Journal of Water Resources Planning and Management

Abstract

Drought and population growth, especially in the western United States, are propelling a need for more efficient irrigation. Smart irrigation controllers, which interface with soil moisture, evapotranspiration (ET), or weather sensors, have been promoted as a demand-side management tool for this purpose. This paper reviews the body of research on residential smart irrigation controllers and their effectiveness. We find that smart irrigation controllers consistently reduce water demand by 15% among general users and more than 40% among indulgent users. Gaps in research include studies addressing peak demand reduction, centralized communication, data verifiability, and human factors of landscape management. Future work may develop techniques for coordinating networks of smart irrigation controllers to enable greater shifting and shaving of discretionary irrigation demands, similar to what is already happening in the electric grid, thereby creating an integrated water distribution system (IWDS). An IWDS may utilize smart irrigation controllers as direct load control devices at customer end points and interface with advanced metering infrastructure (AMI) and supervisory control and data acquisition (SCADA) systems at a central location to create more effective demand-side management (DSM) strategies for water conservation.

Practical Applications

Weather-based irrigation controllers (WBICs) have become the prevalent smart controllers used for residential irrigation scheduling. They are easy to install, have smartphone control, are easy to program, utilize local real-time weather data, prevent operating the irrigation system during rain or other adverse weather events, adjust watering schedules and duration seasonally, conserve water, and reduce water billing costs. In addition to the ease of use and benefits at the residential user level, smart irrigation controllers have the potential to provide the water utility with controls to buffer or shift peak irrigation demands. This buffering and shifting of peak demands have the potential to lower energy costs, decrease the carbon footprint, and delay costly infrastructure upgrades. With technological advancements and the proliferation of smart controllers, the future for water utilities will become more similar to the electrical grid industry. The smart controllers and technology will enable the development of the IWDS. The IWDS will allow the water utility to utilize data from AMI meters, SCADA systems, smart irrigation controllers, artificial intelligence, and other smart technology to operate more effectively and efficiently.

As global warming and climate variability bring about more frequent and intense rainstorms and accelerate sea level rise, our social and built environments are at heightened risk of flood-induced damages and costs. All levels of governance stand to benefit from deepened understanding of possible outcomes resulting from decentralized human behavior in the realm of water resources engineering and management, particularly in coastal areas. Game theory allows scientists to predict preferred strategies and interactions of rational self-interested actors in coalitional games, wherein players increase their individual payoffs through formation of strategic subsets. When applied to infrastructure planning, this practice can be used to identify which coalitions should form to benefit their overall hydrologic system. This research aims to inform green infrastructure decisions in Charleston, South Carolina’s Market Street watershed using a coalitional game theory solution concept, the Shapley value, in combination with rainfall-runoff simulation in storm water management model (SWMM). Results offer insights into stormwater services and flood managers concerning suggested areas of focus for green infrastructure spending and advocacy to reduce flooding and resulting property damage.

Abstract

Intermittent water supply (IWS) networks have distinct and complicated hydraulics. During periods without water supply, IWS networks drain, and consumers rely on stored water; when supply resumes, pipes and consumer storage are refilled. Draining, storage, and filling are not easily represented in standard modeling software. We reviewed 30 ways modelers have represented the hydraulics of IWS in open-source modeling tools and synthesized them into eight distinct methods for quantitative comparison. When selecting methods, modelers face two critical choices: (1) whether to ignore the filling phase, and (2) how to represent consumers as attempting to withdraw their demand: as fast as possible (unrestricted), as fast as possible until a desired volume is received (volume-restricted), or just fast enough to receive a desired volume by the end of supply (flow-restricted). We quantify these choices’ impact on consumer demand satisfaction (volume received/volume desired) and inequality using three test networks under two supply durations, implemented in two different hydraulic solvers (EPANET and EPA-SWMM). Predicted inequality and demand satisfaction were substantially affected by the choice to represent consumer withdrawals as unrestricted, volume-restricted, or flow-restricted, but not by the specific implementation (e.g., three different flow-restricted methods agreed within 0.01%). Volume-restricted methods predict wider inequalities than flow-restricted methods and unrestricted methods predict excessive withdrawal. Modeling filling delayed water provision unequally, reducing the volume received by some consumers (by $\sim 20\%$), especially where water supply is brief. All else being equal, we recommend using volume-restricted methods, especially when modeling system improvements, and including the filling process when studying inequalities.

