饮用水安全一直是水环境领域关注的重点问题。丹江口水库作为南水北调中线饮用水源地，其水质好坏直接影响到输水的安全，而上游支流作为丹江口水库最重要的污染来源之一，其水环境综合管理显得尤为重要。本文选取距离陶岔引水渠首最近的上游支流老灌河流域作为研究对象，开展水环境综合管理的基础研究工作，具体研究内容与结果如下：1、污染源调研分析。根据分析得到老灌河流域以冶金类企业居多，制药和造纸是重要的工业污染源。生活污染以自然排放为主，缺乏必要的收集和处理设施。同时，农业污染源也是区域重要的污染来源。2、水环境质量分析。通过因子分析、聚类分析以及一元线性回归分析方法，对老灌河流域进行污染源解析，水质变化与土地利用的相关性分析，以及水质时空差异性和总体变化趋势分析。结果表明，老灌河流域最重要的污染为自耗氧有机污染，耕地为工业和农业非点源的重要来源。老灌河流域水质的时空间差异性明显，自2006年以来水质整体呈现出改善的趋势，丰水期为年内水质重点控制时段，西峡县和淅川县的两县交界处为水质控制的重点区域。3、水环境质量综合评价。通过单因子评价法、熵权模糊综合评价法、灰色关联水质评价法对2012年水质进行综合评价，得到老灌河2012年水质总体较好，总体能够达到规划要求。三种评价方法中单因子综合评价法最为严格，而熵权模糊综合评价法和灰色关联水质评价法结果较为乐观。4、老灌河水质模拟与预测。应用QUAL2Kw模型为基础建立老灌河河流一维水质模型，通过改进的均方根误差法结合遗传算法进行模型的参数率定，并对模型进行验证和参数区域敏感性分析。5、基于不确定性的QUAL2Kw模型改进。采用拉丁超立方采样和马尔科夫转移分析对已建立的QUAL2Kw模型改进，计算出基准年和不同水文年的水质概率分布以及改变污染负荷条件下的水质概率分布，为流域水环境管理提供了决策支持。

Drinking water security is one of the important issues in water environment field which deserves limelight from the public. The Danjiangkou Reservoir as the water source of the South-to-North Water Diversion Project, its water quality directly affects water safety of the water delivery. The pollution from upstream tributaries is one of the most important pollution sources of the Danjiangkou Reservoir, therefore, the comprehensive water environmental management of these upstream tributaries is of particularly importance. This thesis selected the Laoguanhe river as the research object to carry out the basic research work of water environmental integrated management, because this river is the nearest upstream tributary from Taocha, where is the head of Diversion channel of the South-to-North Water Diversion Project. The detailed contents were as follows: 1. Pollution source analyses. According to the field study, the Laoguanhe River basin harbors a great amount of metallurgical companies; pharmaceutical and papermaking companies were the primary point pollution sources. Domestic sewage was mainly natural emission, and lacked essential collection and treatment facilities. In addition, agricultural pollution was another important regional pollution sources.2. Water quality analyses. The factor analysis, the cluster analysis and monadic linear regression analysis were applied to analyze the pollution source, the correlation of water quality variations and land uses, and the spatial-temporal variation of water quality in the Laoguanhe river basin. The results indicated that the oxygen consumption organic pollution was the most important contamination; the cultivated land was one of the major sources of industry and agricultural non-point source pollution. The spatial-temporal variation of water quality was significant in the Laoguanhe river basin. Since 2006, the water quality had been gradually improved. Wet period is an important water quality control period, and the junction of Xixia County and Xichuan County is a key area of water quality control.3. Water quality assessment. The single factor evaluation method, the entropy weight fuzzy comprehensive evaluation method and the grey correlation evaluation method were applied to evaluate the water quality of the Laoguanhe river basin in 2012. The results showed that the water quality of the Laoguanhe River in 2012 was good, and it can meet the requirement of the 12th five year plan. Among the three methods, the assessment results from the entropy weight fuzzy comprehensive evaluation method and grey correlation evaluation method were positive, while those from the single factor evaluation method were negative. 4. Water quality simulation and prediction. The QUAL2Kw model was applied to build the one-dimensional water quality model for the Laoguanhe River. A developed root mean square error method combined with the genetic algorithm method was used to calibrate the model. Furthermore, the validation and parameter regional sensitivity analysis had been performed with the calibration results.5. The QUAL2Kw model was improved with the uncertainty methods, including Latin Hypercube sampling and Markov transfer analysis. The improved model was used to calculate the water quality probability distribution of the base year and different hydrological years, and the water quality probability distribution with different pollution loads, which would provide a decision support for water environment management.