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Analysis and partitioning of terrestrial water storage in the Yellow River source region
Authors:Xia Li  Zuhao Zhou  Jiajia Liu  Chongyu Xu  Junqiang Xia  Pengxiang Wang  Hao Wang  Yangwen Jia
Institution:1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basins, China Institute of Water Resources and Hydropower Research, Beijing, China;2. Department of Geosciences, University of Oslo, Oslo, Norway;3. State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China;4. Science and Technology Research Institute, China Three Gorges Corporation, Beijing, China
Abstract:In order to increase the capability to understand and quantify the spatial differences in terrestrial water storage (TWS), and to reflect the unique energy balance processes and soil freeze–thaw mechanisms in the Qinghai-Tibet Plateau (QTP), this study improved the energy balance processes of the water and energy transfer processes model, including its surface radiation calculations and snowmelt module. By integrating these improvements, a water and energy transfer processes model in Qinghai-Tibet Plateau (WEP-QTP) for the Yellow River source region (YRSR) is developed. Using the improved WEP-QTP model to perform simulations, we assessed the daily changes in snow cover, soil moisture (SM), permafrost (PM), and groundwater storage (GWS) in the YRSR. Our analysis revealed an increase in TWS of 0.24 mm/yr from 1961 to 2020. Snow water equivalent (SWE), SM, PM, and GWS have proportional contributions of 8.33%, 216.67%, −154.17%, and 29.17% to the increased TWS, respectively. SM is the primary component of TWS. Temperature (T), precipitation (P), evapotranspiration (E), and solar radiation (Rs) influence the spatiotemporal variations in TWS, as well as those of its components. The increase in P is the primary cause for the rise in TWS, SWE, and SM, while the increase in T predominantly contributes to the decrease in PM. Furthermore, permafrost degradation and climate-induced warming and humidification lead to increased infiltration, resulting in elevated GWS.
Keywords:coupled energy-water model  quantitative analysis  spatiotemporal variations  terrestrial water storage (TWS) components  Yellow River source region
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