Conference Agenda

Urban flooding has been an increasingly significant challenge for many cities due to rapid urbanization and climate change, leading to more frequent and intense of extreme events. Recent advances in modelling and prediction of urban flooding have focused on application of machine learning and other AI-oriented methods in modelling and prediction of urban flooding. This paper is a review of the recent development trends and results in modelling and prediction of urban flooding, categorizing them into four sub-topics: 1). Coupled Hydrological-Hydraulic Models, 2). Integration of High-Resolution Data and Remote Sensing in the model simulations, 3). AI-based Flood simulation and Prediction and 4). Climate Adaptation and resilient approaches. These new or improved approaches have been developed and demonstrated in the real case studies internationally. It is concluded that the recent development in urban flooding modelling reflects a shift towards more integrated, data-driven, and climate-resilient approaches. The combination of advanced machine learning and other AI technologies with coupled 1D& 2D hydrological-hydraulic models, high-resolution data can improve the flood simulation accuracy and computation efficiency, and further, the community involvement is enhancing the ability of cities to predict, mitigate, and adapt to urban flooding challenges.

Frequent urban inundations from changing rainfall patterns highlight the need for reliable, timely flood forecasting. This study evaluates inundation prediction under varying storm sewer network simplification and surface grid resolutions using InfoWorks ICM, which supports 1D–2D coupled simulations. Five storm sewer configurations, determined by cumulative watershed area, and five grid resolutions yielded 25 scenarios. Results show that the number of two-dimensional mesh elements influences simulation time more than the number of storm sewer pipes. Coarser grids significantly reduce computation but may oversimplify topographic variability, reducing inundation depths and overestimating inundation extents. Conversely, increasing network detail raises inundation area and depth as well as computation time. For small- and medium-scale basins (e.g., Sinlim4, Daelim), a simplification threshold of 2.25 ha was optimal, while for large basins, 1 ha was best. These thresholds strike a balance between modeling accuracy and simulation speed. Overall, the study underscores the importance of jointly optimizing surface grid resolution and storm sewer network complexity to enhance the accuracy and efficiency of urban inundation forecasting.

This study investigates the challenges of modelling urban catchments, specifically focusing on the interaction between surface runoff and Combined Sewer Overflows (CSOs) in Newcastle upon Tyne. After devastating flooding events, including the 2012 storms, it was shown that Newcastle is highly vulnerability to flooding, which is exacerbated by climate change and urbanisation. The city’s combined sewer system discharges untreated effluent into water bodies through CSOs during heavy rainfall events, causing environmental harm. This study uses the City Catchment Analysis Tool (City-CAT), a hydrodynamic model, to simulate surface runoff and identify contributions to CSO events. The Ouseburn catchment, known for frequent CSO occurrences, is analysed in detail. The results provide an opportunity of bridging the gap between surface runoff management and sewage system operation. The study’s findings aim to improve decision-making and facilitate collaboration between stakeholders. Future work will focus on refining the interaction between surface runoff and response of the drainage system, and on exploring interventions to reduce of flooding and CSO events.

This study addresses the significant challenge of pipe 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 life in urban areas. To tackle this issue, a novel graph-based approach is proposed that converts the UDN into a graph network, 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 UDN resilience. The methodology is validated through a real-case study, analysing return periods of one and two years under 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 UDN resilience.

For decades numerical modelling has been a cornerstone for achieving accurate flood simulations, however, such numerical methods present a severe trade-off between accuracy and computational expense. In recent years, simulation approaches based on neural networks have made large strides in all branches of science enabling fast simulations while also generalizing to permutations of the forcing terms. Several applications for the simulation of hydraulics exist. These include dam break scenarios, river flooding, or flows through water distribution and sewer networks. In this paper, we develop and validate a fully convolutional neural network architecture based on the U-net architecture for the spatio-temporal simulation of pluvial flooding in scenarios where the rainfall has spatial and temporal variation, using various different terrains. This case distinguishes itself by the accumulation of very small surface flows across large areas, resulting in an extremely skewed distribution of water depths. We demonstrate neural network surrogates that achieve CSI scores over 0.8, considering terrains and rain events that were not included in the training data. A conservative estimate for computation speed-up compared to the numerical model is factor 70. Further work will focus on integrating physical constraints and small-scale terrain variations into the architecture.

In a sub-catchment area of the river Wupper in the urban area of Wuppertal, Germany, a hydrodynamic 1D/2D dual drainage model is set up and calibrated by means of the infiltration during a heavy rain event on May 29, 2018. It turns out that even during a heavy rain event in an urban area, a significant proportion of the precipitation infiltrates. In order to estimate infiltration processes during short heavy rain events, established field tests are first examined and evaluated with regard to their applicability to the context of heavy rain events. A physical model is set up in laboratory to verify infiltration processes during heavy rain events by varying individual parameters. The overall goal of the presented study is to develop an easy-to-carry field test that provides information about the infiltration processes depending on site-specific parameters during heavy rain events. Based on the results an infiltration approach will be developed to be used either in 2D urban flood modelling or 1D/2D dual drainage models.

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

The Turin metropolitan area in northwestern Italy, located between the Alps and hilly terrain, faces growing challenges from intense convective weather events, such as heavy rainfall and hailstorms. These events strain the urban drainage network, increasing flood risks and infrastructure damage. This study examines the impact of such phenomena on Turin's drainage system using data from rain gauges, weather radar, and nowcasting simulations. Radar and nowcasting models were calibrated with a dense rain gauge network to simulate drainage system performance under different storm scenarios. Results highlight the weak points of the existing drainage infrastructure in handling extreme convective events, resulting in localized flooding and potential damage. Integrating high-resolution radar and nowcasting data improved predictions and identified vulnerabilities. The study underscores the importance of incorporating nature-based solutions (NBS) into urban planning as a means of enhancing resilience. These findings support the development of a climate adaptation plan to enhance Turin’s drainage network resilience. The study concludes that combining advanced meteorological monitoring, predictive modeling and sustainable infrastructure, is vital to address the growing frequency and intensity of extreme weather events driven by climate change.

As Sustainable Drainage Systems (SuDS)/Low Impact Development (LID) methods are increasingly utilised to manage stormwater in urban areas, it becomes crucial for drainage engineers to accurately represent their hydrological and hydraulic impacts within drainage modelling tools. However, not all tools include explicit SuDS modelling capabilities, or their utilisation may be considered too computationally expensive in some practical contexts. This research examines the potential for representing two common SuDS devices – a green roof and a bioretention cell – using generic hydrological/hydraulic model components. Approaches including initial losses, storage, catchment area disconnection, and reassignment to permeable surfaces were compared against outputs from the SWMM LID module. The findings indicate that all approximation approaches have limitations. Methods involving complete disconnection or transferring catchment areas to pervious surfaces failed to accurately simulate the hydrological dynamics at the device scale. In contrast, approaches based on initial losses and storage showed greater potential, with continuous losses based on evapotranspiration (ET) providing more realistic responses than those using a daily fixed recharge depth.

Urban drainage management is associated with several factors. Stormwater can cause urban flooding and an increase in the amount of sewage treatment plants flowing in. Along with climate change, the frequency of torrential rains is expected to increase in some regions, and accordingly, it is necessary to establish measures to adapt to the climate crisis in sewage treatment plants. In this presentation, an analysis of the vulnerability of the climate crisis to eight sewage treatment plants in Jeju Island (South Korea) is conducted and the contents of the establishment of measures to adapt to the climate crisis are explained.

Deep tunnels play a critical role in urban flood management by temporarily storing excess stormwater to reduce flood risks. Recently, multifunctional deep tunnels that integrate flood control and water reuse have gained attention, but design and operational standards, including those for storage capacity and pump discharge control, remain underdeveloped, highlighting the need for clear criteria. This study proposes an integrated methodology for pump design and optimal operation planning tailored for multifunctional deep tunnel systems. The design framework determines the optimal pump capacities and numbers based on target drainage volume and time constraints, while evaluating various configurations that balance efficiency, maintenance, and energy load. A genetic algorithm is then employed to derive optimal operational strategies that ensure stable performance under variable inflow conditions. The proposed methodology is expected to improve hydraulic performance, enhance operational resilience, and support sustainable urban water management, providing practical guidance for the future design and operation of multifunctional deep tunnel systems.

Forecasting river flow is crucial for providing early warnings and preparing for sudden floods. River flow is mainly affected by rainfall within the basin, leading to the development of various streamflow prediction techniques based on rainfall data. However, predicting total runoff directly from rainfall often results in inaccuracies, as rainfall during dry soil conditions contributes minimally to streamflow. To address this limitation, an improved method is proposed to calculate direct runoff by focusing on effective rainfall. By combining this with base flow contributions, the total streamflow can be accurately estimated, enhancing the reliability of flood forecasting models. In this study, the correlation between effective rainfall and direct runoff, as well as the correlation between total rainfall and total runoff, is analysed and compared. Based on the results of the correlation analysis in this study, Machine Learning (ML) techniques will be applied to achieve the final goal of predicting streamflow in future studies. It is expected that this approach will enhance the efficiency of flood early warning systems, providing more reliable tools for flood preparedness and management.

