Nonlinear response of runoff to atmospheric freezing level height variation based on hybrid prediction models |
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| Authors: | Yanhua Qin Xun Sun Yaning Chen Xun Shi |
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| Institution: | 1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, China;2. School of Geography and Tourism, Qufu Normal University, Rizhao, China;3. School of Geographic Sciences, East China Normal University, Shanghai, China;4. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China;5. Department of Geography, Dartmouth College, Hanover, New Hampshire, USA |
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| Abstract: | ABSTRACTWe use data on the freezing level height (FLH) and summer runoff in the Hotan River, China, from 1960 to 2013, to analyse the nonlinear relationships of atmospheric and hydrological factors at different time scales, by employing three nonlinear decomposition methods. Six hybrid prediction models are established by combining linear regression and back-propagation artificial neural network (BPANN) models. The decomposition results by three nonlinear methods are compared, indicating that the extreme-point symmetric mode decomposition (ESMD) method ensures the best prediction capacity. The runoff and FLH have periods of 3 and 6 years, respectively, at the inter-annual scale, which pass the significance test of 0.05 (P < 0.05) by using the Monte Carlo method, although there were slight differences in the periods at the inter-decadal scale. Among the six models, ESMD-BPANN exhibits the highest accuracy, with good reliability and resolution, according to several performance indicators. The ESMD-BPANN model is thus selected for the simulation and prediction of runoff. |
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| Keywords: | nonlinear trend runoff prediction freezing level height ESMD-BPANN Hotan River Basin |
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