Journal of Water Resources Planning and Management

This study proposes a novel optimal isolation valve placement method for water distribution networks (WDNs) using dynamic programming. The proposed method automatically finds the best places to install isolation valves that lead to minimal pipe isolation risk for the system. It works by gradually removing the least impact valves from a valve redundant system until the desired number of valves is met. The proposed method works for both new systems and established systems. Simulation on a real-life WDN in the East Bay, the eastern region of the San Francisco Bay Area, shows that the system configured by the proposed method outperforms the existing valve configuration. Results show that many widely used empirical valve placement rules (e.g., N, N-1 rules) can be successfully interpreted and reproduced using the proposed method. It helps water utilities balance the number of valves to install (cost) and system reliability (benefit).

There is an urgent need to reduce water loss from drinking water distribution systems. A novel framework that integrates the placement of multiple pressure sensors and localization using geospatial techniques is developed and validated to find leaks/bursts as they occur within district meter areas (DMAs). A data-driven leak/burst localization technique, featuring a novel spatially constrained inverse-distance weighted interpolation technique, was developed that quantifies the change in pressure due to a new leak/burst event using pressure sensors deployed in a DMA. The integrated framework uses the same modeling results and geospatial search techniques in both the optimal sensor placement and leak/burst localization steps. It can be adapted for any data-driven or model-based leak/burst localization technique and is not dependent on high hydraulic model calibration requirements such as high density smart meter deployment. Validation is presented using data from 16 engineered events (field work flushing) conducted in an operational DMA. Results show good agreement between the leak/burst localization performance for real and modeled engineered events, demonstrating that the sensor placement technique can accurately predict the expected performance of an operational DMA. This is particularly the case as the number of optimal sensors increases. Engineered events as small as 3.5% of the peak daily flow (6% of the average daily flow) were correctly localized with search areas containing as few as 14% of the pipes in the DMA (using only four pressure sensors).

Models for short-term optimal scheduling of hydropower stations should consider complex cascade constraints, hydropower station constraints, and unit operation constraints, all of which are inextricably linked. In practical scheduling, schedulers cannot be certain that the constraints they set based on their preferences are necessarily feasible, potentially leading to a no-solution situation. When the model has no solution, it is time-consuming to find conflicting constraints, and it is difficult to take the importance of different constraints into account, which will seriously affect the efficiency of day-ahead planning of hydropower stations. To solve the problem, a constraint grading model for short-term optimal scheduling of cascade hydropower stations is proposed in this paper. In both the flood and dry seasons, the model transforms five violable constraints into soft constraints and ranks them according to their importance, respectively. If the model has no solution, the soft constraints with low importance are automatically violated to obtain a feasible solution. The application example of Beipan cascade demonstrates that this model can effectively solve the problem of conflicting constraints. Moreover, compared with the penalty function method, this model can ensure that more important soft constraints are not violated.

The water management of the Colorado River is at a tipping point. This paper describes water management strategies in the Mexican portion of the Colorado River Basin considering water scarcity scenarios. A water allocation model was constructed representing current and future water demands and supply. The Colorado River system in Mexican territory is used as a case study, and all its water demands are characterized [Irrigation District Rio Colorado (DR-014), Mexicali, San Luis Rio Colorado, Tecate, Tijuana-Rosarito, and Ensenada]. Individual strategies were run by subsystem and then their impact was analyzed systemwide. Performance criteria and a performance-based sustainability index were evaluated to identify water stressors and management strategies to improve water supply for agricultural, urban, and environmental users. Analysis of results shows that the irrigation district (DR-014) is the most affected user due to water cuts because it has the lowest priority and, thus, any reduction in Colorado River allocations affects them directly. A range of water management strategies was investigated, including a no-action scenario. The current system depends on the long-term aquifer overdraft to supply water demand. The reduction of the cultivated area was the strategy that increased the sustainability index the most for DR-014. Agricultural to urban transfers, water use efficiency, wastewater reuse, and desalination are prime possibilities to improve the current water supply in the coastal zone (Tijuana, Rosarito, Ensenada). This research shows the spectrum of possible outcomes that could be expected, ranging from systemwide effects of inaction to the implementation of a portfolio of water management strategies.

