Soil water content (SWC) is one of the most important hydrologic variables; it plays a decisive role in the control of various land surface processes. We simulated SWC using a Soil and Water Assessment Tool (SWAT) model in southern Saskatchewan. SWC was calibrated using measured data and Soil Moisture Active Passive (SMAP) Level-4 for the surface (0–5 cm) SWC for hydrological response units (HRU) at daily and monthly (warm season) intervals for the years 2015 to 2020. We used the SUFI-2 technique in SWAT-CUP, and observed daily instrumented streamflow records, for calibration (1995 to 2004) and validation (2005–2010). The results reveal that the SWAT model performs well with a monthly PBIAS < 10% and Nash–Sutcliffe efficiency (NS) and R² ≥ 0.8 for calibration and validation. The correlation coefficient between ground measurement with SMAP and SWAT products are 0.698 and 0.633, respectively. Moreover, SMAP data of surface SWC coincides well with measurements in terms of both amount and trend compared with the SWAT product. The highest r value occurred in July when the mean r value in SWAT and SMAP were 0.87 to 0.84, and then in June for r value of 0.75. In contrast, the lowest values were in April and May (0.07 and 0.04, respectively) at the beginning of the growing season in southern Saskatchewan. Furthermore, calibration in the SWAT model is based on a batch form whereby parameters are adjusted to corresponding input by modifying simulations with observations. SWAT underestimates the abrupt increase in streamflow during the snowmelt months (April and May). This study achieved the objective of developing a SWAT model that simulates SWC in a prairie watershed, and, therefore, can be used in a subsequent phase of research to estimate future soil moisture conditions under projected climate changes.

Soil water content (SWC) and soil temperature are important hydrologic state variables. Accurate model simulation is critical in hydrologic regimes in cold regions dominated by spring snowmelt. In this study, we developed a combined physically-based soil temperature and energy-balance rain-on-snow (ROS) module for the Soil and Water Assessment Tool (SWAT) model and applied it to the South Saskatchewan River Basin (SSRB). We calibrated the SWAT base (SWAT-B) model and the SWAT modified (SWAT–M) model using daily measured soil temperature and SWC by hydrological response unit (HRU) for the years 2015 to 2020. The results of sensitivity analysis using the SUFI-2 technique in SWAT-CUP indicated that eight parameters have the most significant (p < 0.5) effect on streamflow, soil moisture, and snowmelt. Statistics for the SWAT-B and SWAT-M streamflow models revealed that the new module improved the Nash-Sutcliffe efficiency (NSE) from 0.39 to 0.71 and 0.42 to 0.76 for calibration and validation, respectively. The statistics for SWAT-simulated daily SWC showed that the measured data were a better fit with SWAT-M versus the SWAT-B output. Furthermore, SWAT-B values exceeded SWAT-M output and field measurements, and thus, the range of SWAT-M results was a better fit with observations. SWAT-B tended to underestimate soil temperature in the cold season, while SWAT-M significantly improved soil temperature simulation for winter. This new SWAT module simulated freeze-thaw cycles and captured the influence of snow cover on surface soil ice-water content. Spatial analysis of SWC and soil temperature across the SSRB showed that the SWAT-M model predicted more SWC and lower soil temperature in the western part of SSRB than SWAT-B, with higher soil temperature and lower SWC in the eastern region.

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI < 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

The main objective of this research was to understand the effects of climate change on soil water content (SWC) across the Qu’Appelle River basin in southern Saskatchewan, Canada. The Soil and Water Assessment Tool (SWAT) and output from 11 high-resolution (0.22°) regional climate models (RCM) were used over three 30-year periods: the near future (2021–2050) and distant future (2051–2080) and the historical reference (1975–2005). The RCM data are from the CORDEX North American domain, which employs the RCP8.5 high-emission scenario. SWC was modeled at the hydrological response units (HRU) level and at daily and monthly (warm season only) intervals for 2015–2020. The model was calibrated and validated using SUFI-2 in SWAT-CUP based on observations for streamflow and SWC, including measured data and Soil Moisture Active Passive (SMAP) Level 4 for surface (up to 5 cm deep) soil moisture. Values of the Nash–Sutcliffe model efficiency (NS) ranged from 0.616 and 0.784 and the coefficient of determination (R²) was 0.8 for calibration and 0.82 for validation. Likewise, the correlation coefficients between ground measurements and SWAT were 0.698 and 0.633, respectively. Future climate forcing of the calibrated SWAT model revealed that warmer and drier growing seasons will prevail in the region. Similarly, the ensemble of all RCMs indicated that the mean temperature will increase by 2.1 °C and 3.4 °C for the middle and late periods, respectively, along with a precipitation increase of 10% and 11.2%. SWC is expected to decrease with an increase in potential evapotranspiration, despite an increase in precipitation. Likewise, the annual SWC is expected to decrease by 3.6% and 4% in the middle and late periods, respectively. The monthly SWC changes showed the highest decreases (5.4%) in April in the late period. The spatial pattern of SWC for 11 RCMs was similar such that the northwest and west of the river basin are wetter than the south and east. SWC projections suggest that southern Saskatchewan could experience significant SWC deficiencies in the summer by the end of this century.

Climate change has a potentially significant influence on agricultural production in southern Saskatchewan. Crop yields are susceptible to weather patterns and seasonal fluctuations in this sub-humid region owing to the predominance of rain-fed farming practices. A modified Soil and Water Assessment Tool (SWAT-M) and the output from 10 high-resolution (0.22°) regional climate models (RCMs) were used to develop simulations of spring wheat and rain-fed canola in 296 rural municipalities (RM) for a historical baseline period (1975–2004) and three 30-year future periods: near (2010–2039), middle (2040–2069), and far (2070–2099). We combined SWAT-M with the S-curve method to adjust yield to the original drought stress in the source code and evaluated eight indices of extreme precipitation and temperature. Results of calibration and validation suggest that the simulated crop yields generally agree with observed data. Crop yield showed lesser performance compared with streamflow and soil water content (SWC) along with percent bias, ranging from −9.6% to −14.8%, while streamflow calibration ranges from −5.3% to −7.7%. The multi-model ensemble median showed increasing radiative forcing in the temperature and precipitation indices, such that the RCM-projected weather indices were found to be warmer and wetter than those estimated using regional historical data. The results of simulating canola and spring wheat indicate an increase in crop yield of 17% and 9.7% in the near future, 28.2% and 15.6% in the middle future, and 44.7% and 32% in the far future, respectively. Although, there has been an increase in the median wheat and canola yields, a significant reduction in the annual production is observed. This decline in yield amounts to around 1000 kg/ha and is anticipated to occur in the near and middle future. This trend is quite pronounced in the extreme south and southwest regions. Overall, this innovative research framework, along with the region-specific model outcomes in the form of crop yield projections, will aid in the formulation of future agricultural policies aimed at promoting effective climate adaptation strategies.

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies.