This study provides an in-depth analysis of the frequency of extreme streamflow in Italy, adopting the innovative perspective of the theory of records, and focusing on record-breaking floods. (i.e., annual maximum series, AMS) observed in Italy between 1911 and 2020. Our research employs an extensive dataset of 522 annual maximum series (AMS) of streamflow observed across Italy between 1911 and 2020. We consider three time intervals (1911–2020, 1911–1970, and 1971–2020), and we define pooling-groups of AMSs based on (a) hydrological (e.g., catchment size, mean annual precipitation, etc.) and (b) spatial proximities of the gauged sites. First, within each group and for each time period, we compute the regional average number of record-breaking events (NRbins). Second, with a series of resampling experiments that preserve the spatial correlation among the AMSs, we test the hypothesis that NRbins result from a group of stationary sequences. Our results show spatially coherent patterns of an increasing number of record-breaking floods in central and in northeastern Italy over the last 50 years. In the same time interval, significant deviations in the regional number of record-breaking events from what would be expected for stationary flood sequences seem to be more common in drier climates or at higher altitudes, while the catchment size does not seem to be a meaningful descriptor.

Flooding has always been a huge threat to human society. Global climate change coupled with unsustainable regional planning and urban development may cause more frequent inundations and, consequently, higher societal and economic losses. In order to characterize floods and reduce flood risk, flood simulation tools have been developed and widely applied. Hydrodynamic models for inundation simulation are generally sophisticated, yet they normally require massive setup and computational costs. In contrast, simplified conceptual models may be more easily applied and efficient. Based on the Hierarchical Filling-and-Spilling or Puddle-to-Puddle Dynamic Filling-and-Spilling Algorithms (i.e., HFSAs), Safer_RAIN has been developed as a fast-processing DEM-based model for modelling pluvial flooding over large areas. This study assesses Safer_RAIN applicability outside the context for which it was originally developed by looking at two different inundation problems with point-source flooding volumes: (1) rural inundation modelling associated with levee breaching/overtopping; (2) urban flooding caused by drainage systems outflow volumes.

Urban flooding caused by short and high-intensity rainfall events presents increasing challenges for cities, threatening infrastructure, public safety and economic activity. Accurately representing infiltration processes in hydrodynamic models is critical, as oversimplifying infiltration can lead to significant errors in predicted flood extents and water depths. This study systematically compares two widely used infiltration models—Green-Ampt and Curve Number—implemented within two leading 2D hydraulic models, HEC-RAS and IBER, to assess their influence on urban flood predictions. Simulations were conducted for 26 rainfall events, including both observed and synthetic hyetographs, across two urban neighbourhoods in Pamplona metropolitan area, Spain. Model performance was evaluated using root mean square error, mean absolute error and confusion matrix-derived metrics such as precision, accuracy, specificity, sensitivity and negative predictive value. Results indicate that the choice of infiltration method significantly affects both water depths and inundation extents: while Green-Ampt yields more conservative water depth estimates, Curve Number tends to underestimate flood extents. The comparison between the two hydraulic models has shown that IBER simulates broader flood extents and lower water depth errors compared to HEC-RAS. The findings highlight the importance of selecting appropriate infiltration methods and hydraulic models for reliable urban flood risk assessment, as well as providing guidance for model selection in urban inundation studies.

Pluvial flooding on roadways poses a significant threat to drivers. In addition, frequency of such pluvial floods is increasing due to climate change, urbanization, impervious surfaces, and aging stormwater infrastructure. While physics-based hydrodynamic models provide detailed results, their computational demand and reliance on often-uncertain drainage layouts make them impractical for rapid, real-time urban risk assessment.SaferPlaces (saferplaces.co) addresses this with a web-platform that integrates high-resolution geospatial and climate data from sources like GEE, OSM, and Copernicus to automate the construction of digital-twins for flood risk modelling in urban areas. As part of the SaferPlaces platform, Safer_RAIN, a fast-processing DEM based algorithm that combines a pixel-based Green-Ampt infiltration model with a Hierarchical Filling-and-Spilling Algorithm (HFSA) approach to enable building-level risk assessments. By leveraging cloud-based infrastructure, the platform delivers high-resolution, real-time results for urban planning. However, when compared to 2D hydrodynamic models, Safer_RAIN showed some limitations, including the underestimation of flooded extents due to the absence of hydraulic backwater effects and single flow-path constraints. Therefore, this study introduces an enhancement to Safer_RAIN, termed Kinematic Safer_RAIN, by incorporating a kinematic approach to simulate flooding beyond depressions and integrating a flow-path inundation extension feature. This enhancement utilizes the Height Above Nearest Drainage (HAND) approach, coupled with Manning’s equation, to represent flood inundation along flow paths (e.g. essential for assessing risks to urban road networks).Kinematic Safer_RAIN, featuring flow-path flood extension, was benchmarked against HEC-RAS 2D Rain-on-Grid hydrodynamic simulations using 1m-resolution LiDAR DEMs in two case studies. In the Cottonwood Lake Study Area (USA), Kinematic Safer_RAIN produced maximum flooding extents and water depth distributions that closely match HEC-RAS results. The model was further validated in Pamplona (Spain), using the extreme storm event of July 2010. Kinematic Safer_RAIN successfully identified flood-prone depressions and flooding along flow paths (primarily main roads and lanes), yielding high True Positive Rates and aligning with flood evidence from local newspaper images. This research provides a robust, low-cost, and rapid alternative for authorities to accurately predict roadway flooding risks, bridging the gap between topographic simplicity and hydrodynamic complexity to enhance flood mitigation strategies.Keywords: Flow path flood extension, Hierarchical Filling-and-Spilling Algorithm (HFSA), Height Above Nearest Drainage (HAND), Pluvial Flooding, Safer_RAIN, Kinematic_SaferRAIN, SaferPlaces