Practical Applications

Understanding how water distribution networks perform is complicated, particularly when the network is operated intermittently. Pipes in intermittent networks periodically fill up, supply water, and then drain, forcing consumers to adapt, e.g., by storing water. Intermittent networks represent about 20% of the world’s water pipes, but there are not yet standardized, accessible methods for practitioners and utilities to model the key features of intermittent networks by using or adapting off-the-shelf hydraulic software. Previous methods of adapting hydraulic software for IWS networks disagree on two key choices: how consumers behave and whether to ignore the initial filling of pipes. We quantitatively compare how these varied choices affect the resultant hydraulic predictions. First, we find that when consumers are assumed to fill their storage as fast as they can (volume-restricted), water delivery is less equal than if consumers are assumed to withdraw slower (flow-restricted). Second, we found that including pipe filling delays water supply and magnifies predicted inequalities. Accordingly, we recommend that modelers carefully select methods that reflect how consumers behave in their context. We provide accessibly packaged Python code to enable other hydraulic modelers to efficiently compare, extend, and/or adopt the compared intermittent modeling methods.

Coastal water supply infrastructure systems (CWSISs) are exposed to saltwater intrusion (SWI) exacerbated by sea level rise (SLR) stressors. However, most existing definitions of CWSIS resilience are confined to the system’s global severity, while uncertainties from SLR are not considered. In this paper, we develop a CWSIS model considering uncertainties from SLR and define a new formula for resilience based on three components of system severity, i.e., social severity ($Se{v}_S$) affected by water shortages to end users, regional severity ($Se{v}_R$) caused by water shortages in water treatment plants (WTPs), and technological severity ($Se{v}_T$) considering water shortages in wells. A case study in Xingcheng is designed to (1) examine the resilience response of CWSISs to future SLR, (2) identify vulnerable components, and (3) compare the cost-effectiveness of different measures for enhancing resilience using scenario analysis with full consideration of future uncertainties from SLR. This paper contributes to the development of sustainability assessments for urban water systems subject to future sea level change. Finding response measures with high adaptiveness across a variety of future scenarios is crucial for establishing a sustainable urban water system in the long term.

The recently launched California water price index (NASDAQ: NQH2O) and corresponding futures market provide an opportunity for water users to hedge against seasonal drought risk. While the volume of futures trading remains low, the index can be analyzed as a spatially aggregated price of physical water trades that responds to hydrology and management. This study investigates the extent to which the NQH2O index can be predicted from a combination of reservoir storage anomalies and inflow forecasts throughout the state. Over the available record (November 2013–June 2023), the daily hydrologic time-series are reduced to a set of principal components, which are shown to be nonlinearly correlated with the current and season-ahead price index. The PCs are then used as features in an exponential regression to predict the forward six-month average price. The most accurate model in cross-validation performs with $R^2=0.81$ using four PCs that contain 85% of the total variance in the features. Predictions generally fall within $100/AF of the observed value, with larger errors associated with hydrologic forecast uncertainty during the winter. A threshold-based hedging strategy is developed to analyze the potential cost savings of buying during the winter (November–March) when the forecasted six-month average price is greater than the current index. This strategy shows an average cost reduction of 17% compared with the six-month average price, with reductions above 35% at the onset of dry water years. This study contributes a statistical modeling approach to support the development of more advanced hedging strategies in water portfolios.

Aquifer storage and recovery (ASR) is a potential solution to growing global freshwater insecurity. However, the performance of ASR in saline regions depends on regional hydrogeology and groundwater salinity level. Hydrogeological factors, e.g., hydraulic conductivity, hydraulic gradient, aquifer thickness, aquifer heterogeneity, and hydrodynamic dispersivity, affect freshwater recovery efficiency (RE) prominently along with the groundwater salinity. Therefore, a site feasibility analysis for ASR based on the prevailing hydrogeological conditions is required for its successful operation. This study investigates the influence of these hydrogeological parameters and groundwater salinity level on the performance of ASR. Findings from previous field studies were reviewed, grouped, and analyzed systematically for each hydrogeological factor independently and in combination to understand their influence on RE. The interdependency of the effects caused by each factor is also analyzed. The results show that higher freshwater recovery can be achieved at sites offering moderate hydraulic conductivity, low hydraulic gradient, low hydrodynamic dispersivity, thinner aquifers, and low groundwater salinity. The overall findings of this meta-analysis will help in synthesizing information for site hydrogeological conditions to aid in the decision-making process for ASR application in saline groundwater regions.

