Study On The Runoff Change Characteristic And Forecast Model In Jinghe River Basin

Water Resources is one of the basic elements of supporting human life and economic growth and sustainable development, among them, the change of runoff dominates the whole system changes, due to climate, social development and human activities combined effects, the variation of runoff is complex and shows the unpredictable, random, multi-time scale and nonlinear characteristic. The paper depended on National Key Technology R&D Program(2006BAD11B04) and National Natural Science Foundation of China(50879072), studied the variation characteristics of the hydrological and meteorological series systematically in Jinghe River basin with traditional methods, empirical mode decomposition and wavelet analysis theory and method, in this basis, the forecast model was established to predict the runoff series with empirical mode decomposition, wavelet decomposition and BP artificial neural network. The main research results are as follows:(1) In Jinghe River basin, the annual runoff distribution is extremely uneven and variant, the annual runoff distribution shows a single peak type. The centralized degree of runoff is high, and the annual maximum runoff appears in August. The interannual variations of runoff present the relationship of alternating between wet and dry.(2) After analyzing the trend of annual runoff series in Jinghe River basin with moving average and Kendall rank correlation methods, the annual runoff has a significant downward trend and the average decrease has per decade 113 million stere. Meanwhile, analyzing meteorological series which are the factors causing the changes of runoff, it found that the changes of interannual precipitation affect runoff clearly, and the change tendency of runoff is similar to that of precipitation, but the overall consistency of the runoff variation and temperature variation is not obvious.(3) Using EMD, multiple-time-scale analysis of annual runoff and precipitation of 4 typical hydrologic stations, including Pingliang, Huanxian, Xifengzhen and Changwu stations in Jinghe River basin, The results of investigation showed that the runoff time series has periods that about 2~3, 6~8, 10~12 and 21 years. The precipitation series has periods that about 2~3, 5~7, 10~13 and 18~22 years, the change tendency of runoff is similar to that of precipitation by analyzing the correlation of precipitation EMD components and runoff EMD components.(4) After using complex Morlet wavelet transform of annual runoff and annual precipitation series in Jinghe River basin, and analyzing periodic variation and multi-time scales of hydrological and meteorological time series. The result shows that the series in time-frequency domain have multiple time scales structures, the major periods of runoff time series are about 13 years and 32 years, the minor periods are about 3 years and 21 years, and the annual runoff within the next few years is still in the dry period after 2000. The precipitation series has periods that about 3 years, 6years, 11 years and 18 years.(5) EMD-BP neural network model uses EMD method on the precipitation series in the smooth processing, and gets a set of intrinsic mode component(IMF) and a residual amount(RES), through components as the input, the corresponding annual runoff series for the output, with BP neural network to forecast annual runoff series. WANN prediction model is a neural network model based on wavelet analysis, the model integrates the wavelet transform and neural network organically and fully plays the advantages of them. The two models forecasted annual runoff of Jinghe River basin, the rate of relative error less than 10% is 66.7% and 80%,the rate of relative error less than 20% is 83.3% and 93.3%. The result shows that the two models predict higher accuracy and can be used to forecast annual runoff in Jinghe River basin.