Flood Extent Research Articles

Flood inundation monitoring using multi-source satellite imagery: a knowledge transfer strategy for heterogeneous image change detection

Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation of flood inundation extent during the rainy period. Post-classification comparison is one of the fundamental methods for flood mapping of multi-source imagery, as the image characteristics of SAR and optical imagery are inherently different. However, the accuracy of flood mapping depends on the accuracy of water body delineation from single temporal imagery, which can lead to error propagation in flood extent determination. Change detection methods can extract flood extent directly from pre- and post-event imagery, but the models need to learn a consistent flood feature description between optical and SAR imagery from a limited flood dataset. Here, we propose an automatic method for flood inundation extent extraction using pre- and post-event multi-source imagery that requires only a small number of pseudo-change labels. The methodology adopts a pseudo-Siamese network framework as a Heterogeneous - Flood Inundation Extraction Network (H-FIENet), to detect flood extent between the pre‐ and post-event heterogeneous images. Spatially inconsistent multi-source pre- and post-event imagery was used to create a pseudo flood dataset, and this dataset was used to transfer water body feature knowledge from the pre-trained model to the flood extraction model using a cross-task knowledge transfer strategy. Pre- and post-event images from different satellite sources and different flood scenarios were used to evaluate the performance of H-FIENet. We found that: (1) The methodology produced an overall accuracy of over 0.94 for flood inundation extraction from multi-source heterogeneous pre- and post-event imagery. (2) H-FIENet can detect both expansion and recession of the flood extent using any dual-temporal imagery. Our work makes it possible to automate time-series flood monitoring from multi-source remote sensing imagery.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

A Comparative Study on 2015 and 2023 Chennai Flooding: A Multifactorial Perspective

Floods are highly destructive natural disasters. Climate change and urbanization greatly impact their severity and frequency. Understanding flood causes in urban areas is essential due to significant economic and social impacts. Hydrological data and satellite imagery are critical for assessing and managing flood effects. This study uses satellite images, climate anomalies, reservoir data, and cyclonic activity to examine the 2015 and 2023 floods in Chennai, Kanchipuram, and Thiruvallur districts, Tamil Nadu. Synthetic-aperture radar (SAR) satellite data were used to delineate flood extents, and this information was integrated with reservoir data to understand the hydrological dynamics of floods. The classification and regression tree (CART) model delineates flood zones in Chennai, Kanchipuram, and Thiruvallur during the flood years. The study region is highly susceptible to climatic events such as monsoons and cyclones, leading to recurrent flooding. The region’s reservoirs discharged floodwaters exceeding 35,000 cubic meters per second in 2015 and 15,000 cubic meters per second in 2023. Further, the study examines the roles of the Indian Ocean Dipole (IOD), which reached its peak values of 0.33 and 3.96 (positive IOD), and El Niño in causing floods here. The complex network of waterways and large reservoirs poses challenges for flood management. This research offers valuable insights for improving the region’s flood preparedness, response strategies, and overall disaster management.

REFLEX—A novel method for the rapid estimation of flood extent

<scp>REFLEX</scp>—A novel method for the rapid estimation of flood extent

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

An increase in the spatial extent of European floods over the last 70 years

Abstract. Floods regularly cause substantial damage worldwide. Changing flood characteristics, e.g., due to climate change, pose challenges to flood risk management. The spatial extent of floods is an important indicator of potential impacts, as consequences of widespread floods are particularly difficult to mitigate. The highly uneven station distribution in space and time, however, limits the ability to quantify flood characteristics and, in particular, changes in flood extents over large regions. Here, we use observation-driven routed runoff simulations over the last 70 years in Europe from a state-of-the-art hydrological model (the mesoscale Hydrologic Model – mHM) to identify large spatiotemporally connected flood events. Our identified spatiotemporal flood events compare well against an independent flood impact database. We find that flood extents increase by 11.3 % on average across Europe. This increase occurs over most of Europe, except for parts of eastern and southwestern Europe. Over northern Europe, the increase in flood extent is mainly driven by the overall increase in flood magnitude caused by increasing precipitation and snowmelt. In contrast, the increasing trend in flood extent over central Europe can be attributed to an increase in the spatial extent of heavy precipitation. Overall, our study illustrates the opportunities to combine long-term consistent regional runoff simulations with a spatiotemporal flood detection algorithm to identify large-scale trends in key flood characteristics and their drivers. The detected change in flood extent should be considered in risk assessments as it may challenge flood control and water resource management.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event

