Flood Damage Estimation Research Articles

Improving the Accuracy of Flood Damage Assessments to Residential Structures via the Use of Experimental Data

ABSTRACTThe current practice of flood loss prediction presents limitations in accurately predicting building flood losses at multiple scales. While whole‐building estimates can more accurately predict high‐level losses (i.e., large groups of buildings), a significant analysis error is revealed with small‐scale (i.e., individual, or small groups of buildings) investigation. This research presents a more robust, data driven, small‐scale, flood damage estimation approach for residential buildings. The approach is based on component‐level, depth–damage curves derived from experimental analysis. Structures with standard residential construction materials typical to the south‐eastern United States were built and incrementally flooded for short durations. The materials were assessed to determine the level of damage inflicted. This experimentally derived damage data were then translated into a set of flood depth–damage functions (DDFs). The DDFs were tailored for analysis at smaller scales and incorporated the ability to apply damage uncertainty in damage analysis. To demonstrate the applicability of the experimentally derived DDFs to damage estimation at smaller scales, the functions are applied to a hypothetical building design typical of the south‐eastern United States.

Estimation of River Flood Damages Among Farmers and Small Business in Developing Countries: Evidence from the Kali Lamong Indonesia

Flooding is a severe disaster in the Kali Lamong river basin (58 Km2). For this reason, detailed flood damage estimates are needed to design an optimal flood management strategy. This study aims to estimate flood damage in the Kali Lamong River Basin. The data collection method used cluster sampling in 15 villages with 870 respondents. We surveyed farmers’ households and micro businesses from October to December 2022 to obtain information on flood damage, flood characteristics, and socio-economics characteristics based on the 27-30 September 2022 flood event. Based on those data, we used regression analysis to develop flood damage functions to know how flood characteristics are related to the damage. Our study found that the economic flood damage in the Kali Lamong river basin is estimated to be US$ 2,3 million for farmer’s households and US$ 10,3 million for micro businesses. This study shows that household flood damages 5,2% more than micro business.

Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation

Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation

ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images

We propose an automated lowest floor elevation (LFE) estimation algorithm based on computer vision techniques to leverage the latent information in street view images. Flood depth-damage models use a combination of LFE and flood depth for determining flood risk and extent of damage to properties. We used image segmentation for detecting door bottoms and roadside edges from Google Street View images. The characteristic of equirectangular projection with constant spacing representation of horizontal and vertical angles allows extraction of the pitch angle from the camera to the door bottom. The depth from the camera to the door bottom was obtained from the depthmap paired with the Google Street View image. LFEs were calculated from the pitch angle and the depth. The testbed for application of the proposed method is Meyerland (Harris County, Texas). The results show that the proposed method achieved mean absolute error of 0.190 m (1.18 %) in estimating LFE. The height difference between the street and the lowest floor (HDSL) was estimated to provide information for flood damage estimation. The proposed automatic LFE estimation algorithm using street view images and image segmentation provides a rapid and cost-effective method for LFE estimation compared with the surveys using total station theodolite and unmanned aerial systems. By obtaining more accurate and up-to-date LFE data using the proposed method, city planners, emergency planners and insurance companies could make a more precise estimation of flood damage.

Improved flood depth estimation with SAR image, digital elevation model, and machine learning schemes

Study regionNhat Le river basin, the floodplains of Central coast of Vietnam. Study focusFlood disasters have a significant impact on population and economies worldwide. To accurately assess flood damage, it is crucial to estimate the water depth and predict the potential spread of damage. However, conventional methods of estimating flood depth can be time-consuming and costly due to the large amount of data required from hydraulic and rain-runoff models. To overcome these limitations, we propose a new approach that integrates a machine learning (ML) algorithm with a floodwater depth estimation tool (FwDET) based on SAR imagery and digital elevation models (DEMs). To evaluate our approach, we trained and tested six ML regression algorithms using 804 sets of inventory flood depth data from the historic flood on the Nhat Le River in Vietnam in October 2020 as the dependent variable. This approach significantly improves flood depth prediction accuracy, enabling a faster and more cost-effective estimation of flood damage. New hydrological insights for the regionOur research shows that by combining ML with FwDET, we were able to significantly enhance water depth prediction accuracy. We observed a 25.05% increase in the R2 coefficient and a 13.46% increase in the R coefficient. We recommend integrating satellite imagery, DEM, and ML for flood depth predictions, especially for floodplains with similar topographical conditions as the Nhat Le basin. This approach offers a practical, cost-effective, and near real-time solution for flood response. Implementing this approach can help improve flood damage assessment and mitigate the potential impacts of flood disasters on communities and economies.

