Die Innsbruck OPEN Proceedings sind die multidisziplinäre Diamond Open-Access-Publikationsreihe für begutachtete Konferenzbände und Symposien der Universität Innsbruck. Die Plattform steht allen Universitätsangehörigen zur Verfügung: Voraussetzung für eine Publikation ist lediglich, dass mindestens ein Mitglied des Organisations- oder Herausgeberteams der Universität Innsbruck angehört. Damit bietet das Journal auch den idealen Publikationsort für Tagungen, die in internationaler oder institutioneller Kooperation ausgerichtet werden. Alle Beiträge erhalten einen permanenten Identifikator (DOI), werden dauerhaft archiviert und fallen für Autor*innen ohne Publikationsgebühren an.
Assessing hydrological behaviours of Sustainable Drainage Systemswith the Town Energy Balance Model
The Sustainable Drainage System module of the Town Energy Balance model (TEB-SUDS) was developed based on the Equivalent Sustainable Drainage System (E-SUDS) approach and has previously been applied to assess the hydrological responses of individual SUDS types. This study extends the analysis by evaluating the hydrological responses of aggregations of different compartments within an SUDS facility using the TEB-SUDS module. The presented results are benchmarked against the well- established Low Impact Development (LID) module of Storm Water Management Model (SWMM), demonstrating the high capability of the TEB-SUDS module in modelling the hydrological processes of SUDS facility for stormwater management.
Copyright (c) 2026 Yangzi Qiu, José-Manuel Tunqui Neira, Katia Chancibault, Maire-Christine Gromaire, Ghassan Chebbo (Author) https://creativecommons.org/licenses/by/4.0
HR-PIGNN A High Resolution Prediction Method for Urban Drainage Network: Combining Graph Neural Networks and Discrete Form Physics Informed Neural Networks
This article presents a novel hybrid model combining data and mechanisms for high-resolution water level prediction in pipeline networks. The model utilizes graph convolutional neural networks to integrate network topology information for precise predictions and incorporates de Saint-Venant system equations through physics-driven neural networks. Compared to traditional data-driven, mechanism-driven, and hybrid methods, this model achieves 5-minute, 1-centimeter resolution predictions while maintaining computational efficiency and high accuracy. In an experimental drainage system in Suzhou, China, the model's RMSE for predicting water levels and pipeline flow rates is 0.014 and 0.012, respectively, with NSE values of 0.802 and 0.883. The model's computation time for 24 hours of data at 5-minute intervals is 0.981 seconds.
Copyright (c) 2026 Yizhou Qian, Xin Dong (Author) https://creativecommons.org/licenses/by/4.0
Water level prediction in urban drainage systems using explainable deep learning models
Accurate water level prediction in existing urban drainage systems (UDSs) is critical for reliable forecasting of pluvial flooding impacts and reduction of flood-related damages in cities. Conventional physically based urban drainage modelling approaches are constrained by the need for extensive hydro-meteorological, drainage network and surface terrain data and high computational demands. In this research, more computationally efficient Machine Learning based Feedforward Neural Network (FFNN), multi-head Convolutional Neural Network (CNN) and 1D-CNN models were developed and applied to simulate water levels at a bridge crossing downstream of an existing UDS in Kampala City. The study results suggested that the multi-head CNN Deep Learning model resulted in more superior predictive performance (NSE, RMSE and MAE of 0.564, 0.208, and 0.091) when compared to the physically based PCSWMM model (NSE, RMSE, and MAE of 0.505, 0.221 and 0.098). Furthermore, the SHapley Additive exPlanations (SHAP) approach was applied to explain the underlying processes in the developed ML models and to determine the most influential model parameters. The research demonstrates that explainable Deep Learning models can reliably simulate water levels in UDSs, and provide a robust basis for development of real-time pluvial flood early warning systems in data-scarce cities.
Copyright (c) 2026 Seith N. Mugume, Dorothy Pamela Adeke (Author) https://creativecommons.org/licenses/by/4.0
Urban Pluvial Flood Risk Assessment in Porto: A Multi-Criteria Spatial Analysis Considering Physical and Social Factors
Urban rainfall flooding represents a growing challenge for cities, especially in the context of climate change. Identifying flood-prone areas is crucial for implementing effective monitoring, mitigation and prevention measures. This study proposes a methodology for assessing the risk of urban pluvial flooding in the city of Porto, through a multi-criteria spatial analysis, where topographical, infrastructural, demographic and building elements were combined with physical and social factors. The results make it possible to classify the different areas of the city into four levels of risk: low, medium, high and very high, and to prioritise the zones with the greatest risk, contributing to more efficient management of resources in the mitigation of urban pluvial flooding.
