scholarly journals Flood Loss Models and Risk Analysis for Private Households in Can Tho City, Vietnam

Water ◽  
2017 ◽  
Vol 9 (5) ◽  
pp. 313 ◽  
Author(s):  
Do Chinh ◽  
Nguyen Dung ◽  
Animesh Gain ◽  
Heidi Kreibich
Keyword(s):  
Risk Analysis ◽  
Private Households ◽  
Flood Loss ◽  
Loss Models ◽  
Can Tho City

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 Probabilistic Flood Loss Models for Companies

2020 ◽  
Vol 56 (9) ◽  
Author(s):  
Lukas Schoppa ◽  
Tobias Sieg ◽  
Kristin Vogel ◽  
Gert Zöller ◽  
Heidi Kreibich
Keyword(s):  
Flood Loss ◽  
Loss Models

scholarly journals Residential flood loss estimated from Bayesian multilevel models

2020 ◽  
Author(s):  
Guilherme S. Mohor ◽  
Annegret H. Thieken ◽  
Oliver Korup
Keyword(s):  
Multilevel Models ◽  
Flood Damage ◽  
Inundation Depth ◽  
Flood Loss ◽  
Building Characteristics ◽  
Loss Models ◽  
Precautionary Measures ◽  
Flood Experience ◽  
Natural Groups

Abstract. Model predictions of monetary losses from floods mainly use physical metrics like inundation depth or building characteristics but largely ignore indicators of preparedness. The role of such predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may complicate reliable loss estimation from empirical data.

A Bayesian Monte Carlo Markov Chain Method for Loss Models and Risk Measure Assessments

2009 ◽  
Vol 12 (03) ◽  
pp. 529-543
Author(s):  
Ling Hu ◽  
Yating Yang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Normal Distribution ◽  
Risk Measure ◽  
Markov Chain Method ◽  
Flood Loss ◽  
Bayesian Monte Carlo ◽  
Loss Models ◽  
Log Normal ◽  
Log Normal Distribution

Natural disasters are also known as catastrophes with low frequency but high damages. Typhoons and floods are the major catastrophes which lead to gargantuan losses in Asia. Once a disaster occurs, a broad region will be affected and this will result in huge social loss. If issuers or governments use the wrong loss models or risk measure indexes to price the related insurance products, they will get an inaccurate price and thus be insolvent to the claims. Previous researches often use a Log-Normal distribution to model a catastrophic loss. This is not appropriate since the characteristics of a loss distribution have some empirical facts, including the positive skewness and the heavy-tailed properties. Recently, some studies (McNeil and Frey, 2000; Rootzen and Tajvidi, 2000; Thuring et al., 2008) also point out that using Log-Normal distribution to model a characteristic loss is not suitable. Therefore, we build a typhoon and flood loss model with higher order moments and estimate the parameters through a Bayesian Monte Carlo Markov Chain method. According to the Kolmogorov-Smirnov test, we find that the Pareto distribution is more adaptive for modeling the loss of typhoon and flood. Further, we evaluate different kinds of risk measure indexes through simulating and numerical analysis. It gives the beacon to issuers or governments when they want to issue the insurance products about typhoon and flood loss.

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 Flood loss reduction of private households due to building precautionary measures – lessons learned from the Elbe flood in August 2002

2005 ◽  
Vol 5 (1) ◽  
pp. 117-126 ◽  
Author(s):  
H. Kreibich ◽  
A. H. Thieken ◽  
Th. Petrow ◽  
M. Müller ◽  
B. Merz
Keyword(s):  
Financial Incentives ◽  
Flood Damage ◽  
Lessons Learned ◽  
Information Campaigns ◽  
Private Households ◽  
River Elbe ◽  
Extreme Flood ◽  
Flood Loss ◽  
Prone Area ◽  
Precautionary Measures

