scholarly journals Is flow velocity a significant parameter in flood damage modelling?

2009 ◽  
Vol 9 (5) ◽  
pp. 1679-1692 ◽  
Author(s):  
H. Kreibich ◽  
K. Piroth ◽  
I. Seifert ◽  
H. Maiwald ◽  
U. Kunert ◽  
...  
Keyword(s):  
Flow Velocity ◽  
Water Depth ◽  
Structural Damage ◽  
Residential Buildings ◽  
Flood Damage ◽  
Damage Models ◽  
Damage Modelling ◽  
Impact Parameters ◽  
A Minor ◽  
The Impact

Abstract. Flow velocity is generally presumed to influence flood damage. However, this influence is hardly quantified and virtually no damage models take it into account. Therefore, the influences of flow velocity, water depth and combinations of these two impact parameters on various types of flood damage were investigated in five communities affected by the Elbe catchment flood in Germany in 2002. 2-D hydraulic models with high to medium spatial resolutions were used to calculate the impact parameters at the sites in which damage occurred. A significant influence of flow velocity on structural damage, particularly on roads, could be shown in contrast to a minor influence on monetary losses and business interruption. Forecasts of structural damage to road infrastructure should be based on flow velocity alone. The energy head is suggested as a suitable flood impact parameter for reliable forecasting of structural damage to residential buildings above a critical impact level of 2 m of energy head or water depth. However, general consideration of flow velocity in flood damage modelling, particularly for estimating monetary loss, cannot be recommended.

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

2016 ◽  
Vol 16 (11) ◽  
pp. 2357-2371 ◽  
Author(s):  
Patric Kellermann ◽  
Christine Schönberger ◽  
Annegret H. Thieken
Keyword(s):  
Risk Management ◽  
Risk Aversion ◽  
Flood Risk ◽  
Structural Damage ◽  
Large Scale ◽  
Damage Model ◽  
Flood Damage ◽  
Railway Infrastructure ◽  
Increased Risk ◽  
The Impact

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.

scholarly journals Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

2017 ◽  
Author(s):  
Francesca Carisi ◽  
Kai Schröter ◽  
Alessio Domeneghetti ◽  
Heidi Kreibich ◽  
Attilio Castellarin
Keyword(s):  
Residential Buildings ◽  
Economic Value ◽  
Flood Damage ◽  
Data Sets ◽  
Local Data ◽  
Data Set ◽  
Damage Models ◽  
Flood Loss ◽  
Loss Data ◽  
Loss Models

Abstract. Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 52 km2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth. However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

scholarly journals Evaluation of a Tunnel Underpass Building Scheme in Yunnan Province

2019 ◽  
Vol 136 ◽  
pp. 04019
Author(s):  
Xiwen Yang ◽  
Tiefeng Zhou ◽  
Xiangyang Cui ◽  
Hongyan Guo ◽  
Ke Li
Keyword(s):  
Structural Damage ◽  
Yunnan Province ◽  
Residential Buildings ◽  
Horizontal Displacement ◽  
Vertical Displacement ◽  
Surrounding Rock ◽  
Tunnel Construction ◽  
The Impact

Side-crossing residential buildings in tunnel construction may lead to building subsidence, structural damage by tension and affect the use of buildings. Aiming at the structural damage caused by the side-crossing structure of Re Shuitang Tunnel NO.1, by simulating the influence of tunnel construction on the building, it is concluded that the surrounding rock above the tunnel will be deformed when the tunnel crosses the building. The maximum horizontal displacement is 0.64 mm and the maximum vertical displacement is 4.43 mm. According to the analysis results, the surrounding rock above the tunnel should be strengthened in time, and attention should be paid to the impact of blasting on residential buildings, so as to ensure the safety of buildings and provide reference for future construction.

scholarly journals Large-scale application of the flood damage model Railway Infrastructure Loss (RAIL)

2016 ◽  
Author(s):  
Patric Kellermann ◽  
Christine Schönberger ◽  
Annegret H. Thieken
Keyword(s):  
Risk Management ◽  
Risk Aversion ◽  
Flood Risk ◽  
Structural Damage ◽  
Large Scale ◽  
Damage Model ◽  
Flood Damage ◽  
Railway Infrastructure ◽  
Increased Risk ◽  
The Impact

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 one hundred million euros 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-year, 100-year 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.

scholarly journals Estimating flood damage in Italy: empirical vs expert-based modelling approach

2018 ◽  
Author(s):  
Mattia Amadio ◽  
Anna Rita Scorzini ◽  
Francesca Carisi ◽  
Arthur H. Essenfelder ◽  
Alessio Domeneghetti ◽  
...  
Keyword(s):  
Risk Management ◽  
Flood Hazard ◽  
Residential Buildings ◽  
Economic Value ◽  
Flood Damage ◽  
Machine Learning Techniques ◽  
Flood Events ◽  
Damage Models ◽  
Multivariable Models ◽  
Modelling Approach

