Affiliations 

  • 1 College of Marine Science and Technology, Hubei Key Laboratory of Marine Geological Resources, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China; Centre for Polar Observation and Modelling, School of Earth and Environment, University of Leeds, Leeds, UK
  • 2 College of Marine Science and Technology, Hubei Key Laboratory of Marine Geological Resources, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China; Institute of Geodesy and Geoinformation, University of Bonn, Bonn 53115, Germany. Electronic address: [email protected]
  • 3 College of Marine Science and Technology, Hubei Key Laboratory of Marine Geological Resources, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China. Electronic address: [email protected]
  • 4 College of Marine Science and Technology, Hubei Key Laboratory of Marine Geological Resources, Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China
  • 5 School of Geodesy and Geomatics, Key Laboratory of Geospace Environment and Geodesy, Wuhan University, Wuhan 430079, P. R. China
  • 6 Discipline of Civil Engineering, Monash University Malaysia, Malaysia
Sci Total Environ, 2023 Mar 17;879:162886.
PMID: 36933709 DOI: 10.1016/j.scitotenv.2023.162886

Abstract

Terrestrial water storage anomaly (TWSA) from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on was first exacted by using the forward modeling (FM) method at three different scales over the Yangtze River basin (YRB): whole basin, three middle sub-basins, and eleven small sub-basins (total 15 basins). The spatiotemporal variability of eight hydroclimatic variables, snow water storage change (SnWS), canopy water storage change (CnWS), surface water storage anomaly (SWSA), soil moisture storage anomaly (SMSA), groundwater storage anomaly (GWSA), precipitation (P), evapotranspiration (ET), and runoff (R), and their contribution to TWSA were comprehensively investigated over the YRB. The results showed that the root mean square error of TWS change after FM improved by 17 %, as validated by in situ P, ET, and R data. The seasonal, inter-annual, and trend revealed that TWSA over the YRB increased during 2003-2018. The seasonal TWSA signal increased from the lower to the upper of YRB, but the trend, sub-seasonal, and inter-annual signals receded from the lower to the upper of YRB. The contribution of CnWS to TWSA was small over the YRB. The contribution of SnWS to TWSA occurs mainly in the upper of YRB. The main contributors to TWSA were SMSA (~36 %), SWSA (~33 %), and GWSA (~30 %). GWSA can be affected by TWSA, but other hydrological elements may have a slight impact on groundwater in the YRB. The primary driver of TWSA over the YRB was P (~46 %), followed by ET and R (both ~27 %). The contribution of SMSA, SWSA, and P to TWSA increased from the upper to the lower of YRB. R was the key driver of TWSA in the lower of YRB. The proposed approaches and results of this study can provide valuable new insights for water resource management in the YRB and can be applied globally.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.