年径流系数变化特征及预测模型研究

以北京市漫水河流域为研究对象,对漫水河1956-2016年年降雨量、年径流系数变化特征及径流系数突变性进行分析,以2000-2016年代表现状下垫面条件,采用主成分分析法分析了时段降雨量和年降雨量对年径流系数的影响,并建立了年径流系数与主要降雨因子的线性主成分回归模型及基于LM(Levenberg—Marquardt)算法的BP神经网络模型。结果表明:漫水河流域年径流系数在过去的61年间呈极显著下降趋势,年径流系数从1956年到上世纪70年代初、上世纪70年代末到80年代末、2000年至今有3个急剧的下降趋势;现状下垫面条件下,短时期强降雨对年径流系数的影响较大,采用神经网络模型预测的年径流系数值和实测值相关系数0.99,平均绝对误MAE为0.002 6,均方根误差RMSE值为0.005,与回归模型相比,神经网络方法构建的年径流系数预测模型精度高,预测效果好。

Research on Annual Runoff Coefficient Characteristics and Prediction Model

Taking Manshuihe watershed, Beijing as the research object, the characteristics and abrupt change point of the annual precipitation and runoff coefficient from 1956 to 2016 were analyzed. Principal Component Analysis was used to analyze the influence of hourly rainfall and annual rainfall on annual runoff coefficient based on runoff coefficient under the underlying surface condition of 2000-2016. A linear principal component regression model and BP neural network model based on LM Algorithms for annual runoff coefficient and main rainfall factors was established. The results showed that annual runoff coefficient had been declining significantly in the past 61 years, and had three sharp downward trends from 1956 to early 1970s, late 1970s to late 1980s, 2000 to present. The short-term rainfall had a great influence on annual runoff coefficient under the current underlying surface. The correlation coefficient, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were 0.99, 0.002 6 and 0.005 respectively between annual runoff coefficient predicted by neural network model and measured value. Compared with regression model, the neural network model gave a better result in simulating annual runoff coefficient.