Journal of Water Resources Planning and Management

Urban groundwater supply planning is often achieved at the regional scale while the effects of aquifer exploitation are experienced on a local scale. In the Mexico City metropolitan area, land and water use changes have caused massive overdraft and subsidence, threatening water supply in marginalized communities. While spatially distributed groundwater models have been developed to test model uncertainty and policy interventions, there is little understanding of how regional aggregation of planning objectives can exacerbate existing social inequities. This study develops socially informed spatial analysis to evaluate differential impacts of groundwater pumping policies at multiple scales. We compare four planning objectives at three spatial resolutions to determine if policy preference shows a relationship with socioeconomic indicators. We find that the multiobjective performance of pumping policies can neglect marginalized communities according to multiple indicators. Further, clustering model grid cells according to socioeconomic indicators allows for more accurate analysis of this effect. Finally, multiscale analysis of policy preference considering subregional socioeconomic characteristics can aid in equitable selection of aquifer management alternatives.

The increasing occurrence of prolonged droughts and extreme wet events in California poses significant challenges to the management of the region’s water resources. To address these challenges, the utilization of Flood-Managed Aquifer Recharge (Flood-MAR) has emerged as a potential solution. This study developed a multiobjective hydroeconomic model to assess the economic impact of implementing Flood-MAR and reoperation of Folsom reservoir within the American River Basin. The simulation module consists of a hydrological model and a linear programming groundwater recharge model. The economic impact evaluation considers three main components: the value of groundwater recharge, surface storage, and hydropower generation. The findings demonstrate that the adoption of Flood-MAR and reoperation of Folsom reservoir offer considerable economic benefits, with minimal adverse effects on the downstream system. Two different model solutions were analyzed: one that aimed to maximize recharge and storage benefits, and one that prioritized hydropower generation. The former exhibited an increase in reservoir storage compared with historical operation, along with increased water allocation for groundwater recharge during wet and normal years. The latter showed substantial gains in hydropower generation but occasional drops in reservoir storage below historical levels. Despite these differences, the solution emphasizing recharge and storage benefits was deemed to be more realistic, considering the risk of future droughts and uncertainties in climate and hydrological forecasts. Overall, this research provides a foundation for assessing the economic impact of Flood-MAR implementation in the Folsom reservoir system.

Water infrastructures are critical assets that ensure communities served by them have access to clean and reliable water. Because the cost of creating a water infrastructure is relatively high and the service life of infrastructures ranges from a few decades to more than a century, the process of systematic maintenance and rehabilitation is essential for preserving them at an optimal functioning capacity. From an asset management perspective, water infrastructure maintenance and rehabilitation (M&R) decision-making primarily involves assessing the condition and performance of the infrastructure, accounting for aspects such as complying with regulatory requirements, allocating resources based on system constraints, and mitigating network failure risks. The existing frameworks for water infrastructure maintenance and rehabilitation rely on models that allocate resources for M&R operations based on network condition and criticality under budgeting constraints. However, existing M&R frameworks have not included social equity (e.g., fair and just distribution of water resources) as a variable that directs or influences the M&R decisions. To improve the equitability of water infrastructures, a framework that incorporates social equity into the M&R decision-making is needed to attain equitable resource allocations for M&R projects. Hence, in this study, we developed a framework that optimizes resource allocations for water infrastructure M&R decisions by directly incorporating social equity objectives. The developed social equity objectives were derived from four social equity theories, utilitarianism, Rawlsianism, egalitarianism, and socialism, and integrated into three different policies A, B, and C, to (1) maximize performance, (2) maximize performance and improve access to disadvantaged groups, and (3) minimize the gap in performance between the disadvantaged group and the rest of network, respectively. The developed framework was applied to a numerical case study of a water distribution pipeline network in the City of Portland, Oregon, and the results of the modeling were discussed. The case study findings indicate that Policies B and C, which are equity inclined, without causing considerable economic setbacks result in a smaller performance disparity between the condition scores of assets in privileged (advantaged) versus underprivileged (disadvantaged) neighborhoods. The study results provide evidence that quantitatively incorporating social equity into the decision-making of water infrastructure M&R can improve asset allocations to disadvantaged communities without significantly compromising the broader network’s performance.

