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 â–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-4851 Article 2024 Chen H.; Xiao X.; Chen C.; Chen M.; Li C.; Lu K.; Lin H.; Fang C. Digital twin-based virtual modeling of the Poyang Lake wetland landscapes Environmental Modelling and Software 10.1016/j.envsoft.2024.106168 Virtual wetland landscapes of provide fundamental support for digital twin watershed constructions. However, most digital twin applications in natural environments have focused on static digital scenes and little consideration for wetlands. The Poyang Lake is characterized by seasonal hydrologic changes, with periodic plant community successions, making it necessary to capture dynamic changes in the Poyang Lake ecological landscapes through dynamic three-dimensional (3D) scenes. This study selected typical landscapes to establish digital twin scenarios, presenting the virtual landscapes, distribution characteristics of flora and fauna in the Poyang Lake wetland, and seasonal changes in the lake water levels. The results can be used to restore the virtual Poyang Lake landscapes, including seasonal changes in the water surface, vegetation growth, and migratory bird activities. This construction process can be applied to similar digital twin constructs for flood-prone wetland watersheds, providing insights into digital twin watersheds or nature-oriented digital twin development. Β© 2024 Elsevier Ltd
publications-4852 Article 2025 Piciullo L.; Abraham M.T.; Drøsdal I.N.; Paulsen E.S. An operational IoT-based slope stability forecast using a digital twin Environmental Modelling and Software 10.1016/j.envsoft.2024.106228 The paper investigates the combined use of real-time hydrological monitoring, publicly available meteorological data and hydrological and geotechnical numerical modelling, to develop data-driven models to forecast the stability of a slope. This study showcases a first attempt to integrate these critical aspects into a fully automatic Internet of Thing (IoT)-based local landslide early warning system (Lo-LEWS). The paper uses a validated hydrological numerical model, back-calculated over real monitored conditions, to evaluate the slope stability. The factor of safety (FoS) was computed coupling the commercial package GeoStudio, using transient SEEP/W and Slope. The analyses were conducted for 5 different 1-year datasets encompassing both historical (2019–2020, 2021–2022, 2022–2023) and future projections (2064–2065, 2095–2096) of meteorological variables. Daily variation of hydrological and meteorological variables, along with vegetation indicators were used as inputs to train data-driven models, using polynomial regression (PR) and Random Forest (RF), to forecast daily FoS values. The trained models proved to be effective and were employed to forecast slope stability for the rolling three days. To accurately forecast the FoS, it was essential to incorporate forecasted hydrological, meteorological and vegetation variables into the analysis. The hydrological variables used as inputs for the data-driven models are forecasted using an open-source Python package for the analysis of hydrogeological time series, called Pastas (Collenteur et al., 2019). This model uses historical and forecasted meteorological and vegetation conditions to, specifically, replicate and forecast the time series of volumetric water content (VWC) and pore water pressure (PWP). The forecasted hydrological variables from Pastas, the forecasted meteorological variables as well as Leaf Area Index (LAI) are used as inputs for the trained data-driven models to forecast the FoS values. Finally, a web-based platform (WBP) has been created that automatically runs once a day and perform the following actions: 1) fetches measured and forecasted data using APIs, 2) runs rolling three days forecast based on collected hydrological, meteorological and vegetation variables, and 3) sends the forecasted values back to the Norwegian Geotechnical Institute (NGI) data platform, NGI Live, making them available for real-time visualization in online dashboards. If FoS, VWC or PWP threshold values are exceeded, text messages and emails are sent to the system managers, enabling them to take appropriate actions. The successful implementation of this framework is the result of a collaborative effort across diverse expertise areas, including geotechnics, hydrology, meteorology, instrumentation, and informatics. © 2024 The Authors
publications-4853 Article 2024 Guo S.; Xin K.; Tao T.; Yan H. A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks Water Research 10.1016/j.watres.2024.122318 As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs. Β© 2024 Elsevier Ltd
publications-4854 Article 2024 Elhashimi-Khalifa M.A.; Abbasi B. A framework to characterize, model, and optimize water-energy systems: case study of a novel HDH system Desalination 10.1016/j.desal.2024.117735 The rapid development in water-energy systems calls for advanced modeling and optimization tools to improve the technologies throughputs and minimize their energy consumption. While having a wide footprint in multiple research areas, Artificial Intelligence applications in water-energy systems are still in early stages. There is a need for generalized models to fully characterize parametric relationships, optimize technologies' performance, and have the flexibility to apply to different complex and non-linear systems. This article describes a digital twin framework that can be used for that purpose. This framework provides fundamental understanding of the process as in process-driven models alongside with the accuracy and usability of data-driven methods. The framework's structure focused on modeling and optimization aspects of digital twin models. As a part of developing a novel humidification-dehumidification desalination technology, this framework was used to model energy consumption based on 16 different operational parameters and was accurate to within 1 % of the data. Pairwise parameters sensitivities were identified by the framework for two case studies involving the whole cycle operation and a critical subsystem of the technology. Moreover, the framework was used to optimize the technology performance through data-efficient Bayesian search methods. Different search realms were used in this process. The framework was able to identify minimum and maximum energy consumption regions by sampling only 25 data points out of possible 5157 cases. Β© 2024 Elsevier B.V.
