Changing Water Resources Under El Niño, Climate Change, and Growing Water Demands in Seasonally Dry Tropical Watersheds
Bibliographic record
Abstract
Abstract The wet‐dry tropics of Central America are characterized by long dry seasons, during which communities often struggle for water. High interannual rainfall variability, driven in parts by the El Niño Southern Oscillation (ENSO), increases these challenges. Further, climate change projections indicate that the region will likely become drier. However, research on impacts on water resources at the watershed scale is limited in the region—yet this information is essential for water managers. Therefore, in this research, we quantified the potential impacts of ENSO and four different climate change scenarios on water resources in two watersheds in the wet‐dry tropics of Guanacaste, Costa Rica, using a hydrological model (Water Evaluation and Planning Tool [WEAP]). Given that the watersheds are human used, we also explored different water demand scenarios. Modeling results indicated that an extreme El Niño can reduce groundwater recharge and streamflow by ∼60% relative to ENSO neutral. For 2075–2100, modeling results indicated that while potential evapotranspiration increases, actual evapotranspiration decreases due to limited water availability. Further, climate change may lead to reductions of mean annual streamflow and groundwater recharge by 40%–45% and 26%–28%, respectively, in comparison to the historical baseline. Importantly, high population growth could further hasten potentially irreversible groundwater storage declines. On the other hand, reduction of per‐capita water demand could slow down, or even reverse, the decline of groundwater storage.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".