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A global historical data set of tropical cyclone exposure (TCE-DAT)

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Geiger,  Tobias
Potsdam Institute for Climate Impact Research;

/persons/resource/Katja.Frieler

Frieler,  Katja
Potsdam Institute for Climate Impact Research;

Bresch,  D. N.
External Organizations;

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Zitation

Geiger, T., Frieler, K., Bresch, D. N. (2018): A global historical data set of tropical cyclone exposure (TCE-DAT). - Earth System Science Data, 10, 1, 185-194.
https://doi.org/10.5194/essd-10-185-2018


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_22025
Zusammenfassung
Tropical cyclones pose a major risk to societies worldwide, with about 22 million directly affected people and damages of USD 29 billion on average per year over the last 20 years. While data on observed cyclones tracks (location of the center) and wind speeds are publicly available, these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots (kn). Based on available spatially explicit data on population densities and gross domestic product (GDP) we estimate (1) the number of people and (2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated spatially explicit and aggregated country-event-level exposure data (TCE-DAT) cover the period 1950 to 2015 and are freely available at https://doi.org/10.5880/pik.2017.011 (Geiger at al., 2017c). It is considered key information to (1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, (2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and (3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration.