Journal of Water Resources Planning and Management

Though new water supply source development has always required planning under many forms of uncertainty, climate change presents an added dimension that may exacerbate supply and water quality challenges. Hence, new decision-support tools are needed, and in this paper an approach that incorporates the uncertainty of climate variability and change into a cost assessment framework for a municipal drinking water provider in Colorado is developed and applied. The water utility provider is developing a new source of water supply, but treatment costs are relatively high because of the advanced processes needed to treat the water to desired standards. Furthermore, the new water source has variable salinity concentrations that are not removed by any of the implemented treatment processes, requiring blending with an existing lower salinity water source. This results in an increase in the finished water salinity (as compared with the existing water source), which can have negative impacts on customer satisfaction and the life span of water-using appliances. To plan for the development of this water supply source, an approach is proposed to assess the potential treatment and residential costs associated with the blending of the new water source with an existing source under climate uncertainty. Uncertainty from climate variability is captured through a previously developed stochastic streamflow and water quality simulation method that utilizes climate change scenarios. Results show that the proposed blend strategies incur increased treatment costs and economic impacts for customers. Specifically, a 30% reduction in annual flows from climate change translates into a 12% treatment cost increase and a 22% rise in residential costs.

This research develops a multistage stochastic linear programming (LP) model to assist in the process of water system planning and management under demographic and climate change in Amman, Jordan, over the next 75 years. Climate change is projected to have a gradual exacerbating effect on Amman’s water stress over the next century, and water resources management strategies and policies put in place now will likely influence water use patterns for generations to come. A multistage decision model allows the identification of both adaptation strategies that should be implemented now and actions likely to be needed later, depending on future climate and demographic conditions. For Amman, the model recommends that household water reuse be expanded immediately, large-scale wastewater reclamation begin within 25 years, and mega-scale water import projects be postponed for several decades. Although these recommendations for the future will almost certainly change as additional information is acquired, by identifying now the actions most likely to be needed in the future, options for their implementation can be reserved, and feasibility studies begun.

A modified version of the U.S. Bureau of Reclamation (Reclamation) long-term planning model, Colorado River Simulation System (CRSS), is used to evaluate whether hydrologic model choice has an impact on critical decision variables within the San Juan River Basin when evaluating potential effects of climate change through 2099. The distributed variable infiltration capacity (VIC) model and the lumped National Weather Service (NWS) River Forecast System (RFS) were each used to project future streamflow; these projections of streamflow were then used to force Reclamation’s CRSS model over the San Juan River Basin. Both hydrologic models were compared to evaluate whether or not uncertainty in climatic input generated from general circulation models outweighed differences between the hydrologic models. Differences in methodologies employed by each hydrologic model had a significant effect on projected streamflow within the basin. Both models project decreased water availability under changing climate conditions within the San Juan River Basin, but disagree on the magnitude of the decrease. On average, total naturalized inflow within the San Juan River Basin into the Navajo Reservoir is approximately 15% higher using inflows derived using the VIC model than those inflows developed using the RFS model; average projected tributary inflow from the San Juan River Basin to the Colorado River is approximately 25% higher using inflows derived by using the VIC model than those inflows developed by using the RFS. Overall, there is a higher risk and magnitude of shortage within the San Juan River Basin using streamflow developed with the RFS model as compared with inflow scenarios developed by using the VIC model. Model choice was found to have a significant effect on the evaluation of climate change impacts over the San Juan River Basin.

