Journal of Water Resources Planning and Management

Synthetic hydrologic forecasts are often needed to evaluate water resources planning and management strategies when appropriate historical forecasts are not available. Synthetic forecasts can be generated to evaluate system performance with existing forecast products over historical periods when they were not available, or with different forecast products that do not yet exist. Synthetic forecast generation procedures should produce forecasts that are realistic and have the desired statistical properties. Two synthetic forecast generation techniques are proposed that create a time series of forecasts with (1) the desired mean, (2) the correct variance, and (3) the desired forecast precision. One uses a classical stochastic hydrology approach, and the other the generalized maintenance of variance extension (GMOVE) concept using historical hydrologic series as input. A critique is provided of several published synthetic forecast generation algorithms that produced unrealistic results. The GMOVE methodology is used in a stochastic optimization model of a single reservoir hydropower system. Using forecasts of varying precision, the example illustrates the ability of more precise forecasts to improve system operations.

There is a vast amount of research on downscaling and bias-correcting general circulation model (GCM) data to a regional scale, but research is lacking on whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida, region, this case study investigates: (1) whether GCM and the downscaled, bias-corrected data are able to replicate important historical climate states, and (2) if climate state or transition probabilities in raw GCMs are preserved or lost in translation in the corrected downscaled data. This has important implications in understanding the limitations of bias-correction methods and shortcomings of future projection scenarios. Results showed that the GCM and downscaled and bias-corrected data did a poor job in capturing historical climate states for wet or dry states as well as variability in precipitation, including some extremes associated with El Niño events. Furthermore, the corrected products ended up creating different cycles compared to the original GCMs. Because the corrected products did not preserve GCM historical transition probabilities, more than likely, similar types of deviations will occur for “future” predictions, and therefore another correction could be applied if desired to reproduce the degree of spatial persistence of atmospheric features and climatic states that are hydrologically important.

The objective of this study was to gain a better understanding of the drivers of indoor water consumption in urban multistory residential buildings, to more accurately predict residential water demand, and to identify water saving opportunities. Water meters were installed at each end-use fixture in 15 apartments within two economically diverse green high-rise buildings. Infrared motion loggers recorded occupant presence. Interviews provided sociodemographic data and data about water consuming activities. The average apartment water consumption was $184\mathrm{L}/[\mathrm{capita}(\mathrm{cap})·\mathrm{d}]$ in Building #1 and $260\mathrm{L}/(\mathrm{cap}·\mathrm{d})$ in Building #2. A principal component analysis determined that tenant presence in kitchen, kitchen faucet water consumption, cooking frequency, bathroom faucet water consumption, bathtub water consumption, percentage of tenants going to work/school, percentage of children, and percentage of seniors were major contributors to the variability in total per capita water consumption. A water end-use model taking these drivers into account demonstrated that preferences in bathtub and shower consumption exceeded the effects of dish washing and toilet leaks. When considered in relation to overall water consumption in the city, the tenant per capita water consumption in both buildings was similar.

The adaptation of infrastructure to climate change has benefits in the form of reduced disaster damage within the associated area. The beneficiaries of this adaptation are parties who are motivated to take adaptation actions and share the cost of adaptation. However, a standard for investment cost allocation has not been established. This study suggests a framework for allocating the adaptation cost among participants based on cooperative game theory. Within the game theory, the Shapley value is used for cost allocation based on an estimation of adaptation benefits and cost. To demonstrate the framework, a case study was conducted using an underground floodway project in Seoul, South Korea with six participants. The allocated cost decreases for participants who contribute more to saving the construction cost, whereas it increases for those who benefit more from flood damage prevention. In addition, geographic advantages in cooperation among players promote the chances of reducing the construction cost. This study is expected to assist decision makers in developing an agreement for financing an adaptation project through a rational cost allocation process.

The forest core partitioning algorithm (FCPA) and the fast graph matrix partitioning algorithm (GMPA) have been used to improve efficiency in the determination of the steady-state heads and flows of water distribution systems that have large, complex network graphs. In this paper, a single framework for the FCPA and the GMPA is used to extend their application from demand dependent models to pressure dependent models (PDMs). The PDM topological minor (TM) is characterized, important properties of its key matrices are identified, and efficient evaluation schemes for the key matrices are presented. The TM captures the network’s most important characteristics: It has exactly the same number of loops as the full network, and the flows and heads of those elements not in the TM depend linearly on those of the TM. The inverse of the TM’s Schur complement is shown to be the top, left block of the inverse of the full system Jacobian’s Schur complement, thereby providing information about the system’s essential behavior more economically than is otherwise possible. The new results are applicable to other nonlinear network problems, such as in gas, district heating, and electrical distribution.