Copyright (c) 2026 Pedro Fernandes, Joana Teixeira (Author) https://creativecommons.org/licenses/by/4.0
Integrated modelling solutions for data-driven holistic management of urban water quality – the Urban M2O digital tools
The increasing demands for reducing urban pollutant emissions and protecting water resources necessitate significant amounts of data for designing, implementing, and reviewing pollution control actions. The EU-funded URBAN M2O project aims to address these challenges by developing digital tools, including Digital Urban Water Twins (DUWT) and holistic Data Management Systems (DMS). The DUWT will track pollutants across urban areas and water matrices, supporting in the planning and evaluation of pollution reduction strategies, while the DMS will ensure smooth information transfer across stakeholders and administrative boundaries. These tools will integrate data from novel water quality monitoring technologies, providing urban water managers with updated city-wide data. The project will apply these tools in three European cities, each facing unique challenges. The DUWT will identify pollution hotspots and assess health and environmental risks, while the DMS will facilitate data integration and sharing. The project aims to promote the application of open-source modelling tools for holistic pollution management in urban areas worldwide, fostering collaborations within the urban drainage modelling community.
Copyright (c) 2026 Luca Vezzaro, Henrik Dissing, Heiko Kinzel, Estanislao Pujades-Garnes, Matteo Nicolini, Davide Russo, Francesca Zanello, Wolfgang Rauch (Author) https://creativecommons.org/licenses/by/4.0
Self-supervised learning approach for automatic sewer defect detection
Automated sewer defect detection has advanced through deep learning, particularly supervised methods using CCTV images, but based on large annotated datasets. This study proposes a semi- supervised learning (SSL) approach to reduce the dependency on annotations. The method includes two stages: self-supervised pre-training on unlabelled images using SwAV (Swapping Assignments between multiple Views of the same Image), followed by fine-tuning on labelled images for multi-label image classification. Experiments on the Sewer-ML dataset show that both ImageNet-pre-trained models -supervised and SwAV- outperform models trained from scratch on 1.04 million images, achieving higher F1-scores with just 13k labelled samples. The proposed SSL approach achieves 64.22% precision, 66.06% recall, and a 65.13% F1 score, surpassing the fully supervised baseline. Additionally, scaling up the pre-training dataset further enhances performance. These findings underscore the importance of ImageNet initialization and highlight self-supervised learning as an accurate, scalable, and cost-effective alternative to supervised methods, particularly in data-scarce scenarios.
Copyright (c) 2026 Tugba Yildizli, Tianlong Jia, Jeroen Langeveld, Riccardo Taormina (Author) https://creativecommons.org/licenses/by/4.0
Modelling bioretention systems: does physical-based mean robust?
This study aims to test the robustness of a physical based bioretention cell model, given the inaccuracies inherent to our knowledge of the systems’ hydrodynamic. Field monitoring data on a pilot bioretention cell is compared to HYDRUS 1D modelling results for a range of possible soil properties and bottom condition scenarios. The uncertainties in the hydrodynamic properties of the filtration and transition medias as well as the bottom boundary conditions (BCs) are found to significantly affect modelling results for water content dynamics in the filtration media but have limited impact on the long-term water balance of the system.
Copyright (c) 2026 Tinghao Huang, Jérémie Sage, Didier Técher, Marie-Christine Gromaire (Author) https://creativecommons.org/licenses/by/4.0
Unveiling the hydrologic climate resilience of Blue Green Infrastructure: Do we have our design/modelling numbers right?
As precipitation patterns change, so does the need for revised Blue-Green Infrastructure (BGI) design standards. Holistic hydrologic design and modelling are vital for addressing climate uncertainties and ensuring the long-term integrity of optimized BGI. Within that scope, two key design needs are what size storm event needs to be safely routed through a BGI practice, and what is the precipitation depth of a Water Quality (WQ) event? WQ events principally determine the surface area and storage depths of BGI. The investigation uses North Carolina, USA, as a case study to determine revised design standards for these hydrologic parameters. Of particular interest was the increase, usually substantially (up to 25%), required for the WQ event depth when focusing on the most recent decade of available precipitation data. These results will be used to inform design standards in NC, likely yielding larger, and more protective, BGI in the coming years.
