Journal of Water Resources Planning and Management

This paper presents an application of Reynolds transport theorem (RTT) to California Central Valley water resources system and projects and its utility for integrated water resources management (IWRM). Using long-term historical and estimated natural outflows of the Sacramento and San Joaquin Valleys, it defines Basin Stress Index and uses it to characterize historical beneficial water uses in the two valleys. It then gives an overview of evolutionary water resources utilization policies in California for multiple competing and complementary beneficial uses and how these policies are brought progressively to the modeling environment in support of planning and operating the Central Valley water resources and projects. This paper highlights the resulting modeling complexity and presents insights about the stability of the system due to unique natural characteristics of the Central Valley. The application of RTT to this water resources system is shown by development of regional water balance formulations and statistics that depict how their components influence the stability of model results. The author posits that the development of such bounded water balance formulations will help advance IWRM. The paper also illustrates the complementary nature of system stability and application of theory to a complex problem to provide useful insights for informed decision making and saving model development costs. Finally, the paper suggests that the analysis technique applied to the Central Valley water resources system and world-class water projects may be useful for a pedagogical development for the emergent concept of IWRM for sustainable water resource use.

Southwestern United States paleoclimate reconstructions feature droughts that are longer and higher in magnitude than any water shortage during the twentieth century, and thus could aid water managers in planning for future severe droughts. This research used the robust decision-making (RDM) analytical framework to incorporate paleoclimate information into an analysis of long-range water management for a Southern California water agency. The analysis leverages a water management model to identify near-term management actions that may help mitigate water shortages over a wide range of future conditions reflecting various assumptions about climate, costs, and planning. Results indicate that a regional urban water management plan for 2005 is vulnerable to extended droughts and that enhancing water management actions in the near term reduces the risk of future unmet demand and shortage costs. Comparing results with previous work with the IEUA using climate model projections indicates some differences in outcomes related to climate data characteristics such as mean and maximum temperature. This work highlights some problems and potential benefits associated with using paleoclimate data for water management modeling.

Management strategies for the optimal and sustainable use of groundwater resources are developed based on prescriptive models that use mathematical tools for simulation and optimization together with field data. Because of the uncertainty inherent in the groundwater systems, it is essential to verify the compliance of the implemented strategies to those prescribed by using proper monitoring techniques during and after the implementation stages of the groundwater management project. In this work, an adaptive management approach for optimal management and monitoring of coastal aquifers is proposed. A simulation-optimization approach is used to derive optimal pumping strategy for the management of saltwater intrusion in coastal aquifers. Then, an optimal monitoring network is designed to evaluate the compliance of the aquifer responses in the field with those predicted by the simulation-optimization model. The designed network can be used to monitor the compliance in the field in terms of the salinity concentration levels, which result from the implementation of the optimal pumping strategy. Uncertainty in the values of groundwater parameters and the uncertainty resulting from the deviation of the pumping strategies from the prescribed optimum values are characterized by considering different realizations of these values in the three-dimensional density dependent flow and transport simulation model. A new objective for monitoring is considered in this study. The objective function consists of maximizing the coefficient of variation of the salinity concentration at the monitored locations and minimizing the correlation coefficient between the concentrations at the monitored locations. Using this objective, the monitoring locations are chosen in regions where the uncertainty in the concentration values is highest, and those locations where the correlation between the concentrations of the monitored locations is lowest, so that the redundancy in monitoring data is the least. The concentration data collected at the optimal compliance monitoring locations can be used as feedback information to improve the initially developed optimal coastal aquifer management strategies. The sequential modification of the optimal pumping strategies in stages is illustrated using numerical experiments.

A new method using artificial immune network is presented to identify pipe burst in water-distribution systems. Burst detection is considered as the problem of pattern recognition in the proposed method. An artificial database that includes information on burst events (BEs) is first established. Using the clonal selection algorithm, the artificial immune network is constructed based on the principle of immune system. The burst location is finally identified using the nearest neighbor method. Three offline case studies are illustrated in detail to evaluate the current method. A total of five possible burst locations are identified from 34 nodes in Case Study 1, whereas four possible burst locations are identified from 77 nodes in Case Study 2. The results derived from the first two case studies show that the method can identify the possible burst areas, including the true burst location, using model-simulated results. The data derived from real BEs in Case Study 3 are used to evaluate the proposed method, through which performance of the method is further investigated. Based on all three case studies, the proposed method has the potential to be a useful tool for burst detection.

Contamination warning systems (CWSs) for drinking water distribution systems (WDSs) are used to reduce the potential adverse effects of intentional or accidental WDS contamination. They are designed on the basis of possible contamination events but often address only a narrow range in event conditions. The influence on their performance of conditions different from those assumed in the design generally is not considered. Using results from simulations done with network models for 11 actual WDSs, it is shown here that CWS performance for high-consequence events can degrade substantially (by an order of magnitude) when conditions such as contaminant toxicity and injection time differ from those used in the design. Generally, increasing the number of sensors does not reduce this sensitivity to changed conditions. The significance of uncertain conditions varies substantially among WDSs. As a consequence of performance changes that occur when conditions change, mean-case designs generally outperform worst-case designs when the objective is to minimize worst-case adverse effects over a range of conditions. The results of this work can be used to implement more robust designs for CWSs, while reducing computational requirements.

The effort to manage irrigation water use through the adoption and implementation of agricultural best management practices (BMPs) is crucial for meeting water bodies’ sustainability in water-deficient agricultural areas. This paper presents the development of an efficient decision support tool able to suggest the optimal location for placing high- and low-tech irrigation BMPs, such as deficit irrigation, conveyance efficiency improvement, precision irrigation, and wastewater reuse, and demonstrates its application in the water-scarce Pinios river basin in central Greece. The tool uses the SWAT (soil and water assessment tool) model as the BMP and hydrologic simulator and a multiobjective genetic algorithm, which, based on calculated cost data, optimizes the cost-effectiveness of management schemes across the landscape. From the analysis of the results produced and the optimal river basin BMP configurations under the current and a volumetric water pricing scenario and under a $\pm 25\%$ variation of the cost parameters, the study concludes that a number of different BMPs at an affordable implementation cost, along with a tiered water pricing system that could address socioeconomic heterogeneities, would form a sustainable action plan against desertification in the highly water-deficient Pinios basin. The methodology and tool are considered to be easily applicable in other river basins and could be used to assist in more cost-effective implementation of environmental legislation.

For the purpose of real-time dam operation in large river basins, an integrated simulation and optimization system (ISOS) has been constructed by combining distributed hydrological inflow predictions with rolling horizon decision making. First, with operational quantitative precipitation forecasts (QPFs) over a forecast horizon (FH), the ISOS is applied to obtain an optimal rule for dam releases; second, with the corrected satellite precipitation, the prescribed rule is employed for the reservoir operation over a decision horizon (DH). The ISOS is applied to the Red River Basin ($\mathrm{169,000}{\mathrm{km}}^2$), to test its performance for optimizing multidam releases in the large river basin. Results show that by using the ISOS, two things were simultaneously achieved, reducing the water level at Hanoi to avoid flooding while raising water storage in Hoa Binh reservoir at the end of flood season for better hydropower generation. Through comparing reservoir performances with different FHs, the effective forecast horizon (EFH) was derived for the 3-reservoir system in the Red River.