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

2016 ◽  
Vol 16 (1) ◽  
pp. 15-27 ◽  
Author(s):  
R. Hasanzadeh Nafari ◽  
T. Ngo ◽  
W. Lehman
Keyword(s):  
Loss Function ◽  
Extreme Event ◽  
Flood Damage ◽  
Damage Models ◽  
Flood Loss ◽  
Residential Structures ◽  
Damage Data ◽  
Historic Data ◽  
Flood Damage Estimation ◽  
Observed Damage

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.

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 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 Results comparison and model validation for flood loss functions in Australian geographical conditions

2015 ◽  
Vol 3 (6) ◽  
pp. 3823-3860 ◽  
Author(s):  
R. Hasanzadeh Nafari ◽  
T. Ngo ◽  
W. Lehman
Keyword(s):  
Model Validation ◽  
Flood Damage ◽  
Flood Frequency ◽  
Physical Damage ◽  
Damage Models ◽  
Emergency Response System ◽  
Disaster Impacts ◽  
Damage Data ◽  
Flood Damage Estimation ◽  
Physical Consequences

Abstract. Rapid urbanisation, climate change and unsustainable developments are increasing the risk of floods, namely flood frequency and intensity. 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. This study aims to quantify the direct physical damage to residential structures that are prone to flood phenomena in Australia. In this paper, the physical consequences of two floods from Queensland have been simulated, and the results have been compared with the performance of two selected methodologies and one newly derived model. Based on this analysis, the adaptability and applicability of the selected methodologies will be assessed in terms of Australian geographical conditions. Results obtained from the new empirically-based function and non-adapted methodologies indicate that it is apparent that the precision of flood damage models are strongly dependent on selected stage damage curves, and flood damage estimation without model validation results in inaccurate prediction of losses. Therefore, it is very important to be aware of the associated uncertainties in flood risk assessment, especially if models have not been adapted with real damage data.

scholarly journals Flood loss modelling with FLF-IT: A new Flood Loss Function for Italian residential structures

2017 ◽  
Author(s):  
Roozbeh Hasanzadeh Nafari ◽  
Mattia Amadio ◽  
Tuan Ngo ◽  
Jaroslav Mysiak
Keyword(s):  
Loss Function ◽  
Urban Areas ◽  
Model Calibration ◽  
Epistemic Uncertainty ◽  
Reliable Estimate ◽  
Italian Economy ◽  
Flood Loss ◽  
Residential Structures ◽  
Damage Data ◽  
Validation Procedure

Abstract. The damage triggered by different flood events costs the Italian economy millions of dollars 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 also validated for the prediction of loss ratios and absolute damage values. 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 validation procedure shows that the newly derived function performs well (no bias and only 10 % mean absolute error). 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 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 The object-specific flood damage database HOWAS21

2020 ◽  
Author(s):  
Patric Kellermann ◽  
Kai Schröter ◽  
Annegret H. Thieken ◽  
Sören-Nils Haubrock ◽  
Heidi Kreibich
Keyword(s):  
Flood Damage ◽  
Damage Estimation ◽  
Economic Sectors ◽  
Flood Impacts ◽  
Large Potential ◽  
Damage Data ◽  
Flood Analysis ◽  
Flood Damage Estimation ◽  
Estimation Models

Abstract. The Flood Damage Database HOWAS21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding in Germany. The datasets incorporate various pieces of information about flood impacts, exposure, vulnerability, and direct tangible damage at properties from several economic sectors. The main purpose of development and design of HOWAS21 is to support forensic flood analysis and the derivation of flood damage estimation models. This paper highlights exemplary analyses to demonstrate the use of HOWAS21 flood damage data in these two application areas. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding. HOWAS21 recently enlarged its scope and is now also open for international flood damage data.

scholarly journals The object-specific flood damage database HOWAS 21

2020 ◽  
Vol 20 (9) ◽  
pp. 2503-2519
Author(s):  
Patric Kellermann ◽  
Kai Schröter ◽  
Annegret H. Thieken ◽  
Sören-Nils Haubrock ◽  
Heidi Kreibich
Keyword(s):  
Web Application ◽  
Flood Hazard ◽  
Flood Damage ◽  
Economic Sectors ◽  
Hazard Exposure ◽  
Damage Models ◽  
Large Potential ◽  
Damage Data ◽  
Flood Analysis

Abstract. The Flood Damage Database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. The main purpose of development of HOWAS 21 was to support forensic flood analysis and the derivation of flood damage models. HOWAS 21 was first developed for Germany and currently almost exclusively contains datasets from Germany. However, its scope has recently been enlarged with the aim to serve as an international flood damage database; e.g. its web application is now available in German and English. This paper presents the recent advancements of HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding.

A critical analysis of residential flood damage estimation curves

1986 ◽  
Vol 13 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Edward McBean ◽  
Michael Fortin ◽  
Jack Gorrie
Keyword(s):  
Critical Review ◽  
Critical Analysis ◽  
Price Index ◽  
Consumer Price Index ◽  
Flood Damage ◽  
Damage Estimation ◽  
Damage Data ◽  
Flood Damage Estimation

A critical review of the problems encountered in attempting to quantify flood damage is used to demonstrate inconsistencies, omissions, and variabilities among previous studies and procedures. A reasonable procedure for updating residential depth – damage data from previous studies is shown to involve use of the all-items consumer price index. Recommended strategies for flood damage estimation involve calibration of synthetic stage – damage data to observed flood damage data.

scholarly journals A generic physical vulnerability model for floods: review and concept for data-scarce regions

2020 ◽  
Vol 20 (7) ◽  
pp. 2067-2090 ◽  
Author(s):  
Mark Bawa Malgwi ◽  
Sven Fuchs ◽  
Margreth Keiler
Keyword(s):  
Risk Reduction ◽  
Flood Risk ◽  
Vulnerability Index ◽  
Flood Damage ◽  
Flood Hazards ◽  
Physical Vulnerability ◽  
Damage Models ◽  
Vulnerability Indicators ◽  
Damage Grades ◽  
Observed Damage

Abstract. The use of different methods for physical flood vulnerability assessment has evolved over time, from traditional single-parameter stage–damage curves to multi-parameter approaches such as multivariate or indicator-based models. However, despite the extensive implementation of these models in flood risk assessment globally, a considerable gap remains in their applicability to data-scarce regions. Considering that these regions are mostly areas with a limited capacity to cope with disasters, there is an essential need for assessing the physical vulnerability of the built environment and contributing to an improvement of flood risk reduction. To close this gap, we propose linking approaches with reduced data requirements, such as vulnerability indicators (integrating major damage drivers) and damage grades (integrating frequently observed damage patterns). First, we present a review of current studies of physical vulnerability indicators and flood damage models comprised of stage–damage curves and the multivariate methods that have been applied to predict damage grades. Second, we propose a new conceptual framework for assessing the physical vulnerability of buildings exposed to flood hazards that has been specifically tailored for use in data-scarce regions. This framework is operationalized in three steps: (i) developing a vulnerability index, (ii) identifying regional damage grades, and (iii) linking resulting index classes with damage patterns, utilizing a synthetic “what-if” analysis. The new framework is a first step for enhancing flood damage prediction to support risk reduction in data-scarce regions. It addresses selected gaps in the literature by extending the application of the vulnerability index for damage grade prediction through the use of a synthetic multi-parameter approach. The framework can be adapted to different data-scarce regions and allows for integrating possible modifications to damage drivers and damage grades.

Sign in / Sign up

Export Citation Format

Share Document