Abstract. Flood risk management generally relies on economic assessments performed using flood loss models of different complexity, ranging from simple univariable to more complex multivariable models. These latter accounts for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. In this paper we collected a comprehensive dataset related to three recent major flood events in Northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), buildings characteristics (size, type, quality, economic value) as well as reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performance of four literature flood damage models of different nature and complexity are compared with the performance of univariable, bivariable and multivariable models empirically developed for Italy and tested at the micro scale based upon observed records. The uni- and bivariable models are produced testing linear, logarithmic and square root regression while multivariable models are based on two machine learning techniques, namely Random Forest and Artificial Neural Networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management.

scholarly journals Contribution of insurance data to cost assessment of coastal flood damage to residential buildings: insights gained from Johanna (2008) and Xynthia (2010) storm events

2013 ◽  
Vol 1 (2) ◽  
pp. 829-854
Author(s):  
C. André ◽  
D. Monfort ◽  
M. Bouzit ◽  
C. Vinchon
Keyword(s):  
Residential Buildings ◽  
Flood Damage ◽  
Storm Events ◽  
Methodological Issues ◽  
Damage Functions ◽  
Damage Modelling ◽  
Future Events ◽  
Paper Format ◽  
Insurance Data ◽  
Do So

Abstract. There are a number of methodological issues involved in assessing damage caused by natural hazards. The first is the lack of data, due to the rarity of events and the widely different circumstances in which they occur. Thus, historical data, albeit scarce, should not be neglected when seeking to build ex-ante risk management models. This article analyses the input of insurance data for two recent severe coastal storm events, to examine what causal relationships may exist between hazard characteristics and the level of damage incurred by residential buildings. To do so, data was collected at two levels: from lists of about 4000 damage records, 358 loss adjustment reports were consulted, constituting a detailed damage database. The results show that for flooded residential buildings, over 75% of reconstruction costs are associated with interior elements, damage to structural components remaining very localised and negligible. Further analysis revealed a high scatter between costs and water depth, suggesting that uncertainty remains high in drawing up damage functions with insurance data alone. Due to the paper format of the loss adjustment reports and the lack of harmonisation between their contents, the collection stage called for a considerable amount of work. For future events, establishing a standardised process for archiving damage information could significantly contribute to the production of such empirical damage functions. Nevertheless, complementary sources of data on hazards and asset vulnerability parameters, will definitely still be necessary for damage modelling and multivariate approaches, crossing insurance data with external material, should also be deeper investigated.

Flood damage assessment to economic activities: implementation and transferability of French methodology

2021 ◽  
Author(s):  
Frédéric Grelot ◽  
Marta Galliani ◽  
Pauline Bremond ◽  
Daniela Molinari ◽  
Lilian Pugnet ◽  
...  
Keyword(s):  
Residential Buildings ◽  
Flood Damage ◽  
Economic Activities ◽  
Damage Functions ◽  
Flood Prevention ◽  
Damage Modelling ◽  
French Case ◽  
Damage Data ◽  
The City

<p>Since 2010, a national method is available in France for multi-criteria analysis of flood prevention projects. The method uses national damage functions to estimate losses to the different exposed items, including economic activities. Despite the business sector suffers significant losses in case of flood, flood damage modelling to businesses is less advanced than for other exposed sectors, as e.g. residential buildings. Reasons are many and include: the high variability of activities types composing this sector and then the difficulty of standardisation (above all when contents are considered), and the lack of data to understand and quantify damage and validate existing modelling tools. The collection of damage data in two case studies, in France and in Italy, and the collaboration between two research groups in the two countries allowed to study the applicability, the validity, and the transferability of the French damage functions for economic activities to Italy. Firstly, the functions were tested and validated in a French case study, i.e. the flood that affected the Île-de-France Region in 2016. This validation exercise faced the problem of working with few information about the identity of the activities, and propose a solution; moreover, it allowed to verify the actual availability of input data to implement the functions in France and pointed out the paucity of information to validate the methodology. Testing the functions in a foreign case study, i.e. the flood occurred in 2002 in Italy in the city of Lodi, allowed instead to verify the transferability of the method.</p>

What is the value of a multi-decadal regional database of compensation claims for flood damage modelling and risk management?

