Flood Risk Assessment Research Articles

Modelling the compound floods upon combined rainfall and storm surge events in a low-lying coastal city

Low-lying coastal cities are highly vulnerable to compound floods, which may result from a simultaneous occurrence of storm surges and heavy rainfall. Systematic numerical analysis is essential for projecting flood scenarios upon compound events. In this study, a coupled model that integrates the 1D drainage module with the 2D overland flow module is developed and validated by taking Macau as the study area. The joint cumulative probability distribution between rainfall and storm tide are derived based on the historical typhoon data. The flood hazards are projected under designed scenarios, and an investigation is conducted on the backflow processes through drainage system and effectiveness of installing tide gates. The flood zones are delineated and the contributions of rainfall and storm tide to floods are analyzed. Storm surge exerts a greater influence on the maximum inundation depth in the western Macau Peninsula than heavy precipitation does. As the peak tidal level increases from a 10-year to a 200-year return period, the increments of flooded area ratio to the total land area are approximately 19 %. The backflows through underground drainage system could expand the tide affected area during storm events, as indicated by the backflows in the pipe 1.4 km away from the coastal boundary. By installing tide gates, backflows could be greatly eliminated in the corresponding pipes. The inundation area in the compound flood scenario is not equal to the superposition of the extents by the individual components. The transition area that is affected by both storm tide and rainfall extends from coast to inland areas under the combined effects. This study is expected to furnish valuable insights for flood risk assessment, flood hazard prevention and mitigation in low-lying coastal cities such as Macau.

Integrated flood risk assessment of properties and associated population at county scale for Nebraska, USA

Risk assessment of properties and associated population was conducted for the state of Nebraska, leveraging only open-source datasets. The flood risk framework consisted of interactions among drivers, i.e. hazard, exposure, vulnerability, and response, to assess the risks related to properties and associated populations. To quantify hazard on a county scale, we considered properties at risk of flooding based on a flood score (a higher score represents a greater chance of flooding). Exposure was quantified by considering population density at the county level. We quantified vulnerability under four categories: social, ecological, economic, and health. Response, a relatively newer component in flood risk assessment, was also quantified under three distinct categories: structural, non-structural, and emergency. Overall, we found that counties in eastern Nebraska (Sarpy, Dakota, Wayne, and Adams) have a higher risk of flooding consequences due to more exposure to vulnerable assets such as population and property. The assessment also observed that counties in eastern Nebraska are in the process of improving their flood control measures with dams, levees, and higher insurance coverage that can subdue the risks associated with flooding. The results from this study are anticipated to guide water managers and policymakers in making more effective and locally relevant policies and measures to mitigate flood risks and consequences.

Small Island City Flood Risk Assessment: The Case of Kingston, Jamaica

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country’s socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience.

The Synergic Effects of Climate Variability on Rainfall Distribution over Hare Catchment of Ethiopia

Climate analysis at relevant time scales is important for water resources management, agricultural planning, flood risk assessment, ecological modeling, and climate change adaptation. This study analyzes the spatiotemporal variability of climate on rainfall distribution for the Hare catchment of Ethiopia. Numerous hydroclimatic variables and scenarios were developed to assess the pattern of rainfall during different seasons. The average annual precipitation varies between −37.3%, +33.1%, and −38.2%, +61.2%, for RCP 4.5 and RCP 8.5, respectively. The anticipated declines in mean seasonal rainfall changes for the Bega and Belg seasons range from −69.6% to 88.4% and from −60.6% to 15.2% for RCP 4.5 and RCP 8.5, respectively. Climate models predict that the average periodic precipitation considered for the Kiremt season will vary from −12.1% to 1.33%. The Belg, Kiremt, and Bega seasons will likely see a 28.2%, 12.2%, and 22.6% drop in mean seasonal precipitation, respectively. The decrease in stream flow accompanied by the aforementioned climate scenarios (RCP 4.5 and RCP 8.5) can be as high as 19.6% and 6.7%, respectively. Also, the amount of discharge will reduce in the near future because of a substantial reduction in rainfall and a rise in evapotranspiration in the catchment. This decline in stream flow has its own effect on the future availability of water resources. The research finding is vital to environmental protection authority, decision makers, and scientific community to undertake climate change adaption techniques for rain scare areas. A program combined with multi-RCMs to evaluate climate change effects on hydrometeorology generated a novel approach to this research with appropriate adaptation mechanisms.

Understanding the compound flood risk along the coast of the contiguous United States

Abstract. Compound flooding is a type of flood event caused by multiple flood drivers. The associated risk has usually been assessed using statistics-based analyses or hydrodynamics-based numerical models. This study proposes a compound flood (CF) risk assessment (CFRA) framework for coastal regions in the contiguous United States (CONUS). In this framework, a large-scale river model is coupled with a global ocean reanalysis dataset to (a) evaluate the CF exposure related to the coastal backwater effects on river basins, and (b) generate spatially distributed data for analyzing the CF hazard using a bivariate statistical model of river discharge and storm surge. The two kinds of risk are also combined to achieve a holistic understanding of the continental-scale CF risk. The estimated CF risk shows remarkable inter- and intra-basin variabilities along the CONUS coast with more variabilities in the CF hazard over the US west and Gulf coastal basins. Different risk assessment methods present significantly different patterns in a few key regions such as the San Francisco Bay area, the lower Mississippi River, and Puget Sound. Our results highlight the need to weigh different CF risk measures and avoid using single statistics-based or hydrodynamics-based CFRAs. Uncertainty sources in these CFRAs include the use of gauge observations, which cannot account for the flow physics or resolve the spatial variability of risks, and underestimations of the flood extremes and the dependence of CF drivers in large-scale models, highlighting the importance of understanding the CF risks for developing a more robust CFRA.

Assessing the spatiotemporal impact of users’ exposure and vulnerability to flood risk in urban built environments

Flood risk in an urban built environment depends on the combination of the hazard, the vulnerability of the built environment itself and its infrastructure (referred to as physical vulnerability), and the exposure and vulnerability of the people residing, working or visiting it (i.e., their human condition). However, factors affecting those people vary over space and time depending on the uses of the built environment. This research offers a methodology for combined spatiotemporal flood risk assessment, providing hourly variations in risks due to hazard, physical vulnerability, users’ exposure, and vulnerability. A mesoscale approach is adopted by collecting and managing data for each open space in the urban layout (e.g., street, square) and the facing buildings. In particular, users’ exposure and vulnerability are investigated for indoor and outdoor uses and their temporalities, providing hourly distributions of users’ density, age, familiarity with the built environment, and direct exposure to the floodwaters. Then, the Analytical Hierarchy process is used to combine risk factors. Finally, the application to a case study application (an urban district in Guimarães, Portugal) demonstrates how users’ factors alter the risk over the day within the same mesoscale element and considers different elements which share the same hazard and physical vulnerability.

Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River, Central Europe

BackgroundPrecisely predicting the water levels of rivers is critical for planning and supporting flood hazard and risk assessments and maintaining navigation, irrigation, and water withdrawal for urban areas and industry. In Hungary, the water level of rivers has been recorded since the early nineteenth century, and various water level prediction methods were developed. The Discrete Linear Cascade Model (DLCM) has been used since 1980s. However, its performance is not always reliable under the current climate-driven hydrological changes. Therefore, we aimed to test machine learning algorithms to make 7-day ahead forecasts, choose the best-performing model, and compare it with the actual DLCM.ResultsAccording to the results, the Long Short-Term Memory (LSTM) model provided the best results in all time horizons, giving more precise predictions than the Baseline model, the Linear or Multilayer Perceptron Model. Despite underestimating water levels, the validation of the LSTM model revealed that 68.5‒76.1% of predictions fall within the required precision intervals. Predictions were relatively accurate for low (≤ 239 cm) and flood stages (≥ 650 cm), but became less reliable for medium stages (240–649 cm).ConclusionsThe LSTM model provided better results in all hydrological situations than the DLCM. Though, LSTM is not a novel concept, its encoder–decoder architecture is the best option for solving multi-horizon forecasting problems (or “Many-to-Many” problems), and it can be trained effectively on vast volumes of data. Thus, we recommend testing the LSTM model in similar hydrological conditions (e.g., lowland, medium-sized river with low slope and mobile channel) to get reliable water level forecasts under the rapidly changing climate and various human impacts.Graphical

Disaggregation of the Copernicus Land Use/Land Cover (LULC) and Population Density Data to Fit Mesoscale Flood Risk Assessment Requirements in Partially Urbanized Catchments in Croatia

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment.

