Enabling real-time high-resolution flood forecasting for the entire state of Berlin through multi-GPU accelerated physics-based modeling

Great advancement has been achieved in the last decade in 2D shallow water solvers for flood modelling. However, their application to physically-based flood forecasting continues to be experimental and not widespread. One of the central challenges towards operational flood forecasting with 2D solvers is their computational cost, which needs to be reconciled with the required lead times for forecasts to be of use. Nonetheless, these solvers have great potential to improve flood forecasting predictions, especially when it comes to flash floods, for which the established 1D and conceptual models may be significantly less applicable.The shallow water solver SERGHEI-SWE leverages on robust and efficient numerical techniques and is implemented for High Performance Computing (HPC), allowing its use in supercomputers and opening new opportunities in 2D flood forecasting. In this contribution, we present proof-of-concept simulations of several flood events in different catchment and river systems. We show that, with SERGHEI-SWE, it is possible to run very high resolution flood simulations for large hydrological systems with runtimes significantly lower than the event duration. This property is essential to enable operational forecasting with useful lead times.We run simulations on three river reaches, in the Italian river Po (125 km reach between Boretto and Pontelagoscuro) and in one of its tributaries, the river Secchia (20 km reach), and a meandering reach of the Ebro river through the city of Zaragoza. We also perform flash flood simulations on a 5 km2 district of Nice (France), and in a 50 km2 agricultural catchment in Jaén (Spain). The focus of the exercise is on the computational performance aspect and not on the model performance. The results show that high resolution simulations can be done with runtimes in the order of 100 times faster than real time, potentially allowing a very good forecast lead time. We also explore different combinations of computational resources, model resolution and ensemble size to explore the flexibility of the modelling approach under different computational systems, which may be available for flood forecasting.

<scp>RMetS M</scp>eteorological <scp>M</scp>asterclass <scp>S</scp>eries: Anticipating floods, droughts and heatwaves

Rapid urbanization and climate change jointly shape future flood exposure in coastal cities, particularly in the Global South, where urban growth rates are high and adaptive capacity is often constrained. While scenario-based frameworks such as the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) provide consistent narratives of socioeconomic and climatic change, their integration into spatially explicit urban growth modelling and flood exposure analysis remains limited.This study applies the SLEUTH urban growth model to project high-resolution (30 m) urban expansion trajectories up to 2050 for nine coastal urban agglomerations across Africa, Asia, and Latin America under SSP1, SSP2, and SSP5. SSP-based urban population projections were translated into scenario-specific SLEUTH parameters, enabling consistent representation of divergent socioeconomic pathways. Model calibration was supported by harmonized historical settlement data from the World Settlement Footprint and enhanced through a kernel density–based zonation and spatial tiling approach to capture heterogeneous urban–rural growth dynamics across large study areas. Projected urban extents were overlaid with RCP-based coastal, fluvial, and pluvial flood hazard maps to assess future flood exposure at the 50th and 83rd percentiles for a 100-year return period.Results reveal pronounced inter-city and inter-scenario variability in both urban expansion and flood exposure. Four cities (Dar es Salaam, Ho Chi Minh City, Khulna, and Surat) are projected to expand by more than 50% by 2050 under SSP1 and SSP5. Flood exposure is driven by the combined effects of climate forcing and urban development patterns: high radiative forcing scenarios amplify hazard extent, while socioeconomic pathways strongly influence where and how cities expand into flood-prone areas [1]. In several cities, newly developed urban areas exhibit disproportionately higher exposure than existing settlements, particularly for fluvial and pluvial flooding. However, exposure patterns are highly city-specific, underscoring the limitations of generalized assumptions.The findings demonstrate that future urban flood exposure cannot be explained by climate change alone, but emerges from the interaction between climate scenarios and socioeconomic pathways shaping urban growth. By combining SSP/RCP frameworks with spatially explicit urban growth modelling across multiple cities, this study provides comparative insights relevant for scenario-based urban planning and flood risk management in rapidly urbanizing coastal regions of the Global South.[1] Bachofer, F., Wang, Z., Huth, J., Eisfelder, C., Reimuth, A., &amp; Kuenzer, C. (2026). Urban growth prediction along Shared Socioeconomic Pathways (SSPs) for future flood exposure risk assessment: a cross-continental analysis of coastal cities. Anthropocene Coasts, 9(1). https://doi.org/10.1007/s44218-025-00109-

