The 2020 flood season in Thừa Thiên Huế province, Central Vietnam, was among the most severe in recent history, driven by consecutive tropical storms and prolonged heavy rainfall. Between October and November 2020, a series of storms, including Tropical Storm Linfa, Typhoon Molave, and Typhoon Goni, brought intense precipitation, causing widespread inundation and significant damage to infrastructure and livelihoods. The hydrological complexity of the region, characterized by mountainous terrain, low-lying floodplains, and the extensive Tam Giang-Cau Hai lagoon system, further exacerbated the flood impacts, underscoring the need for advanced monitoring tools to capture the event's dynamics.

This study leverages multi-sensor Synthetic Aperture Radar (SAR) data, including Sentinel-1, Cosmo-Skymed, and TerraSAR-X, to create a high-temporal flood inventory for this hydrologically challenging region. Multi-temporal SAR intensity and coherence data were processed using threshold-based change detection algorithms and normalized difference indices to delineate flood extents. These SAR-based methods, immune to cloud cover, provided continuous observations despite the adverse weather conditions during the flood. Validation was performed using in-situ flood markers and drone imagery, ensuring accuracy in the derived flood maps. To complement SAR data, hydrodynamic modeling using HEC-RAS simulated water flow, inundation depths, and river system behavior, enabling cross-comparison with SAR-derived flood extents.

The 2020 flood event highlighted a challenge often associated with satellite-based flood mapping: image acquisitions seldom capture the peak of the flood. However, the high temporal resolution provided by the combined SAR datasets allowed researchers to track the pulse of the flood, revealing its evolution and alignment with storm events and precipitation patterns. This capability provided critical insights into the timing, extent, and dynamics of flooding, even in a region with complex topography and hydrology.

The high-temporal flood inventory produced in this study enhances understanding of flood dynamics across diverse land-cover types, enabling improved flood risk assessments and adaptive management. The outcomes not only advance flood monitoring methodologies for Vietnam but also demonstrate the value of integrating Earth Observation data with hydrological modeling to support disaster risk reduction efforts. This approach offers scalable solutions for other regions prone to extreme weather events, contributing to global efforts in informed decision-making and adaptive flood management strategies.

How to cite: Bachofer, F., Sogno, P., Schmid, E., Büche, K., Assmann, A., and Nguyen, H. K. L.: Multi-Sensor SAR-Based Flood Mapping for High-Temporal Monitoring of the 2020 Flood Event in Thừa Thiên Huế, Vietnam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3176, https://doi.org/10.5194/egusphere-egu25-3176, 2025.

Bridges are critical infrastructures of the transport network given their high construction costs and limited alternative routes. Flood events are the most frequent cause of damage to transport infrastructure compared to any other natural hazard. Bridge overtopping is a phenomenon with serious safety consequences for drivers and leads to cascading effects such as traffic disruption and reduced efficiency of evacuation and emergency plans. Whereby, proactive management is essential to enhance bridge resilience and ensure user safety.
This work introduces a catchment-scale screening method using GIS and remotely sensed data to assess the propensity of riverine bridges to overtopping. The application of the method is based on the use of elements such as road network (OSM), hydrographic network, and LiDAR-derived Digital Elevation Models of the bare terrain (DTM) and of the surface (DSM). The propensity of bridges to overtopping is evaluated considering the geometric and morphological characteristics of river-roads intersections, independent of hydrological forcing. The method assumes that bridges with intersection heights (H_i), i.e. the difference between the road level (DSM) and river thalweg (DTM), lower than the corresponding cross-section heights (H_s), are more prone to overtopping during floods.
Intersections between roads and the hydrographic network were identified, and H_i values were calculated by extracting elevation differences within a defined buffer. To minimize noise from vegetation and other elements in the DSM, the topographic ruggedness index was employed as a filter, assuming that roads have smooth surfaces compared to the high roughness of vegetation. Field measurements of H_i were performed to validate the remotely sensed H_i values. Riverbanks and their corresponding H_s values were identified using the Iso Cluster Unsupervised Classification approach, testing various morphometric derivatives of the DTM. A combination of profile curvature and maximum difference from mean elevation provided the clusters of landforms corresponding to riverbanks.
The method was applied to the Magra River basin in Italy (970 km²), an area frequently impacted by flood events.
Results indicate that for roads intersecting streams with Strahler order (S) <4 the median height error (∆_he) between remotely sensed and measured H_i is significant (2 m, i.e. 40%). In contrast, the method proved effective for S>3 (∆_he= 0.4 m, i.e. 12%). The mean cross-section width for such streams is 35 m (excluding the main river), which is two orders of magnitude larger than the planimetric accuracy of the DTM (0.3 m). A total of 231 bridges were identified, and approximately 30% exhibited H_i<H_s, indicating a high propensity for overtopping. This approach enables large-scale screening to identify road-river intersections with geometric and morphological predispositions to overtopping. It provides a valuable tool for prioritizing bridges for further hydrologic-hydraulic and traffic disruption modeling, supporting infrastructure resilience, and flood risk management.

