scholarly journals Biases in national and continental flood risk assessments by ignoring spatial dependence

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Viet Dung Nguyen ◽  
Ayse Duha Metin ◽  
Lorenzo Alfieri ◽  
Sergiy Vorogushyn ◽  
Bruno Merz
Keyword(s):  
Flood Risk ◽  
Spatial Dependence ◽  
Large Scale ◽  
Insurance Industry ◽  
Flood Damage ◽  
Risk Assessments ◽  
Local Damage ◽  
The United Kingdom ◽  
Homogeneity Assumption ◽  
Flood Generation

Abstract Recently, flood risk assessments have been extended to national and continental scales. Most of these assessments assume homogeneous scenarios, i.e. the regional risk estimate is obtained by summing up the local estimates, whereas each local damage value has the same probability of exceedance. This homogeneity assumption ignores the spatial variability in the flood generation processes. Here, we develop a multi-site, extreme value statistical model for 379 catchments across Europe, generate synthetic flood time series which consider the spatial correlation between flood peaks in all catchments, and compute corresponding economic damages. We find that the homogeneity assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139%, 188% and 246% for the United Kingdom (UK), Germany and Europe, respectively. Our study demonstrates the importance of considering the spatial dependence patterns, particularly of extremes, in large-scale risk assessments.

scholarly journals The role of spatial dependence for large-scale flood risk estimation

2020 ◽  
Vol 20 (4) ◽  
pp. 967-979 ◽  
Author(s):  
Ayse Duha Metin ◽  
Nguyen Viet Dung ◽  
Kai Schröter ◽  
Sergiy Vorogushyn ◽  
Björn Guse ◽  
...  
Keyword(s):  
Flood Risk ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Cumulative Distribution ◽  
Risk Assessments ◽  
Return Periods ◽  
Risk Estimates ◽  
Flood Peaks ◽  
Complete Dependence

Abstract. Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100 % for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.

scholarly journals Enhancement of large-scale flood damage assessments using building-material-based vulnerability curves for an object-based approach

2019 ◽  
Author(s):  
Johanna Englhardt ◽  
Hans de Moel ◽  
Charles K. Huyck ◽  
Marleen C. de Ruiter ◽  
Jeroen C. J. H. Aerts ◽  
...  
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Rural Areas ◽  
Flood Risk ◽  
Building Material ◽  
Large Scale ◽  
Flood Damage ◽  
Risk Assessments ◽  
Object Based ◽  
Coarse Information

Abstract. In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data is becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia, and found that rural flood risk accounts for about 22 % of simulated damages; rural damages are generally neglected in the typical land-use-based damage models particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries. It also enables comparison across different natural hazard types that also use material-based vulnerability, paving the way to the enhancement of multi-risk assessments.

scholarly journals The role of spatial dependence for large-scale flood risk estimation

2019 ◽  
Author(s):  
Ayse Duha Metin ◽  
Nguyen Viet Dung ◽  
Kai Schröter ◽  
Sergiy Vorogushyn ◽  
Björn Guse ◽  
...  
Keyword(s):  
Flood Risk ◽  
Spatial Dependence ◽  
Large Scale ◽  
Cumulative Distribution ◽  
Risk Assessments ◽  
Return Periods ◽  
Time Dynamics ◽  
Risk Estimates ◽  
Flood Peaks ◽  
Complete Dependence

Abstract. Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence), and (iii) spatially heterogeneous, but uncorrelated scenarios (complete independence). To this end, the model chain RFM (Regional Flood Model) is applied to the Elbe catchment in Germany, accounting for the space-time dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD), however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage in the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.

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.

Analysis of potential flood damage on crops at global scale

2020 ◽  
Author(s):  
Antonio Annis ◽  
Davide Danilo Chiarelli ◽  
Fernando Nardi ◽  
Maria Cristina Rulli
Keyword(s):  
Food Security ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Hazard ◽  
Strong Support ◽  
Flood Damage ◽  
Natural Ecosystems ◽  
Remotely Sensed Data ◽  
Regulated Rivers ◽  
Cropland Area

<p>Most of the food production connected to crops is located in fluvial corridors because of their suitable morphology and fertile soils. The knowledge and large scale quantification of the agricultural resources at flood risk has a crucial importance for improving urban and regional planning. Recent advances in satellite derived products related to land use, digital terrain and hydrologic variables can give a strong support on extensive analyses on cropland areas in floodplains and their interactions with natural ecosystems and human activities. In this work, we present a global assessment of cropland at flood risk in terms of extension, productivity and the related calories adopting the Global Cropland Area Database (GCAD), the Global Floodplain Dataset (GFPLAIN250m), the Global flood hazard maps (GFHM) in conjunction with continental remotely-sensed data representing free flowing (versus artificially regulated) rivers and urban density maps. Spatially distributed and aggregated results of the research allow to identify the most critical areas in terms of food security and floods, thus allowing to support intervention strategies for food security management at large scale and for different socio-economic contexts.</p>

scholarly journals Brief Communication: Critical Infrastructure impacts of the 2021 mid-July western European flood event

2021 ◽  
Author(s):  
Elco Koks ◽  
Kees Van Ginkel ◽  
Margreet Van Marle ◽  
Anne Lemnitzer
Keyword(s):  
The Netherlands ◽  
Flood Risk ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Critical Infrastructure ◽  
Risk Assessments ◽  
Flood Events ◽  
Infrastructure Systems ◽  
River Flood ◽  
Sewage Systems