Flood damage cost prediction using random forest

Floods are one of nature's deadliest catastrophes, causing permanent and catastrophic damage on the socioeconomic system, agriculture and human life. The problems arise when floods could cause a lot of economic damage such as damage to buildings, agriculture and others. Flood damage estimation is a subject of study that has not received much attention. The objective of this research is to explore the Random Forest algorithm in the flood damage cost prediction. The damages specified by the Malaysia’s Department of Irrigation (JPS) are structures such as culverts, MTB bridges, riverbank ruins, concrete main channels, farm roads, hydrological stations, agricultural and water drainage, JPS pump houses and tyres in Terengganu. Terengganu is one of the states in Malaysia which has to endure floods during the monsoon season by the end of the year. The methods employed in this research include data collection, data pre-processing, backend engine coding and user interface design. This project was implemented using the Python programming language. The data were collected from the annual flood report provided by the JPS Negeri Terengganu. The research used the rainfall and streamflow data from the year 2012 to 2022 as attributes to forecast the cost of the JPS structures damages in Terengganu. The prediction results showed that the best model achieved the accuracy of 91.47% with a Mean Squared Logarithmic Error (MSLE) of 0.48 and Coefficient of Determination (R2) of 0.92. In the performance evaluation, the model with 80:20 training and testing data ratio produced the best result in predicting the flood damage cost. The potential enhancements to this research involve extending the scope to encompass all Malaysian states, incorporating diverse flooded structures and adding more input variables for a more improved and more reliable flood prediction system.

Whole value at risk for flood damage estimates through spatial data analysis

Effective disaster risk reduction (DRR) for flooding requires a comprehensive estimate of the whole value at risk (WVAR) to inform appropriate and proportionate mitigation expenditure. Conventional flood risk estimation methods focus on the direct effects of inundation on community value and generally ignore collateral effects on assets and populations outside the flooded area. Consequently, conventional methods tend to underestimate the cost of flooding, leading to an underestimate of the return on DRR investment. Using spatial data analysis in an urban case study for Toronto, Canada, we identify and capture the collateral value at risk (ColVaR) to estimate the WVAR more comprehensively. In our case study, ColVaR (mean estimate) amounts to 70% of direct losses (ColVar = $344 M; direct losses = $475 M CAD), ranging from 20%–150% (ColVar $100–$740 M) when spanning the 90% confidence intervals of our Monte Carlo simulations. Thus, we demonstrate that if the collateral value at risk is ignored, WVAR can be significantly underestimated, potentially leading to reduced disaster risk reduction resource allocations and thereby adding risk exposure for communities. We present an accessible, seven-step process using existing spatial analysis tools and techniques that infrastructure stakeholders and planners can use to estimate ColVaR and better formulate DRR measures for their communities.

Anchoring tools to communities: insights into perceptions of flood informational tools from a flood-prone community in Louisiana, USA

The lack of community-relevant flood informational resources and tools often results in inadequate and divergent understandings of flood risk and can impede communities' ability to function cohesively in the face of increasing flood threats. The current study reports on a set of workshops that the authors conducted with various groups (citizens, city engineers and planners, realtors and builders, and media representatives) within a flood prone community to evaluate how novel hydroinformatic tools that include hydrodynamic modeling, geospatial visualization, and socioeconomic analysis can enhance understanding of flood risk and engagement in flood risk mitigation among diverse community members. The workshops were designed to help identify stakeholder preferences regarding key functionality needed for integrated hydroinformatic technologies and socioeconomic analyses for flood risk reduction. Workshop participants were asked to use and comment on examples of prototype flood risk informational tools, such as: (1) flood damage estimation tool, (2) drivability and emergency accessibility tool, and (3) community-scale social and economic metrics tool. Data gathered from workshops were analyzed using qualitative analysis based on a grounded-theory approach. Data were coded by hand based on themes identified by the research team and incorporated deviant case analysis to ensure minority opinions was represented. The study results are focused on the following main themes and how flood tools can address them: (1) improving the understanding of flood risk and engagement in flood risk mitigation, (2) reducing the gap between individual and community risk, (3) challenges in communicating flood risk information, (4) enhancing relevance to and engagement of the community, and (5) enabling actionable information. Our research demonstrates the need for community-anchored tools and technologies that can illustrate local context, include local historical and simulated events at multiple levels of community impact, enable analyses by flood professionals while also providing simplified tools of use by citizens, and allow individuals to expand their knowledge beyond their homes, businesses, and places of work.

