Yucaipa Integrated Hydrologic Model: simulating future climate scenarios in the Yucaipa Valley Watershed, San Bernardino and Riverside Counties, California
Bibliographic record
Abstract
Managing water resources in semiarid watersheds is challenging because of limited water supply and uncertain impacts of future climate conditions on groundwater resources. This paper examines the impact of future climate changes on the urban Yucaipa Valley watershed in southern California using an integrated hydrological model referred to herein as the Yucaipa model. Coupled Groundwater and Surface-water FLOW (GSFLOW) modeling software was used to simulate the nonlinear relationships between climate trends and precipitation partitioning into evapotranspiration (ET), runoff, and subsurface storage. Four global climate models (also known as general circulation models or GCMs), each with two greenhouse-gas (GHG) scenarios: Representative Concentration Pathway 4.5 (RCP45) (low emissions) and RCP85 (high emissions) are used to project varying future climate conditions. GCMs include the Canadian Earth System Model (CanESM2), Centre National de Recherches Météorologiques Climate Model version 5 (CNRM-CM5), Hadley Centre Global Environment Model version 2 – Earth System (HadGEM2-ES), and Model for Interdisciplinary Research on Climate version 5 (MIROC5) models. RCP85 scenarios tend to be wetter and warmer than RCP45, and in some cases the increased precipitation offsets increased loss to ET. The Yucaipa model's simulated hydrological conditions using climate projections predict decreased groundwater in storage in most scenarios due to increased natural ET, vegetation consumptive use, and streamflow out of the watershed. Only scenarios with substantial increases in annual precipitation were able to maintain groundwater in storage approximately the same as at the end of the historical period. The study also highlights increased future aridity despite increased precipitation, and larger precipitation events, which increase the risk of urban floods and decrease stream leakage.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".