418 research outputs found
Economic modelling for flood risk assessment
Full text linkAerts, J.C.J.H. [Promotor]Moel, H. de [Copromotor
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
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
What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices
Full text linkAtmospheric oscillations are known to drive the large-scale variability of hydrometeorological extremes in Europe, which can trigger flood events and losses. However, to date there are no studies that have assessed the combined influence of different large-scale atmospheric oscillations on the probabilities of flood losses occurring. Therefore, in this study we examine the relationship between five indices of atmospheric oscillation and four classes of flood losses probabilities at subregional European scales. In doing so, we examine different combinations of atmospheric oscillations, both synchronous and seasonally lagged. By applying logistic regressions, we aim to identify regions and seasons where probabilities of flood losses occurring can be estimated by indices of atmospheric oscillation with higher skill than historical probabilities. We show that classes of flood losses can be predicted by synchronous indices of atmospheric oscillation and that in some seasons and regions lagged relationships may exist between the indices of atmospheric oscillation and the probability of flood losses. Furthermore, we find that some models generate increased (or decreased) probability of flood losses occurring when the indices are at their extreme positive or negative phases. A better understanding of the effects of atmospheric oscillations on the likelihood of flood losses occurring represents a step forward in achieving flood resilience in Europe. For instance, improved early predictions of the indices that represent such atmospheric oscillations, or the evidence of a lagged relationship between their teleconnections and floods, can significantly contribute to mitigating the socioeconomic burden of floods
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
Meerlaagsveiligheid buitendijks: Uitkomsten van de workshop in regio Rotterdam Drechtsteden
No full textTot nu toe werden buitendijkse gebieden vaak opgehoogd, maar dit is lastiger in bestaand bebouwd gebied. Bovendien kost het ophogen van de (deel ondergrondse) infrastructuur veel. Daarnaast is wonen aan het water aantrekkelijk. Daarom wordt er steeds vaker ook naar andere oplossingen gekeken, bijvoorbeeld via een meerlaagsveiligheid aanpak. Hierbij wordt gekeken naar 3 lagen: preventie, duurzame ruimtelijke ordening en rampenbeheersing. Integraal waterbeheer, en meerlaagsveiligheid in het bijzonder, wordt in de praktijk bemoeilijkt doordat de betrokkenen tegengestelde en conflicterende belangen hebben. Daarnaast is de ruimtelijke informatie die nodig is voor het planningsproces vaak gedetailleerd en complex. Het interactief communiceren van ruimtelijke informatie heeft een positieve invloed op het planningsproces doordat het begrip en vertrouwen in de informatie wordt vergroot. Daarom is tijdens de workshop gebruik gemaakt van een Touch Table.kennis voor klimaa
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
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