Elsevier Editorial System(tm) for Journal of Hydrology Manuscript Draft
Manuscript Number:
Title: Simulating the impacts of climate change on streamflow and other hydrological variables in the upper North Saskatchewan River basin, Alberta, Canada
Article Type: Special Issue on Hydrology Conference
Keywords: hydrological modeling; climate change; Alberta; Canada; verification analysis; streamflow
Corresponding Author: Dr. Stefan W. Kienzle, Ph.D.
Corresponding Author's Institution: University of Lethbridge
First Author: Stefan W. Kienzle, Ph.D.
Order of Authors: Stefan W. Kienzle, Ph.D.; Michael W Nemeth, MSc; James M Byrne, PhD; Ryan J MacDonald, MSc
Abstract: The ACRU agro-hydrological modeling system provided the framework, containing code to simulate all major hydrological processes, including actual evapotranspiration estimates, to simulate the impacts of climate change in the Cline River watershed, Alberta, Canada, under historical (1961- 1990) and a range of future climate conditions (2010-2039, 2040-2069, and 2070-2099). Whilst uncertainties in the estimation of many hydrological variables were inevitable, verification analyses carried out for the historical baseline period resulted in good to very good simulations of a range of hydrological processes, including daily air temperature, snow water equivalent and streamflow. Five climate change scenarios were selected to cover the range of possible future climate conditions. In order to generate future climate time series, the 30-year baseline time series was perturbed according to predicted changes in air temperature and precipitation. Projected increases in air temperature and precipitation resulted in mean annual increases in potential and actual evapotranspiration, groundwater recharge, soil moisture, and streamflow in the Cline River watershed. Increases in both high and low flow magnitudes and frequencies, and large increases to winter and spring streamflows are predicted for all climate scenarios. Spring runoff and peak streamflows were simulated to occur up to four weeks earlier than in the 1961-1990 baseline period. Predicted changes were simulated to progressively
Cover Letter
Dr. Stefan W. Kienzle Dept. of Geography University of Lethbridge Lethbridge, AB, T1K 3M4 Canada
Lethbridge, Nov. 29, 2010
Dear Editor,
Please consider the submitted manuscript entitled
“Simulating the impacts of climate change on streamflow and other hydrological variables in the upper North Saskatchewan River basin, Alberta, Canada”
for publication in your Special Issue “Hydrology Conference”.
Figures 2 to 6 were published and/or are submitted for publication in the Journals “Hydrological Processes” and “Hydrological Sciences Journal”. I am in the process or acquiring permission to use the Figures for this publication.
I intent and prefer to publish the color Figures in the paper version, however, if the cost of color publication should be prohibitive for me, I would convert some or all Figures into gray scale and re-submit them.
Sincerely,
Stefan W. Kienzle Associate Professor for Hydrology and GIS Manuscript Click here to download Manuscript: Kienzle_etal_JH_2010_Manuscript2.docx Click here to view linked References P a g e | 1
1 Simulating the impacts of climate change on streamflow and
2 other hydrological variables in the upper North Saskatchewan
3 River basin, Alberta, Canada
4
5 Stefan W. Kienzlea*, Michael W. Nemetha, James M. Byrnea, Ryan J. Macdonalda
6 a Department of Geography, University of Lethbridge, Lethbridge, Alberta Water and Environmental
7 Science Building, 4401 University Drive,T1K 3M4, Canada
8 *Corresponding author. E-mail address: [email protected]; Phone: 1.403.380.1875;Department
9 of Geography, University of Lethbridge, Alberta Water and Environmental Science Building, 4401
10 University Drive, Lethbridge, Alberta, T1K 3M4, Canada
11
12 Received; revised; accepted
13
14 Abstract
15 The ACRU agro-hydrological modeling system provided the framework,
16 containing code to simulate all major hydrological processes, including actual
17 evapotranspiration estimates, to simulate the impacts of climate change in the Cline
18 River watershed, Alberta, Canada, under historical (1961-1990) and a range of future
19 climate conditions (2010-2039, 2040-2069, and 2070-2099). Whilst uncertainties in
20 the estimation of many hydrological variables were inevitable, verification analyses
21 carried out for the historical baseline period resulted in good to very good simulations
22 of a range of hydrological processes, including daily air temperature, snow water
23 equivalent and streamflow. Five climate change scenarios were selected to cover the
24 range of possible future climate conditions. In order to generate future climate time
25 series, the 30-year baseline time series was perturbed according to predicted changes P a g e | 2
1 in air temperature and precipitation. Projected increases in air temperature and
2 precipitation resulted in mean annual increases in potential and actual
3 evapotranspiration, groundwater recharge, soil moisture, and streamflow in the Cline
4 River watershed. Increases in both high and low flow magnitudes and frequencies,
5 and large increases to winter and spring streamflows are predicted for all climate
6 scenarios. Spring runoff and peak streamflows were simulated to occur up to four
7 weeks earlier than in the 1961-1990 baseline period. Predicted changes were
8 simulated to progressively increase into the future. A clear shift in the future
9 hydrological regime is predicted, with significantly higher streamflows between
10 October and June, and lower streamflow in July to September.
11
12 Keywords: hydrological modeling; climate change; Alberta; Canada; verification
13 analysis; streamflow
14
15 1. Introduction
16 Mountain watersheds are the key source of water and provide significant water
17 yields to downstream users. The Rocky Mountain watersheds along the eastern slopes
18 of central Alberta, Canada, are predicted to be exposed to increased air temperatures
19 and increased precipitation during the 21st century (IPCC, 2007). In regions which are
20 expected to maintain or exceed historical water yields due to projected increases in
21 precipitation, the hydrological regime is expected to change in terms of timing and
22 magnitudes of seasonal streamflows (Byrne and Kienzle, 2008). In hybrid watersheds
23 both snowmelt and rainfall events occur, and consequently the watershed behavior is
24 dominated by contrasting hydrological processes, and may respond uniquely to
25 changes of the future climate (Loukas and Quick, 1996; Whitfield et al., 2003). Gleick P a g e | 3
1 (1987), Lettenmaier and Gan (1990) and others reported that increasing air
2 temperatures by a few °C could dramatically affect the timing of runoff in mountain
3 watersheds in the western USA, resulting in increased runoff during the cold season,
4 an earlier spring freshet and decreased runoff in the warm season. Recent studies have
5 confirmed that climate change impacts regions where snowpack and glacier melt are
6 significant sources of the annual streamflow (e.g.: Cayan et al., 2001; Jasper et al.,
7 2004; Mote, 2006; Hamlet et al., 2007). Therefore, the understanding of predicted
8 climate change on the hydrological cycle within a watershed is essential for future
9 water resources planning.
10 As snow melt contributes on average (1961-1990) approximately 60% of the
11 mean annual streamflow of the Cline River watershed in western Alberta, Canada, the
12 study site of this research, and glacier melt contributes another 8%, significant
13 changes in the hydrological regime are expected under climate warming. The
14 increased summer runoff from glacier melt can only be short term, because the
15 shrinking glacier area and declining glacier volume will eventually limit the amount
16 of melt water produced, even if climate warming sustains greater melt per unit area
17 (Moore and Demuth, 2001). Watersheds with significant, but rapidly declining,
18 glacier cover have exhibited a strong decreasing trend in glacier melt contributions
19 (Demuth and Pietroniro, 2003) to the extent that many glaciers have already passed
20 the period of maximum glacier melt volumes. Along the Canadian eastern slopes of
21 the Rocky Mountains glacier melt mainly occurs during July, August and September.
22 Therefore, declining glacier melt contributions to streamflow will take place at
23 approximately the same time as streamflow is predicted to decline due to climate
24 warming. P a g e | 4
1 The hydrological response to climate change has been studied through the
2 application of watershed-scale hydrological models driven by GCM-derived scenarios
3 of future climate (e.g. Loukas et al., 2002; Schulze and Perks, 2003; Toth et al., 2006;
4 Nurmohamed et al., 2007; Forbes et al, 2010). Physically-based, spatially distributed
5 hydrological models are an effective means to assess the impacts of climate change on
6 hydrological response, as they are able to capture the spatial variability of
7 hydrological processes throughout complex watersheds (Bathurst et al., 2004). The
8 ACRU agro-hydrological modeling system (Schulze, 1995, updated; Smithers and
9 Schulze, 1995) was applied in this study as it is a physical-conceptual, distributed
10 hydrological modeling system designed to be responsive to changes in land use and
11 climate. One condition of simulating realistic hydrological impacts of climate change
12 is that the model parameters must be carefully validated to ensure that they
13 appropriately represent the hydro-climatological characteristics of the watershed,
14 therefore reducing the uncertainties of future hydrological simulations.
15 The objective of this study was to estimate the impacts of climate change on
16 water yield, streamflow extremes, and the streamflow regimes in the Cline River
17 watershed, a major tributary of the Upper North Saskatchewan River Basin (UNSRB)
18 in Alberta, Canada. This will be achieved by setting up the ACRU model for the
19 baseline period (1961-1990), verifying simulated output against a range of observed
20 hydro-climatological data, and then simulating the hydrological behavior of the
21 watershed for a range of GCM-derived scenarios for the future periods 2010-2039,
22 2040-2069, and 2070-2099.
23
24 2. Study Area P a g e | 5
1 The Cline River watershed is an important watershed, feeding, on average, over
2 40% of the North Saskatchewan River streamflow at Edmonton, Alberta, Canada, and
3 contains Lake Abraham, a major hydro-electric power reservoir (Fig. 1). For the
4 purpose of streamflow verification, the Cline River watershed was divided into two
5 nested sub-watersheds, the upper Watershed 1, and the lower Watershed 2. Although
6 the Cline River watershed was the focus of the impacts analysis, the verification
7 analysis was performed for the approximately 20,500 km2 UNSRB in order to
8 increase the number of hydro-climatological monitoring sites available for
9 verification analyses. The Cline River watershed has an area of 3,856 km2 and
10 consists of alpine, subalpine, and foothills landscapes located on the eastern slopes of
11 Alberta‟s Rocky Mountains. There are no settlements in the watershed, and, other
12 than forestry, no extensive industrial or agricultural activities are present. Dominant
13 land cover includes bare rock (50.6%), coniferous forest (33.0%), and glaciers (6.8%).
14 The watershed ranges in elevation from approximately 1300 m at the outlet gauging
15 station to just under 3,500 m at the continental divide. Based on available PRISM
16 surfaces (Daly, 2008), the mean annual precipitation (1971-2000) for the Cline River
17 watershed is 1002 mm, ranging from 1408 mm in the upper reaches to 510 mm at the
18 outlet. Snow melt contributes approximately 60% to the mean annual streamflow,
19 which is also supplemented by glacial melt from the Columbia Ice Field and the
20 Peyto, Athabasca, and Saskatchewan glaciers (North Saskatchewan Watershed
21 Alliance, 2005). The mean annual glacial melt contribution (1961-1990) to
22 streamflow is approximately 8%, with an annual range of 0.1 to 29%.