Exacerbating water and food insecurity in drylands urge the more efficient use of water in irrigation through volumetric water pricing. The optimum irrigation depths can be determined using a combination of numerical simulation, water costs, and weather forecasts. In this context, we evaluated the effectiveness of three simulation-based schemes to determine irrigation depths that maximize net income during each irrigation interval using the WASH_2D model, which simulates water flow and solute transport through the plant–soil–atmosphere system. Those schemes were three-point (Scheme A) and two-point (Scheme B) schemes, which were used to optimize irrigation depth using three or two simulated cumulative transpiration at different irrigation depths, respectively, considering volumetric water price and weather forecasts; and a refilling scheme (Scheme C), which was used to determine irrigation depth required to return the simulated volumetric water content in the root zone to the field capacity. Those schemes were compared with the typical tensiometer-based automated irrigation scheme (Scheme D) by carrying out a field experiment in a sandy field of the Arid Land Research Center, Tottori University, Japan, using a major crop, sweet potatoes, in 2021. Compared with Scheme D, Schemes A, B, and C achieved 28%, 7%, and 21% higher net income due to applying 26%, 6%, and 17% less water and producing 21%, 5%, and 16% more biomass, respectively. The total simulated net income of Schemes A and B matched those of the measured schemes. Both simulated volumetric water content and actual evapotranspiration were in fair agreement with observed values. Regarding the accuracy of weather forecast, both daily reference evapotranspiration and rainfall forecasts were overestimated, with relative RMS error (RMSE) of 0.81 and 0.77 compared with observed values. In conclusion, both the two- and three-point schemes, which combined simulation, weather forecasts, and water prices, demonstrated significant benefits for farmers in terms of net income and water use compared with the use of basic types and costly automated irrigation systems.

At present, most research on the optimal scheduling of hydropower stations in the day-ahead electricity market focuses on the optimal power generation benefit, which does not consider the ecological impact of the generated discharge flow on downstream rivers. To address this situation, the ecological impact of electricity markets on rivers downstream of annually regulated hydropower stations is studied, and adjustment methods for traditional scheduling methods are proposed to achieve better ecological benefits. To this end, a multitimescale nested forecasting scheduling model is proposed for simulating hydropower station scheduling. In addition, the ecological flow index of the distribution flow method, an easy-to-implement ecological indicator model, was used to quantify the ecological benefits of discharge flows. A hydropower station on the upper course of the Lancang River in China was used to analyze the electricity market’s impact on downstream river ecology. The results show that under the optimal power generation benefit scheme, the ecological benefits are influenced not only by electricity price characteristics but also by multitimescale nested scheduling. Based on an analysis of these two factors, the general rules for the ecological benefits of rivers downstream of annually regulated hydropower stations in the day-ahead electricity market are summarized. In addition, the relationship between power generation and ecological benefits is analyzed for various schemes in a multiobjective scheduling problem framework. Finally, adjustment methods for traditional scheduling methods are proposed to achieve better ecological benefits.

This study compared how traditional data augmentation and the state-of-the-art style-based generative adversarial network (StyleGAN) benefit automated sewer defect detection using a You Only Look Once (YOLO) object detection model. There were 25 augmentation scenarios (13 individual augmentations, 8 combined augmentations, and 3 StyleGAN augmentations, as well as a baseline) investigated, with 3 types of data set sizes (300, 600, and 1,200 images) and 4 types of sewer defects (disjoint, obstacle, residential wall, and tree root). Results showed that geometric transformations generally outperformed color transformations. Among the traditional methods, random cropping worked best to improve the detector performance, meaning that combined effects may not be as strong as a single type of augmentation. It was also noted that not all augmentation measures benefited the detection, as in our case, 24% of the augmentation effects resulted in lower detection accuracy than the baseline scenario. StyleGAN performed remarkably well in improving the data quality through increasing style and diversity. Moreover, data augmentation had a greater impact on improving the detection of residential wall and tree root [with an increase in the mean of average precision ($AP$) of 32% and 22%] in comparison to disjoint and obstacle (21% and 16%), respectively. The findings will benefit the future selection and use of augmentation methods in enhancing the performance of deep learning models.

To meet increasing energy demands, reduce environmental impacts, and increase economic benefits, many water utilities have installed on-site renewable energy generation and storage facilities. These are commonly referred to as behind-the-meter (BTM) energy systems. This paper proposes a systematic optimization approach to the design and evaluation of water distribution systems (WDS) with BTM solar energy. The trade-offs between economic and environmental costs and the benefits these systems can have throughout their design life are demonstrated using a real-world pressurized irrigation system. Due to the large number of decision variables required during the optimization process and the associated high-computational cost, a hydraulic-power-based search-space reduction method has been developed. This method identifies common features of the pipes across a network based on the potential maximum flow and residual pressure head at the outlet of each pipe, so that pipes having similar hydraulic power capacity and thus similar diameters can be grouped together as a single decision variable. This effectively reduces the search space size and significantly increases the optimization efficiency for complex WDS optimization problems such as the optimal pipe design of WDS incorporating BTM solar energy in this study. In this paper, trade-offs between two objective function values [the total life-cycle cost and total life-cycle greenhouse gas (GHG) emissions] are investigated. A reduction in GHG emissions leads to an increase in the total cost and vice versa. It has also been found that the incorporation of BTM solar energy can significantly improve both objective function values. However, the optimal diameters of pipes and therefore the capital costs and GHG emissions are not sensitive to the increase in solar photovoltaic sizes.

