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In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated.

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

In the context of large-scale urban flood modeling, porosity shallow-water models enable a considerable speed-up in computations while preserving information on subgrid topography. Over the last two decades, major improvements have been brought to these models, but a single generally accepted model formulation has not yet been reached. Instead, existing models vary in many respects. Some studies define porosity parameters at the scale of the computational cells or cell interfaces, while others treat the urban area as a continuum and introduce statistically defined porosity parameters. The porosity parameters are considered either isotropic or anisotropic and depth-independent or depth-dependent. The underlying flow models are based either on the full shallow-water equations or approximations thereof, with various flow resistance parameterizations. Here, we provide a review of the spectrum of porosity models developed so far for large-scale urban flood modeling.

The Nilwala river basin is prone to frequent flooding during the southwest monsoon and second intermonsoon periods. Several studies have recommended coupling 1D and 2D models for flood modelling as they provide sufficient descriptive information of floodplains with greater computational efficiency. This study aims to couple a 1D hydrological model (HEC-HMS) with a 2D hydraulic model (iRIC) to simulate flooding in the Nilwala river basin. Hourly rainfall and streamflow data of three flood events were used for calibration and validation of HEC-HMS. The model performed exceptionally well considering the Nash–Sutcliffe coefficient, percent bias, and root mean square error. The flood event of May 2017 was simulated on iRIC using the streamflow hydrographs modelled by HEC-HMS. An overall accuracy of 81.5% was attained when the simulated extent was compared with the surveyed flood extent. The accuracy of the simulated flood depth was assessed using the observed water level at Tudawa gauging station, which yielded an NSE of 0.94, PBIAS of −4.28, RMSE of 0.18 and R2 of 0.95. Thus, the coupled model provided an accurate estimate of the flood extent and depth and can be further developed for hydrological flood forecasting on a regional scale.

This contribution presents an assessment of the potential vulnerabilities to climate variability and change (CV & C) of the critical transportation infrastructure of Caribbean Small Island Developing States (SIDS). It focuses on potential operational disruptions and coastal inundation forced by CV & C on four coastal international airports and four seaports in Jamaica and Saint Lucia which are critical facilitators of international connectivity and socioeconomic development. Impact assessments have been carried out under climatic conditions forced by a 1.5 °C specific warming level (SWL) above pre-industrial levels, as well as for different emission scenarios and time periods in the twenty-first century. Disruptions and increasing costs due to, e.g., more frequent exceedance of high temperature thresholds that could impede transport operations are predicted, even under the 1.5 °C SWL, advocated by the Alliance of Small Island States (AOSIS) and reflected as an aspirational goal in the Paris Climate Agreement. Dynamic modeling of the coastal inundation under different return periods of projected extreme sea levels (ESLs) indicates that the examined airports and seaports will face increasing coastal inundation during the century. Inundation is projected for the airport runways of some of the examined international airports and most of the seaports, even from the 100-year extreme sea level under 1.5 °C SWL. In the absence of effective technical adaptation measures, both operational disruptions and coastal inundation are projected to increasingly affect all examined assets over the course of the century.

Flood risk modeling is essential for effective disaster mitigation, particularly in flood-prone areas such as Hulu Sungai Utara Regency, Indonesia. This study leverages Google Earth Engine (GEE) to integrate multi-source satellite data and machine learning techniques for flood susceptibility mapping. Key geospatial variables, including the Normalized Difference Vegetation Index (NDVI), elevation, distance from rivers, and the Topographic Position Index (TPI), were analyzed using a weighted overlay method within GEE. A supervised classification approach was employed to enhance accuracy, and validation was performed using historical flood event data. The results indicate that 51.66% (47,875.86 ha) of the study area falls into the low-risk category, 42.90% (39,763.08 ha) is at moderate risk, and 5.44% (5,040.36 ha) is highly susceptible to flooding. This study highlights the advantages of GEE in large-scale flood risk assessments by enabling real-time processing, high computational efficiency, and seamless integration of geospatial datasets. The findings provide critical insights for local governments and disaster management agencies to develop proactive flood mitigation strategies.

The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research.

A serious constraint of data availability over flood-prone areas in India limits the potential of carrying out hydrodynamic flood modelling studies. Such difficulties are encountered because of a lack of high-resolution topography, cross-section data of the rivers, and sufficient and accurate calibration and validation data sets. The present study addresses the problems faced in performing a comprehensive 1D-2D coupled flood modelling over a flood disastrous region Jagatsinghpur, Odisha, India. The constraints faced in terms of hydraulic parameters such as water level, discharge, and geometric parameters such as river geometry are investigated. The simulations were performed for a severe flood event in 2011 that incurred heavy socio-economic losses in the district. The establishment of a modelling platform for flood simulation shall elucidate the major constraints faced in hydrodynamic modelling for such flood-prone areas with poor data availability.

Current global riverine flood risk studies assume a constant mean sea level boundary. In reality high sea levels can propagate up a river, impede high river discharge, thus leading to elevated water levels. Riverine flood risk in deltas may therefore be underestimated. This paper presents the first global scale assessment of the joint influence of riverine and coastal drivers of flooding in deltas. We show that if storm surge is ignored, flood depths are significantly underestimated for 9.3% of the expected annual population exposed to riverine flooding. The assessment is based on extreme water levels at 3433 river mouth locations as modeled by a state-of-the-art global river routing model, forced with a multi-model runoff ensemble and bounded by dynamic sea level conditions derived from a global tide and surge reanalysis. We first classified the drivers of riverine flooding at each location into four classes: surge-dominant, discharge-dominant, compound-dominant or insignificant. We then developed a model experiment to quantify the effect of surge on flood hazard and impacts. Drivers of riverine flooding are compound-dominant at 19.7% of the locations analyzed, discharge-dominant at 69.2%, and surge-dominant at 7.8%. Compared to locations with either surge- or discharge-dominant flood drivers, locations with compound-dominant flood drivers generally have larger surge extremes and are located in basins with faster discharge response and/or flat topography. Globally, surge exacerbates 1-in-10 years flood levels at 64.0% of the locations analyzed, with a mean increase of 11 cm. While this increase is generally larger at locations with compound- or surge-dominant flood drivers, flood levels also increase at locations with discharge-dominant flood drivers. This study underlines the importance of including dynamic downstream sea level boundaries in (global) riverine flood risk studies.

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning.