A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Jielong Wang; Yunzhong Shen; Joseph L Awange; Ling Yang

doi:10.1016/j.scitotenv.2023.167030

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Sci Total Environ. 2023 Dec 20:905:167030. doi: 10.1016/j.scitotenv.2023.167030. Epub 2023 Sep 12.

Authors

Jielong Wang¹, Yunzhong Shen², Joseph L Awange³, Ling Yang¹

Affiliations

¹ College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China.
² College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China. Electronic address: yzshen@tongji.edu.cn.
³ School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University, Perth, WA, Australia.

PMID: 37704127
DOI: 10.1016/j.scitotenv.2023.167030

Abstract

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

Keywords: Climate indices; Convolutional neural network; Extreme hydrological events; GRACE; Total water storage anomalies.