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.

Application of smart meters to the residential sector can provide insight into where and when water is used, thereby enabling utilities to achieve an efficient management of water distribution systems. Moreover, detailed information about domestic water use can be obtained by disaggregating smart meter data collected at the household inlet point. In this paper, a rule-based, automated methodology for disaggregating household water-use data into end uses is presented. The methodology is applicable to 1-min temporal resolution data, whose granularity is slightly lower than the one generally used in other methodologies, potentially allowing it to be applied to several contexts in the field of water-use monitoring. The methodology was set up and validated with data collected for 2 months through intrusive monitoring of four households in Bologna, Italy, and represents a pioneering case in which disaggregation performance is directly assessed by the comparison against data collected at each end use. The results obtained showed that the methodology enables household water use to be efficiently disaggregated even if detailed information about end-use features is not available.

Uncertainty in the impacts of climate change and development on freshwater resources pose significant challenges for water resources management. Integrated and adaptive approaches to water resources management are a promising means of addressing uncertainty that afford flexibility in balancing multiple stakeholder objectives. However, guidance on designing such plans is lacking. In this study, we use multiobjective optimization to strategically incorporate green infrastructure (GI) into water resources management plans that maximize reductions in nutrient loads, minimize stormwater runoff, and minimize costs. Robust decision-making methods are applied to the resulting plan options to evaluate how optimized GI implementation varies under different possible future climates and to determine which solutions would be robust under a range of plausible future conditions. We demonstrate these coupled methods using a case study in southern Massachusetts to address water quality issues related to point and nonpoint source nutrients in a rapidly developing watershed.

Socioeconomic characteristics are influencing the temporal and spatial variability of water demand, which are the biggest source of uncertainties within water distribution system modeling. Improving current knowledge of these influences can be utilized to decrease demand uncertainties. This paper aims to link smart water meter data to socioeconomic user characteristics by applying a novel clustering algorithm that uses a dynamic time warping metric on daily demand patterns. The approach is tested on simulated and measured single-family home data sets. It is shown that the novel algorithm performs better compared with commonly used clustering methods, both in finding the right number of clusters as well as assigning patterns correctly. Additionally, the methodology can be used to identify outliers within clusters of demand patterns. Furthermore, this study investigates which socioeconomic characteristics (e.g., employment status and number of residents) are prevalent within single clusters and, consequently, can be linked to the shape of the cluster’s barycenters. In future, the proposed methods in combination with stochastic demand models can be used to fill data gaps in hydraulic models.

Streamflow forecasts can be used to improve reservoir operation decision-making, yet the sensitivity of forecast value to forecast lead time and reservoir characteristics in multiobjective reservoir operations is rarely investigated simultaneously. We incorporate streamflow forecasts into reservoir operation by applying radial basis functions (RBFs) and evaluate the forecast value conditioned on different types of forecast lead time and reservoir characteristics. From a case study of the Danjiangkou Reservoir in the Hanjiang River basin, China, forecast value is assessed by comparing Pareto fronts of forecast-informed and no-forecast reservoir operation rules from multiobjective optimization algorithms maximizing power generation and water supply. Subsequently, we vary the installed hydropower plant capacity and capacity-inflow ratio (ratio of active reservoir storage capacity and annual reservoir inflow volume) of the Danjiangkou Reservoir to investigate corresponding forecast values in terms of magnitude and distribution. The results demonstrate that the inclusion of streamflow forecasts predominantly leads to an increase in power generation in wet years, yet the monthly distribution of generation varies with water supply. Additionally, forecast value increases with forecast lead time, resulting in approximately 10, 15, and 18 million yuan annually for forecast lead times of 10, 20, and 30 days, respectively. Finally, forecast value is demonstrated to generally increase as installed capacity increases and decrease as capacity-inflow ratio increases, however, the distribution of forecast value within a year is more sensitive to the capacity-inflow ratio.

A new approach to solving the problem of the optimal operation of a wellfield under water quality constraints is presented and tested. The approach is related to well activation in which all the wells are operated against an approximately constant head. Under these conditions, the hydraulic solutions of the network can be considered as a set of steady-state solutions that depend on the combination of operated wells. This setting is very practical because, in many cases, groups of wells pump water to the same receptor, which maintains an approximate constant head (for example, a relatively large reservoir or a treatment plant). The methodology utilizes mixed-integer linear programming (MILP) to optimize the combinations of wells that will pump the required volume of water given a threshold water quality constraint at minimum energy. This is by dividing the problem into two subproblems of hydraulics and water quality, whose solutions are combined and embedded into a MILP formulation. To overcome the complexity of water quality simulations, a predictive linear regression formulation is utilized to construct linear surrogate connections between the wells and the resulting water quality outcomes, which are then incorporated into a MILP optimization problem. Two example applications of increasing complexity are explored through base runs and sensitivity analyses. The results show that once the model is tuned to a specific water distribution system, an operational plan can be established that provides good approximate results without the need to construct a detailed simulation-evolutionary optimization algorithm framework.