Journal of Water Resources Planning and Management

Water quality degrades with time as water travels to users through pipes. Water age (as a surrogate quality indicator) is not limited to distribution networks but carries through the residential premise plumbing system to the tap. Poorly designed premise plumbing layouts and intermittent usage patterns can lead to high residence times. This work aims to numerically quantify water age in residential premise plumbing systems using a stochastic demand simulator and a hydraulic solver. Plumbing layouts based on real houses are assessed and modified to determine design practices that lower water age. Results suggest that best practices are to loop the plumbing system or connect water closets at the end of the premise distribution branches to ensure periodic flushing. However, none of these approaches impacts the “last foot” of pipe connecting plumbing fixtures with the premise distribution pipes. The effect of fixture use intensity and water heater types on residence times is also assessed. The introduction of more efficient fixtures with their lower flows over the last several decades has increased residence times. On-demand heaters reduce water ages across all layouts at the outlet and the point of connection of the fixture. Autoflushers, installed in the hot water system, further decrease water age.

Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP.

Aquifer storage and recovery (ASR) in saline unconfined aquifers is challenging as the injected freshwater makes a thin layer above the groundwater table. For this reason, the efficiency of ASR in unconfined saline aquifers has been reported to be low in the literature. Nevertheless, critical factors like depth of freshwater release, length of storage duration, and rate of injection/extraction control the recovery efficiency (RE) of an ASR and can be adjusted to enhance the performance. This study aims to optimize the ASR technique in unconfined saline aquifers to maximize its recovery efficiency through sand tank experiments and numerical modeling. The tank experiments were conducted using a three-dimensional sand tank setup of dimensions $60(\mathrm{length})\times 30(\mathrm{width})\times 60\mathrm{cm}(\mathrm{depth})$, to examine the interactions between injected freshwater and ambient saline groundwater. Variable density groundwater flow modeling was performed to observe the movement of stored freshwater at varying depths within the aquifer and to examine the influence of storage duration and injection/extraction rates on RE. The results from experimental and numerical studies demonstrated that shallow injection depths, higher injection/extraction rates, and smaller storage durations lead to higher freshwater recovery. Deeper injection allows the freshwater packet to move upward due to buoyancy force, resulting in freshwater losses if the injection and recovery are undertaken at the same depth. The overall findings from the present study can be utilized to enhance freshwater recovery in unconfined saline aquifers through the optimization of influencing factors.

Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology.

With the availability of real-time monitoring data, leakage detection for water distribution networks (WDNs) based on data-driven methods has received increasing attention in recent years. Accurate forecasts based on historical data could provide valuable information about the condition of the WDN, and abnormal events could be detected if the observed behavior is substantially different from the typical behavior. Therefore, an accurate forecast model is essential for prediction-based leakage detection methods. While most data-driven methods focus on burst detection, it is also important to develop an early warning system for gradual leakage events because they will cause more water loss due to a longer time to awareness. Therefore, a real-time early leakage detection technique based on a multistep forecasting strategy is proposed in this study. A multistep flow forecasting model is introduced to capture the diurnal, weekly, and seasonal patterns in the historical data. The generated multistep forecasting is further compared with the observed measurements, and residuals are calculated based on cosine distance. Based on the analysis of the residual vector, the gradual leakage event could be detected in a timely manner. The proposed method is applied to the L-town datasets containing one year of real-life flow monitoring data. The results prove the superiority of the proposed multistep prediction model-based method over the traditional one-step prediction model for gradual leakage detection. In addition, the results show that the proposed methodology can detect small gradual leakage events within just a few days while generating no false alarms. The method was further applied to a real-life network and showed consistent results.

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.

Seasonal streamflow forecasts are becoming widely used to improve water reservoir operations, especially in areas where climate teleconnections enable predictability on medium and long lead times. Most existing studies have focused on the assimilation of forecasts into operational decision models, an approach that typically banks on predeveloped forecasts to optimize water release decisions. However, this approach may overlook the potential synergies that stand in co-developing forecast and decision-making models. In other words, the opportunities that lie in coupling both forecast and operational decision models have not yet been explored. Here, we address this gap and contribute a novel approach building on the Evolutionary Multi-Objective Direct Policy Search algorithm to design forecast and decision-making models together. The proposed approach is benchmarked against operating policies not informed by any forecast, as well as by forecast-informed policies relying on predeveloped forecasts (data-driven and perfect). Numerical experiments are conducted on the Angat-Umiray water resources system, Philippines, which is operated primarily for ensuring municipal water supply to Metro Manila and irrigation supply to a large agricultural district. Our results show that the integrated design of forecast models and control policies provides a performance gain with respect to policies informed by predesigned forecasts. This result is particularly interesting because the skill of the integrated forecast models is lower than that of the predeveloped ones, thus suggesting that more accurate forecasts do not necessarily produce better water system operations. Overall, our analysis represents a step towards a deeper integration of streamflow forecast and reservoir operation models.