Performance of nearest neighbour metrics for pluvial flood nowcasts in urban catchments

Aim of the study The study aims to simulate the extent and depth of fluvial and pluvial flooding under different scenarios using the 2D hydraulic MIKE+ model in combination with geographic information systems (GIS). Material and methods As for the fluvial floods, we used steady-state flow conditions for three flood scenarios (Q10, Q100, and Q1000). The modeled flood maps were compared to official flood maps created under the second cycle of EU Floods Directive (2007). Regarding the pluvial flooding, we used the rain-on-grid method, where the rainfall input was set to three constant intensities (20, 40, and 60 mm/hour) under two scenarios: fully saturated soils and infiltration losses. Study area was the Teplica River section (3.68 km) in western Slovakia. Results and conclusions Based on the results, the flood extent difference against the official flood maps was 0.009, 0.075, and 0.123 km2 for Q10, Q100, and Q1000, respectively. In case of 20, 40, and 60 mm/hour rainfall scenarios and fully saturated soils, 13.5, 22.1, and 29.2% of the domain, respectively, had flow depths between 0.005-0.1 m while 2.0, 4.2, and 6.1% of the domain had flow depths above 0.1 m. In case of 20, 40, and 60 mm/hour rainfall scenarios with infiltration losses, 6.0, 14.6, and 22.7% of the domain, respectively, had flow depths between 0.005-0.1 m while 0.4, 2.5, and 4.5% of the domain had flow depths above 0.1 m. When the infiltration rates of land cover classes are applied, pluvial flood extent decreases by 58.2%, 34.4%, and 23.2% for the 20, 40, and 60 mm/hour rainfall scenario, respectively.

Planning Nature Based Solutions against urban pluvial flooding in heritage cities: A spatial multi criteria approach for the city of Florence (Italy)

Reliable hourly flood forecasting using weather radar rainfall data for early warning system is essential for reducing natural disaster risk during extreme typhoon events. This study proposed a novel approach integrated with physics-based WASH123D and HEC-HMS models to forecast 1 h ahead flood level in the Fengshan Creek basin, northern Taiwan. The comparison was done with data-driven support vector machine (SVM) model, and performances were assessed by using statistical indicators (root mean square error, correlation coefficient, the error of time to peak flood level, the error of peak flood). Four typhoons and two plum rain events (with 620 data sets) were selected for the process of model calibration and validation. The model performs better when it used quantitative precipitation estimate radar data rather than rain gauge data. Results of using 1 h ahead quantitative precipitation forecast (QPF) as input for flood forecasting were encouraging but not feasible to use directly for early flood warning system due to errors in peak flood levels and timing. Therefore, the improvement in accuracy of 1 h ahead flood forecasting was done using physics-based approach and SVM model. The systematic comparison revealed that the SVM model is an attractive way out to improve the accuracy of QPF forecasted flood levels but unable to fully describe the flood level patterns in terms of timings and flood peaks, while the results obtained by the physics-based approach were accurate and much better than the SVM model. The approach fully described the physics of hydrograph patterns and outputs have exactly the same 1 h ahead predictions, in excellent agreement with observations. The reliable and accurate reflections of timing and amount of flood peaks in all selected typhoons by a newly developed physics-based approach with its operational nature are recommended to use by the government in the future for early warning to reduce the flood impacts during typhoon events.

