Evaluating Climate Change Impacts on Soil Moisture and Groundwater Resources Within a Lake‐Affected Region
Bibliographic record
Abstract
Abstract The impact of climate change on surface water resources is reasonably well studied. However, the impact on groundwater resources has only been considered by a few studies worldwide. Here we present an analysis of climate change impacts on groundwater resources in a well‐instrumented 6,800‐km2 watershed in the Laurentian Great Lakes Basin. We employ a physics‐based modeling pipeline consisting of an ensemble of high‐resolution regional climate model projections based on the Weather Research and Forecasting model and the fully integrated three‐dimensional hydrologic model HydroGeoSphere. The Weather Research and Forecasting model is run at a resolution as fine as 10 km using two different physics configurations, while HydroGeoSphere simulates the terrestrial hydrosphere at subkilometer scale, from deep groundwater to surface water, including surface water‐groundwater interactions. The two Weather Research and Forecasting model physics configurations exhibit opposite climate change responses in summer precipitation. The hydrologic simulations follow the climate forcing, but due to the memory of the subsurface, differences in summer affect the entire seasonal cycle. In the drier climate scenario groundwater levels and recharge decline, while in the wetter scenario groundwater levels rise (recharge remains unchanged). Soil moisture changes accordingly, but primarily in late summer. It is also shown that the magnitude of climate change impacts on groundwater is strongly modulated by local physiographic features. In particular, regions where the groundwater table is deep (below 2 m; 15% of the area) show a high sensitivity to changes in climate forcing. Furthermore, changes in groundwater levels, recharge, and soil moisture typically occur in the same regions, suggesting potentially compounding impacts.
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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 | 0.001 |
| Open science | 0.000 | 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".