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<p>Climate change and anthropogenic activities have profoundly influenced the hydrological cycle and water resource dynamics, exacerbating global water security challenges. To facilitate water resource management in semi-arid regions and address climate change, it is essential to enhance the predictive capability of hydrological models, particularly in estimating water balance components such as precipitation and evapotranspiration. This study integrates satellite-derived Leaf Area Index (LAI) and phenological data dynamically into the SWAT+ model to refine the vegetation growth modules and mitigate uncertainties. Moreover, addressing the limitation of SWAT+ regarding the spatial homogenization of meteorological data within same sub-basins, this study assigns corresponding high-resolution meteorological grid data to each Hydrological Response Unit (HRU) based on proximity to its centroid, aiming to improve precipitation-streamflow simulation accuracy. The refined SWAT+ model demonstrated improved simulation performance in the Weihe River Basin (WRB). Streamflow simulations at four hydrological stations yielded Nash-Sutcliffe efficiency (NSE) values exceeding 0.68, Kling-Gupta Efficiency (KGE) values ranging between 0.79 and 0.93, and Percent Bias (PBIAS) values below 13%. Evapotranspiration simulations across six stations achieved coefficients of determination (R²) between 0.79 and 0.92. Water balance analysis revealed that the southern Qinling Mountains region possess relatively plentiful blue and green water resources, the Guanzhong Plain is rich in blue water but has limited green water reserves, and the northern Loess Plateau experiences severe water scarcity. Notably, green water within the WRB is more vulnerable during the summer, while blue water exhibits heightened vulnerability throughout the WRB. This indicates that intensified evaporation or abrupt reductions in precipitation could lead to unmet daily water demands, with particularly severe consequences for agriculture. These findings emphasize the value of integrating vegetation phenology dynamics and high-resolution meteorological data in water resource assessment in semi-arid regions. Limitations faced during the research include temporal resolution constraints of remote sensing data and challenges in acquiring high-resolution meteorological datasets. Future research should focus on integrating multi-source remote sensing data and refining the incorporation of finer-scale meteorological datasets to further enhance model reliability and applicability. </p>

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SWAT+ Model Enhanced with Dynamic Phenology Remote Sensing and High-Precision Precipitation Data for Water Resource Vulnerability Assessment in Semi-Arid Regions

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Qingyang Zhang,
  • Junhu Wu,
  • Weibin Liu,
  • Tianle Wang,
  • Yichao Wang

摘要

Climate change and anthropogenic activities have profoundly influenced the hydrological cycle and water resource dynamics, exacerbating global water security challenges. To facilitate water resource management in semi-arid regions and address climate change, it is essential to enhance the predictive capability of hydrological models, particularly in estimating water balance components such as precipitation and evapotranspiration. This study integrates satellite-derived Leaf Area Index (LAI) and phenological data dynamically into the SWAT+ model to refine the vegetation growth modules and mitigate uncertainties. Moreover, addressing the limitation of SWAT+ regarding the spatial homogenization of meteorological data within same sub-basins, this study assigns corresponding high-resolution meteorological grid data to each Hydrological Response Unit (HRU) based on proximity to its centroid, aiming to improve precipitation-streamflow simulation accuracy. The refined SWAT+ model demonstrated improved simulation performance in the Weihe River Basin (WRB). Streamflow simulations at four hydrological stations yielded Nash-Sutcliffe efficiency (NSE) values exceeding 0.68, Kling-Gupta Efficiency (KGE) values ranging between 0.79 and 0.93, and Percent Bias (PBIAS) values below 13%. Evapotranspiration simulations across six stations achieved coefficients of determination (R²) between 0.79 and 0.92. Water balance analysis revealed that the southern Qinling Mountains region possess relatively plentiful blue and green water resources, the Guanzhong Plain is rich in blue water but has limited green water reserves, and the northern Loess Plateau experiences severe water scarcity. Notably, green water within the WRB is more vulnerable during the summer, while blue water exhibits heightened vulnerability throughout the WRB. This indicates that intensified evaporation or abrupt reductions in precipitation could lead to unmet daily water demands, with particularly severe consequences for agriculture. These findings emphasize the value of integrating vegetation phenology dynamics and high-resolution meteorological data in water resource assessment in semi-arid regions. Limitations faced during the research include temporal resolution constraints of remote sensing data and challenges in acquiring high-resolution meteorological datasets. Future research should focus on integrating multi-source remote sensing data and refining the incorporation of finer-scale meteorological datasets to further enhance model reliability and applicability.