The frequency and intensity of temperature extremes have increased worldwide in the past couple of decades. The year 2018 was characterized by record-breaking temperatures in many parts of Europe during spring and summer, which lead to unusual and severe wildfires in central and northern Europe. For example, devastating fires destroyed large areas of intact forests, not only in countries with a long tradition of wildfires but also in countries, such as Sweden, Norway, Finland, and Latvia.  In 2022, Europe was characterized   by a prolonged spring drought, several summer heatwaves and fire activity without precedents for several European countries. In particular, a high number of fires occurred in Germany, Austria, Chechia, Hungary, Slovenia and Romania during the summer months, highlighting the increase of fire-prone conditions in the region linked with an increase of hot and dry conditions.

This work analyses the exceptionality of the 2022 fire season over central Europe. Fire Radiate Power (FRP) from MODIS, burned area and number of fires from EFFIS were used to characterize fire occurrence in 2022. We used ERA5 meteorological parameters, such as the maximum and minimum air 2m temperature, minimum relative humidity, and wind speed to evaluate the severity of the heat extremes from April to August. SPEI for the time scales of 6 and 12 months were computed using ERA5 data to evaluate the drought conditions in spring and summer over the central European region. The Canadian Fire Weather Index (FWI) and sub-indices available from ERA5 data, were used to assess the exceptionality of meteorological fire danger over the region in the summer of 2022.  Moreover, possible FWI trends and sub-indices were also analysed for the period from 1979 to 2022. The impact of drought on vegetation productivity during the spring and summer of 2022 was also evaluated. Results highlight the new fire dynamics in Europe in recent years, with new emergent hot spots, in central and northern European countries. It is thus extremely important to assess of trends of fire danger and changes in fire activity over this region to better define the related activities of fire monitoring, as well as the definition of planning activities and suppression measures towards climate mitigation and adaptation.

Acknowledgements: This study is partially supported by the European Union’s Horizon 2020 research project FirEUrisk (Grant Agreement no. 101003890) and by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and DHEFEUS - 2022.09185.PTDC.

How to cite: Gouveia, C. M., Ramos, A. M., Silva, M. C., Durão, R., Bayar, A. S., and Pinto, J. G.: The exceptionality of the 2022 fire season over Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10974, https://doi.org/10.5194/egusphere-egu24-10974, 2024.

Wildfires present substantial threats to ecosystems and human settlements which increase the importance of monitoring for timely detection and assessment. This study was performed on the Campania provinces—Salerno, Avellino, Benevento, Caserta, and Napoli in Italy—employing a multi-sensor remote sensing approach to elevate wildfire analysis. The first objective is identifying fire patches through Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2020. Although, generating Difference Normalized Burn Ratio (DNBR) and Difference Normalized Difference Vegetation Index (DNDVI) maps from Sentinel-2 images. Integration of MODIS and Sentinel-2 outcomes enhances pinpointing fire-affected areas. Subsequently, Incorporating Landsat 9 images for Land Surface Temperature (LST) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for precipitation trends in five provinces from 2001 to 2020 in Hotspot Polygons generated via First Order Contiguity edges corners algorithm. Pearson correlation coefficients between burnt area, LST, and precipitation are computed. A high correlation coefficient was observed between the mentioned parameters. Wildfire analysis reveals peak burnt areas in 2001, 2007, and 2017 in Avellino and Salerno Province. Change detection maps illustrate significant land cover changes from Forest to Savannas and Shrubland to grasslands in 2001. Avellino province reveals a decreasing trend in Grassland and an increase in Savannas, as the same as observations in Salerno Province. This study considers the analysis of wildfires, connecting burnt areas, climate variables, and land cover changes across the Campania provinces.

Keywords: Wildfire, Fire and Vegetation Indices, Land Cover Changes, Climate Change

How to cite: Dadkhah, H., Rana, D., Ghaderpour, E., and Mazzanti, P.: Integrating Multi-Sensor Remote Sensing Data for Comprehensive spatio-temporal WildfireAssessment In Campania Provinces – Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21377, https://doi.org/10.5194/egusphere-egu24-21377, 2024.

The study is a step forward in the characterization of meteorological environments that favour the evolution of large and extreme fires in Southern Portugal. The region has some fire-prone areas which are recognized by the mega fires occurred in 2003, 2005, and 2018. Two numerical simulations were performed using the Meso-NH non-hydrostatic research model and used to investigate in detail the atmospheric environments of two large fires that occurred on 18^th July 2012 and 19^th June 2020. The simulations were configured using two nested domains with a 375 km × 375 km grid domain (D1) at 2.5 km horizontal resolution and a 150 km × 150 km domain (D2) at 500 m resolution added before the start of the fires. The vertical grid was configured with 50 stretched levels following the terrain. The initial and boundary conditions are provided by the 6-hourly operational ECMWF analyses. The large-scale circulation has been characterised using data obtained from the ECMWF's Meteorological Archival and Retrieval System. In addition to the large-scale circulation, namely the positioning of the Azores anticyclone and the thermal low development over the Iberian Peninsula, the results have shown the important role played by regional orography in creating favourable fire weather conditions. For instance, the high-resolution simulations showed the high daytime temperatures and sometimes overnight, low humidity, and strong wind gusts that favour fire spread. In July 2012, the typical sea breeze circulation affected the fire evolution, whereas the intense downslope winds favoured the fire spread in June 2020. The study brings useful guidelines for interpreting the impact of different mesoscale environments that may produce large fires, namely the orographic effects that can increase the fire susceptibility and vulnerability of some regions. This study was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Ref. PCIF/MPG/0175/2019).