Abstract. Building houses in inundation areas is always a risk, since absolute flood protection is impossible. Where settlements already exist, flood damage must be kept as small as possible. Suitable means are precautionary measures such as elevated building configuration or flood adapted use. However, data about the effects of such measures are rare, and consequently, the efficiency of different precautionary measures is unclear. To improve the knowledge about efficient precautionary measures, approximately 1200 private households, which were affected by the 2002 flood at the river Elbe and its tributaries, were interviewed about the flood damage of their buildings and contents as well as about their precautionary measures. The affected households had little flood experience, i.e. only 15% had experienced a flood before. 59% of the households stated that they did not know, that they live in a flood prone area. Thus, people were not well prepared, e.g. just 11% had used and furnished their house in a flood adapted way and only 6% had a flood adapted building structure. Building precautionary measures are mainly effective in areas with frequent small floods. But also during the extreme flood event in 2002 building measures reduced the flood loss. From the six different building precautionary measures under study, flood adapted use and adapted interior fitting were the most effective ones. They reduced the damage ratio for buildings by 46% and 53%, respectively. The damage ratio for contents was reduced by 48% due to flood adapted use and by 53% due to flood adapted interior fitting. The 2002 flood motivated a relatively large number of people to implement private precautionary measures, but still much more could be done. Hence, to further reduce flood losses, people's motivation to invest in precaution should be improved. More information campaigns and financial incentives should be issued to encourage precautionary measures.

scholarly journals Multi-Variate Analyses of Flood Loss in Can Tho City, Mekong Delta

Water ◽  
2015 ◽  
Vol 8 (1) ◽  
pp. 6 ◽  
Author(s):  
Do Chinh ◽  
Animesh Gain ◽  
Nguyen Dung ◽  
Dagmar Haase ◽  
Heidi Kreibich
Keyword(s):  
Mekong Delta ◽  
Flood Loss ◽  
Can Tho City

scholarly journals Bayesian Data-Driven approach enhances synthetic flood loss models

2020 ◽  
Vol 132 ◽  
pp. 104798 ◽  
Author(s):  
Nivedita Sairam ◽  
Kai Schröter ◽  
Francesca Carisi ◽  
Dennis Wagenaar ◽  
Alessio Domeneghetti ◽  
...  
Keyword(s):  
Data Driven ◽  
Flood Loss ◽  
Data Driven Approach ◽  
Loss Models

scholarly journals Influence of flood frequency on residential building losses

2010 ◽  
Vol 10 (10) ◽  
pp. 2145-2159 ◽  
Author(s):  
F. Elmer ◽  
A. H. Thieken ◽  
I. Pech ◽  
H. Kreibich
Keyword(s):  
Residential Buildings ◽  
Statistical Error ◽  
Residential Building ◽  
Water Levels ◽  
Recurrence Interval ◽  
Flood Events ◽  
Flood Loss ◽  
Loss Data ◽  
Loss Models ◽  
Loss Modelling

Abstract. For the purpose of flood risk analysis, reliable loss models are an indispensable need. The most common models use stage-damage functions relating damage to water depth. They are often derived from empirical flood loss data (i.e. loss data collected after a flood event). However, object specific loss data (e.g. losses of single residential buildings) from recent flood events in Germany showed higher average losses in less probable events, regardless of actual water level. Hence, models that were derived from such data tend to overestimate losses caused by more probable events. Therefore, it is the aim of the study to analyse the relation between flood damage and recurrence interval and to propose a method for considering recurrence interval in flood loss modelling. The survey was based on residential building loss data (n=2158) of recent flood events in 2002, 2005 and 2006 in Germany and on-site recurrence interval of the respective events. We discovered a highly significant positive correlation between loss extent and recurrence interval for classified water levels as well as increasing average losses for longer recurrence intervals within each class. The application of principal component analysis revealed the interrelation between factors that influence the damage extent directly or indirectly, and recurrence interval. No single factor or component could be identified that explained the influence of recurrence interval, which led to the conclusion that recurrence interval cannot substitute, but complement other damage influencing factors in flood loss modelling approaches. Finally, a method was developed to include recurrence interval in typical flood loss models and make them applicable to a wider range of flood events. Validation including statistical error analysis showed that the modified models improve loss estimates in comparison to traditional approaches. The proposed multi-parameter model FLEMOps+r performs particularly well.

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