Journal of Water Resources Planning and Management

Numerical simulation models are a fundamental tool for planning and managing smart water networks—an evolution of water distribution systems in which physical assets are monitored and controlled by information and communication technologies. While simulation models allow us to understand the interactions between physical processes and abstract control strategies, they ignore key implementation aspects of distributed control systems, such as the required communication over digital links. As a result, the effects of anomalies and faults in the communication on the process control cannot be investigated with existing tools. In this work, we fill this gap by introducing DHALSIM (Digital HydrAuLic SIMulator), a numerical modelling platform combining EPANET-based process simulation with a network and host emulation environment, offering a high-fidelity representation of the processes occurring in the cyber domain. We illustrate DHALSIM’s key functionalities by implementing it on a benchmark water distribution system, present case studies of simulated network traffic, and demonstrate how anomalies in the behavior of the communication network affect the process data received by the supervisory control and data acquisition (SCADA) server. In a companion paper, we further illustrate how DHALSIM enables research opportunities in the domain of cyber-physical security. The easily customizable and open source DHALSIM provides a “workbench” for studying smart water networks, developing digital twins, and designing a broad spectrum of engineering solutions.

A fundamental problem in the realm of cyber-physical security of smart water networks is attack detection, a key step towards designing adequate countermeasures. This task is typically carried out by algorithms that analyze time series of process data. However, the nature of the data available to develop these algorithms limits their capabilities: by relying on process data only, one cannot distinguish a cyber-attack from the failure of a system’s component or identify the root cause of an attack. Here, we show that these limitations can be addressed through the joint analysis of process and network data—with the latter representing the information exchanged between the components constituting the Industrial Control System, such as sensors and Programmable Logic Controllers (PLCs). For this purpose, we utilize a dataset generated by digital hydraulic simulator (DHALSIM)—a numerical modelling platform built on a two-way interaction between EPANET version 2.2 and a network emulation tool—which is extended here to include a framework for launching cyber-physical attacks. This paper presents a dataset with realistic network information of a smart water network under cyber-physical attacks and presents an analysis of how that information can enable the development of better intrusion detection systems that can localize and identify attacks. Through this analysis, the dataset provided here, and the open-source availability of DHALSIM, our work paves the way to a novel class of analytics for actionable detection.

Abstract

Conservation in working landscapes is an important aspect of stream restoration. Landowners with agricultural water rights are increasingly adding instream flow as a beneficial use of their allocated water. However, stream restoration projects commonly focus on the quantity of water dedicated to the environment and overlook water quality. For cold-water ecosystems, the preservation of natural thermal regimes is a key to conservation efforts in the face of a changing climate. This study explored the effects of increasing stream flow using alternative water sources (e.g., instream runoff versus offchannel springs) to enhance cold-water stream habitat on a working cattle ranch. Using empirical data to create a steady-state stream flow and water temperature model, we evaluated temperature benefits and management trade-offs for ecological and agricultural water use. The modeled results showed that the source of water and the method used to deliver water to the channel affected the quality and extent of cold-water habitat. Offchannel spring water cooled 7-day average maximum temperatures and 7-day average mean temperatures by as much as 3.7°C and 2.9°C, respectively, but only when potential offchannel aquatic habitat was sacrificed for mainstem habitat. When offchannel, spring-fed habitat was provided, the value of spring water to the main channel decreased. Our findings suggest that source water thermal quality is of paramount importance for enhancing ecosystem function and improving cold-water habitat in working landscapes. In addition, the restoration of a historic spring channel may provide added ecosystem benefits by enhancing aquatic habitat for cold-water species.

Practical Applications

In the western United States, understanding the best possible uses for high-quality cold-water sources may provide the greatest benefits to the environment and adjacent working landscapes. Whereas water purchases and leases tend to focus on water quantity, this study could direct such investments toward higher value sources for the environment. In places like California, where drought has resulted in water right curtailments for agricultural diverters, understanding the best uses for high quality cold water compared with other sources can provide the greatest benefits to both agriculture and the environment.

Temperature is known to impact physical, chemical, and biological processes in Drinking Water Distribution Systems (DWDS), but it is rarely considered or modeled. This research evaluates the impact of considering a finite heat capacity for the ground, which has been assumed infinite in previous DWDS research. The aim of this work is to explore and quantify the region where the difference between considering infinite or finite heat capacity for the ground is significant, i.e., the distance over which water-ground heat transfer interaction is important. A detailed model comparison is carried out for key pipe materials, diameters, and hydraulic conditions. Temperature effects are found to exist for up to tens of kilometers (i.e., several hours) into the DWDS. Whereas the differences found were only a few degrees Celsius, this will affect all reaction rates, such as chlorine decay, and is at the start of the DWDS so will impact the entire downstream network. This work highlights the importance of considering temperature in DWDS, and in particular the finite heat capacity of the ground, in ensuring the provision of safe drinking water.

Abstract

Most scientific contributions addressing cybersecurity issues in water distribution networks (WDNs) propose detection systems without considering the location problem. A methodology for detection and location of cyberattacks in WDNs is proposed in this paper. Structural analysis and neural networks are effectively combined with the control chart adaptive exponential weighted moving average (AEWMA). The proposed detection and location framework requires only data from normal operating conditions and knowledge about the behavioral model of the system. The validity of the methodology was demonstrated with the widely known case study Battle of the Attack Detection Algorithms (BATADAL). The detection method detected all the attacks with a false positive rate (false alarm rate) below 5% and true positive rate (TPR) (i.e., the detection rate) higher than 95%. The location method presents consistent diagnosis results while guaranteeing that the district metering area under attack always is identified.

Practical Applications

Water distribution networks are critical infrastructure for a country because they ensure the distribution of a basic element for life. The current development of electronics and communication technologies has made it possible to achieve automated management of water distribution networks, which has led to better rates of efficiency in water management and control. However, that technological development and the fact that WDNs are critical infrastructures make water distribution networks central targets for cyberattacks that seek to interrupt this basic service, temporarily or permanently affecting production and services. In this paper, a methodology is proposed for the detection and localization of cyberattacks on water distribution networks using computational intelligence tools that do not require new technological investments. The proposed methodology works with the data that the supervision and control system obtain from the real process. The results of the application of the proposed methodology will allow managers to make appropriate decisions to avoid the effects that cybertacks can produce.

The purpose of the paper is to present an approach for real-time pipe burst detection and location estimation, based on the changes in the harmonics and analysis of the corresponding damping that is caused by the burst. The amplitude of each resonance response of the transient pressure wave caused by the burst is damped differently due to the occurrence of the burst. However, utilizing higher order harmonics beyond the first few fundamental frequencies has not previously been possible as it leads to an increasing number of possible solutions to the burst location, which limits the application of the method. An algorithm for damping analysis has been developed which overcomes this limitation, and is able to exclude most of the incorrect solutions when utilizing the information contained in higher order harmonics. Additionally, the gap between data windows can be set so that it enables the real-time data analysis by letting the window gap equal the reciprocal of the sampling rate. Therefore, this algorithm makes utilizing any sequence of harmonics of the signal be possible for real-time burst detection and location estimation. The approach has been verified both numerically and experimentally with acceptable accuracy.

A growing number of studies have investigated how forecast skill, i.e., predictive power, translates into forecast value, i.e., usefulness, for improving forecast-informed decisions. The relationship between skill and value is widely understood to be complex and case-specific, yet few methods enable its systematic exploration using realistic forecast errors. This paper addresses this gap by proposing a single-parameter linear scaling method to generate families of synthetic forecasts with the desired skill improvements on an existing hindcast (a retrospective forecast of already-observed events). The method is applicable to any quantity for which a deterministic or ensemble hindcast is available, and generates a set of forecasts with different skill but strictly proportional errors. This like-for-like comparison preserves the autocorrelation and cross-correlations of errors, and opens the door for thorough, yet easily interpretable, explorations of the relationship between skill and value of a realistic forecast. We apply this new method to seasonal precipitation hindcasts (produced by the fifth generation of the Seasonal forecasting System of the European Centre for Medium-range Weather Forecasts, ECMWF-SEAS5) in order to explore their value for improving the management of a water supply system in the UK. The application showed that although value generally increases with skill, the skill–value relationship is not necessarily linear, and it strongly depends on operational preferences and hydrological conditions (wet or dry years). It also suggests that the forecast families methodology can help water managers and forecast developers identify when a skill increase would be most valuable. This has the potential to foster productive two-way conversations between forecast producers and users.

Improving the operational effectiveness of hydropower systems is becoming more relevant considering the shift to renewable energy sources and the rising social, financial, and environmental costs associated with the construction of new hydropower facilities. This challenge is interdisciplinary as it involves technical, managerial, and institutional aspects. This paper focuses on a technical aspect, more specifically on the relationship between the quality of short-term ensemble streamflow forecasts and the energy produced by a hydropower system. A secondary objective is to measure the contribution of both the meteorological and structural uncertainties on the energy output. To achieve this, a numerical experiment comprising multiple sets of hydrologic ensemble forecasts of different quality and a suite of reservoir optimization models is developed for a case study in Canada (the hydropower system of the Gatineau River basin). These ensemble forecasts are processed by the short-term reservoir operation model in rolling-horizon mode over a planning period of 6 years. Each day, the short-term optimization model seeks to maximize the energy output over the 14-day forecast lead time considering the expected future value of the system derived from a midterm optimization model. The relationship between hydropower generation and common statistical scores characterizing the ensemble forecasts indicates that although there is a link between the quality of the forecasts and the energy production, it is not a one-to-one causal relationship. Our results also show that the diversity of hydrological models is beneficial to the production of energy, indicating that the diversity of model structures compensates the deficiencies of individuals models and adds value to the forecast.

This study examined the benefits and limitations of formulating linear programming models for water distribution network leak detection and localization considering sequences of demands. First, the consistency of sensitivity matrixes for different demand magnitudes and spatially varying nodal demands was investigated to determine if different sensitivity matrixes are needed for each time step and to determine the demands to use for computing the gradient matrixes. Then several unconstrained and constrained models were developed and their detection and localization performance was evaluated for a network in Austin, Texas. To constrain the failure location in time and space, binary (zero–one) integer linear programming (BILP) was used. A set of threshold-based detection rules was applied to test for the anomalies in the time series, and they were compared for a range of realistic leak sizes. Based on the numerical results, the best optimization model was identified considering multiple detection metrics (detection effectiveness, efficiency, and localization). The best BILP model that constrains leak locations spatially and temporally outperformed the unconstrained model in detecting and locating relatively small leaks.

This case study synthesizes strategies that electric power utilities can implement to reduce surface water risks to infrastructure, operations, and regulatory compliance as climate change impacts hydrologic regimes over the next century. The strategies range from the reach scale to watershed scale. A reach-scale example would be evaluating relocation alternatives for a transmission tower along an eroding streambank versus a streambank stabilization strategy. A watershed-scale strategy would involve the value engineering of stormwater management strategies that could be implemented across a catchment that is restorative of a more natural flow regime such as prolonged baseflows and reduced flooding and erosion. The cost-effective watershed-scale strategies highlighted herein include retrofits of existing detention ponds, beaver reintroductions (or discontinued extirpation), riparian reforestation, adding wood to headwater streams, and the removal of postsettlement alluvium from floodplains coinciding with restoration of floodplain wetlands. Many of these strategies are management approaches that could be implemented on utilities’ own property for relatively little cost while appealing to broader societal goals such as environmental restoration. Although costs will vary by setting and program goals, we hope that this article is a launching point for infrastructure managers to consider holistic, watershed-scale approaches to provide durable infrastructure resilience in the face of increased extreme events while contributing to long-term economic, social, and environmental sustainability.