How to cite: Purificação, C., Campos, C., Henkes, A., Kartsios, S., and Couto, F. T.: Meteorological environments leading to two large fires in Southern Portugal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6004, https://doi.org/10.5194/egusphere-egu24-6004, 2024.

Persistent positive anomalies in 500 hPa geopotential height (PPAs) are upper-air circulation patterns associated with surface heatwaves, drought, and consequently fuel aridity, elevated fire weather, and active wildfires. We examined the association between PPA events and surface fire weather and burned area at a pan-European level. Europe-wide, extreme fire weather and wildfires were on average 3.5 and 2.3 times more likely to occur concurrently with a PPA, respectively. PPAs were associated with 43% of pan-European area burned between March and October 2001–2021, and there was a latitudinal increase in the percentage of area burned during PPAs up to 60% over Northern Europe. Burned area was highest in the three days following PPA presence, and fuel moisture indices from the Canadian Fire Weather Index System lagged behind peak PPA strength, demonstrating the role of PPAs in pre-drying fuels. PPAs have been associated with significant wildfire events experienced across Europe, including the 2017 Portugal wildfires, the 2018 UK, Sweden, and Finland wildfires, and the 2021 Greece wildfires. Our findings demonstrate opportunities for developing early warning systems of wildfire danger, having implications for wildfire awareness and preparedness, informing policy, and wildfire management decisions like early mobilisation and resource sharing initiatives within and across Europe.

How to cite: Little, K., Castellanos-Acuna, D., Jain, P., Graham, L., Kettridge, N., and Flannigan, M.: Persistent positive anomalies in geopotential height drive enhanced wildfire activity across Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11570, https://doi.org/10.5194/egusphere-egu24-11570, 2024.

Climate change alters boreal forest dynamics. The risk of boreal forest disturbances are expected to increase by the end of the century compared to the current state. However, projecting the future impacts of climate change on forest disturbances inherently contains uncertainties related to the global climate models.

Here, we study the impact of climate change on forest fires and wind damage using ecosystem model (JSBACH) simulations from 1951 to 2100. The simulations are driven by output from three global climate driver models that have been bias-corrected and downscaled (CORDEX EUR-44 domain). The global models from CMIP5 were run under two forcing scenarios, RCP 4.5 and RCP 8.5. To tackle the uncertainty of climate change projections, we use six climate projections.

In our simulations the fire season in Fennosscandia is projected to extend in both spring and autumn. The fire season is estimated to lengthen by 20-52 days, starting 10-23 days earlier and ending 10-30 days later, by the end of the century. In general, it is expected that the number of fires and burnt area are projected to increase from the reference period (1981-2010) to the end of the century (2071-2100) due to rising temperatures, despite increases in precipitation. However, the amount and direction of change varies significantly between climate projections and locations.

Our preliminary results implicate that the risk for wind damage may change and affect to the number of fires. Wind damage affects the size of litter pools that change the amount of fuel available for fires. Finally, our aim is to study the interaction between forest fires and wind damage in boreal forests.

How to cite: Kinnunen, O., Backman, L., Aalto, J., and Markkanen, T.: Projected impacts of climate change in forest fires and wind damage in Fennoscandia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8971, https://doi.org/10.5194/egusphere-egu24-8971, 2024.

Wildfires are expected to increase under warmer and drier conditions, yet little is known about their potential effects on population at global scale. Here, we developed a novel framework based on statistical wildfire model and spatial demographic data to better understand how global warming alters population exposure to wildfires throughout the world. We sought to model annual burn rate with relevant explanatory variables, such as climate, land cover, and topography to simulate historical and future wildfire frequency at global scale. To do so, we used a Generalized Additive Model combined with historical climate data and an ensemble of CMIP6 climate projections under the SSP5-8.5 scenario. We then analysed population exposure to wildfires combining population count across the wildland–urban interfaces with the simulated historical and future wildfire frequency. Our results indicate that in the present day the highest population exposure to wildfires is in southeast Asia, parts of South America and Africa, due to the large number of people living in wildland–urban interfaces with a high wildfire frequency. All other things being equal, global warming was found to increase population exposure, with an expansion of the regions with high wildfire frequency in east and south Europe, southeastern Asia, parts of North and South America. The estimated increase in population exposure may also imply potential impacts on the built environment and human health in the absence of mitigation or adaptation measures.

How to cite: Galizia, L., Castet, C., and Voulgarakis, A.: Global warming increases population exposure to wildfires , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10990, https://doi.org/10.5194/egusphere-egu24-10990, 2024.

Portugal is one of the European countries that faces significant challenges with wildfires. While lightning-triggered natural fires constitute a minority compared to anthropogenic ones, accurate forecasting of lightning occurrences is crucial for effective prevention. The study assesses the ECMWF model's capability to predict lightning in Portugal over four fire seasons [2019-2022]. Observed lightning data was obtained from the national lightning detector network, aggregated into 0.5° and 1° resolutions over 3-hour periods. The evaluation employs statistical indices from a contingency table to analyze the model's performance. Results indicate an overestimation of lightning occurrences by the ECMWF model, with a Bias greater than 1. The success rate for lightning prediction was 57.7% for a horizontal resolution of 1° and 49% for 0.5°. Additionally, the temporal analysis reveals a time lag between both data, with the model starting to predict lighting before its occurrence and finishing the prediction earlier. These findings are complemented by analyzing the spatial lightning distribution, which led us to identify some weather patterns associated with lightning activity during the study period. For instance, lightning activity was associated with the Iberian thermal low development overlapped by an Upper Level Low and the passage of large-scale features, such as frontal systems. The insights gained from this study have implications for the ECMWF lightning forecast applicability in the context of forecasting natural forest fires in Portugal. The research was funded by the European Union through the CILIFO project (0753-CILIFO-5-E) and also by national funds through FCT Foundation for Science and Technology, I.P. under the PyroC.pt project (PCIF/MPG/0175/2019).