In this study, a multiobjective multistage stochastic mixed-integer quadratic model for water allocation with hedging rules is proposed and investigated. Hedging rules determine different rationing levels among users at certain trigger volumes of reservoirs during droughts. The goal of this study is to provide a water management policy that could alleviate the water shortages caused by droughts and uneven distribution of rainfall in a region with multiple reservoirs. We devise a stochastic mathematical model to determine hedging rules under inflow uncertainty and apply an improved version of the augmented $ϵ$-constraint method to generate the Pareto frontier of two negatively correlated objectives. A real-world case study on the Taizi River Basin in China and numerical comparisons of the proposed stochastic model with other ways of handling uncertainty used in the literature (1) demonstrate the value of the proposed model; and (2) emphasize the importance of adequately considering the uncertainties and multiobjectives in managing multireservoir systems subject to droughts.

Intermittent water distribution systems supply over a billion people worldwide, a number expected to increase due to water scarcity, population growth, and urbanization. This study proposes a multiphase methodology for the definition of strategies (i.e., investments and controls) for the rehabilitation of complex intermittent systems over multiple-year periods. The multiphase methodology was developed in the context of the Battle of Intermittent Water Supply (BIWS) and is based on the use of EPANET 2.2 software for hydraulic modeling in combination with MATLAB version 2019a programming software. First, the hydraulic analysis of the deteriorated network is conducted to localize its main vulnerabilities. Second, multiple intervention strategies for network initial layout and subsequent rehabilitation (i.e., control modifications and investments) are identified considering different subsets of decision variables in turn. The effectiveness of each defined rehabilitation strategy is then quantified by means of a group of indicators reflecting the multiobjective nature of the problem. Finally, the best solution among all the defined rehabilitation strategies is identified and iteratively revised in order to further enhance its effectiveness on the deteriorated system. The application of the proposed rehabilitation method to the realistic and complex water distribution network considered as a case study in the BIWS—consisting of nearly 3,000 nodes—allowed obtaining an effective intervention solution for the reduction in the critical issues related to the system, ensuring 97% of users would be continuously supplied at the end of the rehabilitation period.

Abstract

Water is a limited and highly valuable resource. In many parts of the world, water agencies allocate water according to agreed entitlement systems. The allocations are largely based on water already available in storages and rivers. Water agencies may also issue seasonal water allocation outlooks by anticipating future inflows to the storages and rivers. These outlooks are meant to assist water entitlement holders to plan for their crop planting, irrigation, and participation in water markets. Currently, these outlooks are generally based on historical inflow observations (climatology) and are often determined for a small selection of possible climatic scenarios (e.g., extreme dry, dry, average, and wet). These outlooks have large uncertainties, which require users to manage high risks themselves, leading to inefficient water use. In this study, we investigate the use of ensemble seasonal inflow forecasts to improve the production of seasonal water allocation outlooks through a case study of the Goulburn system in central Victoria, Australia. This is a complex system with active water trade both within the region and outside with the larger connected southern Murray-Darling Basin. In this case study, we integrate Australian Bureau of Meteorology’s seasonal streamflow forecasts with Goulburn-Murray Water’s water allocation to produce fully probabilistic water allocation outlooks. We evaluate the outlooks for three irrigation seasons from 2017 to 2020. We compare these outlooks with those produced from using inflows based on climatology only, an approach akin to the current practice of Goulburn-Murray Water. Using seasonal streamflow forecasts resulted in outlooks up to 60% (average 20%) closer to actual determinations, with uncertainty reduced by up to 65% (average 19%) Improvements were most obvious for short lead times and later in the irrigation season. This is a clear demonstration of how integration of streamflow forecasts can improve end-user products, which can lead to more efficient water use and water market participation.

