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中国沙漠 ›› 2010, Vol. 30 ›› Issue (5): 1234-1238.

• 水文与水资源 • 上一篇    下一篇

塔里木河源流山区径流模拟及不确定性研究

黄 粤1,2, 陈 曦1, 马勇刚3   

  1. 1.中国科学院 新疆生态与地理研究所, 新疆 乌鲁木齐 830011; 2.北京大学 城市与环境学院, 北京 100871; 3.新疆遥感中心, 新疆 乌鲁木齐 830011
  • 收稿日期:2009-12-12 修回日期:2010-03-19 出版日期:2010-09-20 发布日期:2010-09-20

Simulation of Runoff Process in Mountainous Basin of Tarim River based on Distributed Hydrological Model and Uncertainty Analysis

HUANG Yue1,2, CHEN Xi1, MA Yong-gang3   

  1. 1.Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China; 2.College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; 3.Xinjiang Remote Sensing Center, Urumqi 830011, China
  • Received:2009-12-12 Revised:2010-03-19 Online:2010-09-20 Published:2010-09-20

摘要: 自然环境恶劣、站点稀少、观测困难是干旱区水文系统研究面临的基本问题。特殊的自然地理条件给水文过程带来了极大的复杂性和不确定性,也阻碍了干旱区水文过程和机理研究的发展。选取塔里木河源区开都河流域为研究区,采用分布式水文模型MIKE SHE模拟大尺度资料稀缺地区水文过程,将流域内气象、水文站点数据与遥感数据相结合,利用气象、土壤类型、土地利用和地表覆盖、数字高程(DEM)和降雨等资料,模拟流域水文过程;在出山口径流数据的基础上对模型进行率定和验证;分析了模型中的不确定性的来源,探讨模型优化方法。结果表明,MIKE SHE模型能在水文、气象站点稀少,土壤及水文地质数据缺乏的条件下,模拟开都河流域的日径流过程,但精度仍有待提高;通过分析识别出了隐含于模型结构、输入及参数等3个方面的8种不确定性来源。

关键词: 分布式水文模型, MIKESHE, 不确定性, 内陆河

Abstract: Hydrological modelling in ungauged areas especially in inland river basin has become a hotspot of hydrology study in the world. A modified version of the physically-based distributed hydrological model, MIKE SHE, was applied to simulate the runoff process in Kaidu river basin which is one of the most important sources of the Tarim River. The data for modeling, such as precipitation, evapotranspiration, soil type, vegetation type, LAI, DEM are from meteorological stations and remote sensing. The observation data from Dashankou (DSK) and Bayinbuluk (BYBLK) hydrological stations were used for calibration and validation. Uncertainty in runoff process simulation was discussed. Simulation result showed that MIKE SHE Model could be used to simulate the runoff process in mountainous basin of inland river, it can meet acceptable accuracy.

Key words: distributed hydrological model, MIKE SHE, uncertainty, inland river

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