How to cite: Campos, C., Couto, F. T., Santos, F. L. M., Rio, J., Ferreira, T., Purificação, C., and Salgado, R.: Assessing ECMWF Lightning Forecast in Portugal during fire seasons, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6108, https://doi.org/10.5194/egusphere-egu24-6108, 2024.

Lightning-induced wildfires lead to significant loss of life and extensive property damage worldwide. This issue is especially critical in southeast Australia, where such wildfires account for 80-90% of the total area burned, emphasising the need for a comprehensive understanding of the contributing factors and mechanisms that drive these events. This study aims to investigate the complex interactions between climate, topography, lightning activity, and fire events in New South Wales (NSW), Australia. By analysing comprehensive datasets from 2017-2021, including ignition records, meteorological data, topographical information, and fuel characteristics, this research seeks to identify the key factors influencing lightning-attributed wildfires and predict the probability of lightning-caused fire occurrence. A Random Forest model is trained and tested to estimate the probability of fires caused by lightning strikes. Model performance was assessed through the Receiver Operating Characteristic, with an Area Under the Curve (AUC) around 0.7 in the validation datasets, indicating a good agreement between the estimated probabilities and the reported lightning-caused fires. The identified key factors that influence lightning fire ignitions include humidity, elevation, temperature, rainfall, soil moisture, and fuel moisture, highlighting the dominant influence of weather variables on wildfire ignitions. The preliminary results demonstrate a potential link between the geographic distribution of lightning-induced fires and the temperate climate zones, possibly due to the presence of dense vegetation and seasonal weather patterns. Our ongoing efforts focus on further refining the predictive model and conducting a more extensive analysis of the data to enhance our understanding of the dynamics of lightning-induced wildfires. Ultimately the study will provide insights for effective risk management and mitigation of lightning-caused wildfires in the regions prone to wildfires.

How to cite: Zhao, L. and Yebra, M.: Modelling and Analysis of Lightning-Induced Wildfires in Australia , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15304, https://doi.org/10.5194/egusphere-egu24-15304, 2024.

Increasing temperatures, due to climate change lead to more evapotranspiration which increases the possibility of severe drought periods. These trends enhance the risk of wildfire hazards even in humid regions like the Alps. Further, possible changes in the occurrence of thunderstorms can modify the ignition danger of lightning induced wildfires. This study aims to investigate the effect of climate change on the probability of wildfires ignited by lightnings including possible shifts in lightning probability for Austria.

The full analysis is performed on a 1x1 km grid over Austria. Fire ignition danger and drought periods are approached by computing the Fine Fuel Moisture Code (FFMC). Noon temperature and windspeed for the FFMC are estimated by a spatio-temporal GAM (generalized additive model) with a geographic varying cyclic B-spline. The occurrence of lightnings is approached by the Showalter Index, which is validated with data from the Austrian Lightning Detection and Information System (ALDIS) for the period 2011 to 2020. For the historical weather conditions the Spartacus dataset is used for the period 1981-2022. Regarding the future development, five different climate projections are compared.

The historical period showed on average no trend for days with high FFMC values (> 91) for Austria, but already 13% of the study area have a significant positive trend (tested by Mann-Kendall trend test). The trend is even more evident for the climate projections, which show a significant increase in days with FFMC values > 91 for 99% of the study area, with a sharp increase starting about 2050. Possible alterations in thunderstorm activity will strengthen the danger of forest fire ignitions of wildfires in Austria and are posing an increasing threat for forest management and society.

How to cite: Laimighofer, J., Andrade, M. S., Echtler, P., Fuchs, S., Müller, M., Papathoma-Köhle, M., Vacik, H., and Formayer, H.: Effect of climate change on lightning induced forest fires in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18048, https://doi.org/10.5194/egusphere-egu24-18048, 2024.

Fire weather indices are used widely as predictors for landscape fire potential. However, for the United Kingdom (UK: England, N-Ireland, Scotland and Wales) and comparable regions of humid-Atlantic Europe, they do not correlate well with fire occurrence. Here we explore the role of vegetation phenology as a key driver for fire occurrence in the UK.

We mapped satellite-derived fire occurrence and phenology climatology for 2012-2023 onto main fire-affected vegetation cover types within distinct precipitation regions for the UK. This enabled fire occurrence for fuels in different phenological phases to be explored across distinct ‘fuel’ types and regions.

Semi-natural grassland and dwarf shrub-dominated land emerged as the prominent fire affected ecosystems across much of the UK. We found that, critically, fire occurrence for vegetation at its maximum greenness were reduced by a factor of five to six compared to dormant vegetation, despite higher fire weather indices being typically associated with the former.

In contrast to most regions of the world that exhibit more extreme fire weather, fire activity in the UK’s humid Atlantic climate therefore seems strongly governed by vegetation phenology. This suggests that incorporating vegetation phenology is la critical step in the development of robust fire risk and behaviour prediction systems for regions with similar climate. It should be noted, however, that we also found evidence of that this fire-suppressing phenology barrier can be broken during extreme summer heat/drought events, which are likely to increase in frequency and severity under changing climate.  Hence fire weather indices remain critical predictors during the currently still rare extreme summer heat/drought events.

