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MIKE 11/NAM模型在挠力河流域的应用     被引量:44

Application of coulpled MIKE11 /NAM model in Naoli River Basin,northeastern China

文献类型:期刊文献

中文题名:MIKE 11/NAM模型在挠力河流域的应用

英文题名:Application of coulpled MIKE11 /NAM model in Naoli River Basin,northeastern China

作者:林波[1] 刘琪璟[1] 尚鹤[2] 王英伟[3] 隋祥[3]

第一作者:林波

机构:[1]北京林业大学林学院;[2]中国林业科学研究院森林生态环境与保护研究所;[3]东北林业大学林学院

年份:2014

卷号:36

期号:5

起止页码:99-108

中文期刊名:北京林业大学学报

外文期刊名:Journal of Beijing Forestry University

收录:CSTPCD;;北大核心:【北大核心2011】;CSCD:【CSCD2013_2014】;

基金:"948"国家林业局引进项目(2008-4-37);"973"国家重点基础研究发展计划项目(2006CB403307)

语种:中文

中文关键词:NAM模型;MIKE;11;挠力河流域;自动率定

外文关键词:NAM model; MIKE 11 ; Naoli River Basin ; autocalibration

分类号:S715.3

摘要:在水文水资源的管理实践中,气候变化以及农业开发等活动对流域水资源的影响往往无法通过数据直观体现,水文模型常被用于提供水资源管理决策所必须的一些重要信息。集总式概念模型结构简单,仅依赖有限的数据和较少的参数即可对流域主要水文过程进行有效的模拟。本研究将MIKE 11水动力模型和降雨径流模型(NAM)耦合,应用到三江平原挠力河流域。结果表明,MIKE 11/NAM模型可以对挠力河流域降雨径流过程进行较好的模拟,模型在率定期的表现优于验证期。造成模型在验证期模拟效果降低的最主要原因可能是强烈的农业开发活动对流域下垫面性质的改变。20世纪50年代以来三江平原地区高强度的农业开发活动深刻影响了流域水文情势,并且这种影响在近20年仍然持续。本研究所构建模型的不确定性主要来源于输入数据的时空精度、模型结构和最优参数集的选择。
In the practice of water resource management, the effects of land use and climate changes on basin scale water regime have become increasingly concerned, for which data are not directly available. Despite their simplicity, lumped conceptual model can effectively represent the major hydrologic processes based on limited data and a modest numbers of parameters. A coupled MIKE 11 HD/NAM modelling system was constructed to simulate the rainfall-runoff process of Naoli River Basin in northeastern China. Results showed that MIKE l l/NAM was capable of simulating the rainfall/runoff process of Naoli River Basin. Model performance in validation period was generally better than that in calibration period, which was mainly caused by the modification of underlying surface characteristics due to intensive human activities. The ongoing agricultural development since 1950 had significantly changed the hydrological regime of the watershed, and there was no sign that the wetland degradation trend had stopped recently. The uncertainty of our conceptual model comes mainly from input data, model structure and the selection of optimal parameter set.

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