Characterising the Hydrology and Water Resources of a Regulated Cold‐Regions River Basin Using a Land Surface Hydrological Model
Bibliographic record
Abstract
ABSTRACT The MESH hydrological model, driven by a 10 km meteorological reanalysis, was deployed to simulate the Saskatchewan River Basin (SRB), a 406 000 km 2 cold‐region basin in Western Canada with diverse climate zones and extensive human regulation. The model was validated using multi‐source observation and enabled detailed assessment of the basin's water balance components, runoff generation processes and irrigation impacts on hydrology. The model achieved Kling‐Gupta Efficiency values of 0.35–0.85 across 23 streamflow stations (2005–2016), indicating reliable capture of observed flow regimes and reservoir regulation effects. Simulated evapotranspiration correlated strongly with satellite estimates (GLEAM, r = 0.98), and the model realistically reproduced seasonal snowpack dynamics and GRACE‐derived water storage variations, with minor underestimation of peak snow water equivalent. Glacier diagnostics revealed that total runoff from glacier‐covered areas contributes ~2.9% of SRB's mean annual runoff, of which 0.75% is glacier ice melt. Glacier ice melt runoff contributions varied by sub‐basin, with the highest proportions from high‐elevation regions: 1.96% to the North Saskatchewan near Edmonton, 1.14% to the Bow near its mouth, 0.66% to the Oldman and 0.32% to the Red Deer. A negative glacier mass balance trend strongest in southern sub‐basins, signals declining ice reserves and the long‐term vulnerability of glacier‐fed water supplies. Diagnosis of runoff processes revealed significant variability in runoff generation, particularly in mountain headwaters and identified snowmelt as the dominant contributor, involved in 84.2% of runoff generation, broken down as snowmelt 43.4%, rain‐on‐snowmelt 10.2% and mixed events 30.6% of the SRB's annual runoff. Rainfall events contributed 15.8% and events with rainfall involved totalled 56.6% of annual runoff. This highlights the complexity of runoff generation processes in the SRB and the substantial role of snowmelt in sustaining the basin's hydrology. The impact of irrigation on evapotranspiration and streamflow was significant, with irrigation increasing mean annual evapotranspiration by 26.4% and reducing streamflow in key locations by up to 11%. Overall, this study provides a comprehensive and validated understanding of the SRB's hydrology and water resources, emphasising the influence of interactions between natural processes and human interventions. The insights from this research can inform water management strategies, particularly those aimed at adapting to future environmental changes. The findings underscore the importance of MESH as a robust tool for coupled hydrological and water resources modelling in managed, diverse, cold‐regions basins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".