Inundation Model Research Articles

LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs

Abstract. LISFLOOD-FP 8.0 includes second-order discontinuous Galerkin (DG2) and first-order finite-volume (FV1) solvers of the two-dimensional shallow-water equations for modelling a wide range of flows, including rapidly propagating, supercritical flows, shock waves or flows over very smooth surfaces. The solvers are parallelised on multi-core CPU and Nvidia GPU architectures and run existing LISFLOOD-FP modelling scenarios without modification. These new, fully two-dimensional solvers are available alongside the existing local inertia solver (called ACC), which is optimised for multi-core CPUs and integrates with the LISFLOOD-FP sub-grid channel model. The predictive capabilities and computational scalability of the new DG2 and FV1 solvers are studied for two Environment Agency benchmark tests and a real-world fluvial flood simulation driven by rainfall across a 2500 km2 catchment. DG2's second-order-accurate, piecewise-planar representation of topography and flow variables enables predictions on coarse grids that are competitive with FV1 and ACC predictions on 2–4 times finer grids, particularly where river channels are wider than half the grid spacing. Despite the simplified formulation of the local inertia solver, ACC is shown to be spatially second-order-accurate and yields predictions that are close to DG2. The DG2-CPU and FV1-CPU solvers achieve near-optimal scalability up to 16 CPU cores and achieve greater efficiency on grids with fewer than 0.1 million elements. The DG2-GPU and FV1-GPU solvers are most efficient on grids with more than 1 million elements, where the GPU solvers are 2.5–4 times faster than the corresponding 16-core CPU solvers. LISFLOOD-FP 8.0 therefore marks a new step towards operational DG2 flood inundation modelling at the catchment scale. LISFLOOD-FP 8.0 is freely available under the GPL v3 license, with additional documentation and case studies at https://www.seamlesswave.com/LISFLOOD8.0 (last access: 2 June 2021).

Integrated methodology for urban flood inundation modeling: a case study of Ichinomiya River Basin, Japan

Integrated methodology for urban flood inundation modeling: a case study of Ichinomiya River Basin, Japan

Data-driven flood hazard zonation of Italy

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

InundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid system

Abstract. Despite the high historical losses attributed to flood events, Canadian flood mitigation efforts have been hindered by a dearth of current, accessible flood extent/risk models and maps. Such resources often entail large datasets and high computational requirements. This study presents a novel, computationally efficient flood inundation modeling framework (“InundatEd”) using the height above nearest drainage (HAND)-based solution for Manning's equation, implemented in a big-data discrete global grid system (DGGS)-based architecture with a web-GIS (Geographic Information Systems) platform. Specifically, this study aimed to develop, present, and validate InundatEd through binary classification comparisons to recently observed flood events. The framework is divided into multiple swappable modules including GIS pre-processing; regional regression; inundation models; and web-GIS visualization. Extent testing and processing speed results indicate the value of a DGGS-based architecture alongside a simple conceptual inundation model and a dynamic user interface.

A first estimation of tsunami hazard of the Pacific coast of Costa Rica from local and distant seismogenic sources

Costa Rica has been affected by several local and distant tsunamis in the past, but the historical information is scarce and incomplete. Its Pacific coast stretches for over a thousand kilometers, and tsunami hazard has never been evaluated for its full extent. Numerical modeling of tsunami propagation and inundation is a useful tool to assess tsunami hazard, particularly in cases with limited historical information available. Here, we perform a first estimation of tsunami hazard for the Pacific coast of Costa Rica from seismogenic sources, by numerical propagation of 57 local and distant tsunamis to a depth of 20 m. The results of our study identified tsunami sources that are particularly threatening for Costa Rica and determined locations with higher tsunami hazard. For the analysis, the Pacific coast of Costa Rica was divided into segments and subsegments based on differences in continental slope morphology. Subsegments with higher tsunami heights were Southwest Nicoya Peninsula and West Osa Peninsula, and in a lesser extent North Guanacaste, North Nicoya Peninsula, and Central Pacific. Regions with long and gentle slopes and narrow continental shelf were affected by higher tsunami waves, due to more efficient tsunami energy transmission to the shelf and reduced energy loss while traveling through a narrow shelf. On the opposite, steeper continental slopes reflected most of the tsunami energy, causing smaller tsunami heights nearshore, regardless of the shelf width. Nevertheless, other effects played a major role, like curved coastlines that focused tsunami energy, wave refraction, interference, and trapped edge waves. Distant tsunamis dominated the threat, with tsunamis coming from the Tonga-Kermadec and the Colombia-Ecuador Trenches causing the greatest heights due to directivity, and arrival times of about 15 h and 75 min, respectively. Local tsunamis had short arrival times but a localized impact, mainly at the shoreline in front of the generation region but were also affected by tsunami focusing, wave refraction, and edge waves. Outer rise and Osa sources caused the lowest impact within local sources. These results provide a guide for emergency planners to prioritize coastal locations and tsunami sources for tsunami preparedness actions and warning protocols.