Flood risk assessment using an indicator based approach combined with flood risk maps and grid data

The increasing frequency of tropical cyclones, such as typhoons and localized heavy rainfall, increases the risk of flood damage in urban areas around the world. Countries implementing flood prevention projects use qualitative risk assessments, such as flood risk assessments, to support decision-making. However, existing flood risk assessment methods are not able to select and reflect only the damage targets that are directly exposed to flood risk. In addition, existing indexing methods are limited in improving the skewness of the raw data distribution, which distorts the calculated flood risk index. This study conducted a flood risk assessment based on an indicator-based approach using flood risk maps and grid data utilizing indicators (buildings, road area, etc.) from 2016 to 2019 for 44 cities and counties in the Gyeongsang-do Province. The flood risk assessment in this study consists of four items (Hazard, Exposure, Vulnerability, and Capacity) and nine indicators. Instead of using statistical data, we used the overlapped grids with the flood risk map for Exposure and Vulnerability indicators to reflect the flood risk exposed targets in the risk assessment. The limitations of existing indexing methods, such as min–max normalization, were improved by dividing the distribution into ten quantiles and scoring each quantile interval when indexing the indicators. Finally, entropy weights and Euclidean distance were applied to calculate the annual flood risk index of 44 cities and counties. The annual flood risk indices for local governments were arranged in descending order and then, the rankings of flood damage amount in 2019 were compared with the calculated index rankings. The sum and mean of absolute errors were 386 and 9.897, and the root mean square error was calculated as 11.930, which showed an improvement over the existing method. Using the calculated indices, it is possible to create Hazard vs. Capacity graph, Exposure vs. Vulnerability graph, or annual flood risk index graph. These graphs can be utilized to understand the level of exposure to flood risk of local governments and to assess the level of response through the Capacity index that contributes to damage reduction. It is believed that the flood risk assessment method presented in this study not only accurately identify the flood risk for local governments but can also be used to support decision-making processes to strengthen the government’s disaster prevention capabilities.

Infiltration Efficiency Index for GIS Analysis Using Very-High-Spatial-Resolution Data

Infiltration models and impervious surface models have gained significant attention in recent years as crucial tools in urban and environmental planning, to assess the extent of land-surface changes and their impacts on hydrological processes. These models are important for understanding the hydrological dynamics and ecological impacts of urbanization and for the improvement of sustainable land-use planning and stormwater-management strategies. Due to the fact that many authors partially or entirely overlook the significance of the infiltration process in geographic information system (GIS) analyses, there is currently no universally accepted method for creating an infiltration model that is suitable for GIS multicriteria decision analysis (GIS-MCDA). This research paper presents an innovative approach to modeling the infiltration-efficiency index (IEI) for GIS analysis, with a focus on achieving high-quality results. The proposed methodology integrates very-high-resolution (VHR) remote-sensing data, GIS-MCDA, and statistical methods. The methodology was tested and demonstrated on a small sub-catchment in Metković, Croatia. The study developed a VHR IEI model from six specific criteria that produced values between 0 and 0.71. The model revealed that 14.89% of the research area is covered by impervious surfaces. This percentage is relatively favorable when compared to urban areas globally. The majority of the research area (62.79%) has good infiltration efficiency. These areas are predominantly characterized by agricultural land use, encompassing orchards, tangerines, olive groves, vineyards, and a diverse range of low-lying and high vegetation on flat terrain. The IEI model can provide input spatial data for high-resolution GIS analysis of hydrological processes. This model will aid decision-makers in stormwater-management, flood-risk assessment, land-use planning, and the design of green infrastructure. By utilizing the information derived from this study, policymakers can make informed decisions to mitigate flooding risks and promote sustainable urban development.

A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods

Comprehending the changing patterns of flood magnitudes globally, particularly in the context of nonstationary conditions, is crucial for effective flood risk management. This study introduces a unique approach that employs simulated discharge data to unravel these intricate variations. Through a comprehensive analysis of a substantial ensemble of General Circulation Models (GCMs) runoff datasets, we examine the dynamics of nonstationary flood magnitudes on a global scale. A pivotal aspect of our investigation is the development of a reference map, which helps delineate suitable scenarios for applying stationary or nonstationary methods in estimating extreme floods. This map is then employed to compare estimations of 100-year flood magnitudes using both methodologies across specific geographical areas. Our findings distinctly highlight the disparities arising from the use of stationary versus nonstationary approaches for estimating extreme floods. These insights underscore the significance of considering nonstationary for accurate flood risk assessment and mitigation strategies. The practical utility of our reference map in aiding informed decision making for stakeholders and practitioners further underscores its importance. This study contributes to the scholarly understanding of the evolving nature of flood phenomena and provides valuable insights for crafting adaptive measures in response to changing climatic conditions.