23
24 ***** Insert Fig. 1 approximately here *****
25 P a g e | 6
1 3. Methods
2 Impacts of climate change on the hydrological behavior of a watershed can only
3 be simulated successfully if the watershed has been set up in a physically explicit
4 manner, at a meaningful spatial representation, and has been verified against
5 numerous observations to ensure that the key elements of the hydrological cycle are
6 represented realistically (Beven, 1989; Refsgaard, 1997; Loukas et al., 2002). A
7 number of recent upgrades to the ACRU model were made to improve the division of
8 precipitation into snow and rain, the spatial representations of air temperature, and to
9 correct for the sloped area under-estimation in mountain terrain.
10
11 3.1 The ACRU agro-hydrological modeling system
12 The ACRU agro-hydrological modeling system has been developed at the
13 School of Bioresources Engineering and Environmental Hydrology (formerly the
14 Department of Agricultural Engineering) at the University of KwaZulu-Natal,
15 Pietermatitzburg, Republic of South Africa, since the late 1970s (ACRU, 2007).
16 ACRU is a multi-purpose, multi-level, integrated physical-conceptual model that is
17 designed to simulate total evaporation, soil water and reservoir storages, land cover
18 and abstraction impacts, snow water dynamics and streamflow at a daily time step
19 (Fig. 2). The ACRU model revolves around multi-layer soil water budgeting with
20 specific variables governing the atmosphere-plant-soil water interfaces. Surface
21 runoff and infiltration are simulated using a modified SCS equation (Schmidt and
22 Schulze, 1987), where the daily runoff depth is proportional to the antecedent soil
23 moisture content. ACRU often requires extensive GIS pre-processing, such as the
24 delineation of hydrological response units (HRUs), shortwave radiation calculations,
25 and air temperature and precipitation correction factors, as well as to parameterize all P a g e | 7
1 HRUs for soils and land cover dependent variables (Kienzle, 1993; 1996; Schulze et
2 al., 1995; Kienzle, 2010). The ACRU model is further described by Schulze (1995),
3 Smithers and Schulze (1995), Kiker et al. (2006), Nemeth et al. (in review), and
4 Kienzle (2010).
5 The Cline River watershed was divided into hydrological response units
6 (HRUs), which are distributed, relatively homogeneous hydro-climatological units
7 that are assumed to have similar hydrological characteristics and response (Flügel,
8 1995). The HRUs were delineated based on 100 m elevation bands, 11 land cover
9 classes, four mean annual radiation classes, and sub-watershed boundaries. GIS
10 overlay analysis resulted in 308 HRUs, with an average planimetric HRU area of 12.5
11 km2.
12
13 ***** Insert Fig. 2 approximately here *****
14
15 3.2. Recent ACRU updates
16 Recent improvements to ACRU include the estimation of two daily mean air
17 temperatures for each HRU. In the updated version of ACRU, the lapse-rate-adjusted
18 daily air temperatures from the base station are only used for the separation of
19 precipitation into snow and rain, which is based on a curvi-linear relationship between
20 air temperature and the proportion of precipitation falling as snow (Kienzle, 2008).
21 Snowmelt, sublimation, and evapotranspiration are understood to depend on near-
22 ground air temperatures, influenced by the local surface characteristics such as land
23 cover, slope and exposition, time of year, and albedo. In order to enable different
24 daily air temperatures as a function of exposition, i.e., north versus south-facing
25 slopes, or valleys that rarely receive direct incoming radiation, the lapse-rate adjusted P a g e | 8
1 air temperatures are further corrected according to daily incoming radiation and the
2 land cover dependent leaf area index. This method is described in detail by Kienzle
3 (2010) and results in a more realistic spatial distribution of daily air temperature in
4 mountainous terrain than air temperature adjustments dependent on lapse rates only.
5 Two areas were calculated for each HRU: the true, sloped area, and the
6 planimetric area (Fig. 3). The slope area under-estimation factor (SAUEF) determines
7 the ratio by which the sloped area is under-estimated relative to its planimetric area
8 (Kienzle, 2010). Fig. 4 shows the relationship between the slope and the slope area
9 under-estimation. The omission of using two areas for each simulation unit can be
10 significant, as the precipitation depth (dark arrows in Fig. 3) is distributed over a
11 larger area. Consequently, the interception capacity, actual evapotranspiration, and
12 soil storage capacity are increased by the same ratio as the SAUEF (light arrows in
13 Fig. 3), resulting in increased water losses, drier soils, reduced groundwater recharge,
14 and reduced runoff. When the precipitation falls as snow, the snowfall is distributed
15 over the SAUEF-enlarged area, resulting is an equal decrease in soil water equivalent
16 (SWE) per unit area. With similar climatic conditions, the consequence is a shallower
17 snowpack, increased snowmelt from the larger „true‟ area, and an earlier time when
18 all snow from the simulated land unit has melted. Due to the steep slope of the Cline
19 River watershed, the area-underestimation would have been 9.8%, which would have
20 affected interception volumes, soil moisture storages, groundwater recharge rates,
21 actual evapotranspiration volumes, and runoff coefficients (Kienzle, 2010).
22
23 ***** Insert Fig. 3 approximately here *****
24 ***** Insert Fig. 4 approximately here *****
25 P a g e | 9
1 ACRU contains dynamic snow routines, where precipitation is separated into rain and
2 snow based on daily air temperature (Kienzle, 2008), and the snow melt is derived
3 dynamically as a function of net radiation and albedo (Kienzle, 2010). For this study,
4 the authors have added a simple glacier mass balance routine, which contains a
5 dynamic glacier melt function, as well as a routine, where both glacier depth and
6 glacier area change annually as a function of glacier mass balance.
7
8 3.3. ACRU Parameterization
9 All 308 HRUs were parameterized in terms of daily precipitation, daily air
10 temperature, potential evapotranspiration, and hydrologically relevant land cover and
11 soil variables (Nemeth et al., in review). Mean monthly PRISM climate surfaces
12 (Daly, 2008) were used to calculate initial mean monthly precipitation and air
13 temperature correction factors for each HRU in order to account for differences
14 between the climate base station values and the spatially complex terrain of the study
15 area. The estimation of exponential stormflow and baseflow response coefficients for
16 the two sub-watersheds was based on observed hydrograph recession curves from the
17 two observed streamflow time series. The glacier mass balance parameters were based
18 on reported glacier volume estimations (Ommanney, 2002). The glacier melt factors
19 were calibrated to match reported ratios between glacier melt and snow melt factors
20 (Comeau et al., 2009; Shea et al., 2009) and the observed glacier mass balance of
21 Peyto Glacier, situated in the very south-west corner of the Cline River watershed
22 (Fig. 1), which has been extensively monitored since 1965 (Comeau et al., 2009;
23 Demuth and Keller, 2009; Shea et al., 2009). Glacier melt is assumed to occur after
24 the snow pack has completely melted, and is determined on a daily basis as a function
25 of incoming radiation and albedo. P a g e | 10
1
2 3.4. Verification Analyses
3 Simulation results for the 1961-1990 baseline period were compared against
4 observed key elements of the hydrological cycle, including air temperature, snow
5 pack, glacier melt, and streamflow. For both sub-watersheds high coefficients of
6 determination for daily (r2 of 0.81 and 0.82) and monthly (r2 of 0.89 and 0.91)
7 streamflow were achieved. Differences between mean monthly simulated and
8 observed streamflow variances were 9.2% for Watershed 1 and 3.9% for Watershed 2.
9 ACRU simulated the mean annual hydrograph well, especially the timing and
10 magnitude of peak flow and baseflow periods, as well as the shape of the hydrographs
11 (Fig. 5). The annual water yields of the Cline River watershed were replicated for the
12 1961-90 time series with a mean over-estimation of 2.6% for both sub-watersheds.
13 The Nash-Sutcliff coefficients of efficiency for Watersheds 1 and 2 were 0.79 and
14 0.80 for daily and 0.87 and 0.91 for monthly streamflow simulations, indicating a
15 strong association between observed and simulated values (Nemeth et al., in review).
16
17 ***** Insert Fig. 5 approximately here *****
18 ***** Insert Fig. 6 approximately here *****
19
20 Within the larger, 20,500 km2, UNSRB (Fig. 1), additional verification
21 analyses were carried out. Ten independent climate stations at fire observation towers
22 with five to 42 years of daily minimum and maximum air temperature observations
23 (totaling about 37,600 days of observations) were used to verify that daily air
24 temperature values based on the base climate station and adjusted for both monthly
25 varying lapse rates, exposition and land cover were simulated realistically. Air P a g e | 11
1 temperatures were well simulated across the UNSRB, with coefficients of
2 determination for daily air temperatures of 0.88 (n=37,402), and monthly air
3 temperatures of 0.98 (n=499), with an average over-estimation of 0.37°C, and
4 excellent representation of seasonality.
5 Taking into account the difficulties of representative snow measurements
6 within a watershed, snow water equivalent (SWE) was reasonably well simulated
7 when comparing 7,625 daily observations from two snow pillow and 882 observations
8 from 15 snow course time series during the 1980s. The coefficient of determination
9 between simulated and observed daily SWE values was 0.63, with a mean annual
10 under-estimation of 6.1%, and a difference in variance of 4.0%.
11 Based on the verification analyses it was confirmed that ACRU was
12 representing the 1961-90 baseline period realistic in terms of spatial and temporal
13 hydro-meteorological behavior. This provided the fundamental condition required to
14 carry out the estimation of impacts of climate change in the Cline River watershed.
15
16 3.5. Glacier Simulations
17 Fig. 7 presents the three major streamflow components for the mean monthly
18 streamflow (1961-1990). Groundwater recharge occurs from April to July, after which
19 baseflow recedes continuously until March. Stormflow includes snowmelt
20 contributions. Approximately 6.8% of the Cline River watershed is covered by
21 glaciers, which contribute, on average (1961-1990), 9.5% of the mean annual
22 streamflow (Fig. 7), mainly between July and September, with an annual range of 0.1
23 to 29.2%. Mean annual groundwater is simulated to contribute 49.7% to the mean
24 annual streamflow, with the remainder of 40.8% being provided by stormflow. In this
25 mountain watershed, 74.2% of groundwater flow and stormflow are simulated to be P a g e | 12
1 generated by snowmelt. Thus, only 16.3% of the streamflow originates, on average, as
2 runoff from rainfall. The current version of ACRU‟s glacier mass balance model is
3 simple, enabling only the simulation of historical (1961-1990) mean contributions,
4 with approximations of annual variations. The relationships between initial glacier
5 depth and changes in glacier volume and depth during glacier melt are complex, as
6 are the mass balances between the accumulation and ablation areas within the various
7 glaciers in the study area. Whilst the current glacier model is believed to simulate
8 historical glacier melt reasonably well, it is not able to reliably simulate glacier
9 dynamics under future climate regimes. Therefore, only the climate change impacts of
10 all non-glacier contributions to streamflow were simulated and analyzed here.
