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 sm...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(R3)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.展开更多
基金supported by National Natural Science Foundation of China:[grant no U1304402,41977284]Natural science and technology project of Department of Natural Resources of Henan Province:[grant no 2019-378-16].
文摘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(R3)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.