Agricultural regions located in snowmelt-dominated Mediterranean climate basins have been identified as being highly vulnerable to the impacts of climate change. The Maipo basin in central Chile is one such region. Projections of future climate conditions suggest major challenges for this basin. Precipitation levels are projected to decrease by the end of the century, and temperature levels in the mountains are expected to increase by around 3–4°C. Such changes would affect both river discharge and irrigation water demand. This paper illustrates potential climate change impacts on the hydroclimatological regime of the Maipo basin, focusing on irrigated agriculture and its demands on water use rights. The impact assessment was carried out by combining a multisite stochastic weather generator with a disaggregation technique for historical monthly flows of the Maipo river at El Manzano. Demand for irrigation was simulated with a daily water budget model. Data showed that water demands from irrigated agriculture tend to increase as a consequence of the simulated changes in temperature and precipitation. The magnitudes of these changes depend on crop types and their prevalence in the region. It was concluded that the reliability of water allocations under the current water rights system may be strongly negatively affected. In particular, projected hydrological impacts for the climate change scenario considered indicate that the 15th percentile currently used as the basis for water rights may become the 40th or 50th percentile in the future, suggesting that present water allocations will become overcommitted.

The Limarí River basin is one of the most important watersheds in north-central Chile (30°S). Its headwaters lie at the top of the subtropical Andes ($>5,000\mathrm{m}$ above sea level) and the river flows westward into the Pacific Ocean over a length of approximately 200 km. This basin has a marked snowmelt-driven hydrological regime and, in spite of the arid conditions that characterize this region, holds more than 50,000 ha of highly productive agricultural land thanks to its irrigation infrastructure and three interconnected reservoirs. Like many semiarid regions around the world, north-central Chile is expected to become warmer and drier during the 21st century as a consequence of ongoing anthropogenic climate change. The associated reduction in streamflow, changes in hydrograph timing, and enhanced evapotranspiration will undoubtedly threaten agriculture in the Limarí basin and elsewhere in semiarid Chile. In this paper, the effect of temperature and precipitation on surface hydrology, performance of water infrastructure, and irrigation coverage in the Limarí basin is investigated by using the water evaluation and planning (WEAP) model. WEAP was calibrated by using current climate and agriculture patterns, and then forced with a set of 30-year-long climate scenarios, each of them obtained by adding a temperature and precipitation perturbation to the historical time series. This delta approach allows (1) determination of the sensitivity of selected variables to climate change, and (2) obtaining a projection of the effects in irrigation coverage expected for the near and far future (2010–2040 and 2070–2100, respectively). Both aspects are investigated for agricultural districts with varying access to irrigation infrastructure and groundwater; this exercise highlights the relevance of added storage and innovative conjunctive use of surface and groundwater resources for improving the resilience and adaptability of irrigated agriculture in the face of a changing climate.

This study reflects a portion of the riverine analysis for a Federal Emergency Management Agency initiative to estimate the economic risks associated with climate and land use change to the U.S. National Flood Insurance Program. Specifically, this paper investigates how the 1% annual chance flood discharge, $Q_{1\%}$ (equivalent to a 100-year return period flood), may change based on climate change and population projections through the year 2100. Watershed characteristics and observations of climate indicators at 2,357 U.S. Geological Survey gauging stations were used to develop regression relationships to estimate $Q_{1\%}$. Projections of the climate indicators that measure extremes in temperature and precipitation from a suite of global climate models were then used within a Monte Carlo sampling framework to estimate future changes to $Q_{1\%}$ throughout the United States, while also translating the uncertainty resulting from multiple climate model projections into uncertainty in estimating the future $Q_{1\%}$. Population growth models consistent with climate model emission assumptions were used to estimate increases to impervious area over the next century, along with corresponding contributions to the $Q_{1\%}$ estimates. The study provides a screening-level analysis of possible changes to flow and suggests spatial trends across the United States. The results suggest that $Q_{1\%}$ may increase substantially over many areas of the United States over the next century, especially in the Pacific Northwest, the Northeast, and highly urbanized areas.

