M. Dubrovský, M. Salis, P. Štěpánek, P. Duce, P. Zahradníček, J. Meitner, M. Mozny
{"title":"Modelling Present and Future Wildfire Risk with Use of a Fire Weather Index, Spatial Weather Generator and Regional Climate Models","authors":"M. Dubrovský, M. Salis, P. Štěpánek, P. Duce, P. Zahradníček, J. Meitner, M. Mozny","doi":"10.3390/environsciproc2022017130","DOIUrl":null,"url":null,"abstract":"To construct time series for a Fire Weather Index (FWI), input weather series may come from various sources. Observed weather station data or gridded series interpolated from observations are commonly used to produce FWI series representing the present climate. FWI series representing the future may be based on RCM-simulated data or on series synthesized by a stochastic weather generator (WG). In the latter case, WG parameters are calibrated with observed weather data and modified using the climate change (CC) scenarios derived from GCM or RCM simulations. The application of a WG implies some advantages, including: (a) arbitrarily long series may be produced, allowing us to make a probabilistic assessment of CC impacts on the FWI. (b) only selected characteristics of the multi-variate multi-site weather series may be modified when modifying WG parameters before producing the weather series representing the modified climate (the complete CC scenario consists of changes in averages and standard deviations of weather variables, and changes in the temporal and spatial structure of weather series); this allows us to assess the sensitivity of the FWI to changes in individual statistical characteristics of the weather series. We use the spatial daily weather generator SPAGETTA (Dubrovsky et 2020, Theor. Appl. Climatol.) to produce a synthetic weather series representing present and future climates for Czechia (125 weather stations) and Sardinia (15 stations). FWI time series are constructed using both present-climate and future-climate weather series, and the changes in FWI characteristics due to climate change are assessed. The future climate weather series are produced with WG modified using the CC scenarios derived from a set of RCMs. In assessing the results, we focus on high FWI values, spatial extent of the area with high FWI values, and the duration of the periods with a high FWI. The results based on the WG-synthesized weather series are compared with those based on the RCM-simulated series.","PeriodicalId":291013,"journal":{"name":"The Third International Conference on Fire Behavior and Risk","volume":"53 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Third International Conference on Fire Behavior and Risk","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/environsciproc2022017130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
To construct time series for a Fire Weather Index (FWI), input weather series may come from various sources. Observed weather station data or gridded series interpolated from observations are commonly used to produce FWI series representing the present climate. FWI series representing the future may be based on RCM-simulated data or on series synthesized by a stochastic weather generator (WG). In the latter case, WG parameters are calibrated with observed weather data and modified using the climate change (CC) scenarios derived from GCM or RCM simulations. The application of a WG implies some advantages, including: (a) arbitrarily long series may be produced, allowing us to make a probabilistic assessment of CC impacts on the FWI. (b) only selected characteristics of the multi-variate multi-site weather series may be modified when modifying WG parameters before producing the weather series representing the modified climate (the complete CC scenario consists of changes in averages and standard deviations of weather variables, and changes in the temporal and spatial structure of weather series); this allows us to assess the sensitivity of the FWI to changes in individual statistical characteristics of the weather series. We use the spatial daily weather generator SPAGETTA (Dubrovsky et 2020, Theor. Appl. Climatol.) to produce a synthetic weather series representing present and future climates for Czechia (125 weather stations) and Sardinia (15 stations). FWI time series are constructed using both present-climate and future-climate weather series, and the changes in FWI characteristics due to climate change are assessed. The future climate weather series are produced with WG modified using the CC scenarios derived from a set of RCMs. In assessing the results, we focus on high FWI values, spatial extent of the area with high FWI values, and the duration of the periods with a high FWI. The results based on the WG-synthesized weather series are compared with those based on the RCM-simulated series.
为建构火警天气指数(FWI)的时间序列,输入的天气序列可能来自不同来源。气象站观测数据或从观测中插入的网格序列通常用于生成代表当前气候的FWI序列。代表未来的FWI序列可以基于rcm模拟数据,也可以基于随机天气发生器(WG)合成的序列。在后一种情况下,使用观测到的天气数据校准WG参数,并使用来自GCM或RCM模拟的气候变化(CC)情景对其进行修改。使用工作组意味着一些好处,包括:(a)可以产生任意长的序列,使我们能够对CC对FWI的影响进行概率评估。(b)在生成代表修改后气候的天气序列之前,修改WG参数时,只能修改多变量多站点天气序列的选定特征(完整的CC情景包括天气变量的平均值和标准差的变化,以及天气序列的时空结构的变化);这使我们可以评估FWI对天气系列个别统计特征变化的敏感度。我们使用空间每日天气发生器SPAGETTA (Dubrovsky et 2020, theory)。达成。Climatol.)为捷克(125个气象站)和撒丁岛(15个气象站)制作一个代表现在和未来气候的综合天气系列。利用现在气候和未来气候天气序列构建了FWI时间序列,并评估了气候变化对FWI特征的影响。未来气候天气系列是用一组rcm衍生的CC情景修改的WG生成的。在评估结果时,我们重点关注高FWI值、高FWI值区域的空间范围以及高FWI期的持续时间。将基于wg合成天气序列的结果与基于rcm模拟序列的结果进行了比较。