A conceptual model for the estimation of flood damage to power grids

Abstract. Flood damage assessment is a critical aspect in any decision-making process on flood risk management. For this reason, reliable tools for flood damage estimation are required for all the categories of exposed elements. Despite infrastructures can suffer high economic losses in case of flood, compared to other exposed sectors, their flood damage modelling is still a challenging task. This is due, on the one hand, to the structural and dynamic complexity of infrastructure networks, and, on the other hand, to the lack of knowledge and data to investigate damage mechanisms and to calibrate and validate damage models. Grounding on the investigation of the state-of-the-art, this paper presents a conceptualization of flood damage to power grids and reviews the methodologies in the field for an in-depth understanding of the existing modelling approaches, challenges, and limitations. The conceptual model highlights: (i) the different kinds of damage (i.e., direct, indirect, and systemic) the network can suffer, (ii) the hazard, exposure, and vulnerability parameters on which they depend, (iii) the spatial and temporal scales required for their assessment, (iv) the interconnections among power grids and economic activities, and (v) the different recipients of economic losses. The development of the model stresses the importance of dividing the damage assessment into two steps: the estimation of damage in physical units and the consequent economic losses in monetary terms. The variety of damage mechanisms and cascading effects shaping the final damage figure arises, asking for an interdisciplinary and multi-scale evaluation approach. The ultimate objective of the conceptual model is to be an operative tool in support of more comprehensive and reliable flood damage assessments to power grids.

Discrepancies in estimated flood losses on paddy production: Application of damage models on historical flood records of the Northwest States of Peninsular Malaysia

Information of flood damage to crops production is essential for guiding risk-based decisions in reducing future adverse impacts of floods. For paddy, its susceptibility to floods is one of the factors that may influence the loss of paddy’s production. To date, there have been limited studies that have been undertaken to examine in detail how different damage functions (with and without a susceptibility index) influence flood damage estimates of paddy using past recorded flood information. This study attempts to examine flood damage functions of different forms in two conditions: as their stand-alone forms and when being applied for damage and risk estimations. Furthermore, recorded revenues from the past were used to investigate the effects of paddy’s susceptibility to floods on revenues. Historical flood evidence and revenues between 1987 and 2021 occurred on paddy granaries under the Muda Agricultural Development Authority’s (MADA) monitoring located in Perlis and Kedah states of Peninsular Malaysia and were used in this study. Application of three damage functions reveals that the exclusion of susceptibility index from the damage calculation results in significantly overestimations of losses, with a standard deviation reaching as high as 38 and overestimations up to four times that of the function with susceptibility index. The high estimates could hinder the acceptance of risk-based flood assessment (and its results) in flood alleviation decision-making. Furthermore, as the percentage of flooded area increases, the revenue loss increases, indicating the effects of floods on harvested paddy quality. This further highlights the potential role of the susceptibility index in the damage function.

Disaster loss calculation method of urban flood bimodal data fusion based on remote sensing and text

Study regionMajor urban areas in Henan Province of central China. Study focusdata fusion technology is also a key and difficult point in the field of flood research. Remote sensing and text data have different modalities and scales, making fusion difficult. This study proposed a remote sensing and text bimodal data fusion model based on UFCLI, and we validated the spatiotemporal distribution of floods and the calculation results of disaster losses. The results show that through the coupling analysis of remote sensing and text bimodal data, rainstorm and flood events can be fully reproduced in space and time. The proposed UFCLI effectively improves the accuracy of remote sensing single-data inversion for urban flood disaster losses. The flood loss in Henan is 121.98 billion yuan, and the accuracy improvement result is R² increased by 0.08 and MAPE decreased by 0.88. New hydrological insights for the regionIn the case of sudden urban storm flooding with complex spatial and temporal evolution, the traditional hydrological-hydraulic model has many pending parameters, which makes it difficult to accurately calculate large-scale disaster losses. By establishing a theoretical model of bimodal data fusion, we effectively use the complementary spatiotemporal information using remote sensing and text to solve the differences in spatiotemporal scales existing between remote sensing and text data. The timeliness and accuracy of urban flood damage estimation have further improved. Data Availability StatementNot applicable.

