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Integrated SWAT model and statistical downscaling for estimating streamflow response to climate change in the Lake Dianchi watershed, China

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Abstract

Understanding the relationships between hydrological regime and climate change is important for water resources management. In this study, the streamflow response to climate change was investigated in the Lake Dianchi watershed, which is one of the most important eutrophic lakes in China. Daily time-series of temperature and precipitation in the future periods (2020, 2050 and 2080s) were projected from HadCM3 model. Statistical downscaling model (SDSM) and the previously calibrated and validated Soil and water assessment tool (SWAT) model were used to quantify the impacts of climate change on streamflow in this watershed. The results showed that SDSM can well capture the statistical relationships between the large scale climate variables and the observed weather at regional scale. The downscaled results showed that annual average maximum and minimum temperature would rise by 4.28 (3.25) and 4.71 °C (3.33 °C) in the 2080s under A2 (B2) scenario. Annual average precipitation would decrease within the range between 20.34 and 74.12 mm under both scenarios in the future. Based on SWAT model simulation, annual average streamflow would decrease in the future by the declination of −7.12 to −21.83 % and −6.34 to −17.09 % under A2 (B2) scenarios in the outlet of this watershed. The frequency of drought and extreme rainfall events would increase in the future, which is not beneficial to protect Lake Dianchi. This study could lead to a better understanding of the streamflow response under climate change and also raised concerns about the sustainability of future water resources in Lake Dianchi watershed.

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Acknowledgments

This paper was supported by National Natural Science Foundation of China (Grant No. 41401080, 41222002 and U0833603) and “China National Water Pollution Control Program” (2013ZX07102-006). The authors also greatly appreciate the Canadian Climate Change Scenarios Network (CCCSN) for providing the reanalysis products of the NCEP and HadCM3 outputs for the downscaling tool and would like to acknowledge the IPCC Data Distribution Centre for technical support.

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Authors and Affiliations

  1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China

    Jing Zhou

  2. Key Laboratory of Water and Sediment Sciences (MOE), College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China

    Jing Zhou, Yong Liu, Yonghui Yang, Hu Sheng & Huaicheng Guo

  3. College of Arts and Sciences, Beijing Union University, Beijing, 100191, China

    Dan He

  4. Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing, 210042, China

    Yufeng Xie

  5. Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming, 650034, China

    Lei Zhao

  6. Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA, 22030, USA

    Rui Zou

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  1. Jing Zhou
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Zhou, J., He, D., Xie, Y. et al. Integrated SWAT model and statistical downscaling for estimating streamflow response to climate change in the Lake Dianchi watershed, China. Stoch Environ Res Risk Assess 29, 1193–1210 (2015). https://doi.org/10.1007/s00477-015-1037-1

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