Simulating Climate Change Impacts on Surface Water Resources Within a Lake‐Affected Region Using Regional Climate Projections
Bibliographic record
Abstract
Abstract This study aims to assess the impact of climate change on water resources in a large watershed within the Laurentian Great Lakes region, using the fully integrated surface‐subsurface model HydroGeoSphere. The hydrologic model is forced with an ensemble of high‐resolution climate projections from the Weather Research and Forecasting (WRF) model. The latter has been extended with an interactive lake model (FLake) to capture the effect of the Great Lakes on the regional climate. The WRF ensemble encompasses two different moist physics configurations at resolutions of 90, 30, and 10 km, as well as four different initial and boundary conditions, so as to control for natural climate variability. The integrated hydrologic model is run with a representative seasonal cycle, which effectively controls natural climate variability, while remaining computationally tractable with a large integrated model. However, the range of natural variability is also investigated, as are the impacts of climate model resolution and bias correction. The two WRF configurations show opposite climate change responses in summer precipitation, but similar responses otherwise. The hydrologic simulations generally follow the climate forcing; however, due to the memory of the subsurface, the differences in summer propagate throughout the entire seasonal cycle. This results in a set of dry scenarios with reduced streamflow and water availability year‐round and a set of wet scenarios with increased streamflow for all times excluding the spring peak, which does not increase. Most of the analysis focuses on streamflow, but changes in the seasonal cycle of baseflow and groundwater recharge are also analyzed.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".