Flood Loss Estimation Model Research Articles

Relationship between residential house damage and flood characteristics: A case study in the Teesta River Basin, Bangladesh

The flood damage function model is indispensable for flood damage assessment as it can provide useful information for establishing flood protection strategies. For the assessment of residential house damage in different areas, flood damage function models have been traditionally proposed considering only the depth–damage curve. However, flood duration and flood may also have an influence on flood damage. Conventionally, the damage rate was estimated based on information about monetary losses collected from local people or insurance payout databases. This research proposes a new methodology to establish a flood damage function model that considers flood depth, flood duration, and flood velocity. Field surveys were conducted in the Teesta River Basin of rural Bangladesh. For each house, a questionnaire survey was conducted to identify the flood information (flood depth, flood duration, and the qualitative representation of flood velocity), household structure information (area, plinth height, and ceiling height), structural damage mechanism, and the required amount of material and labor to repair the damage after each flood event. Finally, a flood damage function model including the impacts of house types and different flood parameters was established through multiple regression analysis. The consistency of the model was verified through the estimation of different statistical parameters. It can serve as a flood loss estimation model as it correlates flooding with various parameters, such as building materials and their damage mechanism.

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%.

Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar

Flood loss models are essential tools for assessing flood risk. Flood damage assessment provides decision makers with critical information to manage flood hazards. This paper presents a multivariable flood damage assessment based on data from residential building and content damage from the Bago flood event of July 2018. This study aims to identify the influences on building and content losses. We developed a regression-based flood loss estimation model, which incorporates factors such as water depth, flood duration, building material, building age, building condition, number of stories, and floor level. Regression approaches, such as stepwise and best subset regression, were used to create the flood damage model. The selection was based on Akaike’s information criterion (AIC). We found that water depth, flood duration, and building material were the most significant factors determining flood damage in the residential sector.

A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe

AbstractIn view of globally increasing flood losses, a significantly improved and more efficient flood risk management and adaptation policy are needed. One prerequisite is reliable risk assessments on the continental scale. Flood loss modeling and risk assessments for Europe are until now based on regional approaches using deterministic depth‐damage functions. Uncertainties associated with the risk estimation are hardly known. To reduce these shortcomings, we present a novel, consistent approach for probabilistic flood loss modeling for Europe, based on the upscaling of the Bayesian Network Flood Loss Estimation MOdel for the private sector, BN‐FLEMOps. The model is applied on the mesoscale in the whole of Europe and can be adapted to regional situations. BN‐FLEMOps is validated in three case studies in Italy, Austria, and Germany. The officially reported loss figures of the past flood events are within the 95% quantile range of the probabilistic loss estimation, for all three case studies. In the Italian, Austrian, and German case studies, the median loss estimate shows an overestimation by 28% (2.1 million euro) and 305% (5.8 million euro) and an underestimation by 43% (104 million euro), respectively. In two of the three case studies, the performance of the model improved, when updated with empirical damage data from the area of interest. This approach represents a step forward in European wide flood risk modeling, since it delivers consistent flood loss estimates and inherently provides uncertainty information. Further validation and tests with respect to adapting the model to different European regions are recommended.

Establishment of flood damage function models: A case study in the Bago River Basin, Myanmar

This paper derives flood damage function models based on the relationships between flood inundation parameters and damage-aggravating factors for two land use categories: residential and agricultural. This case study considers the Bago River Basin in Myanmar, which is frequently damaged by flooding. A survey was conducted to determine the economic damage to affected populations and properties during a large flood in 2011. House damage, in-house damage, and income loss function models were established for residential areas, along with an agricultural damage function model. Flood water height, floor height from the ground, occurrence of landslide erosion and types of building materials could aggravate house damage, whereas in-house damage is exacerbated by flood water height above plinth level and types of house's plinth level. Income loss scales with flood duration, job category and household level. Flood water height, flood duration and the growth stage of paddies worsen agricultural damage. Knowing the relationship between flooding and its damaging factors, these models can be easily applied to a flood loss estimation model in further research.

Multi-variate residential flood loss estimation model for Jakarta: an approach based on a combination of statistical techniques

Jakarta has endured a series of devastating floods in 2002, 2007 and 2013. In the wake of climate change and complex interaction with diverse socio-economic factors, the flood situation in Jakarta will exacerbate from bad to worse. The city requires quantitative evaluation of flood risk to adapt to the changes arising from both existing and future flooding. Consequently, in this study a comprehensive multi-variate residential flood loss estimation model was developed. The model was developed from the outcome of extensive household surveys performed in the aftermath of the 2013 January floods. In addition, a novel procedure for classifying the flood loss into homogenous groups is proposed and implemented on the data from Jakarta. The proposed approach employed principal component analysis supplemented with correlation analysis and mutual information in identifying the factors that can capture the homogeneity in flood damage and loss data. The identified factors are then used as inputs for cluster analysis to for grouping the data into homogenous groups. Depth–damage curves were developed for these groups. The novel approach for demarcating flood loss data produced groups which showed significant reduction in damage variability and improved loss prediction within the surveyed dataset. Further, a scaled flood loss estimation model was developed which quantified the effect of mitigation measures on flood damage and loss.

Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO.

Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.

Development and evaluation of FLFAcs – A new Flood Loss Function for Australian commercial structures

Commercial building flood losses significantly affect the Australian economy; however, there are not many models for commercial flood damage estimation and their results are not reliable. This study has attempted to derive and develop a new model (FLFAcs) for estimating the magnitude of direct damage on commercial structures. The FLFAcs – Flood Loss Function for Australian commercial structures, was calibrated using empirical data collected from the 2013 flood in Bundaberg, Australia, and considering the inherent uncertainty in the data sample. In addition, the newly derived model has been validated using a K-fold cross-validation procedure. The model performance has also been compared with the Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) and the Federal Emergency Management Agency (FEMA) damage functions from overseas, as well as the ANUFLOOD damage model from Australia.The validation procedure shows very good results for FLFAcs performance (no bias and only five per cent mean absolute error). It also shows that ANUFLOOD, as Australia’s most prevalently used commercial loss estimation model, is still subject to very high uncertainty. Hence, there is an immediate need for a project to build new depth–damage functions for commercial and industrial properties.Awareness of these issues is important for strategic decision-making in flood risk reduction and it could amplify the cognition of decision-makers and insurance companies about flood risk assessment in Australia.

Uncertainty and Sensitivity Analysis of the HAZUS-MH Flood Model

AbstractEstimating flood losses for hazard mitigation planning is an increasingly important aspect of flood risk management. The loss estimation process typically includes sequential analysis of the flood hazard, building characteristics, structural damage, and economic loss. However, the reliability of economic loss estimates from the modeling process is not well understood. This research applies the method of global sensitivity analysis to the HAZUS-MH flood loss estimation model. The work is guided by two research questions: what is the uncertainty of the flood loss estimates, and what is the relative contribution of each model component to the overall uncertainty? Based on the uncertainty analysis, the estimate at the upper bound of loss distribution was found to be a factor of three higher than the lower-bound estimate. The sensitivity analysis revealed the choice of digital elevation data to be the most influential modeling stage. Recommendations are provided so that HAZUS-MH users can develop more ...

Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

The modeling of flood damage is an important component for risk analyses, which are the basis for risk-oriented flood management, risk mapping, and financial appraisals. An automatic urban structure type mapping approach was applied on a land use/land cover classification generated from multispectral Ikonos data and LiDAR (Light Detection And Ranging) data in order to provide spatially detailed information about the building stock of the case study area of Dresden, Germany. The multi-parameter damage models FLEMOps (Flood Loss Estimation Model for the private sector) and regression-tree models have been adapted to the information derived from remote sensing data and were applied on the basis of the urban structure map. To evaluate this approach, which is suitable for risk analyses, as well as for post-disaster event analyses, an estimation of the flood losses caused by the Elbe flood in 2002 was undertaken. The urban structure mapping approach delivered a map with a good accuracy of 74% and on this basis modeled flood losses for the Elbe flood in 2002 in Dresden were in the same order of magnitude as official damage data. It has been shown that single-family houses suffered significantly higher damages than other urban structure types. Consequently, information on their specific location might significantly improve damage modeling, which indicates a high potential of remote sensing methods to further improve risk assessments.

A hydro‐economic modelling framework for flood damage estimation and the role of riparian vegetation

AbstractA modelling framework for the quick estimate of flood inundation and the resultant damages is developed in this paper. The model, called the flood economic impact analysis system (FEIAS), can be applied to a river reach of any hydrogeological river basin. For the development of the integrated modelling framework, three models were employed: (1) a modelling scheme based on the Hydrological Simulation Program FORTRAN model that was developed for any geomorphological river basin, (2) a river flow/floodplain model, and (3) a flood loss estimation model. The first sub‐model of the flood economic impact analysis system simulates the hydrological processes for extended periods of time, and its output is used as input to a second component, the river/floodplain model. The hydraulic model MIKE 11 (quasi‐2D) is the river/floodplain model employed in this study. The simulated flood parameters from the hydraulic model MIKE 11 (quasi‐2D) are passed, at the end of each time step, to a third component, the flood loss model for the estimation of flood damage. In the present work, emphasis was given to the seasonal variation of Manning's coefficient (n), which is an important parameter for the determination of the flood inundation in hydraulic modelling. High values of Manning's coefficient for a channel indicate high flow resistance. The riparian vegetation can have a large impact on channel resistance. The modelling framework developed in this paper was used to investigate the role of riparian vegetation in reducing flood damage. Moreover, it was used to investigate the influence of cutting riparian vegetation scenarios on the flow characteristics. The proposed framework was applied to the downstream part of the Koiliaris River basin in Crete, Greece, and was tested and validated with historical data. Copyright © 2012 John Wiley & Sons, Ltd.