Different methods have been developed in the past to formulate and solve steady-state hydraulics of a water distribution system (WDS). The most widely used method nowadays is probably the global gradient algorithm (GGA). The loop-flow method (also known as the $\mathrm{\Delta }Q$ method) represents a viable alternative to GGA, especially when combined with suitably preprocessed network data. The main advantage of the $\mathrm{\Delta }Q$ method over the GGA is in the smaller number of unknowns to solve for, which is coming from the fact that real WDSs typically have far less loops than nodes. A new loop-flow-type method, relying on the novel triangulation based loops identification algorithm (TRIBAL) that was implemented in the corresponding new hydraulic solver ($\mathrm{\Delta }Q$), is presented in this paper (TRIBAL-$\mathrm{\Delta }Q$). The new method aims to exploit this advantage, while overcoming key drawbacks of existing $\mathrm{\Delta }Q$ methods. The performance of the TRIBAL-$\mathrm{\Delta }Q$-based solver is compared with the GGA-based solver on four large real networks of different complexity and topology. The results obtained demonstrate that, despite requiring an increased number of iterations to converge, the TRIBAL-$\mathrm{\Delta }Q$ method–based solver is substantially computationally faster, has slightly better numerical stability, and is equally accurate in making predictions when compared with the GGA-based hydraulic solver.

Multiobjective simulation-optimization is a useful tool for determining operating rules for water supply that are optimal for multiple management objectives and expected conditions over the planning period. Previous studies have shown the benefits of streamflow forecasts in improving optimized objective performance of reservoir operating rules. This study demonstrates a simple method for incorporating publicly available streamflow forecast information in operational planning for a water grid. Multiobjective optimization is used to find operating rules for a case study that are optimal for three management objectives—maximizing minimum system storage, minimizing operational cost, and minimizing spills from reservoirs—and for forecast inflow scenarios. These forecast-optimized rules are compared to those optimized using inflow scenarios from the historical distribution, representing operation in the absence of forecast information. The results across four seasonal (3-month) planning periods indicate that, on average, operating rules optimized using forecast streamflow information perform slightly better in terms of the management objectives than those optimized using historical inflow information. Using multiple scenarios of inflow that span the forecast distribution increases the robustness of the operating rules and reduces the risk of underperformance because of forecast inaccuracy. The results suggest that incorporating streamflow forecast information into multiobjective simulation-optimization has the potential to provide improvements in seasonal operating rules for a water grid.

Two-stage data envelopment analysis (DEA) methodology was applied in this study to assess the performance of individual districts of a California water utility for calendar year 2014 (CY2014). A bootstrap technique involving the construction of confidence intervals was implemented to overcome the deterministic nature of conventional DEA, and exogenous variables, such as the number of connections and the total annual precipitation in each district, were incorporated into the model to help identify key factors affecting technical efficiency. Two different DEA models were developed to compare the financial and production performance efficiencies of the districts. Although several districts performed weakly from a production perspective, overall the results indicated the strength and high performance of the utility. The findings of this study are expected to provide a useful way to identify the strengths and weaknesses of individual districts and guide subsequent managerial improvement initiatives.

Coastal cliff collapse is a problem faced along many shorelines the world over, especially as cliffs tend to be affected by global climate change. Problems of cliff collapse can benefit from interdisciplinary policy responses that synthesize principles of three paradigms: integrated watershed management, integrated coastal zone management, and water-sensitive urban design. This exploratory, largely empirical research looks at how local and national policies address coastal cliff collapse along Israel’s Mediterranean seashore, in a way that highlights impediments and opportunities for integrated planning. Findings emphasize the importance of addressing urban runoff to prevent coastal cliff collapse using practices originating based on the three paradigms. Conclusions provide insights about policies that could improve the resilience of coastal communities suffering from coastal cliff collapse in the era of climate change. Particularly, greater cross-scale (regional and national) efforts are needed to coordinate proper drainage of the watershed that along coastal cliffs involves integrating principles of watershed management, coastal management, and urban design practices. These should be aimed at implementing practices that reduce phenomena that lead to cliff destabilization, such as ensuring runoff diversion and implementing building setbacks. The case study research leads to recommendations for policy mechanisms that provide opportunities to implement such practices.