Influence and interactions of input factors in urban flood inundation modeling: An examination with variance-based global sensitivity analysis

Flood risk assessment and management in urbanized area experience an increasing reliance on the supporting information provided by numerical flood modeling as a rapid and efficient tool. Although hydrodynamic modeling based on shallow water equations requires less parameters than hydrologic ones, uncertainties of the input factors describing both the driving forces and the complex urbanized environment still have important influences on the accuracy of the urban flood simulation results once induced into the modeling procedure. The influences of input factors of engineering concern in urban flood hydrodynamic modeling in an intensely urbanized area of 0.58 km2 in the city of Chongqing, China with a mixture of different land-use types are investigated in this study by taking advantage of a variance-based global sensitivity analysis methodology with particular focus upon the interaction between the uncertainties of different input factors describing the external driving force, the underlying urban surface, the drainage loss and the spatial resolution of modeling. The global sensitivity analysis results are examined with regard to maximum values of the flood extent, water depth and discharge and their variation in the course of the urban flood propagation. Influences of the input factors and their interactions are observed to vary for different model outputs. From a spatially-distributed perspective, a dependence of the interactions between input factors is found to be upon the relative location of their acting area associated to the land-use type. Antagonistic effects of the input factors describing the underlying urban surface and the drainage loss for the rainfall-induced flood water accumulations and movements are analyzed with the flood evolution. Main effects of different input factors and their interactions are of a certain consistency with the underlying physical processes of the urban flood propagation from both static and temporal perspective of the global sensitivity analysis results.

Detailing the impact of the Storegga Tsunami at Montrose, Scotland

The Storegga tsunami, dated in Norway to 8150±30 cal. years BP, hit many countries bordering the North Sea. Run‐ups of >30 m occurred and 1000s of kilometres of coast were impacted. Whilst recent modelling successfully generated a tsunami wave train, the wave heights and velocities, it under‐estimated wave run‐ups. Work presented here used luminescence to directly date the Storegga tsunami deposits at the type site of Maryton, Aberdeenshire in Scotland. It also undertook sedimentological characterization to establish provenance, and number and relative power of the tsunami waves. Tsunami model refinement used this to better understand coastal inundation. Luminescence ages successfully date Scottish Storegga tsunami deposits to 8100±250 years. Sedimentology showed that at Montrose, three tsunami waves came from the northeast or east, over‐ran pre‐existing marine sands and weathered igneous bedrock on the coastal plain. Incorporation of an inundation model predicts well a tsunami impacting on the Montrose Basin in terms of replicate direction and sediment size. However, under‐estimation of run‐up persisted requiring further consideration of palaeotopography and palaeo‐near‐shore bathymetry for it to agree with sedimentary evidence. Future model evolution incorporating this will be better able to inform on the hazard risk and potential impacts for future high‐magnitude submarine generated tsunami events.

Assessing climate-change-induced flood risk in the Conasauga River watershed: an application of ensemble hydrodynamic inundation modeling

Abstract. This study evaluates the impact of potential future climate change on flood regimes, floodplain protection, and electricity infrastructures across the Conasauga River watershed in the southeastern United States through ensemble hydrodynamic inundation modeling. The ensemble streamflow scenarios were simulated by the Distributed Hydrology Soil Vegetation Model (DHSVM) driven by (1) 1981–2012 Daymet meteorological observations and (2) 11 sets of downscaled global climate models (GCMs) during the 1966–2005 historical and 2011–2050 future periods. Surface inundation was simulated using a GPU-accelerated Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) hydrodynamic model. A total of 9 out of the 11 GCMs exhibit an increase in the mean ensemble flood inundation areas. Moreover, at the 1 % annual exceedance probability level, the flood inundation frequency curves indicate a ∼ 16 km2 increase in floodplain area. The assessment also shows that even after flood-proofing, four of the substations could still be affected in the projected future period. The increase in floodplain area and substation vulnerability highlights the need to account for climate change in floodplain management. Overall, this study provides a proof-of-concept demonstration of how the computationally intensive hydrodynamic inundation modeling can be used to enhance flood frequency maps and vulnerability assessment under the changing climatic conditions.