Coastal Flood Mapping with Two Approaches Based on Observations at Furadouro, Northern Portugal

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD); in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30-year-long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea states according to different return periods and also for performing a sensitivity test for established empirical formulas to estimate wave run-up in this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment, planning, and understanding of coastal responses under extreme weather conditions.

Combining Landsat, VIIRS Night-time Light, and Sentinel-1 SAR for Spatial Flood Risk Assessment in Coastal Area: an Earth Engine Cloud Computing Process

The intensity of development in coastal areas stimulates various potential issues such as flood disasters. This study aims to demonstrate the importance of latest methods and geospatial data as inputs for coastal spatial planning policies in efforts to reduce flood disaster risks. Leveraging spatial analysis with cloud computing through Google Earth Engine (GEE), this research assesses flood risk components—hazards, vulnerability, and capacity. The method involves processing SAR Sentinel-1 data to map flood inundation as a representation of hazards, analyzing Landsat and WorldPop data to evaluate vulnerability, and assessing capacity by utilizing VIIRS nighttime light level imagery to determine economic activities. The chosen research study location is the coastal area of Pekalongan due to frequent flood disasters throughout the year. The results demonstrate that cloud computing is capable of assessing flood risks. The flood inundation model using SAR data covers an area of 2,780 hectares with an accuracy of 96.75%. The analysis also reveals the highest vulnerability level, reaching 15.7% (946.32 hectares) of the total area. The capacity analysis indicates a medium to high level of 15% (913.6 hectares). The assessment of flood risks in the coastal area is dominated by the medium to very high-risk class, covering 43% (2,631.84 hectares) of the area. In conclusion, integrating cloud-based flood risk modeling into spatial planning is crucial, considering disaster resilience for sustainable human habitats.

Analyzing flood characteristics and establishing a monitoring network for the red river area of Hanoi

Flooding is a phenomenon in which river water rises during the rainy season (in the North of Viet Nam from June to September every year), causing flooding in some alluvial grounds outside the Red River dyke in the Hanoi area. This phenomenon occurs frequently with different scales in area and depth of flooding, which depends on the hydro-meteorological conditions of the entire region of Red river and the ability of flow regulation structures and flood drainage on river. The impacts of flooding due to rising river levels not only cause instability in daily life in residential areas but also change river dynamics and cause increased water pressures and seepage pressures on the dikes. Currently, flooding can be largely controlled by regulating the flow of rivers through hydroelectric dams in upstream areas. However, establishing a flood monitoring network and creating a database to assess the impact of flooding is necessary to minimize damage and improve the efficiency of exploitation and use of the Red River. This article focuses on research on flood risk assessment and the basis for establishing a flood monitoring network on the Red River in the Hanoi area to serve sustainable exploitation of the territory.

Quantitative assessment of flooding risk based on predicted evacuation time: A case study in Joso city, Japan

Flooding is a frequent natural hazard, threatening people's lives and properties worldwide. Even though completely preventing damage from flooding is impossible, an appropriate evacuation can help minimize its impact and mitigate human casualties. However, analyzing various flooding information to make quick evacuation judgments is challenging for decision-makers. Therefore, a quantitative flooding risk assessment is needed to support appropriate evacuation decisions. This study introduces the travel time under ideal evacuation conditions to quantify evacuation vulnerability, with Dijkstra's algorithm finding the evacuation route and the Random Forest algorithm predicting the evacuation time. The evacuation vulnerability is then integrated with time-varying flooding hazards and estimated congestion levels to assess evacuation risk. Herein, a case study in Joso, Japan is used as an example to demonstrate the practicality and applicability of the proposed approach. As a result, the prediction model achieved high accuracy with an RMSE of 0.88min and a MAPE of 5.88 %. The proposed approach clearly illustrated the vulnerable areas and times that may hinder an efficient evacuation process. Also, this approach identified and visualized the individual evacuation risk distribution during a given flooding scenario: 49.38 % of buildings were assessed as having the highest-risk: no evacuation ability, while a maximum of 11.86 % and 3.83 % of the buildings were assessed as middle-risk and high-risk. Furthermore, the regional evacuation deadline and priorities were specified to ensure timely and effective evacuation. Overall, this study develops an approach for assessing evacuation vulnerability and risk that could facilitate awareness among the public and provide judgmental bases for evacuation preparedness and decision-making.

