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.
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.
Sci Total Environ. 2023 Jun 25;879:162886. doi: 10.1016/j.scitotenv.2023.162886. Epub 2023 Mar 17.
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.
利用正向建模 (FM) 方法,首次在长江流域 (YRB) 的三个不同尺度上提取了来自重力恢复与气候实验 (GRACE) 和 GRACE 后续任务的陆地水存储异常 (TWSA):整个流域、三个中游子流域和十一个小流域(共 15 个流域)。全面研究了长江流域 8 个水文气象变量(雪水存储变化 (SnWS)、冠层水存储变化 (CnWS)、地表水存储异常 (SWSA)、土壤水分存储异常 (SMSA)、地下水存储异常 (GWSA)、降水 (P)、蒸散量 (ET) 和径流量 (R) 的时空变异性及其对 TWSA 的贡献。结果表明,与原位 P、ET 和 R 数据验证相比,FM 后 TWS 变化的均方根误差提高了 17%。季节性、年际和趋势表明,2003-2018 年期间长江流域的 TWSA 增加。长江流域的季节性 TWSA 信号从上到下增加,但趋势、次季节和年际信号从上到下减弱。长江流域 CnWS 对 TWSA 的贡献较小。长江流域上部地区的 SnWS 对 TWSA 的贡献较大。对 TWSA 的主要贡献来自 SMSA(36%)、SWSA(33%)和 GWSA(30%)。GWSA 可能受到 TWSA 的影响,但其他水文要素对 YRB 地下水的影响可能较小。长江流域 TWSA 的主要驱动因素是 P(46%),其次是 ET 和 R(均为~27%)。SMSA、SWSA 和 P 对 TWSA 的贡献从上到下增加。R 是长江流域下游 TWSA 的关键驱动因素。本研究提出的方法和结果可为长江流域水资源管理提供有价值的新见解,并可在全球范围内应用。