AN ASSESSMENT OF SPATIAL VARIATION OF LAND SURFACE CHARACTERISTICS OF MINNA, NIGER STATE NIGERIA FOR SUSTAINABLE URBANIZATION USING GEOSPATIAL TECHNIQUES

In recent decades, rapid urbanization has resulted in a growing urban population, transformed into regions of exceptional socio-economic value. By removing vegetation and soil, grading the land surface and saturating soil air content, urban developments are more likely to be flooded, which will be further exacerbated by an anticipated increase in the number of intense rainfall events, due to climate change. To date, data collected show that urban pluvial flood events are on the rise for both the UK and China. This paper presents a critical review of existing sustainable approaches to urban flood management, by comparing UK practice with that in China and critically assessing whether lessons can be learnt from the Sponge City initiative. The authors have identified a strategic research plan to ensure that the sponge city initiative can successfully respond to extreme climatic events and tackle pluvial flooding. Hence, this review suggests that future research should focus on (1) the development of a more localized rainfall model for the Chinese climate; (2) the role of retrofit SuDS (Sustainable Drainage Systems) in challenging water environments; (3) the development of a robust SuDS selection tool, ensuring that the most effective devices are installed, based on local factors; and (4) dissemination of current information, and increased understanding of maintenance and whole life-costing, alongside monitoring the success of sponge cities to increase the confidence of decision makers (5) the community engagement and education about sponge cities.

Sudan is increasingly exposed to compound risks from floods and droughts, amplified by conflict, climate variability, fragile infrastructures, and weakened institutional capacities. The APIS (Early Warning and Civil Protection for Floods and Droughts in Sudan) project has developed and tested a set of methodologies to strengthen multi-hazard risk assessment and early warning, tailored to contexts marked by fragility and data scarcity. At the core of this approach is the enhancement of the national early warning system through decision support tools for rain and flood forecasting and drought monitoring, strengthened by the effective use of information provided in operational bulletins disseminated through established procedures. The development of Impact-Based Forecasting (IBF) methodologies, built upon regional-level research and operational experiences, ensured the transfer and contextualization of established practices to the Sudanese domain. Complementing this framework, a high-resolution forecasting chain based on the Weather Research and Forecasting (WRF) model was operationalized, delivering 3 km spatial resolution and 72-hour lead times for key weather variables to support IBF applications and assessing populations potentially affected by severe weather, including extreme rainfall, strong winds, and heatwaves. This system supports IBF applications and the assessment of populations potentially affected by severe weather, including extreme rainfall, strong winds, and heatwaves. The system was further reinforced through the rehabilitation and integration of meteorological and hydrological monitoring stations, enhancing the reliability of real-time observations. A national drought monitoring framework was also established to detect emerging stress conditions and assess related impacts on priority assets. By combining hazard simulations with exposure and vulnerability information, the methodologies demonstrated consistency in generating tailored, real-time early warning products for disaster management authorities and humanitarian partners. A pivotal achievement included the establishment of a joint inter-sectoral operations room, which laid the foundation for sustained collaboration among relevant institutions. This forum fostered a sequential and multi-stakeholder forecasting process, with each member contributing their expertise, significantly enhancing the final product and ensuring its operational viability. Current and future efforts will focus on tailoring impact-based forecasting products for distinct user groups by translating decision-maker–oriented outputs into simplified, community-accessible formats using clear language and intuitive icons to strengthen last-mile early-warning engagement. Case studies from 2024 and 2025 illustrate the effectiveness of this approach, where daily monitoring and forecasting facilitated coordination and reduced the impacts of significant flood events. The Sudan experience underscores the value of regional collaboration in sustaining critical services and embedding multi-risk approaches into both scientific practice and governance frameworks for disaster risk reduction in humanitarian settings.

