Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.

Reservoir systems are subject to several uncertainties that are the result of imperfect knowledge about system behavior and inputs. A major source of uncertainty arises from the inability to predict future inflows. Fortunately, it is often possible to generate probabilistic forecasts of inflow volumes in the form of probability density functions or ensembles. These inflow forecasts can be coupled with stochastic management models to determine reservoir release policies and provide stakeholders with meaningful information of upcoming system responses such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. This information on anticipated system responses is also expressed in the form of forecasts that must reliably represent the actual system behavior when it eventually occurs. The first part of this study presents an assessment methodology that can be used to determine the consistency of ensemble forecasts through the use of relative frequency histograms and minimum spanning trees (MST). This methodology is then used to assess a management model's ability to produce reliable ensemble forecasts. It was found that neglecting to account for hydrologic state variables and improperly modeling the finite management horizon decrease ensemble consistency. Several extensions to the existing management model are also developed and evaluated. The second portion of this study involves the management of the uncertainties in reservoir systems. Traditional management models only find management policies that optimize the expected values of system benefits or costs, thereby not allowing operators and stakeholders to explicitly explore issues related to uncertainty and risk management. A technique that can be used to derive management policies that produce desired probabilistic distributions of reservoir system outputs reflecting stakeholder preferences is developed. This technique can be embedded in a user-interactive framework that can be employed to evaluate the trade-offs and build consensus in multi-objective and multi-stakeholder systems. The methods developed in this dissertation are illustrated in case studies of real reservoir systems, including a seven-reservoir, multi-objective system in California's Central Valley.

35 leading multi-disciplinary scientists with international reputations provide reviews of topical areas of research on uncertainty and reliability related aspects of water resource systems. The volume will be valuable for graduate students, scientists, consultants, administrators, and practising hydrologists and water managers.

Hydraulic, hydrologic and water resources engineers have been concerned for a long time about failure phenomena. One of the major concerns is the definition of a failure event E, of its probability of occurrence PtE), and of the complementary notion of reliability. However, as the stochastic aspects of hydraulics and water resources engineering were developed, words such as "failure," "reliability," and "risk" took on different meanings for different specialists. For example, "risk" is defined in a Bayesian framework as the expected loss resulting from a precisely defined failure event, while according to the practice of stochastic hydraulics it is the probability of occurrence of a failure event. The need to standardize the various concepts and operational definitions generated numerous exciting discussions between the co-editors of this book during 1983-84 when L. Duckstein, under sponsorship of the Alexander von Humboldt Foundation (FRG), was working with E. Plate at the Institute of Hydrology and Water Resources of the University of Karlsruhe. After consulting with the Scientific Affairs Division of NATO, an organizing committee was formed. This comittee - J. Bernier (France), M. Benedini (Italy), S. Sorooshian (U. S. A. ), and co-directors L. Duckstein (U. S. A. ) and E. J. Plate (F. R. G. ) -- brought into being this NATO Advanced Study Institute (ASI). Precisely stated, the purpose of this ASI was to present a tutorial overview of existing work in the broad area of reliability while also pointing out topics for further development.

Water resources are indispensable for the sustainable development of the human society. A variety of hydrological modeling and water resources management tools based on simulation and optimization have been developed to address the current water issues worldwide. However, there are many challenges arising from climate change, human disturbances and enormous uncertainties and complexities. Thus, there is a global need for advanced methodologies that can support the modeling and management of water resources systems in an effective and efficient way. In this dissertation research, a spectrum of methods have been developed to deal with the stochastic modeling and risk-based management problems for water resources systems. These methods include: (i) a Stepwise Clustered Hydrological Inference (SCHI) model that can establish the complex nonlinear relationships between climatic conditions and streamflow for hydrological forecasting; (ii) a flexible and effective hydro-climatic modeling framework based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and stepwise cluster analysis for hydrological modeling under the changing climatic conditions; (iii) a Stepwise-cluster-analysis-based Probabilistic Collocation Expansion (SPCE) method for the stochastic simulation and forecast of hydrologic time series; (iv) a hydrologic frequency analysis framework based on change point analysis and Bayesian parameter estimation to deal with the nonstationarity and uncertainties in hydrological risk analysis; (v) an Interval-parameter Two-stage Fuzzy Stochastic Integer Programming (ITFSIP) model for risk-based flood diversion management under multiple uncertainties. The proposed methods have been applied to the Xiangxi River Watershed in China and the Grand River Watershed in Canada, in order to demonstrate their capabilities and performances in precipitation-runoff modeling, climate change impact analysis, uncertainty quantification, frequency analysis, and systematic water resources and risk management. The major contribution of this research lies in the development of innovative approaches for tackling various uncertainties and complexities in the hydrological cycle and water resources systems. This research can provide scientific and practical bases for robust hydrological modeling and reliable water resources management.