[1]LIU Yan,NIE Lei,NIE Lei,et al.The EEMD-ARIMA Prediction of Runoff at Mountain Pass of Manas River[J].Research of Soil and Water Conservation,2017,24(06):273-280,285.
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Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
24
Number of periods:
2017 06
Page number:
273-280,285
Column:
Public date:
2017-11-24
- Title:
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The EEMD-ARIMA Prediction of Runoff at Mountain Pass of Manas River
- Author(s):
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LIU Yan1,2, NIE Lei' target="_blank" rel="external"> NIE Lei3, NIE Lei' target="_blank" rel="external"> NIE Lei4, SONG Qiuyu5
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1. Institute of Desert Meteorology, CMA, Urumqi 830002, China;
2. Center of Central Asia Atmospheric Science Research, Urumqi 830002, China;
3. College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China;
4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
5. Xinjiang Uygur Meteorological Bureau, Urumqi 830002, China
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- Keywords:
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change of runoff; nonlinear variation; evolution; hydrological station
- CLC:
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P96
- DOI:
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- Abstract:
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Quantitative estimation under warm-humid climate of the runoff variation of arid endorheic river, especially the nonlinear variation, and its trend in the future has directive significance for disposing regional water resources. Taking Manas River estimation under warm-humid climate of the runoff variati Manas Basin for example, in this context, we analyzed the annual average runoff data from 1957 to 2012 of mountain-pass hydrological station Ken Swart by using diversiform algorithms such as Mann-Kendall, Ensemble Empirical Mode Decomposition (EEMD) and Autoregressive Integrated Moving Average Model (ARIMA), then ultimately got the quantitative characteristic of the nonlinear variation of annual average runoff and the trend between years. The results indicated that the annual average and general stream flow increased significantly. After decomposing through EEMD, we obtained 4 IMF components and 1 trend term of the time series of annual average runoff which differed from each other in central frequency, showing that there are varieties of periodic laws of annual average runoff. Introducing the IMF components representing years of intergenerational oscillation into ARIMA to predict annual average runoff, comparing with straightly using ARIMA to predict, we found that its result was more accurate, showing that using EEMD-ARIMA to predict short-term runoff could be positively valuable.