publications-4855 Article 2024 Mahmoud M.M.M.; Darwish R.; Bassiuny A.M. Development of an economic smart aquaponic system based on IoT Journal of Engineering Research (Kuwait) 10.1016/j.jer.2023.08.024 Aquaponics is a sustainable farming practice that combines aquaculture and hydroponics in a recirculating system. The nutrients from the fish waste are used by the plants to grow, and the plants filter the water for the fish. This process is self-sustaining and requires little human intervention. The monitoring and control of aquaponic systems is a complex task. In this research, the Internet of Things (IoT) is utilized to monitor and control the proposed system. In addition, a digital twin (DT) is deployed to digitize the physical system. The results explore that IoT and DT can improve aquaponic systems by collecting data about environmental conditions that is used to regulate plant growth, monitor fish health, optimize crop production, and optimize nutrient recycling. This decreased the need for and cost of human monitoring and improved the system's economics. Β© 2023 The Authors
publications-4856 Review 2024 Bakhtiari V.; Piadeh F.; Chen A.S.; Behzadian K. Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review Expert Systems with Applications 10.1016/j.eswa.2023.121426 Cutting-edge flood visualisation technologies are becoming increasingly important in managing urban flood risks, particularly from the perspective of stakeholders who play a crucial role in controlling and reducing the risks associated with flood events. This review study provides a comprehensive overview of stakeholder analysis in this context, highlighting gaps in current research and paving the way for future investigations. For this purpose, scientific literature and critical analysis are conducted based on identified relevant research works to map the mutual role of stakeholders in this context. This study categorises cutting-edge technologies into four groups - virtual reality, augmented reality, mixed reality, and digital twin - and explores their adoption in engaging various stakeholders across the five key stages of risk management: prevention, mitigation, preparation, response, and recovery. Results show that existing research has primarily concentrated on the support to water utilities and the communication with the general public. However, there is a noticeable gap in research regarding the comprehensive engagement of important stakeholders such as policy-makers, researchers, and insurance providers. Furthermore, the study highlights disparities in the involvement of stakeholders in damage assessment studies, particularly with a lack of representation from policy-makers and researchers. Finally, the study introduces the concept of overlooked key stakeholders and the interconnected impacts they have, which has received relatively little attention in previous research. Β© 2023 The Authors
publications-4857 Article 2024 Ren S.; Bai B.; Wang Y.; Dong F. Adaptive Statistical Error Modeling for Electrical Impedance Tomography with Programmable Resistance Network IEEE Transactions on Instrumentation and Measurement 10.1109/TIM.2024.3427755 Due to the advantage of high temporal resolution, cost-effectiveness, and radiation-free, electrical impedance Tomography (EIT) is considered a promising technique owning a large number of potential industrial and biomedical applications. However, the spatial resolution of EIT is still limited and its imaging results are susceptible to noise. To reduce the impact of measurement noise on the quality of EIT imaging, an adaptive statistical error model (ASEM) is proposed. Unlike noisy models trained by comparing a physical model to its digital twin, ASEM is trained by comparing a digital model to its equal resistance network. The programmable resistance network is configured according to the transfer conductivity matrix derived from the digital model and can be connected to the data acquisition system (DAS) as the physical models. Using the programmable resistance network, a large-scale training dataset can be efficiently collected. To evaluate the performance of the proposed method, a series of experiments were performed with a water tank model. Three different image reconstruction algorithms and one absolute imaging algorithm were tested. The proposed ASEM is trained on 12000 data samples collected by the developed programmable resistance network. The results show that for all tested algorithms, the conductivity reconstruction accuracy is significantly improved using ASEM. Β© 1963-2012 IEEE.
