Journal of Hydrology 521 (2015) 182–195
Contents lists available at ScienceDirect
Journal of Hydrology
journal homepage: www.elsevier.com/locate/jhydrol
Variability in evaporation across the Canadian Prairie region during
drought and non-drought periods
R.N. Armstrong a,b,⇑,1, J.W. Pomeroy a, L.W. Martz a
a
b
Centre for Hydrology, Dept. of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan S7N 5C8, Canada
National Agroclimate Information Service, Science and Technology Branch, Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, Saskatchewan S7N 0X2, Canada
a r t i c l e
i n f o
Article history:
Received 16 June 2014
Received in revised form 28 October 2014
Accepted 24 November 2014
Available online 3 December 2014
This manuscript was handled by Andras
Bardossy, Editor-in-Chief, with the
assistance of Ashish Sharma, Associate
Editor
Keywords:
Spatiotemporal variability
Cold regions hydrological modelling
platform
Actual evaporation
Penman–Monteith
Canadian Prairies
Land surface parameterization
s u m m a r y
Knowledge of changes in spatial and temporal distributions of actual evaporation would be useful for
land surface parameterizations in the Prairie region of Canada. Yet challenges persist for examining
the variability of evaporation from land surfaces and vegetation over such a large region. This is due in
part to the existence of numerous methods of varying complexity for obtaining estimates of evaporation
and a general lack of sufficient measurements to drive detailed models. Integrated approaches may be
applied for distributing evaporation over vast regions using energy and mass balance methods that integrate remote sensing imagery and surface reference data. Whilst informative, previous studies have not
considered the variability of actual evaporation under drought and above normal moisture conditions.
Continuous physically-based simulations were conducted for a 46 year period using the Cold Regions
Hydrological Model (CRHM) platform. The Penman–Monteith model was applied in this platform to calculate estimates of actual evaporation at point locations which had sufficient hourly measurements. Variations in the statistical properties and mapped distributions derived from point-scale modelling via
CRHM were instructional for understanding how evaporation varied spatially and temporally for a baseline normal period (1971–2000) and the years 1999–2005 which included both drought and above normal moisture conditions. The modelling approach was applied successfully for examining the historical
variability of evaporation and can be applied to constrain land surface parameterization schemes; validate more empirical predictive model outputs; inform operational agrometeorological and hydrological
applications in the Canadian Prairies.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
The actual evaporation from land surfaces, which includes
evaporation from soils and vegetation, varies both spatially and
temporally across heterogeneous landscapes. Within the Prairie
region of Western Canada this is due in part to differences in soil
conditions but is largely a result of the regional climate conditions
that range from semi-arid to sub-humid. Consequently, from year
to year the spatial distribution of actual evaporation can vary
widely over this large region. Relative spatiotemporal variations
in evaporation are of particular interest under drought and above
normal moisture conditions for a wide range of hydrological and
meteorological type applications.
⇑ Corresponding author.
E-mail address: [email protected] (R.N. Armstrong).
Permanent address: 330 Campion Crescent, Saskatoon, Saskatchewan S7H 3T9,
Canada.
1
http://dx.doi.org/10.1016/j.jhydrol.2014.11.070
0022-1694/Ó 2014 Elsevier B.V. All rights reserved.
A general issue is that a detailed network of meteorological station observations of daily forcing data such as solar and/or net
radiation, temperature, humidity and wind speed is often lacking
to drive physically-based modelling. As a result estimates of evaporation over extensive areas are often derived via empirical
schemes or indirectly via water and energy budget calculations.
The latter commonly involves the application of complex numerical methods that integrate land surface schemes and climate modelling techniques. In general, water budgets are calculated over
entire watersheds and require reliable accounting of precipitation,
infiltration, evaporation, changes in storage and runoff at a range of
appropriate scales (e.g. Wang et al., 2014a,b).
Energy budget methods integrate remote sensing techniques
that provide measured surface variables over large areas to derive
input data (Courault et al., 2005; Gowda et al., 2008). Remote sensing type approaches commonly use moderate to large scale gridded data to compute simplified energy budgets (e.g. Jackson
et al., 1977; Seguin et al., 1989; Bussières et al., 1997; Allen
et al., 2007; Long et al., 2014). Process-based modelling approaches
R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
have also been developed to integrate remote sensing, for example,
with application to Canada’s landmass for a single year (e.g. Liu
et al., 2003). Difficulties for remote sensing methods continue to
exist, particularly for methods relying on optical and thermal satellite-based sensors, due to the inherent requirement for clear sky
conditions (Long et al., 2014).
Some evaporation modelling methods are purely empirical and
use vegetation properties to index evaporation (e.g. Nagler et al.,
2005). A review by Colaizzi et al. (2006) highlighted the application
of scaling factors to distribute estimates from one-time-of-day
measurements. In contrast, more complex methods have been
developed from, and validated by, intensive field studies. For
example, Mu et al. (2007, 2011) and Fisher et al. (2008) derived
global scale estimates from AVHRR and MODIS imagery and near
surface reference data which were validated against field measurements from the extensive global Fluxnet network.
A general limitation of remote sensing approaches is that evaporation is often estimated indirectly as a residual term of the
energy balance or is distributed on an empirical basis. Due to
uncertainties in key inputs the error associated with residual estimates may be quite large. A more critical problem is these methods
lack an adequate description of the physical process which makes
it impossible to directly improve our understanding of the spatial
or temporal variability of evaporation. In contrast, predictive modelling such as done via land surface schemes is physically-based
but requires complex algorithms to solve the energy and water balances. Specifically, numerical models diagnose the sensible and
latent heat fluxes which are required to parameterize the lower
boundary condition for coupled climate modelling.