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, resulting in frequent flood damage. Despite the wide usage of the dual-drainage model—which relies on minimum time-step synchronization for accurate flood analysis—computational speed remains a major challenge. This study employs the Hyper-Connected Solution for Urban Floods (HC-SURF) model to conduct a sensitivity analysis of fixed time-step flow synchronization for different rainfall runoff approaches (lumped and distributed). Laboratory experiments and real-world urban watershed simulations were used to evaluate the accuracy and efficiency of each approach. While some accuracy loss may occur due to the choice of rainfall runoff method, overall performance in terms of mass balance and maximum inundation area remained excellent. These findings highlight HC-SURF’s potential for rapid and precise urban flood modeling, offering a balanced perspective on computational efficiency and model accuracy.

Stormwater trees are nature-based solutions for stormwater management. Often, the infiltration capacity of existing roadside trees is improved to increase collection of runoff from neighbouring streets and sidewalks. Stormwater trees are considered as a pertinent stormwater control measure in highly urbanised areas, but the contribution of the tree (evapotranspiration) to the water balance remains unclear due to measurement challenges. This extended abstract presents preliminary data from a starting PhD project, aimed at continuously monitoring five stormwater trees and two control trees for water level in an infiltration trench, soil water content and matric potential in the tree pit, and sap flow to characterize the water balance. The seven trees have been progressively equipped since December 2024, with the latest equipped in June 2025. By the conference, several months of data from all trees will be available. This data will be used to calibrate a stormwater tree model in HYDRUS, with a preliminary version to be presented at the conference. A later objective is to assess the hydrological performance of stormwater in future climates using downscaled climatic projections.

This study investigates the optimization of groundwater monitoring systems to address urban water management and climate adaptation challenges. Using continuous water level measurements from 40 sensors across 0.31 km² in a Danish village, the shallow groundwater table and flow patterns is reconstructed in high detail. The analysis identifies areas at risk of flooding and groundwater infiltration into sewer systems, providing actionable insights for municipalities and utilities.

By applying statistical methods, the minimum data density required to maintain accuracy while minimizing costs is calculated. Thus, making the solution economically efficient. This approach offers a transferable framework for stakeholders to balance precision and resource efficiency in monitoring efforts and enables better modeling of the shallow groundwater levels in urban areas. The findings support targeted climate adaptation strategies, such as mitigating urban flood risks and improving infrastructure resilience.

Preliminary results demonstrate the potential of high-resolution data to map vulnerable areas and optimize sensor placement. Future work will integrate machine learning models to predict groundwater dynamics under varying climatic conditions, enhancing decision-making for urban water management.

Urban floods pose serious risks to high-density areas and complex infrastructures, causing social, economic, and environmental damages. Traditional physics-based flood prediction methods are slow and computationally intensive, limiting real-time forecasting. Since 2017, machine learning (ML) and deep learning (DL) models have emerged to accelerate predictions (after initial lengthy training), though approaches vary widely. This study analyzes recent ML and DL efforts (e.g., Decision Trees, Support Vector Machines, Convolutional Neural Networks, Long-Short Term Memory, etc.) for predicting flash flood timing, extent, and urban impact. By reviewing literature datasets on weather, terrain, and historical flood records, we identify key inputs and metrics guiding future research (what to do and what not to do). Common input data include rainfall, slope, elevation, and proximity to rivers and roads, with images also used in DL models. Performance metrics such as precision (0.7-0.98), accuracy (0.64-0.98), and AUC (0.69-0.99) indicate strong model performance. However, most models rely on synthetic data, and challenges persist in validating results with real-world measurements.

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.

In the water sector, the issue of polluting source identification has primarily been investigated concerning pressurised water distribution networks for their direct impact on public health. Although the Water Framework Directive 2000/60/EC and equivalent legislation in numerous countries introduce the principle of polluter pays, water managers must detect the most significant pollutant discharges in sewers. Previous studies have demonstrated that a probabilistic approach to positioning water quality sensors in urban drainage networks exhibits a progressive increase in identification probability obtained through the Bayesian approach. Usually, such studies neglect sensing accuracy, which can be a relevant factor that impedes the detectability of a contamination event or a polluting source. Building upon existing literature, the present study aims to enhance the state-of-the-art source tracking approach by incorporating additional information about sensor accuracy. The strategy was developed to consider the use of fixed and Lagrangian sensors. The methodology is applied to a real-world test case represented by the sub-catchment of the sewer system in Palermo, Italy.

In the last years, cities are increasingly facing challenges associated with urban sustainability and urban water issues. Specifically, the risk associated to extreme rain events in urban areas has dramatically increased. Rapid, unplanned urban expansion disrupts in fact the natural water cycle, causing impermeabilization, increased runoff, and strain on drainage systems. Extreme rainfall events further enhance these issues. Traditional drainage methods are often ineffective in areas with complex topography and protected water bodies, necessitating alternative solutions through hydrological/hydraulic modelling. For this reason, an accurate pluvial flooding modelling in highly urbanized areas becomes fundamental. This study focuses on the Ganzirri village in the north-east part of Sicily (Italy), a flood-prone area with limitations for standard drainage systems. Two software tools are used to evaluate their strengths and weaknesses, offering recommendations for rain-on-grid modelling in challenging environments.

Climate change intensifies challenges for urban drainage systems through altered precipitation patterns and increased flooding risks. This study evaluates the resilience of Graz, Austria’s combined sewer system under future climate scenarios. A novel statistical emulator integrates global and regional climate models to generate ensemble projections, validated against 13 rain gauges. The methodology assesses shifts in precipitation intensity, seasonality, and spatial variability, alongside urban drainage performance via hydrodynamic simulations. Results reveal climate models underestimate short-duration rainfall but project stable annual precipitation with increased dry periods and intensified convective storms. Future scenarios (2041–2050; 2090–2099) indicate rising combined sewer overflow (CSO) frequencies and expanded flood extents, particularly in near-future projections. Spatial flood risk redistribution underscores the need for adaptive strategies addressing both climatic and anthropogenic factors, including urbanization and sustainable drainage solutions. While wastewater treatment plant inflows decrease due to shorter rainfall events.

Diffuse pollution from polluted urban surfaces and transported with stormwater runoff into surface waters remains a significant challenge towards achieving zero pollution from cities. Often it appears to be not clear where what kind of pollution actually originated. We therefore adopted a pollution source area approach to develop a tool named CleanCityCover, which identifies critical source areas of urban diffuse pollution. The tool was parameterised for four study areas in Aarhus for three pollution types – dry deposition, pollution due to traffic and metal roofs. The CleanCityCover is a web-based, interactive tool displaying the spatial patterns of different pollution types, summary of annual runoff quality, results of functional connectivity analysis and a pollutant ranking summary. The tool is validated for heavy metal (zinc, copper and lead) concentrations from the runoff of three of the four study areas in Aarhus. A reasonable validation was achieved at the three study areas with correlation coefficient varying from 0.57 to 0.92.

Stormwater management is facing growing challenges, from climate change and environmental concerns to ageing infrastructure and urbanisation. Whilst hydraulic modelling and optimisation-based approaches have been used in water system planning and management for decades, the potential of ‘smart’ catchments and data-centric approaches is becoming increasingly recognised.

This paper presents the ‘Blue Heart’ case study, in which a catchment subject to complex flood management challenges and where there was historically a poor understanding of interactions between water from different sources has been transformed via a dense network of sensors to provide enhanced knowledge and facilitate smarter stormwater management. The process has been based on the iterative transition framework presented by Sweetapple et al. (2023), and key challenges and lessons learned are presented here. An example application of the collected data in which locations where rising fluvial water levels that are not rainfall-driven and may not be flagged in traditional model-based approaches are identified is also presented as an example of new insights obtainable.

The new European Urban Wastewater Treatment Directive (UWWTD), effective from January 1, 2025, aims to reduce emissions into water bodies. A key challenge is limiting combined sewer system discharges to less than 2% of the annual collected wastewater. Total Suspended Solids (TSS) serve as a crucial parameter to assess pollution levels.

The DBU-funded ENTfrachten project in Cologne explores load-based real-time control to reduce emissions. Eight PKM sensors were installed in the sewer system to measure TSS concentrations. However, sensors placed on the sewer bed provided unreliable data due to sediment buildup. In contrast, measurements during heavy rainfall at the CSOs showed good data quality, making them suitable for model validation and emission assessments.

