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 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.

A faecal transport model was applied to a 11.3 km2 wastewater servicing area in Toronto, Ontario, Canada to explore the role that different wastewater sampling campaigns have on estimating the prevalence of SARS-CoV-2 in a population of 60,000. A stochastic wastewater and water quality model was used to evaluate the effectiveness of 11 sampling campaigns during periods of high and low COVID-19 infection among the population, tested using virtual sampling during dry-weather flow. The virtual sampling campaigns were based on the most common automatic sampler programming capabilities and widely used wastewater-based epidemiology (WBE) sampling campaigns reported in the literature. Sampling campaigns differ in weighting method (time, volume, or flow-weighted sampling), sample count, collection period, or sample time. Results suggest that grab samples should be avoided and/or that sampling campaigns with the greatest sample counts and durations are the most robust at capturing COVID-19 infection among the population. Most surprisingly, changes to the weighting method were negligible indicating that a greater number of samples, and larger sample volumes are preferred. This work suggests that investment in flow monitoring equipment for flow- or volume-weighted sampling will not improve WBE results, and that standard time based sampling is sufficient.

This study presents a general workflow for creating a priority map for blue–green infrastructure (BGI) placement at the city scale, incorporating model-based benefit analysis. This workflow generates a BGI priority map, combining flood hazard and urban heat island maps, that guarantees multi-functional requirements are met. This approach was applied at a small study site in Feldbach, Austria. In the second part, we used the priority map generated to implement six BGI strategies in an integrated 1D-2D urban flood model and a semi-distributed hydrological model at high-priority and low-priority locations. The use of the efficiency index (EI_mod) enabled a multi-objective assessment. The results indicate that all the strategies led to a higher EI_mod when implemented in high-priority locations compared to low-priority locations. Our findings demonstrate that priority maps support decision making regarding where strategies should be implemented, providing remarkable benefits for water management objectives. Additionally, the findings highlight the importance of incorporating potential flooding areas to enhance prioritisation regarding flood hazard indicators. In future assessments, economic parameters, such as cost considerations, should also be integrated in order to optimise BGI placement efficiency.

Urban greening is a cornerstone of climate adaptation strategies, yet its long-term sustainability under changing vegetation dynamics remains poorly understood. This study analyzes 40 years of Landsat data (1984–2024) using normalized difference vegetation index (NDVI) time series to quantify changes in vegetation extent, phenology and interannual stability across four European cities (Paris, Graz, Barcelona and Birmingham). The study investigates long-term phenological trends in heterogeneous urban green spaces. Results showed similar developments in greening measures at all four study sites: In Birmingham, Paris and Barcelona, vegetation extent increased by 12 to 23 absolute percentage points, while Graz showed a moderate increase of 4 percentage points. A general tendency towards a longer growing season was observed across the study sites, driven by directional changes towards an earlier Start of Season (SOS) and a delayed End of Season (EOS), although the magnitude and statistical significance varied between cities. Barcelona exhibited pronounced summer NDVI declines, whereas peak vegetation activity occurred earlier in Paris and slightly later in Birmingham. Interannual variation showed greater differences in smaller, fragmented open green spaces (Coefficient of Variation ≈ 0.3) compared to larger tree-dominated parks (Coefficient of Variation ≈ 0.1). These findings provide a harmonized long-term assessment of urban vegetation dynamics. European cities are becoming greener while simultaneously experiencing shifts in vegetation activity and, depending on location, strong declines during summer months. These long-term changes highlight the importance of considering vegetation dynamics when planning resilient urban green spaces under a changing climate.

The mitigation of urban microclimatic deficiencies as a result of the urbanized environment receives increasing attention by cities, municipalities and the planning community. Priority areas for the implementation of multi-beneficial climate change adaptation measures and the development of mitigation plans need to be identified. Therefore modelling methods producing good results while only requiring moderate data input and computational effort are needed for the widespread application in planning processes. In this study, we test the rapid fine-scale methodology for simulating urban bioclimatic conditions in a 2D environment that was previously introduced by Back et al. (Back et al., 2021). The original methodology uses high resolution land cover classification (Hiscock et al., 2021) from multi-spectral aerial imagery, digital elevation data, and a vector layer of buildings to calculate land surface temperature (LST), mean radiant temperature (MRT), and the Universal Thermal Climate Index (UTCI). We apply this methodology to the rural municipality of Feldbach, Austria, using commercially and openly available satellite imagery. The simulated data is validated against the publicly available monitoring data from the WegenerNet climate station networks (Fuchsberger et al., 2022), which provides high spatial and temporal resolution measurements. The results are evaluated regarding the agreement of relative spatial differences of the simulated variables with the observed data. To be suitable for the identification of priority areas for the implementation of climate change adaptation measures, the methodology is expected to accurately reflect the spatial variability of the simulated variables.Project supported by ESA Network of Resources Initiative.Back, Y., Bach, P. M., Jasper-Tönnies, A., Rauch, W., & Kleidorfer, M. (2021). A rapid fine-scale approach to modelling urban bioclimatic conditions. Science of The Total Environment, 756, 143732. https://doi.org/10.1016/j.scitotenv.2020.143732Fuchsberger, J., Kirchengast, G., Bichler, C., Leuprecht, A., & Kabas, T. (2022). WegenerNet climate station network Level 2 data [Text/csv,application/x-netcdf]. Wegener Center for Climate and Global Change, University of Graz. https://doi.org/10.25364/WEGC/WPS7.1:2022.1Hiscock, O. H., Back, Y., Kleidorfer, M., & Urich, C. (2021). A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management. Water Resources Management, 35(4), 1339–1352. https://doi.org/10.1007/s11269-021-02790-x