Road criticality assessment to improve commutes during floods

The combined effects of climate change and urbanization have led to an increase in urban flooding due to extreme precipitation events, which show significant spatial variation within cities. Therefore, the development of a fine community-scale flooding model is crucial for urban flood prevention and control. This study presents the Urban Flood Inundation Model (UFIM), a refined model that integrates high-resolution topographic data, dynamic real-time 1D–2D coupling, and backflow effects to simulate flood inundation at community scale. The key findings are as follows: (1) Due to its built-in one-to-two-dimensional (1D–2D) connection modeling approach, the model can consider backflow effects, rendering water accumulation simulation more realistic and enhancing simulation accuracy. (2) UFIM achieved high accuracy with a precision of 0.81, recall of 0.77, Probability of Detection (POD) of 94%, and Nash-Sutcliffe Efficiency (NSE) between 0.785 and 0.800 under various actual precipitation events. (3) The model effectively simulated urban flood inundation for different return periods (1-, 5-, 10-, 20-, and 50-years), and different community types (including residential, functional and comprehensive communities). These results highlight the UFIM's potential to support refined flood risk assessment across diverse community types, inform adaptive urban drainage design, and enhance decision-making for resilient planning and emergency response.

&amp;lt;p&amp;gt;&amp;lt;em&amp;gt;Flood inundation forecast maps provide an essential tool for disaster management teams to aid planning and preparation ahead of a flood event in order to mitigate the impacts of flooding on the community. Evaluating the accuracy of forecast flood maps is essential for model development and improving future flood predictions and can be achieved by comparison with flood maps derived from remote-sensing observations. Conventional, quantitative binary verification measures typically provide a domain averaged score, at grid level, of forecast skill. This score is dependent on the magnitude of the flood and the spatial scale of the flood map. Binary scores have limited physical meaning and do not indicate location specific variations in forecast skill that enable targeted model improvements to be made. A new, scale-selective approach is presented to evaluate forecast flood inundation maps against Synthetic Aperture Radar (SAR)-derived observed flood extents. We evaluate forecast flood maps out to 10-days lead time for the Rivers Wye and Lugg (UK) during Storm Dennis, February 2020. A neighbourhood approach based on the Fraction Skill Score is applied to assess the spatial scale at which the forecast becomes skilful at capturing the observed flood. This skilful scale varies with location and when combined with a contingency map creates a novel categorical scale map, a valuable visual tool for model evaluation and development. The impact of model improvements on forecast flood map accuracy skill scores are often masked by large areas of correctly predicted flooded/unflooded cells. To address this, the accuracy of the flood-edge location is evaluated: this provides a physically meaningful verification measure of the forecast flood-edge discrepancy. Representation errors are introduced where remote sensing observations capture the flood extent at different spatial resolutions in comparison with the model. We evaluate the sensitivity of the verification measures to the resolution of the SAR-derived flood map. &amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;em&amp;gt;An ensemble of forecast flood inundation maps has the potential to represent the uncertainty in the flood forecast and provides a location specific, probabilistic, likelihood of flooding. This gives valuable information to flood forecasters, flood risk managers and insurers and will ultimately benefit people living in flood prone areas. We apply a scale selective approach to evaluate the spatial predictability of forecast ensemble flood maps. An ensemble forecast of flooding of the Brahmaputra in the Assam region, August 2017, is evaluated using flood extents derived from Sentinel-1 SAR images. The results are presented on a Spatial Spread-Skill (SSS) map, indicating where the flood map ensemble is over-, under- or well-spread. Overall, emphasis on scale, rather than domain-average score, means that comparisons can be made across different flooding scenarios and forecast systems and between forecasts at different spatial scales.&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;