The initial project phase revealed that the current measurement concept is insufficient for full control implementation. A second phase is now underway to address data gaps by optimizing sensor placement and developing an improved control strategy to enhance wastewater management.

In Taiwan, river water levels are often monitored manually or by camera-based systems that rely on physical staff gauges and human interpretation. However, such methods can be limited by environmental factors (e.g., inaccessible sites without a gauge, or gauges that become obscured or soiled). To address these limitations, this study proposes an automated river water level monitoring system that combines virtual gauges and a deep learning CNN model (HRNet w18). First, an orthorectification process is used to generate a virtual gauge within the image frame, eliminating the need for on-site installation of a physical gauge. Next, the HRNet w18 model accurately detects the water surface, mitigating errors from debris or gauge contamination. Both real-world river images and laboratory flume simulations were used to train and validate the model, ensuring robust performance under various flow and wave conditions. By capturing a series of images over time and calculating an averaged water level, this approach accounts for natural wave fluctuations and can be adapted to different river scales. Experimental results show that this method significantly reduces costs and labor while providing real-time, accurate water level data for disaster prevention and water resource management.

Urban drainage networks are ageing and deteriorating, however as they are predominantly buried it is difficult and costly to inspect these assets to understand their condition. Furthermore, data on these networks can be missing or unreliable, creating significant challenges for effectively managing these vital assets. Changes in flows due to urbanisation, population and climate, as well as tightening regulations for environmental and public health protection increase the requirements on water utilities for management of these assets. Inspection technologies are subject of much research, however in the near and midterm future there will remain uncertainties around the condition and status of many assets. It is therefore important that utilities can better target inspection and intervention to optimally manage their urban drainage networks. The poster will present analysis of asset and operational failure data from several urban drainage networks supplied by two UK water utilities. Self-organising maps will be utilised to improve understanding of these datasets and show where there are commonalities and differences between the studied networks.

Regional Municipality of Peel’s wastewater collection system has about 56000 maintenance holes, 58000 sanitary sewers with a total length 3670km, pipe sizes range from 200mm to 3000mm. It also has 34 sewage pumping stations and two wastewater treatment plants. The system currently serves 1.5 million population with strong growth in the future. A region wide wastewater model has been calibrated and validated using over 300 flow monitoring stations with multiple years’ flow monitoring data and Gauge Adjusted Radar Rainfall (GARR), which provides much higher degree of rainfall spatial resolution compared to rain gauges. Creating such a large-scale model has been very challenging and many lessons learned. The model has been a critical decision-making tool, which has been used for region’s Wastewater Master Plan study, Rainfall Derived Infiltration and Inflow (RDII) investigation and reduction, all region’s Environment Assessment (EA) projects and Capital works, Basement Flooding investigation, potentially for Real Time Control study.

This study presents the initial findings from the ORCHESTRA project, which aims to enhance sustainable and resilient urban water management in South Tyrol. The project combines advanced digital technologies, such as digital twins, Artificial Intelligence (AI), and machine learning, with Nature-based Solutions (NbS) and green technologies to develop an intelligent, adaptive water management system. The application of digital twins is crucial as it provides real-time simulation and monitoring of urban water systems. Moreover, AI-driven analysis of IoT sensor data constitutes the basis for dynamic decision-making in the context of predictive water management and optimised network safety.

The integration of digital technologies and green infrastructure will support sustainable urban development by contributing to informed urban planning while promoting resilience. Two case studies in Bolzano have been identified to demonstrate the practical application of digital twins and nature-based solutions (NbS). This research underscores the role of digital technologies in promoting sustainability within urban landscapes while advocating for a comprehensive, interdisciplinary approach to urban planning. Future steps include the expansion of the digital twin framework, alongside detailed cost-benefit analyses, stakeholders engagement, and the identification and resolution of implementation challenges to advance sustainable, intelligent, and resilient cities.

DREINCAM is an intelligent urban drainage management system developed for Madrid Region to address the increasing challenges posed by climate change and urbanization. This project is supported by the European Union's Recovery, Transformation, and Resilience Plan. The system integrates advanced technologies for climatological analysis, network information, and sensorization, enabling real-time decision-making through precise data and predictive modelling.

A core feature of DREINCAM is its use of advanced hydrodynamic and mathematical modelling. Real-time models simulate the basin and network responses to precipitation, predicting runoff volumes, velocities, and flood risks within minutes. Complementary offline scenario-based models provide deeper insights into dry and wet weather behaviours, supporting proactive infrastructure management. The climatology module utilizes high-resolution radar forecasting, while the network and sensorization modules ensure accurate infrastructure monitoring and water quality assessment.

By optimizing urban drainage operations and reducing environmental impacts, DREINCAM prepares Madrid’s Region infrastructure for stricter regulatory requirements and climate uncertainties. Its modular and replicable design demonstrates significant potential for application in other regions, serving as a model for sustainable urban water management.

This paper describes the outcome of a research project that investigated the stormwater quality and quantity implications of transforming detention ponds into multi-purpose facilities in Cape Town. Increasing urbanisation has led to a predominance of impervious surfaces causing increased stormwater runoff, decreased groundwater recharge, and lowered water quality in the receiving waters. Sustainable Drainage Systems (SuDS), such as stormwater detention ponds, can mitigate impacts of urbanisation and climate change, achieve environmental benefits, and provide recreation and social amenities. However, to fully realise their potential, a paradigm shift is required in the city’s approach to stormwater management. This study focused on a former detention pond in Mitchell’s Plain that has been retrofitted for extended detention and infiltration. Stormwater treatment efficiency and infiltration potential were evaluated for water quality and quantity using a mixed methods research philosophy that combined field experiments, laboratory investigations, and computational hydraulic simulations using the PCSWMM software to enable a long-term assessment of the likely performance of the retrofits. The water quality and quantity results demonstrated that retrofitting the School Pond with the additional SuDS components enhanced its multifunctionality, improving water quality and reducing stormwater outflow volumes and thus contributing to more sustainable stormwater management

This work presents the optimization of installation sites for sustainable urban drainage (SUD) interventions to attenuate flooding in urban drainage systems. The algorithm used in the analysis is a multi-objective genetic algorithm, in which three objectives are considered, including the total cost of SUD installations to be minimized, the total flooding volume to be minimized, and a third novel function to consider satisfaction of the community’s call for action, obtained by means of participatory mapping, to be maximized. For each sub-catchment present in an urban drainage system, the decision variables include the rate of impervious areas to be converted to SUDs. The applications on a real case study, consisting of the calibrated EPASWMM model of a part of the Genoa urban drainage system, will show the effectiveness of the methodology and the comparison with an optimization approach neglecting the community’s call for action.

Drainage systems in many cities are unintentionally loaded by groundwater infiltrating through damaged or non-watertight pipes, as well as by groundwater from forced underground drainage. With anticipated climate changes, this problem is expected to worsen, particularly in cities with dynamic shallow groundwater tables. Addressing the problem requires comprehensive mapping of a potentially very heterogeneous system. This involves understanding the local groundwater table’s level and dynamics, soil types and characteristics, as well as the condition of the drainage infrastructure.

Monitoring at an experimental site in Denmark shows great variability in the shallow groundwater table caused by capillary forces in the soil causing significant increases in groundwater levels posterior to rainfall events. Groundwater levels are observed to increase tenfold in response to the rainfall depths for example meaning a 50 cm increase in the water level in response to 50 mm of rain. Areas where the drainage system is located in this dynamic zone of shallow groundwater therefore experience a great variability in infiltration. In this study, the hydrological processes in the soil are modelled and compared to estimated flow rates based on a decomposition of flow from drainage system pumping stations.

Since the 1980s, alongside China's large-scale urban construction, urban stormwater and sewage drainage systems have also undergone rapid development. By the end of 2023, the total length of urban drainage pipelines in China reached 952,500 km, with over 5,000 urban sewage treatment plants in operation, and a total treatment capacity of 227 million cubic meters per day. However, the rapid development of urban drainage systems has gradually exposed serious issues, primarily in the following three areas: (1) The lack of proper planning and design for stormwater major drainage systems in urban development has led to an increasing risk of urban flooding; (2) illegal connection of stormwater and sewage pipes, the abnormal "combined sewer" system, and a lack of effective operation and maintenance management, leading to severe issues such as sedimentation and overflow risks; (3) low pollutant treatment efficiency in sewage systems. This paper uses the TK River Basin as a case study, employing a comprehensive approach that combines continuous monitoring, a physics-based model, and a data-driven model to conduct an integrated assessment of performance and risk in the existing urban stormwater and sewage drainage systems, providing support for the renovation, upgrading, and operation and maintenance management of urban drainage system.

Low impact development (LID) technologies, such as permeable pavements and bioretention systems, have been proven effective in promoting infiltration and preventing excessive urban runoff. However, the sustainability of such technologies under extreme weather remains underexplored. In response, an extreme weather exposure assessment of LIDs in the major cities of South Korea was done through climate modelling and developing an exposure index (EI). Quantile mapping was conducted to bias-correct extreme weather projection data, which were utilized for the development of the EI. Bias-corrected projections revealed significant rainfall-related extremes, with days with precipitation ≥ 20 mm increasing under the SSP5-8.5 scenario, peaking at 37 days in Incheon by late-century. Meanwhile, projections on the annual maximum value of daily maximum precipitation showed mixed trends, with significant increases in Seoul. Temperature extremes, including yearly maximum daily maximum temperature (TXx) and heat wave days, increased, particularly under SSP5-8.5, while cold wave days declined. Seoul recorded the highest EI for permeable pavements at 0.616 under SSP5-8.5. Moreover, high bioretention EIs were found in Daegu at 0.377 under SSP5-8.5, emphasizing the need for resilient LID designs to address growing climate variability in urban areas.

Flood risk is increasing due to climate change and small rural communities are increasingly impacted. Constrained governmental budgets lead to inadequate funding for smaller communities leaving them at ever-increasing risk. Integrating small scale pumped hydro into new fluvial flood attenuation provides an opportunity to generate revenue from the purchase and sale of electricity. This revenue may provide the necessary funding to enable a flood risk management scheme to progress. A review of literature found no existing methodology for combined assessment of flood risk and energy storage assets. Therefore, a bespoke methodology was developed to evaluate proposals at a community in Dawlish, Devon, UK. Key parameters from flood risk management and energy power generation were collated into a methodology for testing using river flow data from the study site. Key performance indicators were generated for a range of configurations. Initial results suggested an installation could provide modest positive economic returns whilst delivering desired flood risk benefits. Further work is recommended to refine the methodology, test sensitivity and establish the range of sites where this technique may be adopted.

The digital RAIN (DRAIN) project is a research-oriented project, initiated to develop a tool capable of doing stormwater modelling in the drainage network (1D) and the surface runoff (2D) within a GIS interface. It leverages both SWMM and IBER, two powerful tools in terms of hydraulic and hydrological modelling, to produce high-resolution outputs. This developed tool is available for free as a plugin in QGIS, a platform for collaborative development. It serves as a valuable asset in terms of water management providing vital support to improve water quality and mitigate urban floods enhancing the holistic approach to water management in the cities.

Urbanization and climate change have heightened urban flooding, necessitating stormwater modeling approaches tailored to urban contexts. This study compares two modeling approaches using PCSWMM for an urban catchment in Ottawa, Canada: (1) a topographic model, which simulates drainage patterns based on a 0.25-meter DEM, and (2) a lot-level model, which represents idealized drainage by routing runoff directly to roads. While the topographic approach reflects conventional subcatchment delineation, the lot-level approach is designed to evaluate impacts of property-level actions on flooding. Both models, provided by the City of Ottawa, were calibrated using flow and precipitation data for 11 storm events and validated for 6. Calibration showed correlations ranging from 0.61 to 0.97 (average 0.84) and KGEs averaging 0.49, while validation achieved correlations of 0.84–0.86 and KGEs between 0.42 and 0.34. A 69-mm storm event was simulated to assess flood distribution differences. The topographic model identified greater backyard flooding due to terrain-driven routing, whereas the lot-level model predicted higher street inundation caused by direct routing to roads. While no surface water level data were available for model calibration, results reflect relative differences due to routing assumptions. This analysis informs targeted flood management strategies by aligning modeling methodologies with specific mitigation objectives.

This study explores the potential of machine learning (ML) for predicting flow in sewer systems, using data generated by the Storm Water Management Model (SWMM). A Long Short-Term Memory (LSTM) network, chosen for its effective handling of time series data, was trained using both hypothetical and real rainfall data. The final model achieved a mean error of 4.6 % in predicting peak flows and demonstrated a speed up to 600 times faster than traditional hydrodynamic models. Tested with 5-fold cross-validation, the model exhibited significant improvements in accuracy and speed compared to its initial version, largely due to enhanced complexity in the model architecture. However, when applied to a more complex sewer system, a decrease in accuracy was observed, underscoring the need for further validation with real-world data. These findings illustrate the promising potential of ML models to boost real-time prediction efficiency but also highlight the need for model adaptation in more complex scenarios.

Accurate representation of urban blue-green infrastructure is required for continued implementation, and improvement. While monitoring data provides ground-truth information of these systems function, models can offer expected performance under a wider range of conditions. For larger blue-green infrastructure systems, like constructed stormwater wetlands (CSW), the hydraulics of flow through these systems is fundamental to the water quantity and quality treatment they can provide. Hydraulic models can supplement monitoring data to provide a holistic understanding of these systems. For two-dimensional hydraulic models, the terrain input dictates model accuracy. Different terrain models have various pros and cons with respect to the time and computational requirements for collecting and processing the data, the expense, the accuracy of representation of flow path and system storage, and the ease of use for ultimate use in a two-dimensional hydraulics. This work explores terrain models for input to a two-dimensional HEC-RAS model of a CSW.

Facing the increasing global challenges posed by climate change and urban densification, sustainable urban planning has become ever more important. To effectively mitigate urban heat islands and urban overheating, the use of Blue-Green Infrastructure offers a viable solution. Using the software tools PCSWMM and SWMM-UrbanEVA, we examine how redesigning a previously sealed urban square in Innsbruck with Blue-Green infrastructure affects the local water balance. Additionally, we simulate the effects of both states (before and after the redesign) on the water balance considering changes in the temperature and precipitation patterns due to climate change for the near future (2031-2060) and far future (2071-2100). Finally, we compare different soil compositions to examine their effectiveness in terms of water storage and evaporation. Our findings show that increasing vegetation, which can store and evaporate water, leads to higher evaporation rates at the site, positively impacting cooling of the area and thereby improving its quality as a public space.

This study demonstrates the potential of optimization techniques for real-time water disaggregation, particularly in situations where direct measurements of individual end-uses are infeasible. We utilized European Union (EU) consumption pattern of different end-use water as baselines from the literature and minimized the discrepancy between observed and reference consumption patterns (EU) to accurately allocate total water consumption across four major end-uses: shower and bathtub, taps, washing machine, and toilet flushing. We found an average water consumption of 128 liters per person per day (l/p/d), with distinct variations on the temporal scales. The weekly variation is 128 l/p/d on weekdays and 126 l/p/d on weekends, while the seasonal variation is 140, 115, 127, and 129 l/p/d on summer, winter, fall, and spring, respectively. Disaggregation results indicate that toilet flushing constitutes the largest share of daily water-use (39 l/p/d, 30.5%), followed by shower and bathtub (32 l/p/d, 25%), taps (30 l/p/d, 23.1%), and washing machines (27 l/p/d, 21.3%). However, the proportional distribution of water consumption over 4 end-uses are subject to be changed based on daily, weekly and seasonal variations. Further work will utilize this gained understanding to estimate the produced wastewater loads with a 1 hour resolution.

Monitoring the hydrological performance of green roofs (i.e. the amount of rainfall received, evapo-transpirated, and released as controlled outflow or overflow) is required to improve their design, to develop and refine modelling tools and to anticipate climate change. However, collecting high resolution (1 min) data remains challenging. This abstract describes experimental research carried out on the GROOF platform at INSA Lyon, to compare the performances of four green roofs, and to understand the physical parameters (climate, design) that drive their performance. Over two years of 1 min data are analysed (June 2022-Sep 2024) and presented in this abstract.

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 (NBS_SW), 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 NBS_SW 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 NBS_SW, 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.

Retention ponds are highly regarded among Sustainable Drainage Systems (SuDS) for their ability to simultaneously manage flood peaks as well as provide a degree of water quality improvement. It is essential that water quality improvement be reliably modelled by stormwater software. Many factors play a role in the removal of pollutants in retention ponds, but it is likely that the most significant is the hydraulic retention time (HRT). Seven retention ponds have been selected for study across three sites in Cape Town, South Africa: five at the Greenbay Eco Estate, one at the Fynbos Lifestyle Estate and one next to Paddocks Shopping Centre in Milnerton. These ponds predominantly serve middle- to upper-income residential areas, each having moderate to low quality inflows which subsequently discharge into downstream natural environments. This study aimed to develop appropriate methods for data collection and used these to monitor the treatment levels in each of these ponds on a continuous basis over the period of one year. These will be used in the development of retention pond treatment equations for the South African context, where they are severely lacking.

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.