publications-4858 Book chapter 2024 Lu W.; Ma Q.; Sun T.; Zhang X.; Yao Q.; Liu R.; He B.; Gourbesville P. Digital Twin Technologies for Flood Management in Large Catchment: Challenges and Operational Solution Springer Water 10.1007/978-981-97-4072-7_4 The concept of digital twin is originated from aerospace technology which aims to monitoring the physical processes of the machinery. After many years development, not only its concept but also its scope has been strongly extended which brings many opportunities for other industries. In theory, the digital technologies are able to represent the microscopic process of any objects, which can improve the user’s understanding of the progress and give chance to the user to control the occurrence and development of the object's process. However, in the water industry, especially the flood management in large-scaled catchment, how to introduce the digital technologies into the current working process and system and what kind of added values can it bring has became a hot top in the discussion among many water committees. On one hand, in large catchments, many physical process in water cycle are still unclear and difficult to find the way to well represent in the computer, on another hand, the core point in the current working process of the flood defense is more focus on the monitoring of macroscopic state of the catchment. One BIM model or even several specific models cannot fix with the requirement of current managers. Therefore, how to balance the macroscopic demands and the microscopic outputs is the core challenges in the digital application of the flood management in large-scaled catchment. With the application in Haihe River Basin (around 320,600 km2) in China, a new system framework and modelling strategy is presented in this paper, which indicates the added values produced by new digital technologies in the smodelling analysis of β€_x009c_23Β·7β€_x009d_ catastrophic flood disaster. The system was just setted up and used in the daily work of current managers in China. The operational strategy presented in this paper can be migrated to other large-scaled catchments in the world. Β© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
publications-4859 Conference paper 2024 Ranson J.; Lonjou V.; Helfrich S.; Rogers L. Satellite Data for a Coastal Zone Digital Twin Use Case International Geoscience and Remote Sensing Symposium (IGARSS) 10.1109/IGARSS53475.2024.10641719 Currently, more than two billion people live in or near-coastal zones at the ocean-land interface with almost a billion more living in adjacent low-lying coastal areas. These areas and populations are at risk from increasingly severe storms and longer-term sea level rise resulting in coastal erosion, water pollution, urban and agricultural inundation and ecosystems degradation. Earth Science Digital Twins, that is, the combination of data, models, and AI/ML technologies to simulate Earth system processes and enables short- and long-term forecasts, provide understanding and actionable information to reduce risks to humans, infrastructure and ecosystems. Satellite and in situ data are critical components of a coastal zone digital twin (CZDT) to provide timely and spatially relevant input data for models and serve as a check or validation of digital twin performance.The goal of this paper is to introduce a CZDT concept being developed as part the Satellite Climate Observatory with joint participation of NASA, NOAA and CNES and discuss initial use cases, satellite data, modeling, and advanced technology components. Β© 2024 IEEE.
publications-4860 Conference paper 2024 Kabir M.R.; Mila S.A.; Ray S. AQUATWIN: A Digital Twin Framework for Early Detection of Water Contamination Interdisciplinary Conference on Electrics and Computer, INTCEC 2024 10.1109/INTCEC61833.2024.10603171 The stability and well-being of human societies and communities depend on having access to clean, safe water. The sustainability of our civilization rests on our ability to guarantee that the water bodies we utilize are free of harmful contaminants. Digital twins are emerging as a powerful tool for managing complex systems in a variety of Internet of Things applications, including water quality monitoring. In this paper, we discuss the development and implementation of a digital twin for water quality monitoring that mimics sensory data integrated with a structural model of the water quality system to create a multipurpose simulation of water quality conditions. The model can be used to explore changes in water quality based on various scenarios. It employs establishing effective communication and coordination between computational processes, facilitating the utilization of waste inputs for conducting data analysis through machine learning techniques. Β© 2024 IEEE.