Flood inundation simulations based on GSMaP satellite rainfall data in Jakarta, Indonesia

Jakarta is the capital of Indonesia and is considered one of the most vulnerable cities to climate-related disasters, including flooding, sea-level rise, and storm surges. Therefore, the development of a flood-forecasting system for Jakarta is crucial. However, the accurate prediction of flooding in Jakarta is challenging because of the short flood concentration time in highly urbanized basins and the shortage of rainfall data in poorly gauged areas. The aim of this study is to simulate recent flood inundation using global satellite mapping of precipitation (GSMaP) products. The GSMaP products (NRT and Gauge V7) were evaluated and compared with hourly observation data from five ground stations in the Ciliwung River Basin. In addition, a rainfall-runoff and flood inundation model was applied to the target basin. The results of the analysis showed that the GSMaP Gauge data were more accurate than the GSMaP NRT data. However, the GSMaP Gauge cannot be used to provide real-time rainfall data and is, therefore, inadequate for real-time flood forecasting. We conclude that the GSMaP Gauge is suitable for replicating past flood events, but it is challenging to use the GSMaP NRT for real-time flood forecasting in Jakarta.

GIS-based hazard and vulnerability assessment of a torrential watershed

Torrents are ephemeral streams which usually carry heavy bedload and flash flows. The frequent changes in its course caused by meandering and migration lead to extensive damage to environment in valley and alluvial land. In the present study, application programming interface (API) was developed to analyze temporal Landsat data in Google Earth Engine(GEE) to understand land use/land cover and vegetation characteristics in the Tangri (Dangri) River, India, and Indian Remote Sensing Satellites data were used for hazard and vulnerability assessment. It is observed that because of various conservation activities as well as due to watershed inhabitants’ intervention, the areas under cropland, orchard/plantation and grass/scrub have increased. The monthly mean enhanced vegetation index (EVI) has also increased in the watershed. Although, the average annual soil loss is high, i.e., 40.4 t/ha/yr. It is highest for mountain physiographic unit (67.6 t/ha/yr.) and scrub forest (61.5 t/ha/yr.). Based on flood inundation modeling, it is estimated that for two-, five-, ten-, twenty-five-, fifty- and hundred-year return periods, 1.42–16.14% areas of watershed are likely to get inundated. Torrents’ vulnerability analysis was carried out using analytical hierarchy process (AHP) to derive composite vulnerability index by aggregating weights of various parameters and sub-parameters. The alternatives for the treatment of vulnerable sections in torrential regime were evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and ELimination Et Choice Translating REality (ELECTRE) models. The methodology demonstrated has potential to analyze torrential systems and also for their proficient planning, monitoring and management.

Accounting for DEM Error in Sea Level Rise Assessment within Riverine Regions; Case Study from the Shatt Al-Arab River Region

Global elevation datasets such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) are the best available terrain data in many parts of the world. Consequently, SRTM is widely used for understanding the risk of coastal inundation due to climate change-induced sea level rise. However, SRTM elevations are prone to error, giving rise to uncertainty in the quality of the inundation projections. This study investigated the error propagation model for the Shatt al-Arab River region (SARR) to understand the impact of DEM error on an inundation model in this sensitive, low-lying coastal region. The analysis involved three stages. First, a multiple regression model, parameterized from the Mississippi River delta region, was used to generate an expected DEM error surface for the SARR. This surface was subtracted from the SRTM DEM for the SARR to adjust it. Second, residuals from this model were simulated for the SARR. Modelled residuals were subtracted from the adjusted SRTM to produce 50 DEM realizations capturing potential elevation variation. Third, the DEM realizations were each used in a geospatial “bathtub” inundation model to estimate flooding area in the region given 1 m of sea level rise. Across all realizations, the area predicted to flood covered about 50% of the entire region, while predicted flooding using the raw SRTM covered only about 28%, indicating substantial underprediction of the affected area when error was not accounted for. This study can be an applicable approach within such environments worldwide.

Quantifying Slopes as a Driver of Forest to Marsh Conversion Using Geospatial Techniques: Application to Chesapeake Bay Coastal-Plain, United States

Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.

