Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Changing natural streamflow conditions apply pressure on water supply systems globally. Understanding potential vulnerabilities using IPCC-endorsed top-down impact assessments, however, is limited due to uncertainties in climate and/or hydrological models. In recent years, bottom-up stress tests have been proposed to avoid some of the uncertainties in top-down assessments, but the uncertainty in bottom-up approaches and its impact on vulnerability assessments are poorly understood. Here, we aim at addressing uncertainties that originate from synthetic realizations of regional streamflow with which the system vulnerability is mapped and assessed. Four regional streamflow generation schemes are used to form alternative hypotheses for performing a bottom-up impact assessment in a large-scale water supply system under changing conditions. Our findings suggest that despite having different levels of realism, none of the schemes can dominate others in terms of reproducing all historical streamflow characteristics considered. There can also be significant differences in the results of impact assessments, particularly in terms of variability in long-term streamflow characteristics and system performance. These differences cause uncertainty in assessing risk in system performance and stress-response relationships under changing conditions.

Hydropower release decision making relies on multisource information, such as climate conditions, downstream water quality, inflow and storage, regulation and engineering constraints, and so on. The decision tree (DT) method is one of the commonly used techniques to simulate reservoir operation and release strategies because of its simplicity and effectiveness. However, the performances and simulation accuracy vary among different DT models due to many structures and splitting rules associated with each DT model. In this study, we propose a dynamic merge technique (DMerge), which adopts a concept from particle swarm optimization, to postprocess outputs from different DT models with the purpose of increasing the simulation accuracy and producing a model ensemble with dynamically changing weights throughout the validation phase. A case study of Shasta Lake in northern California is presented, where the daily hydropower releases are predicted and compared using the DMerge, AdaBoost DT, random forest, and extremely randomized trees methods. Results show that the DMerge method has the best statistics compared to other popular DT algorithms. Furthermore, scenario tests were carried out to analyze the sensitivity to model inputs (i.e., hydrological condition, reservoir storage and regulation, climate phenomenon indices, and water quality) with respect to explaining the variability of hydropower releases. According to the results, we found that the hydropower releases are a complex decision-making process and water quality and climate conditions could play an even more significant role than both hydrological forcing and system states in our case study. The proposed DMerge method is a robust and efficient technique in solving water-energy prediction and simulation problems, and it is suitable for joint use with other data-driven approaches.

Predicting water demand is necessary to ensure a secure water supply to homes and businesses. With great uncertainty around future changes in the climate and in UK households, it is essential to accurately determine the effect of weather on water consumption. A systematic approach based on smart demand-metering data and customer characteristics (e.g., metering status and garden ownership) was used to investigate the sensitivity of household water consumption to weather, for different consumer types and time-varying parameters. The following weather variables were analyzed: air temperature, soil temperature, humidity, precipitation, and sunshine hours. Results indicated that the effect of the weather on water consumption is moderate in the UK. This effect was more significant for affluent customers with high monthly variations in consumption and medium-occupancy households; and during work days, summers, and evenings. Sunshine hours, humidity, and air temperature were the most influential weather variables. Soil temperature had a milder influence, whereas daily rainfall had minimal impact.

Due to high in-channel sedimentation rates, the Yellow River Delta of China has changed course frequently in its history, with huge socioeconomic impacts. The following questions are addressed: What is the best timing and location of deliberate avulsions? Can temporary floodways lessen the cost and flooding impacts of avulsion strategies? To address these questions, a simulation-based optimization model is developed that is novel in its integration of economics, hydraulics, and sediment dynamics. The framework considers tradeoffs between the cost of avulsion-mitigation strategies and expected flooding risk. Four design variables (avulsion timing and location, floodway size, and operation rule) are optimized. The results suggest that locating the deliberate avulsion as far upstream as possible reduces total cost. However, the level of reduction of channel capacity at which a deliberate avulsion should be triggered is sensitive to assumed cost parameters. Meanwhile, the optimal floodway size is either the largest possible size or none at all, depending on the frequency of avulsion and floodway cost. Floodway costs influence decisions less than costs of avulsions and flood damage costs.

Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step toward sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events, and in an organized manner, to prioritize the use of available resources to restore service rapidly while minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve the resilience of a postdisaster WDS through identifying optimal sequencing of recovery actions. To address this gap, the authors propose a new dynamic optimization framework in this study in which the resilience of a postdisaster WDS is evaluated using six different metrics. A tailored genetic algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include the following: (1) the near-optimal sequencing of a recovery strategy heavily depends on the damage properties of the WDS; (2) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time; and (3) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS.

Evaluation of the operational efficiency of a stormwater detention facility may involve deterministic simulation or application of probabilistic models derived from runoff statistics. Using a probabilistic approach, a model for runoff capture efficiency at a facility is derived that is functionally related to storage capacity and release rate. The runoff storage/release cycle considers carryover storage capacity from a preceding storm, an ensuing interevent dry period, and probable overflow from the following storm occurrence. Hydrologic variables are assumed independent, identically distributed exponential variates. The model allows for the adoption of different release rates for the interevent time and subsequent intraevent duration. A novel feature of the probabilistic model consists in treating the carryover storage capacity as a random variable. The model is validated with published results from various studies, including bioretention applications. It allows construction of attainable capture efficiency envelope curves, as a measure of reliability, and estimation of controlled release rates that would increase the runoff capture efficiency of a facility.

With the growing trend in extreme weather events, establishing an optimal investment plan for infrastructure adaptation has attracted considerable research interest aimed at minimizing future damage in urban areas that have limited resources. Most previous studies focused on optimization problems of infrastructure adaptation for climate change in a single urban district, but multiple key variables of an adaptation plan—investment target, amount, and timing—should be developed simultaneously for interconnected urban districts. Thus, there is a knowledge gap regarding optimization for infrastructure adaptation in interconnected urban districts. This study suggests a model for establishing an infrastructure adaptation plan in urban areas under various climate change scenarios using an optimization method. The proposed model derives the infrastructure investment plan, which includes the key decision variables of investment amount, location, and timing. The model was applied to derive an investment plan for a sewer system covering the 25 administrative districts in Seoul under six future scenarios. The proposed method aims to provide a basis for adaptation planning for urban infrastructure, maximizing benefits from the limited resources.

Various reliability measures have been developed to evaluate design and operational aspects of water distribution system (WDS) performance. Conventional reliability indicators mainly focus on nodal supply conditions, such as nodal pressure and available discharge. Here, a new link-based reliability index is proposed that considers pipe head loss–distribution throughout a network. The proposed index, the hydraulic uniformity index (HUI), is approximated using the equivalent hydraulic gradient and intends to distribute the hydraulic gradient as uniformly as possible throughout the network by enlarging steep-gradient pipes and reducing the size of low-gradient pipes, while satisfying the design constraints. The HUI is intended to overcome the shortcomings of conventional node-based indices by synchronizing the evaluation components with the design elements for intuitive system evaluation. An applied study using the Hanoi benchmark network demonstrates that the proposed HUI can be used as an indicator to evaluate system design and operational reliability.