The U.S. EPA’s Global Change Research Program (GCRP) recently began an effort to address gaps in the current knowledge of the sensitivity of U.S. streamflow, nutrient (nitrogen and phosphorus), and sediment loading to potential future climate change, and the methodological challenges associated with integrating existing tools (e.g., climate models, land-use models, watershed models) and data sets to address these scientific questions. The study is based on watershed modeling in 20 large U.S. watersheds. The purpose of this paper is to describe the overall structure of this ongoing effort—methods, sites, models, and scenarios—and discuss preliminary results generated to date for a subset of the watersheds. Specifically, a representative set of modeling results are reviewed that encompass the full suite of sensitivity tests explored in this project. These results illustrate a number of key methodological issues, sensitivities, and uncertainties associated with carrying out these types of climate change/hydrologic impacts assessments, including: the sensitivity of simulated changes to the watershed model used; the sensitivity of simulated changes to climate model and downscaling approach used; and the interaction between climate change and other key forcing factors, specifically urbanization and change in atmospheric ${\mathrm{CO}}_2$ concentration. In addition, this suite of results provides an overview of the response to climate change in different geographic regions and the different sensitivities of a variety of flow and water quality end points.

Spring-run Chinook salmon (Oncorhynchus tshawytscha) are particularly vulnerable to climate change because adults over-summer in freshwater streams before spawning in autumn. We examined streamflow and water temperature regimes that could lead to long-term reductions in spring-run Chinook salmon (SRCS) in a California stream and evaluated management adaptations to ameliorate these impacts. Bias-corrected and spatially downscaled climate data from six general circulation models and two emission scenarios for the period 2010–2099 were used as input to two linked models: a water evaluation and planning (WEAP) model to simulate weekly mean streamflow and water temperature in Butte Creek, California that were used as input to SALMOD, a spatially explicit and size/stage structured model of salmon population dynamics in freshwater systems. For all climate scenarios and model combinations, WEAP yielded lower summer base flows and higher water temperatures relative to historical conditions, while SALMOD yielded increased adult summer thermal mortality and population declines. Of management adaptations tested, only ceasing water diversion for power production from the summer holding reach resulted in cooler water temperatures, more adults surviving to spawn, and extended population survival time, albeit with a significant loss of power production. The most important conclusion of this work is that long-term survival of SRCS in Butte Creek is unlikely in the face of climate change and that simple changes to water operations are not likely to dramatically change vulnerability to extinction.

Climate is one of the most important factors leading to changes in the hydrologic and environmental characteristics of river basins. When considering water quality, the natural factors should be weighed against anthropogenic factors (such as urbanization, increased population, and higher water demand) that may increase or decrease the effect of climatic modifications. Any prediction of future climatic and anthropogenic scenarios is affected by uncertainty and the modelling tools that are used for evaluating their impact on receiving water bodies. The present paper is aimed at investigating the complexity of such analyses and the uncertainty related to future impact predictions based on limited databases. Specifically, the study is focused on quantifying the impact of urbanization and social changes coupled with climate on river water quality under uncertainty. The study was carried out at a small catchment scale (less than $100{\mathrm{km}}^2$), an analysis that has been lacking from the field so far. The analysis took into account both anthropogenic change and climate determined from observed data. Because of inherent uncertainty in the model caused by the complexity of the model and its calibration, the analysis was performed considering the effect of such uncertainty on reliability by means of Monte Carlo simulations. This analysis has been applied to a monitored semiurbanized basin in Sicily (Italy). The study showed that the proposed analyses can useful for planners and urban managers; however, the uncertainty connected with mathematical modeling and small available databases has to be taken into account because it can affect decision making. The impacts of anthropogenic changes and climate can interact, thus requiring that such factors be jointly analyzed. A larger effort in monitoring and characterization of natural and urban water systems is needed to increase the reliability of models and to improve the effectiveness of decision making based on such models.

