Flood Damage Models Research Articles

Recent Changes of Floods and Related Impacts in Niger Based on the ANADIA Niger Flood Database

During the last two decades, the sub-Saharan region has experienced unusual floods that have differentially impacted the region. No official and precise data regarding flood damage and impacts on the population are available, and the magnitude of events are not easily evaluated. Most previous studies have investigated this new threat using data derived from local media sources or world disaster databases. The aim of this study was to provide the scientific community and policy makers with an updated and reliable referenced data source concerning floods in Niger between 1998 and 2015, at national, regional and sub-regional scales. Reliable information regarding floods was derived from the national official flood damage database (ANADIA DB) showing their impact on the country. During the investigated period, considerable numbers regarding flood impacts were found (about 4000 settlements and 1.7 million people were affected by floods). The analysis also indicates a sudden increase in flood impacts since 2010. Regions in the south-west (Tillabery, Dosso and Niamey district) are the most affected; however, this kind of risk involves the whole country, and some particularly vulnerable areas have been identified. A data modeling comprehensive framework based on remotely sensed rainfall (climate hazards group infrared precipitation with stations (CHIRPS)) and vegetation index (moderate resolution imagery spectroradiometer normalized difference vegetation index (MODIS NDVI)) datasets data along with census data were used to investigate which variables are most able to explain the recent and sudden Niger flood vulnerability detected at the departmental scale. Only a few statistically significant flood damage models were found (61 out of 297), due essentially to the non-linearity of the increase in damage time series compared to environmental and climatic trends. The population increase is the most significant variable at national level; however, at regional and sub-regional scales, different patterns provided evidence to identify local triggers for vulnerability.

Regional and Temporal Transferability of Multivariable Flood Damage Models

AbstractReliable flood damage assessment is important for decision‐making in flood risk management. Flood damage assessment is often done with damage curves based only on water depth. These depth‐damage curves are usually developed based on data from a specific location and specific flood conditions. Such depth‐damage curves tend to be applied outside the scope of their validity. Validation studies show that in such cases depth‐damage curve are not very reliable, probably due to excluded influencing variables. The expectation is that the inclusion of more variables in a damage function will improve its transferability. We compare multi‐variable models based on Bayesian Networks and Random Forests developed on the basis of flood damage data sets from Germany and The Netherlands. The performance of the models is tested on a validation sub‐set of both countries' data. The models are also updated with data from the other country and then tested again. The results show that the German models (BN/RF‐FLEMOps) perform better in the Netherlands than the Dutch models (BN/RF‐Meuse) perform in Germany. This is probably because the FLEMOps models are based on more heterogeneous data than the Meuse models. The FLEMOps models, therefore, are better able to capture damages processes from other events and in other locations. Model performance improves via updating the models with data from the location to which the model is transferred to. The results show that there is high potential to develop improved damage models, by training multi‐variable models with heterogeneous data, for example from multiple flood events and locations.

Multi-objective optimal design for flood risk management with resilience objectives

In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.

Multi-variable flood damage modelling with limited data using supervised learning approaches

Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Several recent studies have shown that supervised learning techniques applied to a multi-variable data set can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive data set, which is rarely available, and this is currently holding back the widespread application of these techniques. In this paper we enrich a data set of residential building and contents damage from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2-D flood simulations are used to add information on flow velocity, flood duration and the return period to the data set, and cadastre data are used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched data set in combination with the supervised learning techniques delivers a 20 % reduction in the mean absolute error, compared to a simple model only based on the water depth, despite several limitations of the enriched data set. We find that with our data set, the tree-based methods perform better than the Bayesian network.

Tree‐based flood damage modeling of companies: Damage processes and model performance

AbstractReliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage‐influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector‐specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector‐specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector‐specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector‐specific data acquisition and a consideration of sector‐specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.