A Novel Hybrid Approach Based on Cellular Automata and a Digital Elevation Model for Rapid Flood Assessment

An efficient inundation model is necessary for emergency flood responses during storm events. Cellular automata (CA)-based flood models have been proven to produce rapid results while maintaining a certain degree of accuracy. However, the need for computational resources dramatically increases when the number of grid cells increases. Digital elevation model (DEM)-based models generate results even faster, but the simplified governing equations within the models fail to reflect temporal flood evolution. To achieve rapid flood modeling while maintaining model simplicity, a novel two-dimensional hybrid inundation model (HIM) was developed by combining the CA- and DEM-based concepts. Given the temporal flood evolution generated by the CA concept, final finer-scale predictions were obtained by applying the DEM-based concept. The performance of this model was compared to those of widely used, physically based hydraulic models using three UK Environment Agency (EA) benchmark test cases. The HIM yielded consistent prediction results but was faster than the CA-based model. Finally, a comparison was made against flood observations, and the overall root mean squared error (RMSE) for flood depth was 0.388–0.400 m. Considering the uncertainty in the observed flood depths, the HIM shows promising potential to serve as an intermediate tool for emergency response in practical cases.

Cooperative operation of the Grand Ethiopian Renaissance Dam reduces Nile riverine floods

AbstractThe construction of the Grand Ethiopian Renaissance Dam (GERD) on the Nile River has triggered much debate between Ethiopia, Sudan, and Egypt on the dam's effects. Once completed, the GERD will be Africa's largest hydropower plant. This study analyzes the implications of cooperative long‐term operation of the GERD for Nile riverine flooding downstream of the dam. A daily river system model of the Eastern Nile is developed and used to examine how cooperative long‐term operation of the GERD would affect the occurrence of three flood alarm levels (alert, critical, and flooding) in Sudan downstream of the dam. A reconnaissance‐level flood inundation model is developed for the Nile within Khartoum State (Sudan's capital) to assess the GERD's impacts on the flood extent in the state based on simulated flows from the river system model. Assuming the GERD is operated to achieve a 90% power reliability and active upstream‐downstream data sharing between Ethiopia and Sudan on the dam's daily outflows, results show that the GERD would reduce the occurrence of the three alarm levels. Based on 34 simulated river flow sequences, the proportion of years with at least one flooding alarm day at Khartoum Gage declined from 37% without the GERD to 11% with the GERD. Seasonal coordination and planning between Ethiopia and Sudan are necessary to mitigate the remaining riverine flood hazard. Although cooperative long‐term operation of the GERD could play a positive role in reducing the riverine flood hazard in Sudan, the associated river flow alterations would adversely impact recession agriculture and the environment.

The Impact of Flooding on Settlement Along the Jangkok River Mataram, Indonesia

Floods are natural disasters that have harmed populations in several places in the world. Flooding in urban areas can be caused by coastal floods or river floods. One of the flood-prone areas in Lombok is the Jangkok River, the largest rivers that cross the city of Mataram. Floods have consequences that impact the social and economic activities. Direct impacts of flooding can include damage to property and infrastructure, especially in the settlement area. The purposes of this research are to analyze the flood-prone areas and to build a flood modeling of the Jangkok River. The data used for the analysis combined a topographic map scale 1:5.000, DEM LiDAR, land system, and rainfall data from the Tropical Rainfall Measuring Mission. The result shows that along the Jangkok River at a distance of 100 m estimated as the flood-prone areas. River flood modeling shows settlements have very high potential affected by floods caused by overflowing water from the Jangkok River. Modeling of flood inundation for the return period of 10 years, 50 years, and 100 years shows that the area of inundated settlements periodically expands the flooded area of 70.95 ha, 97.17 ha, and 108.41 ha. This study is expected to be an input to improve the preparation of disaster mitigation-based spatial planning in many coastal cities in Indonesia.

Estimating River Channel Bathymetry in Large Scale Flood Inundation Models

AbstractFlood inundation modeling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modeling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFMs are only accurate for kinematic water surface profiles and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced model error compared to a target water surface profile by 66% and eliminated bias due to backwater effects. For a large‐scale test case in Mozambique this reduced flood extents by 40% and floodplain storage by 79% at the 5 years return period. The wet bias associated with uniform flow derived channels could have significant implications for modeling the role floodplains play in attenuating river discharges, potentially overstating their role.

