It has been shown that climate change impacts the overall health of a river's ecosystem. Although predicting river health under climate change would be useful for stakeholders to adapt to the change and better conserve river health, little research on this topic exists. This paper presents amethodology predicting river health under different climate change scenarios. First, a multi-source, distributed, time-variant gain hydrological model (MS-DTVGM) was used to predict the runoff from a mountainous river in eastern China using the data from three existing IPCC5 climate change models (RCP2.6, RCP4.5, and RCP8.4). Next, a model was developed to predict the river's water quality under these scenarios. Finally, a multidimensional response model utilizing hydrology, water quality, and biology was used to predict the river's biological status and ascertain the impact of climate change on its overall health. The river is in a mountainous area near Jinan City, one of China's first "pilot" cities recognized as a "healthy water ecological community." Our results predict that the overall health of the Yufu River, which is minimally influenced by human activities, will improve by 2030 due to the increased river flow due to an increase in rainfall frequency and subsequent peak runoff. However, the total nitrogen concentration is predicted to increase, which is a potential eutrophication risk. Therefore, effective control of nitrogen pollutants entering the river will be necessary. The increase in flow velocity (the annual average increase is similar to 0.5 m/s) is favorable for fish reproduction. Our methods and results will provide scientific guidance for policy makers and river managers and will help people to better understand how global climate change impacts river health. (c) 2018 Elsevier B.V. All rights reserved.
1.Beijing Normal Univ, Coll Water Sci, Beijing Key Lab Urban Hydrol Cycle & Sponge City, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Sch Geog, Fac Geog Sci, Beijing 100875, Peoples R China 3.Jinan Survey Bur Hydrol & Water Resources, Jinan 250013, Shandong, Peoples R China 4.Univ Technol, Fac Sci, Sch Life Sci, Sydney, NSW 2007, Australia 5.UdS, CNRS, UMR 7357, ICube, 300 Bld Sebastien Brant,CS 10413, F-67412 Illkirch Graffenstaden, France
Recommended Citation:
Zhao, C. S.,Yang, Y.,Yang, S. T.,et al. Predicting future river health in a minimally influenced mountainous area under climate change[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2019-01-01,656:1373-1385