How to cite: Doerr, S., Nikonovas, T., Santin, C., Clay, G., Belcher, C., and Kettridge, N.: Spatio-temporal patters of fire in humid-Atlantic Europe: is vegetation phenology rather than fire weather the key driver? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8266, https://doi.org/10.5194/egusphere-egu24-8266, 2024.

The altering climate patterns contribute to variations in precipitation, temperature and overall ecosystem conditions, influencing the composition and combustibility of forest fuels in the alpine regions. Changes in vegetation patterns, with shifts in species distribution and the prevalence of dry, flammable materials, increases the risk through wildfires. Rising temperatures and prolonged periods of drought enhance the likelihood of ignition and intensify fire behavior. Thus, this research aims to carry out a comprehensive vegetation analysis to characterize the different fuel types in Austria and to provide the scientific basis for developing a detailed forest fuel map, considering various types of vegetation, topography and land cover. We use statistical models to predict fuel characteristics based on vegetation type and empirical data collected during field surveys in the recent years. The spatial distribution of fuel types will be related to an analysis of the location of historical fire data for the time period 2001-2023. A statistical analysis is done to identify clusters, patterns, and relationships for different fuel types. This integrated methodology not only enriches our understanding of the complex interconnection between vegetation fire ignition and behavior, but also provides the scientific basis for developing targeted strategies in forest fire management and improving prevention measures in Austria.

How to cite: Silva Andrade, M., M. Müller, M., Kuhnen, K., and Vacik, H.: Relation between the distribution of fuel types and forest fires in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11983, https://doi.org/10.5194/egusphere-egu24-11983, 2024.

Over the past two decades wildfires have been increasingly disturbing many ecosystems worldwide. Among them, the Mediterranean-like climate regions have been strongly affected by recurrent events, as widely seen during the fire seasons of 2003, 2005, 2017 and 2022 in Portugal and northern Spain, as well as in Greece and southern Italy in 2007, 2021 and 2023. Additionally, Chile experienced significant fire seasons in 2015 and 2017, California faced destructive wildfires in 2018, 2020 and 2021, and Australia was affected by severe wildfires during 2019-2020.

Even though there is an observed increase of fire frequency over fire-prone regions, the Mediterranean ecosystems are in general well adapted to fire through several mechanisms to tolerate exposure to extreme conditions and recover from fire. However, climate change has been exacerbating the frequency and severity of climate extreme events, so that the pace of recovery of ecosystems from fires may be impaired, enhancing the potential of irreversible changes in vegetation communities. 

Here we assess the recovery of global Mediterranean vegetation after recurrent fires over the past two decades based on Enhanced Vegetation Index (EVI) retrieved from the MODIS sensor. To do so, we apply a statistical model to assess the recovery rate of vegetation repeatedly burned across different land cover types. Moreover, we study how fire severity, pre-fire state of vegetation and post-fire climate conditions modulate the recovery rates. Our results show a significant influence of fire severity on vegetation recovery rates globally across all Mediterranean regions, suggesting that higher severity levels may trigger the activation of the ecosystem's recovery mechanisms. Nevertheless, we also find a modulating effect of post-fire climate conditions, particularly air temperature and precipitation, on the recovery rates of burned vegetation, which highlights how compounding effects of changing disturbance regimes and climate change might destabilize ecosystems.

This study was supported by the doctoral Grant PRT/BD/154296/2022 financed by FCT under the MIT Portugal Program and was performed under the framework of DHEFEUS project, funded by Portuguese Fundação para a Ciência e a Tecnologia (FCT) (https://doi.org/10.54499/2022.09185.PTDC). The work was also funded by the FCT I.P./MCTES through national funds (PIDDAC) UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). A.R. is supported by the FCT through national funds from the MCTES within the Faculty of Sciences of University of Lisbon, through https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006.

How to cite: Ermitão, T., Gouveia, C., Bastos, A., and Russo, A.: Global patterns of Mediterranean ecosystems recovery from recurrent fires, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5375, https://doi.org/10.5194/egusphere-egu24-5375, 2024.

The 2007 Zaca Fire burned about 240,000 acres of land north of Lake Cachuma, which supplies water to Santa Barbara, CA. USGS Streamgage 11124500 was able to record pre and post-fire stream discharge for the affected Santa Cruz Creek Watershed, of which 67% was burned. 80% of this burn was severe, which raises concern for extreme flood events following the wildfire. It has been observed that the extreme temperatures in wildfires not only damage vegetation but soils as well -- wildfires much more so than comparatively mild prescribed burns. Our research proposes analyzing precipitation and stream discharge data from the affected Santa Cruz Creek Watershed to quantify the effects of such widespread and severe wildfire. This study uses the Hydrologic Modeling System (HEC-HMS) from the Army Corps of Engineers (ACE) to perform event-based, lumped modeling. It also uses Gridded Surface Subsurface Hydrologic Analysis (GSSHA) to perform physics-based modeling of the same watershed. Doing so should deepen our understanding of the effects of increasingly common and severe wildfires on watershed characteristics like infiltration and streamflow.

How to cite: Walters, A., Pradhan, N. R., Floyd, I., and Lakshmi, V.: Modeling Post-Wildfire Rainfall Events in the Santa Cruz Creek Watershed using HEC-HMS and GSSHA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20928, https://doi.org/10.5194/egusphere-egu24-20928, 2024.