11
12 ***** Insert Fig. 7 approximately here *****
13
14 3.6. Global Climate Model (GCM) scenario selection
15 A total of 41 climate change scenarios were available from the Pacific Climate
16 Impacts Consortium (PCIC). In order to provide regional estimates of air temperature
17 and precipitation changes, and to reduce the influence imposed by a single grid cell,
18 which can be as large as 400 km by 400 km, the average of the four GCM grid cells
19 closest to the study area were calculated for each scenario. Three future time series
20 were assessed: 2020s (2010-2039), 2050s (2040-2069), and 2080s (2070-2099).
21 Averages of 30-year periods are typically used in scenario construction and result in a
22 better representation of the scenarios than a shorter period (IPCC, 2007). The
23 observed daily climate time series for 1961-90 was used as the baseline period. This
24 period is used instead of the 1971-2000 time period, as it is assumed that it has not yet
25 been significantly impacted by climate change (Diaz-Nieto and Wilby, 2005). P a g e | 13
1 IPCC (2007) recommended using more than one GCM in impact assessments
2 to demonstrate how a range of air temperature and precipitation changes may affect a
3 given region. In order to provide the range of predicted climate change impacts on
4 streamflow, five scenarios were selected using the method developed by Barrow and
5 Yu (2005). These scenarios were chosen from the 41 climate scenarios provided by
6 PCIC (Table 1). Following Barrow and Yu (2005), the climate scenario selection was
7 based on predicted mean air temperature and percent precipitation changes for the
8 2050 spring (MAM) time period. The 2050 time period provides a farther view into
9 the future than the 2020 time period, but is associated with less uncertainty than the
10 results for the 2080s. This method has also been recently applied for climate impact
11 studies by Forbes et al. (2010) and MacDonald et al. (2010).
12
13 ***** Insert Table 1 approximately here *****
14
15 Forecast GCM changes in air temperature and precipitation were used to
16 create a graph of 41 climate scenarios for the spring 2050 time period (Fig. 8). The
17 selection of GCM scenarios for the sensitivity analysis is based on the creation of four
18 quadrants separated by the median air temperature change (here: +1.75°C) and the
19 median precipitation changes (here: +12.0%). Five scenarios were selected based on
20 their projection of the range of possible future climates: one from each quadrant (A,
21 B, C, D) and the median (M) (Fig. 8, Table 2). The selected five GCMs were using
22 three different emissions scenarios, A2, A1B, and B1. The A2 emissions scenario
23 represents moderate economic growth and rapid population increases, while the A1B
24 emissions scenario represents a world with rapid economic growth, a mix of
25 technological development, and continued fossil fuel use. The B1 emissions scenario P a g e | 14
1 is the introduction of clean technology and very small population growth
2 (Nakicenovic et al., 2000).
3 As is evident in Fig. 8, the estimated changes in air temperature and
4 precipitation vary greatly between various GCM scenarios. All model outputs predict
5 an increase in air temperature and precipitation. Projected air temperature changes
6 range in magnitude from 0.5°C to 4.1°C, relative to historical conditions. Projected
7 changes in precipitation reveal a consistent increase in precipitation relative to
8 historical conditions, ranging from 2% to 27%.
9
10 ***** Insert Fig. 8 approximately here *****
11
12 3.7. Regional downscaling
13 GCMs are assumed to accurately represent climate at a global scale, but are
14 inaccurate when simulating regional climate (Evans and Schreider, 2002). Therefore,
15 GCM output needs to be downscaled to local or regional climate to evaluate climate
16 change impacts at a watershed scale. To construct regional climate change scenarios
17 based on GCM output, a widely used procedure, the „delta‟ method, was applied (Hay
18 et al., 2000; Arnell, 2004). The delta method is commonly used for the assessment of
19 climate change impacts, and has been used to downscale CGM output in diverse
20 geographical regions (e.g. Wilby et al., 1999; Merritt et al., 2006; Markoff and Cullen,
21 2007; Nogués-Bravo et al., 2007; Lui et al., 2009; MacDonald et al., (2010); Forbes et
22 al., 2010). The „delta‟ method of downscaling uses projected monthly changes in air
23 temperature and precipitation based on results from each selected scenario to perturb
24 the historical (1961-1990) daily climate record. P a g e | 15
1 New 30-year time series representing changes in air temperature and
2 precipitation predicted by the selected GCMs for the 2020s, 2050s and 2080s were
3 used as input to the ACRU model to perturb observed daily air temperature values at
4 the driver station for the 1961-90 period (Table 2). The driver station is the climate
5 station located near the outlet of the Cline River watershed (Fig. 1) and is used to
6 „drive‟ the daily climatological conditions across the entire watershed. To derive daily
7 minimum and maximum air temperature changes, a Fourier transform (Epstein and
8 Ramirez 1991; Morrison et al. 2002) was applied to the monthly data. In order to
9 derive a future air temperature time series for each HRU, the perturbed air
10 temperature time series were adjusted using the monthly changing lapse rates, after
11 which the air temperatures were further corrected according to the HRU‟s radiation
12 input and land cover according to the method described by Kienzle (2010). Percent
13 changes in precipitation amounts were also calculated relative to the 1961-90 period.
14 Daily precipitation values from 1961-90 were adjusted by the percentage change for
15 each of the future periods. Table 2 lists the predicted monthly changes for each of the
16 scenarios relative to the historical base period.
17
18 ***** Insert Table 2 approximately here *****
19
20 4. RESULTS
21 4.1. Water Balance
22 Simulated mean annual hydrological changes of the entire Cline River
23 watershed were analyzed for the five climate scenario outputs for each of the three
24 future time periods. A summary of mean watershed percent changes in potential
25 evapotranspiration (PET), actual evapotranspiration (AET), soil moisture, ground P a g e | 16
1 water (GW) recharge and SWE relative to the baseline period is presented in Table 3.
2 All simulations indicate a progressively increasing change of all variables with time.
3 With the exception of the “A” scenario in the 2020 period, all simulations result in a
4 large increase in mean annual PET, with an average of all scenarios ranging from
5 11.5% in the 2020s to 27% in the 2080s, and a modest increase in AET, with an
6 average of 2.5% in the 2020s to 8.5% in the 2080s. Similarly, with the exception of
7 the “D” scenario in the 2020 period, all scenarios result in continuous increases in soil
8 moisture and groundwater recharge. While soil moisture is simulated to have a very
9 small increase, ranging from an average of all scenarios of 0.5% in the 2020s to 2.6%
10 in the 2080s, groundwater recharge is simulated to increase modestly, from an
11 average of all scenarios of 1.6% in the 2020s to 8.0% in the 2080s. The seasonal
12 distribution of baseflow is simulated to shift towards increased flows from December
13 to March and a progressively earlier groundwater recharge, leading to an earlier peak
14 in baseflow by as much as a month by the 2080s, resulting in an earlier baseflow
15 recession and subsequently much lower baseflow between July and October relative
16 to the baseline period (Fig. 9). All scenarios result in very severe decreases in SWE,
17 from an average decrease of 46.7% in the 2020s to 65.7% in the 2080s. With the
18 exception of Scenario B, the peak was simulated to still occur in April in all future
19 scenarios (Fig. 10), with a severe decline in SWE throughout the year. There is an
20 almost complete loss of SWE during August to October for Scenarios B and D, and to
21 a lesser extent Scenarios A and C, which means that even in the highest elevations
22 most of snow is simulated to melt out.
23
24 ***** Insert Fig. 9 approximately here *****
25 ***** Insert Fig. 10 approximately here ***** P a g e | 17
1 ***** Insert Table 3 approximately here *****
2 ***** Insert Fig. 11 approximately here *****
3
4 4.2. Water Yield
5 Mean annual water yields are simulated to increase during the three future
6 time periods (Table 4). The mean annual increase of the five scenarios is 1.1% for the
7 2020s, ranging from -2.0% with the “D” scenario to 3.7% with the “C” scenario. The
8 mean annual water yield is predicted to increase in the 2050s by 5.6%, varying from
9 2.3% with the “B” scenario to 9.0% with the “M” scenario. Respectively, the mean
10 increases in mean annual water yield for the 2080s are simulated to be 11.5%, with
11 the lowest increase of 7.5% with the “C” scenario, and the largest increase of 17.8%
12 with the “M” scenario.
13
14 ***** Insert Table 4 approximately here *****
15
16 Seasonal changes in future streamflow are listed in Table 5. With the
17 exception of simulated summer and fall streamflows in Scenario M in the 2050 and
18 2080s, all climate change scenarios result in an increase in streamflow in winter and
19 spring, and a decrease in summer and fall.
20
21 ***** Insert Table 5 approximately here *****
22
23 The mean weekly absolute and percent streamflow changes relative to the
24 observed baseline period (1961-1990) provide evidence that all five climate scenarios
25 result in a similar change in future streamflow patterns (Fig. 11). Peak streamflows P a g e | 18
1 are simulated to increase and occur earlier in all 5 scenarios and all three time periods,
2 relative to the baseline period. Also, starting approximately in Week 40 (early
3 October), and lasting until approximately Week 12 (end of March), streamflow is
4 simulated to increase significantly. Of importance is the period between
5 approximately Week 27 (mid July) and Week 39 (end of September), when future
6 streamflow is reduced by between about 25% in the 2020 period to about 33% in the
7 2080 period. As expected, simulations further in the future consistently result in a
8 wider spread of predicted streamflow behavior, resulting in increased uncertainties.
9
10 ***** Insert Fig. 11 approximately here *****
11
12 4.3. Flow Duration Curves
13 Flow duration curves (FDCs) show the percentage of time that streamflow is
14 likely to equal or exceed a given value. FDCs based on daily streamflow for the 2020
15 and 2080 time periods are presented in Fig. 12, illustrating the projected range of
16 changes. All scenarios in both the 2020s and 2080s are simulated to increase in both
17 peak flows and low flows in terms of frequency and magnitude, relative to the
18 baseline period.
19
20 ***** Insert Fig. 12 approximately here *****
21 ***** Insert Fig. 13 approximately here *****
22 ***** Insert Fig. 14 approximately here *****
23
24 In order to reveal the predicted impacts of climate change on extreme flow
25 conditions, the annual minimum and maximum streamflow series for the 2020 and P a g e | 19
1 2080 time periods were calculated (Figs. 11 and 12). All of the annual minimum
2 flows are predicted to increase considerably, with the lowest streamflows increasing
3 the most in magnitudes and decreasing in frequency (Fig. 13). For example, the
4 lowest streamflow during the 30-year baseline time period has an exceedance
5 probability of 97%, and a magnitude of 2.3 m3s-1. The 97% exceedance probability
6 may have a streamflow of approximately 7.1 m3s-1 in the 2020s, and approximately
7 8.6 m3s-1 in the 2080s.