Practical Applications

This paper demonstrates how skillful forecasts of streamflow can be used to produce meaningful products for end users. Specifically, improvements are made to outlooks of water that will be made available to irrigators in dry years. The new outlooks are up to 60% closer, with up to 65% less uncertainty. These improved outlooks allow entitlement holders to more confidently plan for the irrigation season, leading in turn to more efficient water use. The method presented here should provide benefits for any water management systems where skillful streamflow forecasts are available. Beyond its use for high-reliability water share outlooks, it could easily be adapted for other water manager applications such as outlooks of low-reliability water shares in very dry years, or risk of spill (insufficient storage capacity) in wet years.

From distributed or semidistributed hydrological models, it is possible to identify where land use decisions can bring greater economic benefits in relation to water use. This study aimed to integrate simulations of a hydrological model varying the land use in four reforestation scenarios that influence the economic benefits of provision environmental services, including the user’s alternatives as a key parameter and finally estimating the economic value of such benefits. The “avoided costs” method was used for economic valuation of flow regularization service and adequacy of water quality in the Piracicaba River basin, aiming to generate various economic instruments to support decision makers. The cost for water storage was estimated from the rising rain volume infiltrating the soil from the change in the dynamics of land use, whereas the cost of maintaining water quality was obtained from the variation between the effects of scenarios in nitrogen, phosphorus, and sediment loads. The valuation methodology allowed us to estimate the avoided costs, which will mainly contribute to application of economic instruments to manage water resources, directing land use decisions in hydrographic basins, guiding environmental impact assessment studies, and the definition of mitigation measures.

Water is largely used in urban-environment human activities; thus, it is essential to properly understand consumption characteristics so that measures to ensure a more efficient use of water resources can be implemented. This study aims to evaluate how different factors impact water consumption in office buildings using longitudinal and cross-sectional approaches in a case study conducted in southern Brazil. Data from 53 offices were used to build statistical water consumption models using the multiple linear regression method. In addition, the impact of the COVID-19 pandemic on water consumption in this building typology was assessed. The significant variables for the per capita water consumption regression model are the age of the building and the total number of employees. For consumption per floor-plan area (${\mathrm{m}}^3/{\mathrm{m}}^2/\text{year}$), the variables are whether the building is owned or rented, its age, its size, and the total number of employees. The COVID-19 pandemic impact analysis showed a 10% reduction in the average water consumption during the first wave, when considering all offices in the sample. The analysis by business activity showed a reduction of 28% and 27% in the average consumption of water in law and accounting firms, respectively. In contrast, it went up by 15% in engineering offices during the same period. The analysis of the impact of the pandemic and the investigation of the main drivers of water consumption can provide parameters to promote water conservation in office buildings and can be used in the management of urban water demand.

Due to the uncertainty of daily reservoir inflow and market clearing power price, the market-oriented operation of cascade hydropower stations faces both transaction risk in the bidding stage and performance risk in the operation stage. It is difficult to obtain the fluctuation range of uncertain variables and establish a reasonable bidding strategy when the expected power sale profit is met. This work developed a medium-term scheduling and transaction decision method for cascade hydropower stations based on information gap decision theory (IGDT) and prospect theory. For performance risk in the operation stage, the IGDT-based robust optimization model solves the maximum fluctuation range of the uncertain variables under different expected profits from power sale. For transaction risk in the bidding stage, prospect theory analyzes the bidding strategy of cascade hydropower stations according to the fluctuation range of uncertain variables, and the corresponding bidding decision model and solution are provided. A cascaded hydropower system in Southwest China was used as a case study to verify the proposed method. The results showed that this method could provide an acceptable fluctuation range of the uncertain variables and a bidding strategy to ensure the expected profit. According to different risk preferences, decision makers can evaluate a range of strategies to optimize bidding and operation, to guarantee the expected profit of cascaded hydropower stations.

This paper describes a public domain semiautomated data screening and quality assurance processing tool useful for both discrete and continuous data. This application serves both a field research project assessing salinity and methylmercury drainage load control options and the requirements of a public domain watershed-based water quality forecasting model used to provide decision support for short-term salinity management in the San Joaquin River Basin of California. The US Army Corps of Engineers HEC-DSSVue data management and visualization platform was enhanced in the former application with Python scripts that performed the basic data quality assurance functions. Analogous tools were developed for use on the HOSTGATOR cloud server using the same Python scripts and algorithms to migrate quality-controlled data directly to a server for use by the Watershed Analysis Risk Management Framework (WARMF) water quality forecasting model. The Python scripts for both applications are easily adaptable by other potential users who do not currently have the resources to implement an enterprise-level hydrological data management and real-time quality assurance software system.