Gully pots serve as an active separation system designed to capture particles carried by stormwater runoff. Gully pots are crucial in preventing blockages downstream in the drainage pipe systems and for mitigating flooding. With increasing emphasis on sustainable stormwater management, gully pots are also being utilised to reduce pollutant loads into aquatic environments. Despite their widespread use in urban drainage systems, a noticeable gap exists in understanding the efficiency of gully pots. In this research, a full-scale (1:1) gully pot was constructed to examine the flow field within gully pots. This full-scale pilot was used to evaluate the effects of flow rate and grate direction on the hydrodynamic conditions in the gully pot. When the grate was parallel to the flow direction, the jet penetration length was greater, and the flow field exhibited asymmetry. In contrast, in the perpendicular grate configuration, the jet penetrated centrally through the middle of the sump. The results demonstrated the impact of flow rate and grate direction on water flow behaviour, which can also influence the separation efficiency of the gully pot. The experimental results obtained using this full-scale pilot of a gully pot provide a validation benchmark for further numerical investigations on gully pots.

We explore the potential for block-based interventions to improve stormwater management and comply with the European Urban Wastewater Directive by reducing combined sewer overflow (CSO). Urban blocks—defined as polygons enclosed by cleaned road networks—serve as spatial units for identifying suitable Low Impact Developments (LIDs). Using UrbanWaterBlocks, an open-source Python toolkit, we automate the delineation of urban blocks, calculation of key spatial and demographic attributes, and assessment of decentralization potential for stormwater interventions. The methodology was demonstrated on two districts of Leipzig, Germany—Mitte and Süd—where LID types such as bioretention cells and infiltration shafts were considered. Our approach supports scalable, reproducible, and data-driven pre-feasibility studies for decentralized stormwater management in urban catchments with limited data.

Culverts are critical stormwater management structures that prevent flooding and transportation disruptions. However the increasing intensity and frequency of extreme weather events and changes in land use requires a comprehensive assessment of their hydraulic capacity and resilience. Therefore, uncertainty-aware methods are essential for predicting culvert failure risk and its potential impacts on the upstream communities. This study presents a probabilistic framework to assess the upstream flooding extent resulting from culvert failures across New York State. By considering a range of storm return periods, this framework explores how variations in rainfall event severity influence the cumulative flooded area, with a focus on the spatial distribution of failure impacts across culverts. The maximum tolerable discharge is primarily determined by the culvert size (and other hydraulic parameters of the culvert). A simplified inundation mapping method is then applied to assess the resulting flood extent associated with this discharge. This approach offers valuable and scalable insights into the relationship between rainfall events and upstream flooding due to culver failure, providing decision-makers with a probabilistic tool to evaluate the risk to culvert infrastructure. It also supports multi-objective optimization strategies for prioritizing maintenance and replacement based on failure probability and the potential impacts on communities.

Fostering dialogue and integration among various domain models allows for a focused examination of specific aspects of the urban environment, including pluvial flooding, fluvial flooding, coastal flooding, heat, energy, mobility, and health. This interdisciplinary approach acknowledges that climate change impacts are interconnected and that effective solutions require a holistic understanding. Rather than studying adaptation measures in isolation, the KNOWING project establishes a dialogue between models to optimise and evaluate the performance of the adaptation and mitigation measures. This enables the quantification of both the individual and combined effects of interventions. The integrated approach provides a more comprehensive assessment of the effectiveness of nature-based solutions (NBS) and other adaptation strategies. A case study in Granollers, Spain, exemplifies this methodology by using a coupled 1D/2D hydrological and hydrodynamic model to simulate the impact of NBS on both pluvial and fluvial flooding, while also considering effects on urban heat. Integrating these models allows for a more nuanced and accurate prediction of the overall impact of interventions, leading to more effective and sustainable urban planning and management strategies.

As the effects of climate change continue to unfold, pluvial flooding has become a significant concern. While the development of complex models for simulating pluvial flooding events has advanced, challenges related to input data persist. In this research we want to assess the sensitivity of flood models to the level of detail in the description of the topography at small scales. The case study presented below examines a section of a neighbourhood in Genoa, where cases of pluvial flooding are frequently reported.

Climate change has increased stress on urban drainage, leading to a rise in application of urban nature-based solutions (NBS) such as vegetated biofilters, wetlands, and green roofs. However, researchers have realized that their multiple benefits can only be achieved and maintained through proper monitoring and maintenance. Traditional monitoring methods are expensive and technically demanding, making low-cost alternatives necessary. Plants, a key part of NBS, can serve as condition indicators due to their response to external conditions (temperature, drought, pollution). This work aimed to explore the relationship between plant parameters (measured using novel low-cost multi-spectral plant monitoring device (Plant-O-Meter)) and the water management performance of urban NBS (by measuring the soil moisture). This was tested at two fully monitored NBS research facilities: (1) biofiltration system and (2) green roof. We explored the relationship between the data obtained with Plant-O-Meter, which is an affordable multispectral device, and high-cost hyper-spectral camera. Results show that Plant-O-Meter measurements of different plant parameters are correlated with soil moisture in the NBS system, across the range of conditions. Basic relationships were established between Plant-O-Meter and hyper-spectral camera. This research provides a foundation for identifying plant traits that can be used for NBS modelling and condition assessment, triggering maintenance actions.

This paper presents a fully automated network elevation repair algorithm designed to fill in missing elevation data in Urban Drainage System (UDS) models, as the first step in designing Digital Twins (DTs) of these systems The algorithm is developed as a tool to assist users in efficiently setting up UDS models, particularly for large, real-life networks. The process involves importing network layout from GIS database, identifying missing data, and applying the algorithm to correct network elevations. The algorithm is based on graph theory, developed in C++ and implemented in the “SewNet” module of 3DNet software package. The proposed methodology is tested on a part of the UDS in Belgrade, covering a hilly area of approximately 436 ha. The results demonstrate that the algorithm can quickly and effectively correct network elevation, even for extensive stormwater and wastewater systems, significantly reducing the model’s setup time compared to manual approach, making it a practical and time-saving solution for urban drainage network modelling.

Build-up/wash-off water quality (WQ) models are frequently used to assess stormwater runoff quality. In this work we explore the possibility of using continuous turbidity data to calibrate and validate such models under realistic conditions. By using continuous data, a much larger amount of rainfall events can be used for calibration than it is feasible with grab samples. The approach shows that the build-up rate used in these conceptual models can vary widely between storm events. The time dependence of the build-up rates from different catchment types and the link with external factors, such as wind speed and traffic volume were investigated. This study shows that emission factors for different vehicle types previously published in the literature can be used to estimate the build-up rate for streets.

The NATALIE project, funded by Horizon Europe, aims to promote the broader adoption of Nature-Based Solutions (NBS) by providing evidence of their benefits. The project explores methodologies, models, and resilience assessment tools to address climate challenges, including water-related climatic hazards in urban areas, such as pluvial flooding, droughts, and the associated water quality issues. The NATALIE methodological framework involves analyzing regional climate and socio-economic scenarios, utilizing downscaled climate change projections, and conducting a comprehensive hazard and impact assessment using tailored performance indicators. It also examines the mitigation mechanisms of NBS and their implementation in physically-based modelling. The project underscores the importance of land use maps in hazard modelling and exposure assessment, presenting a method to downscale land use changes within future socio-economic scenarios. NATALIE features eight case studies, including two that focus on urban drainage in the Canary Islands (Spain) and Bucharest (Romania): the first addressing non-point source pollution in the natural reserve of Maspalomas (Gran Canaria), and the latter focusing on freshwater habitat restoration in Văcărești Natural Park (Bucharest).

Decentralised Nature-based Solutions (NBS), such as swales and green roofs, contribute to flood mitigation during heavy rainfall events. The quantitative impact of these systems on flood mitigation can be determined using 1D/2D coupled models. However, developing such models requires comprehensive geospatial data and technical expertise. This article presents a methodology to transfer the detailed flood reduction effects of NBS from a 1D/2D simulation to other catchment areas using effect curves. For this the following information is required: the total catchment area, the total roof area and the potential for implementing NBS. This includes the green area available for infiltration systems (roof area is connected to the systems) and the roof area that can be converted to green roofs. Given these characteristics, the validation shows that the effect curves can be used for an initial estimation of NBS performance in reducing floods in the new catchment area. However, simulations are required for a detailed assessment of NBS performance due to the heterogeneity of catchment areas.

The topic of receiving water contamination and pollution from intermittent discharges from combined sewer overflows (CSOs) has attracted recent public awareness due to the ecological and public health consequences of these discharges; both domestically in the United Kingdom (UK) and in Europe. Current real time control systems to reduce the impact of CSO intermittent discharges on the receiving water quality associate spill volume to pollutant loading. However, this approach does not directly measure the impact of spills on the receiving water quality. For an impact-based real time control system, incoming data from upstream and downstream receiving water quality monitors needs to be dynamically and autonomously scrutinised to direct control outputs. Filtering of noise from receiving water quality data is important both for regulatory and compliance determinations, as well as for real time control decisions. Signal filters are tested to filter stochastic noise from open data on receiving water quality parameters while preserving features that are beneficial for real time control.

Brussels' complex sewer network, with around hundred regularly-spilling combined sewer overflows (CSOs), contributes significantly to its surface water courses pollution, threatening compliance with the European Water Framework and the new Urban Wastewater Treatment Directives. Identifying which specific rainfall events trigger different CSO occurrences is essential for implementing solutions like real-time control. However, many studies on CSOs rely on sparse or low-resolution rainfall data. This study investigates CSO occurrences for rainfall events in 2023 and 2024 using high-resolution rainfall data in the Brussels Capital Region, specifically radar-based precipitation with 1 km spatial and 5-minute temporal resolution. Nine overflow locations were selected, which discharge into two different surface water bodies at varying frequencies. We quantify overflow frequency and duration, and link these to rainfall event metrics. Future work will include more overflow locations, and will aim at identifying specific rainfall patterns that disproportionately contribute to CSO at the different locations.

The design and operation of urban sanitation and drainage networks have always been largely overlooked in the integral water cycle. In this sense, Geographic Information Systems (GIS) and mathematical models for networks play a key role in both the design and exploitation phases. This project demonstrates the viability of carrying out a long-term strategy for asset management of urban sanitation and drainage networks with the use of open-source technologies. In the design phase, a complete analysis of the terrain can be carried out along with sizing and selection of materials, and design of auxiliary structures of network elements. In the exploitation phase, this set of technologies working in solidarity will allow us to efficiently work on the activities and processes necessary to maintain and operate the systems efficiently and effectively. This includes preventive and corrective maintenance, monitoring and control, emergency management, or resource optimization ensuring seamless operations. This project demonstrates the possibility of carrying out all the work professionally stated with open-source technologies, which opens the door to the universalization of urban sanitation management, regardless of the degree of maturity or available capital since access to technology has become possible in this regard.

Contemporary resilience assessments predominantly emphasize physical factors while overlooking transformation factors, particularly carbon emissions. This study investigates the correlation between carbon emissions from hydraulic engineering projects and regional resilience, proposing an integrated quantitative analysis model. The methodology encompasses two primary components: a resilience function and a carbon footprint cost function, which are synthesized into a unified framework to quantify resilience improvement efficiency per unit of carbon footprint. This research presents a novel approach for evaluating and comparing the effectiveness of various engineering strategies, thereby promoting sustainable development in climate-vulnerable regions.

In this paper we present the analysis of the clogging process of various types of permeable pavements using different sediment sources and methodologies at UDC and IKT laboratories. The work analyses the influence of the type of sediment used to study the performance of these types of urban drainage assets and discuss how the laboratory results can be used for long-term modelling of the clogging of permeable pavements.

The increasing demand for real-time environmental monitoring has spurred the creation of cost-effective, energy-efficient solutions. This paper presents the BoSL board, a low-power, low-cost IoT device for environmental data logging and control. Bridging the gap between expensive commercial loggers and basic hobbyist units like Arduino, the BoSL board offers affordability and advanced features. Equipped with an ATmega640 microcontroller, it supports flexible sensor integration, autonomous operation, and LTE data transmission. Its design includes advanced power management, a watchdog timer for error recovery, and versatility for various environmental applications. Case studies in urban discharge detection, wetland monitoring, and remote actuator control highlight its reliability in demanding field conditions. The BoSL board addresses existing limitations, providing scalable, reliable, and accessible solutions for long-term, unattended deployments.

Water bodies in Europe face pressure from multiple sources. This condition is especially critical in areas affected by combined sewer overflows, urban runoff from impervious surfaces and highways, agricultural land uses, forests, and leakages. Modelling and quantification of pollutant sources and transport in sewers are crucial to support municipal planning and design efficient mitigation measures. This study applied the SWMM model to simulate pollutant transportation, focusing on Total Nitrogen (TN) and Ammonia (NH4-N) in urban sewer systems. A lumped SWMM model demonstrated good performance in simulating stormwater runoff and NH4-N transport, showing reliable accuracy for estimating pollutant loads at the catchment outlet. However, the lumped model is limited in identifying specific pollution sources or problematic areas within the drainage network. To address this, a methodology was developed and demonstrated by analysing TN loads. The results revealed differences between observed and expected values based on land use, suggesting additional pollution sources in certain locations. The methodology also identified parts of the network requiring more attention due to large differences between measured and expected loads. Future work will focus on refining this methodology and applying it to multiple pollutants to improve urban water quality management.

Urban runoff is recognized major transport vector of pollutants, and therefore, a significant contributor to the deterioration of receiving waterbodies. Effective implementation of stormwater modelling and management requires sufficient knowledge of the quantity, quality, and temporal distribution of stormwater discharges. This study monitors stormwater pollutants exported from a defined watershed. Data of the contributing watershed was analysed, and an advanced remote-controlled automatic water monitoring system was developed and established. Preliminary results showed a good detection of water in the drainage pipe after rain event. Additionally, water was found to be present in the pipe during the dry season. Lab measurements identified human chemical markers (carbamazepine, caffeine and paracetamol), which may imply on sanitary wastewater contamination in stormwater runoff.

The study attempts to better understand the effect of many parameters on contamination of stormwater released to the sea, and consequently aid in assessing the risks to coastal marine environment and human health. Investigating factors that affect stormwater quality and developing forecasting models for such phenomena will enable the development of a runoff management policy, and to predict the impact that future rain events will have on quality and quantity of drainage water released to the Mediterranean Sea, the receiving water body.

This study analyzes extreme flood events (with return periods of 200–1000 years) that could occur in the Oncheon Stream basin in Busan, South Korea, comparing a traditional Quantitative Risk Assessment (QRA) approach with the HEC-LifeSim model developed by the U.S. Army Corps of Engineers (USACE). Using unsteady flood simulation results from HEC-RAS 1D, we precisely estimate potential loss of life and construct Frequency–Number of fatalities (F–N) curves. While conventional equation-based QRA methods do not adequately account for dynamic evacuation processes, HEC-LifeSim integrates evacuation time, population characteristics, and traffic networks to quantitatively estimate the number of fatalities. Consequently, fatalities are quantified through physical processes, leading to a more intuitive and comprehensive risk assessment. The analysis reveals that, due to the combined effects of urbanization and climate change, certain reaches of Oncheon Stream face severe flood risks exceeding the conventional 100-year design threshold. Future research will aim to incorporate real evacuation behavior data and climate change scenarios into HEC-LifeSim, thereby enhancing the reliability of flood risk assessments and decision-making support.

This study investigates the ability of the bioretention module in the EPA SWMM software to describe tree-based stormwater management solutions using 2 years monitoring data from a pilot system with 3 maple trees. The analysis is conducted by running simulations for a set of configurations within the space of model parameters and then comparing their outputs to available measurements. The ability to simulate outflow dynamics, soil water storage variations and overall water balance is evaluated considering a variety of performance indicators. The approach allows identifying configurations that satisfactorily replicate the different observations independently. For instance, Nash Sutliffe Efficiency respectively reaches 0.6 and 0.9 for outflow and soil water storage variations, and volumetric error for outflow often remains below 10%. Yet, achieving good performance simultaneously for the aspects analysed (outflow and soil water storage dynamics as well as cumulative outflow volume) remains challenging.

The accuracy of rainfall-runoff models is essential for understanding climate variability, managing water resources, and mitigating flood risks. Rainfall data, as a crucial input to hydrological processes, significantly contributes to the uncertainty of these models. High temporal resolution is needed to understand and evaluate rainfall variability and its impact on urban drainage management and design when dealing with urban areas. Focusing on the Sicilian region (Italy), an approach for the assimilation of rainfall data has been studied, integrating satellite and ground observations to enhance urban drainage design through the precise identification of design storms. The methodology's core is based on the bootstrap method for evaluating spatial uncertainty in Intensity-Duration-Frequency (IDF) curves within a comprehensive rain gauge network. Findings derived from a dataset encompassing more than 200 low-resolution rainfall stations (in place for more than 50 years on average) and more than 80 high-resolution ones (in place from 2005) reveal high variability in uncertainty ranging highlighting areas within the urban core that necessitate targeted improvements for data accuracy. The research contributes to developing resilient urban water systems by advancing precision in hydrological assessments. The proposed analysis can create a unitary rainfall analysis approach to be applied at the regional scale.

Combined sewer overflows (CSO) strongly impact water quality of receiving water bodies. The spilled volumes increase due to climate change. It becomes more and more urgent to find solutions in order to mitigate these CSO. Nature based solutions (NBS) are effective infrastructures which help to better control runoff and hence contribute to decrease the spilled water quantity. However, the efficiency of their implementation is impacted by the location selected for their installation. In order to find the relevant location where NBS have to be implemented, an original hydrological and hydraulic disconnection model (HHDM) was developed. The use of this HHDM enabled to reveal the characteristics of the disconnected regions that allow to mitigate CSO. Different levels of deployment of the disconnection strategy were simulated for each sub-catchment of the city of Figeac-France. Initially, the results showed that peripheral catchments with large impervious areas contribute little to the outflow. Additionally, the implementation of NBS on a small, urbanized sub-catchment has a greater capacity to reduce overflows than that of a peripheral sub-catchment with a large impermeable area but low runoff capacity. Subsequently, territorial characteristics and proximity to the targeted combined sewer overflow structures play a key role in contributing to overflows.

