scholarly journals Residential flood loss estimated from Bayesian multilevel models

2021 ◽  
Vol 21 (5) ◽  
pp. 1599-1614
Author(s):  
Guilherme S. Mohor ◽  
Annegret H. Thieken ◽  
Oliver Korup
Keyword(s):  
Water Depth ◽  
Multilevel Models ◽  
Flood Damage ◽  
Flood Loss ◽  
Loss Estimations ◽  
Loss Models ◽  
Precautionary Measures ◽  
Flood Experience ◽  
Natural Groups ◽  
Do So

Abstract. Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how 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 unduly generalise the model and systematically bias loss estimations from empirical data.

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.

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 Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar

2020 ◽  
Vol 15 (3) ◽  
pp. 300-311 ◽  
Author(s):  
Win Win Zin ◽  
Akiyuki Kawasaki ◽  
Georg Hörmann ◽  
Ralph Allen Acierto ◽  
Zin Mar Lar Tin San ◽  
...  
Keyword(s):  
Water Depth ◽  
Building Material ◽  
Damage Assessment ◽  
Flood Damage ◽  
Loss Estimation ◽  
Flood Hazards ◽  
Estimation Model ◽  
Flood Duration ◽  
Flood Loss ◽  
Flood Damage Assessment

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.

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 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 New insights into flood warning reception and emergency response by affected parties

2017 ◽  
Vol 17 (12) ◽  
pp. 2075-2092 ◽  
Author(s):  
Heidi Kreibich ◽  
Meike Müller ◽  
Kai Schröter ◽  
Annegret H. Thieken
Keyword(s):  
Emergency Response ◽  
Regional Differences ◽  
Flood Damage ◽  
Emergency Plan ◽  
Emergency Response System ◽  
Flood Warning ◽  
Private Households ◽  
Flood Experience ◽  
Flood Warnings ◽  
Early Warnings

Abstract. Flood damage can be mitigated if the parties at risk are reached by flood warnings and if they know how to react appropriately. To gain more knowledge about warning reception and emergency response of private households and companies, surveys were undertaken after the August 2002 and the June 2013 floods in Germany. Despite pronounced regional differences, the results show a clear overall picture: in 2002, early warnings did not work well; e.g. many households (27 %) and companies (45 %) stated that they had not received any flood warnings. Additionally, the preparedness of private households and companies was low in 2002, mainly due to a lack of flood experience. After the 2002 flood, many initiatives were launched and investments undertaken to improve flood risk management, including early warnings and an emergency response in Germany. In 2013, only a small share of the affected households (5 %) and companies (3 %) were not reached by any warnings. Additionally, private households and companies were better prepared. For instance, the share of companies which have an emergency plan in place has increased from 10 % in 2002 to 34 % in 2013. However, there is still room for improvement, which needs to be triggered mainly by effective risk and emergency communication. The challenge is to continuously maintain and advance an integrated early warning and emergency response system even without the occurrence of extreme floods.

scholarly journals How awareness and confidence affect flood-risk precautionary behavior of Greek citizens: the role of perceptual and emotional mechanisms

2019 ◽  
Vol 19 (7) ◽  
pp. 1329-1346 ◽  
Author(s):  
Katerina Papagiannaki ◽  
Vassiliki Kotroni ◽  
Kostas Lagouvardos ◽  
Giorgos Papagiannakis
Keyword(s):  
Flood Risk ◽  
Structural Equation ◽  
Online Survey ◽  
Equation Modeling ◽  
Emotional Processes ◽  
Precautionary Behavior ◽  
Negative Effect ◽  
Precautionary Measures ◽  
Flood Experience

