![]() The global dataset HYDE 25 spans from years 10,000 BC to 2017 AD for both land-use and population, but has a resolution of only 5 arc-minutes (9 km on the equator). For example, HILDA 22, 23, 24 includes only highly aggregated land cover for the European Union countries, though with a high 1 km resolution covering years 1900 to 2010. In case of floods, high-exposure areas tend to be better protected 19 and less vulnerable 20, while land-use can locally modulate river discharge stronger than climate change 21.Īvailable historic reconstructions of exposure have limited utility for climate change attribution in a long perspective, either due to low resolution, limited spatial coverage or covering only a particular component of exposure. Quantifying changes in exposure, such as land use type, population, economic output, value of assets, and the uncertainty of it is vital not only due to its large direct influence on the observed impacts, but also indirect effects. Many studies indicated no upwards trend in natural hazard direct economic loss in Europe, USA or Australia when corrected for growth in exposure 14, 15, 16, 17, 18. Finally, only a small fraction of wildfires in Europe are caused by natural sources, making the human factors fundamental in understanding the frequency of those disasters 13. Windstorm damage in Europe was shown not to increase after correcting for exposure increase 10 with attribution being complicated by contrasting trends in hazard 11 and very high uncertainty on vulnerability functions 12. Estimates on the value of assets in a given location (exposure) and flood vulnerability functions, which indicate the share of assets that are lost at a given intensity of flood, vary drastically between countries 6, 7, 8, 9. 5 has shown that flood protection was the biggest source of uncertainty in coastal flood risk assessments in test sites in the Iberian Peninsula. For example, flood risk in the Rhine basin was found to be least sensitive to change in atmospheric forcing, but more to changes in reservoir capacity, dike height, land use, asset value or private precautionary measures 4. Case studies have indicated strong influence of additional drivers especially for floods, where the uncertainty of the present risk is already high. However, less quantification is available for systems with strong non-climatic drivers of change 3. There is growing research that quantifies the effects of the changing climate on the world’s natural, managed and human systems 1, 2. Global mean temperature has surpassed 1 ☌ warming compared to pre-industrial times. This allows the separation of the effects of climate change from the effects of exposure change. Raster datasets generated by the model enable reconstructing exposure within the footprint of any extreme event both at the time of occurrence and anytime between 18. It consists of algorithms to reallocate baseline (2011) land use and population for any given year based on a large collection of historical subnational- and national-level statistics, and then disaggregate data on production and tangible assets by economic sector into a high-resolution grid. The HANZE v2.0 (Historical Analysis of Natural HaZards in Europe) dataset presented in this study quantifies the evolution of key socioeconomic drivers in Europe since 1870, namely land use, population, economic activity and assets. However, the effects of climate change are obscured in the observed impact data series due to the rapid evolution of the social and economic circumstances in which the events occurred. Understanding the influence of climate change on past extreme weather impacts is a vital research task.
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