Conference Agenda

Climate change, by altering rainfall patterns and intensities, exacerbates the risk of pluvial flooding in urban areas. Efficient planning of mitigative measures necessitates advanced methods for urban drainage modelling. Accurate representation of sub-catchments with their actual boundaries is a key aspect in this process. Current methods for sub-catchment delineation often produce sub-catchments encompassing entire urban districts. However, it is well-established that only portions of these districts contribute to runoff, as water from other areas fails to reach inlets due to ground obstacles or soil depressions. This study introduces and evaluates a methodology for delineating high-fidelity sub-catchments in urban areas. The process accounts for ground surface depressions and major obstacles, such as street curbs and buildings, creating a sub-catchment for each inlet in the study area. It also generates key properties required by modelling software e.g. EPA SWMM. The methodology is tested on a combined wastewater system model of the City of Tallinn’s. Results indicate that the proposed method is effective, and the modelling outcomes using high-fidelity catchments are reliable.

Accurate flowrate data in a catchment area is essential for city stakeholders that needs to plan future developments for the next decades. Hydraulics models are powerful tools to rely on for such decisions. The lack of models around the world is mainly due to their development cost which is directly correlated to the data volume requirement. The rise of open-source datasets for geography, population, urban infrastructures provide an opportunity to deploy urban drainage on almost any point of the earth. This study aims at developing two automatic model generators, the first one using GIS dataset as input aims at being robust to poorly filled GIS while the second is solely relying on data from publicly accessible websites for auto configuration and calibration. The codes were applied to reproduce the urban drainage system of Dijon Metropole for which the simulated flowrates were compared to observed flowrates at Dijon water resource recovery facility inlet.

The increasing presence of nature-based solutions within urban catchments have revealed the necessity of properly addressing uncertainties associated with green areas while calibrating drainage models, even including new types of measurements. Soil moisture data have the potential to complement or substitute flow rate measurements in this process. However, the selection of sensor locations is important to ensure that data are reliable and useful for model calibration and the efficient usage of resources during the installation and data collection phase. In this paper, a model-based approach is used to identify locations to collect soil moisture data for the calibration of a suburban drainage model. Coupled with physical observations, the model can be successfully employed to select green areas within the urban catchment that are most suitable for placing sensors.

In urban areas experiencing heavy rainfall, inadequate drainage system capacity can result in runoff overflowing from manholes, triggering urban flooding and disruptions to essential infrastructure, such as roadways. Developing a fast and robust model is crucial in addressing this functionality failure in urban drainage networks (UDNs) and enhancing resilience. This research introduces a novel method for calculating flood volume in UDNs and evaluating their resilience using graph theory. The proposed graph-based approach employs modified graph metrics to route runoff through conduits, accurately identifying overflow conditions for precise flood volume calculations. Applied to a real-world case study in an alpine region, the resilience values were calculated for rainfall durations of 10, 15, 20, and 30 minutes across return periods ranging from 1 to 100 years. The results demonstrate a strong consistency between the graph-based approach and the Storm Water Management Model (SWMM) utilizing dynamic wave methods for flow routing, highlighting the effectiveness and reliability of the proposed methodology.

Urban rainfall flooding represents a growing challenge for cities, especially in the context of climate change. Identifying flood-prone areas is crucial for implementing effective mitigation and prevention measures.

This study proposes a methodology for assessing the risk of urban pluvial flooding in the city of Porto, through a multi-criteria spatial analysis, where topographical, infrastructural, demographic and building elements were combined with physical and social factors.

The results make it possible to classify the different areas of the city into four levels of risk: low, medium, high and very high, and to prioritise the zones with the greatest risk, contributing to more efficient management of resources in the mitigation of urban pluvial flooding.

Modelling urban stormwater networks (USNs) provides valuable insights into their performance and assists in improving management strategies. However, a common and significant challenge arises from incomplete information about USNs, where essential network data (e.g., sewer diameters) are unavailable, hindering reliable hydrodynamic analysis. To address this issue, we propose an efficient and fully automated graph-based data retrieval model for USNs with incomplete information. The model automatically infers missing physical attributes, including sewer diameter and slope, by considering the topological features (e.g., connectivity) and hierarchical patterns observed in sewer diameter variations. The framework was tested on a real-world USN with a complete dataset, where data gaps were artificially introduced by randomly removing sewer diameter and slope information ranging from 10% to 90%. Each data gap scenario was repeated 100 times, resulting in 900 incomplete USN configurations. The results demonstrated that the model efficiently retrieved missing data with high accuracy, achieving up to 90% data recovery while accurately reproducing hydrodynamic attributes, such as flow rates. This model provides an efficient tool for water utilities managing incomplete USNs, enabling them to conduct various hydrodynamic analyses.