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Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network

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Abstract

Climate projections for the Huaihe River Basin, China, for the years 2001–2100 are derived from the ECHAM5/MPI-OM model based on observed precipitation and temperature data covering 1964–2007. Streamflow for the Huaihe River under three emission scenarios (SRES-A2, A1B, B1) from 2010 to 2100 is then projected by applying artificial neural networks (ANN). The results show that annual streamflow will change significantly under the three scenarios from 2010 to 2100. The interannual fluctuations cover a significant increasing streamflow trend under the SRES-A2 scenario (2051–2085). The streamflow trend declines gradually under the SRES-A1B scenario (2024–2037), and shows no obvious trend under the SRES-B1 scenario. From 2010 to 2100, the correlation coefficient between the observed and modeled streamflow in SRES-A2 scenario is the best of the three scenarios. Combining SRES-A2 scenario of the ECHAM5 model and ANN might therefore be the best approach for assessing and projecting future water resources in the Huaihe basin and other catchments. Compared to the observed period of streamflows, the projected periodicity of streamflows shows significant changes under different emission scenarios. Under A2 scenario and A1B scenario, the period would delay to about 32–33a and 27–28a, respectively, but under B1 scenario, the period would not change, as it is about 5–6a and the observed period is about 7–8a. All this might affect drought/flood management, water supply and irrigation projects in the Huaihe River basin.

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Acknowledgements

The leading work for the preparation of this paper has been financially supported by the National Natural Science Foundation of China (project no. 40601017, 40771040, 40701028), and funds were received from the Anhui Province meteorological bureau (No. HRM200702) and the Natural Science Foundation of Anhui (090415212). The authors also thank the Max Planck Institute for Meteorology, Hamburg, Germany, which has provided the valuable ECHAM5/MPI-OM model. Discussions with Dr Chen Youmin of Max-Planck-Institute for Biogeochemistry (Germany) are also gratefully acknowledged. Dr Marco Gemmer is a Visiting Professor and integrated expert of CIM at the China Meteorological Administration, supported by the German Government through the CIM programme (www.cimonline.de).

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Authors and Affiliations

  1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China

    Chao Gao & Bo Liu

  2. National Climate Centre-Centre on Climate Change, China Meteorological Administration, Beijing, 100081, China

    Marco Gemmer & Buda Su

  3. School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Xiaofan Zeng

  4. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu, 241000, China

    Chao Gao

  5. Hydrological Bureau of the Jiangsu Province, Nanjing, 210029, China

    Yuhua Wen

  6. Graduate School of the Chinese Academy of Sciences, Beijing, 100039, China

    Chao Gao & Bo Liu

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  1. Chao Gao
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Correspondence to Marco Gemmer.

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Gao, C., Gemmer, M., Zeng, X. et al. Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stoch Environ Res Risk Assess 24, 685–697 (2010). https://doi.org/10.1007/s00477-009-0355-6

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