Abstract

This study applied statistical approaches (e.g., Spearman’s rank correlation coefficient, regression tree analyses) to characterize Army site-specific factors (e.g., water demand, installation size, climate zone, Army mission type, utility privatization, water source, population, installation status on mission capacity) correlated with Army installation water unit price ($\mathrm{USD}·{\mathrm{kgal}}^{-1}$). The results from Spearman’s rank correlation coefficient for individual factors showed that annual water consumption, size of installation, and population were the major influencing factors (positive correlation) to annual water billed. For Army installation water unit prices, however, negative correlation with annual water consumption and less significant correlation with climate zone were observed. This could lead to a failure in promoting water conservation in water-stressed areas with limited water supplies. From the results of regression tree analyses with combination of characterized variables, installation mission type, type of primary water source, and assured access to water were statistically significant factors to Army installation water unit prices. The regression trees provided coarser but actionable insights while clustering water unit prices by the influencing factors. The results of this study support site-specific reconsideration of water pricing and further development of installation water security and resiliency through deeper understanding of factors correlated with installation water unit price. Also, this research adds to existing studies on water infrastructure system characteristics, specialized use cases and water price across scales and locations at the United States.

Practical Applications

Implementations of emerging water conservation technologies are often challenging due to the low market price of water, which could lead to a limited cost savings relative to the investment. While disruptions to water supply can affect many communities and businesses, understanding of factors correlated with water price may provide implications to promote water conservation efforts especially in water stress regions. This study explores significance of site-specific factors correlated with US Army installation water price to support prescriptive modifications to water pricing and further development of water security and resiliency.

Abstract

Severe droughts, water-use conflicts, and minimum outflow requirements have caused infeasibilities in modeling large-scale complex hydrothermal systems. This paper presents and discusses a new formulation of objective function and constraints to better represent multiple water uses and deal with infeasibilities in the HIDROTERM model, a nonlinear programming (NLP) model developed to optimize the operation of the Brazilian interconnected hydrothermal system. The system is one of the largest in the world. In the last 10 years, approximately 72% of electricity consumed in Brazil has been supplied by hydropower plants, but with increasing shares from thermal, wind, and, more recently, solar power generation. Because of hydrological changes in recent years and the additional operational constraints imposed by environmental agencies, the use of models for decision making, such as HIDROTERM, frequently encounters the problem of infeasibility. To avoid this problem, a new objective function is proposed in HIDROTERM that incorporates economic penalties when the preset requirements are not met. Based on economic considerations, some hard constraints are removed from the constraint set and added to the objective function as penalty terms. These constraints are tightly linked with multiple water uses, including environmental protection, flood control, consumptive uses, and navigation purposes. The proposed change in the objective function and constraints avoids the problem of infeasibilities and makes it possible to apply the model to extreme conditions. Additionally, this formulation helps identify and minimize the risk, duration, and intensity of unavoidable non-compliance occurrences related to multiple water uses.

Practical Applications

The presented new version of the HIDROTERM model can be used to study and support decision making in the management and operation of large-scale hydrothermal systems. These systems can be formed by a set of hydraulically connected individual hydropower plants and reservoirs, located within a set of electrically connected subsystems. Each subsystem contains demands and other power plants, including thermal, wind, nuclear, and solar plants, and exchanges. The model has been developed with continued improvements since 2008 and applied to large systems such as the Brazilian Interconnected Power System (BIPS) with over 150 reservoirs. A graphical user interface is provided. The objective function is to minimize the total costs of additional thermal dispatch and exchanges, as well as avoid or minimize deficits in energy supply in subsystems, minimum releases, consumptive uses, and minimum storages in reservoirs. The decision variables are the power and nonpower releases for each hydropower plant and the thermal dispatches and exchanges for each subsystem. Some typical applications include (1) monthly planning operation of the system, considering a planning horizon from 3 to 5 years; (2) diagnostic studies about reliability and resilience of existing systems, processing the same system configuration to a set of different hydrological or energy demand scenarios; (3) impact analysis of changes in operational rules and constraints related to multiple uses of water; and (4) planning and evaluation of expansion alternatives. The model can be applied to a complete hydrothermal system or subsets of it, including subsystems, basins, cascades, or a single hydropower plant.

The integrity and accuracy of river-terrain data directly affect the results of open-channel one-dimensional hydrodynamic numerical simulation. However, there are topographically uncharted rivers in the real world, and their hydrodynamic processes are difficult to simulate. This study investigated topographically uncharted rivers and investigated publicly available digital elevation model data to generalize their cross sections. The sensitivity of the shape parameters of the generalized cross sections and river roughness to open-channel hydrodynamic simulation was analyzed using the Latin hypercube one factor at a time method. The results show that the sensitivity of the bottom width of the cross section was considerably less than that of the bottom elevation and roughness. The sensitive parameters were optimized by a data-driven method, and the optimized section was applied to the simulation model calculation. The proposed key factor identification and cross-section generalization methods greatly improved the calculation speed. The method was applied to the topographically uncharted river of the Zhentouba I-Shaping II to simulate the hydrodynamic processes of the whole river. The mean error in the water level was verified to be less than 0.15 m, and the Nash-Sutcliffe efficiency coefficient was greater than 0.9, which proves the validity of the method.