High-Resolution, Integrated Hydrological Modeling of Climate Change Impacts on a Semi-Arid Urban Watershed in Niamey, Niger
Bibliographic record
Abstract
This study evaluates the impact of climate change on water resources in a large, semi-arid urban watershed located in the Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface–subsurface HydroGeoSphere model at a high spatial resolution. Historical (1980–2005) and projected (2020–2050) climate scenarios, derived from the outputs of three regional climate models (RCMs) under the regional climate projection (RCP) 4.5 scenario, were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predicted increases of up to 1.6% in annual rainfall and of 1.58 °C for mean annual temperatures between the historical and projected periods. The durations of the minimum environmental flow (MEF) conditions, required to supply drinking and agricultural water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater from 2020–2030, and then they will be reduced for the 2030–2050 statistical periods. All three RCMs consistently project a rise in groundwater table of more than 10 m in topographically high zones, where the groundwater table is deep, and an increase of 2 m in the shallow groundwater table.
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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 | 0.000 |
| Research integrity | 0.001 | 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".