Hydrological responses to co-impacts of climate change and land use/cover change based on CMIP6 in the Ganjiang River, Poyang Lake basin

环境科学 气候变化 土地覆盖 支流 土地利用、土地利用的变化和林业 地表径流 分水岭 水流 水文学(农业) 水循环 流域 土地利用 降水 水资源 水土评价工具 构造盆地 全球变化 气候模式 地理 生态学 地质学 气象学 地图学 岩土工程 生物 古生物学 机器学习 计算机科学
作者
L. Gong,Xiang Zhang,Guoyan Pan,Jianmin Zhao,Yupei Zhao
出处
期刊:Anthropocene [Elsevier BV]
卷期号:41: 100368-100368 被引量:7
标识
DOI:10.1016/j.ancene.2023.100368
摘要

Climate change and Land Use/Cover Change, affected by human activity, are the two main factors influencing the regional water cycle and water management. However, studies of co-impacts based on future scenario predictions are still lacking. This study proposed a complete methodology for simulating future changes in water resources and distinguishing the independent and synergistic effects of climate change and land use change. The coupling prediction model of land use and the global climate models were used for scenario predictions; the hydrological model and statistical methods were used for simulations and analyses. The Ganjiang River, the largest tributary of Poyang Lake, is chosen as the study area. In the future, the main trend of change in land use would be the expansion of construction land in the northern part of the basin, and the future annual precipitation and temperature (p < 0.5) would increase. In this basin, runoff is more sensitive to climate change than to land use/cover change, and the synergistic effects are not substantial. Most climate scenarios showed a significant change in monthly peak runoff. The current peak is in June; this is projected to decrease with the simulated future peak in August, causing problems in basin flood control and Poyang Lake water level regulation. This study proposed a methodology integrating the global climate models with predicted land use scenarios and tested the feasibility at the watershed scale by the case study. It can serve as a reference for co-impact studies considering different scenarios and be extended to basins with similar areas, underlying surface variation intensity, or hydro-climatic characteristics, valuable for sustainable water resources management in the Anthropocene.
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