Copyright (c) 2026 Luis Angel Sañudo-Fontaneda, William Frederick Hunt, Kátia Fernandes, Jared Bowden (Author) https://creativecommons.org/licenses/by/4.0
Evaluating Urban Flooding and Economic Losses in 332 Chinese Cities under Future Climate Scenario
With climate change and urbanization, urban flooding has intensified, causing significant socio- economic damages. Existing studies lack evaluation of flooding risks from a national scale or multi-city comparative perspective. This study aims to assess the urban flooding risks of 332 cities in China under the Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP-RCP) SSP2-RCP4.5 and SSP5-RCP8.5 for the years 2020, 2040, 2060, and 2080, and to explore the spatial clustering effects and risk driving factors. The results show that over 80% of cities are projected to experience considerable increases in flooding volume and economic losses compared to 2020, with higher growth rates in North, Northeast and Southern China. Factors such as urban land compactness and gross domestic product have important impacts on flooding risks. This study offers scientific references for formulating targeted urban flooding management policies and planning.
Copyright (c) 2026 Ruyi Li, Xin Dong (Author) https://creativecommons.org/licenses/by/4.0
Statistical Analysis of Wastewater Drivers in All Large Treatment Plants of England
The availability of geospatial data across the water cycle is expanding globally, with England offering a comprehensive range of high-quality, openly accessible datasets. Concurrently, wastewater systems face significant challenges, including network capacity limitations, an increasing number of spills, and the threat of emerging pollutants to water bodies and public health. To address these issues, we developed the first national dataset of wastewater signatures and sewershed characteristics across 799 sewershed and applied multiple linear regression (MLR) models to identify the key drivers and sources of wastewater flows and spills. Focusing on large wastewater treatment plants across England, the study incorporates diverse catchment characteristics and effluent signatures. Results indicate that precipitation is the primary driver of effluent flow variability and spills. This key relationship appears at the national scale, while at company/regional scale the signal becomes less clear, highlighting the value of national-scale analyses. These findings provide a foundation for further development of this dataset to aid in modelling of wastewater dynamics and the development of targeted management strategies.
Copyright (c) 2026 Robin Maes-Prior, Barnaby Dobson, Ana Mijic (Author) https://creativecommons.org/licenses/by/4.0
Fast urban drainage model generator using global open data sources
Accurate flowrate data in a catchment area is essential for city stakeholders that needs to plan future developments for the next decades. Hydraulics models are powerful tools to rely on for such decisions. The lack of models around the world is mainly due to their development cost which is directly correlated to the data volume requirement. The rise of open-source datasets for geography, population, urban infrastructures provide an opportunity to deploy urban drainage on almost any point of the earth. This study aims at developing two automatic model generators, the first one using GIS dataset as input aims at being robust to poorly filled GIS while the second is solely relying on data from publicly accessible websites for auto configuration and calibration. The codes were applied to reproduce the urban drainage system of Dijon Metropole for which the simulated flowrates were compared to observed flowrates at Dijon water resource recovery facility inlet.
Copyright (c) 2026 Thibaud Maruejouls, Delmas Simon, Abdelghani Zaid, Philippe Ginestet, Marcello Serrao, Wolfgang Rauch (Author) https://creativecommons.org/licenses/by/4.0
All models are wrong - more or less! Added value of model calibration for dry and wet weather in Munich
Hydraulic models form the basis for far-reaching investment and operational decisions as well as emission- or immission-based considerations. Model calibration and validation are essential to ensure adequate model quality and to assess model uncertainties, particularly in large and complex networks. This study presents the methodology for the calibration of Munich's extensive hydrodynamic sewer network model, comprising 2,400 km of sewers and serving an area of 18,000 hectares. The calibration process is based on a comprehensive 18-month measurement campaign, which included flow, water level, and rainfall data collected from more than 500 temporary and permanent monitoring stations. The calibration process identified and corrected several model inaccuracies, including errors in flow paths, connection elevations, operational settings and assumptions on infiltration water discharge. The results demonstrate the critical role of measurement data in refining hydraulic models for large, highly meshed networks. In a future workflow, quality measurements will be included in the calibration process. The project impressively shows that (all) models are more or less wrong if not calibrated to measurement data. How wrong can only be answered within the framework of a model calibration by proofed measured data and thus whether the model is suitable for the respective application.