Firefighting has become increasingly difficult and costly due to climate change. In response, new tools, including online platforms, are emerging to help prevent and promptly combat ever more destructive wildfires. While those initiatives only provide maps of fire risk based on environmental and climatic conditions, which in general have a medium predictive capability, fire propagation models, although successful in predicting fire behavior and spread, particularly at local scale, can become impractical during emergency situations, since they require lots of spatial data that must be obtained, processed and input by the user. To overcome these limitations, we have developed a fire-spread prediction system for the Brazilian Cerrado, the biome most affected by wildfires in South America. The system, named as FISC-Cerrado, automatically uploads hot pixels and satellite data to calculate maps of fuels loads, vegetation moisture, and post-probability of burning for simulating fire spread thrice a day for the entire Cerrado at 25 ha and for nine conservation units at 0.09 ha spatial resolution. Unlike the requirements to operate fire spread models, the user-friendly interface of FISC-Cerrado, alongside the automatization of the entire chain of tasks, allows its use by practitioners who do not have technical skills, such as GIS knowledge. Model results together with ancillary data, e.g., historical burned areas and annual CO₂ emissions from fires, are available on an interactive web-platform (https://csr.ufmg.br/fipcerrado/en/), which is being used for daily operations by the fire brigades of the selected conservations units. 

How to cite: Oliveira, U. and Soares-Filho, B.: The FISC-Cerrado near-real time web-system for predicting fire spread, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6456, https://doi.org/10.5194/egusphere-egu24-6456, 2024.

In 2017, Portugal was affected by several mega-fire episodes, which led the convective clouds formation, i.e., pyroCumulus (pyroCu) or pyroCumulonimbus (pyroCb). The pyroCb plays a crucial role in the fire front evolution through feedback processes between the atmosphere and the fire, including increased burn and spread rates by surface wind speed and direction variations. In order to investigate the pyro-convective activity during mega-fire events, numerical simulations were performed with the Meso-NH atmospheric model coupled to the ForeFire fire propagation model. The present study considers the mega-fires occurred in Pedrógão Grande and Góis on June 17, 2017, and in Quiaios on October 15, 2017. The experiments were configured into three nested domains with horizontal resolution of 2000 m (600 km × 600 km), 400 m (120 km × 120 km) and 80 m (24 km × 24 km) for the innermost model. The vertical resolution is the same for all the nested domains, with 50 levels and a first level above the ground at 30 m height. Initial and lateral boundary conditions for the outer domain were provided by ECMWF analysis, with updates every 6 h. Heat and water vapour were emitted into the atmosphere using the ForeFire model. In this case, the fire front evolution is directly imposed from a pre-defined time of arrival map (one-way coupling) and obtained from official reports. The results from the simulation of 80 m horizontal resolution showed that in the Pedrógão Grande mega-fire, the violent fire-driven convection manifested as a pyroCb cloud. The convective column penetrated the upper troposphere, and an intense outflow originated from the pyroCb cloud. In Quiaios mega-fire, the simulation also well represented the pyro-convection phenomenon, characterised by a northward-oriented smoke plume and the development of a pyroCu cloud. This study has provided important insights into the numerical modelling of pyroconvective clouds using Meso-NH/ForeFire simulations. This study was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Ref. PCIF/MPG/0175/2019).

How to cite: Couto, F. T., Campos, C., Filippi, J.-B., Baggio, R., Purificação, C., Santos, F. L. M., and Salgado, R.: Towards a better understanding of pyroconvective clouds using Meso-NH/ForeFire coupled model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5991, https://doi.org/10.5194/egusphere-egu24-5991, 2024.

We present a comprehensive simulation toolset for the analysis and prediction of wind fields, wildfire spread, and the propagation of their resulting smoke. It is composed of three physical simulation models that work together: HDWind, PhyFire and PhyNX.

The wind field simulation model, HDWind, is a mass consistent vertical diffusion wind field model based on an asymptotic approximation of the Navier-Stokes equations, providing a 3D wind field (which satisfies the incompressibility condition in the air layer) governed by a 2D equation that id adjusted to meteorological data obtained in a small number of points by solving an optimal control problem. PhyFire is a simplified 2D one-phase fire spread simulation model based on the principles of mass and energy conservation and that considers the radiation and convection (i.e., driven by wind and terrain slope) means of propagation, featuring the most relevant 3D effects, the influence of humidity, ambient temperature, wind, and the fuel types as well as their moisture content. The atmospheric dispersion model PhyNX is an urban scale Eulerian non-reactive multilayer air pollution model, able to describe convection, turbulent diffusion, and emission, considering the 3D wind field provided by the HDWind, and the smoke emission provided by PhyFire. The three models are solved using mainly the finite element method and some numerical and computational procedures to reduce the computational cost.

The required data to feed the simulation models such as cartographic and meteorological information are obtained from online geospatial information systems (GIS) in an automated way with little user intervention, thanks to the integration of the models with the geospatial library GDAL/OGR, which enables easy interpolation with most used standard GIS formats and services. An integration of this toolset into an easy-to-use webgis platform for their exploitation for professionals in the field of wildfire prevention will be demonstrated.

How to cite: Asensio, M. I., Cascón, J. M., and Iglesias, J. M.:  A comprehensive wind-fire-smoke simulation tool based on physical models and geospatial information., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2647, https://doi.org/10.5194/egusphere-egu24-2647, 2024.

The growing frequency of wildfires globally has highlighted the importance of immediate fire forecasting. Traditional high-accuracy fire spread simulations, like cellular automata and computational fluid dynamics, are detailed but require extensive computational resources and time. Consequently, there has been a significant push towards developing machine learning-based fire prediction models. These models, while effective, tend to be specific to certain regions and demand a large volume of simulation data for training, leading to considerable computational demands across various ecoregions.

In response, this study introduces a generative approach using three-dimensional Vector-Quantized Variational Autoencoders. This method is designed to create spatial-temporal sequences predicting the progression of future wildfires in specific ecoregions. The effectiveness of this model was evaluated in the context of the Chimney fire, a notable recent wildfire in California. The results demonstrate that the model effectively produces realistic and structured fire scenarios, incorporating influential geophysical factors like vegetation and terrain slope. Additionally, the data generated by this model were used to develop and train a surrogate model for wildfire spread prediction. This surrogate model was successfully validated using both simulated data and actual data from the Chimney fire incident.

How to cite: Cheng, S. and Arcucci, R.: VQ-VAE generative model of spatial-temporal wildfire propagation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20768, https://doi.org/10.5194/egusphere-egu24-20768, 2024.

Meteorological fire danger has been steadily increasing over Europe in the last decades, not only over the Mediterranean South that is recurrently affected by extreme fire weather and where the largest fire events take place, but also over Central, Eastern and Northern countries that are facing more and more events. The two most recent examples are the devastating fires in Rhodes and northern Greece in 2023, and those in France, Spain, Portugal, Slovenia and Czechia in 2022 when the total of burnt area almost reached the record value of 2017. The increase in severity of fire events is of major concern for all European countries, but special attention should be devoted to Central Europe where large fires, usually driven by the compound effect of droughts and heatwaves (e.g., 2018, 2022), are posing new challenges at the levels of fire management and fire forecasting.

We present a statistical model of energy released by wildfires that allows calibrating the Canadian Fire Weather Index (FWI) over three major land cover types (forest, shrub, and agriculture) covering an area encompassing Central Europe (3.5º-17ºE and 45º-62ºN). The model consists of a doubly truncated lognormal body distribution with generalized Pareto tails (DaCamara et al., 2023) that incorporates FWI as a covariate of its parameters. For each land cover type, the model is fitted to the set of observed values (from 2001 to 2022) of the logarithm of Fire Radiative Power associated to hotspots as detected by the MODIS instrument on-board Terra and Aqua platforms. For each model, goodness of fit is evaluated by using the Anderson-Darling test to assess the strength of the evidence against the null hypothesis that the sample follows the distribution.

The fitted models allow estimating for each land cover type the probability of exceedance of a predefined threshold of log(FRP) for each day and grid point. Five classes of fire danger (low, moderate, high, very high, and extreme) for each land cover type are then defined by analyzing the spatial and temporal variability of the distribution of pixels among classes as well as the distribution among classes of FRP associated to hotspots, such that classes of higher fire danger tend to concentrate in the fire season, and fires with high values of FRP occur in pixels classified in the classes of high, very high and extreme danger. The procedure is further validated by examining several case studies that were chosen because of unusually intense fire events or because of the high number of occurrences.

This work was supported by EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) and by Instituto Dom Luiz (IDL), a research unit financed with national funds (PIDDAC) by FCT (UIDB/50019/2020).

References:

DaCamara, C. C., Libonati, R., Nunes, S. A., de Zea Bermudez, P., & Pereira, J. M. C. (2023). Global-scale statistical modelling of the radiative power released by vegetation fires using a doubly truncated lognormal body distribution with generalized Pareto tails. Physica A: Statistical Mechanics and Its Applications, 625. https://doi.org/10.1016/j.physa.2023.129049

How to cite: DaCamara, C. C., Oliveira, M. P., Nunes, S. A., Trigo, R. M., and Trigo, I. F.: Early warning meteorological fire danger over Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17121, https://doi.org/10.5194/egusphere-egu24-17121, 2024.

The accurate prediction of the Fire Weather Index (FWI) is vital for effective wildfire management and climate-resilient planning. Multisite fire hazard forecasts are crucial for resource allocation, early intervention in high-risk areas, and identifying potential “megafire” threats from multiple simultaneous fire spots. Therefore, it is very important to account for the spatial consistency of these forecasts. This study examines the performance of Convolutional Neural Networks (CNNs) as a Statistical Downscaling (SD) technique for predicting FWI in different locations in the Iberian Peninsula. We contrast CNNs with two conventional SD methods: Generalized Linear Models and analogs. Using daily observed FWI data as predictands and ERA-Interim fields as predictors under a cross-validation setup, we discover that the CNN-Multi-Site-Multi-Gaussian (CNN-MSMG) model outperforms in daily FWI forecasting. This model integrates the covariance structure of the predictands into the CNN design, producing spatially consistent FWI forecasts. Furthermore, CNN-MSMG shows desirable features for estimating fire hazard in the climate change scenario, such as strong spatial consistency of extreme events and the capacity to generalize to new climate situations. These findings have important implications for improving FWI forecast accuracy and strengthening wildfire risk evaluation under climate change.

How to cite: Mirones, O., Baño-Medina, J., Bedia, J., Brands, S., and Santa Cruz, M.: Spatially Consistent Fire Weather Index Predictions using Convolutional Neural Networks in Diverse Iberian Locations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15610, https://doi.org/10.5194/egusphere-egu24-15610, 2024.

Increasing temperatures and harsher drought conditions in recent decades have enhanced the risk of wildfire in many regions across the globe. Recent fire activity in Central Europe raised concerns about the possible expansion of the fire weather danger conditions under climate change outside the present-day fire-prone regions, such as the Mediterranean Basin. Here, we employ the widely used Canadian Fire Weather Index (FWI) system to assess the historical and future trends in the fire weather danger for Central Europe. Calculation of the originally proposed FWI requires utilizing noon-time temperature, relative humidity, wind, and accumulated precipitation. Using the ERA5 reanalysis dataset, we make sensitivity analyses with different combinations of alternative input data for noon-time meteorological parameters and estimate their biases.