Exploring the potential of multivariate depth‐damage and rainfall‐damage models

In Europe, floods are among the natural catastrophes that cause the largest economic damage. This article explores the potential of two distinct types of multivariate flood damage models: ‘depth‐damage’ models and ‘rainfall‐damage’ models. We use survey data of 346 Flemish households that were victim of pluvial floods complemented with rainfall data from both rain gauges and weather radars. In the econometrical analysis, a Tobit estimation technique is used to deal with the issue of zero damage observations. The results show that in the ‘depth‐damage’ models flood depth has a significant impact on the damage. In the ‘rainfall‐damage’ models there is a significant impact of rainfall accumulation on the damage when using the gauge rainfall data as predictor, but not when using the radar rainfall data. Finally, non‐hazard indicators are found to be important for explaining pluvial flood damage in both ‘depth‐damage’ and ‘rainfall‐damage’ models.

Estimation of river flood damages in Jakarta, Indonesia

Flooding is a serious problem in Jakarta, and detailed estimation of flood damage is necessary to design optimal flood management strategies. This study aims to estimate flood damage in a densely populated area in Jakarta by means of a survey, to develop the relationship between flood characteristics and flood damage, and to compare the damage estimates from the survey with the damage estimates obtained by a flood damage model for Jakarta, i.e. the damage scanner model. We collected data on economic losses of the January 2013 flood in a survey of flood-affected households and business units in Pesanggrahan River. The actual flood damage in the survey area is US$ 0.5 million for the residential sector and US$ 0.7 million for the business sector. The flood damage for a similar event in the same area based on the damage scanner model is estimated to be US$ 1.3 million for the residential sector and US$ 9.2 million for the business sector. The flood damage estimates obtained by the survey approach are lower compared to the damage scanner approach due to different ways in obtaining flood damage data and in defining the maximum flood damage per object, the different spatial levels of analysis, and uncertainties in constructing the flood damage curves that were applied in the damage scanner model.

INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis

Abstract. Methodologies to estimate economic flood damages are increasingly important for flood risk assessment and management. In this work, we present a new synthetic flood damage model based on a component-by-component analysis of physical damage to buildings. The damage functions are designed using an expert-based approach with the support of existing scientific and technical literature, loss adjustment studies, and damage surveys carried out for past flood events in Italy. The model structure is designed to be transparent and flexible, and therefore it can be applied in different geographical contexts and adapted to the actual knowledge of hazard and vulnerability variables. The model has been tested in a recent flood event in northern Italy. Validation results provided good estimates of post-event damages, with similar or superior performances when compared with other damage models available in the literature. In addition, a local sensitivity analysis was performed in order to identify the hazard variables that have more influence on damage assessment results.

Large-scale application of the flood damage model RAilway Infrastructure Loss (RAIL)

Abstract. Experience has shown that river floods can significantly hamper the reliability of railway networks and cause extensive structural damage and disruption. As a result, the national railway operator in Austria had to cope with financial losses of more than EUR 100 million due to flooding in recent years. Comprehensive information on potential flood risk hot spots as well as on expected flood damage in Austria is therefore needed for strategic flood risk management. In view of this, the flood damage model RAIL (RAilway Infrastructure Loss) was applied to estimate (1) the expected structural flood damage and (2) the resulting repair costs of railway infrastructure due to a 30-, 100- and 300-year flood in the Austrian Mur River catchment. The results were then used to calculate the expected annual damage of the railway subnetwork and subsequently analysed in terms of their sensitivity to key model assumptions. Additionally, the impact of risk aversion on the estimates was investigated, and the overall results were briefly discussed against the background of climate change and possibly resulting changes in flood risk. The findings indicate that the RAIL model is capable of supporting decision-making in risk management by providing comprehensive risk information on the catchment level. It is furthermore demonstrated that an increased risk aversion of the railway operator has a marked influence on flood damage estimates for the study area and, hence, should be considered with regard to the development of risk management strategies.

