With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules.

Optimal Operation of Multireservoir Systems: State-of-the-Art Review

Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events.

Challenges in modeling and predicting floods and droughts: A review

Reservoir operations play an important role in water resources planning and management. During periods of drought, when inflows to the reservoir are low, it may not be possible to satisfy the target delivery (planned demand), and shortage occurs. Reservoir hedging rules, which include a set of rule curves and rationing factors, are used to guide the operation under such a situation. In particular, hedging rules are employed to balance the current shortage in supply with future storage to avoid severe water shortages in the future. In general, hedging rules are determined at the planning stage, or at the early stage of a reservoir’s operating life. However, after years of operation, the reservoir system, as well as the demand characteristics, may change. Hence, it becomes necessary to reevaluate and update the existing hedging rules to incorporate changes that have taken place. The purpose of this paper is to devise new hedging rules for an existing multireservoir system. An original mixed-integer nonlinea...

Optimization of Hedging Rules for Reservoir Operations

Considerable research has been carried out on the optimization of water distribution systems WDSs over the last three decades. In previous research, attention has mainly focused on the minimization of cost, due to the high expenditure associated with the construction and maintenance of such systems. However, the impacts of WDSs on the environment usually have not been considered adequately. The recent increasing awareness of sustainability and climate change, especially global warming, has led to research where greenhouse gas GHG emissions are considered. In the study described in this paper a multiobjective genetic algorithm for WDS optimization has been used as an explorative tool to investigate the trade-offs between the traditional economic objective of minimizing costs and an additional environmental objective of minimizing GHG emissions. The impacts of minimizing GHG emissions on the results of WDS optimization have been explored for a case study in this paper. The results indicate that the inclusion of GHG emission minimization as one of the objectives results in significant trade-offs between the economic and environmental objectives. Furthermore, a sensitivity analysis has been conducted by using different discount rates in a present value analysis for computing both ongoing costs and GHG emissions. The results obtained show that the Pareto-optimal front is very sensitive to the discount rates used. As a result, the selection of discount rates has a significant impact on final decision making.

/pdf/accounting-for-greenhouse-gas-emissions-in-multiobjective-1qn6czhsjq.pdf

Accounting for Greenhouse Gas Emissions in Multiobjective Genetic Algorithm Optimization of Water Distribution Systems

Floods, droughts, water scarcity, and water contamination are some among many water problems that are present today and will be even more noticeable in the future. In the past, many different tools have been used for simulation and optimization of complex water resources systems in order to provide an improved basis for decision making. The continuing evolution of information technology (hardware and software) creates a good environment for the transition to new tools. Application of the systems approach to water resources planning, management, and operations has been established as one of the most important advances made in the field of water resources engineering. Based on the lessons learned, this contribution provides my personal view on the tools to be used in the future. Two paradigm shifts are discussed. The first one is focusing on the complexity of the water resources domain and the complexity of the modelling tools in an environment characterised by continuous rapid technological develo...

One View of the Future

[1] Numerous computer models for river basin planning and management have been developed and used extensively since the mid-1970s. Early developments have relied on the use of network flow algorithms (NFA), due mainly to higher execution speed than the standard Simplex solvers. However, subsequent efforts to include proper modeling of hydraulic and hydrologic constraints introduced iterative schemes into the NFA-based models, which diminished the initial advantages in execution speed and which also caused concerns over the accuracy of the convergence schemes. Hence full-blown commercial linear programming (LP) solvers were introduced as a replacement to the iterative solution strategy of the NFA approach. This paper demonstrates one possible failure to solve a simple allocation problem using the NFA-based model and shows how this problem can be solved using the standard LP approach. It then identifies cases when even a full-blown LP approach cannot properly model two critical aspects of river basin management, one related to reservoirs with multiple outflows and the other one related to modeling of hydrologic channel routing. For NFA-based models the failures are the result of the inability to include relationships between flows on different model components directly into the search process. For the models based on LP solvers, the failures are caused by the fact that integrated reservoir outflow capacity between the starting and the ending storage levels is assumed over the entire length of the assumed time step, while the actual outflow can only take place during the portion of the time step when the storage level is above the invert of the outlet structure.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2007WR006192