Remote sensing information and forecasted reanalysis outputs
have been used to force model simulations (e.g. Szeto, 2007;
Szeto et al., 2008). Fernandes et al. (2007) applied the land surface
scheme, EALCO (Ecological Assimilation of Land and Climate
Observations) to generate a Canada wide examination of evaporation trends at climate stations with available records. More
recently, Wang et al. (2013) examined monthly and seasonal averages of evaporation obtained from EALCO using assimilated gridded land surface information for a Canada wide 30 year
simulation (1979–2008).
In general, these types of land and climate assimilation studies
are informative and provide methodologies for distributing estimates of evaporation across Canada and examining historical
trends. A limitation of forecasting approaches is that the models
are relatively complex to parameterize and apply for less experienced users. More importantly, continuity equations for conserving energy and mass must always be solved. So predictions are
seldom constrained by available measurements. Rather these
observations are generally used to verify the accuracy of outputs
and correct notable biases.
Whilst informative, previous large scale studies have not considered how the distribution of actual evaporation might vary during periods of drought and above normal moisture conditions.
Spatial and temporal variability in meteorological and surface state
conditions, particularly in the Canadian Prairies, is an important
concern for a variety of hydrological and meteorological research,
operational and predictive applications. For example, knowledge
of changes in the spatiotemporal distribution of evaporation can
be used for constraining and verifying land surface parameterizations under different surface state conditions; for improving coupled modelling of surface–atmosphere fluxes. This is crucial for
improving regional climate modelling and weather forecasting.
This information is also important for agrometeorological and
hydrological operational applications such as determining crop
water demand, flood forecasting and irrigation scheduling.
Without detailed sets of point-scale evaporation measurements
(e.g. via eddy covariance) it is difficult to generate spatial and
183
temporal distributions of evaporation with absolute certainty.
Calculating physically-based estimates of actual evaporation
directly (i.e. not as a residual or based on vegetation indexes) can
help to reduce the uncertainty. Models designed for this purpose
include stand-alone point-scale equations which are often
integrated with processed-based hydrological simulations. However,
input requirements for existing methods can vary widely
depending on theoretical considerations and model complexity.
In the present study a physically-based hydrological model was
assembled in the Cold Regions Hydrological Model (CRHM) platform. The classical form of the Penman–Monteith (Monteith,
1965) equation was applied to simulate long term estimates of
actual evaporation during the snow free period at selected point
locations in the Canadian Prairies. The model was driven by meteorological records covering a 46 year period from Jan 1960 to Dec
2005. The variability of seasonal estimates of evaporation was
examined for a baseline normal period for the years 1971–2000.
Daily estimates of evaporation were also examined for a highly
variable period characterized by drought and above average wet
conditions which occurred in the region during 1999–2005.
A key objective of the analysis was to compare the properties of
statistical and mapped distributions to better understand the variability of evaporation during a baseline normal period and a period characterized by drought and above normal moisture
conditions; which is not well known for the Canadian Prairies.
The distribution of annual growing season evaporation for the
baseline normal period was also used as a reference for calculating
exceedance fraction maps based on the individual years from 1999
to 2005. The general spatial and temporal variability of evaporation was further quantified by computing the coefficient of variation among the point locations across the Prairie region.
2. Study region
The spatial extent for this study was limited to the Prairie ecozone as shown in Marshall et al. (1996). A map of the region and 15
station locations with suitable data sets for the modelling is provided in Fig. 1. The Prairie ecozone extends across the southern portions of Alberta, Saskatchewan and Manitoba, and into the United
States. The Canadian portion covers an area of approximately
435,000 km2 and more than half of this area is characterized as a
semi-arid region known historically as the Palliser Triangle. An idealized conceptual boundary of the Palliser region spanning portions
of southern Alberta and Saskatchewan, Canada is shown in Fig. 1.
2.1. General climate of the ‘Palliser Triangle’ region of Canada
In 1863, Captain John Palliser described a portion of the Prairie
region of Western Canada which seemed too arid for agriculture
purposes and potentially for settlement (Spry, 1959). Since that
report, conceptual boundaries of the ‘Palliser Triangle’ have been
delineated over time. Subsequently, archived station records confirm that this general area has historically been the driest region
of western Canada, but has also been interspersed with periods
of above normal moisture conditions. Cycles of major droughts
have occurred during the 1930s, 1960s and 1980s (Khandekar,
2004); and more recently during a period from 1999 to 2005,
which also included episodic, rapid shifts to well above normal
moisture conditions.
The conceptual boundary of Palliser’s semi-arid zone generally
fluctuates depending on the prevailing climate conditions. Historically, there has been notable differences in climate conditions
observed within the Palliser region compared to the surrounding
sub-humid region. Fig. 2 shows maps for the 1971–2000 normal
climate conditions for the period May 1–Sept 30. These maps were
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Fig. 1. Map of Western Canada showing outlines of the Prairie ecozone (outer black line) and Palliser Triangle region (inner black line) and locations of selected Environment
Canada stations.
Fig. 2. Maps showing the 1971–2000 normal climate conditions for the growing season (May 1–September 30) and station locations.
generated based on a spline interpolation applied to archived rainfall, air temperature, relative humidity (RH) and wind speed data
for 15 Environment Canada stations. The regional differences are
highlighted by the general trends in these key climate variables.
The maps show higher rainfall (mm) and RH (%) towards the
northwestern and eastern edges of the Prairie ecozone. Historically,
rainfall and RH have been the lowest in the southwestern portion of
the Palliser Triangle in the area of Medicine Hat and Lethbridge,
R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Alberta (see Fig. 1). As expected, air temperatures are observed to
decline with increasing latitude and elevations along the edge of
the western cordillera. Generally, wind speed increases from the
northwest corner of the Prairie ecozone toward the southeast portion of the Palliser Triangle region. On average higher wind speeds
are observed across a broad region that is bounded by Swift Current,
Regina and Estevan (see Fig. 1).
3. Methods
3.1. The CRHM Model
The Cold Regions Hydrological Model (CRHM) platform contains
a suite of physically-based hydrological process algorithms which
were developed through extensive field investigations. These processes are fundamental to the hydrological interactions within
northern cold region environments. Pomeroy et al. (2007) have
provided a comprehensive overview of CRHM core meteorological
and hydrological processes and so these will not be discussed in
detail here. Rather the present discussion focuses on the unique
treatment of cold region and snow free period processes which
were applied in CRHM for the long term continuous modelling
185
approach used for this study. The availability of these process modules in CRHM is crucial for tracking changes in surface state conditions known to the cold regions of Western Canada.
For this study, a continuous simulation of the hydrological
interactions for both the cold snow covered and snow free periods
was used. It is important to note that with the exception of field
research, required model inputs such as incoming solar radiation,
net radiation and soil moisture are seldom measured or available
as archived records for the majority of Canada. As a result, specific
meteorological and hydrological process modules were assembled
to estimate the components of the surface energy and mass balances. A flowchart is provided in Fig. 3 showing links between
the respective modules assembled in CRHM.
In CRHM, spatial arrangements of biophysical landscape elements in a basin are treated as individual hydrological response
units (HRU). Each HRU has a specific set of parameters (e.g. area,
slope, aspect, elevation, albedo, land cover type, etc.) and has a
specified flow connection within the network, if a connection
exists. Energy and mass balances are driven by the available forcing
meteorology and applied to each HRU independently. The transfer
of water and energy is applied at discrete time steps. For the present
study a daily time step was used.
Fig. 3. Flowchart of CRHM hydrological modules assembled for modelling evaporation at climate stations across the Prairie region.
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Energy balance calculations included the seasonal estimation of
incoming solar radiation, atmospheric transmissivity, surface net
shortwave and net longwave energy components needed to obtain
the net radiation for driving the evaporation process. For the winter/spring period the spring melt energy was estimated as a function
of the snow pack properties and meteorological state conditions.
Mass balance calculations for the winter snow covered period
included blowing snow transport and snow sublimation, as well as
infiltration into frozen soils. For the snow free period the mass balance included soil moisture accounting and estimation of the actual
evaporation from the surface cover using the Penman–Montieth
equation.
Meteorological inputs required for CRHM include solar or net
radiation, air temperature, humidity, wind speed and precipitation.
CRHM also provides empirical relationships and standard modelling techniques to compute estimates of meteorological forcing
data when observations are not available. Specifically, these techniques are applicable to the calculation of energy balance components based on the current meteorological conditions, time of year,
latitude, surface elevation, and associated astronomical earth–sun
calculations.
3.2. Virtual basin hydrologic response units
For CRHM modelling purposes each station location was treated
conceptually as a virtual basin consisting of three HRUs (Fig. 4). This
type of approach was applied for simplicity and to standardize the
modelling to focus on the general spatiotemporal variability in
evaporation across the region. The HRU selection and parameterization was based on field observations collected during a study in
2006 and 2007 at the St. Denis National Wildlife Area, located in central Saskatchewan; which included eddy covariance measurements.
The first HRU was treated as a source of snow redistribution
which is an important factor in the redistribution of water in the
Prairies due to wind transport by blowing snow. This HRU was covered by a standard cereal crop that alternated between crop/stubble (fallow). In the winter period snow was captured when stubble
was present and could be transported to other HRUs when the
snowpack exceeded the stubble height, or in fallow years. The second HRU was treated as a perennial mixed alfalfa–grass type surface (primarily consisting of alfalfa) and is the primary focus of
the evaporation analysis. It was assumed that no lateral transport
of surface moisture occurred between the crop and alfalfa–grass
HRUs. Any runoff generated from these HRUs was routed directly
to a third HRU, treated as a grass/coulee, which served as the
virtual basin outlet.
Fig. 4. Diagram of conceptualized virtual basin with 3 HRUs.
All HRUs were assumed to be relatively flat in order to eliminate
variations in slope and aspect which could exert a strong influence
on the net radiation balance and seasonal estimates of evaporation.
Constant rooting depths were set for the HRUs based on historical
field research that included detailed soil excavations (Weaver,
1926, 1968). A rooting depth of 1.5 m was assumed for the cropped
HRU which is typical of fibrous root systems for domestic wheat or
barely cereal crops. A depth of 3 m was assumed for the Alfalfa –
grass mix which included tall perennial grass species. This depth
was considered reasonable as the alfalfa tap root has been
observed to extend to several metres or more into the soil even
under drought conditions.
3.3. Data sources and vegetation tracking
3.3.1. Archived meteorological data
Environment Canada meteorological stations within the Prairie
ecozone were selected based on the availability of long term hourly
data of air temperature, relative humidity, wind speed, and daily
observations of snowfall and rainfall. A total of 15 stations with
the most complete continuous hourly records over a period from
1960 to 2005 were selected and data gaps in the records were filled
using data from nearby stations. Where more than one suitable station was available data gaps were filled using the average values.
3.3.2. Soil types
Soil infiltration capacities and water holding capacities can be
highly variable across large regions depending on differences in
soil textures. These variations influence the amount of soil water
that may be accessible by growing vegetation. For simplicity, a
bulk soil type was determined for each location based on an analysis of the SLC v3.1.1 (Soil Landscapes of Canada Working Group,
2007). The database consists of compiled soil survey maps at a
scale of 1:1 million.