A High-Resolution Flood Inundation Archive (2016–the Present) from Sentinel-1 SAR Imagery over CONUS

AbstractMost existing inundation inventories are based on surveys, news, or passive remote sensing imagery. Affected by spatiotemporal resolution or weather conditions, these inventories are limited in spatial details or coverage. Satellite synthetic aperture radar (SAR) data have recently enabled flood mapping at unprecedented spatiotemporal resolution. However, the bottleneck in producing SAR-based flood maps is the requirement of expert manual processing to maintain acceptable accuracy by most SAR-driven mapping techniques. To fill the vacancy, we generate a high-resolution (10 m) flood inundation dataset over the contiguous United States (CONUS) from nearly the entire Sentinel-1 SAR archive (from January 2016 to the present), using a recently developed automated Radar Produced Inundation Diary (RAPID) system. RAPID uses U.S. Geological Survey (USGS) water watch system and accumulated precipitation to identify SAR images that potentially overlap a flood event. The dataset include inundation events with the temporal scale from several days to months. Concluded from all 559 overlapping images in the period from 2016 to the first half of 2019, the comparison of the proposed dataset against the USGS Dynamic Surface Water Extent (DSWE) product yields an overall, user, producer agreements, and critical success index of 99.06%, 87.63%, 91.76%, and 81.23%, respectively, demonstrating the high accuracy of the proposed dataset and the robustness of the automated system. We anticipate this archive to facilitate many applications, including large-scale flood loss and risk assessment, and inundation model calibration and validation.

A Flood Inundation Modeling Approach for Urban and Rural Areas in Lake and Large-Scale River Basins

Fluvial floods are one of the primary natural hazards to our society, and the associated flood risk should always be evaluated for present and future conditions. The European Union’s (EU) Floods Directive highlights the importance of flood mapping as a key stage for detecting vulnerable areas, assessing floods’ impacts, and identifying damages and compensation plans. The implementation of the EU Flood Directive in Greece is challenging because of its geophysical and climatic variability and diverse hydrologic and hydraulic conditions. This study addressed this challenge by modeling of design rainfall at the sub-watershed level and subsequent estimation of flood design hydrographs using the Natural Resources Conservation Service (NRCS) Unit Hydrograph Procedure. The HEC-RAS 2D model was used for flood routing, estimation of flood attributes (i.e., water depths and flow velocities), and mapping of inundated areas. The modeling approach was applied at two complex and ungauged representative basins: The Lake Pamvotida basin located in the Epirus Region of the wet Western Greece, and the Pinios River basin located in the Thessaly Region of the drier Central Greece, a basin with a complex dendritic hydrographic system, expanding to more than 1188 river-km. The proposed modeling approach aimed at better estimation and mapping of flood inundation areas including relative uncertainties and providing guidance to professionals and academics.

Flooded with Error: Handling Uncertainty in SRTM for the Assessment of Sea Level Rise in the Mississippi River Delta

Digital elevation data are essential to estimate coastal vulnerability to flooding due to sea-level rise. Shuttle Radar Topography Mission (SRTM) 1 arc-second global is considered the best free global digital elevation data set available. Inundation estimates from SRTM, however, are subject to uncertainty due to inaccuracies in the elevation data. Small systematic errors in low, flat areas can generate large errors in inundation models, and SRTM is subject to positive bias in the presence of vegetation canopy, such as along channels and within marshes. In this study, we conducted an error assessment and developed a statistical error model for SRTM to improve the quality of elevation data in the Mississippi River Delta (MRD) region. Vegetation cover, SRTM elevation, and slope were found to be closely associated with SRTM error for a random sample of 10,000 small sites across the MRD region, with an ordinary least squares regression model using these variables explaining over 80 percent of the variation in error. Residuals from this model were spatially autocorrelated, and a variogram model was readily fit to them. We conclude by speculating on the utility of application of this model, developed for the MRD region, to similar near-coastal riverine regions around the world.

Scenario-Based Real-Time Flood Prediction with Logistic Regression

This study proposed a real-time flood extent prediction method to shorten the time it takes from the flood occurrence to an alert issuance. This method uses logistic regression to generate a flood probability discriminant for each grid constituting the study area, and then predicts the flood extent with the amount of runoff caused by rainfall. In order to generate the flood probability discriminant for each grid, a two-dimensional (2D) flood inundation model was verified by applying the Typhoon Chaba, which caused great damage to the study area in 2016. Then, 100 probability rainfall scenarios were created by combining the return period, duration, and time distribution using past observation rainfall data, and rainfall-runoff–inundation relation databases were built for each scenario by applying hydrodynamic and hydrological models. A flood probability discriminant based on logistic regression was generated for each grid by using whether the grid was flooded (1 or 0) for the runoff amount in the database. When the runoff amount is input to the generated discriminant, the flood probability on the target grid is calculated by the coefficients, so that the flood extent is quickly predicted. The proposed method predicted the flood extent in a few seconds in both cases and showed high accuracy with 83.6~98.4% and 74.4~99.1%, respectively, in the application of scenario rainfall and actual rainfall.