Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century
Bibliographic record
Abstract
Study region Saudi Arabia. Study focus The major goal of this study is to downscale GRACE (Gravity Recovery and Climate Experiment) groundwater storage (GWS) anomalies to assess the local-scale vulnerabilities of groundwater changes across western regions of Saudi Arabia (Al Jumum, Makkah, Jeddah, and Bahrah). This was accomplished by using multi-model ensemble machine learning (ML) approach leveraging Random Forest, CART, and Gradient Tree Boosting algorithms within Google Earth Engine (GEE). Additionally, we used the downscaled GWS and CMIP6 climate data with the Generalized Additive Model (GAM) to project the future GWS changes under climate change. New hydrological insights for the region The ensemble results demonstrated robust performance (R² = 0.92 and RMSE = 20 mm) compared to the individual model (R² = 0.84–0.88 and RMSE = 25–28 mm). The areas of higher groundwater depletion were predominantly observed in Jeddah and Makkah, with average annual rates of − 165 mm/year and − 150 mm/year, respectively, from 2002 to 2023. The total volumetric losses range from 11.38 km³ to 15.31 km³ across different sub-regions. Seasonally, the peak GWS drop (-90 to − 125 mm) was detected during the summer months (April–July), aligning with periods of maximum water demand. Several key drivers that control the GWS changes were also identified, including anthropogenic effects, local climate anomalies, and large-scale climate oscillations. Projections for GWS reveal an irreversible decline throughout the 21st Century with potential reductions surpassing − 216 mm/year in high-emission scenarios (SSP5-8.5). The developed approach is transferable to other regions for quantitative assessment of local groundwater changes.
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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".