Abstract. Germany, Belgium and The Netherlands were hit by extreme precipitation and flooding in July 2021. This Brief Communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed during the first six months after the event. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that large-scale risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts. This may be the result of limited availability of validation material. As such, this study will not only help to better understand how critical infrastructure can be affected by flooding, but can also be used as validation material for future flood risk assessments.

scholarly journals Enhancement of large-scale flood risk assessments using building-material-based vulnerability curves for an object-based approach in urban and rural areas

2019 ◽  
Vol 19 (8) ◽  
pp. 1703-1722 ◽  
Author(s):  
Johanna Englhardt ◽  
Hans de Moel ◽  
Charles K. Huyck ◽  
Marleen C. de Ruiter ◽  
Jeroen C. J. H. Aerts ◽  
...  
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Rural Areas ◽  
Flood Risk ◽  
Building Material ◽  
Large Scale ◽  
Risk Assessments ◽  
Object Based ◽  
Coarse Information ◽  
Urban And Rural Areas

Abstract. In this study, we developed an enhanced approach for large-scale flood damage and risk assessments that uses characteristics of buildings and the built environment as object-based information to represent exposure and vulnerability to flooding. Most current large-scale assessments use an aggregated land-use category to represent the exposure, treating all exposed elements the same. For large areas where previously only coarse information existed such as in Africa, more detailed exposure data are becoming available. For our approach, a direct relation between the construction type and building material of the exposed elements is used to develop vulnerability curves. We further present a method to differentiate flood risk in urban and rural areas based on characteristics of the built environment. We applied the model to Ethiopia and found that rural flood risk accounts for about 22 % of simulated damage; rural damage is generally neglected in the typical land-use-based damage models, particularly at this scale. Our approach is particularly interesting for studies in areas where there is a large variation in construction types in the building stock, such as developing countries.

scholarly journals Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1834 ◽  
Author(s):  
Raffaele Albano ◽  
Caterina Samela ◽  
Iulia Crăciun ◽  
Salvatore Manfreda ◽  
Jan Adamowski ◽  
...  
Keyword(s):  
Land Use ◽  
Flood Risk ◽  
Large Scale ◽  
Binary Classification ◽  
Depth Map ◽  
Return Time ◽  
Flood Damage ◽  
Landsat 8 ◽  
Machine Learning Classification ◽  
Cost Efficient

Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

Large Scale Flood Damage Mapping: the case study of Romania Country

2020 ◽  
Author(s):  
Raffaele Albano ◽  
Aurelia Sole ◽  
Salvatore Manfreda ◽  
Caterina Samela ◽  
Iulia Craciun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
High Resolution ◽  
Flood Risk ◽  
Large Scale ◽  
Flood Damage ◽  
Emergency Operations ◽  
Hazard Exposure ◽  
Cost Efficient ◽  
Damage Mapping

<p>A large-scale flood risk analysis that properly evaluates and quantifies the three components of risk (hazard, exposure and vulnerability) is essential in order to support national and global policies, emergency operations and land use management. For example, governments can use risk information for the prioritisation of investments to implement measures for flood damage reduction, for emergency operations and for land-use policies, while reinsurance companies can improve the estimation of the flood risk-based insurance premiums.</p><p>Nevertheless, limits in time and data represent significant limitation this kind of applications: i) the significant amount of data and parameters required for the calibration and validation of traditional model; ii) the moderate/coarse resolution of data available at global scale and the sparse availability of high-resolution data that may affect the accuracy of analysis results; iii) the high cost and computational demand of hydraulic models. However, the growing availability of data from new technologies of Earth observation (EO) and environmental monitoring combined with the advances in newly developed algorithms (e.g. machine learning) have extended the range of possibilities for geoscientists, updating and re-inventing the way highly resource- and data-intensive processes, such as risk management and communication, are carried out.</p><p>The present study proposes a cost-efficient method for large-scale analysis and mapping of direct economic flood damage at medium resolution in data-scarce environments. The proposed methodological framework consists of three main stages. The first step concerns the derivation of a water depth map through a Digital Elevation Model (30m resolution)-based geomorphic method that uses supervised linear binary classification. The second step aims to realize an exposure map on the basis of a supervised land use classification through the use of a machine learning technique: the information extracted from Landsat-8 remotely sensed optical images were utilized in combination with the discontinuous (i.e. available for a few large cities in Europe) existing high-resolution Urban-Atlas land use maps in order to obtain a land-use map with a resolution of 30 m. Finally, the flood economic damage mapping was carried out using the results of the two previous steps in a GIS algorithm, developed by authors, based on the vulnerability (depth-damage) curves method. The proposed integrated framework has been tested in Romania for a 100-years return time event. The resulting map (at 30 m resolution) covers the entire Romanian territory including minor order rivers, which are often neglected in large-scale analyses.</p><p>The demonstrative application shows how the description of flood risk may particularly benefit from the integrated use of geomorphic methods, machine learning algorithms and EO freely available monitoring data. The ability of the proposed cost-efficient model to carry out high-resolution and large-scale analyses in data-scarce environments allows performing future risk assessments keeping abreast of temporal and spatial changes in terms of hazard, exposure and vulnerability.</p><p><em>Acknowledgement: This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme.</em></p>

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.

Sign in / Sign up

Export Citation Format

Share Document