Digitalization and data-centric engineering provide significant benefits to the water sector, but they also introduce uncertainties due to the necessary simplifications of complex network models. Accurate modelling of base wastewater flow (BWF), the primary revenue source for water utilities and a major factor in environmental and public health risks, is especially critical. However, large networks with extensive datasets amplify the challenges of balancing modelling fidelity and efficiency. This study evaluates a newly proposed methodology for defining BWF designed to expedite the model development process for operational wastewater networks of any layout. The suitability of the method is assessed by comparing two models with different levels of detail, both representing the operational wastewater network of Tallinn, Estonia. The results demonstrate that the proposed method performs effectively when the level of detail is appropriately matched to the modelling objectives.

Stormwater modelling software operates on fundamental hydraulic and hydrological principles to predict and simulate the flow and movement of stormwater in large catchments. There are significant gaps in studies that use both modelling and field measurements to evaluate the water quantity control performance of stormwater management (SWM) ponds many years after construction. This study aims to provide new insights into the ability of design hydrologic models to predict the actual performance of aging SWM ponds. Models created during the design and planning stages of two SWM ponds in Vaughan, Canada were re-created in Visual OTTHYMO (VO), and modelled water levels were compared against observed pond depths for eight rainfall events at each pond. The results showed that 50% of the 16 modelled runs reasonably reflected the observed conditions, with 88% (14 of 16 events) achieving normalized root mean square error (RMSE) values below 0.5 and 50% attaining R² values greater than 0.7. A one-way sensitivity analysis showed that total impervious area (TIMP), modified curve number (CN*), and final infiltration rate (F_c) had a significant impact on the modelled peak runoff rates. To improve model performance, measuring the onsite discharge and re-creating the stage-storage-discharge relationship for as-built conditions is recommended.

Numerical modeling environments are subject to continuous evolution, leading to frequent software updates that often render custom solver modifications obsolete. This work presents the newly developed solver, incompressibleVoFTransfer, which is fully compatible with OpenFOAM 11. The implementation process, challenges in maintaining backward compatibility, and the advantages of modular solvers over legacy structures are discussed. Initial validation results indicate that the new solvers maintain consistency with previous implementations while offering improved maintainability and future-proofing for upcoming OpenFOAM versions.

To limit the downstream hydraulic load and/or upstream water level, hydraulic structures as side weirs are commonly used in combined sewer systems (CSSs) and urban drainage systems (UDSs). The flow conditions established around and over the side weir are complex, hence particular care must be taken in describing their discharge capacity. Fundamental issue is the assessment of the side weir discharge coefficient. Modern tools for UDS management, such as Digital Twins, require reliable estimation of the side weir discharge capacity, allowing for efficient monitoring and modelling of their operation during significant rain events. Here, a particular case study of the Belgrade UDS long side weir is analysed, where the single point measurements of water level were taken to quantify the total spill volume. It was illustrated that by employing existing models for discharge coefficient estimation, uncertainty can reach unacceptably high levels. Future steps and monitoring campaign is discussed to allow for more reliable estimation of the side weir operation.

Reliable design storms are indispensable for climate-resilient drainage in the Alpine province of South Tyrol, where steep orography produces sub-kilometre rainfall contrasts that existing atlases fail to capture. The present study proposes a comprehensive methodology for interpolating intense precipitation data in South Tyrol. Utilising advanced statistical techniques, including exponential distribution and Ordinary Kriging interpolation, the study offers a reproducible workflow that converts 5-minute gauge records from several stations in South Tyrol into a 1 x 1 km grid of Intensity-Duration-Frequency (IDF) curves. The objective of the study was to enhance hydrological modelling and flood risk assessment across various durations and return periods. After rigorous quality control, the annual maxima are fitted with the exponential distribution and the scale (u) and slope (w) parameters are then regionalised by 3D ordinary kriging within six homogeneous subregions. The approach was validated against observed station data, thereby confirming its robustness. The novel grid has demonstrated efficacy in reducing interpolation error whilst concomitantly enhancing the resolution of the valley-ridge gradient characteristic of convective Alpine storms. The released dataset provides the first kilometre-scale basis for urban drainage design, flood mapping and climate adaptation planning in South Tyrol and offers a transferable framework for other mountain regions.

Rapid urbanisation and climate change exacerbate flood risks, particularly rapidly evolving areas. This study provides a methodology to evaluate the effectiveness of Blue-Green Infrastructure (BGI) strategies in mitigating pluvial flood risks, using Antananarivo, Madagascar, as a case study. Results showed that buildings and people affected by flooding can be minimised by planning BGIs before land use change, and through the use of spatially distributed strategies, or strategies favouring blue infrastructure. The Net Present Value (NPV) analysis indicates that while most strategies are economically viable, hydrology-focused strategies yield the highest returns, and that implementation rates between 7-10% of the total case study area show the best balance between costs and benefits. These findings provide valuable insights and tools for urban planners and policymakers in designing sustainable flood mitigation strategies in rapidly developing, data scarce peri urban areas.

This project focuses on the simplification of 1D modeling of Barcelona’s drainage network for assessing Combined Sewer Overflows (CSO) and aids in proposing a cost-effective and sustainable innovative solution. The project is done by integrating, Giswater, a water digitization open-source tool, that identifies the CSO points under different rainfall events. With this tool, hydrological data and urban drainage networks are integrated together achieving a calibrated model for both stormwater and dry water flows. Scalable measures including the Sustainable Urban Drainage Systems (SUDS) are proposed to effectively manage the sewer network in Barcelona in case of flooding and enhance its environmental sustainability.

The study proposes a novel six-step methodology for the development of a city scale hydrological-hydraulic models with adequate spatial resolution. In an era of almost unlimited data, the key challenge is the selection of appropriate data sources and the rational use of data for time efficiency. The novel methodology uses the socio-economic sectors of the city of Girona and a simplified version of the Copernicus urban atlas data to create subcatchments of homogeneous land use type and similar sizes. Network topological consistency was conducted using a novel Python algorithm, based on graph theory. Flow routing from the catchments to the network considers terrain slope and maximum flow path distance. The result exhibits a spatial resolution capable of considering land use properties, while also avoiding a very high spatial resolution, to prevent complexities in the calibration process. The methodology was applied to Girona, Spain, resulting in 351 subcatchments. Preliminary results indicate that this semi-automated approach effectively balances spatial resolution and computational efficiency, providing a robust framework for urban hydrological-hydraulic modelling. Ongoing calibration efforts aim to further refine the model.

The accelerating pace of urbanization has led to significant accumulation of pollutions in receiving water bodies through stormwater runoff, threatening both aquatic ecosystems and human health. Capturing pollution dynamics requires effective monitoring systems to provide high-frequency measurements while maintain cost-effective for widespread deployment. This study aims at testing a low-cost, non-contact proximal sensing system for urban water quality monitoring. A low-cost (<AUD120) multispectral sensor has been developed through casing and electrical design and its performance has been assessed in the laboratory using real-world samples from four sites in Australia. Both SLR and PLS model can achieve good predictive performance for turbidity (R2>0.93). This result approves the feasibility for turbidity measurement of various water sources. The outcome of this research aims to provide an innovative and reliable solution for urban water quality monitoring that can be deployed at field continuously with minimal maintenance required, which could help us better understand the temporal and spatial dynamics of water pollution.

Green roofs are an important and versatile type of blue-green infrastructure with multiple application potentials and several benefits. Irrigation of green roofs can enhance these benefits and greywater provides a sustainable source of irrigation water. This paper describes a water balance model for the evaluation of green roof irrigation strategies and storage tank size optimization. The model accounts for different water sources, irrigation strategies and green roof types. The model operates with openaccess data, provides site-specific results for Germany and can be easily adapted for other regions. The model was calibrated with experimental data for green roofs with different irrigation intensities from Dresden green roof research facility. It obtained satisfactory to good performance, with Nash-Sutcliffe efficiencies of 0.59 - 0.71 for daily runoff, 0.66 - 0.82 for substrate moisture and 0.69 - 0.79 for actual evapotranspiration. Long-term simulation for the same site revealed that irrigation significantly reduces drought periods for green roofs and increases evaporation by up to 65 %. Irrigation with greywater provided higher supply reliability than rainwater for the same storage tank size. A combined supply with both water sources enabled complete supply reliability during the simulation period with a minimum relative storage level of 35%.

Plastic wastes sourced from urban areas can be retained and transported by urban drainage systems. However, comprehensive research on the transport of plastics in these systems is still required. This study examines the efficiency of gully pots in trapping macroplastics during rainfall events of different intensity, focusing on the influence of flow conditions, plastic types and the presence of sediments. Using a 1:1 scale gully pot model, with water flow ranging from 0.2 to 2.0 l/s we measured the transport and retention of bottles, caps, cigarette filters, and snack wrappings. The results revealed that high discharges significantly reduce plastic removal efficiency –i.e., less plastics remain trapped in the gully pot—, while sediments accelerate plastic transport, especially at high discharges. The study highlights the role of urban drainage systems to mitigate plastic pollution in aquatic environments.

Past studies have demonstrated that optimizing the control of the integrated urban drainage system can enhance system performance instead of incurring the high costs of updating expensive facilities. This study focuses on the drainage system management of the Wutong Mountain River Basin in Longgang District, Shenzhen. For the drainage network, wastewater treatment plant, and river water systems in the study area, SWMM, ASM, and Delft3D models were constructed. By coupling these models, control objectives were set with two sub-objectives: environmental and economic. The environmental sub-objective includes considerations for node pressure, wastewater plant water quality, and river water quality, while the economic sub-objective includes factors such as reagent dosage, aeration blower operation time, pump operation time, and operational frequency. The study aims to optimize the operation of the integrated urban drainage system. To address the low operational efficiency of mechanistic models, a deep reinforcement learning model was developed to improve computational speed, enabling real-time control. The expected results, compared to existing strategies, suggest a 5% reduction in economic costs while achieving a 10% reduction in river pollution. Moreover, the computational time, compared to mechanistic models, is reduced from 30 minutes to under 10 seconds.

To characterise the impact of climate change on urban water management, hydrological models need data from climate projections at finer spatial and temporal resolutions than climate models can produce. Downscaling methods are therefore necessary. Thus, an existing method, using local weather types (LWT), has been enriched to also adequately downscale rainfall events in intensity and dynamics so that all the meteorological variables required by the hydro-climatic Town Energy Balance (TEB) model are produced. By comparing with observed hourly data, four different approaches have been evaluated, by LWT or random approaches, and show very good results in terms of both daily precipitation duration and maximum intensity.

This study evaluates the performance of retrofitted smart and passive Sustainable Drainage Systems (SuDS) at a household scale. Over a three-month monitoring period, data were collected from seven tanks across six systems (three smart and three passive) to assess their rainwater attenuation capacity. Smart tanks, leveraging autonomous operation and weather forecasts, effectively mitigated overflows, while passive tanks, relying on fixed drainage mechanisms, experienced frequent overflows. The findings highlight the advantages of smart SuDS in improving stormwater management and the potential for passive systems under moderate conditions. These results contribute to advancing urban drainage solutions.

Urban flooding, driven by climate change and the expansion of effective impervious areas (EIA), poses growing challenges for sustainable urban development. Among nature-based solutions, green roofs (GRs) have gained attention for their potential to reduce surface runoff, promote infiltration, and restore aspects of the urban hydrological cycle. However, in Brazil, the adoption of GRs remains limited due to regulatory gaps and a lack of basin-scale impact studies. This research investigates the hydrological effects of GR retrofitting in a representative urban watershed in São José dos Campos, Brazil, through scenario-based modeling using the CAbc hydrological software. Scenarios were developed based on local policy frameworks, simulating varying degrees of GR implementation. The results suggest that GRs can enhance stormwater management by reducing runoff and delaying peak flows, especially under frequent, moderate rainfall events. Nonetheless, their performance tends to diminish during high-intensity storms, indicating the need for integration with other drainage strategies. The study highlights the value of GRs as part of a broader sustainable urban drainage system and supports the development of policies that incentivize their use to improve resilience in rapidly urbanizing regions.

Urban trees provide a wide range of ecosystem services, including urban cooling, flood control, biodiversity, and air quality improvement. However, their impact depends on their physical attributes, seasonality, placement, and distribution in urban settings. This study focuses on the role of trees in stormwater management. Using the modified Gash and Rutter formulation, we developed a spatially distributed model to simulate individual tree’s interception capacity and throughfall. The model was applied to individual trees with time-varying leaf area index (LAI) under different rainfall intensity, and canopy saturation conditions. Model validation against measurements showed strong agreement for total throughfall (R² ≃ 0.83) and interception (R² ≃ 0.61). At the urban scale, applying the model to a pilot area in Copenhagen, Denmark, resulted in a total runoff reduction of 15.2%, with local contributions varying between 2% and 43% based on tree characteristics. However, tree effectiveness was primarily driven by rainfall intensity and tree seasonality. Further research is needed to refine species-specific interception capacity and improve LAI-based parameterization in tree interception. Integrating this approach with hydrodynamic simulations could enhance understanding of trees’ local flood control contributions, supporting urban planning and tree-planting strategies to optimize city resilience.

Blue Green Infrastructure (BGI) is widely implemented as an adaptive stormwater management strategy to reduce flood risks in urban areas. However, increasing greenery in cities raises water demand during prolonged droughts due to higher evaporation rates. This study examines the impact of commonly used BGI types on both private and public domains at the city scale under future climate projections in the Netherlands. Using Rotterdam and Eindhoven as case studies, we explore their urban transitions guidelines and policies in the context of climate change. Based on this, we introduce several scenarios to test the impact of intensifying greenery on the urban water balance across diverse urban settings. The aim is to identify the most effective BGI implementation strategies and determine optimal locations for water harvesting at the city scale to ensure sufficient water availability for the viability of urban greenery.

The implementation of pollution-based real-time control (P-RTC) is still hindered by several aspects, one being the lack of a methodology to assess whether and where there is potential for P-RTC. For this purpose, a simplified approach is proposed which aims to calculate a theoretical potential for local P‑RTC. The methodology is shown based on a literature review and first case-study related results are presented.

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.

The paper presents a methodology for the simulation of surface flows on urban streets during stormwater events. In a novel way with respect to past research, the sewer capacity is dynamically evaluated based on simulations to account for its variability on time during the flood event. Specifically, the methodology is based on the use of a 1D model of the sewer network and of a 1D model of the urban surface network. First, the sewer network model is used to calculate actual sewer capacity during the flood. Such results are then used to obtain a “reduced” hyetograph, which is adopted as input for the simulation of the surface network model. The methodology was applied to an urban catchment in southern Italy affected by problems of pluvial flooding. Field data - comprising direct measurements and indirect estimations (from video-clips) of flows in streets - were used for calibration and validation of the methodology. The outcomes demonstrated good agreement between simulated and observed data, indicating the reliability of the methodology for urban surface flow modelling.

Convection-permitting climate models (CPMs) can resolve convection-scale processes and improve estimates of short-duration, extreme precipitation events that are critical for urban drainage models. Their outputs, however, still require bias correction to match station-scale resolution. Quantile-mapping (QM), commonly used for bias-correction due to its simplicity, has limitations that need to be evaluated for CPMs. This study tests five QM variations to bias-correct and downscale simulations from a CPM for over 70 weather stations in Switzerland. For each station, ten years of data from the CPM, COSMO-CLM, is downscaled from a 2.2 km grid to the station scale at a 30-minute interval. Techniques to increase robustness of the QM approach were tested, including adding a temporal moving window, spatially pooling surrounding grid cells, and extending the observational record. Cross-validation shows that all QM methods reduce wet biases in the raw CPM output up to the 98^th quantile. Only the moving window technique and its combination with spatial pooling effectively reduce higher quantile biases. However, QM methods may distort the climate change signal, especially in rainfall frequency indices. Despite added computational effort, the moving window technique is recommended for robust CPM downscaling in urban drainage.

Source control solutions (SCSs) are some of the most effective methods for mitigating Combined Sewer Overflows (CSOs), while offering numerous environmental and urban benefits including improved water quality. Quebec City, with its mix of separate and combined sewer networks, faces frequent CSO events. The city aims to reduce CSO frequency and volume through innovative approaches. This study evaluated the integration of gray and green SCSs to mitigate CSO volume and frequency. Using a SWMM model, the performance of integrating bioretention cells, permeable pavement, and small underground retention chambers was assessed in a 150 ha catchment. These measures reduced CSO volumes, compared to the reference scenario, by 56% from May to October 2017, the year where the highest total rainfall between 2010 and 2019 was recorded. Partial network separation in targeted areas further improved CSO reductions to 63%. This integrated approach highlights the effectiveness of combining green and gray infrastructure with network modifications to improve system performance. The findings indicate that bioretention cells are the most cost-effective solution, providing the highest cost efficiency per unit of CSO volume reduced. These strategies support Quebec City’s sustainability goals and contribute to a resilient urban water management system.