive high intensity thunderstorms was not
modeled, but as my modeling shows, peak discharge is a direct function of infiltration
rates. In such a scenario, since the soil would be somewhat saturated from the first
storm, less water would be able to infiltrate into the soil, which would lead to more
runoff from a second storm of equal rainfall intensity.
Reflecting the small drainage area of upper Gordon Gulch, the modeled peak
discharges are just a small fraction of the measured peak discharges of Boulder Creek,
and stream flow from upper Gordon Gulch alone does not cause any immediate flood
danger in Boulder Canyon. However, as there are numerous watersheds like upper
Gordon Gulch in the drainage area of Boulder Creek, the effect of a number of large
thunderstorms at once could cause possible flood danger. The natural example would
be a precipitation event like the one that caused the Big Thompson Flood in 1976.
Future work includes an expansion of infiltration measurements and soil
analysis, to the entirety of the Gordon Gulch watershed. An expansion of model
parameters is also an important aspect of future research. This will increase the
accuracy of modeled peak discharges if sub-surface flow contributing to peak
discharge can be estimated fairly accurately. A thorough analysis of the entire basin
would give a larger database for drawing conclusions about controls on infiltration
rates. With a complete set of infiltration measurements, the peak discharge can be
modeled for the entire watershed, which would give a better indicator for the
possibilities of flooding in the basin, and the possible impact a flood will have on the
residents at the bottom of Gordon Gulch.
83
With the introduction of a gaging station at the outlet of upper Gordon
Gulch, a comparison can be made between measured and modeled data, and the
discrepancies between the values can be used to find weaknesses in the models, and
with improvements a valid method of modeling peak discharges in a small watershed
can be developed. This model can then be applied to other similar watersheds.
84
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Gable, D. J., 1980, Geologic map of the Gold Hill quadrangle, Boulder County,
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Gregory, J. H., Dukes, M. D., Miller, G. L., Jones, P. H., 2005, Analysis of Double
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Technical University of Munich.
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86
Martin, D. A., Moody, J. A., 2001, Comparison of soil infiltration rates in burned and
