我国大部分地区水资源开发利用程度普遍较高，特别是北方水资源严重紧缺地区，水资源开发利用程度高达80%，已超过国际公认的水资源利用程度警示线40%，水资源成为经济发展的重大制约因素，水资源承受着巨大压力。针对水资源供求矛盾日渐尖锐的现状，如何对区域水资源承压状态进行全面、必要的动态测评和风险预警，如何对水资源风险来源、风险发生概率及其损失程度等问题做出科学合理的回答，如何有效减小风险损失及尽早预防不安全性的升级，是目前亟待解决的关键问题。在水资源合理配置与实现经济社会可持续发展的目标下，水资源管理需要适应新形势，目前缺乏一套完整的水资源压力测评与风险预警的理论体系，且尚未在案例研究的实践中发展完善理论框架，将风险管理的理念引入到水资源管理中形成风险调控策略将是研究的一个趋势。本文研究水资源压力测评与风险预警方法，探索风险调控技术策略，并结合我国华北地区水资源供需矛盾较为突出且水资源演变趋势较为明显的行政区域—河北省进行案例分析，主要研究内容有以下几个方面：⑴水资源的诸多问题与人类活动密不可分，根据虚拟水与水足迹的概念，从消费角度衡量和刻画了人类对水资源系统的真实占用和影响程度，建立了水资源利用与人类消费模式的联系。以水足迹为基础，从水资源供需平衡、经济社会、农业生产和生态环境四个方面构建了水资源压力测评指标体系，并构建了水足迹-Vague集模型，对研究区各市级行政区进行水资源压力测评，划分高水资源压力（红区）、中水资源压力（黄区）和低水资源压力（蓝区）状态，为水资源风险预警的研究奠定基础。⑵将风险的概念和思想引入到水资源压力评价中，研究水资源系统本身运行的可靠性，即风险事件的成因和风险事件出现的概率。根据研究区经济社会发展趋势及水资源压力测评结果，采用有限可列马尔科夫链来描述水资源风险状态，建立马尔科夫链风险预警模型，对未来年份水资源状态进行风险预警，预测未来水资源压力状态的转移概率，重点关注各类型区向红区发展的可能性。在揭示区域水资源的现状和未来状态演变趋势的研究基础上，将预测风险水平与目标风险水平进行比较，当预测的风险标记与目标水平存在偏差时，就需要调整管理决策手段或者进行风险调控。基于控制论基本原理，采用状态方程作为控制对象的数学模型，描述水资源系统的动态演化过程，并按协调偏差进行反馈控制，直至达到偏差最小和实现风险概率降低的控制目标，提出水资源风险控制机理和控制框架，并解析出人口、GDP、土地利用类型这些控制变量，探讨人口发展规模和人口结构调控、经济增长方式调控和土地利用类型调控的技术方法。⑶在上述理论框架体系和方法探讨的基础上，以河北省11个市级行政区为研究对象，系统开展了水资源压力测评、水资源风险预警和调控研究。案例研究结果表明，河北省各行政区水足迹总体呈现上升趋势，说明人类对水资源的消费水平和利用程度呈增长趋势，主要驱动因素为人口数量增加、城市发展以及经济水平的提高等，水资源利用压力很大，属于高水资源压力和中水资源压力状态的区域居多；如果按照现有趋势发展，预测出2015年河北省各行政区水资源出现黄色和红色预警状态的可能性很大，其中最大概率达0.6942，出现在保定地区；针对风险预警结果提出风险调控策略，案例研究对北方干旱半干旱地区水资源科学管理具有示范与借鉴作用。

In most parts of China, water resources development and utilization has reached or exceeded the international warning level, especially in the northern arid and semi-arid regions, while water security is becoming increasingly prominent. The conflict between water resources supply and demand is becoming more and more serious. Therefore it is important to conduct comprehensive dynamic monitoring and risk warning of water resources stress, to seek a scientific and reasonable answer to water risk sources, occurrence probability and the losses of water risk, to reduce the risk losses effectively and to prevent an insecure situation as soon as possible. In the goal of rational allocation of water resources and sustainable development of society and economy, water resources management should explore ways to adapt to the new situation. At present, there is no complete and effective theoretical system of water stress assessment and risk early warning, and the lack of theoretical framework as well as implication of case studies are problems. It will become a research trend which needs further expansion to introduce risk management into water resources management. Based on water stress assessment and risk early warning, risk control technology is explored by combining case analysis of Hebei province which has serious conflicts between water supply and demand. The main content is as follows:⑴Many issues of water resources are closely and some inseparably associated with human activities. Based on the concept of virtual water and water footprint, real occupation and consumption of water resources by humans from the perspective of consumption is measured and characterized, and then a relationship between water resources utilization and human consumption patterns is established. Based on water footprint, the index system of water stress assessment including four aspects which are the water supply and demand balance, socio-economy, agricultural production and ecological environment is founded. Furthermore, the water footprints vague model is described in its role to assess water stress, and water stress assessment of every municipality of Hebei is analyzed. All regions are divided into three levels, namely high water stress (red zone), general water stress (yellow zone) and low water stress (blue zone), which lays the foundation of the further research on water risk warning.⑵The operational reliability of water resources system and the probability occurrence of risk events is studied by bringing the ideas and concepts of risk into water resources stress assessment. According to the socio-economic development trends of the study area and the results of water stress assessment, a Markov chain risk warning model is built to describe the risk status of water resources and to predict the probability of transition of the water stress state in the future. This part is intended to focus on the possibility that three water stress levels might change into higher water stress level. Based on revealing the current state and evolution trend of regional water resources, we compare the forecasting risk level with the target risk level and focus on the deviation between them, and then adjust the management method of decision-making and risk control. Based on the basic principles of control theory, using a state equation to describe dynamic evolution of the control object and the system, a feedback control is made aimed at minimizing coordination deviation and reducing risk probability. Moreover, a water system control mechanism and control framework is proposed, and the control variables including population, GDP, and the land use type are analyzed. Then the control technologies of population scale development, economic growth mode and land use type are investigated.⑶Base on investigation of the theoretical framework and methods above, 11 municipalities in Hebei are chosen as the research object, and water stess assessment, risk early warning and risk control management are systematically carried out. The case study shows that along with the rising population density and urban development, the water footprint takes on an upward trend, which indicates that water demand is increasing, and the situation places high stress on water resources. The safety status of water resources is not good with most regions belonging to the high water stress or general water stress level. If this develops in accordance with current trends, the water resources in Hebei will possibly appear in the yellow and red warning status by the year 2015. The maximum probability is up to 0.6942 which will appear in Baoding. The case studies can demonstrate and provide reference for scientific management of water resources in the northern arid and semi-arid regions.