Open data analysis of terrestrial water storage and water availability in the Middle East: Spatiotemporal trends, hydroclimatic drivers, and socio-ecological implications
Bibliographic record
Abstract
This study examines the spatiotemporal variability of Terrestrial Water Storage (TWS) and Water Availability (WA) across the Middle East (ME) from 2002 to 2024 using exclusively open-access datasets, including GRACE/GRACE-FO mascon solutions, GLDAS-Noah simulations, CHIRPS precipitation records, and global aridity indices. The contributions of six hydroclimatic variables, such as snow water equivalent, canopy water storage, soil moisture storage, groundwater storage, precipitation, and evapotranspiration, to TWS and WA were quantified through component contribution ratio analysis and Least-Squares Cross Wavelet Analysis (LSCWA). The harmonized and reconstructed datasets provided here are openly accessible, enabling reproducibility and further regional water studies. Results reveal a critical decline in ME water storage, with an average depletion of −45 km 3 annually, and widespread WA deficits affecting about half the region. Groundwater storage emerged as the dominant contributor to TWS variability, particularly under arid and hyper-arid conditions, whereas soil moisture and snow water played stronger roles in humid zones. The coherency analysis indicates that annual cycles of TWS and WA were strongly linked with hydroclimatic drivers before 2020 but weakened in subsequent years. These findings, underpinned by openly shared datasets, provide essential resources and insights for water management strategies and sustainable policy development in one of the world's most water-stressed regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".