Journal of Water Resources Planning and Management

The sensitivity of forecast-informed reservoir operating policies to forecast attributes (lead-time and skill) in many-objective water systems has been well-established. However, the viability of forecast-informed operations as a climate change adaptation strategy remains underexplored, especially in many-objective systems with complex trade-offs across interests. Little is known about the relationships between forecast attribute and policy robustness under deep uncertainty in future conditions and the relationships between forecast-informed performance and future hydrologic state. This study explores the sensitivity of forecast-informed policy robustness to forecast lead-time and skill in the outflow management plan of the Lake Ontario basin. We create water supply forecasts at four different subseasonal-to-annual lead-times and two levels of skill and further employ a many-objective evolutionary algorithm to discover policies tailored for each forecast case, historical supply conditions, and six objectives. We also leverage a partnership with decision-makers to identify a subset of candidate policies, which are reevaluated under a large set of plausible hydrologic conditions that reflect stationary and nonstationary climates. Scenario discovery techniques are used to map attributes of future hydrology to forecast-informed policy performance. Results show policy robustness is directly related to forecast lead-time, where policies conditioned on 12-month forecasts were more robust under future hydrology. Policies tailored for noisier long-lead forecasts were more robust under a wide range of plausible futures compared with policies trained to perfect forecasts, which highlights the potential to overfit control policies to historical information, even for a forecast-informed policy with perfect foresight. The relationship between performance and the hydrologic regime is dependent on the complexity of the interactions between control decisions and objectives. A threshold of objective performance as a function of supply conditions can support adaptive management of the system. However, more complex interactions make it difficult to identify simple hydrologic indicators that can serve as triggers for dynamic management.

Most previous studies in the field of sensor placement have focused on only one aim. In this study, pressure sensor placement is done for calibration and leakage detection simultaneously to use pressure data optimally to save money and time. The sensor placement method implemented in this paper consists of two main parts: clustering the nodes of a water distribution network (WDN) and determining the representative node of each cluster as the sensors’ location. A new multiview clustering approach is implemented to cluster nodes of a WDN based on two pressure sensitivity matrices. In fact, two different aspects of nodes’ characteristics are used for clustering. The representative node from each cluster is also chosen based on global sensitivity and the number of detection criteria. The Sobol method is used for global sensitivity analysis, and the number of detections is calculated with the local sensitivity matrices. The performance of sensor placement is evaluated individually and collectively for different goals in the Anytown network. The accuracy of network calibration with the sample design proposed in this study is equal to 0.0707 m, which is the lowest value between previous studies. Leakage detection also has a significant performance ratio than random pressure sampling. Furthermore, this new method of sensor placement is evaluated in the more extensive networks of the C-town and the Modena, which have high complexity. The performance of the presented pressure sampling is significantly different from the random pressure sampling, and the pressure data collected at the selected nodes can be used for the calibration procedure efficiently.

Blockage, which arises from accumulated materials like sand, stones, sediment, uncontrolled growth of tree roots within pipes, and the inadvertent partial closure of in-line valves, poses a pervasive challenge in the operational sphere of water distribution networks (WDNs). This results in significant head loss in the pipes, leading to disruptions in the water supply. Identifying such blockages conventionally requires specialized human resources and is often encumbered by cost and time challenges. This paper introduces a method for precisely determining the location and magnitude of blockages within WDNs. This methodology is firmly grounded in a calibration-optimization framework, harnessing a specially crafted hybrid optimization algorithm known as PSOHS, which combines the strengths of particle swarm optimization (PSO) and harmony search (HS) algorithms. Detailed comparisons are drawn between simulated pressure data (derived from the hydraulic network model) and corresponding field data at multiple points to minimize disparities, thereby facilitating the precise identification of potential blockages in a WDN. The proposed method undergoes rigorous evaluation across two benchmark networks (modified Poulakis and Hanoi) under diverse scenarios as well as on an actual WDN. Results from over 60,000 simulated blockage scenarios underscore the method’s remarkable proficiency, achieving blockage localization accuracy of 99.9% and magnitude estimation with a maximum absolute error (MAE) of less than 3%. Particularly noteworthy is the superior performance of the PSOHS algorithm as proposed, surpassing the PSO algorithm by enhancing blockage localization accuracy by a minimum of 10.5% and reducing MAE by 3%. Further, adopting the proposed algorithm over the HS algorithm yields improvements of 27.5% and 3.5%, respectively. Applying the proposed method in an actual network effectively pinpointed its blockage. Consequently, this algorithm presents a valuable tool for WDN operators, augmenting their capacity for effective network management.