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87
Appendix A
Complete table of soil analysis data
Grain Size Class
Sand
Fine Sand
Very Fine Sand
Silt and Clay
Sample
Sample Identity Environment
% >250µm % 250µm - 125µm %125µm - 62.5 µm Wt % < 62,5µm
EMB-GG-7/28/08-1
451
Forest soil
69.69
16.28
3.78
9.32
EMB-GG-7/28/08-2
452
Open Area Hillslope
63.23
20.10
7.34
8.53
EMB-GG-7/28/08-3
453
Open Area Hillslope
62.44
18.02
8.11
10.74
EMB-GG-7/30/08-1
454
Ridge
74.90
19.26
1.17
4.34
EMB-GG-7/30/08-2
455
Forest soil
63.38
15.99
6.51
13.66
EMB-GG-7/30/08-3
456
Forest soil
69.59
22.73
1.73
5.55
EMB-GG-7/30/08-5
458
Forest soil
80.46
14.62
1.86
2.63
EMB-GG-7/30/08-6
459
Ridge
71.25
22.67
1.60
4.07
EMB-GG-7/30/08-7
460
Ridge
76.38
14.23
2.80
6.14
EMB-GG-7/31/08-1
461
Forest soil
60.29
21.12
5.30
12.82
EMB-GG-7/31/08-2
462
Forest soil
67.15
17.57
4.94
9.08
EMB-GG-7/31/08-3
472
Forest soil
67.13
27.50
1.89
2.95
EMB-GG-7/31/08-4
473
Forest soil
67.71
22.66
3.13
6.04
EMB-GG-7/31/08-5
474
Forest soil
63.14
28.63
2.52
5.01
EMB-GG-7/31/08-6
476
Forest soil
66.29
18.83
5.75
8.62
EMB-GG-7/31/08-7
477
Forest soil
72.64
21.63
1.68
3.67
EMB-GG-8/2/08-1
478
Meadow
71.58
22.96
1.29
3.68
N/A
EMB-GG-8/2/08-2
EMB-GG-8/2/08-3
EMB-GG-8/2/08-4
EMB-GG-8/2/08-5
EMB-GG-8/2/08-6
EMB-GG-8/4/08-1
EMB-GG-8/4/08-2
EMB-GG-8/4/08-3
EMB-GG-8/4/08-4
EMB-GG-8/4/08-5
EMB-GG-8/4/08-6
EMB-GG-8/5/08-1
EMB-GG-8/5/08-2
EMB-GG-8/5/08-3
EMB-GG-8/5/08-4
EMB-GG-8/5/08-5
EMB-GG-8/5/08-6
479
480
481
EMB 5
EMB 6
EMB 7
594
595
596
597
598
599
600
602
603
604
605
606
Mean
Std
Trail
Open Area Hillslope
Open Area Hillslope
Road
Road
Road
Forest soil
Forest soil
Forest soil
Forest soil
Road
Road
Forest soil
Forest soil
Trail
Forest soil
Forest soil
Road
57.49
71.36
62.04
75.06
79.68
65.80
75.00
66.70
64.64
71.09
75.69
65.34
78.13
73.65
75.16
68.91
73.09
69.59
5.77
22.59
19.62
27.21
19.96
13.72
26.41
15.49
20.81
20.58
22.57
14.15
21.40
12.88
13.33
12.71
15.99
22.35
19.61
4.39
8.40
2.19
2.23
1.19
2.17
2.17
2.70
3.27
4.14
1.14
6.08
4.47
3.78
6.63
3.44
6.76
0.97
3.62
2.20
11.04
6.27
8.13
3.54
4.15
5.06
6.41
8.92
10.14
4.86
3.90
8.05
4.79
6.29
8.34
8.25
3.36
6.72
2.90
Silt
Clay
% Silt % Clay
8.81
0.16
8.36
0.16
10.31 0.24
4.19
0.09
12.93 0.32
5.33
0.13
2.49
0.10
3.78
0.09
5.87
0.15
11.14 0.36
8.77
0.16
2.75
0.10
5.69
0.14
4.86
0.15
8.35
0.19
3.52
0.09
3.52
0.09
10.18
6.01
7.55
3.40
3.78
4.79
6.18
7.96
9.58
4.34
3.52
6.77
4.61
6.10
7.88
7.69
3.19
6.30
2.68
88
0.34
0.26
0.30
0.14
0.11
0.17
0.23
0.30
0.29
0.17
0.17
0.19
0.12
0.18
0.22
0.23
0.09
0.18
0.08
Infiltration Rate
Sum
98.72
99.19
99.12
99.62
99.11
99.52
99.53
99.40
99.43
98.21
98.59
99.37
99.32
99.30
