珠江流域气候与土地利用变化的水文响应情景模拟

粤港澳大湾区的发展建设依赖于珠江流域的水资源供给。在气候变暖和土地利用快速变化的综合影响下，未来珠江流域的水资源变化存在不确定性。本项目拟综合运用数据同化、情景预测、土地利用和水文模拟等技术，在RCP-SSP情景框架下预测未来气候和土地利用变化情景组合下珠江流域的水文响应。具体内容包括：1）联合GRACE重力卫星、SMOS卫星土壤湿度和径流观测数据对SWAT模型进行数据同化，以提高水文模型的输出精度；2）利用FLUS模型模拟共享社会经济路径（SSPs）情景数据驱动的未来多类土地利用格局演化；3）基于CMIP6集合模式预测的典型浓度路径（RCP）气候变化情景数据和SSP情景驱动的土地利用模拟数据，构建RCP-SSP框架下的气候与土地利用变化情景组合，并利用SWAT模型预测珠江流域未来的水文响应并评估其情景差异。研究成果可为珠江流域水资源管理、应对和减缓气候变化风险的政策制定提供决策依据。

The construction and development of the Guangdong-Hong Kong-Macao Greater Bay Area relies on the water supply from the Pearl River Basin (PRB). There is uncertainty of the future water resources changes in PRB under the comprehensive impacts of global warming and rapid land use changes. This project aims to predict the hydrological response to scenario combinations of future climate and land use changes in PRB under the RCP-SSP scenario framework by using technologies including data assimilation, scenario prediction, land use and hydrological simulation. The research contents include: 1) Multisource data assimilation of the SWAT model by integrating GRACE gravity satellite, SMOS satellite soil moisture and runoff observation data to improve the hydrological model accuracy; 2) Project the future evolution of multiple types of land use patterns which are driven by the Shared Socioeconomic Pathway (SSP) scenario data via a land use model named FLUS; 3) Build a series of scenario combinations of climate and land use changes under the RCP-SSP framework based on the Representative Concentration Pathway (RCP) climate change scenario data provided by the CMIP6 GCM models and the simulated land use patterns driven by the SSP scenario data, predict and assess the future hydrological response to different scenario combinations in PRB through the SWAT model. The research results will support the policy making decisions of water resources management, climate change mitigation and adaptation.