Nelson Churchill River Basin (NCRB), Canada, and USA. Soil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model’s soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale. Our study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21–0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis.

Nelson Churchill River Basin (NCRB), Canada, and USA. Soil temperature and moisture are essential variables that fluctuate based on soil depth, controlling several sub-surface hydrologic processes. The Hydrological Predictions for the Environment (HYPE) model’s soil profile depth can vary up to four meters, discretized into three soil layers. Here, we further discretized the HYPE subsurface domain to accommodate up to seven soil layers to improve the representation of subsurface thermodynamics and water transfer more accurately. Soil moisture data from different locations across NCRB are collected from 2013 to 2017 for model calibration. We use multi-objective optimization (MOO) to account for streamflow and soil moisture variability and improve the model fidelity at a continental scale. Our study demonstrates that MOO significantly improves soil moisture simulation from the median Kling Gupta Efficiency (KGE) of 0.21–0.66 without deteriorating the streamflow performance. Streamflow and soil moisture simulation performance improvements are statistically insignificant between the original three-layer and seven-layer discretization of HYPE. However, the finer discretization model shows improved simulation in sub-surface components such as the evapotranspiration when verified against reanalysis products, indicating a 12 % underestimation of evapotranspiration from the three-layer HYPE model. The improvement of the discretized HYPE model and simulating the soil temperature at finer vertical resolution makes it a prospective model for permafrost identification and climate change analysis.

Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.

Intercomparison studies play an important, but limited role in understanding the usefulness and limitations of currently available hydrological models. Comparison studies are often limited to well-behaved hydrological regimes, where rainfall-runoff processes dominate the hydrological response. These efforts have not covered western Canada due to the difficulty in simulating that region’s complex cold region hydrology with varying spatiotemporal contributing areas. This intercomparison study is the first of a series of studies under the intercomparison project of the international and interprovincial transboundary Nelson-Churchill River Basin (NCRB) in North America (Nelson-MIP), which encompasses different ecozones with major areas of the non-contributing Prairie potholes, forests, glaciers, mountains, and permafrost. The performance of eight hydrological and land surface models is compared at different unregulated watersheds within the NCRB. This is done to assess the models’ streamflow performance and overall fidelity without and with calibration, to capture the underlying physics of the region and to better understand why models struggle to accurately simulate its hydrology. Results show that some of the participating models have difficulties in simulating streamflow and/or internal hydrological variables (e.g., evapotranspiration) over Prairie watersheds but most models performed well elsewhere. This stems from model structural deficiencies, despite the various models being well calibrated to observed streamflow. Some model structural changes are identified for the participating models for future improvement. The outcomes of this study offer guidance for practitioners for the accurate prediction of NCRB streamflow, and for increasing confidence in future projections of water resources supply and management.

The North American prairie region is known for its poorly defined drainage system with numerous surface depressions that lead to variable contributing areas for streamflow generation. Current approaches of representing surface depressions are either simplistic or computationally demanding. In this study, a variable contributing area algorithm is implemented in the HYdrological Predictions for the Environment (HYPE) model and evaluated in the Canadian prairies. HYPE's local lake module is replaced with a Hysteretic Depressional Storage (HDS) algorithm to estimate the variable contributing fractions of subbasins. The modified model shows significant improvements in simulating the streamflows of two prairie basins in Saskatchewan, Canada. The modified model can replicate the hysteretic relationships between the water volume and contributing area of the basins. With the inclusion of the HDS algorithm in HYPE, the global HYPE modelling community can now simulate an important hydrological phenomenon, previously unavailable in the model.