8 Most of the annual maximum streamflows in the 2020s and 2080s are
9 increasing for all scenarios. When compared to the baseline period, the “B” scenario
10 is simulated to have lower maximum streamflows above the 60% exceedance
11 probability in the 2020s, and above the 40% exceedance probability in the 2080s (Fig.
12 14). The frequency and magnitude of maximum peak flows are increasing for all
13 scenarios for each time period, with the highest flows occurring more often compared
14 to the baseline period. For example, the exceedance probability for the base period of
15 the maximum flow of approximately 795 m3s-1 is about 3%. The average of the same
16 magnitude flow is simulated to have an exceedance probability around 5% in the
17 2020s, and around 7% in the 2080s.
18
19 5. DISCUSSION
20 5.1. Projected changes in PET and AET
21 Based on the simulated hydrological cycle within the Cline River watershed,
22 the average increases in PET for the 2020, 2050 and 2080 time periods are projected
23 to be 11.5%, 20%, and 27% respectively. This is the result of projected increased air
24 temperatures, as wind, relative humidity, and incoming radiation (decreased on days
25 with precipitation) were kept constant due to uncertainty of their respective future P a g e | 20
1 behavior. With increasing air temperatures the air above the ground has a higher
2 potential vapor pressure deficit, resulting in increased potential evapotranspiration.
3 Average increases in AET are simulated to be 2.5% for the 2020s, 5.5% for the 2050s,
4 and 8.5% for the 2080s. These trends in projected increases in PET and AET are
5 consistent with general future intensification of the hydrological cycle (Hamlet et al.,
6 2007) and results reported by Zhang et al. (2009), who found positive trends for the
7 1983-2005 period of increased AET in North American boreal regions. As AET is
8 dependent on both atmospheric moisture demand (PET) and available soil moisture,
9 future AET increase is governed by soil moisture conditions within each HRU. In
10 regions with a natural soil moisture deficit, such as the prairies, increases of AET
11 under future climate conditions were simulated to be negligible (Forbes et al., 2010).
12 Simulated future increases in AET are likely due to the projected earlier snow melt
13 and increases in precipitation during May and June (Table 2), as the largest increases
14 in AET are simulated to occur between April and July, after which the increases in
15 AET become very modest.
16 Some factors that will affect AET in future climate scenarios that are not
17 accounted for in the simulations are the rise in CO2 concentration and subsequent
18 changes in plant activity and growth. Bio-physical thresholds for plant species are
19 expected to change under regional climate change conditions, thus altering net
20 primary productivity (Cramer et al., 2001) and, consequently, actual
21 evapotranspiration and runoff. Increased climate warming and CO2 levels can result in
22 increased AET with an earlier onset and longer growing season, and a change in
23 vegetation structure, such as leaf area index (LAI) and canopy coverage (Frederick
24 and Major, 1997; Parmesan and Yohe, 2003; Zhang et al., 2009). However,
25 observations in laboratory and field studies have shown that increases in P a g e | 21
1 concentrations of CO2 can stimulate net photosynthesis and decrease stomatal
2 conductance, thereby decreasing AET rates, as less water is required for a given unit
3 of CO2 uptake (Rosenberg et al., 1990; Levis et al., 2000; Long et al., 2004). There
4 exists considerable uncertainty as to the magnitudes and directions of the relevant
5 feedbacks and feedforwards between a future climate, CO2 content, and the
6 adjustments of plant species to these conditions. Thus, reported climate impact
7 simulations did not account for changes in land cover as a response to climate change
8 such as those discussed by Veldcamp and Lambin (2001).
9
10 5.2. Projected changes in soil moisture, ground water recharge, and SWE
11 Increased air temperatures and changes in precipitation regimes can result in
12 changes in soil moisture and evapotranspiration rates. Based on simulations from the
13 selected climate scenarios, the mean annual soil moisture is projected to increase by
14 0.5% in the 2020s, 1.4% by the 2050s, and 2.6% by the 2080s. It has been suggested
15 that an earlier snow melt could result in increased soil moisture earlier in the spring, if
16 the soil is not frozen, a time when potential evaporation is low (Barnett et al., 2005),
17 and that soil moisture deficits can be expected in the summer (Saunders and Byrne,
18 1995; Gleick and Chalecki, 1999). This is indeed the case in the Cline River
19 watershed, as soil moisture was simulated to increase between November and April,
20 and to decrease between May and August for all five climate scenarios. Therefore, the
21 major limitation to a future increase in actual evapotranspiration is the progressively
22 reduced soil moisture storage between May and August, when potential
23 evapotranspiration is the highest (Eckhardt and Ulbrich, 2003; Sauchyn and
24 Kulshreshtha, 2008). P a g e | 22
1 The mean annual groundwater recharge was simulated to increase, on average,
2 by 1.6% in the 2020s, 4.1% in the 2050s, and 8.0 % in the 2080s. Previous studies
3 indicate that the rate of groundwater recharge could increase by as much as 53%
4 (Jyrkama and Sykes, 2007), or decrease by as much as 50% (Eckhardt and Ulbrich,
5 2003) as a result of climate change. Warmer winter air temperatures will reduce the
6 extent of ground frost and cause an earlier shift in the spring melt, allowing more
7 water to infiltrate into the ground (Eckhardt and Ulbrich, 2003), leading to increased
8 soil moisture, as stated above. Despite the importance of groundwater as a resource,
9 temporal and spatial changes and rates of recharge across Canada are unknown, and
10 research on the impacts of climate change remains limited (Hoffmann et al., 1998;
11 Jyrkama and Sykes, 2007). Future mean seasonal Cline River groundwater discharge
12 is simulated to be higher in winter and spring, peaking earlier and higher, and to
13 recess earlier, resulting in much reduced baseflow from July to November (Fig. 9).
14 The future projected increases in winter streamflows (Fig. 11) are mainly the result of
15 increased baseflow, as groundwater recharge is simulated to increase due to projected
16 increased winter precipitation, especially between March and April (Fig. 9, Table 2).
17 Another factor is the decreased proportion of mean annual precipitation falling as
18 snow, decreasing from an average of 66.4% during 1961-1990 to 63.4% (2020s),
19 61.8% (2050s) and 59.2% (2080s) in this mountain watershed. As, on the 1961-1990
20 average, about 40% of the Cline River watershed is above the mean annual snow line
21 of 2293 m, there is always a significant snowpack left at the end of the melt season,
22 which typically extends to September (Fig. 10). All climate scenarios were simulated
23 to significantly decrease in snowpack relative to the baseline period, despite the
24 potentially large increase in winter precipitation (Table 2). This can be explained by a P a g e | 23
1 shift of more precipitation falling as rain, and an increase in snow melt due to
2 increased winter and spring temperatures.
3
4 5.3. Projected changes to streamflow
5 The five climate scenarios project increases in mean annual precipitation from
6 an average of 1.2%, 11% and 16.6% in the 2020s, 2050s, and 2080s, respectively
7 (Table 2). These projections are consistent with a study in the headwaters of the
8 UNSRB (Demuth and Pietroniro, 2003) that used five GCMs to project air
9 temperature and precipitation centered around the 2050s and projected increased air
10 temperatures, particularly for the winter and spring periods. Another study, an
11 evaluation of eleven GCM scenarios for the Canadian Prairie Provinces (Töyrä et al.,
12 2005), also found projected mean annual precipitation increases in both the 2050 and
13 2080 time periods, with winter precipitation projected to increase, summer and spring
14 precipitation projected to decrease, and annual mean air temperatures projected to
15 increase in every season. Increases in winter streamflows (DJF) are likely due to
16 increased winter baseflows projected by all of the models used (Fig. 9). With
17 projected decreases in SWE and warmer air temperatures, increased winter flows are
18 likely the result of an increase of rain events on both frozen and un-frozen soil.
19 Hydrological simulations predict that these precipitation changes will only force small
20 changes in mean annual AET, ranging from 2.5% to 8.5% due to limitations in
21 summer soil moisture levels. The consequence is a progressive increase in mean
22 annual streamflow in the future.
23 A focus of this study was to investigate how changes in climate will affect
24 seasonality of streamflow, variance of annual streamflow, and annual water yields. As
25 all future scenario simulations of streamflow did not include glacial melt P a g e | 24
1 contributions, as discussed earlier, the results will all be altered by the future melt
2 behavior of the glaciers. Hopkinson and Young (1998) have stated that glaciers in the
3 Bow Valley, just south of Cline River watershed, could disappear within
4 approximately 150 years if they continue to deplete at observed rates. It can,
5 therefore, be assumed that glacial contributions will occur into the 2080s in the Cline
6 River watershed. As the glacier melt occurs predominantly between July and
7 September, its contributions will increase simulated streamflows reported here,
8 particularly during drought years. The severe decline in future SWE at higher
9 elevations, evident by the decline in summer SWE for the entire Cline River
10 watershed (Fig. 10), would have strong impacts on the glacier mass balance. As the
11 snowline rises in elevation, the accumulation area decreases, while the ablation areas
12 increases. The result is a decrease in glacier input, with an associated increase in
13 glacier output, which will result in accelerated strong decline in glacier volume and
14 area in the future, with associated impacts on the decline of summer streamflows in
15 the future, exacerbating the simulated decline in streamflow due to reduced baseflow.
16 However, at this time the magnitude that glacier melt contributions will have on
17 future streamflow scenarios is unknown. Future integration of a glacier melt model
18 into ACRU will help to improve currently available simulation results.
19 5.4. Seasonality
20 All future streamflow simulations result in progressively earlier spring flows.
21 There is shift towards earlier peak streamflow, which was simulated to be, on average,
22 18 days in the 2020s, 21 days in the 2050s, and 26 days in the 2080s (Fig. 11). The
23 snowpack would melt out earlier due to warmer spring air temperatures. Increases in
24 spring rainfall could add to the melting snowpack when it falls on frozen ground or
25 bare rock. An earlier spring melt and increased streamflow in the spring are consistent P a g e | 25
1 with other climate change impact studies (Burn, 1994; Barnett et al., 2005; Rood et
2 al., 2008; Sauchyn and Kulshreshtha, 2008). Projected increases in winter
3 streamflows are due to projected higher baseflows, which are simulated to be fed by
4 increases in soil moisture and groundwater recharge.