Pluvial floods, intensified by short-duration and high-intensity storms, are becoming increasingly frequent and severe due to climate change and urbanization. SaferPlaces addresses this with a digital twin platform that integrates high-resolution geospatial and climate data from sources such as Google Earth Engine (GEE), Open Street Map (OSM), Microsoft Planetary, Amazon, and Copernicus. These datasets are automatically integrated to construct detailed, multi-layered urban digital twins, enabling real-time flood hazard and risk modelling. As part of the SaferPlaces platform, Safer_RAIN, a fast-processing Hierarchical Filling-&-Spilling Algorithm (HFSA), combines spatially distributed rainfall input and infiltration simulation through a pixel-based Green-Ampt model (see https://saferplaces.co/) and enabling building-by-building flood risk modeling across large urban areas. Leveraging the platform’s cloud-based infrastructure, Safer_RAIN can efficiently run computationally intensive simulations at high resolution, delivering real-time results that support effective urban planning and climate resilience strategies. Comparisons with traditional 2D hydrodynamic models revealed limitations in Safer_RAIN, such as underestimation of flooded areas due to the lack of hydraulic backwater effect and single flow path constraints. We present significant enhancements of the SaferPlaces platform that were recently developed to address these challenges. These include: (1) incorporating a weir equation using a simplified kinematic approach to account for backwater effect, (2) introducing a travel-time distribution for water within watersheds and (3) implementing flow path flood extension using a Height Above Nearest Drainage (HAND) approach. These improvements are tested in a synthetic case study and applied to a set of real flooding events in urban areas of Pamplona, Spain. With its ability to run scalable simulations in real time and integrate diverse datasets, Safer_RAIN, as part of SaferPlaces' digital twin platform, offers a transformative solution for flood risk intelligence, empowering cities to build preparedness and enhance climate resilience.Keyword: Digital twin, Hierarchical Filling-&-Spilling Algorithms, 2D hydrodynamic models, pluvial flooding

Urban areas are especially vulnerable to the effects of pluvial flooding due to the high population density and concentration of valuable resources as well as the fact that heavy precipitation events are becoming more common and more intense because of climate change. The use of high-resolution hydrologic-hydraulic numerical models for pluvial flood risk assessments in large metropolitan areas is still very resource-intensive. Several studies have pointed out the potential of fast-processing DEM-based methods, like Hierarchical Filling-&-Spilling Algorithms (HFSAs), considering the increasing availability of LiDAR (Light Detection and Ranging) high-resolution DEMs (Digital Elevation Models). We have developed a fast-processing HFSA, as part of a web-based digital twin solution for flood risk intelligence (see https://saferplaces.co/), that enables building-by-building pluvial flooding hazard and risk modelling in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. In this study, we upgrade SaferPlaces’ HFSA based on kinematic wave approximation to depression points and analysing the impact of flow contributions from one cell to another on the formulation of travel time, as well as the backwater effect in depression-related watershed areas. We present the first applications of the kinematic HFSA, comparing it with two state-of-the-art fully 2D rain-on-grid inundation models (i.e. HEC-RAS and UnTRIM). We discuss the potential and limitations of Saferplaces' upgraded HFSA and identify future research possibilities.Keywords: Hierarchical Filling-&-Spilling Algorithms, kinematic wave, pluvial flooding, rain on grid.