Acknowledgments
This study was founded by the European Union - Next Generation EU through the PRIN 2022 call powered by MUR, within the project “FLOOD@ROAD” (Prot. 202257JJSJ).

How to cite: Amaddii, M., Castelli, F., and Arrighi, C.: A catchment-scale screening tool for the assessment of bridge overtopping using GIS and LiDAR-derived digital elevation models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7115, https://doi.org/10.5194/egusphere-egu25-7115, 2025.

Devastating floods in the Mediterranean region are caused by heavy rainfall. Flood forecasting systems are essential in Maghreb countries like Morocco to reduce the consequences and impacts of floods. Developing such a system for ungauged areas is challenging. Even though there is a shortage of observed data, remote sensing products offer a promising solution to fill these data gaps. Different soil moisture and precipitation products are evaluated against in situ data for flood modeling applications. Using an event-based hydrological model with an hourly time step, the results show that observed soil moisture is strongly related to the SMOS-IC satellite product and the ERA5 reanalysis. The comparison of soil moisture records allowed us to calculate the initial soil moisture state using the Soil Conservation Service Curve Number (SCS-CN). Daily in situ soil moisture data may not represent basin soil moisture conditions; however, ASCAT, SMOS-IC, and ERA5 products performed similarly in terms of validation for flood modeling. The daily time step may not accurately represent the saturation state just before a flood, as soil moisture in these semi-arid areas is depleted more quickly after rainfall. For the hourly time step, the initial soil moisture conditions of the SCS-CN model were found to be more accurately represented by ERA5 and in situ data. This work highlights the potential of remote sensing products to improve flood forecasting in semi-arid regions, providing valuable information for the development of robust hydrological models where traditional data are scarce.

How to cite: El Khalki, E. M., Yves, T., Christian, M., Luca, B., Vincent, S., Simon, G., and Mohamed Elmehdi, S.: Global Soil Moisture Products for Flood Modeling in a Semi-Arid Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7445, https://doi.org/10.5194/egusphere-egu25-7445, 2025.

River deltas are home to more than 400 million people worldwide, being fundamental centers for industry, and ecosystems of great ecological and economic importance. Some of the most densely populated rural and urban areas are in low-lying deltaic regions, such as the Mekong Delta. These areas are highly vulnerable to the impacts of climate change on coastal-river floods, which are driven by several factors, such as sea level rise, extreme river flows or storm surges. To mitigate these effects, accurate integrated coastal-river hydraulic models are essential for enhancing predictive capabilities for compound flooding events and developing effective contingency plans. However, the accuracy of hydraulic models is often limited by the quality of available observations. Developing reliable datasets for coastal-river domains involves addressing several challenges, including a) the high spatial and temporal variability of coastal-estuary dynamics, b) the complex morphology of delta regions characterized by extensive floodplains, braided river channels, and man-made structures, and c) the lack of continuous coastal-river datasets.

Traditional in-situ monitoring provides data only at widely spaced stations, which limits coverage. As a results, satellite Earth Observation (EO) has emerged as a solution to generate datasets with large spatial coverage and high spatial resolution. The Surface Water and Ocean Topography (SWOT) mission is the first dedicated mission to monitor surface water, while also providing ocean height measurements, making it ideal to overcome the monitoring challenges in coastal-river domains. The SWOT mission, with a 120 km wide swath, offers large spatial coverage that can deliver water surface elevation (WSE) and surface water extent observations for rivers as narrow as 50 meters. Additionally, the mission offers a revisit time of 21 days, delivering 2-6 observations in each cycle.

In this study we utilize SWOT observations over the Mekong Delta to generate continuous datasets that span from the river to the ocean. These datasets are used to inform and validate an integrated coastal-river hydraulic model of the Mekong Delta. The SWOT L2_HR_Raster product is exploited at a 100-meter resolution, to derive coastal and estuarine WSE time series and surface water extent. This dataset has the capability to map complex river morphological structures at a temporal resolution previously unattainable by satellite EO missions. It can also capture the effects of ocean tides and storm surges on river water levels, as well as the impact of high river flows on coastal domains. Moreover, the 2D nature of the L2_HR_Raster product can deliver not only river-ocean WSE profiles, but also coastal longitudinal ocean height, to better understand the effect of high river flows in near-coastal areas.

The results provide continuous coastal-river datasets mapping the interplay between near coastal and estuarine dynamics, as well as the complex morphology of the Mekong Delta region. The datasets are used to calibrate and validate a hydraulic model of the Mekong Delta that integrates river and coastal zones to accurately simulate WSE and surface water extent in deltaic regions. The integrated model supports better prediction capabilities for compound flooding simulations and the impacts of climate change on the coastal and estuarine environments.

How to cite: Coppo Frias, M., Kittel, C. M. M., Nielsen, K., Musaeus, A. F., Toettrup, C., and Bauer-Gottwein, P.: Integrated coastal-river water surface elevation datasets derived from SWOT to improve compound flooding simulations over the Mekong Delta, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10980, https://doi.org/10.5194/egusphere-egu25-10980, 2025.

Climate change has increased the frequency and intensity of extreme weather events, leading to greater risks for vulnerable urban areas. Inadequate infrastructure often exacerbates vulnerability of many areas, resulting in significant socioeconomic losses from climate-related hazards and in particular flooding. Satellite services, smart technologies such as GIS-based Digital Twin help to simulate flooding scenarios to support urban planning and decision-making and provide monitoring and short-term forecasting of floods thus contributing to enhance climate resilience and to strengthen financial risk strategies.

To ensure that these systems operate effectively, the validation of their predicted  outputs in terms of flooding maps is crucial. This task is usually possibly carried out using the satellite-based data available and particularly those from Synthetic Aperture Radar (SAR), which are effective in various meteorological conditions. In urban areas, the application of state-of-the-art SAR-based methods for flood detection is challenging due to the complexity of effects caused by the radar backscattering from built environments.

This study focuses on validating flood maps for urbanized environments based on a consolidated approach that reprocesses the clustering result with fuzzy logic approach (Pulvirenti et al. 2023, DOI: 10.3390/w15071353) and here improved to better estimate flooding in urban areas. The method was applied to a severe precipitation event in November 2023 in Tuscany, Italy, which caused multiple flood episodes. Our focus was on the Florence-Prato-Pistoia plain, the most densely populated area in Tuscany. On November 2, heavy rainfall began in the early afternoon, accumulating 130-170 mm within 5-6 hours. This led to the first flood episodes after 19:00 local time, resulting in several casualties.

Copernicus Rapid Mapper was activated on 03/11/2023, 04:21 (Local time = UTC+1). It produced an estimate of flooded area mainly using one COSMO-SkyMed image, collected on November 6, after a second storm occurred in the night between 4 and 5 November. In our analysis we used two images. For the common image, good spatial correspondence was obtained. However, due to the late availability of satellite images, critical early floods were missing.

This work takes this case study to address the opportunity and challenges of flood detection in urban areas using satellite data. While highlighting the importance of having a satellite flood mapping service, some drawbacks are also pointed out, such as the lack of revising time that can imply missing early stages of floods to early implement search and rescue operations. Projects to improve revisiting time are related to the emergence of next generation constellations, such the ASI/ESA IRIDE multisatellite and multiservice constellation. In case of fast evolving phenomena, such as the one considered in this study, a higher time resolution of flood mapping would increase the chance to obtain data even in the first floods. In practice, resorting to modelling and sensor data coupled in digital twins eventually integrated with obtained from citizens science will be still unavoidable. This is demonstrated within the SCORE project (https://score-eu-project.eu/), a four-year EU-funded project aiming to increase climate resilience in European coastal cities (Coastal City Living Labs - CCLLs).

How to cite: Carlini, B., Baldini, L., Adirosi, E., Serafino, G., Scognamiglio, G., and Paranunzio, R.: Satellite Mapping Analysis of the November 2023 Flood in Prato, Tuscany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11828, https://doi.org/10.5194/egusphere-egu25-11828, 2025.

Flash floods have been responsible for some of the most catastrophic events globally. The extensive range of effects and the varying severity of impacts present significant challenges in accurately understanding the damage caused by a flood event, thereby hindering our capacity to predict future consequences. When evaluating flood impacts and their severity, most existing approaches rely on qualitative descriptions (e.g., major, catastrophic, etc.) or examine the impacts from a single perspective or discipline, such as economic losses. In this study, the Flash Flood Impact Severity Scale (FFISS) is employed to evaluate, map, and categorize the impacts of two flash floods that occurred in the Lilas River in Greece in 2009 and 2020. The goal of this application is to analyze the varying severity levels and how one flood event can influence the impacts of a subsequent event. The methodology involved extensive fieldwork, including the collection of ground-based and aerial observations using UAV technology to document the impacts. These observations were subsequently georeferenced, followed by applying the Flash Flood Impact Severity Scale (FFISS) and creating detailed maps to assess and evaluate the severity of impacts associated with the two flood events. The results indicate that, despite the higher water levels during the second flood, areas previously affected show lower severity values. This reduction is attributed to the community’s gradual adaptation, improvements in infrastructure, and significant local widening of the river channel. Conversely, newly flooded areas during the second event exhibit high severity levels. Overall, applying the FFISS reveals spatial patterns of impact severity, offering insights into the local nature of floods while suggesting a potential reduction in overall risk during the post-flood period.

How to cite: Spyrou, N.-I., Diakakis, M., Mavroulis, S., Deligiannakis, G., Andreadakis, E., Filis, C., Kotsi, E., Antoniadis, Z., Melaki, M., Gogou, M., Katsetsiadou, K.-N., Stanota, E.-S., Skourtsos, E., Vassilakis, E. V., and Lekkas, E.: Ground observations and UAV mapping to support a GIS-based implementation of the Flash Flood Impact Severity Scale (FFISS) for the 2009 and 2020 flash floods in Evia, Greece., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13306, https://doi.org/10.5194/egusphere-egu25-13306, 2025.

Paleochannels are natural features in floodplains, and their identification and geometric characterization can guide river restoration and natural flood management interventions. This study focuses on identifying the network of dendritic drainage patterns in a portion of the Lincolnshire fens near Billinghay. A semi-automatic approach was developed for identifying paleochannels and performing a morphometric analysis of these features.

A high-resolution LiDAR data survey from 2022 was downloaded from the UK environment portal. The LiDAR digital terrain model has a resolution of 2 m and vertical accuracy of +/- 15 cm. The raw LiDAR point cloud was pre-processes using CloudCompare. An initial ground-level extraction was performed with automatic filters and further refined by identifying and removing additional anthropogenic features such as roads, buildings, and artificial levees along canals, using a vector data analysis. The dendritic drainage channels of the particular study site (6.78 km²) were isolated using a semi-automatic selection with specific elevation filters. The differences in elevation between the paleochannel surface and the surrounding flat areas were used to define distinct elevation ranges for different altimetric bands. Points within these ranges were selected and reclassified to create a preliminary morphological model of the paleochannels. Discontinuous segments were interpolated, and areas with missing values were resampled, resulting in a consistent and detailed representation of the paleochannels.