Abstract

Wastewater affordability is a pressing concern in the US as the cost of collecting and treating wastewater continues to rise rapidly, and reports have revealed that millions of residents are experiencing a lack of equitable access to affordable wastewater management. The USEPA has established an affordability metric based on 2.5% of median household income (MHI) and affordability is typically interpreted as the monthly utility bill. However, this approach is not applicable to the 25% of US households that are not connected to networked sewer. This study developed the first statewide methodology for mapping wastewater affordability for users of both networked and onsite wastewater treatment systems (OWTS) based on USEPA guidelines. The methodology used local data from the Alabama Department of Environmental Management (ADEM), utilities, and the US Census Bureau. This article presents a novel methodology for quantifying water and wastewater affordability challenges for large geographical areas while maintaining the accuracy of small-spatial scale analysis. This study also incorporated income inequalities by using census tract-level household income data to estimate the number of households in Alabama with unaffordable wastewater access. This study revealed that wastewater access affordability challenges are more widespread than indicated by traditional MHI-based analysis and are likely affecting approximately 445,000 households in Alabama (23.7%). This study also showed that expansions in the available funding and types of eligible applicants for grant programs that subsidize OWTS capital costs could reduce OWTS affordability challenges eightfold. We propose using this methodology to quantify affordability challenges alongside a suite of approaches to address wastewater affordability in the US at the utility and household levels to preserve the human right to affordable sanitation.

Practical Applications

Wastewater affordability is a key aspect of providing sustainable wastewater collection and treatment for US residents. The ongoing discussions about wastewater access affordability are often centered around customers of sewer utilities and neglect to consider the affordability challenges of residents with OWTS (commonly known as septic tank systems). This study develops a methodology for analyzing statewide wastewater access affordability for both residents with sewer bills and users of OWTS based on annual household incomes at the census tract level. This study reveals that wastewater affordability challenges affect both users of centralized sewer networks and OWTS including approximately 445,000 households in Alabama (23.7%).

This study investigated the current and potential future impact of climate change on water resources in the Minjiang River Basin, Fujian Province, China. Coupling a statistical downscaling model (SDSM) model and the Soil and Water Assessment Tool (SWAT) model, and considering the Coupled Model Intercomparison Project (CMIP) CMIP5 Representative Concentration Pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, we found a notable upward trend (2.9%, 7.5%, and 12.3% increase, respectively) in multiyear average runoff. A system dynamics feedback model was applied to optimize water resource allocation for Fuzhou, Sanming, and Nanping cities in the Minjiang River Basin, considering the runoff predicted by the SWAT model under climate change. The prediction results show that industrial water use steadily decreases, whereas water consumption in the tertiary sector increases concurrently. By 2050, water allocation schemes have reached the level of moderately developed countries, with these schemes taking into consideration the constraints imposed by red lines for water usage upper limit in China. These findings provide insights into the current and potential challenges posed by climate change, and can guide effective water resource planning for Minjiang River Basin cities.

Statistical water main failure models that improve our understanding of main breaks may help water utilities allocate resources more efficiently. A variety of statistical models have been developed, but few actively seek to replicate empirical main break behavior. Furthermore, the prevailing conceptual model of how failure risk changes over the lifetime of a water main, which includes self-excitation, is based on limited empirical evidence. We investigate self-excitation and pipe aging behavior using data describing a large cohort of water mains, present a statistical model that includes self-excitation, and compare the performance of several published models both with and without self-excitation. The failure data suggest that temporal clustering is occurring, which may be caused by self-excitation; however, the modeling results suggest that including self-excitation in failure models may not be worth the additional required resources. Researchers and practitioners should investigate their data and assess their specific goals and available resources to determine which modeling approach is most appropriate.