Abstract

The Rainwater Harvesting Tool is a publicly available web-based geographic information system tool. It was developed using geospatial analysis in combination with historic zone improvement plan (ZIP) Code–level monthly average precipitation and evapotranspiration data across the US to help select potential locations for harvesting rainwater. Rainwater harvesting can provide a key alternative water source for a variety of uses, including landscape irrigation, vehicle washing, cooling tower make-up, dust suppression, and toilet flushing. Rainwater harvesting can help to diversify water sources for institutional, commercial, and residential buildings and offset the use of treated potable water. This tool aims to help organizations strategically target locations in which to implement rainwater harvesting systems. The metric used in the tool is called the rainwater harvesting potential, which is a normalized metric. The rainwater harvesting potential describes the amount of rainwater that reasonably can be collected and stored at a specific location. This metric was used to rank areas delineated by ZIP Codes across the US from lowest to highest to show the relative availability of rainwater for harvesting. Two mapping layers are included in the tool: a layer that shows the general year-round rainwater harvesting potential, and a layer that specifically shows the potential for harvesting rainwater to supply irrigation water. The Rainwater Harvesting Tool allows users to view the overall trends across the US and view the data at a scale at which the ZIP Code boundaries are clearly delineated. The tool can be used to help organizations with buildings located in multiple regions to strategically identify where to install rainwater harvesting systems and prioritize locations that may be optimal for rainwater harvesting.

Practical Applications

The Rainwater Harvesting Tool is a web-based geographic information system tool, which is free and available to the public. The tool presents the rainwater harvesting potential across the US to help determine locations that are potentially good candidates for harvesting rainwater and capable of meeting the water demands of institutional, commercial, and residential buildings. Rainwater can supply water for irrigation, cooling towers, vehicle washing, toilet flushing, and so forth, and offsets the use of treated potable water. The tool contains two mapping layers. The first shows the general potential for capturing rainwater and indicates the relative ability of a given location to collect rainwater throughout the year for beneficial use. The second identifies areas with favorable precipitation patterns for collecting rainwater during the irrigation season. The Rainwater Harvesting Tool provides interactive functions within a single browser session. Users enter specific locations (by ZIP Code), and the tool outputs a relative rank of the harvesting potential. The amount of rain potentially harvested each month also is provided. This portfolio planning, decision support tool can be utilized by organizations to target locations that are most conducive for rainwater harvesting across many locations.

The use of performance indicator systems is essential to evaluate the performance of water supply systems. They allow the quantification of the goals and objectives for service provision and are presented as the best alternative for monitoring the efficiency of these systems, with direct applications for managers, regulatory agencies, investors, users, society, and the providers themselves. Nevertheless, technicians and managers still have difficulty in interpreting the global performance of the systems because indicators present different characteristics and evaluations. The main limitation for global assessment is the lack of a broader view in the simultaneous analysis of a set of indicators. To overcome this challenge, aggregating individual indicators by a multicriteria analysis for decision support can be a solution for building synthetic global indices. The literature contains few works that focused on the performance of a global indicator that provides a holistic perspective. Thus, the aim of this study is to develop a multicriteria methodology based on the Grey Technique for Order of Preference by Similarity to Ideal Solution (Grey-TOPSIS) method to evaluate the quality of operation of the services of water supply systems. TOPSIS was chosen to assist in solving decision-making problems and the grey technique was chosen to assist in imprecise data and make the model more concise and real. The proposal was applied to Brazilian companies, and the results were obtained by a performance ranking. The study also allowed a robustness analysis for two different stages, one analysis for changes in weight and the other for the exclusion of criteria, and thus the validation of the application was highlighted. Therefore, by the results and their validation, it was possible to identify the positive contribution this methodology can add to the operation and management of water supply systems worldwide.

This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab-${\mathrm{SIC}}^2$ is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily.