Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-3921	article	2013	Zhang, Yongliang and Zhang, Yongliang and Zhang, Yongliang and Zhang, Yongliang and Wu, Yueying and Wu, Yueying and Wu, Yueying and Wu, Yueying and Yu, Hai and Yu, Hai and Dong, Zhanfeng and Dong, Zhanfeng and Dong, Zhanfeng and Dong, Zhanfeng and Zhang, Bing and Zhang, Bing and Zhang, Bing	Trade-offs in designing water pollution trading policy with multiple objectives: A case study in the Tai Lake Basin, China	Environmental Science & Policy	10.1016/j.envsci.2013.07.002
publications-3922	article	2022	Kadinski, Leonid and Kadinski, Leonid and Salcedo, Camilo and Salcedo, Camilo and Boccelli, Dominic L. and Boccelli, Dominic L. and Berglund, Emily Zechman and Berglund, Emily Zechman and Ostfeld, Avi and Ostfeld, Avi	A Hybrid Data-Driven-Agent-Based Modelling Framework for Water Distribution Systems Contamination Response during COVID-19	Water	10.3390/w14071088	Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent-based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID-19 pandemic and their location in the network. The results indicate that the ANN-based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80\%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less.
publications-3923	article	2022	Tiedmann, Helena R. and Tiedmann, Helena R. and Spearing, Lauryn A. and Spearing, Lauryn A. and Sela, Lina and Sela, Lina and Kinney, Kerry and Kinney, Kerry A. and Kirisits, Mary Jo and Kirisits, Mary Jo and Katz, Lynn E. and Katz, Lynn E. and Kaminsky, Jessica and Kaminsky, Jessica and Faust, Kasey M. and Faust, Kasey M.	Modeling in the COVID-19 Pandemic: Overcoming the Water Sectorβ€™s Data Struggles to Realize the Potential of Hydraulic Models	Journal of Water Resources Planning and Management	10.1061/(asce)wr.1943-5452.0001561	Hydraulic models can provide efficient and cost-effective ways for water utilities to evaluate changes in operating conditions (e.g., population dynamics, disasters), thereby increasing system resiliency during crises. Unfortunately, model development remains out of reach for many utilities because of high software costs, data needs, or personnel requirements. This study seeks to classify hydraulic modeling data needs, identify success factors and challenges associated with model development, and determine whether modeling a subzone of a larger water distribution network can provide useful insights during a crisis, specifically the COVID-19 pandemic. At the pandemic onset, we began developing a hydraulic model of the water distribution system of the University of Texas at Austin campusβ€”a subsystem of the water distribution network of Austin, Texasβ€”to understand how spatiotemporal changes in water demands impacted system performance. We found that the completed model can offer useful insight into the impacts of demand changes within the modeled subsystem (e.g., potential locations of water stagnation). However, the data collection and processing challenges encountered (e.g., siloed collection efforts, lack of standardization, lengthy processing) reflect barriers to model development and use. The amount of time required to gather and process the necessary data shows that model development cannot occur during a time-sensitive crisis, likely rendering any insight too late for use. Here, we make recommendations to address data-related challenges and support utilities in incorporating hydraulic modeling into emergency planning.
publications-3924	article	2022	Hassani, Hossein and Hassani, Hossein and Huang, Xu and Huang, Xu and MacFeely, Steve and MacFeely, Steve	Enabling Digital Twins to Support the UN SDGs	Big data and cognitive computing	10.3390/bdcc6040115	Digitalisation has enjoyed rapid acceleration during the COVID-19 pandemic on top of the already fast-paced expansion impacting almost every aspect of daily life. Digital twin technology, which is considered a building block of Metaverse and an important pillar of Industrial revolution 4.0, has also received growing interest. Apart from its significant contribution to intelligent manufacturing, there has been considerable discussion on its implementation and the as yet undiscovered potential. This paper reviews the current trajectory of digital twin applications in supporting general sustainability, in the context of the 17 UN SDGs. Furthermore, it connects researchers and readers from different fields with the aim of achieving a better understanding of emerging digital twin technologies, the current values this technology has brought to support UN SDGs, and identify areas with potential for future research to better contribute to achieving the remaining tasks of Agenda 2030.
publications-3925	article	2022	DiCarlo, Morgan and DiCarlo, Morgan Faye and Berglund, Emily Zechman and Berglund, Emily Zechman	Using Advanced Metering Infrastructure Data to Evaluate Consumer Compliance with Water Advisories during a Water Service Interruption	Water Research	10.1016/j.watres.2022.118802	- A large smart water meter dataset with more than 16,000 meters is analyzed. - Explores compliance to utility advisories during a water service interruption. - Methods determine the percent compliance and volume of water saved by consumers. - Less than a third of meters compiled to advisories to reduce water use. Water main breaks disrupt services provided by utilities and result in Water Service Interruptions (WSIs). Water utilities can manage WSIs through water advisories, which request that consumers limit their water use. The performance of water advisories depends on consumer compliance and decisions to conserve water. This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). AMI provides high temporal and spatial resolution of water consumption data, which is analyzed to identify changes in water use behaviors. This research explores water use changes during a major water main break in Orange County, North Carolina, that caused a significant WSI, limiting water supply for more than 80,000 people. Customers were asked to reduce water use to essential purposes only and to boil water over the course of two days in November 2018. This research analyzes hourly consumption data to evaluate water consumption trends during the WSI and in response to water advisories. Statistical analysis is used to estimate the number of consumers who complied with utility notifications and to evaluate the volume of water saved. Regression analysis is applied to explore compliance across different user segments. Results provide insight about the level and variation of water conservation that can be expected during a WSI.
publications-3926	article	2022	Kadinski, Leonid and Kadinski, Leonid and Vizanko, Brent and Vizanko, Brent and Berglund, Emily Zechman and Berglund, Emily and Ostfeld, Avi and Ostfeld, Avi	A Socio-Technological Framework for Optimizing Water Utility Strategies and Resilience to Pandemic Changes and Contamination Events		10.1061/9780784484258.085	Water distribution systems are critical infrastructure that deliver high quality drinking water to its consumers. Contamination events in water distribution systems (WDS) are emergencies that can cause distress in the population and require quick response from the responsible utility manager. While regular water quality parameters are monitored at water treatment facilities, it is still a challenge to monitor water quality in the WDS itself. Various models have been developed to explore the reactions and interactions of relevant stakeholders during a contamination event including agent-based modelling. Furthermore, recent research has shown that water demands have significantly changed during the COVID-19 pandemic, and these changes can affect the operation and management of water infrastructure. In this study, an agent-based modelling framework is developed to explore social dynamics and reactions of water consumers and a utility manager during a contamination event, while considering a pandemic demand scenario. Furthermore, innovative response and recovery methods to a contamination event are explored for rehabilitating the water network after a water quality deterioration. Graph theory algorithms are used to place mobile sensor equipment for surveying the water quality in specific network parts, and the distribution system is clustered by the status of endangerment. The Bayesian Belief Network (BBN) was developed using survey data around risk perceptions and social distancing behaviour that were collected during the COVID-19 pandemic. The agent-based model (ABM) was developed using output from the BBN and water use data that were collected during the COVID-19 pandemic. The ABM is coupled with hydraulic simulation of the water infrastructure to evaluate changes in hydraulic performance. The model can be used to explore long and short-term consequences of the pandemic on water distribution systemsβ€™ management, design, and operations; develop and optimize strategies of how to deal with changes in around water distribution systems due to the pandemic; and investigate how resilient water utilities can cope with additional catastrophic events such as a contamination of a water system during a global or local pandemic related shutdown.
publications-3927	article	2023	Ramos, Helena M. and Kuriqi, Alban and Coronado-HernΓ΅ndez, Γ“scar E. and JimΓ©nez, Petra Amparo LΓ³pez and SΓ΅nchez, Modesto PΓ©rez	Are digital twins improving urban-water systems efficiency and sustainable development goals?	Urban Water Journal	10.1080/1573062x.2023.2180396	The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology, which enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 Mβ‚¬, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs).A digital twin is a tool, which enables a real-time simulation of the water systems and therefore, the water managers can make a decision in the management of the water system over time. The use of these new interaction tool implies the improvement of the awareness of the whole system and it lies in improving the sustainability and efficiency of the water systems with the integration of measurements. The research proposed a methodology to integrate GIS and water models, being the main goal the integration of social, economic, environmental and technical issues. This integration enables improvement in the accuracy and reliability of data and it increases the performance of water systems. This study proposes a pressure-reduction strategy and the implementation of pumps as turbines (PATs), applicable in Sta Cruz, Madeira water system. The use of the developed digital twin model assures a decrease of 3.3 hm3 in water-demand volume, increasing renewable generation by micro-hydropower up to 1.2 GWh. These actions would result in savings above 1.5 Mβ‚¬, decreasing around 530 tons of CO2 emissions each year. The consideration of these values implies the improvement of different indicators, which allows the evaluation of different targets linked to sustainable development goals (SDGs).
publications-3928	article	2023	Metcalfe, Brett and Boshuizen, Hendriek C. and Bulens, J.D. and Koehorst, Jasper J.	Digital twin maturity levels: a theoretical framework for defining capabilities and goals in the life and environmental sciences		10.12688/f1000research.137262.1	<ns4:p><ns4:bold>Background</ns4:bold>: Digital twins (DT) are the coupling of a real-world physical asset to a virtual representation to provide insight and actionable knowledge. The benefits of DT are considered to include improvements in reproducibility, reliability of interventions, increased productivity, as well as increased time for innovation. For instance, a DT could be used to boost agricultural productivity whilst also meeting various targets (e.g., biodiversity, sustainability). Or a DT could be used to monitor a cell culture, predict interactions, and make subtle adjustments to maintain the environment allowing researchers to conduct other work. Yet in developing DT two fundamental questions emerge: β€What will the DT capabilities be?β€™ (i.e., the range of features and possible actions) and β€What will the DT do?β€™ (i.e., which capabilities will it utilise). </ns4:p><ns4:p> <ns4:bold>Methods</ns4:bold>: Here we discuss a theoretical framework for DTs developed during Wageningen University & Researchβ€™s Investment Programme on DTs that aims to answer these questions. Focusing on the Life and Environmental Sciences to help developers and stakeholders to agree on the capabilities, purpose, and goal of a DT. As well as identifying iterative design stages that may help set interim development goals such as a minimum viable product.</ns4:p><ns4:p> <ns4:bold>Results</ns4:bold>: This framework defines a DT as sitting at one of five maturity, or capability, levels associated with specific types of DT: a status, an informative, a predictive, an optimisation, and an autonomous twin.</ns4:p><ns4:p> <ns4:bold>Conclusions</ns4:bold>: The aim of DTs is to make better, data-driven, decisions yet there can be a gulf between expectations of what a Digital Twin will do and the reality. The five maturity levels outlined here can be used to first identify and communicate about the type of Digital Twin required for a particular project prior to DT development. Bridging the gap between what project leads, developers, and stakeholders envision the end-product will be.</ns4:p>
publications-3929	article	2023	Evangelista, Stefania and Nardi, Mariantonia and Padulano, Roberta and Cristo, Cristiana Di and Giudice, Giuseppe Del	Analysis of the effects of COVID-19 restriction policies on drinking water consumption by smart water network data filtering		10.2166/ws.2023.208	Abstract People's habits changed during the COVID-19 pandemic and the consequent containment policies, with numerous implications in all fields. In particular, restrictions had important consequences for drinking water consumption. The present work analyses this influence in the Soccavo district of Naples (Campania), in Italy, during the two periods of strongest restrictions in 2020: the national Lockdown (March 11β€“May 3) and the autumn Red Zone (November 16β€“December 6). A large amount of data, referred to single-household flowmeters connected to a Smart Water Grid acquisition system, was collected for the years 2019 (considered the average reference year) and 2020. The first step was the preliminary filtering of the data, by identification and elimination of anomalies and outliers, as well as anomalous annual patterns, through clustering and classification. The second step consisted of the comparison of the same meters in two consecutive years considering the daily and weekly average hourly patterns, the average daily patterns of midweek days, Saturdays, and Sundays, respectively, and the total daily volumes. The results are consistent with those in the literature. Some general trends in literature data were sought and pointed out in the present paper.
publications-3930	article	2023	Coronado-HernΓ΅ndez, Γ“scar E. and Fuertes-Miquel, Vicente S. and SΓ΅nchez, Modesto PΓ©rez and Coronado-HernΓ΅ndez, Jairo R. and QuiΓ±ones-BolaΓ±os, Γ‰dgar and Ramos, Helena M.	Dynamic effects of a regulating valve in the assessment of water leakages in single pipelines		10.21203/rs.3.rs-3276460/v1	Abstract Water losses in water distribution systems are typically analysed using extended period simulations, where its numerical resolution is commonly achieved using the gradient method. These models assume that adjustments to regulating valves occur, either manually or automatically, over an extended period of time, then the system inertia can be neglected. This research introduces the development of a rigid water column model for analysing water leakages in single pipelines, which can be employed to account for regulation valve adjustments in shorter time periods, thereby providing greater accuracy when assessing water losses. The application to a case study is presented to analyse pressure variations and leakage flow patterns over 30, 60, and 180 s. A comparison between the extended period simulation and rigid water column model is presented in order to note the order of magnitude on leakages when the system inertia is not considered. The results confirm that is crucial for water utilities the consideration of inertial system to simulate adequately opening and closure manoeuvres in water distribution systems, since according to the case study the extended period simulation can overestimated or underestimated the total leakage volume in percentages of 37.1 and 55.2 \%, respectively.