99.40
99.55
99.44
Sorting
1.4715
1.4980
1.4338
1.2445
1.5765
1.2934
1.1695
1.1390
1.4295
1.5677
1.3838
0.9886
1.2837
1.1219
1.3852
1.0752
1.4526
98.98
99.44
99.33
99.74
99.46
99.35
99.60
99.02
99.23
99.30
99.60
98.18
99.51
99.89
99.41
99.58
99.68
1.4849
1.2616
1.2883
1.2510
1.5362
1.5463
1.5129
1.2719
1.2208
1.2997
1.3834
1.4226
1.6194
1.4498
1.1391
1.4498
1.1391
171.43
257.14
150
248.28
42.61
295.08
409.09
450.00
342.86
64.86
163.64
720
553.85
782.61
253.52
211.76
1384.62
17.92
236.29
1894.74
9.84
19.39
23.18
68.57
33.42
218.18
68.18
5.68
19.32
236.84
281.25
7.39
52.31
400
6.82
1st
12.54
8.89
18.82
12.54
10.19
8.62
2nd
19.60
24.04
36.06
25.87
14.90
17.25
3rd
19.60
26.66
24.30
37.63
22.74
22.74
Average Resistance
Moisture content
4th Resistance 10cm Resistance 15cm Resistance 20cm
21.17
16.07
17.25
18.23
8.15
39.72
16.46
19.86
24.83
5.24
29.79
27.44
26.39
27.24
4.33
39.20
19.21
25.35
28.81
3.18
24.30
12.54
15.94
18.03
2.66
25.09
12.94
16.20
18.42
4.83
3.14
5.49
8.89
4.70
3.92
6.27
6.27
9.41
29.01
14.11
8.36
101.92
47.56
54.10
12.54
8.36
13.33
23.52
31.36
18.03
13.07
6.27
15.68
10.19
5.75
66.38
7.84
10.98
17.25
11.76
9.41
19.60
19.34
16.20
24.30
22.47
9.41
74.48
38.15
50.18
15.68
13.07
17.25
44.69
43.90
56.45
25.09
18.82
42.34
24.30
8.89
116.03
29.79
17.25
21.43
10.19
7.06
20.38
24.57
16.73
22.74
39.72
10.45
72.13
27.44
23.00
22.74
7.84
22.74
29.79
14.63
14.90
28.22
13.59
36.59
39.98
15.68
13.07
24.30
44.69
69.78
25.87
20.91
23.52
38.15
37.63
26.66
129.36
23.00
31.36
17.25
8.89
44.69
36.85
46.26
21.95
33.97
28.22
29.79
35.28
26.66
117.60
5.49
8.23
13.07
8.23
6.66
12.94
12.81
12.81
26.66
18.29
8.89
88.20
42.86
52.14
14.11
10.71
15.29
34.10
37.63
37.24
19.08
12.54
29.01
17.25
7.32
91.21
13.59
11.24
15.85
8.89
6.79
15.42
16.73
14.11
25.35
25.44
9.41
28.22
15.29
17.64
12.35
7.06
17.25
19.99
14.24
22.74
26.13
10.45
40.77
48.09
14.63
11.50
18.29
37.63
48.35
33.45
19.69
16.20
32.06
24.04
13.76
103.92
36.33
43.90
15.29
10.85
24.89
37.44
47.82
30.58
23.26
19.21
31.49
26.85
16.99
107.34
LOI
14.21
13.19
6.57
4.16
4.75
3.38
3.08
1.99
11.03
7.13
9.93
2.95
5.21
7.02
6.74
4.54
11.72
7.68
15.15
6.61
6.41
7.47
2.77
6.66
3.58
3.78
3.10
7.03
3.82
2.90
3.03
3.62
9.92
5.46
4.56
4.53
2.42
5.46
2.75
5.22
7.31
0.10
2.73
3.62
8.75
5.86
5.26
5.69
2.71
5.59
8.96
8.83
2.08
3.72
3.20
1.80
Appendix B
Complete table of field measurements
Samplenumber Location
Soilsample
Waypoint
1L Min
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Trail
Road
Meadow
Meadow
Meadow
Meadow
Forest soil
Forest soil
Open Area Hillslope
Open Area Hillslope
Ridge
Forest soil
Forest soil
Forest soil
Forest soil
Ridge
Ridge