This study uses an ensemble of regional climate models (RCM) from the EURO-CORDEX domain. We first compare the results from ERA5 with the RCM ensemble for the historical period. Then, we analyze future projections for Central Europe under different global warming levels (+2 K and +3 K). Results indicate that the fire-prone areas consistently increase under warmer climate conditions, including emerging fire-prone regions in Central and Northern Europe.

How to cite: Bayar, A. S., Ramos, A. M., Gouveia, C., and Pinto, J. G.: Projections of the Fire Weather Danger over Central Europe using EURO-CORDEX simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18455, https://doi.org/10.5194/egusphere-egu24-18455, 2024.

We developed a comprehensive fire risk assessment framework for Indian forests, divided into five distinct forest zones (Himalayan, Northeast, Central India, Deccan, and Western Ghats) characterized by diverse climatic conditions and forest types. This framework focused on three primary triggering factors: weather, fuel availability, and anthropogenic ignition.

For the weather factor, we considered the Fire Weather Index (FWI) module of the Canadian Forest Fire Danger Rating System with ECMWF's ERA5 reanalysis as meteorological inputs over the period 2003-2021. As fire weather is a dominant factor in causing fires, we developed a robust system to predict fire weather danger. We evaluated the simulated FWI against MODIS active fire data and observed that FWI was a good enough metric for fire weather danger assessment. FWI was categorized into five danger classes through an ensemble approach based on logistic regression, FWI percentiles, percentage of fires, and K-means clustering. We introduced machine learning techniques to reduce the subjective decisions in these methods. This increased the efficiency of the danger rating system to detect fire probability well by 30-50%. A rigorous evaluation of the danger classes revealed that there was no overlap of central tendencies between different methods in the ensemble. The defined danger classes demonstrated coherent values for evaluative parameters, with a consistently high hit rate, low hits due to chance, moderate correct rejections, and an acceptable false alarm ratio.

Addressing fuel availability, we used vegetation indices (MODIS normalized difference and enhanced vegetation indices) and topographic features (aspect, elevation and slope from FLDAS land surface model). The anthropogenic ignition factor consisted of population density and land use information. In India, fragmented forests cohabitate with human settlements and agricultural lands. To quantify the impact of anthropogenic ignition on fire occurrences, we computed the percentage of built-up and agricultural area within each grid cell. We used machine learning predictive algorithms such as multiple linear regression with interactions, support vector machines, decision trees and neural networks to integrate these triggering factors with fire count as the target variable, We selected the highest-performing system as the risk assessment framework.

This country-scale fire risk assessment provides insights into regional exposure variations and serves as a foundational step towards establishing an operational fire risk assessment system for India. This framework will be of help to operational fire management agencies, enabling enhanced prediction of fire danger and informed decision-making.

How to cite: Barik, A. and Baidya Roy, S.: Machine learning based fire danger assessment framework for Indian forests , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14686, https://doi.org/10.5194/egusphere-egu24-14686, 2024.

The Satellite Application Facility for Land Surface Analysis (LSA SAF), that is part of EUMETSAT’s ground segment, operationally disseminates daily forecasts of meteorological fire danger over Mediterranean Europe. The so-called Fire Risk Map (FRM) product relies on estimates of the probability of exceedance of predefined thresholds of daily released energy by active fires as derived from a Generalized Pareto model that uses FWI as covariate for the scale parameter; FWI, the Fire Weather Index (FWI), is part of the Canadian Fire Weather Index System and has proven to be very suitable to rate fire danger over Europe.

The aim of this study is to extend the procedure to Northern and Central Europe making use of a statistical model of Fire Radiative Power (FRP) as derived from MODIS observations over Europe covering the period 2000-2022. Following the approach developed by DaCamara et al. (2023), the statistical model consists of an 8 parameter, doubly truncated lognormal body distribution with generalized Pareto tails, using FWI as a covariate of its parameters.

First, Europe is divided into eight regions according to the recorded number of hotspots and to the averaged FRP over the study period. Each one of those regions is then stratified into three subregions according to the dominant land cover type, i.e., forest, shrub, or agriculture. For each of the subregions, a statistical model is fitted to the sample of historical records of FRP together with the associated sample of FWI values obtained from the Copernicus Emergency Management Service.

The fitted models are then applied to Europe to generate monthly climatological values of probability of exceedance of prescribed thresholds of FRP. This information is used to define appropriate limits for classes of fire danger (i.e. low, moderate, high, very high and extreme) for each subregion of Europe. Finally, these classes are validated by analyzing the distribution of recorded FRP among the classes and by examining maps for extreme fire events.

This work was supported by EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF) and by Instituto Dom Luiz (IDL), a research unit financed with national funds (PIDDAC) by FCT (UIDB/50019/2020).

References:

DaCamara, C. C., Libonati, R., Nunes, S. A., de Zea Bermudez, P., & Pereira, J. M. C. (2023). Global-scale statistical modelling of the radiative power released by vegetation fires using a doubly truncated lognormal body distribution with generalized Pareto tails. Physica A: Statistical Mechanics and Its Applications, 625. https://doi.org/10.1016/j.physa.2023.129049

How to cite: Ponte Oliveira, M., A. Nunes, S., C. DaCamara, C., M. Trigo, R., and F. Trigo, I.: Assessing meteorological fire danger over Europe based on a statistical model of satellite-derived fire radiative power, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-795, https://doi.org/10.5194/egusphere-egu24-795, 2024.

