Modelling the Future: Groundwater Responses to Climate Change in Talomo-Lipadas Watershed, Davao City, Philippines
Bibliographic record
Abstract
This research investigates the long-term impact of climate change on groundwater recharge (seepage) within the Talomo-Lipadas Watershed, Davao City, Philippines, over the next eighty-nine (89) years. Employing the Statistical Downscaling Method (SDSM), stationscale climate scenarios were generated for three future time slices centered on 2020 (2011-20140), 2050 (2041-2070), and 2080 (2071-2100). These scenarios, indicating a projected increase in temperature within the watershed, were then used as input for the BROOK90 hydrological model to simulate groundwater recharge. The modelling results project a decline in groundwater supply from 109.01 million cubic meters (MCM) in 2020 to 103.53 MCM in 2050 and further down to 99.81 MCM by 2080. This projected decrease in groundwater recharge has significant implications beyond just water availability. Reduced groundwater flow can impact baseflow in rivers, affecting aquatic ecosystems and potentially exacerbating water scarcity during dry periods. Decreased recharge also has implications for other water-related sectors, including agriculture (irrigation), industry (water supply), and domestic water use, potentially leading to increased competition for dwindling resources. These findings underscore the urgent need for adaptation strategies to mitigate the effects of climate change on groundwater recharge within the Talomo-Lipadas Watershed. Further research employing diverse hydrological models is recommended to validate these findings and provide a more robust basis for developing sustainable water management plans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".