DATE2022-05-30 23:49:50
TITLEApplication of the Spatial Weather Generator in Modelling Present and Future Wildfire Risk
AUTHORSMartin Dubrovsky (1,3) ,Michele Salis (2) ,Petr Stepanek (3) ,Pavel Zahradnicek (3) ,Jan Meitner (3) ,Pierpaolo Duce (2)
  1. 1) Institute Of Atmospheric Physics, Czech Academy Of Sciences, Prague (Czechia) ,2) Institute Of Bioeconomy, National Research Council, Sassari (Italy) ,3) Global Change Research Institute, Czech Academy Of Sciences, Brno (Czechia)
ABSTRACTThe occurrence and intensity of wildfires in Mediterranean has increased during the last decades and is assumed to further rise due to the forthcoming climate change (CC), which is in Mediterranean characterized (based on GCM and RCM projections) by increasing temperature (all seasons of the year) and decreasing precipitation (especially in summer). In our contribution we present a summary of results of the experiment, in which we model the present-climate and future-climate wildfire risk in two European regions: one Mediterranean region – Sardinia, where the wildfires represent a serious environmental issue, and one Central European region – Czechia, where the forest fires are not so frequent presently, but their occurrence shows an increasing trend as a reasult of the changing climate and the wildfires may soon become a significant problem. To characterize the dynamics of the wildfire risk, we produce daily time series of the Fire Weather Index (FWI) using the daily weather series coming from three sources: (a) weather observations made at irregulary distributed weather stations (125 stations in Czechia, 15 stations in Sardinia; these data are used only for the present climate simulations), (b) synthetic weather data produced by spatial multi-variate weather generator SPAGETTA (Dubrovsky et 2020, Theor. Appl. Climatol.) calibrated with the observed weather data, and (c) gridded surface weather series simulated by Regional Climate Models (CORDEX database). The main focus in our experiment is put on results obtained by the weather generator (WG). To produce synthetic series representing the future climate, the WG parameters calibrated with observational data are modified by change factors which are based on a comparison of WG parameters derived from future vs. reference time slices of RCM-simulated weather series. Appart from the speed of the WGs (which are much faster than RCMs and GCMs), their employment implies some advantages, especially: (a) Arbitrarily long series may be produced, which allows to make a probabilistic assessment of the CC impacts. (b) WGs may produce future-climate synthetic series even for emission scenarios, for which RCM or GCM simulations are not available (by using the pattern scaling approach, in which the standardized RCM/GCM-based CC scenarios related to 1 K rise in global mean temperature are scaled by change in global mean temperature projected by simple climate model MAGICC for selected emission scenario. (c) Only selected statistical characteristics of the multi-variate multi-site weather series may be modified (the complete CC scenario consists of changes in averages and standard deviations of weather variables, together with the changes in temporal and spatial correlations of the weather series); this allows to assess sensitivity of FWI characteristics to changes in individual statistical characteristics of the weather series. In assessing impacts of the climate change on wildfire risk, we focus on changes in (i) high FWI values, (ii) spatial extent of area with high FWI values, and (iii) duration of the periods with high FWI. The results based on weather series synthesized by SPAGETTA are compared with results based on direct use of RCM-simulated surface weather series.