Estimating the geographical flood extent is a key element in impact-based flood forecasting and crucial for countries with long coastlines, and places where storm surges pose a significant risk, such as Denmark. For local flood mitigation measures and climate adaptation strategies, inundation mapping is often performed using physical models. However, in the context of flood forecasting and early warning, these are computationally demanding and therefore may not be able to provide timely forecasts and effective warnings.The Danish Meteorological Institute (DMI) has developed a real-time flood forecasting system for storm surge events in Denmark together with the company SCALGO. This system couples the HBM regional oceanic storm surge forecasting model, developed by DMI, with a rapid inundation mapping, developed by SCALGO, using a 0.4 m resolution Digital Elevation Model (DEM). All inland pixels in the DEM are connected to a coastline pixel through pre-computed hydrological flow paths. The predicted water level from the storm surge model at each coastline pixel is then instantaneously projected inland through the pre-mapped flow paths. This study evaluates the performance of the flood forecasting system on the Oct. 20-21 (2023) storm surge event, with an estimated return period of over 100 years and affecting large parts of southern Denmark (and northern Germany).This flood forecasting system creates a simple inundation mapping based on forecasted sea levels based on a high-resolution DEM modified to account for hydrological flow processes. This real-time flood mapping allows for a visualization of full five-day ocean model forecasts updated continuously at 6h intervals and has been operational for flood warning since October 2022, to supplement DMIs operational ocean forecasting system [1].&amp;#160;The evaluation is performed by comparing the inundation map from the flood forecasting system with media reports, photographs, and other data sources to get an overview of spatial and temporal accuracy and accuracy of the severity of the event. We see a large overlap between areas with forecasted flood risks and actual flooded areas. In some cases, the extent of the flooding differs from the area at risk due to errors in the DEM or local emergency services mitigation strategies.We conclude that the flood forecasting system is useful for identifying coastal areas at risk. While it does not account for detailed physics of flow on land, it is able to reflect the effects of, even very local, geographical variations in sea level that determine the distributions of local-scale flood risk. The current inundation mapping does not currently include the impact of waves, which resulted in larger differences between predictions and actual flooded areas, for easterly-facing locations exposed to large waves. Proposed activities to include the effect of waves will therefore improve the flood forecasting system.&amp;#160;[1] Andr&amp;#233;e, E., Su, J., Larsen, M. A. D., Madsen, K. S., &amp;amp; Drews, M. (2021). Simulating major storm surge events in a complex coastal region. Ocean Modelling, 162, 101802.

In the context of rapid global population growth and extensive economic development, urbanization is expanding rapidly. The expansion of urbanization brings about increasingly complex challenges for cities, and flooding is one of the disasters faced. Climate change has led to a significant increase in extreme hydrological events, particularly a sharp rise in rainfall intensity, further elevating the risk of flooding in low-lying urban areas. The study area is located in Taipei City, characterized by low-lying terrain surrounded by mountains, and is influenced by subtropical climate. The frequent occurrence of heavy rainfall during the monsoon season and typhoons contributes to frequent flooding events, with the additional impact of climate change increasing the risk of intense rainfall. Therefore, the real-time prediction of regional flooding and its application in urban management becomes an imperative task, aiding in early warning, effective flood risk response, and ensuring sustainable urban development. This study utilizes the Double-Encoder Transformer model for real-time flood forecasting leveraging dual-encoder architecture to process and analyze diverse data types relevant predicting floods. One encoder could be dedicated to interpreting meteorological data, such as rainfall spatial distribution. This encoder focuses on extracting and understanding the complex patterns in weather-related data, which are crucial for predicting the likelihood of flooding. The second encoder, on the other hand, could handle geographical and environmental data, including terrain topology, and land use patterns. This encoder is adept at understanding how environmental factors contribute to flood risk in specific areas. By concurrently processing these two streams of information, the Double-Encoder Transformer can create a more comprehensive prediction model. It can identify correlations between meteorological conditions and environmental responses, leading to more accurate and timely flood forecasts. This approach enhances the model's ability to predict not only when and where floods might occur but also their potential severity, aiding in disaster preparedness and resource allocation. Overall, the application of the Double-Encoder Transformer in flood forecasting represents a significant advancement in disaster management, leveraging AI's power to integrate and analyze complex, multi-faceted data for better, more informed decision-making in critical situations.