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 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 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 How are flood risk estimates affected by the choice of return-periods?

2011 ◽  
Vol 11 (12) ◽  
pp. 3181-3195 ◽  
Author(s):  
P. J. Ward ◽  
H. de Moel ◽  
J. C. J. H. Aerts
Keyword(s):  
Flood Risk ◽  
Damage Model ◽  
Area Under The Curve ◽  
Flood Management ◽  
Exceedance Probability ◽  
Return Periods ◽  
Inundation Maps ◽  
Risk Estimates ◽  
Risk Curve ◽  
Inundation Extent

Abstract. Flood management is more and more adopting a risk based approach, whereby flood risk is the product of the probability and consequences of flooding. One of the most common approaches in flood risk assessment is to estimate the damage that would occur for floods of several exceedance probabilities (or return periods), to plot these on an exceedance probability-loss curve (risk curve) and to estimate risk as the area under the curve. However, there is little insight into how the selection of the return-periods (which ones and how many) used to calculate risk actually affects the final risk calculation. To gain such insights, we developed and validated an inundation model capable of rapidly simulating inundation extent and depth, and dynamically coupled this to an existing damage model. The method was applied to a section of the River Meuse in the southeast of the Netherlands. Firstly, we estimated risk based on a risk curve using yearly return periods from 2 to 10 000 yr (€ 34 million p.a.). We found that the overall risk is greatly affected by the number of return periods used to construct the risk curve, with over-estimations of annual risk between 33% and 100% when only three return periods are used. In addition, binary assumptions on dike failure can have a large effect (a factor two difference) on risk estimates. Also, the minimum and maximum return period considered in the curve affects the risk estimate considerably. The results suggest that more research is needed to develop relatively simple inundation models that can be used to produce large numbers of inundation maps, complementary to more complex 2-D–3-D hydrodynamic models. It also suggests that research into flood risk could benefit by paying more attention to the damage caused by relatively high probability floods.

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 Reassessing flood frequency for the River Trent through the inclusion of historical flood information since ad 1320

2012 ◽  
Vol 44 (2) ◽  
pp. 215-233 ◽  
Author(s):  
Neil Macdonald
Keyword(s):  
Flood Risk ◽  
River Flow ◽  
Historical Data ◽  
Flood Frequency ◽  
Return Periods ◽  
Risk Estimates ◽  
Historical Series ◽  
Single Station ◽  
Multi Method Approach ◽  
Method Approach

The estimation of return periods for floods likely to have significant societal impact is challenging unless suitably long records exist. Relatively few sites across the UK provide a continuous record of river level or discharge over 50 years, whilst records extending back to the nineteenth century are rare. This represents a significant problem in providing robust and reliable estimates of flood risk, as relatively short records often fail to include an adequate sample of large floods. The inclusion of historical flood levels/magnitudes prior to instrumental river flow recording presents a valuable opportunity to extend this dataset. This paper examines the value of using historical data (both documentary and epigraphic) to augment existing gauged records for the River Trent in Central England, as part of a multi-method approach to assessing flood risk. Single station and pooled methods are compared with flood risk estimates based on an augmented historical series (1795–2008) using the generalised logistic and generalised Pareto distributions. The value of using an even longer, but less reliable, extended historical series (1320–2008) is also examined. It is recommended that modelling flood risk for return periods >100 years should incorporate historical data, where available, and that a multi-method approach increases confidence in flood risk estimates.

scholarly journals Coupling a global hydrodynamic algorithm and a regional hydrological model for large-scale flood inundation simulations

2017 ◽  
Vol 49 (2) ◽  
pp. 438-449 ◽  
Author(s):  
Shaochun Huang ◽  
Fred F. Hattermann
Keyword(s):  
Flood Risk ◽  
Large Scale ◽  
Hydrological Model ◽  
Regional Scale ◽  
Risk Assessments ◽  
Flood Inundation ◽  
Coupled Models ◽  
Macro Scale ◽  
Extreme Flood ◽  
Routing Method

Abstract To bridge the gap between 1D and 2D hydraulic models for regional scale assessment and global river routing models, we coupled the CaMa-Flood (Catchment-based Macro-scale Floodplain) model and the regional hydrological model SWIM (Soil and Water Integrated Model) as a tool for large-scale flood risk assessments. As a proof-of-concept study, we tested the coupled models in a meso-scale catchment in Germany. The Mulde River has a catchment area of ca. 6,171 km2 and is a sub-catchment of the Elbe River. The modified CaMa-Flood model routes the sub-basin-based daily runoff generated by SWIM along the river network and estimates the river discharge as well as flood inundation areas. The results show that the CaMa-Flood hydrodynamic algorithm can reproduce the daily discharges from 1991 to 2003 well. It outperforms the Muskingum flow routing method (the default routing method in the SWIM) for the 2002 extreme flood event. The simulated flood inundation area in August 2002 is comparable with the observations along the main river. However, problems may occur in upstream areas. The results presented here show the potential of the coupled models for flood risk assessments along large rivers.

scholarly journals Process-based flood risk assessment for Germany

2021 ◽  
Author(s):  
Nivedita Sairam ◽  
Fabio Brill ◽  
Tobias Sieg ◽  
Patric Kellermann ◽  
Kai Schröter ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Resolution ◽  
Flood Risk ◽  
Large Scale ◽  
Systems Approach ◽  
Economic Damage ◽  
Flood Risk Assessment ◽  
Continuous Simulation ◽  
Hazard Exposure ◽  
Risk Estimates

<p>Floods affect people worldwide and account for more than USD 100 billion losses on average every year. Hazard, Exposure and Vulnerability are the three components that influence flood risk. Flood Risk Management (FRM) decisions especially, with respect to new flood defense schemes and resilience initiatives are generally taken based on the assessment of impacts for hazard scenarios. Current large-scale studies are comprehensive in terms of sectors covered in impact assessment. However, these studies often deploy generalized data and methods on the model components resulting in coarse risk estimates with low spatial resolution.</p><p>In this study, we use process-based models with 100m resolution on the national scale within a systems approach to develop and simulate a 5000 year flood event catalogue for Germany. The events are then analyzed per economic sector, including residential, commercial and agriculture sectors. The risk chain includes continuous simulation of high-resolution hazard maps, obtained from coupled hydrology and hydraulic models; NUTS3-level exposure asset values further disaggregated to ATKIS land-use data and calibrated object-level vulnerability models that provide high-resolution quantification of economic damage. Spatial dependence of flood events is addressed by the continuous simulation approach. For each model component in the risk assessment (hazard, exposure and vulnerability), uncertainty in data and methods are integrated into the risk predictions. Based on these simulations, we present a sector-wise flood risk assessment for Germany along with the reliability of the risk estimates. This process-based, systemic flood risk assessment is valuable for policy making, adaptation planning and estimating insurance premiums.</p>

scholarly journals River-dike-floodplain system Interactions and temporal dynamics for large-scale flood risk assessment

2021 ◽  
Author(s):  
Mostafa Farrag ◽  
Sergiy Vorogushyn ◽  
Dung Viet Nguyen ◽  
Karin de Bruijn ◽  
Bruno Merz
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Flood Risk Assessment ◽  
River Dike

scholarly journals Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

2019 ◽  
Vol 33 ◽  
pp. 5668-5675 ◽  
Author(s):  
Huaxiu Yao ◽  
Xianfeng Tang ◽  
Hua Wei ◽  
Guanjie Zheng ◽  
Zhenhui Li
Keyword(s):  
Real World ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Dynamic Network ◽  
Research Field ◽  
Traffic Prediction ◽  
Unified Framework ◽  
Gating Mechanism ◽  
Spatial Dependencies

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

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