The SLC database contains information for 3–5 soil layers and
two or more soil components which describe the stoniness, slope
gradient, and percent occupied by each component typically down
to a profile depth of 1 m. The soil layer information identified the
specific properties of the soil such as the type of soil, horizon name,
percentages of sand, silt and clay, and bulk density etc which was
relevant for parameterizing the soils at the various locations. The
bulk soil type was determined at each location using a standard
soil texture triangle based on the computed weighted average of
sand, silt, and clay percentages for soil layers within the soil profile.
Representative parameters (e.g. porosity, hydraulic conductivity,
and threshold ratios for moisture limited evaporation rates, etc.)
were set from CRHM look-up tables based on the specified bulk soil
type (e.g. loam, clay, clay-loam, etc.).
3.3.3. Tracking vegetation growth
Changes in vegetation height and leaf area were tracked over
the growing season. Continuous vegetation height inputs for an
ideal crop and tall grass were estimated using a linear regression
between observed heights collected during the field study at the
St. Denis National Wildlife Area (SDNWA) in 2006 which included
crops and the mixed field of alfalfa and other tall grasses which
reached heights of 1 m or more. Leaf area was assumed to vary
between the minimum and maximum values as a linear function
of relative changes in vegetation height.
For the cropped HRU cultivation and fallow periods were
assumed to alternate annually. In cropped years, vegetation
growth was assumed to start in early June and maturity was
reached in mid-September. This is a typical life cycle for a cereal
crop from emergence to harvest; i.e. 90–110 days. A post-harvest
stubble height of 20 cm was set for capturing blowing snow. For
the alfalfa–grass HRU new growth was started in early May and
R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
was shut down at the end of September. A post-harvest stubble
height of 20 cm was set for blowing snow capture and new growth
was initiated the next spring.
3.4. Estimating key evaporation factors: Energy and water availability
Energy and water availability are two key factors required for
estimating actual evaporation. For this study the general form of
the Penman–Monteith (Monteith, 1965) equation was applied to
calculate daily estimates of evaporation during the snow free
growing season period. This method combines the simplified equations for the energy balance and water vapour transfer, and also
requires estimates of the aerodynamic and canopy resistances that
influence evaporation rates. Application of this method and modelling assumptions for field studies in the Prairie region of Canada
have been described previously by Armstrong et al. (2008, 2010).
Under conditions when soil water is in abundant supply the
evaporation rate can be computed without imposing soil moisture
limitations (i.e. the continuity equation). In this case the evaporation process is mainly driven by the energy available for converting
water to vapour, and is enhanced or limited by variations in temperature, humidity and wind speed. Across the Prairie ecozone soil
water availability is often highly variable on a seasonal basis and is
typically a key limitation during the summer period. Under these
conditions continuity (i.e. conservation of mass) must be enforced
for estimating the moisture limited evaporation rate.
In combination methods the evaporation process is mainly driven by the available surface energy as a function of the net radiation
balance; the sum of net shortwave and longwave radiation. Meteorological inputs required for this calculation include incoming solar
radiation, air temperature, vapour pressure and sunshine hours. A
principal component of the net radiation balance is incoming solar
radiation. This is computed as a function of the solar radiation to
the top of the atmosphere, and estimates of atmospheric transmittance derived from the daily range of air temperatures and the altitude (Annandale et al., 2001; Shook and Pomeroy, 2011; ).
When soil water is in limited supply conservation of mass must
be enforced and the actual evaporation rate is restricted as a function of soil moisture limits and soil texture properties. The simulations performed for this study applied functions based on
developments by Zahner (1967) and modifications by Leavesley
et al. (1983). This method requires tracking of the soil wetness
ratio, which is the ratio of current soil moisture, h, to the maximum
water holding capacity of the soil, hmax.
The actual evaporation rate is defined here as E and the soil
moisture limited rate as EL. When water is not a limitation the soil
moisture tension is low and soil water is depleted without restrictions at the actual evaporation rate E. For example, for the case of a
clay-loam soil the evaporation rate is allowed to continue unrestricted while h/hmax > 0.67, and
EL ¼ E:
ð1Þ
Under drying conditions, while 0.67 > h/hmax > 0.33 the soil
moisture tension increases and soil water depletion is reduced as
a function of the available soil moisture, where
EL ¼
h
E:
hmax
ð2Þ
Soil water availability is considered to be severely limited when
h/hmax < 0.33, (e.g. very dry to drought conditions). In this case soil
moisture tension increases more rapidly and soil water depletion is
severely restricted. In this case the evaporation rate is reduced to
EL ¼ 0:5
h
E:
hmax
ð3Þ
187
Variations in the soil moisture balance and soil texture properties were used to parameterize the Penman–Monteith canopy
resistance term required for the water vapour transfer equation.
3.5. Modelling period, initial conditions and analysis methods
The availability of long term meteorological forcing data
allowed for continuous hydrologic simulations to be run for a period of 46 years from 1960 to 2005. This allowed the hydrological
mass balance to stabilize over a long period. In other words the initial starting conditions were not a critical factor beyond the first
few years of the simulation. The distributions of actual estimates
of growing season evaporation were of general interest for two
key periods across the Prairie region. This included a normal period
(1971–2000) that served as a baseline reference for relative comparisons against individual years from 1999 to 2005. This period
was characterized by a mix of drought and above normal moisture
conditions across the region. Results for the individual years spanning 1999–2005 were used to assess the spatial and temporal variability of the seasonal evaporation totals.
Statistical and graphical analysis was done using the ‘R’ software environment (R Core Team, 2013). Results were summarized
for point locations via boxplots and cumulative probability distributions. Boxplots were used to describe the data graphically based
on seven statistical measures. The 1st and 3rd quartiles (interquartile range) are indicated by the lower and upper limits of the box
frame; also equivalent to the 25th and 75th percentiles. Within
the box frame, a line and point show the location of the median
and mean values respectively. Whiskers extending from the frame
indicate the minimum and maximum values within 1.5 times the
interquartile range. Points falling outside the whisker limits are
considered outliers relative to the majority of data values.