5 The predicted late summer and early fall decreases in streamflow (Fig. 6) are
6 likely over-estimated due to the exclusion of glacier melt in the simulations,
7 introducing a small systematic error in the streamflow simulations under climate
8 change conditions. Studies within the Cline River watershed have shown that glacial
9 melt runoff can contribute up to 70% of the streamflow in late summer and early fall
10 (Loijens, 1974; Comeau et al., 2009). Projected higher air temperatures in summer
11 and fall, coinciding with smaller increases in precipitation, are also likely contributing
12 to projected decreased late summer and fall streamflows relative to the baseline
13 period.
14
15 5.5. Extreme events
16 Figs. 10 to 12 show the simulated exceedance probabilities for both baseline
17 and future streamflow for the 2020s and 2080s. Fig. 12 illustrates an increase in daily
18 streamflow over the 30-year period for the 2020s and 2080s relative to the baseline
19 period. Both time periods exhibit a similar change in low, median, and high
20 streamflow, progressively increasing in magnitude and frequency, with slight
21 decreases, relative to the baseline period, in the 15 to 40% exceedance probabilities.
22 These same projections are evident in the 2050s. Figs. 11 and 12 highlight the fact
23 that the minimum and some maximum streamflows are expected to increase in both
24 magnitude and frequency. The lowest minimum annual streamflow in the 2020s and P a g e | 26
1 2080s is estimated to be at least two and a half times the magnitude of the lowest
2 minimum annual streamflow in the baseline 1961-90 time period (Fig. 13). Medium
3 minimum annual streamflows (50% exceedance probability) during the baseline
4 period are simulated to shift to an exceedance probability of between 64% and 77% in
5 the 2020s, and between 81% and 97% in the 2080s. This suggests that low flows
6 could be less of an issue than floods for water managers in and downstream of the
7 study area. However, as the climate models did not account for possible future
8 changes in precipitation frequency and magnitude, the impacts of neither the potential
9 increase in dry periods during any year nor the impacts of multi-year droughts could
10 be simulated.
11 The highest estimated peak flows were estimated to increase between 11 and
12 19% during the 2020s, and between 14 and 35% during the 2080s (Fig. 14). The
13 frequency of flood events is also expected to increase. For example, the 20-year flood
14 would become a 17- to 14-year flood in the 2020s and a 12- to 8-year flood in the
15 2080s. Increases in high flows are mainly the result of increased rainfall during the
16 spring (Table 2).
17 5.6. Streamflow Volumes
18 Mean monthly streamflow volumes were analyzed for all five climate change
19 scenarios and for the three future time periods. Average annual changes in water
20 yields were projected to be positive, except for small decreases projected by two
21 scenarios in the 2020s (Table 4). Changes in monthly streamflow for each time period
22 are more complex, with the largest increases between November and May, and large
23 decreases in July to September streamflow (Table 5, Fig. 11). The largest percent
24 decreases were simulated to be in August and the greatest increases in April.
25 Increased flows in these winter and spring months are attributed to both the warmer P a g e | 27
1 projected air temperatures and increases in precipitation (Table 2), causing earlier
2 snow melt and an earlier transition of precipitation falling as rain. Results showing an
3 increase in winter runoff, a reduction in snowmelt derived streamflow generation, and
4 a future reduction in summer flows are consistent with other studies in the eastern
5 Rocky Mountains (Rood et al., 2005; Stewart et al., 2005; Lapp et al., 2005; Byrne
6 and Kienzle, 2008; Sauchyn and Kulshreshtha, 2008).
7 Rood et al. (2005) concluded that streamflow has declined in the North
8 American central Rocky Mountains over the past century, and that this region could
9 see streamflows decline by a further 10% by 2050. Results from the simulated climate
10 scenarios for the glaciated and mountainous Cline River watershed indicate a net
11 increase in future streamflows, with water yields increasing, on average, by 7% in the
12 2050s. Clair et al. (1998) reported that a doubling of CO2 would result in an increase
13 between 12 and 21% in mean annual runoff in the Montane Cordillera and Boreal
14 Plains regions of Canada, which corresponds with some of the 2080 scenario results
15 reported here.
16
17 5.7. Uncertainty in climate projections and hydrological simulations
18 Physically based hydrological models and their ability to assess the effects of
19 climate change on water resources have been applied in a variety of studies (Bathurst
20 and O'Connell, 1992; Barnett et al., 2005; Barrow and Yu, 2005; Forbes et al., 2010).
21 However, it must be recognized that major limitations of physically based distributed
22 models include the availability and quality of the spatial distribution of all variables
23 representing the bio-physical characteristics of the watershed and the temporal
24 fluctuations of the hydro-climatological time series across the entire watershed. The P a g e | 28
1 selected scenarios produce a wide range of projected changes in the hydrological
2 regime of the Cline River watershed. However, for all time periods there is a large
3 degree of variation of predicted annual and seasonal flow volumes (Fig. 11).
4 As in most watersheds, data required to properly simulate all major hydrological
5 processes were limited in the Cline River watershed. Land cover data were only
6 available in generalized form, such as the land class “coniferous forest”, which was
7 assumed to be uniform in species, age, and density across the entire watershed. Other
8 variables, such as plant transpiration coefficients (PTCs), were nonexistent and were
9 estimated for the most prevalent land cover classes from observed meteorological and
10 flux data from grassland and coniferous forest sites from Fluxnet Canada and
11 AmeriFlux flux towers at Alberta grassland, Saskatchewan aspen forest and Colorado
12 coniferous forest sites (Nemeth et al., in review). Generally, climate stations in
13 mountain regions fail to represent the full range of climate conditions prevalent in a
14 high-relief environment, because they are situated at lower elevations. This was the
15 case in this study. Although some climate stations did exist at higher elevations, their
16 records were short and often seasonal, rendering them inadequate for long-term
17 climate studies (Luckman, 1998). However, the high elevation air temperature
18 observations were used to verify the simulated climate representations at those sites.
19 Due to the lack of soils data, many assumptions and generalizations had to be
20 made for the estimation of each HRUs soil hydrological properties. A method was
21 developed to derive soils information for each HRU using soils data collected for
22 approximately 10% of the study area and associating soil depth and water holding
23 parameters with land cover. Based on the analysis of sparsely available soils data, it
24 was established that there were no statistically significant relationships that could be
25 derived between soil properties, such as texture and depth, and terrain attributes, such P a g e | 29
1 as elevation, slope, or aspect. This is in agreement with the findings by Rahman et al.
2 (1996), who found, in a study of Rocky Mountain forest soils, that relationships
3 among soil properties and terrain attributes were statistically non-significant. Based
4 on the lack of soils data and the broad and uniform assumptions made over the
5 complex study area, large uncertainties remain in the realistic distribution of soil
6 hydrological variables across the study area, thus introducing uncertainty into the
7 simulated streamflow results (Grayson and Bloeschl, 2000).
8 The comparison of simulated against observed snow pillow and snow survey
9 time series revealed the problem of a realistic, HRU-wide, representation of SWE, as
10 the current version of ACRU only distinguished between either forest or non-forest
11 snow hydrology, while most SWE observations were in transition zones between
12 dense forest and open areas. This introduced further uncertainties in the selection of
13 the most appropriate snow variables, such as snow interception, snow sublimation,
14 and snow melt.
15
16 5.8. GCM scenario uncertainty
17 The selected climate change scenarios from the PCIC provided the basis for a
18 guided sensitivity analysis of the possible impact of climate change on water
19 resources in the Cline River watershed. Using GCMs as input for water resource
20 forecasting is inherently associated with uncertainties, including the spatial scale of
21 GCMs, the accuracy of simulated climate in time and space, scenario development
22 based on unknown factors such as population and energy demand growth forecasts,
23 changes in land cover as a response to altered climates, and whether assumptions of
24 parameterization under present climates will hold true for future climates (Lins et al.,
25 1997; Merritt et al., 2006). As is evident from Table 2, precipitation estimates from P a g e | 30
1 various GCMs are notoriously uncertain in both magnitude and timing (Merritt et al.,
2 2006), thus significantly affecting simulated responses in both actual
3 evapotranspiration and streamflow. However, all five selected climate scenarios
4 predict principally similar and progressive future changes in the precipitation regime,
5 such as large increases in spring precipitation. Summer precipitation forecasts are
6 much more uncertain, as the magnitudes and directions of future precipitation are very
7 diverse, ranging from increases to decreases in precipitation during June to September
8 (Table 2).
9
10 6. CONCLUSIONS
11 The ACRU agro-hydrological modeling system provided the framework,
12 containing code to simulate all major hydrological processes, including actual
13 evapotranspiration estimates, to simulate the impacts of climate change in the Cline
14 River watershed, Alberta, Canada, under historical (1961-1990) and a range of future
15 climate conditions (2010-2039, 2040-2069, and 2070-2099). Whilst uncertainties in
16 the estimation of many hydrological variables were inevitable, verification analyses
17 carried out for the historical baseline period resulted in good to very good simulations
18 of a range of hydrological processes, including daily air temperature, snow water
19 equivalent and streamflow. Five climate change scenarios were selected to cover the
20 range of possible future climate conditions. In order to generate future climate time
21 series, the 30-year baseline time series was perturbed according to predicted changes
22 in air temperature and precipitation.
23 In the Cline River watershed, projected increases in air temperature and
24 precipitation were simulated to result in large increases in mean annual potential
25 evapotranspiration, but only small increases in actual evapotranspiration, and modest P a g e | 31
1 increases in soil moisture and groundwater recharge, resulting in modest increases in
2 streamflow. Increases in both high and low flow magnitudes and frequencies, as well
3 as large increases to winter and spring streamflows, are predicted for all climate
4 scenarios. Spring runoff and peak streamflows were simulated to occur up to four
5 weeks earlier than in the 1961-1990 baseline period. Predicted changes were
6 simulated to progressively increase into the future. A clear shift in the future
7 hydrological regime is predicted, with significantly higher streamflows between
8 October and June, and lower streamflow in July to September. The reduction in
9 streamflow during the July to September period is the result of inter-dependent
10 changes in hydrological processes, i.e. decreased summer precipitation, higher
11 evapotranspiration, drier soils, and reduced groundwater recharge. Current inadequate
12 simulation of future glacial melt prevented the modeling of future glacier melt
13 contributions to streamflow, and was, subsequently, omitted in all hydrological
14 simulations. As glacier melt water contributed an average of 9.5% to streamflow in
15 the Cline River watershed during the baseline period (Loijens, 1974; Comeau, 2008),
16 future streamflow contributions during the melt period July to September are expected
17 to remain significant for some time, especially during drought years, but will decline
18 over time, as the period of peak glacier melt has already been surpassed (Demuth and
19 Pietroniro, 2003). The combination of increased glacier melt rates in the future and
20 reduced snow accumulation in higher elevations due to a rising snowline will result in
21 accelerated decline in glacier melt contributions to streamflow between July and
22 September.
23 In order to improve hydrological simulations, better spatially distributed
24 climate data, improved knowledge of mountain soils, and the integration of both a P a g e | 32
1 dynamic and process-based glacier accumulation and ablation model and an
2 ecological land cover adaptation model, sensitive to climate change, are required.
