Regional Modeling of Climate Change Impacts on Groundwater Resources Sustainability in Peninsular Malaysia
Bibliographic record
Abstract
Projection of climate for the 2020s and 2080s from an ensemble of global climate models (GCMs) run under A2, A1B and B1 emission scenarios are used for regional modeling of climate change impacts on groundwater resources sustainability in Peninsular Malaysia. Few studies that have modeled climate change impacts on groundwater resources used the physically-based surface-subsurface flow model. In this paper, the suite of GCM outputs were modeled for the impact studies via integrative approach involving empirical modeling equation, GIS-based geostatistical technique and model forecasting accuracy optimization. A range of predictions is obtained by modeling the precipitation and temperature change factors derived from fifteen (15) GCMs forced with three (3) future emission scenarios for 50 years periods between 2000 to 2049 (2020s) and 2050 to 2099 (2080s) reference to the baseline period (1950 to 1999). The ensemble average suggests there will be a 1% reduction in monthly recharge in 2020s and 7-10% recharge increment in 2080s across the study area. The spread of predictions for recharge and PET rates across the area ranges from 12.05 to 17.83 mm/day and 3.72 to 4.05, respectively. Geostatistical analysis enabled generation of recharge rate and PET rate prediction maps. The prediction maps were classified into low (L), medium (M) and high (H) recharge and PET rated zones in GIS environment. The recharge rate model map revealed that more than 60% of the area coverage in the study area is characterized by low rating recharge, particularly within 2020s future period. The multiple climate models results provided the highest likelihood mean estimate as well as a measure of its uncertainty and less probable outcomes. Results suggest that recharge reduction in the area is an evidence of water resources scarcity in 2020s. The results of this work provided the basis for the inclusion of representative climate scenarios into the Peninsular Malaysia water resources’ existing decision support system model useful for policy and decision making in the area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".