基于SWAT-LUC模型与多灵敏度实验的密云水库上游径流变化归因

Attribution analysis of runoff variation in the upstream of Miyun Reservoir based on SWAT-LUC model and multi-sensitivity experiments

摘要

为定量区分气候变化与人类活动对水库上游径流变化的贡献，以北京市重要饮用水源地潮白河流域密云水库上游为研究区，基于2006−2023年气象、土地利用变化、水库调水及土壤等数据，构建能够反映逐年土地利用动态变化的土壤与水评估工具-土地利用变化（soil and water assessment tool-land use change, SWAT-LUC）分布式水文模型，并设计多灵敏度数值实验，定量拆分气候变化（降水和气温）与人类活动（土地利用变化和水库调水）对白河与潮河水系径流变化的贡献。结果表明：研究期内白河与潮河水系径流量均呈不显著增加趋势；降水是径流变化最主要的驱动因素，其对白河与潮河径流增加的年贡献量分别为0.494 9和0.295 2 m³/s；植被面积增加对径流具有微弱的影响；水库调水减少则显著抑制了白河水系径流的增加。研究结果可为密云水库流域水资源管理与调控提供参考。

Abstract

Climate change and human activities have been identified as two major forces that are significantly altering runoff processes in reservoir upstream regions. However, quantifying their contributions is a challenge due to the complex interactions within hydrological systems. The Chaobai River basin upstream of the Miyun Reservoir was selected as the study area, which is an important drinking water source for Beijing. Existing research on runoff attribution in this region shows significant disparities, largely resulting from methodological differences, such as the use of static land use representations, neglect of reservoir regulation effects, and the difficulty in analyzing the impacts of short-term climate and human activities. This study aims to fill these gaps by developing a dynamic hydrological modelling framework that considers annual land use changes and reservoir operations to quantitatively attribute runoff variations to specific climatic and anthropogenic factors. A SWAT-LUC hydrological model, an enhanced version of the Soil and Water Assessment Tool (SWAT) capable of incorporating annual land use dynamics, was developed for the Miyun Reservoir's upstream basin. Input data included meteorological records, annual land use maps from the CLCD dataset, soil data from the World Soil Database and detailed reservoir operation parameters for the Yunzhou and Baihebao Reservoirs. Using monthly streamflow data from five hydrological stations in the Bai River and Chao River basins, the model was validated from 2016 to 2023 after being calibrated from 2007 to 2015. Model performance was evaluated using the Nash-Sutcliffe efficiency coefficient and the coefficient of determination. A multi-sensitivity experimental framework was used, consisting of four scenario simulations with precipitation, temperature, land use, and reservoir diversion sequentially fixed at their 2006 levels and one control simulation with all factors changing naturally. These experiments used a joint solution method to distinguish between the individual contributions of climate change and human activities to the observed runoff trends. The calibrated SWAT-LUC model demonstrated acceptable performance in simulating the hydrological processes. During the calibration period, Nash-Sutcliffe coefficients ranged from 0.50 to 0.82, and coefficients of determination ranged from 0.67 to 0.88 across all five stations. During the validation period, these metrics ranged from 0.67 to 0.93 and 0.77 to 0.94, respectively. Between 2006 and 2023, both the Bai River and Chao River basins observed non-significant increasing trends in annual runoff. Precipitation was identified as the dominant driver of runoff variation, contributing an annual increase of 0.494 9 m³/s in the Bai River basin and 0.295 2 m³/s in the Chao River basin. Temperature exhibited opposing effects, contributing a slight increase in the Bai River basin but a decrease in the Chao River basin. Increased vegetation area had a weak impact on runoff in both basins. Reduced water diversion from the Baihebao Reservoir significantly suppressed the runoff increase in the Bai River basin, with an annual contribution of negative 0.012 7 m³/s, an effect greater than that of land use change. The results indicate that climate factors, especially precipitation, were the primary drivers of runoff variation in

关键词

径流变化 / 气候变化 / 土地利用变化 / 水库调水 / SWAT-LUC模型

Key words

runoff variation / climate change / land use change / reservoir water diversion / SWAT-LUC model

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基金资助

河北省重点研发计划项目(22374205D)

河北省省级水利科技计划项目(HBSL2025-04)