Flood Extent Delineation and Exposure Assessment in Senegal Using the Google Earth Engine: The 2022 Event

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin

AbstractSatellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.

Quantifying urban flood extent using satellite imagery and machine learning

Quantifying urban flood extent using satellite imagery and machine learning

A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.

Enhancing resilience: Integrating future flood modeling and socio-economic analysis in the face of climate change impacts

As climate change intensifies, future floods will become more severe in some areas with geographic variation, necessitating that local and regional governments implement systems to provide information for climate adaptation, particularly for vulnerable populations. Therefore, we aimed to develop a methodology to identify areas that are at an increased risk from future floods and independently socially vulnerable. In this study, 100-year recurrence interval flood extents and depths were estimated using an ensemble of six independent Coupled Model Intercomparison Project Phase 6 climate models for a past and future period under the highest-emissions climate scenario. The flood inundation results were related to social vulnerability for two selected study areas in the Mississippi River Basin. The range of flood extents and depths for both time periods were estimated, and differences were evaluated to determine the effects from climate change. To identify at-risk areas, the relationship between the spatial distribution of flood depths and vulnerability was then assessed. Finally, an analysis of the current and future damages on infrastructure from flooding on residential housing was performed to determine whether damages are correlated with higher vulnerability areas. Results show in every flooding scenario, flood extents and depths are increasing in the future compared with the past, ranging from an increase of 6 to 76 km2 in extent across both locations. A statistically significant relationship between spatial clusters of flooding and of vulnerability was found. The infrastructure analysis found that residential structures in the most vulnerable census tracts are 6 to 59 times more likely to experience moderate damage compared with the least vulnerable tracts depending on scenario. Overall, a framework was established to holistically understand the hydrologic and socioeconomic impacts of climate change, and a methodology was developed to use for allocating resources at the local scale.

Fluvial flood inundation and socio-economic impact model based on open data

Abstract. Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting.

Advancing process-based flood frequency analysis for assessing flood hazard and population flood exposure

Recent studies have showcased the use of process-based hydrological models with Stochastic Storm Transposition (SST) techniques to conduct Flood Frequency Analysis (FFA). This framework, referred hereby FFA-SST, has proved to be a robust strategy to estimate peak flows of specific annual exceedance probability (e.g., 100-year peak flow) that can reflect natural and anthropogenic disturbances, including changes in land use and meteorological patterns. With the objective of advancing the FFA-SST framework, this study presents for the first time the use of a spatially-resolved Integrated Surface-Subsurface Hydrological Model (ISSHM) to conduct FFA-SST. This allows us to extend the analysis from peak flow responses to flood extent, enabling a unique view and analysis of flood hazard and population flood exposure at the basin scale. As a proof-of-concept, we used the ISSHM, Amanzi–ATS, and the SST model, RainyDay, to conduct FFA-SST by simulating the flood response to 5000 annual synthetic storm events in a ∼2000km2 Southeast Texas watershed. We demonstrate that ATS, without site-specific calibration, provides a robust process-based representation of peak flows, flood extent, streamflow, evapotranspiration, soil moisture content, and water storage changes. Our results and analyses, covering frequency curves up to a 500-year return period for peak flows, basin inundation fractions, and the number of people exposed to flooding, offer a unique perspective to analyze flood impacts across spatial scales. Overall, this study provides critical insights for flood risk management by extending the FFA-SST framework to include both flood hazard and population flood exposure analyses at the basin scale. Such an approach will empower stakeholders and disaster emergency agencies with a more comprehensive understanding of flood impacts across the entire basin domain, facilitating informed decision-making for flood risk assessment and management.