A web-based analytical urban flood damage and loss estimation framework

A web-based analytical urban flood damage and loss estimation framework

Quantitative Flood Risk Assessment in Drammenselva River, Norway

Floods are frequent natural hazards, triggering significant negative consequences for the economy every year. Their impact is expected to increase in the near future due to socio-economic development and climate change. In order to minimize the probability and magnitude of expected economic losses and compensation costs, it is essential that flood risk managers are properly informed about potential damage related to hazard features and exposure. In this paper, a flood damage estimation method was proposed for the assessment of flood risk in the Drammen River basin by using a hydraulic model, GIS, and a flood loss estimation model. Hazard variables such as flood depth, flood extent, and flood velocity were computed for the current and future climatic scenarios using the hydraulic model for flood damage assessment. To visualize the flood extent, velocity, depth, and their impact, the results of modelling are illustrated in the form of flood inundation maps produced in GIS. A flood loss estimate included buildings and other infrastructure that are major exposures in flood-prone areas. The flood damage model is formulated based on stage–damage relationships between different flood depths and land-use categories. It calculates the economic loss related to different land-use features based on the simulated flood parameter obtained from the hydraulic model from 100- to 1000-year return periods. For the case study, the results show that the highest proportion of the total damage in each repetition interval (approximately 90–92%) is expected to occur in buildings. In addition, results showed that the effects of climate change will raise the total damage from floods by 20.26%.

Developing a framework for the assessment of current and future flood risk in Venice, Italy

Abstract. Flooding causes serious impacts on the old town of Venice, its residents, and its cultural heritage. Despite this existence-defining condition, limited scientific knowledge on flood risk of the old town of Venice is available to support decisions to mitigate existing and future flood impacts. Therefore, this study proposes a risk assessment framework to provide a methodical and flexible instrument for decision-making for flood risk management in Venice. We first use a state-of-the-art hydrodynamic urban model to identify the hazard characteristics inside the city of Venice. Exposure, vulnerability, and corresponding damage are then modeled by a multi-parametric, micro-scale damage model which is adapted to the specific context of Venice with its dense urban structure and high risk awareness. Furthermore, a set of individual protection scenarios are implemented to account for possible variability in flood preparedness of the residents. This developed risk assessment framework was tested for the flood event of 12 November 2019 and proved able to reproduce flood characteristics and resulting damage well. A scenario analysis based on a meteorological event like 12 November 2019 was conducted to derive flood damage estimates for the year 2060 for a set of sea level rise scenarios in combination with a (partially) functioning storm surge barrier, the Modulo Sperimentale Elettromeccanico (MOSE). The analysis suggests that a functioning MOSE barrier could prevent flood damage for the considered storm event and sea level scenarios almost entirely. A partially closed MOSE barrier (open Lido inlet) could reduce the damage by up to 34 % for optimistic sea level rise prognoses. However, damage could be 10 % to 600 % higher in 2060 compared to 2019 for a partial closure of the storm surge barrier, depending on different levels of individual protection.

Spatial Estimates of Flood Damage and Risk Are Influenced by the Underpinning DEM Resolution: A Case Study in Kuala Lumpur, Malaysia

The sensitivity of simulated flood depth and area to DEM resolution are acknowledged, but their effects on flood damage and risk estimates are less well understood. This study sought to analyse the relative benefits of using global DEMs of different resolution sizes, 5 m AW3D Standard, 12.5 m ALOS PALSAR and 30 m SRTM, to simulate flood inundation, damage and risk. The HEC-RAS 2D model was adopted for flood simulations, and the Toba River in the Klang River Basin in Malaysia was chosen for the case study. Simulated inundation areas from AW3D coincide the most with reported flooded areas, but the coarser-resolution DEMs did capture some of the reported flooded areas. The inundation area increased as the resolution got finer. As a result, AW3D returned almost double flood damage and risk estimates compared to ALOS PALSAR, and almost quadruple compared to SRTM for building-level damage and risk analysis. The findings indicate that a finer-resolution DEM improves inundation modelling and could provide greater flood damage and risk estimates compared to a coarser DEM. However, DEMs of coarser resolution remain useful in data-scarce regions or for large-scale assessments in efforts to manage flood risk.

Deriving First Floor Elevations within Residential Communities Located in Galveston Using UAS Based Data

Flood damages occur when just one inch of water enters a residential household and models of flood damage estimation are sensitive to first-floor elevation (FFE). The current sources for FFEs consist of costly survey-based elevation certificates (ECs) or assumptions based on year built, foundation type, and flood zone. We sought to address these limitations by establishing the role of an Unmanned Aerial System (UAS) to efficiently derive accurate FFEs. Four residential communities within Galveston Island, Texas were selected to assess efficient flight parameters required for UAS photogrammetry within the built environment. A real-time kinematic positioning enabled (RTK) UAS was then used to gather georeferenced aerial imagery and create detailed 3D photogrammetric models with ±0.02 m horizontal and ±0.05 m vertical accuracies. From these residential models, FFEs and other structural measurements present in traditional ECs were obtained. Comparative statistical analyses were performed using the UAS-based measurements and traditional EC measurements. UAS based FFE measurements achieved 0.16 m mean absolute error (MAE) across all comparative observations and were not statistically different from traditional EC measures. We conclude the RTK enabled UAS approach is an efficient, cost-effective method in establishing accurate FFEs and other flood-sensitive measures in residential communities.