2021 ◽  
Author(s):  
Axelle Doppagne ◽  
Pierre Archambeau ◽  
Jacques Teller ◽  
Anna Rita Scorzini ◽  
Daniela Molinari ◽  
...  
Keyword(s):  
Flood Damage ◽  
State Agency ◽  
Flow Depth ◽  
Flood Events ◽  
Explanatory Variables ◽  
Damage Models ◽  
Damage Modelling ◽  
Spatial Coverage ◽  
Compensation Claims ◽  
Building Level

<p>Flood damage modelling is a key component of flood risk modelling, assessment and management. Reliable empirical data of flood damage are essential to support the development and validation of flood damage models. However, such datasets remain scarce and incomplete, particularly those combining a large spatial coverage (e.g., regional, national) over a long time period (e.g., several decades) with a detailed resolution (e.g., address-level data).</p><p>In this research, we analysed a database of 27,000 compensation claims submitted to a Belgian state agency (Disaster Fund). It covers 104 natural disasters of various types (incl. floods, storms, rockslides …) which occurred in the Walloon region in Belgium between 1993 and 2019. The region extends over parts of the Meuse and of the Scheldt river basins. The registered amounts of damage at the building level were estimated by state-designated experts. They are classified in six categories. While roughly half of the registered disasters are pluvial flooding events, they account for less than a quarter of the total claimed damage. In contrast, riverine floods correspond to about one third of the registered events, but they lead to one half of the claimed damage.</p><p>A detailed analysis of the data was undertaken for a limited number of major riverine flood events (1993, 1995, 2002), which have caused a very large portion of the total damage. By geo-referencing the postal address of each individual building, it was possible to assign each claim to a specific river reach. This enabled pointing at the most flood prone river stretches in an objective way. Then, using cadastral data, each type and amount of damage could be attributed to a specific building.</p><p>To explore the value of the database for elaborating and validating damage models, the claimed damage data at the building level were related to estimates of hydraulic variables for the corresponding flood events. To do so, we used an existing database of results of 2D hydrodynamic modelling, covering 1,200+ km of river reaches and providing raster files at a spatial resolution ranging from 2 m to 5 m for computed flow depth and velocity in the floodplains. The attribution of flow depth to individual buildings was performed either by averaging the computed flow depths around the building footprint or by considering the maximum value.</p><p>The correlation between claimed damage at the building level and attributed flow depth is relatively low, irrespective of the flow depth attribution method. This may result from the high uncertainty affecting each of these variables. It also hints at the necessity of using multivariable damage models which account for a broader range of explanatory variables than the sole flow depth (flow velocity, characteristics of building material and equipment, building age, etc.). This will be discussed in the presentation and further explored in the next steps of this research.</p><p>Data for this analysis were provided by the Belgian regional agency SPW-IAS in July 2020. Due to privacy reasons, data at the address-level may not be disseminated in the scientific community; but results of data processing may be shared at an aggregated level.</p>

scholarly journals Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy)

2018 ◽  
Vol 18 (7) ◽  
pp. 2057-2079 ◽  
Author(s):  
Francesca Carisi ◽  
Kai Schröter ◽  
Alessio Domeneghetti ◽  
Heidi Kreibich ◽  
Attilio Castellarin
Keyword(s):  
Residential Buildings ◽  
Flood Damage ◽  
Local Data ◽  
Explanatory Variables ◽  
Damage Models ◽  
Private Households ◽  
Flood Loss ◽  
Multivariable Models ◽  
Loss Models ◽  
Flood Damage Estimation

Abstract. Flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data, which is often rooted in a lack of protocols and reference procedures for compiling loss datasets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia River flood event of January 2014, when a sudden levee breach caused the inundation of nearly 52 km2 in northern Italy. After this event local authorities collected a comprehensive flood loss dataset of affected private households including building footprints and structures and damages to buildings and contents. The dataset was enriched with further information compiled by us, including economic building values, maximum water depths, velocities and flood durations for each building. By analyzing this dataset we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multivariable loss models for residential buildings and contents. The accuracy of the proposed models is compared with that of several flood damage models reported in the literature, providing additional insights into the transferability of the models among different contexts. Our results show that (1) even simple univariable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multivariable models that consider several explanatory variables outperform univariable models, which use only water depth. However, multivariable models can only be effectively developed and applied if sufficient and detailed information is available.

scholarly journals Testing empirical and synthetic flood damage models: the case of Italy

2019 ◽  
Vol 19 (3) ◽  
pp. 661-678 ◽  
Author(s):  
Mattia Amadio ◽  
Anna Rita Scorzini ◽  
Francesca Carisi ◽  
Arthur H. Essenfelder ◽  
Alessio Domeneghetti ◽  
...  
Keyword(s):  
Risk Management ◽  
Flood Hazard ◽  
Residential Buildings ◽  
Flood Damage ◽  
Machine Learning Techniques ◽  
Flood Events ◽  
Ancillary Data ◽  
Data Set ◽  
Damage Models ◽  
Multivariable Models

Abstract. Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models.

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