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.

For cold regions, an ice cover reduces channel conveyance and hydroelectricity generation potential. Therefore, predicting the impact of ice cover on a river-reservoir system is of critical importance for hydro producers. Ice impact can be described using historical records, where typical conditions are characterized by a daily median ice factor (IF) curve. The daily median IF curve works well only for past years with typical climatic conditions. Moreover, the median curve would not respond to climate-induced changes in the ice cover. In this research, a novel statistical (ST) model, named ST-IF, is developed to simulate the impact of river ice on the conveyance of the Nelson River West Channel (NRWC) as a function of daily air temperature. ST-IF uses a series of statistically based functions, including regression and threshold functions to estimate different characteristics of IF, such as its initial and peak values, and its daily distribution during ice-on period. Model performance was evaluated against historical records and the daily median value of the ice cover impact. Results showed that ST-IF significantly improved the simulation of each year-specific IF curve in NRWC compared to the daily median curve. Moreover, the model was used to predict the impact of ice cover under future climate conditions using 19 climate simulations. Results showed that, due to the predicted warmer future, ice cover is expected to take longer to fully form. This leads to longer Ice Stabilization Program duration, higher program implementation cost, and potential additional downstream stakeholder impacts. In addition, earlier ice impact peak date, shorter ice impact duration, and lower ice impact magnitude leading to overall higher winter hydroelectricity generation potential for Manitoba Hydro are expected in the future. Such future alterations intensify from near to far future time periods.

For cold regions, an ice cover reduces channel conveyance and hydroelectricity generation potential. Therefore, predicting the impact of ice cover on a river-reservoir system is of critical importance for hydro producers. Ice impact can be described using historical records, where typical conditions are characterized by a daily median ice factor (IF) curve. The daily median IF curve works well only for past years with typical climatic conditions. Moreover, the median curve would not respond to climate-induced changes in the ice cover. In this research, a novel statistical (ST) model, named ST-IF, is developed to simulate the impact of river ice on the conveyance of the Nelson River West Channel (NRWC) as a function of daily air temperature. ST-IF uses a series of statistically based functions, including regression and threshold functions to estimate different characteristics of IF, such as its initial and peak values, and its daily distribution during ice-on period. Model performance was evaluated against historical records and the daily median value of the ice cover impact. Results showed that ST-IF significantly improved the simulation of each year-specific IF curve in NRWC compared to the daily median curve. Moreover, the model was used to predict the impact of ice cover under future climate conditions using 19 climate simulations. Results showed that, due to the predicted warmer future, ice cover is expected to take longer to fully form. This leads to longer Ice Stabilization Program duration, higher program implementation cost, and potential additional downstream stakeholder impacts. In addition, earlier ice impact peak date, shorter ice impact duration, and lower ice impact magnitude leading to overall higher winter hydroelectricity generation potential for Manitoba Hydro are expected in the future. Such future alterations intensify from near to far future time periods.

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.

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society. • Sensitivity analysis (SA) should be promoted as an independent discipline. • Several grand challenges hinder full realization of the benefits of SA. • The potential of SA for systems modeling & machine learning is untapped. • New prospects exist for SA to support uncertainty quantification & decision making. • Coordination rather than consensus is key to cross-fertilize new ideas.

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society. • Sensitivity analysis (SA) should be promoted as an independent discipline. • Several grand challenges hinder full realization of the benefits of SA. • The potential of SA for systems modeling & machine learning is untapped. • New prospects exist for SA to support uncertainty quantification & decision making. • Coordination rather than consensus is key to cross-fertilize new ideas.

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.

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.