Abstract Operational flood forecasting in Canada is a provincial responsibility that is carried out by several entities across the country. However, the increasing costs and impacts of floods require better and nationally coordinated flood prediction systems. A more coherent flood forecasting framework for Canada can enable implementing advanced prediction capabilities across the different entities with responsibility for flood forecasting. Recently, the Canadian meteorological and hydrological services were tasked to develop a national flow guidance system. Alongside this initiative, the Global Water Futures program has been advancing cold regions process understanding, hydrological modeling, and forecasting. A community of practice was established for industry, academia, and decision‐makers to share viewpoints on hydrological challenges. Taken together, these initiatives are paving the way towards a national flood forecasting framework. In this article, forecasting challenges are identified (with a focus on cold regions), and recommendations are made to promote the creation of this framework. These include the need for cooperation, well‐defined governance, and better knowledge mobilization. Opportunities and challenges posed by the increasing data availability globally are also highlighted. Advances in each of these areas are positioning Canada as a major contributor to the international operational flood forecasting landscape. This article highlights a route towards the deployment of capacities across large geographical domains.

Abstract Operational flood forecasting in Canada is a provincial responsibility that is carried out by several entities across the country. However, the increasing costs and impacts of floods require better and nationally coordinated flood prediction systems. A more coherent flood forecasting framework for Canada can enable implementing advanced prediction capabilities across the different entities with responsibility for flood forecasting. Recently, the Canadian meteorological and hydrological services were tasked to develop a national flow guidance system. Alongside this initiative, the Global Water Futures program has been advancing cold regions process understanding, hydrological modeling, and forecasting. A community of practice was established for industry, academia, and decision‐makers to share viewpoints on hydrological challenges. Taken together, these initiatives are paving the way towards a national flood forecasting framework. In this article, forecasting challenges are identified (with a focus on cold regions), and recommendations are made to promote the creation of this framework. These include the need for cooperation, well‐defined governance, and better knowledge mobilization. Opportunities and challenges posed by the increasing data availability globally are also highlighted. Advances in each of these areas are positioning Canada as a major contributor to the international operational flood forecasting landscape. This article highlights a route towards the deployment of capacities across large geographical domains.

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.

Abstract The proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.

Lower Nelson River Basin, Manitoba, Canada Hydroelectricity makes up almost 97% of electricity generated in Manitoba, of which over 70% of its generation capacity is installed along the Lower Nelson River (LNR). In this study, 19 climate projections representing ~ 87% of climatic variability over Hudson Bay Drainage Basin are applied to coupled hydrologic-operations models to estimate water supply and hydropower generation potential changes under future climates. Future inflow to the forebay of the main hydropower generating stations along LNR is expected to increase in spring and summer but decrease in winter and fall. Consequently, hydropower generation potential is projected to increase for spring, the historical flood season, which may lead to reduced reservoir inflow retention efficiency. In extremely dry climatic simulations, winter seasons see a reduction in reservoir inflow and hydropower generation potential, up to 35% and 37% in 2021–2050 and 2041–2070, respectively. Projected changes in reservoir inflow and hydropower generation potential continue to diverge over time, with dry scenarios becoming drier and wet becoming wetter, yielding high basin climate sensitivity and uncertainty with system supply and generation potential. Despite the presence of statistically significant individual trends and changes, there is a low agreement within the climate ensemble. Analysis of system robustness shows adjustment of the operations along LNR should be considered over time to better leverage changing seasonal water supply. • Unique dynamic coupling of climate-hydrologic-operations models. • Projected reservoir inflow and hydropower generation potential for LNRB. • No significant change or trend in mean or median values due to uncertainty. • Wet seasons are getting wetter, dry seasons are getting drier. • Increase in uncertainty and extremes under future climates poses operational challenge.

AbstractHydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequen...

AbstractHydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequen...

Oxygen-18 and deuterium were measured in streamflow samples collected from 331 gauging stations across Canada during 2013 to 2019. This dataset includes 9206 isotopic analyses made on 4603 individual water samples, and an additional 1259 analysis repeats for quality assurance/quality control. We also include arithmetic and flow-weighted averages, and other basic statistics for stations where adequate data were available. Station data are provided including station code, name, province, latitude, longitude and drainage area. Flow data were extracted from the historical database of the Water Survey of Canada. Details on the preliminary application of these data are provided in “ 18 O and 2 H in streamflow across Canada” [1] . Overall, these data are expected to be useful when combined with precipitation datasets and analytical or numerical models for water resource management and planning, including tracing streamflow source, water balance, evapotranspiration partitioning, residence time analysis, and early detection of climate and land use changes in Canada.

Abstract This study develops a novel reservoir regulation routine, incorporated into a continental-scale hydrologic model in the Nelson, Churchill, Yenisey, Ob, and Lena basins. This regulation routine is integrated into the Hydrological Predictions for the Environment (HYPE) hydrologic model, used for continental-scale applications. Applying this daily timestep regulation routine at 19 reservoirs in the Arctic Ocean watershed, performance is shown to improve upon the reservoir regulation currently available in the HYPE model when testing outflow and storage Nash Sutcliffe Efficiencies (NSEs). Improvements stem from intra-annually variable storage rule curves and a variety of stage-dependent outflow functions, improving simulation skill (median NSE increases of 0.18 over 21 reservoir outflow records and 0.49 over 19 reservoir storage records). This new, reservoir regulation routine is suitable for continental-scale modelling by deriving varying, rather than fixed, threshold water surface levels and associated outflow rules in a programmatic way for multiple reservoirs.

The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.

Funding and in-kind support for analytical costs and logistics was provided by Environment and Climate Change Canada via a Grants and Contributions Agreement and by InnoTech Alberta via an Internal Investment Grant.

Accurate representation of flow sources in process‐based hydrologic models remains challenging for remote, data‐scarce regions. This study applies stable isotope tracers (18O and 2H) in water as auxiliary data for the calibration of the isoWATFLOOD™ model. The most efficient method of those evaluated for introducing isotope data into model calibration was the PA‐DDS multiobjective search algorithm. The compromise solutions incorporating isotope data performed slightly inferior in terms of streamflow simulation compared to the calibrated solution using streamflow data only. However, the former solution outperformed the latter one in terms of isotope simulation. Approximation of the model parameter uncertainty into internal flow path partitioning was explored. Inclusion of isotope error facilitated a broader examination of the total parameter space, resulting in significant differences in internal storage and flow paths, most significantly for soil storage and evapotranspiration loss. Isotope‐optimized calibration reduced evaporation rates and increased soil moisture content within the model, impacting soil water velocity but not streamflow celerity. Flow‐only calibration resulted in artificially narrow model prediction bounds, significantly underestimating the propagation of parameter uncertainty, while isotope‐informed calibrations yielded more reliable and robust bound on model predictions. Our findings demonstrate that the accuracy of a complex, spatially distributed, and process‐based model cannot be judged from one summative flow‐based model performance evaluation metric alone.

Calibration of hydrological models is challenging in high-latitude regions where hydrometric data are minimal. Process-based models are needed to predict future changes in water supply, yet often w...

ABSTRACTThis study evaluates the 1981–2010 spatiotemporal differences in six available climate datasets (daily total precipitation and mean air temperature) over the Lower Nelson River Basin (LNRB)...