290 research outputs found
Economic modelling for flood risk assessment
Full text linkAerts, J.C.J.H. [Promotor]Moel, H. de [Copromotor
Estimation of global tropical cyclone wind speed probabilities using the STORM dataset
No full textTropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments
STORM tropical cyclone wind speed return periods
No full textDatasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see https://www.nature.com/articles/s41597-020-0381-2). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every ocean basin. The STORM_FIXED_WIND_SPEED dataset contains return periods for a fixed set of maximum wind speeds at 10 km resolution in every ocean basin. The STORM_CITIES dataset contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from a selection of 18 coastal cities. The STORM_ISLANDS contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from the capital city of an island. We included the Small Island Developing States and a set of other islands.</span
STORM EC-Earth present climate synthetic tropical cyclone tracks
No full textDatasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in prep.). The dataset is generated using data the EC-Earth model and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.</span
STORM IBTrACS present climate synthetic tropical cyclone tracks
No full textDatasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in review). The dataset is generated using historical data from IBTrACS and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.</span
Generation of a global synthetic tropical cyclone hazard dataset using STORM
No full textOver the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions
Natural Hazards in a Digital World:Algorithms for Using Social Media in Disaster Management
Full text linkRising waves of indecision:How financial incentives can support flood risk management
Full text linkFloods have a devastating impact on society, costing thousands of lives and billions of dollars annually. Scientific projections indicate that flood risk is expected to increase in the future, driven by socio-economic growth and climate change. However, managing flood risk is a complex and costly process that requires decision-making with uncertain future conditions under the fear of making irreversible, inefficient choices. To support decision-makers, flood risk assessments provide estimates of the monetary impacts of floods or the economic efficiency of adaptation investments, although they often lack spatial or temporal dynamics. In addition, homeowners also make decisions at an individual level, such as implementing building-level adaptation measures or purchasing flood insurance. Homeowners’ decisions often deviate from rationality, as it is difficult for individuals to estimate the probability and associated damage of a potential flood. This PhD dissertation explores the extent to which we can incorporate the decision-making dynamics of governments, households, and flood insurance into a flood risk assessment at different spatial scales, and how this may improve flood risk management, applied to cases in the US
Sea Change To Nature-based Solutions: A Coastal Flood Risk Perspective
Full text linkCoastal floods are one of the deadliest and costliest of natural hazards, triggering or contributing to economic disruption, displacement, (mental) health implications, environmental disasters, poverty traps, and geomorphic change. In the coming century, coastal communities are projected to face increases in coastal flood risk. To prevent this increase in coastal flood risk, or even reduce risk below today’s levels, adaptation strategies are necessary. To make informed decisions on what measures to take, it is important to better understand the effectiveness of such coastal flood risk adaptation strategies, preferably beyond just monetary terms. Therefore, the overall aim of this thesis is to disentangle drivers of coastal flood risk and assess costs and benefits of adaptation strategies. By doing so, the thesis improves upon conventional flood risk assessments by taking steps into the direction of integrated and holistic assessments that include Nature-based Solutions and valuing of adaptation beyond monetary terms. This thesis uses artificial intelligence in the form of deep learning to construct a model to predict storm surges (which may lead to coastal flooding) at the global scale. Next to this, a risk model has been constructed to assess the effectiveness of adaptation strategies (based on structural measures, Nature-based Solutions, or a combination which is called “hybrid” measures). We assess risk by using projections of sea-level rise, socioeconomic change, subsidence, foreshore vegetation and restoration potential. Next to this we use vulnerability data, like poverty dynamics, to assess effectiveness of adaptation measures beyond monetary values. The results show that EAD increases by a factor of 150 between 2010 and 2080, if no adaptation were to take place, and that 15 countries account for approximately 90% of this increase. Moreover, sea-level rise contributed the most to the increase in coastal flood risk, but socioeconomic change and subsidence also play an important role at the regional scale. Furthermore, the results show that implementing Nature-based Solution, like conservation and restoration of foreshore vegetation, can contribute a large share to reduce flood risk and will next to structural measures, likes dikes and levees, increase the feasibility of adaptation strategies for two-thirds (68%) of the regions assessed. Moreover, we show that restoration of mangroves contribute to the safeguarding of communities by providing coastal flood protection benefits. Therefore, implementing adaptation in low- and middle-income countries could contribute to the resilience of people in poverty, poverty alleviation and help tackle poverty traps. Overall, the results of this thesis contribute to international initiatives such as the Sendai Framework for Disaster Risk Reduction and can be used to inform policy makers and development agencies on risks from global to regional level. In order to bridge the gap between academia and the risk management community, we integrated the results into the Aqueduct Global Floods webtool (www.wri.org/floods). This webtool allows any user to examine current and future risk, as well as the benefits of strucutral flood protection at the sub-national scale. Implementing adaptation measures, such as mangrove restoration, in LMICs could contribute to the resilience of people in poverty, decrease the risk of displacement and migration, and tackle poverty traps. The loss of these ecosystems disproportionally affects vulnerable groups and communities that live close to the coast and often heavily depend on natural resources. The results can help policymakers to assess the threat of coastal flooding and design sustainable adaptation measures considering poverty dynamics
Hoogwater 2021: Feiten en Duiding
No full textIn juli 2021 zijn grote delen van Limburg getroffen door hevige regenval en overstromingen. Ook delen van België en Duitsland overstroomden met zeer veel schade en verlies aan mensenlevens tot gevolg. Dit betrof een extreme en ongeëvenaarde gebeurtenis met enorme impact. Daarom is naar aanleiding van de overstromingen deze verkenning uitgevoerd om een eerste stap te maken om beschikbare informatie over deze gebeurtenis te verzamelen en analyseren. Het onderzoek is uitgevoerd door een breed consortium (TU Delft, Deltares, HKV Lijn in Water, VU Amsterdam, Universiteit Utrecht, KNMI, WUR, Erasmus MC en Universiteit Twente) in opdracht van het Expertise Netwerk Waterveiligheid (ENW). Een overstroming heeft effect op de hele maatschappij. Daarom zijn niet alleen hydrologische en civieltechnische onderwerpen beschouwd, maar ook de maatschappelijke gevolgen van overstromingen, de crisisrespons en de gezondheidseffecten.Contributors (in alphabetical order): Nathalie Asselman (Deltares), Hermjan Barneveld (HKV / Wageningen UR), Jules Beersma (KNMI), Eline Boelee (Deltares), Wouter Botzen (VU Amsterdam), Eefke Copper (TU Delft), Dim Coumou (KNMI), Karin de Bruijn (Deltares), Anniek de Jong (Deltares), Jurjen de Jong (Deltares), Hans de Moel (VU Amsterdam), Ferdinand Diermanse (Deltares), Astrid Fischer (Evides) , Gert-Jan Geerling (Deltares), Marie-Louise Geurts (WML), Rob Groenland (KNMI), Mark Hegnauer (Deltares), Bas Jonkman (TU Delft), Nicole Jungermann (KNMI), Frans Klijn (Deltares), Andre Koelewijn (Deltares), Matthijs Kok (HKV / TU Delft), Elco Koks (VU Amsterdam), Bas Kolen (HKV / TU Delft), Marion Koopmans (Erasmus MC), Laurens Leunge (Deltares), Hans Middelkoop (Utrecht University), Roelof Moll (TU Delft), Jaap Mos (Dunea), Sjoukje Philip (KNMI), Gerbert Pleijter (HKV), Joost Pol (HKV / TU Delft), Stephan Rikkert (TU Delft), Guus Rongen (TU Delft), Rinus Scheele (KNMI), Julius Schlumberger (TU Delft), Peter Siegmund (KNMI), Kymo Slager (Deltares), Frederiek Sperna Weiland (Deltares), Bart Strijker (HKV / TU Delft), Henk v.d. Brink (KNMI), Janko van Beek (Erasmus MC), Marion van den Bulk (TU Delft), Bart van den Hurk (Deltares), Tim van Emmerik (Wageningen UR), Kees van Ginkel (VU Amsterdam / Deltares), Mick van Haren (TU Delft), Margreet van Marle (Deltares), Malou van Schaijk (TU Delft), Dennis Wagenaar (Nanyang TU), Davide Wüthrich (TU Delft)Hydraulic Structures and Flood RiskSafety and Security ScienceCoastal Engineerin
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