3 The findings reported here are in line with general predictions and previous
4 studies in other regions. Although uncertainties exist as to the quantification of the
5 impacts of climate change on the hydrological regime in the Cline River watershed,
6 the estimated trends in soil moisture, snow pack, actual evapotranspiration,
7 groundwater, and streamflow serve as guidelines to water resources managers,
8 ecologists and foresters in similar regions.
9
10 Acknowledgements This research was funded by the combined grant (#40286) by
11 EPCOR Water Supply and the Natural Sciences and Engineering Research Council of
12 Canada (NSERC). We thank Dr. Hester Jiskoot for her help with the development of
13 our basic glacier model.
14
15
16 REFERENCES
17 Arnell, N.W., 2004. Climate change and global water resources. Global Environ. 18 Change 9, 31-49. 19 Barnett, T.P., Adam, J.C., Lettenmaier, D.P., 2005. Potential impacts of a warming 20 climate on water availability in snow dominated regions. Nature 438, 303-309. 21 Barrow, E., Yu, G., 2005. Climate scenarios for Alberta: A report prepared for the 22 Prairie Adaptation Research Collaborative (PARC) in co-operation with 23 Alberta Environment. 1-73. 24 Bathurst, J. C., O‟Connell, P. E., 1992. Future of distributed modelling: The Systeme 25 Hydrologique Europeen. Hydrol. Processes 6, 265–277. 26 Bathurst, J.C., Ewen, J. Parkin, G., O‟Connell, P.E., Cooper, J.D., 2004. Validation of 27 catchment models for predicting land-use and climate change impacts. 3. 28 Blind validation for internal and outlet responses. J. Hydrol. 287, 74-94. 29 Beven, K., 1989. Changing ideas in hydrology- the case of physically based models. 30 J. Hydrol. 105, 157-172. 31 Burn, D.H., 1994. Hydrologic effects of climatic-change in West-Central Canada. J. 32 Hydrol. 160, 53-70. P a g e | 33
1 Byrne, J., Kienzle, S.W., 2008. The Prairies. In: Lemmon, D, Warren, F., Bush, E. 2 and Lacroix, J. (Eds.): From Impacts to Adaptation: Canada in a Changing 3 Climate 2007. Report to the Government of Canada, Chapter 7. 4 Cayan, D.R., Kammerdiener, S., Dettinger, M.D., Caprio, J.M., Peterson, D.H., 2001. 5 Changes in the onset of spring in the western United States. Bull. Am. 6 Meteorol. Soc. 82, 399–415. 7 Clair, T.A., Ehrman, J., Higuchi, K., 1998. Changes to the runoff of Canadian 8 ecozones under a doubled CO2 atmosphere. J. Fish. Aquat. Sci. 55, 2464-2477. 9 Comeau, L.E.L., Pietroniro, A., Demuth, M.N., 2009. Glacier Contribution to the 10 North and South Saskatchewan Rivers. Hydrol. Processes 23, 2640-2653. 11 Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., Brovkin, V., 12 Cox, P.M., Fisher, V.; Foley, J.A.; Friend, A.D., Kucharik, C., Lomas, M.R., 13 Ramankutty, N., Sitch, S., Smith, B., White, A., Young-Molling, C., 2001. 14 Global response of terrestrial ecosystem structure and function to CO2 and 15 climate change: results from six dynamic global vegetation models. Global 16 Change Biol. 7, 357-372. 17 Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris 18 PA., 2008. Physiographically-sensitive mapping of temperature and 19 precipitation across the conterminous United States. Int. J. Climatol. 28, 2031- 20 2064. 21 Demuth, M.N., Pietroniro, A., 2003. The impact of climate change on the glaciers of 22 the Canadian Rocky Mountain eastern slopes and implications for water 23 resource-related adaptation in the Canadian Prairies: Phase I- Headwaters for 24 the North Saskatchewan River Basin. Report for the Climate Change Action 25 Fund- Prairie Adaptation Research Collaborative, 1-111. 26 Demuth, M.N., Keller, R., 2009. An assessment of the mass balance of Peyto Glacier 27 (1966–1995) and its relation to recent and past century climate variability. In: 28 Demuth, M.N., Munro, D.S., Young, G.J. (Eds.), Peyto Glacier: One Century 29 of Science, National Hydrology Research Institute Science Report No. 8, 83– 30 132. 31 Diaz-Nieto, J., Wilby, R.L., 2005. A comparison of statistical downscaling and 32 climate change factor methods: impacts on low flows in the River Thames, 33 United Kingdom, Clim. Change 69, 245-268. doi: 10.1007/s10584-005-1157- 34 6. 35 Eckhardt, K., Ulbrich, U., 2003. Potential impacts of climate change on groundwater 36 recharge and streamflow in a central European low mountain range. J. Hydrol. 37 284, 244-252. 38 Epstein, D., Ramirez, J.A. 1994. Spatial disaggregation for studies of climatic 39 hydrologic sensitivity. J. of Hydraul. Eng. 120, 1449-1467. 40 Evans, J., Schreider, S., 2002. Hydrological impacts of climate change on inflows to 41 Perth, Australia. Clim. Change 55, 361-393. 42 Flügel, W.A., 1995. Delineating hydrological response units by geographical 43 information-system analyses for regional hydrological modelling using 44 PRMS/MMS in the drainage-basin of the River Brol, Germany. Hydrol. 45 Processes 9, 423-436. 46 Forbes, K.A., Kienzle, S.W., Coburn, C.A., Byrne, J.M., Rasmussen, J., 2010. 47 Modelling the impacts of selected GCM derived climate scenarios on the 48 future hydrology of a hybrid watershed in the Oldman River watershed, 49 Alberta, Canada. Clim. Change. DOI 10.1007/s10584-010-9890-x P a g e | 34
1 Frederick, K.D., Major, D.C., 1997. Climate change and water resources. Clim. 2 Change 37, 7-24. 3 Gleick, P.H., 1987. Regional hydrologic consequences of increases in atmospheric 4 CO2 and other trace gases. Clim. Change 10, 137-160. 5 Gleick, P.H., Chalecki, E.L., 1999. The impacts of climatic changes for water 6 resources of the Colorado and Sacramento-San Joaquin River Basins. J. Am. 7 Water Resour. Assoc. 35, 1429-1441. 8 Grayson, R., Blöschl, G., 2000. Spatial Patterns in Catchment Hydrology: 9 Observations and Modelling, Cambridge University Press, Cambridge, p. 404. 10 Hamlet, A.F., Mote, P.W., Clark, M.P., Lettenmeier, D.P., 2007. Twentieth-century 11 trends in runoff, evapotranspiration, and soil moisture in the western United 12 States. J. Climate 20, 1468-1486. 13 Hay, L.E., Wilby, R.L., Leavesley, G.H., 2000. A comparison of delta change and 14 downscaled GCM scenario for three mountainous basins in the United States. 15 J. Am. Water Resour. Assoc. 36, 387-397. 16 Hofmann, N., Mortsch, L., Donner, S., Duncan, K., Kreutzwiser, R., Kulshreshtha, S., 17 Piggott, A., Schellenberg, S., Schertzerand, B., Slivitzky, M., 1998. Climate 18 change and variability: impacts on Canadian water in Responding to Global 19 Climate Change. National Sectoral Issue, Environment Canada, Canada 20 Country Study: Climate Impacts and Adaptation 7, 1-120. 21 Hopkinson, C., Young, G.J., 1998. The effect of glacier wastage on the flow of the 22 Bow River at Banff, Alberta, 1951-1993. Hydrol. Processes 12, 1745-1762. 23 IPCC, 2007. Climate Change 2007: Synthesis Report. Contribution of Working 24 Groups I, II and III to the Fourth Assessment Report of the Intergovernmental 25 Panel on Climate Change. [Core Writing Team, Pachauri, R. K. and Reisinger, 26 A. (eds.)]. IPCC, Geneva, Switzerland, 104pp. Available at 27 http://www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr_spm.pdf. 28 Jasper, K., Calanca, P., Gyalistras, D., Fuhrer, J., 2004. Differential impacts of 29 climate change on hydrology of two river basins. Climate Res. 26, 113-129. 30 Jyrkama, M.I., Sykes, J.F., 2007. The impact of climate change on spatially varying 31 groundwater recharge in the Grand River watershed (Ontario). J. Hydrol. 338, 32 237-250. 33 Kienzle, S.W., 1993. Application of a GIS for simulating hydrological responses in 34 developing regions. In: HydroGIS 93: Application of Geographical 35 Information Systems in Hydrology and Water Resources Management (Proc. 36 of the Vienna Conference, Austria, April 1993). IAHS Publications no. 211, 37 309-318. 38 Kienzle, S.W., 1996. Using DTMs and GIS to define input variables for hydrological 39 and geomorphological analysis. In: HydroGIS 96: Application of 40 Geographical Information Systems in Hydrology and Water Resources 41 Management (Proc. of the Vienna Conference, Austria, April 1996). IAHS 42 Publications no. 235, 183-190. 43 Kienzle, S.W., Lorenz, S.A., Schulze, R.E., 1997. Hydrology and Water Quality of 44 the Mgeni Catchment. Water Research Commission, Pretoria, Report 45 TT87/97, 1-88. 46 Kienzle, S.W., 2008. A new temperature based method to separate rain and snow. 47 Hydrol. Processes 22, 5067-5085. 48 Kienzle, S.W., Schmidt, J., 2008. Hydrological impacts of irrigated agriculture in the 49 Manuherikia Catchment, Otago, New Zealand. J. Hydrol. (NZ) 47, 67-83. P a g e | 35
