基于降雨-径流试验的高寒草甸产流机制研究

对黄河源区高寒草甸产流机制进行研究,为河源区生态环境保护、水资源科学规划管理提供基础水文理论认知。基于野外降雨-径流试验,通过回归分析构建考虑坡度和覆被因子的暴雨和中小雨产流计算模式,精度良好,能够反映暴雨和中小雨2种降雨模式下高寒草甸产流的一般特征。试验结果表明,因草甸土-植体系蓄容能力较强,高寒草甸区地表产流主要发生在暴雨模式下,中小雨模式下的地表产流量一般很小。模式应用表明,草甸发生退化后,暴雨和中小雨入渗均有减少,暴雨模式下退化草甸比高覆草甸入渗减少约12%,中小雨模式下减少约3%。     

考虑降雨的下垫面变化对径流的影响分析

以下垫面覆被条件变化来表征的人类活动是水文循环研究中的核心问题,对流域发电、水资源管理以及社会经济可持续发展起到至关重要的影响。本文采用趋势分析法分析右江水电站控制流域内的年降雨量以及年径流量的变化特征及趋势,在利用Arc GIS分析流域下垫面变化的基础上,分别采用多元统计模型和考虑降雨的多元统计模型对径流系数进行模拟。结果表明年降雨量与年径流量的总体变化趋势是一致的,对年径流影响最大的要素是年降雨量,将降雨作为自变量放入回归模型后,模型拟合效果明显提高。     

基于支持向量机(SVM)的祁连山典型小流域日降水-径流模拟研究

及时准确的日径流预测在流域水资源的合理规划、利用及管理中具有十分重要的作用。本文以支持向量机(SVM)模型为基础,以祁连山典型小流域-排露沟流域为研究区域,建立了流域日降水-径流模型,对流域未来1~7 d的日径流量进行了模拟预测。为检验SVM模型的有效性,模拟结果与人工神经网络(ANN)模型预测结果进行了对比。结果表明:SVM和ANN均表现出了很高的精度;但相比于传统的ANN模型,SVM模型的预测精度显著提高。表明SVM模型在半干旱山区小流域径流预测中有更好的适用性,可以用于流域中长期日径流预测,是资料有限的条件下中长期日径流预测的有效工具。     

桃林口水库降水径流变化特征分析

受气候条件变化及人类活动的影响,近年来桃林口水库降水径流特征发生了显著变化。根据Mann-Kendall检验法对流域内典型测站降水径流的时空变化特征进行分析,结果表明,桃林口水库降水过程具有显著的下降趋势,且降水中心有向中上游流域转移的趋势;加之进入20世纪70年代以来,人类活动的加剧,导致入库水资源量显著减少。     

论城市地表径流损害的赔偿

城市地表径流污染有其独特的特征,现行的环境法律规范对此缺乏规范指导。本文分析了城市地表径流造成的损害与传统环境问题的不同特性,以及适用法律的特点,说明完善立法、建立政府环境赔偿基金和相应机构是解决问题的根本途径     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

A Genetic Programming Approach to Rainfall-Runoff Modelling

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a transparent and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models.     

人工神经网络评价城市化对径流生成的影响

探讨使用人工神经网络（ANN）模型模拟城市化地区的降雨径流关系。建立城市化地区的降雨径流关系模型，并把反映城市化进程的不透水面积比（PIA）这一重要参数加入输入模式中，用人工智能手段研究城市化对降雨径流关系的影响。大量的数值实验证明，当神经网络被适当地配置以后，可以再现潜在的降雨径流关系，可以用来生成精确且符合实际的预测结果。而当城市化参数作为输入模式的一部分时，可以较准确模拟涨洪段，而退水段的性能不佳。     

Neural nets for modelling rainfall-runoff transformations

To obtain river flow data, a neural network (NN) is developed and applied to rainfall-runoff transformation. The NN has been built considering a hidden two layer net and the sigmoidal has been used as a response function. Training is conducted using a back-propagation learning rule. In the input layer, both areal and point data values may be considered. The capability to provide a suitable forecast of river runoff has been examined for the Araxisi watershed in Sardinia. Experiments have been made dividing the total extension of observed data into three ten-year periods, assuming each as a training set, learning the NN and simulating the other two decades over the same period. The obtained model efficiency confirms the capability of this approach to supplying a useful tool in the evaluation of rainfall-runoff transformations.     

高台漫流区铁路桥涵洪灾预警雨量分析

为降低漫流区水害对兰新铁路桥涵的危害,利用暴雨洪水管理模型(SWMM)对高台漫流区洪水进行模拟分析,得到该漫流区部分铁路桥涵预警雨量.首先通过ArcGis建立集总式与半分布式两种SWMM模型,用6场降雨对模型参数进行率定,利用精度评价指标比较两种模型的优劣,进而探讨了SWMM模型对漫流区的适用性;最后采用水位反推法,得...