4. Results and discussion
4.1. Comparison of modelled evaporation estimates with
measurements and integrated remote sensing assimilation methods in
the Prairies
This section briefly compares the realism of CRHM Penman–
Monteith model estimates of evaporation with available measurements and remote sensing techniques that have been partially verified using flux measurements. A key limitation is that reporting on
long term measurements (e.g. eddy covariance) or remote sensing
techniques for crop type surfaces is very limited for the Canadian
Prairie region. Measurements have been mostly restricted to intensive field studies and boreal forest locations have received the
greatest attention in this regard. It should be noted that eddy
covariance may be of limited use during wet periods, and in the
case of continuous remote sensing (e.g. satellites), key surface variables are obtained using optical (e.g. albedo) and thermal (i.e. LST)
sensors which is limited to clear-sky conditions. New approaches
to obtaining surface fluxes have seen the development of complex
methods that integrate land surface modelling and remote sensing
data assimilation which have been applied more recently to examine historical trends across Canada (e.g. Wang et al., 2013).
In the current study an alfalfa–grass HRU, the main focus of the
evaporation analysis, was treated as a simple and standardized
cover within each virtual basin for the 15 Prairie locations. CRHM
modelled total growing season estimates of evaporation among
the 15 locations ranged from approximately 150–200 mm (under
drought) to over 400 mm under above normal moisture conditions
in 2005. Much lower estimates were obtained in the semi-arid
region (e.g. Lethbridge and Medicine Hat) due to the higher variability in climate and soil moisture conditions which included
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drought. Higher estimates were obtained under above normal
moisture conditions in the semi-arid region and within the subhumid zone. Modelled peak daily estimates were found to be in
the order of 2.8 mm/day under drought to between 4.5 mm/day
and over 5 mm/day under more favourable conditions (e.g. both
at the Lethbridge location).
The CRHM-derived point estimates compare well with the magnitudes of eddy covariance measurements and estimates derived
from integrated remote sensing methods. This includes measurements collected for the Lethbridge Ameriflux temperate grassland
during 1998–2006. Under drought conditions (2000 and 2001) the
CRHM total growing season evaporation was between approximately 150–200 mm at Lethbridge, Alberta. For the same years,
Wever et al. (2002) and Zha et al. (2010) reported measured (eddy
covariance) totals for various growing season periods in the order
of 175–220 mm. A similar agreement was found for evaporation
derived during above normal moisture in 2005 between CRHM
derived evaporation (e.g. see Fig. 17) and the over 400 mm measured at Lethbridge (Zha et al., 2010). For the same site Wever
et al. (2002) indicated peak estimates were in the order of
4.5 mm/day in 1998 prior to the onset of drought and only
3 mm/day under drought in 2000 which are very similar to the
CRHM estimates stated above (and to be discussed further).
Further, an intensive field study was conducted during the summer in central Saskatchewan in 1991 around the time of the peak
evaporation period for the region. During this study, Bussières et al.
(1997) obtained an average of 5 mm/day based on GOES-7 satellite
observations. Granger and Bussières (2005) presented regional
maps for remote sensing estimates derived on July 14 from NOAA
AVHRR observations. For the land surface portions of the map the
estimates largely ranged from between 3 mm and 6 mm. More
recent, integrated EALCO modelling studies produced estimates
of long term average annual evaporation aggregated for the Prairie
ecozone in the order of >300 mm (Fernandes et al., 2007; Wang
et al., 2013).
4.2. Interannual variation of growing season total evaporation for the
1971–2000 normal period
Analysis for the normal period from 1971 to 2000 focused on
the estimates of evaporation obtained from the mixed alfalfa–grass
HRU for the growing season period from May 1 to Sept 30. This
period was used to develop baseline estimates of growing season
evaporation for comparisons against estimates obtained for the
period of drought and above normal moisture conditions. Figs. 5
and 6 show the interannual variability of growing season total
evaporation for stations located in the sub-humid and semi-arid
zones respectively. Striking differences are apparent based on a
comparison of the distributions for the two climatic zones. The
range of seasonal estimates for stations located in the sub-humid
region (Fig. 5) was much narrower than for locations within the
semi-arid Palliser region (Fig. 6). Estimates were generally higher
in the sub-humid zone ranging from 340 mm to 410 mm with
median and mean values ranging between 360 mm and 390 mm.
In contrast seasonal estimates for nearly all stations located in
the semi-arid region were highly variable. The North Battleford
location which is considered to be near the edge, but within, the
sub-humid zone, exhibited similar variability to stations in the
semi-arid region. For these cases seasonal estimates were broadly
distributed and the median and mean values ranged from only
280 mm to 350 mm. For the case of Estevan which is located within
the Palliser region, over 60% of the seasonal values were distributed
within a very narrow range which was more characteristic of the
sub-humid locations.
Variations in the shape and location of the distributions of
actual evaporation can reasonably be expected given the variability
in climate and soils across the region. However, it is very interesting to note the characteristic differences among distributions for
locations within the sub-humid zone compared to those within
the semi-arid Palliser region. In general, shifts among distributions
across the range of estimates are mainly attributed to regional
variations in precipitation. Seasonal variations in evaporation can
also be partly attributed to differences in soil properties that control water storage capacities, infiltration, and runoff generation.
The daily depletion of soil moisture progressively becomes more
restricted during periods of drying and less restricted following
infiltration events. The magnitude of these variations will depend
on the hydraulic properties of different soil classes. For most locations within the sub-humid zone (except North Battleford) the distributions were observed to be relatively uniform and clustered
around higher seasonal totals. This suggests average annual precipitation is sufficient to maintain favourable soil moisture levels at
locations influenced by the sub-humid prairie climate.
