Hydroclimatic assessment of water resources of low Pacific islands: evaluating sensitivity to climatic change and variability
Bibliographic record
Abstract
Abstract For many of the low islands of the tropical Pacific, freshwater is a scarce resource. Water catchment areas are small and groundwater storage is a shallow fresh water lens. The high hydraulic conductivities of the coral and sand substrate means surface water is limited. Realization of the possible impact of climate change has highlighted the sensitivity of island communities to the availability of water. However, impact evaluation requires specialized data as well as appropriate sensitivity assessment methodologies. This is the second of a two part study. The first addressed the data problem by assembling and validating a suitable database. The second develops an island water balance model and applies a sensitivity assessment. Data are at a 2.5° × 2.5° latitude–longitude grid resolution for the Pacific bounded by coordinates 30°S to 30°N and 155°E to 120°W. Output is in the form of Climate Change Sensitivity Index maps that show the impact on the spatial redistribution of climate‐determined freshwater resources under various climate scenarios. The method allows for estimation of water deficits or surpluses for low islands located in any part of the study area. Areas of high sensitivity to climatic change are those that sit between margins of very wet and very dry zones. Their extent is determined by the gradients at the margins. Steep gradients define small areas of high sensitivity, whereas gentle gradients appear as large areas of high sensitivity. Adjustments to the model for differing local surface conditions on different islands can be easily made, which allows a sensitivity assessment of individual islands, even for islands with no climate station data. The approach could be a powerful tool to gain useful information on the influence of climate change on freshwater resources of low islands. Planning decision‐making is possible without knowing precisely the magnitude of climate change that might occur.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".