This paper presents a novel methodology for the coupled optimization of water distribution network (WDN) pipe sizing and isolation valve placement. The methodology is based on the effective application of biobjective genetic algorithm optimization to simultaneously minimize installation cost and average demand shortfall due to segment isolations, followed by a postprocessing financial analysis including assessment of valve maintenance costs. For speeding up convergence, a constraint is implemented in the optimization to enforce the telescopic property of pipe diameters, which are expected to shrink from source(s) to external parts of the WDN. The methodology is applied to two case studies with different levels of complexity, enabling the results to be compared with the traditional approach based on decoupled least-cost optimization of pipe sizing and placement of isolation valves at all (N_valve rule) or all but one (N-1_valve rule) pipes connected to the generic demand node. The least-cost pipe design equipped with the N_valve rule valve placement is always dominated. The adoption of the N-1_valve rule yields very close results to the optimization, as long as the best among the very numerous possible N-1_valve layouts is suitably identified. However, more beneficial solutions featuring lower valve maintenance costs can be selected from the optimal solutions obtained from the novel methodology. The use of the weighted average demand shortfall as objective function, instead of the simple average demand shortfall, tends to produce a larger variety of solutions.

Water shortage and salinization are the main challenges confronted by irrigated agriculture in semiarid and arid regions. As one of the top-three largest irrigation districts in China, the Hetao Irrigation District faces pressing challenges to reduce irrigation water use and mitigate soil and groundwater salinization. Managing water and salinity requires understanding and analyzing not only the hydrologic and agronomic processes involved but the cropping and irrigation decision-making of farmers. With this aim, we build a hydroeconomic optimization model, which integrates the agrohydrological processes of water and salt balance in the crop root zone and underlying aquifer into economic optimization of water allocation and crop production. The positive mathematical programming method is used to calibrate the model such that it can replicate base-year observations. Model results highlight that a portfolio of interventions is necessary for alleviating soil and groundwater salinization and maintaining agricultural productivity and revenues; it is thus crucial to understand their long-term, cumulative effects for the sake of sustainable irrigated agricultural production. The model can assist decision-making for water and salinity management in the HID and offer useful insights for water and salinity management in irrigated agriculture of other arid regions in the world.

Detecting pipe bursts in water distribution systems (WDSs) is of critical importance for urban infrastructure maintenance. A pipe burst can be detected from measurements that are continuously collected from hydraulic meters installed in WDSs, with widely accepted statistical process control techniques. However, the significant autocorrelation inevitably embedded in the continuously collected hydraulic measurements makes it extremely difficult for existing methods to accurately estimate the breakout time and the magnitude of a burst. To overcome the limitation, this paper proposes a new method to model the autocorrelation patterns with functional basis expansion. Functional regression is adopted to detect the pipe burst by decomposing the hydraulic measurements into three components: the normal components, the burst-induced anomaly component, and noises. A regularized estimation algorithm is developed to identify the three components by incorporating the knowledge of the impacts of bursts on the autocorrelation patterns in hydraulic measurements. A simulated water distribution network is built through EPANET. Analysis results based on the simulated data show that the proposed method not only outperforms existing methods with higher burst detectability and lower false alarm rate, but can also estimate the burst starting time, and magnitude estimation.

Reservoirs balance multiple conflicting objectives, including flood control and water supply. In California, shifts in seasonal hydrologic patterns under climate change will amplify the difficulties in balancing flood control with water supply. Current flood control policies are based on fixed seasonal rule curves determined by the observed timing and magnitude of floods in the record. These rule curves generally require the release of wet season inflows, reducing the available stored water for use during the dry season. Here we investigate the potential for forecast-informed reservoir operations (FIRO) to increase water supply availability while minimizing additional flood risk at 14 reservoirs in the Sacramento, San Joaquin, and Tulare river basins. We use a differential evolution algorithm to train risk-based reservoir operation policies with an ensemble of historical forecasts over the period 2013–2023. Results show an average 8.1% increase in storage normalized by capacity, though this varies across reservoirs. The forecast-informed policies also reduce the occurrence of high-magnitude releases throughout the system. The accumulation of benefits is sensitive to the timing and magnitude of flood events, and most of the cumulative benefit is obtained during a few years. Under cross-validation, we find that large floods are needed in the training data to avoid overfitting the policy. We further examine the relationship between reservoir properties and FIRO benefits, finding that the ratio of peak inflow magnitude to maximum safe release correlates with increased storage under the FIRO policy, while the ratio of mean inflow to capacity correlates to the reduction of high-magnitude releases. This study highlights how adaptive reservoir management policies can yield water supply benefits without an increase in flood risk, given adequate historical data for policy training. These policies may be a valuable adaptation to climate change but require careful validation and out-of-sample testing.