As a result, seasonal totals of actual evaporation were less likely
to be restricted by soil moisture limitations over the growing season. In contrast there was much larger variability amongst distributions for the semi-arid locations (except Estevan). These
seasonal totals were scattered across a large range because of the
Interannual Variability of Evaporation
0.6
0.8
Brandon
Calgary
Edmonton
Red Deer
Winnipeg
Yorkton
0.2
0.4
Cumulative Fraction
350
300
250
0.0
150
200
Evaporation (mm)
400
450
1.0
Interannual Variability of Evaporation
n
n
er
ry
on
eg
ndo Calga
ont ed De
nip Yorkto
Win
R
Edm
Bra
Location
150
200
250
300
350
400
450
Evaporation (mm)
Fig. 5. Boxplots and cumulative distributions for the interannual variability of growing season evaporation among stations in the sub-humid zone.
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Interannual Variability of Evaporation
0.6
0.8
Estevan
Lethbridge
Medicine Hat
North Battleford
Regina
Saskatoon
Swift Current
0.2
0.4
Cumulative Fraction
350
300
250
0.0
150
200
Evaporation (mm)
400
450
1.0
Interannual Variability of Evaporation
n
n idge
a
rd
at
nt
r
eva
eH
lefo Regin skatoo Curre
Est Lethb edicin h Batt
a
ft
i
S
w
t
S
M Nor
150
200
250
300
350
400
450
Evaporation (mm)
Location
Fig. 6. Boxplots and cumulative distributions for the interannual variability of growing season evaporation among stations within the Palliser Triangle and for North
Battleford, SK.
Daily Evaporation at Edmonton
0.8
0.6
0.2
0.4
Cumulative Fraction
6
5
4
3
2
1999
2000
2001
2002*
2003*
2004*
2005
Normal
0
0.0
1
Evaporation (mm/day)
7
8
1.0
Daily Evaporation at Edmonton
’99
’00
’01
’02
’03
’04
’05 ’71−’00
Year
0
1
2
3
4
5
Evaporation (mm/day)
Fig. 7. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at Edmonton.
increased variability of annual precipitation and stronger seasonal
moisture limitations. For these cases relative differences in soil
properties among the locations would have an influence on the soil
moisture limited evaporation rates on a daily basis, and also variations in seasonal evaporation totals.
4.3. Interannual variation of growing season daily evaporation for a
period of drought and above normal moisture conditions
In the following sections, graphical summaries of the interannual variability for growing season daily estimates for the mixed
alfalfa–grass HRU for 1999–2005 are provided for selected stations.
This period was of interest due to the rapid shifts from drought to
above normal moisture conditions across the region. Growing season daily estimates of evaporation for 1971–2000 comprised the
reference distribution which was used for a two-sample K–S test
against the distributions derived for the individual years from
1999 to 2005. Significant differences (Prob. < 0.05) between the
distributions for the normal period and the years 1999–2005 are
denoted in the figures by an asterisk.
4.3.1. Sub-humid zone
Graphical summaries are provided below for several stations
located in the sub-humid zone. These include Edmonton, AB
(Fig. 7), Yorkton, SK (Fig. 8), and Winnipeg, MB (Fig. 9). Under normal to wet conditions, evaporation rates for the alfalfa–grass HRU
peaked at approximately between 4 mm/day and 4.5 mm/day during the growing season. This is consistent with observations
obtained over the mixed grass field (4.4 mm/day) during the
SDNWA field study in 2006, and for other studied over grass fields
as reported by Kelliher et al. (1993), Meyers (2001), Wever et al.
(2002) and Baldocchi et al. (2004).
A comparison of distributions for the sub-humid locations
under dry to wet conditions showed that the interannual variability among daily estimates increased with the transition to rapid
drying and drought conditions. During periods of extended
drought large proportions of the distributions expectedly shift
toward lower values. Under more normal to wet conditions
approximately 60% of the daily evaporation rates were less than
3 mm/day. Under drought conditions between 40% and 60% of
the daily estimates fell below 2 mm/day.
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Daily Evaporation at Yorkton
0.8
0.6
0.2
0.4
Cumulative Fraction
6
5
4
3
2
1999
2000
2001*
2002*
2003*
2004*
2005
Normal
0
0.0
1
Evaporation (mm/day)
7
8
1.0
Daily Evaporation at Yorkton
’99
’00
’01
’02
’03
’04
0
’05 ’71−’00
1
2
3
4
5
Evaporation (mm/day)
Year
Fig. 8. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at Yorkton.
Daily Evaporation at Winnipeg
0.6
1999*
2000
2001*
2002*
2003*
2004*
2005
Normal
0
0.0
1
0.2
0.4
Cumulative Fraction
6
5
4
3
2
Evaporation (mm/day)
0.8
7
8
1.0
Daily Evaporation at Winnipeg
’99
’00
’01
’02
’03
’04
’05 ’71−’00
Year
0
1
2
3
4
5
Evaporation (mm/day)
Fig. 9. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at Winnipeg.
As shown in the figures, only a few estimates out of nearly 4900
values were between 5 mm/day and 8 mm/day. This can be attributed to evaporative losses in the early spring from bare soils under
saturated conditions, when the surface resistance was set to zero
prior to new growth. Adjusting the surface resistance to zero under
certain conditions (e.g. surface saturation) is a conventional
approach. However the literature lacks guidance for increasing
the surface resistance under fully saturated soil conditions which
would effectively reduce the estimates for these rare occurrences.
Further partitioning of the surface heat flux into increased soil
warming would also reduce the energy available for evaporation
to occur. As such, it is unclear if these higher bare soil estimates
are truly representative of the unrestricted evaporation rate in
the spring prior to new growth.
