水文、气象站点稀少，观测困难是多年来困扰和阻碍干旱半干旱地区内陆水文过程模拟和水文预报研究的主要原因，无资料或缺资料地区的水文预报是近10年来国际水文学科研究的一个热点。本文选择干旱区典型的开都河流域，以多源遥感数据为基础，结合无资料或缺资料地区区域化方法，实现干旱区大尺度流域分布式水文建模的参数化；构建基于MIKE-SHE模型的干旱区缺资料流域水文预报工具，对流域水文过程进行模拟；通过不确定性分析揭示模型参数化方案中各要素对模型精度影响机理，特别径流模拟结果对气象驱动和陆面特征参数的空间异质性与尺度选择的响应机制。通过研究得到了以下主要结论：（1）研究区水文特征参数具有很强的地域分布规律，分析了不同站点间降水、盘蒸发和气温之间的关系，建立了降水于地形之间的定量关系；（2）在具有物理基础的流域分布式水文模型——MIKE-SHE模型结构基础上，结合流域水文循环特点和数据状况，构建了适合干旱区大中尺度无资料或缺资料流域的分布式水文模型，验证结果表明：MIKE-SHE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，模型效率系数达到0.7以上，率定期与验证期水量平衡误差均小于3%，模拟径流与实测径流高度相关。（3）对FY_2C和MODIS系列遥感反演产品进行了预处理与分析，利用遥感估算的降水、蒸散、叶面积指数优化模型的气象驱动输入和陆表参数，用遥感反演土壤含水量和积雪覆盖数据对模拟结果进行空间验证；结果表明，遥感反演参数能够在一定程度上提高模拟精度，特别是空间模拟结果的精度。（4）从数据输入、参数选取和模型结构等角度揭示导致研究区水文预报不确定性的主导因素；重点研究气象驱动因子的空间变异、模型网格尺度以及非饱和带、饱和带参数对模拟精度的影响机制，并将不确定性分析结果反馈到参数优化中，探讨模型中存在的主要问题以及可能的改进。Due to the Spare gauge distribution and observation difficults, hydrologic modeling and streamflow prediction of ungauged basins is an unsolved scientific problem to the hydrologic community. And it is also a problem that hinders the development of hydraulically modeling in arid inland river basin. One way to address this problem is to improve hydrologic modeling capability through the use of spatial data and spatially distributed physically based models. This dissertation is composed of three part focused on: （1）A GIS-aided MIKE SHE model was applied to simulate the stream flow in an ungauged Kaidu River Basin （19012km2） with arid climate, which is one of the most important sources of Tarim River. The observed discharge at Dashankou station was used for calibration and validation. It is shown that a well-calibrated MIKE SHE model with five free parameters is able to produce consistent results with model efficiency coefficients greater than 0.7, water balance error lower than 3%, and the simulated discharge is highly correlated to observed discharge. （2）Remote sensing was used to derive spatially distributed inputs of the hydrologic model；rainfall, potential evapotranspiration, and leaf area index were used to improve the description of climate driver and land surface characteristic. The remote sensing driven model shows better results than the gauge driven model. （3）Uncertainties were selected for investigation depending on how significantly they affected the model’s decision variables. Model development of distributed models involves considerable uncertainty and 8 sources of model uncertainty were identified and analyzed. And the effects of grid scale, rainfall inputs and three important parameters on runoff simulation were analysed