In 2005, thermoelectric power accounted for 41% of all freshwater withdrawals and roughly 3% of all consumptive use in the United States. With the demand for electricity projected to increase by 24% by 2035 concerns have been raised as to the availability of water for this growing industry; particularly, as the siting of several new thermoelectric facilities have been challenged on the basis of water supply. To address this concern we estimate the potential impact of water availability on future expansion of the thermoelectric power industry. Specifically, both the extent and location of thermoelectric developments at risk due to limited fresh water supply is estimated for a variety of alternative energy futures that differ according to the assumed mix of fuels utilized in new plant construction. According to the analyzed scenarios water consumption for thermoelectric power generation is projected to increase by 36–43% between 1995 and 2035, with much of this development expected to occur in basins with rapidly growing demands in the nonthermoelectric sectors. To identify where this thermoelectric development might be problematic, projected future thermoelectric production has been mapped onto basins subject to limited water availability. For the purposes of this study, water availability is defined as a local ratio of water demand to physical water supply. Results suggest that 10–19% of all new thermoelectric power production is likely to be sited in watersheds with limited surface and/or groundwater availability. These problematic watersheds are largely located in the West.

Storm water management parameters that are significantly influenced by local climatic conditions are the runoff coefficient ($C$) and the water-quality volume (WQV). Two classes of methods were investigated for estimating regional variations in $C$ and WQV in Florida: interpolation methods and cluster-analysis methods. The results show that the inverse-distance-squared (IDW-2) interpolation method based on 45 reference stations distributed throughout the state provides the most accurate estimates of $C$ and WQV, with a relative mean absolute error (RMAE) of 9% in estimating $C$ and an RMAE of 13%–18% in estimating WQV. Although cluster means provide less accurate estimates of $C$ and WQV, cluster analyses show that Florida can be divided into three regions with similar values of $C$ and five regions with similar values of WQV. Cross validation shows that using cluster means instead of the IDW-2 method increases the RMAE by approximately 20% when estimating either $C$ or WQV. A site-specific example illustrates the roles of $C$ and WQV in the design of storm water management systems in Florida.

This paper designs a stylized model representing farming in water-scarce regions of South India to address the problem of managing depleting groundwater assets facing the threat of irreversible loss. Circumstances under which it may be optimal to forgo sustainable water use are evaluated in the context of farmers’ wealth and land endowments, crop choices, and risk of groundwater loss. Several policy and management implications are derived. Additionally, it is argued here that attaining livelihood resilience will entail a transformation process involving tradeoffs between different capital assets where it may be optimal for a farmer to forego the objective of maintaining groundwater sustainability. However, mere forgoing of sustainability may not ensure resilience for all. Initial wealth and risk endowments come into play, especially in heterogeneous communities.

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler’s experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems.

This paper presents an effective approach for calibrating an extended period simulation (EPS) model of water distribution system. By using the benchmark model calibration problem, set up for Battle of Water Calibration Networks (BWCN), the EPS model calibration is solved in a progressive manner, optimizing model parameters and engineering judgments. The model calibration approach consists of multiple steps, including: (1) constructing the initial EPS model with the given system and supervisory control and data acquisition (SCADA) data, (2) calibrating pipe properties and pump curves for static and fire flow test data, and (3) calibrating EPS model demands and controls for given SCADA data over 167 h. The calibration was iteratively conducted per district meter area (DMA) and then fine-tuned for the whole system. Although the field data contained some noise, good model calibration was achieved for BWCN. By using the practically measurable hydraulic attributes of pump flows, pressures, and tank levels provided for BWCN, the performance of the calibrated model is compared with those calibrated by 14 teams around the world participating in the BWCN.

