Journal of Water Resources Planning and Management

Analyses of water resource allocation problems can be undertaken using multicriteria decision analysis (MCDA), yet various sources of uncertainty exist in the application of MCDA methods, including the definition of criteria weights and the assignment of criteria performance values. Sensitivity analysis can be used to analyze the effects of these uncertainties on the ranking of alternatives; however, many existing methods have been found to have numerous limitations when applied to MCDA. In this paper, a reliability-based approach is proposed that enables the decision maker to examine the robustness of a solution, allowing a decision to be made with confidence that the alternative chosen is the best performing alternative under the range of probable circumstances. The proposed stochastic method involves defining the uncertainty in the input values using probability distributions, performing a reliability analysis by Monte Carlo simulation and undertaking a significance analysis using the Spearman rank correlation coefficient. The outcomes of the approach include a distribution of the total values of each alternative based upon the expected range of input parameter values. The benefits of the approach are illustrated by applying it to two water resources case studies.

A market-based water supply problem is modeled as an optimization problem maximizing net benefit from water use such that all the concerned parties use an economically optimal allocation considering damages due to total dissolved solids (TDS) and institutional constraints. These institutional constraints include water supply requirements under international treaty and historical allocations of water to agriculture. The income from water in municipal use, based on the concept of consumer surplus, is utilized in the objective function along with benefit from agricultural uses, supply costs, and damage costs due to poor quality water, which are estimated using available data. The research assesses the impact that system-wide optimal allocation of water will have in increasing monetary benefits and reducing water salinity levels in terms of TDS along the reach of the Rio Grande from Elephant Butte, New Mexico, to Fort Quitman, Texas. The analysis is performed under three institutional constraints, which include flow regulation at the reservoir, restrictions on trade between states, and allowing trade between the states. Among these scenarios, allowing trade between the states results in the best solution in terms of both net benefit and reducing damage due to poor quality water.

A conditional reliability model (CRM) is presented for assessing reliabilities for meeting water needs over the next several months, conditioned upon current reservoir storage contents. The CRM procedure was developed in conjunction with a generalized river/reservoir system model called the Water Rights Analysis Package (WRAP). The CRM methodology is presented in the context of the WRAP model, but the process is applicable to other models as well. A new concept of conditional frequency duration curves, representing probability distributions of naturalized streamflows conditioned on preceding simulated reservoir storage, is incorporated in the CRM methodology. The results of the model are probability-weighted values of storage or diversion from many short-term simulations that start with the same storage condition. Applications of the model in annual operational planning, drought contingency planning, and reservoir resilience analysis are presented, along with pertinent discussions of the fundamental concepts of the model and comparison with the alternative, simpler approach of assuming each short-term simulation is equally likely to occur.

A new way of incorporating fuzzy logic concepts is introduced in order to better capture and manage some uncertainties in applying stochastic dynamic programming (SDP) formulations for reservoir operation. A model called fuzzy-state stochastic dynamic programming (FSDP), which takes into account both uncertainties due to random nature of hydrological variables and imprecision due to variable discretization is introduced in this study. In this model, fuzzy transition probabilities for stochastic hydrologic state variables are calculated by defining a fuzzy Markov chain. These fuzzy probabilities are derived based on fuzzy frequency concept considering different frequencies for different points of a class interval. In order to show how effective the proposed method is, FSDP was applied to the Zayandeh–Rud river–reservoir system in Isfahan, in the central part of Iran, and was compared with a demand driven stochastic dynamic programming model. The results show the robustness of the FSDP solutions with respect to the type of discretization scheme used in calculating the transition probabilities.

Presented herein is a robust approach to calibrating water quality models for water quality management using sparse field data. The calibration procedure adopts genetic algorithms (GAs) to inversely solve the governing equations, along with an alternating fitness method to maintain solution diversity. The proposed approach is illustrated with a total phosphorus model of the Triadelphia Reservoir in Maryland. A series of deterministic and stochastic alternating fitness GA schemes are implemented and compared with a standard GA. Significantly higher diversity is observed in the solutions obtained by the alternating fitness method than by the standard process. The diversified solutions obtained by the alternating fitness GA method are then classified into several patterns using a parameter pattern recognition model. The best solutions to each pattern are then chosen for further projection analyses, which generate a range of prediction results that provide decision makers with information for formulating sound pollution control schemes.

The California State Department of Water Resources and the United States Bureau of Reclamation Mid-Pacific Region have developed a general-purpose reservoir–river basin simulation model for the planning and management of the State Water Project and the federal Central Valley Project. The California Water Resources Simulation Model brings a fundamental change to modeling of these systems. Model users specify system objectives as input to the model. System description and operational constraints are specified using a new water resources engineering simulation language. A mixed integer linear programming solver efficiently routes water through the system network given the user-defined priorities or weights. Simulation cycles at different temporal scales allow for successive layering of constraints. The power and flexibility of the model is demonstrated by its ability to simulate the operation of complex new environmental water accounts.

Increasing demands for water by competing users in semiarid regions pose new challenges for water resources managers. Decision makers must understand the interactions between surface water, groundwater, and the environmental system. Additionally, the decisions made with regard to water transfer and allocation must take into consideration the diverse objectives that include water supply, cost efficiency, and ecosystem protection. The work presented herein demonstrates the use of groundwater simulation and optimization to construct a decision support system (DSS) for solving a groundwater management problem associated with the Upper San Pedro River Basin, located in southeastern Arizona. The case is treated as a multiobjective optimization problem in which environmental objectives are explicitly considered by minimizing the magnitude and extent of drawdown within a prespecified region. The approach adopted uses the constraint method to derive the tradeoffs among three competing objectives. Once the proposed algorithm identifies a set of efficient solutions (alternatives), concepts borrowed from fuzzy set theory are applied to rank the alternatives and to assist decision makers in selecting a suitable policy among them, each of which is optimum with regard to its goal and the corresponding consequences.

Using benefits transfer methods, this paper assesses the downstream economic consequences of development designs that promote greater on-site water retention. It concludes that on-site retention provides many services that are conceptually distinct but empirically intertwined. Flood mitigation and water quality protection are the most important of these services. For residential properties, the economic value of those services is on the order of 0–5% of market value depending on the difference that retention makes to downstream flood exposure. For water quality improvements, the increases range up to 15% of market value for waterside residences where clarity of the water quality is greatly improved. The increases are much less for improvements that are less visible, properties that are not developed, and properties not adjacent to the watercourse. Our best estimate of total benefits to property owners is 2–5% of property value on average for all properties in the flood plain. The public sector realizes additional benefits through smaller bridges, culverts, and other drainage infrastructure and through increased aquifer recharge. Cities and industries may avoid costly upgrades to waste water treatment facilities if low flows increase. It is difficult to generalize about the economic value of the latter effects.

The temporal and spatial characterization of unknown groundwater pollution sources remains an important problem in effective aquifer remediation and assessment of associated health risks. The characterization of contaminated source involves identifying spatially and temporally varying source locations, injection rates, and release periods. The proposed methodology exploits the universal function approximation capability of a feed forward multilayer artificial neural network (ANN) to identify the unknown pollution sources. The ANN is trained to identify source characteristics based on simulated contaminant concentration measurement data at specified observation locations in the aquifer. These concentrations are simulated for a large set of randomly generated pollution source fluxes. The back-propagation algorithm is used for training the ANN, with each corresponding set of source fluxes and resulting concentration measurement constituting a pattern for training the ANN. Performance of this methodology is evaluated for various data availability, measurement error, and source location scenarios. The developed ANNs are capable of identifying unknown groundwater pollution sources at multiple locations using erroneous measurement data.