A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran

Study region: Tasht-Bakhtegan Basin, Iran Study focus: The main objectives of this study are to reconstruct and downscale GRACE data from a coarse resolution of 1-degree to a finer resolution of 1-km. This was accomplished using a robust and flexible multi-scale approach, leveraging machine learning...

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Main Authors: Arezo Mohtaram, Hossein Shafizadeh-Moghadam, Hamed Ketabchi
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S221458182400435X
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author Arezo Mohtaram
Hossein Shafizadeh-Moghadam
Hamed Ketabchi
author_facet Arezo Mohtaram
Hossein Shafizadeh-Moghadam
Hamed Ketabchi
author_sort Arezo Mohtaram
collection DOAJ
description Study region: Tasht-Bakhtegan Basin, Iran Study focus: The main objectives of this study are to reconstruct and downscale GRACE data from a coarse resolution of 1-degree to a finer resolution of 1-km. This was accomplished using a robust and flexible multi-scale approach, leveraging machine learning algorithms, specifically random forest and LightGBM. The models were meticulously calibrated and thoroughly evaluated across various spatial scales. Additionally, the study examined the lag effects of influential covariates in the downscaling process, further enhancing model accuracy. New hydrological insights for the region.The multi-scale calibration of the models provided new insights into the relationship between terrestrial water storage anomalies (TWSa) and various environmental and hydrological factors. It was found that precipitation and land surface temperature (LST) were the most influential covariates in the reconstruction and downscaling process. Specifically, precipitation with a two-month delay, LST with a three-month delay, and evapotranspiration with an eight-month delay exhibited the highest correlations with TWSa. These findings offer valuable insights into the temporal influence of key hydrological variables on TWSa within the region, shedding light on how delayed responses of precipitation, LST, and evapotranspiration affect groundwater storage. This enhances the understanding of the underlying dynamics governing hydrological variability in the study area.
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series Journal of Hydrology: Regional Studies
spelling doaj-art-f7dcfa663e1c48d4bb1b849176b272052025-01-22T05:41:58ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102086A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, IranArezo Mohtaram0Hossein Shafizadeh-Moghadam1Hamed Ketabchi2Department of Water Engineering and Management, Tarbiat Modares University, Tehran, IranCorresponding author.; Department of Water Engineering and Management, Tarbiat Modares University, Tehran, IranDepartment of Water Engineering and Management, Tarbiat Modares University, Tehran, IranStudy region: Tasht-Bakhtegan Basin, Iran Study focus: The main objectives of this study are to reconstruct and downscale GRACE data from a coarse resolution of 1-degree to a finer resolution of 1-km. This was accomplished using a robust and flexible multi-scale approach, leveraging machine learning algorithms, specifically random forest and LightGBM. The models were meticulously calibrated and thoroughly evaluated across various spatial scales. Additionally, the study examined the lag effects of influential covariates in the downscaling process, further enhancing model accuracy. New hydrological insights for the region.The multi-scale calibration of the models provided new insights into the relationship between terrestrial water storage anomalies (TWSa) and various environmental and hydrological factors. It was found that precipitation and land surface temperature (LST) were the most influential covariates in the reconstruction and downscaling process. Specifically, precipitation with a two-month delay, LST with a three-month delay, and evapotranspiration with an eight-month delay exhibited the highest correlations with TWSa. These findings offer valuable insights into the temporal influence of key hydrological variables on TWSa within the region, shedding light on how delayed responses of precipitation, LST, and evapotranspiration affect groundwater storage. This enhances the understanding of the underlying dynamics governing hydrological variability in the study area.http://www.sciencedirect.com/science/article/pii/S221458182400435XGRACETotal water storageLightGBMClustering
spellingShingle Arezo Mohtaram
Hossein Shafizadeh-Moghadam
Hamed Ketabchi
A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
Journal of Hydrology: Regional Studies
GRACE
Total water storage
LightGBM
Clustering
title A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
title_full A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
title_fullStr A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
title_full_unstemmed A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
title_short A flexible multi-scale approach for downscaling GRACE-derived groundwater storage anomaly using LightGBM and random forest in the Tashk-Bakhtegan Basin, Iran
title_sort flexible multi scale approach for downscaling grace derived groundwater storage anomaly using lightgbm and random forest in the tashk bakhtegan basin iran
topic GRACE
Total water storage
LightGBM
Clustering
url http://www.sciencedirect.com/science/article/pii/S221458182400435X
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