Abstract. This study examines the mechanisms of flood-risk precautionary behavior among Greek citizens. To that end, we specify and test a mediation model in which awareness-raising factors and confidence attitudes influence the citizens' current flood preparedness and preparedness intention through perceptual and emotional processes. Raw data were obtained via an online survey that received 1855 responses. Causal relations were tested by means of structural equation modeling (SEM). Overall, results indicate that risk perception and worry are significant drivers of preparedness intention. In particular, they act as mediating variables, explaining how flood experience, access to more risk information, vulnerability awareness, and trust in authorities affect citizens' intention to invest in precautionary measures. Especially trust was found to have a negative effect on worry, leading to lower preparedness levels. Worry was also found to have a significant role in explaining the current preparedness levels. Interestingly, citizens who had already undertaken precautionary measures in the past appear to be more willing to invest in more measures. Implications for improving flood-risk management in Greece are discussed.

scholarly journals Flood loss modelling with FLF-IT: a new flood loss function for Italian residential structures

2017 ◽  
Vol 17 (7) ◽  
pp. 1047-1059 ◽  
Author(s):  
Roozbeh Hasanzadeh Nafari ◽  
Mattia Amadio ◽  
Tuan Ngo ◽  
Jaroslav Mysiak
Keyword(s):  
Loss Function ◽  
Water Depth ◽  
Urban Areas ◽  
Model Calibration ◽  
Epistemic Uncertainty ◽  
Reliable Estimate ◽  
Flood Loss ◽  
Residential Structures ◽  
Damage Data ◽  
Validation Procedure

Abstract. The damage triggered by different flood events costs the Italian economy millions of euros each year. This cost is likely to increase in the future due to climate variability and economic development. In order to avoid or reduce such significant financial losses, risk management requires tools which can provide a reliable estimate of potential flood impacts across the country. Flood loss functions are an internationally accepted method for estimating physical flood damage in urban areas. In this study, we derived a new flood loss function for Italian residential structures (FLF-IT), on the basis of empirical damage data collected from a recent flood event in the region of Emilia-Romagna. The function was developed based on a new Australian approach (FLFA), which represents the confidence limits that exist around the parameterized functional depth–damage relationship. After model calibration, the performance of the model was validated for the prediction of loss ratios and absolute damage values. It was also contrasted with an uncalibrated relative model with frequent usage in Europe. In this regard, a three-fold cross-validation procedure was carried out over the empirical sample to measure the range of uncertainty from the actual damage data. The predictive capability has also been studied for some sub-classes of water depth. The validation procedure shows that the newly derived function performs well (no bias and only 10 % mean absolute error), especially when the water depth is high. Results of these validation tests illustrate the importance of model calibration. The advantages of the FLF-IT model over other Italian models include calibration with empirical data, consideration of the epistemic uncertainty of data, and the ability to change parameters based on building practices across Italy.

scholarly journals Does Gender Top Family Ties? Within-Couple and between-Sibling Sharing of Elderly Care

2019 ◽  
Author(s):  
Matteo Luppi ◽  
Tiziana Nazio
Keyword(s):  
Multilevel Models ◽  
Elderly Care ◽  
European Countries ◽  
Sex Composition ◽  
Family Ties ◽  
Female Partners ◽  
Share Data ◽  
Transfers Of Care ◽  
Lower Contribution ◽  
Do So

Abstract Most elderly care continues to be delivered informally within families. Yet we still lack a thorough understanding of how care responsibilities are shared across both family ties and generations. We explore the gender dimension of caregiving in the distribution of elderly care between couple members (care provided to parents and parents-in-law and to children or grandchildren) and its associations with siblings' sex composition in a range of European countries. Using SHARE data and multinomial multilevel models, we test how responsibility for elderly care is shared across children and mediated by their partners and their siblings' sex composition as well as how it is combined with other downward care responsibilities, towards children and grandchildren. Results confirm the very gendered nature of elderly care. But who do men shift elderly care responsibilities to? We find that elderly care is more likely shifted to sisters than brothers, especially when caregiving becomes intense. We also find that the lower contribution by sons does not seem to prompt transfers of care responsibilities to their female partners within couples. Finally, although upward and downward caring responsibilities might compete, we find that individuals who are more inclined to provide care tend to do so in both directions.

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