IMPROVING THE ASSESSMENT OF THE ECOLOGICAL STATE OF SURFACE WATERS BY SATELLITE DATA

The article presents the results of research into the ecological state of surface waters using newly developed scientific approaches based on the use of high spatial resolution satellite data. The systematization of all available indicators and the development of new methodological approaches significantly improves the existing methodology for determining the ecological state of water bodies, which is the goal of research. In the studies of water bodies within the city of Kyiv, Obolon Bay and Lake Verbne, the optimal set of such indicators as spectral indices was determined, which made it possible to comprehensively assess the state of water bodies: NDWI - for distinguishing vegetation and water bodies, GCI - to assess the extent of blue-green algae, NDTI - to determine turbidity, IO - to determine the presence of soluble iron in water, NDSI - to assess the extent of flooding. According to the NDSI, NDWI indices and a combination of the red and infrared channels of the Sentinel-2 L2A satellite, the flooding of the floodplain of the Irpin River was traced, caused by the destruction of a hydraulic structure near the village of Kozarovichi. Wave abrasion of the shores was studied on the example of the Kremenchuk Reservoir near the villages of Pronozivka and Mozoliivka using Landsat4 (1984) and Landsat8 (2016) satellite images. The study of the reshaping of the coastline near the village of Tsybli in the Kyiv region used the method of determining spatio-temporal changes of the coastline as a result of its erosion. The assessment of spatio-temporal changes of the coastline should be carried out taking into account the water levels on the dates of the measurements. The conducted studies established that all the identified evaluation indicators can be grouped as biological, hydro morphological, and physicochemical, and it can be concluded that methodical approaches to assessing the ecological state of surface waters using satellite data are based on established cause-and-effect relationships of processes affecting water objects.

SAR-based dynamic information retrieving of the Beijing-Tianjin-Hebei flood-inundation happened in July 2023, North China

Climate change-induced rainfalls have elevated the likelihood of flood occurrences, disrupting severely the normal functioning of people. Especially in the geographically unique Beijing-Tianjin-Hebei (BTH) plain in north China, an unanticipated sudden and long-duration flood-inundation happened in the summer of 2023 (referred to as the ‘23.7’ BTH flood). To address the dynamic information and recreate the flood evolution, we constructed a flood retrieving and analysis procedure to elucidate the development process and its impacts. First, accurately extract the temporal inundation extent of floods. Subsequently, we implemented the selected knowledge-driven algorithm to retrieve the dynamic information of flood evolution, including inundation extent, water depth, and shortest inundation duration. Furthermore, hazard-bearing bodies like cultivated land, population, and buildings were analyzed to improve our understanding of the flood impact. We uncovered that the ‘23.7’ BTH flood had caused extensive inundation and notably affected the Daqing River System, where the flood breached the river bank and submerged both buildings (0.96 km2) and cultivated land (241.46 km2), and led to prolonged inundation in the Baiyangdian area, which persisted for up to 30 days. This disastrous event highlights the great requirements to boost upstream flood storage capacity and enhance preparedness and rapid response capabilities for abnormal floods.