Copyright (c) 2026 Holger Hoppe, Daniela Böckmann, Ehsan Rabiei, Dieter Sitzmann, Stefan Braunschmidt, Tobias Knödlseder (Author) https://creativecommons.org/licenses/by/4.0
A Blind Dive into the Unknown: Water Quality without Metadata
The need to monitor urban water quality is increasing due to the ecotoxicological and health risks urban contaminants pose to water resources. Moreover, the list of contaminants of potential concern is increasing, amplifying the challenges posed by the multiple urban water matrices and sites to be analysed by various analytical methods. Without shared efforts towards a common standardization of datasets and metadata, these monitoring activities will result in datasets fragmented in space, time and context. We propose a first draft for standardizing metadata in urban water quality monitoring, initiating a discussion among the urban drainage community to increase the potential for dataset reuse. Also, this standard will contribute to support the efforts of early career researchers, who are often the main actors in data collection. The proposed data format will allow (re)connecting datasets to generate new knowledge, favour reanalysis and bridge the gap between hydroinformatics and cheminformatics.
Copyright (c) 2026 Vincent Pons, Kefeng Zhang, Luca Vezzaro, Helene Österlund, Tone Merete Muthanna, Viviane Furrer, Lena Mutzner (Author) https://creativecommons.org/licenses/by/4.0
Modelling green roof hydrologic performances for past and future climate in eight cities around the world
The Olympluie project investigates the modelled hydrological performance of green roofs under varying climatic conditions in eight cities in the world, through long-term simulations (13–29 years) for both historical and future climate data. It also examines performance metrics (e.g. evapotranspiration efficiency) for different green roof designs (varying substrate depths, underlying storage capacities, etc.). Simulations revealed that green roof performance varies significantly with geography, while the analysis of future climate scenarios (2071–2099) for the city of Lyon, France, projected a moderate drop in green roof performance. However, the variability across 12 future climate rainfall timeseries was substantial. These results highlight the necessity of site-specific designs to optimize green roof efficiency under both current and future conditions and confirms the potential of green roofs to mitigate urban runoff. However, in a context of climate change, uncertainty surrounding future rainfall patterns is high and must be accounted for in future design.
Copyright (c) 2026 Jean-Luc Bertrand-Krajewski, Solène Barbera, Guillemette Duval, Lôide Angelini-Sobrinha, Vincent Pons, Jérémie Bonneau, Jean-Christophe Grimard (Author) https://creativecommons.org/licenses/by/4.0
Forecast and Real-Time-Control for the Sewer System of Warsaw, Poland
The Sewer Department of the city of Warsaw, Poland in collaboration with ITWH GmbH restructures and expands the sewer network. The objective of the initiative is to reduce combined sewer overflow (CSO) volume and limit the number of registered CSO events. A real-time-control system is established to facilitate centralised control of pumping stations and storage channels, thereby ensuring efficient utilisation of retention space. A digital hydraulic twin of the sewer network is developed. This tool allows real-time access to current conditions and predictions of hydraulic conditions up to two hours into the future. The creation of a fine-resolution precipitation forecast is facilitated by the incorporation of radar, rain gauge, and disdrometer data, which serves as an input for the hydraulic prediction feature. Real-time data of water level and flow measurements is directly used in the digital twin for an online calibration of the sewer network through the adjustment of water levels. The accuracy of the volume prediction is heavily dependent on the initial calibration and is repeatedly affected by changes in the sewer network, as manual readjustment is necessary.
Copyright (c) 2026 Robert Sämann, Patrick Schönfeld, Lothar Fuchs, Thomas Beeneken, Paweł Pęczek, Bartosz Zaborski (Author) https://creativecommons.org/licenses/by/4.0
Modelling tray-based modular blue-roof systems by using EPA- SWMM
This study proposes a model to simulate the hydraulic/hydrological behaviour of modular tray-based blue roof systems during rainfall events. The model was developed using EPA-SWMM software and it was applied to a full-scale pilot of modular blue roof installed in south Italy. The paper firstly provides a description of the pilot installation. Then, the conceptualization of the modular blue roof system in the software is outlined. Available components and tools in the software were arranged and customized to reproduce the system behaviour. Model parameters were set based on the geometrical and hydraulic characteristics of the pilot. The model was validated by simulating the system behaviour during 9 rainfall events recorded on the pilot between 2018 and 2024. Results of the model application to the pilot show a good ability of the model to reproduce the behaviour of the system during precipitation events. The model opens perspective to the analysis of the broader benefits of implementing such systems at the scale of the urban catchment, providing valuable opportunities for sustainable urban water management.
