Rainfall runoff models are frequently used for design processes for urban infrastructure. The most sensitive input for these models is precipitation data. Therefore, it is crucial to account for temporal and spatial variability of rainfall events as accurately as possible to avoid misleading simulation results. This paper aims to show the significant errors that can occur by using rainfall measurement resolutions in urban environments that are too coarse. We analyzed the spatial variability of rainfall events from two years with the validated data of 22 rain gauges spread out over an urban catchment of 125 km². By looking at the interstation correlation of the rain gauges for different classes of rainfall intensities, we found that rainfall events with low and intermediate intensities show a good interstation correlation. However, the correlation drops significantly for heavy rainfall events suggesting higher spatial variability for more intense rainstorms. Further, we analyzed the possible deviation from the spatial rainfall interpolation that uses all available rain gauges when reducing the number of rain gauges to interpolate the spatial rainfall for 24 chosen events. With these analyses we found that reducing the available information by half results in deviations of up to 25% for events with return periods shorter than one year and 45% for events with longer return periods. Assuming uniformly distributed rainfall over the entire catchment resulted in deviations of up to 75% and 125%, respectively. These findings are supported by the work of past research projects and underline the necessity of a high spatial measurement density in order to account for spatial variability of intense rainstorms.

Climate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.

This study employs a comprehensive multi-objective efficiency index (EI) to assess urban flood mitigation strategies. The EI enables the simple interpretation of a mitigation strategy’s efficiency with a value range between −1 (low efficiency) and 1 (high efficiency), which represents a practical communication tool for decision makers, engineers, and researchers. This was tested at the study site of Feldbach (Austria) with an integrated 1D–2D urban flood model and a distributed hydrological model. A total of 112 scenarios were analysed for six mitigation strategies, which were built from three future challenge scenarios, two observed heavy storm events, and two hydrological pre-conditions. For the given study site, the analysis identifies mitigation strategies implemented in rural boundary areas as the most effective. A novel aspect of this study is the consideration of the urban water balance change, highlighting its impact on the EI. The analysis highlights the importance of analysing each relevant process separately to determine the EI in order to understand why a mitigation strategy is more or less efficient.

The Python package swmm_api addresses a critical gap in urban drainage modeling by providing a flexible, script-based tool for managing SWMM models. Recognizing the limitations of existing solutions, this study developed a Python-based approach that seamlessly integrates SWMM model creation, editing, analysis, and visualization within Python’s extensive ecosystem. The package offers intuitive, dictionary-like interactions with model components, enabling manipulation of input files and extraction of results as structured data. It supports advanced GIS integration, sensitivity analysis, calibration, and uncertainty estimation through libraries like GeoPandas, SALib, and SPOTPY. Results demonstrate significant efficiency improvements in repetitive tasks, including batch simulations, sensitivity analyses, and automated GIS data processing, exemplified by practical applications such as model updates for municipal sewer systems. The package significantly enhances reproducibility and facilitates transparent sharing of scientific workflows. Overall, swmm_api provides researchers and practitioners with a robust, adaptable solution for streamlined urban drainage modeling.

The article presents an innovative method for simplifying urban drainage models. This approach strategically reduces complexity while preserving accuracy in large-scale, high-resolution models such as those of the city of Graz. It involves the selective removal and aggregation of sewer network elements like subcatchments and conduits, ensuring vital features like storage nodes and flow controls remain intact for precise simulations. Despite significant reductions in model components, the simplified version maintains high accuracy in hydrological and hydraulic aspects, including water balance components like infiltration loss, surface runoff, and external outflow. The method proves equally effective across both land cover and sewer shed-based models, offering computational efficiencies that speed-up processing by 20–45 times for the study site. This is particularly beneficial for rapid decision-making and resource optimization in urban planning. The model also adeptly predicts flood events, especially from larger, infrequent rainfall, although an overly restrictive flow capacity can refine flood predictions at the expense of other flow characteristics. Ultimately, this streamlined approach allows for the quick creation of simplified, yet accurate, models from high-resolution city-scale hydrodynamic data or digital twins, facilitating efficient and timely analyses in urban drainage management.

Due to the ongoing climate and energy crisis, wastewater is becoming increasingly important as a source of renewable energy. In urban areas, heat recovery from the sewer is considered a promising approach, as the locations of supply and demand are close to each other. In this context, it is crucial that negative impacts on temperature-sensitive processes in the downstream wastewater treatment plant are strictly avoided. To support the necessary planning and authorization, this paper presents a model-based approach to assess the thermal energy level of the wastewater at any given location in the sewer, taking into account the influent temperature constraints of the wastewater treatment plant. The entire modelling is based on the open-source software SWMM 5, which was extended by a temperature model. The concept showed its practicability and informative value during a full-scale field application in the sewer systems of the Austrian city of Graz. All modelling is based on freely available software, which makes the approach easy transferable to other cities with comparable infrastructural boundary conditions.