4.3.2. Semi-arid zone
Graphical summaries were generated for several locations having a semi-arid climate, which included Lethbridge, AB (Fig. 10),
North Battleford, SK (Fig. 11), and Saskatoon, SK (Fig. 12). During
the period of drought and above normal moisture conditions from
1999 to 2005 the variability in the daily estimates for these
locations was much greater than for the sub-humid locations.
Under normal to wet conditions evaporation rates during the
growing season also peaked at approximately between 4 mm/day
and 5 mm/day. A few higher estimates attributed to bare soils
and saturated conditions were also observed in this case.
The shapes of seasonal cumulative distributions at these locations were generally different from those for the sub-humid zone.
In these cases the position of the interquartile ranges and median
values shown in the boxplots varied widely as a result of highly
variable moisture and meteorological conditions. Of particular
note were the severe drought conditions at Lethbridge, AB in
2000 and 2001 when peak estimates were in the order of only
2.8 mm/day and 3.5 mm/day respectively. These estimates were
consistent with observations reported for grasses under drought
conditions as reported by Meyers (2001) and Wever et al. (2002)
for an AmeriFlux grass site located at Lethbridge, Alberta.
In general, differences in the positions and shapes of distributions within the semi-arid region can be attributed to dramatic
and rapid shifts between drought and above normal moisture conditions. When conditions were either extremely wet or dry, yearly
distributions of growing season evaporation clustered around
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Daily Evaporation at Lethbridge
0.8
0.6
0.2
0.4
Cumulative Fraction
6
5
4
3
2
1999*
2000*
2001*
2002*
2003*
2004
2005*
Normal
0
0.0
1
Evaporation (mm/day)
7
8
1.0
Daily Evaporation at Lethbridge
’99
’00
’01
’02
’03
’04
0
’05 ’71−’00
1
2
3
4
5
Evaporation (mm/day)
Year
Fig. 10. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at Lethbridge.
Daily Evaporation at North Battleford
0.8
0.6
0.2
0.4
Cumulative Fraction
6
5
4
3
2
1999*
2000
2001*
2002*
2003*
2004
2005*
Normal
0
0.0
1
Evaporation (mm/day)
7
8
1.0
Daily Evaporation at North Battleford
’99
’00
’01
’02
’03
’04
0
’05 ’71−’00
1
2
3
4
5
Evaporation (mm/day)
Year
Fig. 11. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at North Battleford.
Daily Evaporation at Saskatoon
0.8
0.6
0.2
0.4
Cumulative Fraction
6
5
4
3
2
1999*
2000*
2001*
2002*
2003*
2004*
2005*
Normal
0
0.0
1
Evaporation (mm/day)
7
8
1.0
Daily Evaporation at Saskatoon
’99
’00
’01
’02
’03
Year
’04
’05 ’71−’00
0
1
2
3
4
5
Evaporation (mm/day)
Fig. 12. Boxplot and cumulative distributions for the interannual variability of growing season daily evaporation at Saskatoon.
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
1.0
Growing Season Evaporation Among Sites
0.6
0.4
0.2
Cumulative Fraction
0.8
1999
2000
2001*
2002
2003
2004
2005
Normal
0.0
0
Evaporation (mm)
50 100 150 200 250 300 350 400 450
Growing Season Evaporation
’99
’00
’01
’02
’03
’04
’05 ’71−’00
Year
0
50
100 150 200 250 300 350 400 450
Evaporation (mm)
Fig. 13. Boxplot and cumulative distributions for the regional variation of growing season evaporation among the 15 locations.
higher or lower evaporation rates respectively. For individual years,
however, the shape and position of the distributions for each location varied due to the spatial and temporal variations in soil moisture across the region. Distributions across the semi-arid region for
2001 were the general exception due to the areal extent of the
severe drought conditions.
4.4. Regional variation in growing season evaporation among stations
An analysis of variations in growing season estimates of evaporation was also conducted among all 15 locations for the years
from 1999 to 2005. Despite the limited number of locations, results
presented here depict the general variability in actual evaporation
as driven by differences in climate factors and soil moisture conditions. Maps showing the distribution of evaporation and exceedance fractions relative to estimates for the normal period are
useful for highlighting variations in the spatial organization of
drought and above normal moisture conditions over a series of
years.
Boxplots and cumulative distributions of the growing season
total evaporation are provided in Fig. 13. Spatial variations in seasonal evaporation totals among the locations are characterized by
positional differences in the interquartile ranges and median values. For example, the interquartile range of estimates for all locations in the driest year (2001) was in the order of 130 mm;
approximately 260 mm to 390 mm of evaporation. For the wettest
year (2005) the range in estimates was reduced to just 17 mm and
evaporation across the region was between 358 mm and 375 mm.
Understandably, mean and median values of the estimates are
more easily influenced when only a limited number of point locations are available. In some years (e.g. 1999–2002) the lower range
of estimates (and outliers) had a large influence on the mean value.
Fig. 13 showed that there can be large variability among estimates
falling below the median value which is mainly attributed here to
large differences in soil moisture. For the higher range of estimates
water availability is less of a limitation and evaporation from relatively flat surfaces is mainly driven by energy availability; which is
much less variable than soil moisture for this region on a seasonal
basis across similar latitudes.
The variability in estimates of evaporation increased across the
region as the drought progressed from 1999 to 2001. More notably,
two extreme lower seasonal totals of evaporation (outliers) for
2000 and 2001 highlighted the severe drought conditions experienced at the Medicine Hat, AB and Lethbridge, AB locations. A rapid
shift to above normal moisture conditions ensued in 2002 across
the southern portion of the region due to higher spring precipitation. For 2003–2005 a gradual decline in the variability of seasonal
totals across the region was observed with a transition toward a
more uniform moisture state.