Abstract

Recent studies have demonstrated some advantages of using advanced heuristic algorithms to identify near-Pareto-optimal future locations, types, and sizes for stormwater low-impact development and green infrastructure (LID/GI) across a given urban landscape. However, previous optimization studies did not consider social equity as an objective, which poses problems because urban green infrastructure often is distributed inequitably. Increasing access to LID/GI in historically marginalized areas is a prominent environmental justice issue, and increasingly is becoming a primary consideration when prioritizing future locations, types, and sizes of urban LID/GI. This study integrated a novel spatial social equity objective [LID/GI–Social Vulnerability Index (SVI) correlation objective, $\rho$] into a multiobjective LID/GI optimization model. The LID/GI-SVI correlation is an objective that directs the optimization algorithm to search for LID/GI distributions that maximize the linear correlation between LID/GI implementation and subbasins with higher estimated percentages of historically marginalized people. Our analysis focused on understanding the impacts of the LID/GI-SVI correlation objective on a LID/GI optimization model. This modeling study demonstrates that (1) the LID/GI-SVI correlation objective can be used to direct optimization algorithms to search for LID/GI distributions that can achieve runoff management objectives, increase green LID/GI implementation in more marginalized areas, and explore the potential trade-offs or synergies between hydrologic and equity goals; (2) LID/GI optimization formulations that consider only hydrologic objectives likely will not result in equitable LID/GI distributions; (3) LID/GI distributions that perform well on the LID/GI-SVI correlation may be composed of different types of LID/GI than less-equitable but more hydrologically favorable LID/GI distributions; and (4) for our study area, including spatial equity as an objective resulted in modest reductions in the hydrologic performance of near-Pareto-optimal LID/GI distributions.

Practical Applications

Low-impact development or stormwater green infrastructure (LID/GI) has been implemented across the globe to address the environmental issues of increased impervious surfaces, aging infrastructure, elevated stormwater runoff due to climate change, and to meet non-point-source discharge permit requirements. Recent research has demonstrated the use of advanced algorithms to help identify optimal locations, sizes, and types of LID/GI that will maximize local runoff reduction benefits. Although runoff control is a primary objective of LID/GI, many cities in the US have been striving to prioritize local areas with high proportions of historically underserved or vulnerable populations for implementation. In tune with this trend, we developed a novel equity objective function that forces a proportional relationship between LID/GI implementation and indicators of social vulnerability. We tested the impact of this new objective within a multiobjective LID/GI optimization algorithm and found minimal trade-offs between runoff and equity objectives in our study area.

Influenced by climate warming and human disturbance, the contradiction between flood control and water supply of the rivers-connected lake is becoming more prominent, and the prevailing dispatch system mainly aimed at flood control is a serious challenge for storage allocation. This paper presents a lake impoundment in advance method during flood recession period, which preliminarily explores the traditional dispatch model and couples it with a high-precision hydrodynamic model. A one- and two-dimensional coupled hydrodynamic model was constructed for integrated flood regulation in the “river-lake-flood detention basin” system. Then a sample library of water storage strategies was developed, which combined impounding times and gate operation schemes based on previous engineering experience. Thus, by combining large-scale numerical simulations of flood scenarios and risk-benefit assessment, the dispatching schemes facilitating the combined use of gates during the post-flood period under different wet-dry conditions were determined. The results show that: (1) the hydrological-hydrodynamic model has high accuracy for realizing the elaborate simulation of flood evolution and inundation dynamic between lands and lakes, with the average error controlled from 0.03 to 0.14 m, (2) the strategy extraction method of post-flood impoundment in advance for the lake basin is proposed, which takes the impounding times, gate scheduling rules, and inflow scenarios into account comprehensively, and (3) under the optimal storage strategies, the water storage efficiency is significantly improved, with the water level overrun rate controlled at 19.24%, the terminal storage level raised by 0.96 m, and the storage capacity increased by $17.0\times {10}^8{\mathrm{m}}^3$ on average. The outcomes will provide support for flood control and water storage improvement during the post-flood period, which is beneficial to the utilization and management of flood resources.

The equivalent relationships between flood prevention storage of a cascade reservoir system provide useful information for real-time operation. However, those relationships are implicitly hinted at in the optimized operation rules of downstream reservoirs, which may add difficultly in providing feasible operation strategies for engineering practice. In this study, we investigate the equivalent relationship between cascade reservoirs using a flood regional composition-based framework coupled with reservoir inflow and river flow routing techniques. We select the cascade reservoirs in the downstream Jinsha River and Three Gorges Reservoir (TGR) as the case study. Results reveal that (1) the equivalent relationship between flood prevention storage of cascade reservoirs in the downstream Jinsha River and TGR is approximately linear-related; (2) the conversion coefficient of equivalent relationship depends on the flood type, and the intercept term of equivalent relationship is mainly influenced by the design flood frequency; and (3) the TGR flood prevention storage (or water level) can be dynamically adjusted according to the corresponding reserved flood prevention storage of cascade reservoirs in the downstream Jinsha River, which can generate $3.56\mathrm{billion}\mathrm{kW}·\mathrm{h}$ ($+8.5\%$) extra hydropower during flood season without increasing flood control risks.

Due to the complex feedback cycle relationship among water resources, energy, and food, the agricultural water management mode and concept with a single object or two objects as the core can no longer meet the inherent requirements of the coordinated, sustained, and healthy development of economy and society. A two-stage multiobjective decision-making method based on a $\theta$-dominance evolutionary algorithm ($\theta$-DEA) is proposed for the optimization of agricultural systems with a water-energy-food nexus. First, the Pareto noninferior solution set of the model is obtained by the evolutionary algorithm based on $\theta$-DEA. Then, the economic benefit and carbon emission of each solution are evaluated, and the optimal solution is selected by using the multiobjective decision-making method based on interval-valued hesitant fuzzy sets and the technique for order preference by similarity to ideal solution (TOPSIS). Through a case study of the Hetao Irrigation District (HID), the best optimization results are obtained when the carbon footprint is $4.16\times {10}^9\mathrm{kg}{\mathrm{CO}}_2$ eq, and the economic benefit is $8.09\times {10}^9$ yuan. The example shows that the model can well select the optimal allocation solution of water resources, which not only has high economic benefits and low pollution discharge but also meets the subjective wishes of decision-makers. The model has good practicability and stability.