Analysis of Groundwater Depletion in the Saskatchewan River Basin in Canada from Coupled SWAT-MODFLOW and Satellite Gravimetry
Bibliographic record
Abstract
The Saskatchewan River Basin (SRB) of central Canada plays a crucial role in the Canadian Prairies. Yet, climate change and human action constitute a real threat to its hydrological processes. This study aims to evaluate and analyze groundwater spatial and temporal dynamics in the SRB. Groundwater information was derived and compared using two different approaches: (1) a mathematical modeling framework coupling the Soil and Water Assessment Tool (SWAT) and the Modular hydrologic model (MODFLOW) and (2) gravimetric satellite observations from the Gravity Recovery and Climate Experiment (GRACE) mission and its follow-on (GRACE-FO). Both methods show generalized groundwater depletion in the SRB that can reach −1 m during the study period (2002–2019). Maximum depletion appeared especially after 2011. The water balance simulated by SWAT-MODFLOW showed that SRB could be compartmented roughly into three main zones. The mountainous area in the extreme west of the basin is the first zone, which is the most dynamic zone in terms of recharge, reaching +0.5 m. The second zone is the central area, where agricultural and industrial activities predominate, as well as potable water supplies. This zone is the least rechargeable and most intensively exploited area, with depletion ranging from +0.2 to −0.4 m during the 2002 to 2011 period and up to −1 m from 2011 to 2019. Finally, the third zone is the northern area that is dominated by boreal forest. Here, exploitation is average, but the soil does not demonstrate significant storage power. Briefly, the main contribution of this research is the quantification of groundwater depletion in the large basin of the SRB using two different methods: process-oriented and satellite-oriented methods. The next step of this research work will focus on the development of artificial intelligence approaches to estimate groundwater depletion from a combination of GRACE/GRACE-FO and a set of multisource remote sensing data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".