Forest soil
Forest soil
Forest soil
Forest soil
Forest soil
Forest soil
Forest soil
Forest soil
Meadow
Trail
Open Area Hillslope
Open Area Hillslope
Road
Road
Road
Forest soil
Forest soil
Forest soil
Forest soil
Road
Road
Forest soil
Forest soil
Forest soil
Trail
Forest soil
Gravel Covered
Road
EMB-GG-7/28/08-1
EMB-GG-7/28/08-2
EMB-GG-7/28/08-3
EMB-GG-7/30/08-1
EMB-GG-7/30/08-2
EMB-GG-7/30/08-3 and EMB-GG-7/30/08-4-pineneedles
EMB-GG-7/30/08-5
EMB-GG-7/30/08-6
EMB-GG-7/30/08-7
EMB-GG-7/31/08-1
EMB-GG-7/31/08-2
EMB-GG-7/31/08-3
EMB-GG-7/31/08-4
EMB-GG-7/31/08-5
EMB-GG-7/31/08-6
EMB-GG-7/31/08-7
EMB-GG-8/2/08-1
EMB-GG-8/2/08-2
EMB-GG-8/2/08-3
EMB-GG-8/2/08-4
EMB-GG-8/2/08-5
EMB-GG-8/2/08-6
EMB-GG-8/4/08-1
EMB-GG-8/4/08-2
EMB-GG-8/4/08-3
EMB-GG-8/4/08-4
EMB-GG-8/4/08-5
EMB-GG-8/4/08-6
EMB-GG-8/5/08-1
EMB-GG-8/5/08-2
EMB-GG-8/5/08-3
EMB-GG-8/5/08-4
EMB-GG-8/5/08-5
EMB-GG-8/5/08-6
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
472
473
474
475
476
477
478
479
480
481
EMB 5
EMB 6
EMB 7
594
595
596
597
598
599
600
601
602
603
604
605
606
4
4
1
3
2
11
18
9
8
7
9
9
12
14
6
9
2
11
2
2
12
0
30
19
9
15
Aborted
15
7
1L Seconds
Rate(mm/hr) 2L Min
Steady state
2L Seconds Rate(mm/hr) 3L Min
3L Seconds
Rate(mm/hr) 4L Min
10
35
45
26
46
0
0
57
250ml in 20min
38
250ml in 20min
19
2
20
24
20
18
53
38
aborted because of leak
26
43
0
27.35 for 0.5 liter
30
50
38
145ml in 25.30
170 ml in 25.00
15
51.00min for 500ml
0
25.00min for 500ml
30.00min for 50ml
30.00min for 170ml
5
818.18
743.80
1948.05
992.94
1232.20
309.92
189.39
342.62
42.61
394.88
42.61
465.93
377.39
365.26
274.93
237.84
541.13
344.93
1294.59
6
5
3
4
3
11
45
50
7
0
45
30
505.05
584.42
1093.83
852.27
909.09
296.44
7
7
3
5
5
11
5
14
40
30
30
20
481.28
471.30
929.75
619.83
619.83
300.80
12
10
280.20
12
40
269.14
9
45
349.65
9
45
349.65
7
8
9
20
17
5
6
3
20
55
0
6
39
10
25
50
464.88
382.33
378.79
169.61
193.15
659.82
531.29
889.33
8
33
18
4
6
1
out of water
aborted
33
43
12
10
0
52
398.72
101.11
187.31
818.18
568.18
1826.30
298.17
1254.88
1704.55
17.92
272.73
4090.91
111.29
19.39
23.18
177.10
33.42
378.79
68.18
5.68
19.32
226.02
12
3
1
14
27
50
278.67
988.14
1859.50
3
1
24
40
1002.67
2045.45
12
0
33
55
271.64
3719.01
1.00
0
3409.09
34
35
98.58
8
30
401.07
8
50
385.93
12
52
264.96
12
24
274.93
49
30.00min for 65ml
32.35min for 500ml
52
30.00min for 60ml
215.54
7.39
52.31
433.36
6.82
12
0
284.09
11
15
303.03
7
30
454.55
89
4L Seconds
Rate
4
15
802.13904
3
1
5
57
1105.6511
1748.2517
Minus 1cm Min Minus 1cm seconds Rate
Minus 2cm Min Minus 2cm seconds
Rate
Falling Head
Minus 3cm Min Minus 3 cm Seconds
Rate
Minus 4cm Min Minus 4cm Seconds
Rate
Average rate 1st 5cm
2
1
0
1
1
2
3
2
14
30
50
0
20
0
0
15
268.66
400.00
720.00
600.00
450.00
300.00
200.00
266.67
2
1
0
1
2
1
3
2
40
15
56
0
25
40
30
25
225.00
480.00
642.86
600.00
248.28
360.00
171.43
248.28
3
1
1
1
3
2
4
2
34
29
14
0
5
20
0
15
168.22
404.49
486.49
600.00
194.59
257.14
150.00
266.67
1
0
0
3
2
3
2
18
50
45
30
15
40
17
461.54
720.00
800.00
171.43
266.67
163.64
262.77
220.63
436.51
642.34
650.00
266.07
295.95
171.27
261.10
1
57
307.69
1
55
313.04
2
0
300.00
2
2
295.08
303.95
1
1
1
9
3
0
0
0
24
20
45
15
40
45
55
40
428.57
450.00
342.86
64.86
163.64
800.00
654.55
900.00
1
1
1
7
3
0
0
0
16
19
25
45
44
50
55
40
473.68
455.70
423.53
77.42
160.71
720.00
654.55
900.00
1
1
1
8
2
0
1
0
28
11
25
7
37
47
5
44
409.09
507.04
423.53
73.92
229.30
765.96
553.85
818.18
1
1
25
10
423.53
514.29
2
1
0
20
0
25
257.14
600.00
1440.00
2
1
0
22
55
26
253.52
313.04
1384.62
2
2
0
8
5
24
2
25
17
30
248.28
2117.65
38.71
2
0
30
20
17
30
257.14
2117.65
9.84
2
0
15
7
45
77.42
6
30
92.31
2
40
225.00
2
45
2
20
257.14
2
2
7
283.46
1
16
473.68
0
46
782.61
433.72
481.76
396.64
72.07
184.55
761.99
620.98
850.20
281.25
288.00
1500.00
2
0
50
25
211.76
1440.00
263.97
353.20
1441.15
33
17
235.29
2117.65
0
19
1894.74
8
45
68.57
79.43
218.18
1
40
360.00
267.73
23
251.75
2
32
236.84
2
27
244.90
247.66
2
5
288.00
2
8
281.25
2
0
300.00
288.18
1
30
400.00
1
14
486.49
246.90
2061.92
24.27
453.39
90
Penetrometer 1
2nd 5cm 3rd 5cm 4th 5cm 1st 5cm
Penetrometer 2
2nd 5cm 3rd 5cm 4th 5cm 1st 5cm
7
5
6
10
22
14
19
27
47
28
33
47
66
42
43
69
9
8
5
7
27
18
14
22
50
33
29
41
81
50
51
50
4
3
3
3
6
13
11
11
10
24
27
31
17
15
56
47
51
26
22
2
3
6
3
2
8
8
15
10
7
28
16
21
17
11
88
31
35
37
14?
4
5
9
17
10
6
65
23
32
9
5
11
11
20
23
10
15
18
20
30
33
12
160
52
71
16
13
21
37
53
59
23
28
37
30
43
54
14
41
65
40
53
72
17
4
4
4
20
8
5
18
15
15
38
18
12
31
29
27
54
33
21
47
44
36
63
51
32
82
103
24
19
36
65
103
92
33
99
133
32
27
62
92
134
120
52
17
37
7
7
6
19
20
32
62
20
16
18
50
43
49
81
32
26
34
79
82
8
28
4
23
4
3
37
16
42
16
7
116
30
61
29
17
229
47
74
47
36
4
4
9
4
65
16
39
28
9
172
Penetrometer 3
Slope in degrees
2nd 5cm 3rd 5cm 4th 5cm Above At site Below
8
24
39
49
3
5
16
14
30
24
44
33
9
5
29
10
69
19
31
64
91
46
32
65
99
110
51
80
103
115
4
12
21
24
49
76
7
22
31
50
32
69
63
rock
51
89
90
6
3
4
27
4
51
75
4
25
12
61
36
113
51
188
20
17
12
10
flat
9
12
11
5
flat
flat
3
17
10
14
14
6
10
16
flat
flat
17
12
flat
5
2
16
9
10
13
flat
8
8
16
6
flat
8
11
flat
10
20
10
10
8
19
14
10
5
9
8
4
7
10
10
4
6
11
10
16
8
10
9
12
7
12
9
17
15
6
10
15
6
10
9
12
5
3
12
3
4
10
6
4
9
9
13
11
5
7
5
4
7
6
17
11
9
6
10
6