1 Kienzle, S.W., 2010. Effects of area under-estimations of sloped mountain terrain on 2 simulated hydrological behaviour. Hydrol. Processes, DOI: 10.1002/hyp.7886. 3 Kiker, G.A., Clark, D.J., Martinez C.J., Schulze, R.E., 2006. A Java-based, object- 4 oriented modeling system for southern African hydrology. Trans. ASABE 49, 5 1419–1433. 6 Lapp, S., Byrne, J., Townshend, I., Kienzle, S., 2005. Climate warming impacts on 7 snowpack accumulation in an Alpine watershed: a GIS based modeling 8 approach. Int. J. Climatol. 25, 521–536. 9 Lettenmaier, D. P., Gan, T., 1990. An exploratory analysis of the hydrologic effects of 10 global warming on the Sacramento–San Joaquin River Basin, California. 11 Water Resour. Res. 26, 69–86. 12 Levis, S., Foley, J. A., Pollard, D., 2000. Large-scale vegetation feedbacks on a 13 doubled CO2 climate. J. Climate 13, 1313-1325. 14 Lins, H.F., Wolock, D.M., McCabe, G.J., 1997. Scale and modelling issues in water 15 resources planning. Clim. Change 37, 63–88. 16 Loijens, H.S., 1974. Streamflow formation in the Mistaya River Basin, Rocky 17 Mountains, Canada. Western Snow Conference 43, 86-95. 18 Long, S.P., Ainsworth, E.A., Rogers, A., Ort, D.R., 2004. Rising atmospheric carbon 19 dioxide: plants FACE the future. Ann. Rev. Plant Biol., 55, 591–628. 20 Loukas, A., Quick, M.C., 1996. The effect of climate change on floods in British 21 Columbia. Nord. Hydrol. 30, 1-256. 22 Loukas, A., Vasiliades, L., Dalezios, N.R., 2002. Potential climate change impacts on 23 flood producing mechanisms in southern British Columbia, Canada using the 24 CGCMA1 simulation results. J. Hydrol. 259, 163-188. 25 Luckman, B.H., 1998. Landscape and climate change in the central Canadian Rockies 26 during the 20th century. Can. Geogr. 42, 319-337. 27 Liu, Z., Xu, Z., Huang, J., Charles, S.P., Fu, G., 2009. Impacts of climate change on 28 hydrological processes in the headwater catchment of the Tarim River basin, 29 China. Hydrol. Processes 24(2), 196-208. 30 MacDonald R.J., Byrne, J.M., Kienzle, S.W., Larson, R.P., 2010. Assessing the 31 potential impacts of climate change on mountain snowpack in the St. Mary 32 River watershed, Montana. J. Hydrometeorol. DOI: 10.1175/2010JHM1294.1 33 Markoff, M.S., Cullen, A.C., 2007. Impact of climate change on Pacific Northwest 34 hydropower. Clim. Change, 87, 451-469. 35 Merritt, W.S., Alila, Y., Barton, M., Taylor, B., Cohen, S. Neilsen, D., 2006. 36 Hydrologic response to scenarios of climate change in sub watersheds of the 37 Okanagan basin, British Columbia. J. Hydrol. 326, 79-108. 38 Moore, R.D., Demuth, M.N., 2001. Mass balance and streamflow variability at Place 39 Glacier, Canada, in relation to recent climate fluctuations. Hydrol. Processes 40 15, 3473-3486. 41 Mote, P.W., 2006. Climate driven variability and trends in mountain snowpack in 42 western North America. J. Climate 19, 6209-6220. 43 Morrison, J., Quick, M.C., Foreman, M.G.G., 2002. Climate change in the Fraser 44 River watershed: flow and temperature projections. J. Hydrol. 263, 230-244. 45 Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J., Gaffin, S., 2000. 46 Special Report on Emissions Scenarios: A Special Report of Working Group 47 III of the Intergovernmental Panel on Climate Change, Cambridge University 48 Press, Cambridge, U.K., 599 pp. Available online at: 49 http://www.grida.no/climate/ipcc/emission/index.htm. P a g e | 36
1 Nemeth, M.W., Kienzle, S.W., Byrne, J.M. Multi-variable verification of 2 hydrological processes in the upper North Saskatchewan River basin, Alberta, 3 Canada. Hydrolog. Sci. J. (under review) 4 Nogués-Bravo, D., Araujo, M.B., Errea, M.P., Martinez-Rica, J.P., 2007. Exposure of 5 global mountain systems to climate warming during the 21st Century. Global 6 Environ. Change 17, 420-428. 7 North Saskatchewan Watershed Alliance, 2005. State of the North Saskatchewan 8 Watershed Report 2005 - A Foundation for Collaborative Watershed 9 Management. North Saskatchewan Watershed Alliance, Edmonton, Alberta. 10 202 pp. 11 Nurmohamed, R., Naipal, S., De Smedt, F., 2007. Modeling hydrological response 12 of the Upper Suiname river basin to climate change. J. Spatial Hydrol. 7(1), 13 1–22. 14 Ommanney, C.S.L., 2002. Glaciers of the Canadian Rockies. In Ferrigno, J. and R.S. 15 Williams, Jr, eds. Satellite image atlas of glaciers of the world. Denver, CO, 16 United States Geological Survey, J199–J289. (USGS Professional Paper 1386- 17 J.) 18 Parmesan, C., Yohe, G., 2003. A globally coherent fingerprint of climate change 19 impacts across natural systems. Nature 421, 37-42. 20 Refsgaard, J.C., 1997. Parameterisation, calibration and validation of distributed 21 hydrological models. J. Hydrol. 198, 69-97. 22 Rood, S.B., Samuelson, G.M., Weber, J.K., Wywrot, K.A., 2005. Twentieth-century 23 decline in streamflows from the hydrographic apex of North America. J. 24 Hydrol. 306, 215-233. 25 Rood, S.B., Pan, J., Gill, K.M., Franks, C.G., Samuleson, G.M., Shepherd, A., 2008. 26 Declining summer flows of Rocky Mountain rivers: Changing seasonal 27 hydrology and probable impacts on floodplain forests. J. Hydrol. 349, 397- 28 410. 29 Rosenberg, N.J., Kimball, B.A.,Martin, P., Cooper, C.F., 1990. From climate and 30 CO2 enrichment to evapotranspiration. In Waggoner, P.E. (ed.) Climate 31 change and U.S. water resources. John Wiley and Sons, New York, 151–175. 32 Sauchyn, D., Kulshreshtha, S., 2008. Climate change impacts on Canada‟s Prairie 33 Provinces: A summary of our state of knowledge, from “Prairies” in From 34 Impacts to Adaptation: Canada in a Changing Climate 2007, edited by D. 35 Lemmen et al., Government of Canada, Ottawa. 36 Saunders, I.R., Byrne, J.M., 1995. Seasonal climate and climatic changes in the 37 Canadian Prairies simulated by the CCC GCM. Atmos. Ocean 33, 621-641. 38 Schmidt, E.J., Schulze, R.E., 1987. Flood volume and peak discharge from small 39 catchments in southern Africa, based on the SCS technique. Water Research 40 Commission, Pretoria, Technical Transfer Report TT/3/87. 164pp. 41 Smithers J., Schulze, R.E., 1995. ACRU Agrohydrological Modelling System User 42 Manual. Water Research Commission, Report TT 70/95, Water Research 43 Commission, Pretoria, Republic of South Africa. 44 Schulze, R.E., 1995. Hydrology and Agrohydrology: a text to accompany the ACRU 45 3.00 agrohydrological modelling system. Report TT69/95. Water Research 46 Commission, Pretoria, RSA. 125 47 Schulze, R.E., Perks, L.A., 2003. The potential threat of significant changes to 48 hydrological responses in southern Africa as a result of climate change: a 49 threshold analysis on when these could occur, and where the vulnerable areas 50 are. In: Schulze RE (ed) Modelling as a tool in integrated water resources P a g e | 37
1 management: conceptual issues and case study applications. WRC Report 2 749/1/02, Water Research Commission, Pretoria, RSA, Chapter 14, pp 250– 3 258. 4 Shea, J. M., Moore, R. D., Stahl, K. (2009). Derivation of melt factors from glacier 5 mass balance records in western Canada. J. Glaciol., 55(189), 123-130. 6 Stewart, I.T., Cayan, D.R., Dettinger, M.D. (2005). Changes toward earlier 7 streamflow timing across western North America. J. Climate, 18, 1136-1155. 8 Toth, B., Pietroniro, A., Conly, M.F., Kouwen, N., 2006. Modelling climate change 9 impacts in the Peace and Athabasca catchment and delta: I-hydrological model 10 application. Hydrol. Processes 20, 4197–4214. 11 Töyrä, J., Pietroniro, A., Bonsal, B. (2005). Evaluation of GCM simulated climate 12 over the Canadian Prairie Provinces. Can. Water Resour. J., 30(3), 245-262. 13 Veldcamp, A. and Lambin E.F. 2001 Predicting land-use change. Agr. Ecosyst. 14 Environ. 85, 1-6. 15 Whitfield, P.H., Fraser, D., Cohen, S., 2003. Climate change impacts on water in 16 Georgia Basin/Puget Sound–special issue. Can. Water Resour. J. 28, 523–529. 17 Wilby, R.L., Hay, L.E., Leavesley, G.H. (1999). A comparison of downscaled and 18 raw GCM output: implications for climate change scenarios in the San Juan 19 River basin, Colorado. J. Hydrol. 225, 67-91. 20 Zhang, K., Kimball, J.S., Mu, Q., Jones, L.A., Goetz, S.J., Running, S.W. (2009). 21 Satellite based analysis of northern ET trends and associated changes in the 22 regional water balance from 1983 to 2005. J. Hydrol. 379, 92-110. List_of_Referees Click here to download Manuscript: Kienzle_etal_2010_JH_Referee_List.docx Click here to view linked References
List of Potential Referees
Alain Pietroniro, PhD National Hydrology Research Centre, 11 Innovation Blvd., Saskatoon, SK S7N 2X8, Canada Phone: 1.306.975.4394 E-Mail: [email protected]
Gregory Kiker, PhD Department of Agricultural and Biological Engineering 291 Frazier Rogers Hall, PO Box 110570, University of Florida, Gainesville, FL 32611-0570, USA Phone: 1.352.392.1864 E-Mail: [email protected]
Jeff Smithers, PhD, Head of the Department School of Bioresources Engineering and Environmental Hydrology University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scotsville, 3209, South Africa Phone: +.27.33.260.3210 E-Mail: [email protected]
Roland Schulze, PhD, Professor Emeritus School of Bioresources Engineering & Environmental Hydrology University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scotsville, 3209, South Africa Phone: +.27.33.260.5489 E-mail: [email protected]
David Sauchyn, PhD Prairie Adaptation Research Collaborative (PARC) University of Regina, 2 Research Drive, Regina, S4S 0A2, Saskatchewan, Canada Phone: 1.306.337.2299 E-Mail: [email protected]
Figure_01 Click here to download high resolution image Figure_02 Click here to download high resolution image Figure_03 Click here to download high resolution image Figure_04 Click here to download high resolution image Figure_05 Click here to download high resolution image Figure_06 Click here to download high resolution image Figure_07 Click here to download high resolution image Figure_08 Click here to download high resolution image Figure_09 Click here to download high resolution image Figure_10 Click here to download high resolution image Figure_11 Click here to download high resolution image Figure_12 Click here to download high resolution image Figure_13 Click here to download high resolution image Figure_14 Click here to download high resolution image Figure_Captions
Figures and Captions
Fig. 1. The Cline River watershed as part of the Upper North Saskatchewan River Basin
Fig. 2. Key elements of the ACRU agro-hydrological modeling system
Fig. 3. On sloped areas, precipitation is distributed over a larger area than the planimetric area, and total evapotranspiration losses are also larger than from the planimetric area.