F28: A novel coupling strategy for 1D/2D hydraulic models for flood risk assessment of the Mekong Delta

Abstract Coupling models of different dimensions is one of the most important yet under-represented challenges. This paper introduces a new modeling strategy to streamline a more flexible and effective integrated one-dimensional (1D)/two-dimensional (2D) model for floodplains along lowland rivers. The 1D model, utilizing the finite volume method, solves the Saint–Venant equations, while the 2D mesh employs unstructured quadrilateral elements. The two strategies couple the 1D/2D models: direct 1D/2D connection by the law of mass conservation at supernode, and lateral 1D/2D model connection by spillways at riverbank. The coupling strategy in F28 guarantees the water balance and the conservation of momentum at the integrated 1D/2D nodes. The model was applied to the Mekong Delta to address the capacity of hydrodynamic simulations integrating various water infrastructures. Results showed that the developed model has a strong potential to be applied to other lowland rivers worldwide with complex infrastructures.

An empirical flood fatality model for Italy using random forest algorithm

Due to the increasing occurrence of natural hazards, such as floods, a significant number of lives are lost each year worldwide. The risk of experiencing catastrophic losses from flooding is exacerbated due to the changing climate and increasing anthropogenic activities. Consequently, predicting the conditions leading to fatalities is crucial in the assessment of flood risk. However, the existing modeling capabilities in this field are limited, emphasizing the critical need for the development of such tools. Here, we show that the occurrence of flood fatalities can be estimated using a random forest (RF) algorithm applied to nine explanatory variables characterizing each fatality. Furthermore, by converting the RF model outcomes into a user-friendly tool, it is possible to predict the probability of the occurrence of flood-related fatalities, based on variables describing hazard intensity and the environmental and sociodemographic conditions contributing to such events. Our results represent an initial attempt towards a predictive model of flood fatalities in the Italian context. They reveal the key factors that together influence flood fatalities, enabling the prediction of such occurrences. These findings can serve as a foundational framework for quantitatively assessing the risk to the population from such events and as a valuable resource for identifying strategies to mitigate flood risk.

Stochastic Models of Rainfall

Rainfall is the main input to most hydrological systems. To assess flood risk for a catchment area, hydrologists use models that require long series of subdaily, perhaps even subhourly, rainfall data, ideally from locations that cover the area. If historical data are not sufficient for this purpose, an alternative is to simulate synthetic data from a suitably calibrated model. We review stochastic models that have a mechanistic structure, intended to mimic physical features of the rainfall processes, and are constructed using stationary point processes. We describe models for temporal and spatial-temporal rainfall and consider how they can be fitted to data. We provide an example application using a temporal model and an illustration of data simulated from a spatial-temporal model. We discuss how these models can contribute to the simulation of future rainfall that reflects our changing climate.

Flood risk assessment of subway stations based on projection pursuit model optimized by whale algorithm: A case study of Changzhou, China

With rapid modernization, traffic jams have become a part of people's daily lives. An increasing number of cities have begun to build subways to solve this problem. This study focuses on the flood risk analysis of subway stations, which is often overlooked. However, such disasters cause serious mass casualties and huge property losses. Therefore, this is of great concern. This study applies the method of projection pursuit model optimized by whale algorithm to evaluate the flood disasters of subway stations. The results showed that the flood risk level of 11 subway stations was very low, that of 21 subway stations was low, that of seven subway stations was moderate, and that of one subway station was high. Compared to the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the application of the projection pursuit model based on the whale algorithm has higher precision and better global searchability. The main contribution of this study is to provide a new method and idea for the field of flood risk assessment of subway stations, and to provide a scientific model and reference for local rail companies. This study used the whale algorithm to optimize the projection pursuit model and applied it to the flood risk assessment of subway stations. This is an innovation in this field that has strong engineering application significance.

Assessment of flood risk by using geospatial techniques in Wabi Shebele River Sub-basin, West Hararghe Zone, southeastern Ethiopia

Floods are produced as a result of several factors such as human-induced land-use changes, topographic conditions, and climate change. This study aimed to assess flood risk using geographic information system-based multi-criteria evaluation techniques in the Wabi Shebele River Sub-basin. Six parameters, namely, soil type, slope, land use and land cover, proximity to rivers, rainfall, and elevation, were used. The eigenvector of weight for each factor was computed in IDRISI Selva 17.0. Results show that the study area was subjected to a very high 527.31 km2 (9.42%), high 2133.66 km2 (38.12%), moderate 1906.44 km2 (34.06%), low 847.35 km2 (15.14%), and a very low 182.54 km2 (3.26%) flood risk, respectively. Results reveal that the Wabi Shebele River Sub-basin is highly vulnerable to flooding. This study assists policymakers with regard to land-use planning. To reduce the effects of flood risk, integrated watershed management must be implemented in the study area.