Shortcomings of linear programming in optimizing river basin allocation

We combined the methods and advantages of stochastic and paleo-hydrology to estimate 900 years of weekly flows for the North and South Saskatchewan Rivers at Edmonton and Medicine Hat, Alberta, respectively. Regression models of water-year streamflow were constructed using historical naturalized flow data and a pool of 196 tree-ring (earlywood, latewood and annual) ring-width chronologies from 76 sites. The tree-ring models accounted for up to 80% of the inter-annual variability in historical naturalized flows. We developed a new algorithm for generating stochastic time series of weekly flows constrained by the statistical properties of both the historical record and proxy streamflow data, and by the necessary condition that weekly flows correlate between the end of one year and the start of the next. A second innovation, enabled by the density of our tree-ring network, is to derive the paleohydrology from an ensemble of 100 statistically significant reconstructions at each gauge. Using paleoclimatic data to generate long series of weekly flow estimates augments the short historical record with an expanded range of hydrologic variability, including sequences of wet and dry years of greater length and severity. This unique hydrometric time series will enable evaluation of the reliability of current water supply and management systems given the range of hydro-climatic variability and extremes contained in the stochastic paleohydrology. It also could inform evaluation of the uncertainty in climate model projections, given that internal hydro-climatic variability is the dominant source of uncertainty.

Nine Hundred Years of Weekly Streamflows: Stochastic Downscaling of Ensemble Tree-Ring Reconstructions

This paper presents an algorithm for generating stationary stochastic hydrologic time series at multiple sites. The ideas in this paper constitute a radical departure from commonly accepted methodologies. The approach relies on the recent advances in statistical science for simulating random variables with arbitrary marginal distributions and a given covariance structure, and on an algorithm for re-ordering the generated sub-sets of each synthetic year of data such that the annual auto-correlation of desired lag is maintained, along with the autocorrelations between the end of the preceding year and the beginning of the current year. The main features of the proposed algorithm are simplicity and ease of implementation. A numerical test is presented containing the generation of 1000 years of weekly stochastic series for four sites based on the 84 years of historical natural weekly flows from Southern Alberta in Canada.

A simple method for effective multi-site generation of stochastic hydrologic time series

A number of computer models for river basin planning and management have been developed by various agencies and used extensively since the mid-1970s. Most of the early developments have been based on the use of heuristic weight factors to represent priorities of allocation, and specialized optimization algorithms that were based on the use of network flow algorithms (NFAs). While these algorithms were at first considerably faster than the standard Simplex solvers, their handling of flow constraints was simplistic, which eventually led to the use of iterative schemes for handling nonnetwork constraints. This paper critically examines the notion that iterations applied in combination with NFA are a good vehicle for handling nonnetwork constraints. The failures are demonstrated on several variants of a simple problem with two reservoirs in series.

Limitations of network flow algorithms in river basin modeling.

This paper deals with minimizing the total cost of pumping in a liquid pipeline. Previous experience with the most common solution procedures in pipeline optimization is discussed along with their strengths and weaknesses. The proposed method is an evolutionary algorithm with two distinct features: (1) The search is restricted to feasible region only; and (2) it utilizes a floating point decision variable rather than integer or binary as is the case with most other similar approaches. A numerical example is presented as a basis for verification of the proposed method and its comparison with the existing solver that utilizes the nonlinear Newtonian search. The proposed method provides promising improvements in terms of optimality when compared to the widespread gradient search methods because it does not involve evaluation of the gradient of the objective function. It also provides potential to improve the performance of previous evolutionary programs because it restricts the search to the feasible region,...

Evolutionary Algorithm for Minimization of Pumping Cost

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