Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning

Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based mo...

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Bibliographic Details
Main Authors: Pengao Li, Haiyang Yu, Peng Zhou, Ping Zhang, Ruili Wang
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Digital Earth
Subjects:
Online Access:http://dx.doi.org/10.1080/17538947.2023.2242316
Description
Summary:Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact.
ISSN:1753-8947
1753-8955