The dendritic drainage network was characterized in terms of Strahler order, sinuosity, length, and location of connection nodes. Additionally, several cross-sectional profiles were generated and a Python script was developed to quantify the width, depth, and area between the crest of the paleosurface and the ground level. Reaches of paleochannels of higher Strahler order were found to be deeper and wider. The sinuosity is lower for the reaches in the upper part of the dendritic network. Interestingly, the channels are located in areas that are highly convex compared to the surrounding flat areas. The total surface area occupied by the identified paleochannels in the study site is approximately 1.8 km², which represents a significant portion of the floodplain.

The geometry of the identified enclosed basin and of the dendritic network are being used to test a morphodynamic model in order to identify the sea level and tidal ranges responsible for the formation of the paleochannels.

How to cite: Imbrogno, G., Cianflone, G., Dominici, R., Maruca, G., De Cesare, P., Schuerch, M., and Mao, L.: Semi-Automatic Extraction and Morphometric Characterization of Paleochannels using LiDAR Data: A Case Study in the fens of Lincolnshire, England, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13757, https://doi.org/10.5194/egusphere-egu25-13757, 2025.

Despite the significant increase in Earth Observation (EO) satellites, the frequency of cloud-free imagery at sufficiently high spatial resolutions for timely inundation mapping remains a significant challenge. Obtaining more frequent flood extent estimations would contribute to our understanding of flood dynamics and increase the likelihood of capturing the flood peak, which often evades EO acquisitions. Integrating complementary data from multiple sensors is a potential solution to overcome limitations posed by temporal resolution, spatial resolution, cloud cover, adverse weather, or light conditions. Surface water fractions, indicating the proportion of a pixel covered by water, can be derived from a variety of sensors that passively sense across different spectral ranges daily. However, the fractional coverages are derived at various spatial resolutions, necessitating a methodology to harmonize and combine the information to obtain a comprehensive flood map at a meaningful resolution. The present study proposes a methodology to seamlessly combine data from Low-Earth Orbiting (LEO) multispectral, Geostationary-orbiting (GEO) multispectral, and Passive Microwave (PMW) sensors. The proposed approach is tested on the February 2022 flood event in Brisbane, Australia, and fuse data from Visible Infrared Imaging Radiometer Suite (VIIRS), the Himawari 8/9 Advanced Himawari Imager (AHI), and the Special Sensor Microwave Imager/Sounder (SSMIS). These sensors offer complementary strengths in flood detection, including sub-daily imagery from VIIRS and AHI, and fractional water estimates beneath cloud cover from SSMIS.

Surface water fractions, representing the fraction of a pixel covered by water, are derived from VIIRS, AHI, and SSMIS at spatial resolutions of 375 m, 1 km, and 25 km, respectively. These surface water fractions are subsequently homogenized via downscaling and fused to obtain an aggregated flood map. A Digital Terrain Model and its derivatives, including the Slope, Topographic Water Index, Height Above Nearest Drainage, and Flow Accumulation, and water frequency information are utilized to downscale and distribute the surface water fractions in physically plausible ways. This disaggregation process produces comparable flood maps from all sensors. These maps are thereafter combined to yield a single detailed flood map. This multi-sensor framework ensures the consistent generation of flood maps at a meaningful spatial and temporal resolution, compensating for the unavailability of moderate- to high-resolution imagery due to satellite revisit timing and cloud obstruction. The proposed approach enables more frequent generation of detailed flood maps, providing valuable insights into inundation dynamics to scientists and decision makers.

How to cite: Campo, C., Tamagnone, P., Schumann, G., Choy, S., Duc Tran, T., and Kuleshov, Y.: Closing the Gap: Towards Consistent Flood Extent Retrieval with Multi-Sensor Data Fusion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16950, https://doi.org/10.5194/egusphere-egu25-16950, 2025.