Integrating Wavelet Analysis and BPANN to Simulate the Annual Runoff With Regional Climate Change: A Case Study of Yarkand River, Northwest China
Integrating Wavelet Analysis and BPANN to Simulate the Annual Runoff With Regional Climate Change: A Case Study of Yarkand River, Northwest China
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DOI：
10.1007/s11269-014-0625-z
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发表时间：
2014-05
影响因子：
4.3
通讯作者：
Jianhua Xu;Yaning Chen;Weihong Li;Qin Nie;Chunan Song;Chunmeng Wei
中科院分区：
文献类型：
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作者：
Jianhua Xu;Yaning Chen;Weihong Li;Qin Nie;Chunan Song;Chunmeng Wei
选取叶尔羌河作为我国西北内陆河的典型代表，利用1957 ~ 2008年的水文气象资料，分析了叶尔羌河流域水文气候过程的变化规律，并结合小波分析建立了年径流量与年平均气温、年降水量变化的集成模型和人工神经网络（ANN）在不同时间尺度下的预测结果。结果表明，水文气候过程的格局在时间上具有尺度依赖性。在16年和32年的时间尺度上，AR呈单调增加的趋势，AAT和AP的趋势相似。但在2年、4年和8年时间尺度上，AR随AAT和AP的波动呈现非线性变化。基于小波分解的反向传播人工神经网络（BPANNBWD）很好地模拟了AR随AAT和AP的变化。与传统统计模型相比，BPANNBWD模型在各时间尺度上的模拟效果均优于多元线性回归模型。结果还表明，在较大的时间尺度（如16年或32年）的模拟效果优于在较小的时间尺度（如2年或4年）。
Selecting the Yarkand River as a typical representative of an inland river in northwest China, We identified the variation pattern of hydro-climatic process based on the hydrological and meteorological data during the period of 1957 ~ 2008, and constructed an integrated model to simulate the change of annual runoff (AR) with annual average temperature (AAT) and annual precipitation (AP) by combining wavelet analysis (WA) and artificial neural network (ANN) at different time scale. The results showed that the pattern of hydro-climatic process is scale-dependent in time. At 16-year and 32-year time scale, AR presents a monotonically increasing trend with the similar trend of AAT and AP. But at 2-year, 4-year, and 8-year time scale, AR exhibits a nonlinear variation with fluctuations of AAT and AP. The back propagation artificial neural network based on wavelet decomposition (BPANNBWD) well simulated the change of AR with AAT and AP at the all five time scales. Compared to the traditional statistics model, the simulation effect of BPANNBWD is better than that of multiple linear regression (MLR) at every time scale. The results also revealed the fact that the simulation effect at a larger time scale (e.g. 16-year or 32-year scale) is better than that at a smaller time scale (e.g. 2-year or 4-year scale).