edu.ucar.opensky::articles:43888
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2025-12-24T17:43:55.843014
ISO 19115-2 Geographic Information - Metadata - Part 2:
Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
https://n2t.net/ark:/85065/d7wq086f
https://doi.org/10.1594/PANGAEA.954742?format=html#download
https
Related Dataset #1
GLWS 2.0: A global product that provides total water storage anomalies, groundwater, soil moisture and surface water with a spatial resolution of 0.5° from 2003 to 2019
https://doi.org/10.24381/cds.68d2bb30
https
Related Dataset #2
ERA5-Land monthly averaged data from 1950 to present
https://doi.org/10.48690/1529167
https
Related Dataset #3
LandScan Global 2022
Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century
2025-08-01T00:00:00Z
publication
https://n2t.net/ark:/85065/d7wq086f
https://doi.org/10.1016/j.ejrh.2025.102552
Arshad, A.
author
Shafeeque, M.
author
Tran, T. N. D.
author
Mirchi, A.
author
Xiang, Z.
author
He, Cenlin
author
AghaKouchak, A.
author
Besnier, J.
author
Rahman, M. M.
author
UCAR/NCAR - Library
publisher
<h3 class="u-h4 u-margin-m-top u-margin-xs-bottom" style="box-sizing:border-box;font-size:20px !important;font-weight:normal !important;line-height:30px !important;margin-bottom:8px !important;margin-left:0px;margin-right:0px;margin-top:24px !important;padding:0px;" id="sect0010">Study region</h3><div class="u-margin-s-bottom" style="box-sizing:border-box;margin-bottom:16px !important;margin-left:0px;margin-right:0px;margin-top:0px;padding:0px;" id="sp0090">Saudi Arabia.</div></div><div style="-webkit-text-stroke-width:0px;box-sizing:border-box;color:rgb(31, 31, 31);font-family:ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif;font-size:16px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;margin:0px;orphans:2;padding:0px;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;" id="abs0015"><h3 class="u-h4 u-margin-m-top u-margin-xs-bottom" style="box-sizing:border-box;font-size:20px !important;font-weight:normal !important;line-height:30px !important;margin-bottom:8px !important;margin-left:0px;margin-right:0px;margin-top:24px !important;padding:0px;" id="sect0015">Study focus</h3><div class="u-margin-s-bottom" style="box-sizing:border-box;margin-bottom:16px !important;margin-left:0px;margin-right:0px;margin-top:0px;padding:0px;" id="sp0095">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.</div></div><div style="-webkit-text-stroke-width:0px;box-sizing:border-box;color:rgb(31, 31, 31);font-family:ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif;font-size:16px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;margin:0px;orphans:2;padding:0px;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;" id="abs0020"><h3 class="u-h4 u-margin-m-top u-margin-xs-bottom" style="box-sizing:border-box;font-size:20px !important;font-weight:normal !important;line-height:30px !important;margin-bottom:8px !important;margin-left:0px;margin-right:0px;margin-top:24px !important;padding:0px;" id="sect0020">New hydrological insights for the region</h3><div class="u-margin-s-bottom" style="box-sizing:border-box;margin-bottom:16px !important;margin-left:0px;margin-right:0px;margin-top:0px;padding:0px;" id="sp0100">The ensemble results demonstrated robust performance (R² = 0.92 and RMSE = 20â¯mm) compared to the individual model (R² = 0.84â0.88 and RMSE = 25â28â¯mm). The areas of higher groundwater depletion were predominantly observed in Jeddah and Makkah, with average annual rates of â 165â¯mm/year and â 150â¯mm/year, respectively, from 2002 to 2023. The total volumetric losses range from 11.38â¯km³ to 15.31â¯km³ across different sub-regions. Seasonally, the peak GWS drop (-90 to â 125â¯mm) was detected during the summer months (AprilâJuly), 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 â 216â¯mm/year in high-emission scenarios (SSP5-8.5). The developed approach is transferable to other regions for quantitative assessment of local groundwater changes.</div>
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