Projection of precipitation variability over the highlands of Yemen by statistical down-scaling for the period 2026–2100
Bibliographic record
Abstract
Climate change significantly affects the management of environmental resources, particularly through changes in the amount and variability of local climate variables, such as precipitation. However, current projections from Global Climate Models (GCMs) are not directly applicable to local-scale impact modeling due to their coarse spatial resolution and inherent biases. To address this challenge, the Statistical Down-Scaling Model (SDSM) is employed to downscale daily precipitation, a crucial input for impact assessment models. This study focuses on the highlands of Yemen, a region highly vulnerable to climate change and precipitation variability. Due to limited and incomplete local climate data, we utilized the best available precipitation datasets, including the Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), to fill in missing station data. Historical and future predictors derived from the National Center for Environmental Prediction (NCEP) reanalysis and the Canadian Earth System Model Phase 2 (CanESM2) were used to generate future precipitation scenarios, which were compared with the ensemble means from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We also used the Shared Socioeconomic Pathways (SSPs) scenarios, specifically SSP126 and SSP585, to evaluate potential future changes in precipitation. Results indicate a projected increase in seasonal precipitation during the 2030s (2026–2050), 2060s (2051–2075), and 2090s (2076–2100). The western highlands, including Al Mahwit, Rymah, and parts of Sana'a governorate, are expected to experience precipitation increases of up to 55 %. Under RCP2.6, the short rainy season (March–May) is projected to increase up to 14 %, while under RCP8.5, this increase could reach 24 %. The long rainy season (June–August) is expected to increase by 6 % under RCP2.6 and 27 % under RCP8.5. The dry season (December–February) could see increases of 18 % under RCP2.6 and 46 % under RCP8.5, while the autumn season (September–November) may experience a substantial rise of 61 %–101 %. At the annual timescale, precipitation is projected to increase up to 34 % higher than the baseline period (1991–2020) across the region. These projections indicate that the highlands of Yemen will experience wetter conditions in the 21st century. The findings provide valuable insights for developing adaptation strategies for water and environmental resource management, considering the potential future impacts of climate change in the region. • Projection of future precipitation trends in the highlands of Yemen using the Statistical Downscaling Model (SDSM) for the period 2026–2100. • Significant seasonal and annual increases in precipitation projected under both RCP2.6 and RCP8.5 climate scenarios. • The highest projected precipitation increases, mainly in autumn, occur in the western highlands of Yemen. • Results provide critical insights for water resource management and climate adaptation strategies in a region highly vulnerable to climate change.
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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 | 0.000 | 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".