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Application of developed Grid-GA distributed hydrologic model in semi-humid and semi-arid basin 总被引：1，自引：0，他引：1
A grid and Green-Ampt based(Grid-GA)distributed hydrologic physical model was developed for flood simulation and forecasting in semi-humid and semi-arid basin. Based on topographical information of each grid cell extracted from the digital elevation model (DEM) and Green-Ampt infiltration method, the Grid-GA model takes into consideration the redistribution of water content, and consists of vegetation and root interception, evapotranspiration, runoff generation via the excess infiltration mechanism, runoff concentration, and flow routing. The downslope redistribution of soil moisture is explicitly calculated on a grid basis, and water exchange among grids within runoff routing along the river drainage networks is taken into consideration. The proposed model and Xin’anjiang model were applied to the upper Lushi basin in the Luohe River, a tributary of the Yellow River, with an area of 4 716 km2 for flood simulation. Results show that both models perform well in flood simulation and can be used for flood forecasting in semi-humid and semi-arid region. 相似文献
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本对流域状态和最优化特性是如何影响模型功能方面进行了研究．在假设流域诸多初始条件下，使用SCE-UA优化算法通过日模型率定新安江模型．结果发现流域的初始条件和最优化特性对待优化的参数有非常大的影响，并且使模型过程参数的响应程度变小．研究结果表明变化的初始条件对总产流的影响并不是太大． 相似文献
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由于水文模型是对物理过程的简化,用以描述事物最主要的物理过程,从而数学模型受许多不确定因素的影响.因此,提出了一种耦合了人工神经网络(ANN)和新安江概念模型以提高径流预报精度的方法.该方法用最新的观测资料和新安江模型中产生的径流剩余误差/流量预报结果,其工作原理为用神经网络模型预报新安江模型误差,并作为新数据引入,使径流预报得到改进.对互补的神经网络模型而言,使用的变量要以特定格式输入以符合新安江模型的要求.结果表明,与单独用新安江模型预报相比,互补模型的洪水预报精度有明显提高. 相似文献
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