Influence of sewer network models on urban flood damage assessment based on coupled 1D/2D models

One of the main steps in assessing flood risk in urbanised areas is the quantification of damage costs. Damage is often estimated based on depth‐damage curves for which depth maps are obtained (ideally) from coupled flood models. While the comparative analysis of flood damage models has been extensively researched in the literature, the influence of the underlying sewer network model has not been investigated. In this study, a 2D locally inertial equations flood risk analysis model (GWM) is coupled with two stormwater models (SWMM, tested with three different configurations, and SIPSON). The assessment of the network performance is made through the total exchanged volume between the surface and stormwater system, the surcharged conduits and manholes, maximum overland inundation and costs using a stepped version of the multi‐coloured manual depth‐damage curves for continuous urban fabric. The models behave similarly; however, they do show differences in the head pressures, number of surcharged manholes and maximum depth in some locations. The case study results show that despite the good agreement in damage between the four configurations (≈6% maximum disagreement), some localised high differences in maximum depth observed [0.25 (m)] exist. It was also shown that SWMM needs to be calibrated in order to perform similarly to Preissmann slot models.

Calibration and validation of FLFA<sub>rs</sub> -- a new flood loss function for Australian residential structures

Abstract. Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods. Flood is a frequent natural hazard that has significant financial consequences for Australia. The emergency response system in Australia is very successful and has saved many lives over the years. However, the preparedness for natural disaster impacts in terms of loss reduction and damage mitigation has been less successful. In this paper, a newly derived flood loss function for Australian residential structures (FLFArs) has been presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of the method developed in this work (contrasted to one Australian model and one model from USA) has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of the selected methodologies has been performed in terms of Australian geographical conditions. Results obtained from the new empirically based function (FLFArs) and the other models indicate that it is apparent that the precision of flood damage models is strongly dependent on selected stage damage curves, and flood damage estimation without model calibration might result in inaccurate predictions of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been calibrated with real damage data.

Uncertainty in flood damage estimates and its potential effect on investment decisions

Abstract. This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage functions and maximum damages can have large effects on flood damage estimates. This explanation is then used to quantify the uncertainty in the damage estimates with a Monte Carlo analysis. The Monte Carlo analysis uses a damage function library with 272 functions from seven different flood damage models. The paper shows that the resulting uncertainties in estimated damages are in the order of magnitude of a factor of 2 to 5. The uncertainty is typically larger for flood events with small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Estimating flood damage to railway infrastructure – the case study of the March River flood in 2006 at the Austrian Northern Railway

Abstract. Models for estimating flood losses to infrastructure are rare and their reliability is seldom investigated although infrastructure losses might contribute considerably to the overall flood losses. In this paper, an empirical modelling approach for estimating direct structural flood damage to railway infrastructure and associated financial losses is presented. Via a combination of event data, i.e. photo-documented damage on the Northern Railway in Lower Austria caused by the March River flood in 2006, and simulated flood characteristics, i.e. water levels, flow velocities and combinations thereof, the correlations between physical flood impact parameters and damage occurred to the railway track were investigated and subsequently rendered into a damage model. After calibrating the loss estimation using recorded repair costs of the Austrian Federal Railways, the model was applied to three synthetic scenarios with return periods of 30, 100 and 300 years of March River flooding. Finally, the model results are compared to depth-damage-curve-based approaches for the infrastructure sector obtained from the Rhine Atlas damage model and the Damage Scanner model. The results of this case study indicate a good performance of our two-stage model approach. However, due to a lack of independent event and damage data, the model could not yet be validated. Future research in natural risk should focus on the development of event and damage documentation procedures to overcome this significant hurdle in flood damage modelling.

Multivariate pluvial flood damage models

Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods.

Assessing the operation rules of a reservoir system based on a detailed modelling chain

Abstract. According to available climate change scenarios for Belgium, drier summers and wetter winters are expected. In this study, we focus on two multi-purpose reservoirs located in the Vesdre catchment, which is part of the Meuse basin. The current operation rules of the reservoirs are first analysed. Next, the impacts of two climate change scenarios are assessed and enhanced operation rules are proposed to mitigate these impacts. For this purpose, an integrated model of the catchment was used. It includes a hydrological model, one-dimensional and two-dimensional hydraulic models of the river and its main tributaries, a model of the reservoir system and a flood damage model. Five performance indicators of the reservoir system have been defined, reflecting its ability to provide sufficient drinking water, to control floods, to produce hydropower and to reduce low-flow conditions. As shown by the results, enhanced operation rules may improve the drinking water potential and the low-flow augmentation while the existing operation rules are efficient for flood control and for hydropower production.

