Reconstructing Total Water Storage Anomalies Over the Lake Victoria Basin (1971–2022) Using an Enhanced RecNet Model DOI Creative Commons
Jielong Wang, Yunzhong Shen, Joseph L. Awange

et al.

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(9)

Published: May 2, 2025

Abstract Relatively short records of Total Water Storage Anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) its Follow‐On (GRACE‐FO) missions have impeded our understanding their full range long‐term variability over Lake Victoria Basin (LVB). This study introduces an Enhanced RecNet (ERecNet) to reconstruct LVB's TWSA 1971 2022 using precipitation Victoria's level data. ERecNet integrates a multi‐layer perceptron combination gridded basin‐averaged loss functions for improving reconstruction performance. Our results reveal that can successfully variations, outperforming hydrological models reanalysis products in capturing trends amplitudes. The aligns closely with lake patterns while effectively closing water balance budget. provides first both human‐ climate‐driven data LVB, offering valuable insights into variability.

Language: Английский

A novel generative adversarial network and downscaling scheme for GRACE/GRACE-FO products: Exemplified by the Yangtze and Nile River Basins DOI
Jielong Wang, Yunzhong Shen, Joseph L. Awange

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 969, P. 178874 - 178874

Published: Feb. 24, 2025

Language: Английский

Citations

1
Reconstructing Total Water Storage Anomalies Over the Lake Victoria Basin (1971–2022) Using an Enhanced RecNet Model DOI Creative Commons
Jielong Wang, Yunzhong Shen, Joseph L. Awange

et al.

Geophysical Research Letters, Journal Year: 2025, Volume and Issue: 52(9)

Published: May 2, 2025

Abstract Relatively short records of Total Water Storage Anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) its Follow‐On (GRACE‐FO) missions have impeded our understanding their full range long‐term variability over Lake Victoria Basin (LVB). This study introduces an Enhanced RecNet (ERecNet) to reconstruct LVB's TWSA 1971 2022 using precipitation Victoria's level data. ERecNet integrates a multi‐layer perceptron combination gridded basin‐averaged loss functions for improving reconstruction performance. Our results reveal that can successfully variations, outperforming hydrological models reanalysis products in capturing trends amplitudes. The aligns closely with lake patterns while effectively closing water balance budget. provides first both human‐ climate‐driven data LVB, offering valuable insights into variability.

Language: Английский

Citations

0