Municipal authorities face many challenges in maintaining an effective water distribution service (WDS) because of aging infrastructure and financial limitations. Effective leak detection methods minimize the cost of pipe inspection and reduce the cost of leaks caused by deterioration in the pipe. The pressure residual procedure for leakage detection was performed by comparing the pressure data of certain district meter area (DMA) inner nodes with an estimation using the simulation of a mathematical network model. The effects of leakage in WDSs indicate that the pressure exponent is dependent on the geometry of the orifice, which is considered a constant factor in the applied method. This paper investigates the influence of pipe material in the pressure residual method and determines the element change in the sensitivity matrix. For accuracy, the geographic information technology (spatial data) was used to calculate the connection density around the exact nodes in the network model. The influence of the external load (soil movement and excavation work) on the metallic pipe material is beyond the scope of this paper, because leaks caused by these factors are often obvious and are accompanied by a huge pressure drop inside the distribution network.

The implementation of state estimation techniques to water systems enables the hydraulic state of a given network to be computed at any time. However, errors in both measurements and model parameters can severely affect the quality of the state estimate, thus sensitivity analysis is crucial to assess its performance. The aim of this paper is to provide general explicit expressions for the sensitivities of the objective function and the primal variables of the state estimation problem with respect to both measurements and roughness parameters based on the perturbation of the Karush-Kuhn-Tucker (KKT) conditions. Additionally, among all the possible applications of sensitivity analysis, two specific forms of such analysis for water systems are presented: identifiability of roughness parameters, and linear state estimate approximation. The merit of these applications is illustrated by means of a case study, which highlights the usefulness of compact sensitivity formulae to further understanding of state estimation solutions.

Water transfers between utilities can be a cost-effective substitute for supply infrastructure expansions when seeking to reduce vulnerability to drought. Water utility employees may choose not to use water transfers, however, if they are risk averse, have nonmonetary preferences against transfers, or are uninformed about the benefits of transfers. A pretest is conducted for two surveys that address these issues. The results indicate that, on average, employees are indeed risk averse and prefer supply expansions to transfers, but they become more receptive to transfers after learning more about them. In addition, there is considerable heterogeneity in beliefs; hence, a subset of water utilities may be inclined to use water transfers.

Water quality sensors placed in water distribution systems (WDSs) are critical for detecting accidental or intentional contamination intrusion. This motivates research to optimally place a limited number of sensors for a given WDS aimed to maximize the detection effectiveness (e.g., the detection likelihood and time to detection). Typically, effectiveness of a sensor placement strategy (SPS) is assessed using the expected impact across a number of contamination scenarios. Despite the value of such an approach, it may provide limited information on the SPS’s comprehensive properties in detection, such as the SPS’s ability to detect events with different levels of consequences, or to reduce the impacts from undetectable events. To address this limitation, this study investigates the underlying characteristics of the SPS’s effectiveness for contamination detection using a set of metrics focusing on detection time, consumption of contaminated water, the number of contaminated demand nodes, and the contaminated spatial distance. The former two are derived from detectable contamination scenarios and the latter two are computed from both detectable and undetectable contamination scenarios. The proposed method is illustrated for two real-world WDSs, and the results reveal the underlying properties of the SPS’s utility in contamination detection, which is significantly more informative than the measure of expected impacts. Such improved understanding provides guidance for selecting the most appropriate SPS and for improving preparedness for contamination events.

This paper examines the performance of the minimum monthly antecedent precipitation index (MMAPI) and other covariates in modeling pipe failures using the nonhomogeneous Poisson process (NHPP). Monthly time lag is introduced into the MMAPI to allow predictions to be made using past values. The influence on pipe failure for most covariates was as expected, except for the ageing factor. It is possible that other significant time-dependent factors have been omitted in the model, which led to underestimations of the ageing factor for asbestos cement pipes and for models calibrated using a longer training period. The influence of time lag in the MMAPI was also investigated for practical use. The predictions for the total number of failures from models with 1-month (T1) and 2-month (T2) time lag in the MMAPI were not as accurate as the model with no time lag in the MMAPI (T0). The T1 and T2 models allow predictions to be made for the next 1 and 2 months, respectively. This is useful for small-diameter pipes that are often repaired after reaching a certain threshold. The predictions can be considered as the number of repair jobs in the future and allow sufficient resources (e.g., workers and materials) to be allocated in time, especially for networks with failures influenced by climate factors.