1C21 Development of the Evaluation Method on Measures against Inundation at an Underground Station(Safety-Infrastructure)

In recent years, heavy rainfall in Japan has been increasing due to global warming although it originally has high precipitation. Over the past years, some underground stations were inundated due to heavy rainfall. Moreover, there is a possibility that more underground stations would be inundated in the future. Therefore, it is important to consider appropriate measures against inundation because flood in underground stations would cause serious damages which endanger the safety of passengers. In this research, we developed the prototype of system to evaluate measures against inundation at underground stations in case of flood damages with the simulation technique. We carried out case studies to verify the effects of some prevention against inundation by this prototype-system. As a result, by the prototype of system, we can calculate the time required for evacuation, the number of passengers who walk through inundated spots and, finally evaluate the effects of measures against inundation. Also, by case study with the developed system in a virtual station, we confirmed that it is possible to verify the effects of combinations with each countermeasure against inundation. In conclusion, we could achieve some positive results in the development of this system. However, our system is not enough practical to be utilized at this stage. At this stage, we are considering to improve flood damage model in order to be system based on the actual phenomenon than the present. Finally, we would like to develop this system continuously to be utilized practically in order to contribute to promote countermeasures against inundation in existing stations.

Are flood damage models useful outside their original context?

For researchers and decision makers trying to understand the risks of extreme weather, complex damage prediction models—which in some cases consider physical, sociological, and economic variables—are proving to be a valuable tool. Yet at the same time, the effects of extreme weather are highly context specific: A snow storm easily shrugged off by Canadians can cripple the southwest United States, for instance.

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.

Assessment of the effectiveness of flood adaptation strategies for HCMC

Abstract. Coastal cities are vulnerable to flooding, and flood risk to coastal cities will increase due to sea-level rise. Moreover, Asian cities in particular are subject to considerable population growth and associated urban developments, increasing this risk even more. Empirical data on vulnerability and the cost and benefits of flood risk reduction measures are therefore paramount for sustainable development of these cities. This paper presents an approach to explore the impacts of sea-level rise and socio-economic developments on flood risk for the flood-prone District 4 in Ho Chi Minh City, Vietnam, and to develop and evaluate the effects of different adaptation strategies (new levees, dry- and wet proofing of buildings and elevating roads and buildings). A flood damage model was developed to simulate current and future flood risk using the results from a household survey to establish stage–damage curves for residential buildings. The model has been used to assess the effects of several participatory developed adaptation strategies to reduce flood risk, expressed in expected annual damage (EAD). Adaptation strategies were evaluated assuming combinations of both sea-level scenarios and land-use scenarios. Together with information on costs of these strategies, we calculated the benefit–cost ratio and net present value for the adaptation strategies until 2100, taking into account depreciation rates of 2.5% and 5%. The results of this modelling study indicate that the current flood risk in District 4 is USD 0.31 million per year, increasing up to USD 0.78 million per year in 2100. The net present value and benefit–cost ratios using a discount rate of 5 % range from USD −107 to −1.5 million, and from 0.086 to 0.796 for the different strategies. Using a discount rate of 2.5% leads to an increase in both net present value and benefit–cost ratio. The adaptation strategies wet-proofing and dry-proofing generate the best results using these economic indicators. The information on different strategies will be used by the government of Ho Chi Minh City to determine a new flood protection strategy. Future research should focus on gathering empirical data right after a flood on the occurring damage, as this appears to be the most uncertain factor in the risk assessment.

How useful are complex flood damage models?

AbstractWe investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade‐off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network‐based predictions in space‐time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.