Fig. 4. Sloped area under-estimation as a function of slope
Fig. 5. Simulated and observed daily streamflow at the Cline River watershed outlet (1961-1990) (Nemeth et al., in review)
Fig. 6. Simulated and observed daily streamflow for the period 1961-1990 using (a) a normal scale and (b) a log scale (Nemeth et al., in review)
Fig. 7. Mean monthly streamflow components of the Cline River (1961-1990)
Fig. 8. Available and selected GCM scenarios for the Spring (MAM) 2050 time period; the dashed lines show the respective median changes.
Fig. 9. Simulated seasonal change in mean annual baseflow for the 2020s and 2080s.
Fig. 10. Simulated seasonal change in mean annual snow water equivalent for the 2020s and 2080s.
Fig. 11. Mean weekly simulated streamflows for five simulated climate change and the baseline (1961- 1990) scenarios for the periods 2020s, 2050s and 2080s, and simulated percent changes in mean weekly streamflows relative to historical streamflows; the grey area indicates a predicted decline in streamflow.
Fig. 12. Simulated exceedance probability of daily streamflow for the 2020s and 2080s.
Fig. 13. Simulated exceedance probability of annual minimum streamflow for the 2020s and 2080s.
Fig. 14: Simulated exceedance probability of annual maximum streamflow for the 2020s and 2080s.
Table 1 GCMs and scenarios available from the PCIC
Table 2 Mean monthly GCM projections of air temperature and precipitation for 2020, 2050, and 2080 time periods. Changes in air temperature (°C) and precipitation (%) are relative to the 1961-90 baseline period. Values based on the 2050 Spring (MAM) scenarios are gray-shaded, while negative precipitation changes are in bold. Table 3 Simulated percent change, relative to the baseline 1961-1990 time period, in mean annual potential (PET) and actual (AET) evapotranspiration, soil moisture, ground water (GW) recharge, and snow water equivalent (SWE); declining values are in bold.
Table 4 Simulated mean annual changes in water yield, in % relative to 1961-90 baseline period (negative values in bold).
Table 5 Simulated seasonal changes in streamflow, in % relative to the 1961-90 baseline period
Table_01 Click here to download Table: Kienzle_etal_2010_JH_Table01.docx
Table 1
GCMs and scenarios available from the PCIC
SRES Modeling Center Country Model Simulations Canadian Center for Climate Modeling and Canada CCCMA CGCM3 A1B, A2, B1 Analysis National Center for Atmospheric Research USA NCAR CCSM30 A1B, B1 Bjerknes Centre for Climate Research Norway BCCR BCM20 A1B, A2, B1 CSIRO Atmospheric Research Australia CSIRO MK30 A1B, A2, B1 NASA/Goddard Institute for Space Studies USA GISS AOM A1B, B1 Institute for Numerical Mathematics Russia INM CM30 A1B, A2, B1 Center for Climate System Research (The University of Tokyo), National Institute for MIROC32 HIRES and Environmental Studies, and Frontier Japan A1B, A2, B1 MIROC32 MEDRES Research Center for Global Change (JAMSTEC)
Table_02 Click here to download Table: Kienzle_etal_2010_JH_Table02.docx
Table 2
Mean monthly GCM projections of air temperature and precipitation for 2020, 2050, and 2080 time periods. Changes in air temperature (°C) and precipitation (%) are relative to the 1961-90 baseline period. Values based on the 2050 Spring (MAM) scenarios are gray-shaded, while negative precipitation changes are in bold.
Scenario Time Period Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual Mean Monthly Air temperature Changes (°C)
A 1.3 -0.1 -0.5 0.5 0.8 0.4 0.9 0.5 0.8 0.5 1.6 0.2 0.6 B 2020 2.1 1.7 1.9 1.5 2.8 2.2 2.1 2.2 2.2 1.5 1.3 1.5 1.9 C Mean = 1.5 5.1 2.2 1.0 0.9 1.0 1.0 1.5 2.1 1.9 2.6 1.9 2.2 1.9 D 2.4 1.5 0.6 1.2 2.0 2.3 2.6 2.3 3.0 1.9 1.4 3.0 2.0
M 0.7 1.5 0.5 0.3 0.8 1.2 1.3 1.4 1.2 0.9 1.4 2.5 1.1
A 2.0 -0.3 0.7 1.1 1.0 1.1 2.0 1.7 1.8 1.1 1.7 2.4 1.3 B 2050 3.2 3.9 3.7 3.7 5.0 4.3 4.4 4.1 4.1 3.0 3.0 4.8 3.9 C Mean = 2.4 3.2 1.9 1.4 1.1 1.1 2.4 2.6 2.6 2.2 2.5 1.6 1.9 2.0 D 4.1 2.8 3.0 2.2 2.5 4.1 4.3 3.5 3.8 2.0 2.3 3.5 3.1
M 2.4 2.1 1.3 0.8 1.6 1.6 2.0 2.2 2.2 1.3 1.8 2.8 1.8
A 3.4 0.2 1.3 1.3 2.2 2.6 3.9 3.8 3.2 2.0 3.7 3.8 2.6 B 2080 5.6 5.0 5.5 6.1 6.6 5.6 6.0 6.3 5.3 4.4 4.5 6.1 5.6 C Mean = 3.4 5.1 2.7 2.6 2.3 1.7 2.7 3.0 3.3 3.0 2.5 2.0 2.6 2.8 D 6.1 5.0 2.8 2.7 3.5 5.0 4.3 5.1 4.0 2.7 3.6 4.1 4.1
M 2.6 1.9 1.6 1.2 1.7 2.0 2.2 2.6 1.9 1.1 2.4 2.8 2.0
Mean Monthly Precipitation Changes (%)
A 13.0 -4.0 13.0 2.0 7.0 5.0 0.0 2.0 -8.0 15.0 16.0 10.0 5.9 - - 2020 B 19.0 10.0 -1.0 10.0 12.0 0.0 10.0 -1.0 1.0 8.0 16.0 14.0 4.2 - C Mean = 5.1 24.0 14.0 8.0 5.0 7.0 6.0 10.0 18.0 -8.0 -1.0 10.0 -5.0 4.3 - - - D 9.0 -3.0 -4.0 11.0 2.0 11.0 12.0 13.0 18.0 13.0 6.0 12.0 1.2 M 6.0 25.0 2.0 8.0 6.0 3.0 13.0 13.0 11.0 17.0 13.0 3.0 10.0
- A 18.0 15.0 28.0 24.0 17.0 3.0 -5.0 -7.0 18.0 14.0 10.0 20.0 9.9 - - 2050 B 15.0 3.0 20.0 25.0 23.0 -5.0 31.0 11.0 7.0 17.0 25.0 9.0 8.1 - C Mean = 9.1 10.0 35.0 5.0 6.0 5.0 24.0 -2.0 15.0 -6.0 8.0 7.0 -2.0 6.3 - D 7.0 23.0 -1.0 17.0 8.0 8.0 7.0 -6.0 11.0 17.0 21.0 12.0 8.5 M 6.0 21.0 12.0 9.0 19.0 -1.0 10.0 17.0 10.0 16.0 15.0 16.0 12.5
- A 5.0 9.0 25.0 24.0 16.0 7.0 0.0 13.0 -4.0 28.0 39.0 28.0 13.7 - - - 2080 B 35.0 34.0 18.0 40.0 19.0 -4.0 26.0 17.0 11.0 43.0 36.0 19.0 15.5 Mean = - C 14.5 21.0 18.0 14.0 11.0 31.0 26.0 -2.0 21.0 -1.0 0.0 14.0 1.0 9.3
D 11.0 18.0 6.0 30.0 12.0 17.0 19.0 6.0 -7.0 21.0 21.0 5.0 13.3 M 24.0 31.0 9.0 32.0 18.0 -2.0 10.0 14.0 26.0 39.0 18.0 30.0 20.8
Table_03 Click here to download Table: Kienzle_etal_2010_JH_Table03.docx
Table 3
Simulated percent change, relative to the baseline 1961-1990 time period, in mean annual
potential (PET) and actual (AET) evapotranspiration, soil moisture, ground water (GW)
recharge, and snow water equivalent (SWE); declining values are in bold.
Period Climate Scenario PET AET Soil Moisture GW Recharge SWE A 3.4 -0.4 0.8 5.1 -17 B 16.6 3.6 0.0 0.1 -63 C 11.9 4.0 0.9 3.6 -49 2020 D 17.6 2.4 -0.1 -3.0 -65 M 8.2 2.8 1.1 2.4 -40 Average 11.5 2.5 0.5 1.6 -47 A 11.1 2.5 1.0 3.7 -48 B 33.7 6.1 0.5 0.7 -76 C 16.8 5.4 0.8 4.3 -61 2050 D 25.8 7.8 1.5 2.5 -71 M 12.3 6.4 3.5 9.4 -49 Average 19.9 5.6 1.4 4.1 -61 A 20.2 3.9 2.9 7.9 -64 B 47.4 10.7 1.0 8.0 -79 C 21.0 6.9 1.1 4.1 -66 2080 D 33.4 12.5 2.4 2.8 -77 M 12.8 8.7 5.3 17.0 -43 Average 27.0 8.5 2.5 8.0 -66
Table_04 Click here to download Table: Kienzle_etal_2010_JH_Table04.docx
Table 4
Simulated mean annual changes in water yield, in % relative to 1961-90 baseline period
(negative values in bold).
Scenario 2020 2050 2080 A 2.2 3.0 10.8 B -0.1 2.3 12.4 C 3.7 6.6 7.5 D -2.0 7.2 9.0 M 1.5 9.0 17.8 Average 1.1 5.6 11.5
Table_05 Click here to download Table: Kienzle_etal_2010_JH_Table05.docx
Table 5
Simulated seasonal changes in streamflow, in % relative to the 1961-90 baseline period
Season Period A B C D M Average Winter 2020s 81 38 25 47 41 46 (DJF) 2050s 55 107 47 86 71 73 2080s 89 197 84 140 89 120 Spring 2020s 28 61 21 40 20 34 (MAM) 2050s 51 138 22 77 38 65 2080s 63 220 72 101 56 102 Summer 2020s -4 -11 -1 -9 -3 -5 (JJA) 2050s -1 -24 -1 -5 0 -6 2080s -3 -32 -1 -12 7 -8 Fall 2020s -4 -10 -5 -16 -5 -8 (SON) 2050s -9 -13 -6 -14 6 -7 2080s -11 -4 -6 -8 15 -3
Background dataset for online publication only Click here to download Background dataset for online publication only: Research_Highlights.docx