挠力河流域河流径流量对气候变化的敏感性分析

流域径流量对气候变化的敏感性分析是理解气候变化对流域水资源影响的重要手段。本文利用非更新式人工神经网络(ANN)模型,以年平均降雨、年最低气温和最高气温为输入参数,年平均径流量为输出变量,构建了三江平原挠力河流域的径流量预测ANN模型;并根据IPCC第四次报告的气候变化模式,设定了9种不同的气候变化情景,利用构建的ANN模型分析了流域径流量对气候变化的敏感性。结果表明:构建的人工神经网络模型能够较好的模拟径流量,可用于气候变化的敏感性分析;挠力河流域上游径流量对气候变化的敏感性要大于中游区域的,降水变化对径流量的影响大于气温对其产生的影响。

Assessing the sensitivity of runoff to climate change in Naoli River watershed,Northeast China

Planning for potential future changes in water yield requires an understanding not only of possible climatic changes but also how a catchment hydrology might respond to such changes.Understanding catchment responsiveness,the sensitivity of catchment stream flow to changes in climatic conditions,was a useful method to examine the impacts of climate change on water resources in a watershed.In this paper,the artificial neural network(ANN) model of Naoli River watershed was constructed to assess the sensitivity of runoff to different climatic conditions.The model took the annual mean rainfall,annual minimum temperature and maximum temperature as the input parameters,and the annual mean runoff as the output parameter.The accuracy of the model was also assessed based on the statistical methods.According to the fourth reports of IPCC,we assumed nine climate change scenarios.Under the scenarios,the sensitivity of runoff to climatic condition was assessed.The results showed that: the constructed ANN model could better simulate runoff,and it could be used for sensitivity analysis of climate change;the runoff in the upper reaches of the watershed was more sensitive than that in the middle reaches,and the runoff was more sensitive to the rainfall than to the temperature.The artificial neural network(ANN) model is a simple method used to analyze the sensitivity of runoff to climatic conditions,but this method does not place emphasis on the spatial variation in runoff sensitivity.