Estimation of flood losses to agricultural crops using remote sensing

The estimation of flood damage is an important component of risk-oriented flood design, risk mapping, financial appraisals and comparative risk analyses. However, research on flood loss modelling, especially in the agricultural sector, has not yet gained much attention. Agricultural losses strongly depend on the crops affected, which need to be predicted accurately. Therefore, three different methods to predict flood-affected crops using remote sensing and ancillary data were developed, applied and validated. These methods are: (a) a hierarchical classification based on standard curves of spectral response using satellite images, (b) disaggregation of crop statistics using a Monte Carlo simulation and probabilities of crops to be cultivated on specific soils and (c) analysis of crop rotation with data mining Net Bayesian Classifiers (NBC) using soil data and crop data derived from a multi-year satellite image analysis. A flood loss estimation model for crops was applied and validated in flood detention areas (polders) at the Havel River (Untere Havelniederung) in Germany. The polders were used for temporary storage of flood water during the extreme flood event in August 2002. The flood loss to crops during the extreme flood event in August 2002 was estimated based on the results of the three crop prediction methods. The loss estimates were then compared with official loss data for validation purposes. The analysis of crop rotation with NBC obtained the best result, with 66% of crops correctly classified. The accuracy of the other methods reached 34% with identification using Normalized Difference Vegetation Index (NDVI) standard curves and 19% using disaggregation of crop statistics. The results were confirmed by evaluating the loss estimation procedure, in which the damage model using affected crops estimated by NBC showed the smallest overall deviation (1%) when compared to the official losses. Remote sensing offers various possibilities for the improvement of agricultural flood loss estimation. However, crop prediction and loss modelling are still quite uncertain and further research is needed.

Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector

The estimation of flood loss is difficult, especially in the commercial sector, because of its great inhomogeneity. However, the reliability of loss modelling is fairly unknown, since flood-loss models are scarcely validated. The newly developed Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) was validated on the micro-scale using a leave-one-out cross-validation procedure. Additionally, different meso-scale loss functions were compared. Meso-scale model application was undertaken in 19 municipalities which were affected during the 2002 flood in Germany. Model results were compared with the results of three other loss models, as well as with official loss records. The micro-scale validation shows very good results, with no bias and mean absolute errors between 23 and 31%. The meso-scale validation indicates that FLEMOcs provides good results, especially in large areas with many affected companies where high losses are expected. Citation Seifert, I., Kreibich, H., Merz, B. & Thieken, A. H. (2010) Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector. Hydrol. Sci. J. 55(8), 1315–1324.

Development of FLEMOcs – a new model for the estimation of flood losses in the commercial sector

The estimation of flood damage is an important component for risk-oriented flood design, risk mapping, financial appraisals and comparative risk analyses. However, research on flood-loss modelling, especially in the commercial sector, has not gained much attention so far. Therefore, extensive data about flood losses were collected for affected companies via telephone surveys after the floods of 2002, 2005 and 2006 in Germany. Potential loss determining factors were analysed. The new Flood Loss Estimation MOdel for the commercial sector (FLEMOcs) was developed on the basis of 642 loss cases. Losses are estimated depending on water depth, sector and company size as well as precaution and contamination. The model can be applied to the micro-scale, i.e. to single production sites as well as to the meso-scale, i.e. land-use units, thus enabling its countrywide application. Citation Kreibich, H., Seifert, I., Merz, B. & Thieken, A. H. (2010) Development of FLEMOcs – a new model for the estimation of flood losses in the commercial sector. Hydrol. Sci. J. 55(8), 1302–1314.

A mathematical model for flood loss estimation

This paper introduces an integrated model for flood loss estimation in a river basin. The model is the combination of a physically based distributed hydrologic model and a distributed flood loss estimation model. The hydrologic model considers major processes of the water cycle through physically based governing equations, which are solved to simulate the propagation of water in each of these processes. It is designed to consider the man-made flood control structures, such as river embankments, retarding basins, etc. which affect flooding characteristics. The loss estimation model is formulated based on stage-damage relationships between different flood inundation parameters and landuse features. It calculates the economic loss to different landuse features based on the simulated flood parameters obtained from the hydrologic model for any flood event. A case study illustrates the real world application of the integrated model to a medium size river basin in Japan, which is frequently affected by floods. The simulated river discharge and surface inundation by the flood model show good agreement with the observations. Urban flood loss simulated by the loss estimation model with the simulated flood parameters agrees with the estimated damage using post flood surveyed parameters.