Are interactions important in estimating flood damage to economic entities? The case of wine-making in France

Abstract. Estimating flood damage, although crucial for assessing flood risk and for designing mitigation policies, continues to face numerous challenges, notably the assessment of indirect damage. It is widely accepted that damage other than direct damage can account for a significant proportion of total damage. Yet due to scarcer data sources and lack of knowledge on links within and between economic activities, indirect impacts have received less attention than direct impacts. Furthermore, attempts to grasp indirect damage through economic models have not gone below regional levels. Even though local communities can be devastated by flood events without this being reflected in regional accounts, few studies have been conducted from a microeconomic perspective at local level. What is more, the standard practices applied at this level of analysis tackle entities but ignore how they may be linked. This paper addresses these two challenges by building a novel agent-based model of a local agricultural production chain (a French cooperative wine-making system), utilized as a virtual laboratory for the ex ante estimation of flood impacts. We show how overlooking existing interactions between economic entities in production chains can result in either overestimation (double counting) or underestimation (wrong estimation of the consequences for the activity) of flood damage. Our results also reveal that considering interactions requires thorough characterization of their spatial configuration. Based on both the application of our method and the results obtained, we propose balanced recommendations for flood damage estimation at local level.

The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

The accurate estimation of flood risk depends on, among other factors, a correct delineation of the floodable area and its associated hydrodynamic parameters. This characterization becomes fundamental in the flood hazard analyses that are carried out in urban areas. To achieve this objective, it is necessary to have a correct characterization of the topography, both inside the riverbed (bathymetry) and outside it. Outside the riverbed, the LiDAR data led to an important improvement, but not so inside the riverbed. To overcome these deficiencies, different models with simplified bathymetry or modified inflow hydrographs were used. Here, we present a model that is based upon the calibration of the Manning’s n value inside the riverbed. The use of abnormally low Manning’s n values made it possible to reproduce both the extent of the flooded area and the flow depth value within it (outside the riverbed) in an acceptable manner. The reduction in the average error in the flow depth value from 50–75 cm (models without bathymetry and “natural” Manning’s n values) to only about 10 cm (models without bathymetry and “calibrated” Manning’s n values), was propagated towards a reduction in the estimation of direct flood damage, which fell from 25–30% to about 5%.

Development of flood damage assessment method for residential areas considering various house types for Bago Region of Myanmar

Development of a flood damage estimation method that considers house type is important to evaluate the effectiveness of different adaptation options. This study aimed to analyze flood impact on residential areas and household damage, to develop a new, more accurate, method for flood damage assessment, considering different house types. To do this it was necessary to understand the vulnerability of different house types and household assets, and to develop flood damage functions which accounted for these variations. We considered the Bago River basin of Myanmar as a study area. A detailed household questionnaire survey was conducted to collect data. Using this data, new flood damage functions for residential buildings and assets for different types of house were developed by considering building characteristics such as construction materials, number of stories, and height of plinth level from the ground. The actual and potential flood damage data were combined to develop flood damage functions with improved accuracy for extreme floods. Then, flood damage to residential buildings and assets for recent floods were assessed by developing a grid-based approach, integrating hydrologic-hydraulic and flood loss estimation models. The reduction in expected annual damage by use of different flood adaptation options for residential buildings was also evaluated quantitatively. The results of this study show that elevating the plinth level of the building and adding an additional story to the house can significantly reduce the flood damage to residential buildings and assets, and these results provide useful information for establishing flood protection adaptation measures.

A Revised Approach to Flood Damage Estimation in Flood Risk Maps and Flood Risk Management Plans, Poland

This article describes the determination of asset values in residential and industrial areas in Poland that can be used in the preparation of flood hazard and risk maps (FRMs) and flood risk management plans (FRMPs). In the Floods Directive’s first cycle of implementation (2010–2015), German indicators of asset value were adapted to Polish conditions. This approach was used due to the lack of national statistics necessary to determine the value of property. In this study, the asset values in residential and industrial areas were prepared on the basis of the nationwide household wealth survey conducted in 2016 by the Narodowy Bank Polski (the central bank of Poland) and other data available from Statistics Poland. The proposed approach enables the determination of asset values based on real, validated data. The obtained indicators result in higher and more realistic values of the assets of households (buildings with contents) and the assets of companies (fixed assets and stocks) operating in both residential and industrial areas. The proposed approach, as an extension in relation to the first planning cycle, has been implemented in the second cycle of FRMP preparation in Poland.