Copyright (c) 2026 Aurora Gullotta, Leonardo Bayas-Jiménez, Alberto Campisano (Author) https://creativecommons.org/licenses/by/4.0
Understanding and improving the functioning of stormwater nature-based solutions under climate extremes – Towards a unified modeling framework for the GreenStorm project
The European GreenStorm project aims to better deploy nature-based solutions for the adaptation to different climate extremes. The project focuses on stormwater management solutions (NBSSW ), across a diversity of types and climates. A key step in the project is to develop and use a unified modeling framework at the facility scale to improve both their design and performance assessment. Based on a set of previously monitored of NBSSW constituted by the GreenStorm project consortium, a generic hydrological operating diagram is first proposed, illustrating the main physical processes to consider. Drawing from this diagram, the Hydrus software has been selected but some limitations have at the same time been pre-identified, pointing to areas for consolidating the modeling approach. A single method for evaluating and using the modeling framework is also proposed, in a view to its use in predictive mode: for a given NBSSW, it combines a large number of simulations run with prior parameters intervals, the characterization of the observations corresponding to the climatic extremes of interest, a sensitivity study to identify influential and non-influential parameters, an identification of acceptable simulation and set of parameters, and an analyze how close/different is the accepted parameter distribution compared to prior one.
Copyright (c) 2026 Emmanuel Berthier, Ahmeda Assann Ouédraogo, Jérémie Sage, Konstantinos Soulis, Ico Broekhuizen, Anna Palla, Marie-Christine Gromaire (Author) https://creativecommons.org/licenses/by/4.0
Development of a generic machine learning model for flowrate generation in catchments using a global database
This study introduces a generic data-driven model for urban hydraulic modelling, designed to estimate flow rates in catchment areas. The model employs a machine learning architecture trained on historical observational data from 30 Water Resource Recovery Facility (WRRF) sites across France, covering diverse hydraulic and environmental conditions. A generic data-driven model integrates principles of machine learning with extensive, varied datasets to create adaptable tools that generalize across different locations and conditions without requiring site-specific recalibration. In this case, the model predicts peak flow curves, enabling the estimation of peak and nominal flow rates over time intervals ranging from 1 hour to 6 months. The database was constructed from observational data measured in the sewage networks and at the inlets of wastewater treatment plants managed by Suez Eau France, supplemented with data on land cover, soil type and rainfall from Open Data sources. The model’s effectiveness was evaluated on a pilot site, validating its versatility. This tool serves as a valuable decision-support resource for engineers and consultants in urban water management. By leveraging machine learning and a robust, diverse dataset, this approach enhances reliability, adaptability, and efficiency in addressing complex urban hydraulic challenges.
Copyright (c) 2026 Karim Claudio, Thibaud Maruejouls, Marcello Serrao, Abdelghani Zaid, Philippe Ginestet, Wolfgang Rauch (Author) https://creativecommons.org/licenses/by/4.0
Methodology for Choosing Optimal Parameters for Real-time MPC in Urban Drainage Systems
The main goal of Urban Drainage Systems (UDS) is to properly handle rainwater, surface runoff, and sewage, minimising overflow and other health hazards. Combined Sewer Overflow (CSO) events are still a major challenge in UDS management, and many software techniques are used to reduce overflow events, for example Model Predictive Control (MPC). By applying an optimisation routine using a model of the UDS, MPC can reduce the impact or even avoid CSO events. However, for this method to be effective, it requires the selection of several parameters, which is typically done through trial-and-error. This paper proposes a methodology to identify the MPC’s parameters by using a Sensitivity Analysis (SA) combined with a Genetic Algorithm (GA). The methodology consists of identifying regions of effectiveness of the parameters for desired outputs using the SA and applying the knowledge into a GA which finds the best parameters. The results showed that SA reduced the computational cost for the GA while providing an excellent combination of parameters. The novel methodology is applied to a simple network with 3 CSO tanks, reducing the system’s total volume overflow over a 1-year period by 14% compared to when MPC is not implemented.
Copyright (c) 2026 Rodrigo da Silva Gesser, Sabuj Biswas, Holger Voos, Alex Cornelissen, Georges Schutz (Author) https://creativecommons.org/licenses/by/4.0
Performance evaluation of a correlation-based spectroscopic method for ammonia equivalents in sewers and WWTP influents
Ammonia monitoring in wastewater is essential for environmental protection and process optimization, yet traditional methods are costly and maintenance-intensive. This study evaluates the potential of in-situ UV/Vis spectrometers for monitoring ammonia equivalents in sewers and WWTP influents, which has been overlooked in literature. We assess three calibration approaches—multiple linear regression, partial least squares, and random forest—across two catchments in Switzerland. Initial results show promising predictive performance (R² of best performing models between 0.6 and 0.8). Wavelength importance analysis shows a clear reliance on UV wavelengths for ammonia predictions.
Copyright (c) 2026 Nicolas Neuenhofer, Pierre Lechevallier, Jörg Rieckermann (Author) https://creativecommons.org/licenses/by/4.0
Implementation of a Real-Time decision support system to reduce pollutant load- discharges in Madrid combined sewer system based on off-line and real time modelling
The LIFE RUBIES project aims to develop and deploy an operational tool in Lille (France) and Madrid (Spain) to reduce the impact of combined sewer overflows (CSO) during rain events, by controlling water pollution in real-time. Real-time control will enable decision making over the flow and storage of urban wastewater to prevent pollution, leveraging the coupling of sensors, models, and controllers. This paper presents the work conducted in the Madrid case study to implement and test the system, improving the operation of part of its sanitation facilities (two stormwater tanks with a total capacity of 600.000 m3, two wastewater treatment plants, and the corresponding sewage network), and showcases some preliminary results. The project started in October 2021 and will conclude in December 2025.
Copyright (c) 2026 Antonio Lastra, Alejandro Carrasco, Ángel Villanueva, David Sunyer, Jordi Meseguer, Bernat Joseph-Duran, Gabriela Cembrano, Luis Romero, Vicenç Puig, Joaquin Suárez, Juan Alfredo Jácome, Abraham Martínez (Author) https://creativecommons.org/licenses/by/4.0
The effect of minimum inter-event dry period definition for storm event identification and SuDS hydrological performance evaluation
Urban drainage practitioners and academics increasingly recognise the value of using continuous rainfall/outflow data to derive a comprehensive understanding of how vegetated Sustainable Drainage Systems (SuDS) respond to long time-series rainfall inputs. For event-based analysis, an agreed definition of ‘an event’ is crucial. Typically events are defined based on a Minimum Inter-Event Time (MIT). The objective of this research was to understand how the specific MIT impacts upon event metrics and on the selection of suitable initial conditions for design storm simulations. This was achieved using modelled outflow from a representative green roof (GR) and bioretention cell (BIO) combined with MITs of 6, 12, 24 and 48 hours. The choice of MIT used to separate events in rainfall/outflow analysis has an impact on the derived performance metrics, particularly for devices offering significant detention. The analysis has also highlighted that – for SuDS that do not include infiltration – the most probable retention storage is small, around 5 mm for typical green roofs and 0 mm for bioretention cells in a temperate climate.
Copyright (c) 2026 Virginia Stovin, Shuxin Ren, Simon De-Ville (Author) https://creativecommons.org/licenses/by/4.0
Modelling of Three Permeable Car Parks with SWMM model
Three permeable car park systems were simulated with SWMM LID model. For the two vegetated permeable car parks (ECOVEGETAL Mousses and ECOVEGETAL Green), the choice was made to use the SWMM bioretention module while the permeable pavement module was used for a mineral permeable car park (ECOVEGETAL Minéral). One year of rainfall and potential evapotranspiration were used to simulated water flows through the structure of these parking lots. Calibration and sensibility analysis were conducted and quality of the simulations were estimated with a Nash–Sutcliffe efficiency coefficient and a water budget criterion. Good results were obtained for the mineral and ECOVEGETAL Mousses car parks. Bioretention module seems not suitable if the structure of the parking is too complex.
Copyright (c) 2026 Lucie Varnede, David Ramier, Pierre Georgel, Marie-Christine Gromaire (Author) https://creativecommons.org/licenses/by/4.0
Long-term monitoring of sediment accumulation in gully pots using thermal analysis
The sediment accumulation in urban drainage systems can reduce their hydraulic capacity. The installation of sediment monitoring equipment is very limited due to economic and technical factors, such as the high price of existing solutions or the low coverage of these systems for data transfer. This study presents the long-term monitoring of sediment accumulation in six gully pots using temperature- based devices across two urban catchments in Zurich (Switzerland). The monitoring campaign aimed to evaluate the performance of MONTSE devices under real-world conditions, considering seasonal temperature variations. Results showed high accuracy in sediment depth estimations, with mean errors of less than 20 mm compared to in-situ measurements, as well as maximum accumulation rates ranging from 9 to 35 mm/day. Therefore, continuous monitoring combined with prior experiences on sediment accumulation is proposed as an optimal strategy to ensure the optimal operation of urban drainage systems.
Copyright (c) 2026 Manuel Regueiro-Picallo, Simon Bloem, Christian Ebi, Jörg Rieckermann (Author) https://creativecommons.org/licenses/by/4.0
Wall of Waters: Identification of realistic worst-case scenarios for extreme rainstorms and simulation of hazard attributes of resulting flash floods
Extreme rainfall storms can generate such extreme flash floods that the so-called wall of water phenomenon occurs, which is an immensely fast rise of a wavefront through a street canyon or a natural water course often causing particularly devastating flood damages. This study presents a categorisation method to identify reasonable worst-case rainstorm events from station data that result in catastrophic flash flood events. The identified 24 events were shown to be considerably more extreme than typically employed design storms with a return interval of 100 years. In the selection of the extremes, special care was taken to consider different hazard attributes, which are associated with intrinsically different observed hyetograph shapes, including rectangular, short and long triangle, and double peak shape categories. Hydrodynamic simulations were carried out using the 2D robust shallow water model hms ++ to quantify the flooding impact of the 24 rainfall events in a small urbanised catchment. The simulated hazard attributes showed clear differences as a function of hyetograph shape with strong variations in peak discharges, peak water depths, hazard velocities and flashiness (shortest time to crest). The use of real heavy rainfall series is therefore essential for the generation and further analysis of wall of water events.
Copyright (c) 2026 Eva Paton, Boney Joseph, Reinhard Hinkelmann, Franziska Tügel (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Flood Risk Management in Jeju Island: Grid-Based AI Prediction and Assessment
Jeju Island, a volcanic island in Korea, exhibits distinct hydrogeomorphological features that pose challenges for disaster management. Traditional hydrological models and qualitative risk assessment methods developed for inland areas are insufficient for addressing Jeju Island’s unique conditions. A localized approach is necessary to integrate hydrogeomorphological and urban drainage factors with hazard-related data. This study develops a grid-based AI flood risk assessment method tailored to Jeju Island’s characteristics, considering urban drainage systems to improve prediction accuracy. The objectives are to: (1) establish a grid-type flood damage and flood risk influencing factor database that reflects Jeju Island's unique hydrogeomorphological and hazard characteristics, (2) create a binary classification deep learning model integrating hydrological, hazard, and urban drainage factors to assess flood risk, and (3) propose warning standards based on flood damage data and risk assessments, accounting for the specific disaster patterns of Jeju Island. This study seeks to overcome the limitations of existing disaster management approaches by leveraging AI and hydrological analysis technologies, addressing Jeju Island's increasing vulnerability to disasters. Through the integration of grid-based data and advanced ML models, this research aims to provide a comprehensive framework for predicting and managing flood risks, ultimately enhancing resilience and safety on the area.
Copyright (c) 2026 Hyeontae Moon, Kyung-Tak Kim, Gilho Kim (Author) https://creativecommons.org/licenses/by/4.0
A graph-based approach for structural resilience assessment in the urban drainage network
This study addresses the significant challenge of conduit blockage in urban drainage networks (UDNs), commonly caused by sediment transfer during runoff. Such blockages can lead to flooding, even during low-intensity rainfall, thereby disrupting urban traffic and infrastructure. To tackle this issue, a novel approach proposed a topology-based approach that converts the urban drainage network into a graph- based model, enabling the identification of critical pipes for strategic interventions. By implementing modified graph theory metrics, flood volume, and total runoff were calculated for single pipe failure scenarios, facilitating the assessment of urban drainage network resilience. The methodology is validated through one real-case study, analysing return periods of one and two years and varying durations of 10, 30, and 60 minutes. Results demonstrate that the graph-based method exhibits a high degree of consistency with the established stormwater management model (SWMM), highlighting its potential as an effective tool for improving urban drainage resilience and mitigating conduit failure risks.
Copyright (c) 2026 Mohammad Rajabi, Mohsen Hajibabaei, Aun Dastgir, Robert Sitzenfrei (Author) https://creativecommons.org/licenses/by/4.0
Flood Resilience Assessment of Urban Drainage Systems: A Graph Theory Perspective
In urban areas experiencing intense rainfall, inadequate drainage network capacity can lead to runoff flooding from manholes, resulting in urban floods and disruptions to various infrastructure, including road networks. Developing a fast and robust model is essential for addressing this functionality failure in urban drainage networks (UDNs) and enhancing resilience. This research introduces a novel method for calculating flood volume in urban drainage networks using the graph theory method. The graph- based approach proposes modified graph metrics to rout the runoff in conduits, enabling the identification of overflow conditions for accurate flood calculations. This methodology is applied in a real case study in an alpine city area, and the resilience values calculated for rainfall durations of 10, 15, 20, and 30 minutes, along with return periods of 1 to 100 years, demonstrate a strong consistency with results obtained from a stormwater management model (SWMM) utilizing dynamic wave methods for flow routing.
Copyright (c) 2026 Mohammad Rajabi, Mohsen Hajibabaei, Robert Sitzenfrei (Author) https://creativecommons.org/licenses/by/4.0
Modelling assessment of Bioretention Zones using GIS and SWMM
This study assesses the potential implementation of Bioretention Zones (BRZ) in the Bogota campus at the Universidad Nacional Colombia. The goal of the paper is to present a simplified methodology that considers a BRZ scenario for mitigating runoff peaks in the campus. The project methodology is three folded: (1) a suitability analysis for BRZ implementation, (2) configuration and calibration of a rainfall- runoff model, and (3) simulation and assessment of one BRZ scenario in the study area. The GIS-based suitability analysis revealed that at least 10% of the delineated urban catchment may be suitable for BRZ implementation. An 11-BRZ scenario that captures runoff from 11.9% of the catchment area resulted in a subcatchment’s peak-runoff reduction between 10% to 42%. However, the overall peak reduction may not achieve the 30% peak reduction required by Colombian regulations. Besides the potential location of BRZ, the potential subcatchment runoff is suggested as a planning variable to improve the assessment of this SUDS typology for peak-runoff reduction.
Copyright (c) 2026 Néstor Mancipe, Diana Beltrán, María Fernanda Riaño, Estiveen Rodríguez, John Sandoval (Author) https://creativecommons.org/licenses/by/4.0
Valuing high density sensing on local watercourses: reflections on extreme rainfall responses at a UK lead local flood authority
Lead local flood authority Central Bedfordshire Council (CBC) faced an overwhelming emergency response following unprecedented rainfall (up to 148mm/day) in September 2024. In a changed climate, we can read CBC as “any lead local flood authority” and the lessons drawn from two weeks of 24-hour emergency response as widely applicable. In this paper we evaluate the response to the back-to-back storm events which occurred between 21- 29 September 2024 and delivered rainfall totalling 396% of the monthly average, overwhelming watercourses and infrastructure and flooding over 200 homes. We present the key findings from interviews with emergency responders from a range of partner organisations conducted within weeks of the emergency occurring. CBC was in the unusual position of having one highly sensored catchment, as a result of its participation in a government-funded flood innovation pilot programme. Flood risk managers’ experience of having access to this data was positive, highlighting the benefits of sensor-driven flood monitoring and prediction systems for emergency responders. Key results highlight challenges in handling public enquiries, limitations in current flood warning systems, and the potential of sensors and a streamlined flood reporting platform to enhance response efficiency. Recommendations include expanding sensor coverage and integrating a centralised public reporting framework.
Copyright (c) 2026 Lorna Devenish, Mike Gibson, Sharon Russell-Verma, Chris Sweetapple, Peter Melville-Shreeve (Author) https://creativecommons.org/licenses/by/4.0