Uncertainty in flood risk assessments

2012 ◽  
Author(s):  
H de Moel ◽  
W Botzen ◽  
J Aerts
Keyword(s):  
Flood Risk ◽  
Risk Assessments

scholarly journals A framework for global river flood risk assessments

2013 ◽  
Vol 17 (5) ◽  
pp. 1871-1892 ◽  
Author(s):  
H. C. Winsemius ◽  
L. P. H. Van Beek ◽  
B. Jongman ◽  
P. J. Ward ◽  
A. Bouwman
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Hydrological Model ◽  
Flood Hazard ◽  
Probability Distributions ◽  
Flood Routing ◽  
Risk Assessments ◽  
First Case ◽  
Impact Models ◽  
Damage Estimates

Abstract. There is an increasing need for strategic global assessments of flood risks in current and future conditions. In this paper, we propose a framework for global flood risk assessment for river floods, which can be applied in current conditions, as well as in future conditions due to climate and socio-economic changes. The framework's goal is to establish flood hazard and impact estimates at a high enough resolution to allow for their combination into a risk estimate, which can be used for strategic global flood risk assessments. The framework estimates hazard at a resolution of ~ 1 km2 using global forcing datasets of the current (or in scenario mode, future) climate, a global hydrological model, a global flood-routing model, and more importantly, an inundation downscaling routine. The second component of the framework combines hazard with flood impact models at the same resolution (e.g. damage, affected GDP, and affected population) to establish indicators for flood risk (e.g. annual expected damage, affected GDP, and affected population). The framework has been applied using the global hydrological model PCR-GLOBWB, which includes an optional global flood routing model DynRout, combined with scenarios from the Integrated Model to Assess the Global Environment (IMAGE). We performed downscaling of the hazard probability distributions to 1 km2 resolution with a new downscaling algorithm, applied on Bangladesh as a first case study application area. We demonstrate the risk assessment approach in Bangladesh based on GDP per capita data, population, and land use maps for 2010 and 2050. Validation of the hazard estimates has been performed using the Dartmouth Flood Observatory database. This was done by comparing a high return period flood with the maximum observed extent, as well as by comparing a time series of a single event with Dartmouth imagery of the event. Validation of modelled damage estimates was performed using observed damage estimates from the EM-DAT database and World Bank sources. We discuss and show sensitivities of the estimated risks with regard to the use of different climate input sets, decisions made in the downscaling algorithm, and different approaches to establish impact models.

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 Sensitivity of Coastal Flood Risk Assessments to Digital Elevation Models

Water ◽  
2012 ◽  
Vol 4 (3) ◽  
pp. 568-579 ◽  
Author(s):  
Bas van de Sande ◽  
Joost Lansen ◽  
Claartje Hoyng
Keyword(s):  
Flood Risk ◽  
Digital Elevation Models ◽  
Risk Assessments ◽  
Coastal Flood ◽  
Digital Elevation ◽  
Coastal Flood Risk

scholarly journals FLOOD RISK ASSESSMENTS - RESULTS, APPLICATIONS AND FUTURE REQUIREMENTS

2014 ◽  
Vol 1 (34) ◽  
pp. 15 ◽  
Author(s):  
Andreas Kortenhaus ◽  
Hocine Oumeraci
Keyword(s):  
Flood Risk ◽  
Risk Assessments

The importance of domino effects in flood risk assessments: a case study from the VNK2 project

2015 ◽  
Vol 13 (3) ◽  
pp. 305-313
Author(s):  
Wouter Lambertus Anthonie Ter Horst ◽  
Rudolf Bernard Jongejan
Keyword(s):  
Flood Risk ◽  
Risk Assessments

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.

Flood Risk Analysis

2017 ◽  
Author(s):  
Bruno Merz
Keyword(s):  
Risk Management ◽  
Risk Analysis ◽  
Flood Risk ◽  
Nuclear Power ◽  
Cognitive Biases ◽  
Flood Risk Management ◽  
Risk Assessments ◽  
Runoff Generation ◽  
Knowledge Modeling ◽  
Damage Scenarios

Floods affect more people worldwide than any other natural hazard. Flood risk results from the interplay of a range of processes. For river floods, these are the flood-triggering processes in the atmosphere, runoff generation in the catchment, flood waves traveling through the river network, possibly flood defense failure, and finally, inundation and damage processes in the flooded areas. In addition, ripple effects, such as regional or even global supply chain disruptions, may occur. Effective and efficient flood risk management requires understanding and quantifying the flood risk and its possible future developments. Hence, risk analysis is a key element of flood risk management. Risk assessments can be structured according to three questions: What can go wrong? How likely is it that it will happen? If it goes wrong, what are the consequences? Before answering these questions, the system boundaries, the processes to be included, and the detail of the analysis need to be carefully selected. One of the greatest challenges in flood risk analyses is the identification of the set of failure or damage scenarios. Often, extreme events beyond the experience of the analyst are missing, which may bias the risk estimate. Another challenge is the estimation of probabilities. There are at most a few observed events where data on the flood situation, such as inundation extent, depth, and loss are available. That means that even in the most optimistic situation there are only a few data points to validate the risk estimates. The situation is even more delicate when the risk has to be quantified for important infrastructure objects, such as breaching of a large dam or flooding of a nuclear power plant. Such events are practically unrepeatable. Hence, estimating of probabilities needs to be based on all available evidence, using observations whenever possible, but also including theoretical knowledge, modeling, specific investigations, experience, or expert judgment. As a result, flood risk assessments are often associated with large uncertainties. Examples abound where authorities, people at risk, and disaster management have been taken by surprise due to unexpected failure scenarios. This is not only a consequence of the complexity of flood risk systems, but may also be attributed to cognitive biases, such as being overconfident in the risk assessment. Hence, it is essential to ask: How wrong can the risk analysis be and still guarantee that the outcome is acceptable?

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 Flood Risk Assessments: Applications and Uncertainties

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2096
Author(s):  
Andrés Díez-Herrero ◽  
Julio Garrote
Keyword(s):  
Flood Risk ◽  
Power Plants ◽  
Risk Assessments ◽  
Infrastructure Management ◽  
Special Issue ◽  
Flash Flooding ◽  
River Flooding ◽  
Mountain Communities ◽  
Research Findings ◽  
Study Sites

The present Special Issue brought together recent research findings in Flood Risk Assessments (FRA) and contains contributions on advanced techniques and real cases where FRA have been carried out. The 16 research contributions highlight various processes and related topics where FRA have been applied and the main benefits and improved knowledge derived from them, as well as their replicability in other study sites. The published papers can be classified into three major categories. (a) First, there are those papers focused on improving the methods and results of FRA over different scenarios of both flooding types (river flooding or flash flooding) and flooding areas (urban, non-urban, small mountain communities). (b) Second, there are studies that investigate the application of FRA to diverse topics such as “land urban planning” or “vulnerable infrastructure management (dams, power plants)”. (c) Finally, there is a third group of papers which are focused on the assessment of the sources of uncertainties in FRA, with the aim of improving the results and making it more consistent with the real world.

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