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Investigating the Local-scale Fluctuations of Groundwater Storage by Using Downscaled GRACE/GRACE-FO JPL Mascon Product Based on Machine Learning (ML) Algorithm

Author

Listed:
  • Behnam Khorrami

    (Dokuz Eylul University)

  • Shoaib Ali

    (Northeast Agricultural University)

  • Orhan Gündüz

    (Izmir Institute of Technology)

Abstract

Groundwater storage is of grave significance for humanity by sustaining the required water for agricultural irrigation, industry, and domestic use. Notwithstanding the impressive contribution of the state-of-the-art Gravity Recovery and Climate Experiment (GRACE) to detecting the groundwater storage anomaly (GWSA), its feasibility for the characterization of GWSA variation hotspots over small scales is still a major challenge due to its coarse resolution. In this study, a spatial water balance approach is proposed to enhance the spatial depiction of groundwater storage and depletion changes that can detect the hotspots of GWSA variation. In this study, Random Forest Machine Learning (RFML) model was utilized to simulate fine-resolution (10 km) groundwater storage based on the coarse resolution (50 km) of GRACE observations. To this end, parameters including soil moisture, snow water, evapotranspiration, precipitation, surface runoff, surface elevation, and GRACE data were integrated into the RFML model. The results show that with a correlation of above 0.98, the RFML model is very successful in simulating the fine-resolution groundwater storage over the Western Anatolian Basin (WAB), Türkiye. The results indicate an estimated annual depletion rate of 0.14 km3/year for the groundwater storage of the WAB, which is equivalent to about 2.57 km3 of total groundwater depletion from 2003 to 2020. The findings also suggest that the downscaled GWSA is in harmony with the original GWSA in terms of temporal variations. The validation of the results demonstrates that the correlation is increased from 0.56 (for the GRACE-derived GWSA) to 0.60 (for the downscaled GWSA) over the WAB.

Suggested Citation

  • Behnam Khorrami & Shoaib Ali & Orhan Gündüz, 2023. "Investigating the Local-scale Fluctuations of Groundwater Storage by Using Downscaled GRACE/GRACE-FO JPL Mascon Product Based on Machine Learning (ML) Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3439-3456, July.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:9:d:10.1007_s11269-023-03509-w
    DOI: 10.1007/s11269-023-03509-w
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    References listed on IDEAS

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    1. Erhao Meng & Shengzhi Huang & Qiang Huang & Linyin Cheng & Wei Fang, 2021. "The Reconstruction and Extension of Terrestrial Water Storage Based on a Combined Prediction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5291-5306, December.
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