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学院发表文章

Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects

发布日期:2023-12-27浏览次数:信息来源:土地科学与技术学院

Baogui Li   Lili Tan   Xueliang Zhang   Junyu Qi   Gary W. Marek   Yingxuan Li   Xiaojie Dong   Wenjie Zhao   Ting Chen   Puyu Feng   De Li Liu   Raghavan Srinivasan   Yong Chen

Abstract

Study region

Haihe River Basin (HRB) in North China Plain.

Study focus

Changes in streamflow due to climate change and human activities are highly uncertain. An improved Soil and Water Assessment Tool (SWAT) model equipped with a dynamic CO2 input method was used to quantify the environmental change impacts on streamflow in the HRB. Streamflow changes were analyzed based on 22 bias-corrected CMIP6 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 during two 30-year periods of the middle (2041–2070) and end (2071–2100) of the 21st century relative to the historical period (1971–2000).

New hydrological insights for the region

Long-term simulations of annual streamflow in the HRB demonstrated substantial discrepancies between the dynamic and constant CO2 input methods under the SSP1-2.6 and SSP5-8.5 scenarios, which highlighted the importance of using the dynamic CO2 input method for streamflow simulations. Spatio-temporal analysis using the improved SWAT model revealed that streamflow generally increased under four emission scenarios compared to the historical period. In the HRB, annual streamflow in the downstream plains was higher than in the upstream mountains, and this was more evident under the highest emission scenario of SSP5-8.5. Monthly streamflow showed considerable intra-annual variability that was generally greater from July to November. The strongest increment in streamflow was projected in the SSP3-7.0 and SSP5-8.5 scenarios, indicating that flood risk would possibly increase in the late 21st century.


Keywords

Spatio-temporal variations; CMIP6; SSPs; Haihe River Basin; Streamflow; Environmental change


https://doi.org/10.1016/j.ejrh.2023.101547