Investigating terrestrial water storage change in a western Canadian river basin with GRACE/GRACE-FO and fully-integrated groundwater–surface water modelling
Bibliographic record
Abstract
Abstract. As hydrological trends shift in response to a warming climate, accurate characterization of hydrologic conditions and hydrologic change are imperative for water resources management, which is particularly important in the Canadian Prairies. In the study herein, a HydroGeoSphere (HGS) fully integrated groundwater–surface water (GW–SW) model is employed to evaluate trends and drivers of surface and subsurface water storage changes in the South Saskatchewan River Basin (SSRB). Terrestrial water storage anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) are compared to HGS results; strong correlation is identified. The HGS solution facilitates decomposition of TWSA into constituent water storage components, namely surface water, soil moisture, and groundwater, and the GRACE/GRACE-FO solutions are used to validate the regional-scale TWSA and the interannual trends present in the SSRB TWSA time series. Meteorological and oceanic drivers and their impact on interannual hydrological trends in the SSRB are examined. Time-frequency analysis reveals a harmonic trend present in the SSRB TWSA with a period of 2.7–3.0 years, the inverse of which is present in the Oceanic Niño Index. The largest intra-annual water storage fluctuation is found in the soil profile, followed by snowpack, while groundwater experiences longer, multi-year cyclicity. Warmer oceanic conditions align with dry conditions in the SSRB and less snowpack, which leads to negative TWSA anomalies. Incorporating both high-resolution GW–SW models and regional-scale satellite gravimetry-derived estimates of TWSA facilitates a comprehensive analysis of hydrological dynamics in the Canadian Prairies and improved characterization of surface water and groundwater storage changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".