A framework for national-scale coastal storm hazards early warning

National weather forecasting agencies routinely issue a range of hazard warnings. But to our knowledge, along sandy coastlines where storm waves and storm surge can result in widespread but location-specific beach erosion and beachfront flooding, no national-scale early warning service for these hazards is presently operational. This paper outlines the scientific basis and implementation of a new framework for large area coastal storm hazards forecasting, currently being tested along the southwest (Indian Ocean) and southeast (Pacific Ocean) coasts of Australia. The system provides 7-day rolling predictions of localized beach erosion and/or coastal flooding linked to forecasted extreme weather events. Coastal setting influences the nature and occurrence of these hazards, with sandy beaches along wave-dominated coasts more prone to erosion and at surge-dominated coasts to flooding. An existing nearshore water-level forecasting system and a new inshore wave modeling capability are used to forecast beach erosion and coastal flooding at every 100 m along the shore. At the regional scale O(100–1 000 km of coastline), a threshold-based decision tree model categorises the predicted extent, location, and severity of erosion and flooding. At a more local scale O(100–1 000 m), physics-based modeling using XBeach focuses on vulnerable or high-value locations, providing specific storm hazard indicators tailored to local needs. This two-tier approach is feasible for national implementation due to the reduced computational effort, limiting intensive modeling to pre-identified critical locations. Delft-FEWS manages the data and modeling workflow, ensuring scalability and compatibility with existing forecast infrastructure. Initial evaluations of the system are promising, with a detailed 2-year evaluation in progress. Future enhancements could include the use of satellite imagery for real-time beach width and dune topography assimilation and exploring alternative modeling approaches to further improve forecast accuracy.

Flood risk zoning of cascade reservoir dam break based on a 1D-2D coupled hydrodynamic model: A case study on the Jinsha-Yalong River

Large-scale earthquakes and huge landslide surges may cause the failure of cascade reservoirs, resulting in massive downstream flooding. The flood risk zoning of cascade reservoir dam breaks is crucial for local governments to formulate emergency plans and for individuals to garner their awareness of the flood risks. This paper presents a flood risk zoning approach based on a one-dimensional and two-dimensional coupled hydrodynamic model. The flooding area simulated by the coupled hydrodynamic model was divided into a Deadly Zone (D-zone) and an Escape Zone based on whether there is sufficient time for the public to evacuate the danger area under a specific evacuation generation time (EGT). A case study was conducted on the Jinsha-Yalong River (JYR), a confluence river located in Panzhihua City, to map the risk zones. This study considered a dam-break flood in Yalong River encountering two river flood scenarios in Jinsha River (scenario A: the annual average flow; scenario B: a 1000-year return period flood). Two evacuation scenarios (EGT of 30 and 60 min) were also considered; thereafter, four combined evacuation scenarios (A-EGT30, A-EGT60, B-EGT30, and B-EGT60) were simulated. The coupled hydrodynamic model is demonstrated to be effective through the analysis of water balance and hydrographs of special section discharge. The results show that: (1) in comparison to scenario A, scenario B reached its maximum flood extent approximately 5 min earlier, signifying that flood spread speed increases with the increase in flood magnitude; (2) the D-zone area was 127.7 % larger in scenario A-EGT60 compared to A-EGT30 and 120.8 % larger in scenario B-EGT60 compared to B-EGT30. These findings underscore the importance of flood scenarios and EGT on flood risk zones. The proposed risk zoning method can help government agencies in the JYR and major river regions worldwide with flood management and emergency strategy development.

The Role of Different Total Water Level Definitions in Coastal Flood Modelling on a Low-Elevation Dune System

The present study investigates different combinations and methods for estimating the extreme Total Water Level (TWL) and its implications for predicting flood extension caused by coastal storms. This study analyses various TWL components and approaches and assesses how different methodologies alter flood predictions, with implications for warning systems and emergency responses. Using different combinations of individual TWL components, flood extension simulations were conducted using a hydrodynamic model in the Volano Beach area (Emilia-Romagna, Italy). A real coastal storm event was used as a reference for comparison. The findings indicate that the selection of individual TWL components and calculation methods significantly impacts flood extension predictions. The approaches, which involve calculating extreme values from a combined time series or the water level time series plus the extreme value of wave setup, yield the most realistic results, excluding the runup component. In comparison, the other combinations overestimate the flood. Incorporating hydromorphological models like XBeach could enhance the accuracy of runup estimations and improve the overall method reliability. Despite limitations such as runup estimation and the use of generic regional parameters, this study underscores the importance of the TWL combination selection in accurately predicting flood extents, emphasising the need for context-specific adaptations in environmental contexts.