The regional variability of evaporation among all locations was
quantified annually via changes in the Coefficient of Variation, CV
(Fig. 14). In general, the yearly changes in CV appeared to be linear.
Interestingly, the magnitude and direction of the changes under
drying and wetting conditions were considerably different. Variations in seasonal estimates among the semi-arid and sub-humid
locations became larger as the drought conditions intensified from
1999 to 2001. As a result, consecutive sharp increases in the CV
were observed for 2000 and 2001. A sharp reduction in the CV
was noted in 2002 followed by a more progressive decline from
2003 through 2005. In the latter case, variability among the point
estimates was greatly reduced as much wetter conditions ensued
across the region.
The observed transition in the CV gives rise to an interesting
question regarding the potential effects of antecedent conditions,
and also the influence of prolonged drought. Based on the results
Fig. 14. Coefficient of variation for seasonal evaporation among the locations from
1999 to 2005.
R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
193
Fig. 15. Growing season evaporation and exceedance fraction maps for 2000 and 2001.
Fig. 16. Growing season evaporation and exceedance fraction maps for 2002 and 2003.
of the CV analysis, the increase or reduction in the variability of the
estimates may depend on whether the surface hydrological conditions (and relative extent of the areas impacted), are likely to be in
a transitional state from drying to wetting or vice versa. For example, where the initial state condition is uniformly wet the CV will
undoubtedly increase under a rapid or progress shift toward a state
of drying.
In contrast, more rapid shifts to wetter conditions and reduced
variability are possible when well above normal precipitation and
deluges occur, effectively offsetting drought or drying conditions. It
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R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
Fig. 17. Growing season evaporation and exceedance fraction maps for 2004 and 2005.
is unclear, however, whether the CV would have increased further
or suddenly declined in the case of more prolonged or extensive
drying within the region. Statistically, changes in the CV each year
can only describe potential initial states from which further drying
or wetting may ensue. Unfortunately, a multitude of spatial distributions can produce similar CV values for the region. In essence,
the results show that detailed estimates of evaporation should be
obtained to capture the potential variability within larger regions.
Variations in the mapped distributions of evaporation for the
years 2000–2005 are provided in Fig. 15 (2000–2001), Fig. 16
(2002–2003) and Fig. 17 (2004–2005). These maps show yearly
changes in the spatial distributions of seasonal evaporation and
exceedance fractions during the progression through the drought
and wet periods. The exceedance maps are useful in that these
show the fraction of seasonal estimates for the 1971–2000 baseline
normal period that are exceeded by seasonal estimates obtained
for the years 2000–2005. When displayed in this manner variations
in seasonal evaporation compared to the baseline normal period
are instructive for understanding the relative magnitudes of
changes during drought and above normal moisture conditions.
5. Summary and conclusions
The spatial and temporal variability of physically-based actual
estimates of evaporation were examined at 15 locations across
the Prairie region. A hydrological model was assembled within
CRHM and run continuously over a 46 year period from Jan 1,
1960 to Dec 31, 2005. The Penman–Monteith combination evaporation model was integrated with physically-based algorithms
describing processes relevant to Canadian Prairie hydrology and
parameterized with archived meteorological station forcing data.
Growing season (May 1 to Sept 30) daily estimates and seasonal
totals of evaporation for a perennial alfalfa–grass type surface were
analysed yearly for a baseline normal period (1971–2000) and for a
period characterized by both drought and above normal moisture
conditions (1999–2005).
The continuous modelling approach used here offers some
advantages over other methods that calculate estimates of evaporation as residuals of energy and water balances, or rely on empirical or simple aridity indexes. More importantly, results showed
how spatially variable and temporally dynamic estimates of actual
evaporation can be as a result of the complex physical interactions
and interdependencies between surface states and atmospheric
conditions. Within the sub-humid zone of the Canadian Prairie
ecozone distributions of growing season evaporation where clustered within a narrow range and were relatively similar in shape.
In contrast, distributions for locations in the semi-arid Palliser Triangle region were generally very broad and highly variable from
year to year predominantly as a result of rapid shifts in the climate
and surface state conditions.
For years when water availability differed among the point
locations, particularly when severe drought conditions ensued in
the semi-arid zone, the variability among point scale estimates
was much larger but rapidly and sharply declined under a period
of enhanced precipitation. Although not explicitly analysed here,
variations in soil properties for locations characterized by semiarid climate within the Palliser region, also likely had an influence
in reducing seasonal totals of evaporation. Based on the observed
behaviour among distributions and changes in CV values, one
might also reasonably expect at some point for there to be reduced
variability across the region under more uniform drought conditions that extends further into the sub-humid region.
Estimates of actual evaporation are typically not considered as
primary descriptors of drought conditions in Canada. Distributed
evaporation and exceedance fraction maps were instructional for
showing yearly variations in seasonal estimates under rapid
changes in drought and above normal moisture conditions across
the Canadian Prairies. Specifically, the pattern of evaporation changed rapidly and dynamically during the period and the exceedance
R.N. Armstrong et al. / Journal of Hydrology 521 (2015) 182–195
maps provided a diagnosis of the severity of drought compared to
what might be expected for a baseline normal period. Such information may find application for parameterizing initial state conditions for operational agrometeorology, for evaluating predictive
land surface models or for verifying the lower boundary conditions
required for coupled surface–atmosphere and forecast modelling.
Acknowledgements
Funding support for this study was provided by the Drought
Research Initiative (Canadian Foundation for Climate and Atmospheric Sciences) and the Canada Research Chairs programme.
The authors are grateful to the anonymous reviewers who provided valuable comments and editorial suggestions which have
enhanced the manuscript for publication.
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