Global warming caused by human activities presents serious global risks. Individuals, governments, and industries need to be more energy efficient and contribute to the mitigation of global warming by reducing their greenhouse gas (GHG) emissions. In previous research, GHG emission reduction has been identified as one important criterion in improving the sustainability of urban infrastructure and urban water systems. Within the water industry, opportunities exist for reducing GHG emissions by improving pumping efficiency via the use of variable-speed pumps (VSPs). Previously, VSPs have been used in the optimization of the operation of existing water distribution systems (WDSs). However, in WDS design optimization problems, fixed-speed pumps (FSPs) are commonly used. In this study, a pump power estimation method, developed using a false position method based optimization approach, is proposed to incorporate VSPs in the conceptual design or planning of water transmission systems (WTSs), using optimization. This pump power estimation method is implemented within the solution evaluation process via a multiobjective genetic algorithm approach. A case study is used to demonstrate the application of the pump power estimation method in estimating pump power and associated energy consumption of VSPs and FSPs in WTS optimization. In addition, comparisons are made between variable-speed pumping and fixed-speed pumping in multiobjective WTS optimization accounting for total cost and GHG emissions. The results show that the use of variable-speed pumping leads to significant savings in both total cost and GHG emissions from WTSs for the case study considered.

Municipal storage tanks are normally sized according to inherently conservative design guidelines. An alternative way to determine the required size of a tank, on the basis of a stochastic analysis of the system, was proposed in a previous study, in which it was recommended that tanks should be sized for a minimum reliability of one failure in 10 years at the most critical time of the year, typically under seasonal peak demand conditions. In this study, the same method is used to investigate the impact of different user demand parameters on tank reliability. It was concluded that the supply ratio, defined as the ratio of the source capacity to the average demand in the week considered, is the most important demand-related factor affecting tank reliability. It is shown that the reliability of tanks varies greatly throughout the year, and it is recommended that municipalities do everything possible to ensure that their system runs smoothly over the seasonal peak demand period. Several other important demand factors affecting tank reliability are also identified. It is concluded that the optimal combination of source capacity and tank size should be determined on the basis of economic factors, and that it is likely to be system specific.

The security of drinking water distribution operation is an important issue that has received increasing interest within the last few years. The U.S. EPA has issued guidelines for water utilities regarding which qualitative and quantitative metrics to monitor, and what response actions to take from the moment a contamination event alarm has been triggered, until the contamination has been accommodated and the system has returned to normal operation. Expanded sampling is a type of response action in which the water utilities examine water quality at certain locations in the network after a contamination event has been detected to help evaluate the contamination impact and locate the source-area. In this work, we propose a computational approach, based on decision trees, for choosing a sequence of nodes in the distribution network to perform expanded sampling, such that the water contamination impact is evaluated and the source-area is isolated, with as few manual quality samplings as possible. To illustrate the solution methodology, we present results based on a simplified and a benchmark water distribution system.

Nutrient loads were estimated for the 34 watersheds draining into Cayuga Lake, New York, through use of a watershed model. Financial and human resources were very limited for the project, but significant cost savings were achieved through project decisions. Chief among these were selection of a watershed model that did not require calibration and use of historic water quality monitoring data for model testing. Savings were also obtained by extrapolating soil properties from related information (nutrient contents from organic matter), substituting literature concentrations for missing point source data, extrapolating septic system performance from one area to another, and use of synthetic weather data generated from a model. None of the decisions was remarkable in itself, but together, they permitted a watershed study and its associated modeling to be accomplished with modest resources.

Contamination warning systems are being designed to protect water distribution systems against deliberate contamination intrusions. To design a contamination warning system, contamination intrusion events need to be selected. Because contamination intrusions are random, even for a medium-size network the theoretical number of possible injection events is huge, and thus the number of contamination events which can be considered in the design process is limited. To effectively cope with the threat of contamination events there is a need to identify those critical instances. A straightforward approach of enumerating all possible contamination intrusions from which critical events can be selected is limited to small systems. As critical events are rare the probability of revealing them using common Monte Carlo randomized simulations is very small or requires an extensive impractical computational amount of trials. In this study a methodology utilizing importance sampling and cross entropy based on a recent published work of the authors is further tested on real-sized water distribution systems of increasing complexity. The results demonstrate the robustness of the methodology in terms of improved run times, suggesting computational feasibility for problems in which size prevents full enumeration or application of direct Monte Carlo simulation techniques.