This work investigates the occurrence, parameters, and consequences of fires in satellite images that can be directly exploited by several combinations of different multispectral image bands.

When we want to understand the semantics of a recorded digital image, we can cut it into smaller-size image patches and routinely classify these image patches via common unsupervised or supervised image classification techniques. In addition, when we include some clever interactive learning steps to attach semantic labels to the hitherto mathematically classified image patches, this should allow for a highly automated and powerful image understanding procedure.

On the other hand, starting with simple examples, the application-oriented analysis and exploitation of Sentinel-2 images can combine and display selected colour bands and their combinations. This has already been discussed in many (mostly GIS-oriented) publications ranging from the straightforward assignment of directly available pseudo-RGB colour bands up to advanced machine learning approaches for the extraction of content-related information (such as image feature descriptors or indices) [1-4]. Further, we will also refer to a few recently published advanced information extraction tools [5-10].

As an alternative to these (mostly conventional) image classifications, we describe a powerful semantic image classification technique that starts with the generation of topics (instead of classes) that was originally described by [11].Here, the resulting topic maps can be further combined and be used for colour band displays and their interpretation. When we combine the properties and capabilities of Sentinel-2 images with topic interpretation techniques, the most interesting question is whether a semantic interpretation based on topic maps outperforms common feature-based approaches.

To this end, we selected several Sentinel-2 multi-band images comprising different geographical areas affected by fires. This presentation shows the actual impact of various band combinations of Sentinel-2 channels and illustrates the band-dependent appearance of Fires, Smoke, Clouds, and other specific categories linked to the investigated continental areas. The basic algorithm being used for this investigation is Latent Dirichlet Allocation that has been applied as a data mining tool to discover patterns in the data, combined with automated band selection approaches.

The combination of automated image classification and multi-colour visualization seems to be an interesting alternative to Deep Learning.

[1] https://gisgeograpy,com/sentinel-2-bands-combinations

[2] https://worldofittech.com/sentinel2-bands-and-combinations

[3] https://giscrack.com/list-of-band-combinations-in-sentinel-2a

[4] https://eo4geocourses/github.io/IGIK_Sentinel2-Data-and-Vegetation-Indices

[5] A. Revill et al., The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development, Remote Sensing, 11(17), 2018.

[6] K. Kowalski et al., A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series, Remote Sensing of Environment, 286, 2023.

[7] M.K. Vanderhoof et al., High-frequency Time Series Comparison of Sentinel-1 and Sentinel-2 Satellites for Mapping Open and Vegetated Water Across the United States, Remote Sensing of Environment, 288, 2023.

[8] E.C. Rodriguez-Garlito et al., Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained with Spectral Indices, JSTARS, 16, pp.2889-2899, 2023

[9] Z. Chen, et al., Mapping Mangrove Using a Red-Edge Mangrove Index (REMI) Based on Sentinel-2 Multispectral Images, TGRS, 61, pp.1-11, 2023.

[10] A. Temenos, Interpretable Deep Learning, GRSL, 20, pp.1-5, 2023.

[11] D.M. Blei, et al., Latent Dirichlet Allocation, Journal of Machine Learning Research, 3, pp.993-1022, 2003.

How to cite: Dumitru, O., Schwarz, G., and Karmakar, C.: Automated Selection of Sentinel-2 Spectral Bands for Fire Detection, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1324, https://doi.org/10.5194/egusphere-egu24-1324, 2024.

Until recently, forest fires were considered a rare phenomenon in the temperate forests of Central Europe due to the moderate summer temperatures and the humid climate. However, many of those forests (e.g. monocultures of Picea abies, Norway Spruce) were affected by bark beetle infestations in the past years and recent fires such as in the Bohemian-Saxon Switzerland in 2022 raised widespread debates about the effects of forest mortality on fuel accumulation and hence fire occurrence and severity. Here we mapped and investigated fuel types, fire severity and started to continuously monitor fuel moisture in the Bohemian-Saxon Switzerland. We enhanced a European fuel type classification with a class for dead and dying spruce and mapped fuel types. Satellite observations from VIIRS, Sentinel-2 and Landsat were used to map fire intensity and severity of the fire from 2022.

We found the highest fire intensities at sites with dead spruce forests and single beech trees. Burn severity was moderate with high variability across all fuel types but highest severities occurred in dead spruce stands. Fire severity derived from satellite observation correlated positively with char height and torched trees, especially seen in dead spruce stands, which was likely caused by the high amount of dry fine woody debris and the initial natural regeneration. Our results demonstrate that surface fuel accumulation from past bark beetle disturbances resulted in more intense fires and higher burn severity. The results demonstrate that the recent rapid changes in Central European temperate forests cause a need for a dynamic mapping and monitoring of fuel types and fuel moisture for fire risk assessment and for cross-border fire risk management in landscapes previously not considered as fire-prone.

How to cite: Marrs, C., Beetz, K., Kranz, J., Bauer, K., Avouris, E., Poděbradská, M., Kinalczyk, D., and Forkel, M.: Investigating fuels, fuel moisture and fire severity in the Bohemian-Saxon Switzerland region (Czech Republic/Germany): the need for dynamic fire risk assessment and management , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7851, https://doi.org/10.5194/egusphere-egu24-7851, 2024.

As socio-natural hazards, future extreme wildfires across Europe are exacerbated by two main drivers: climate change and socioeconomic dynamics. While modelling studies account for changes in fire danger and area burned under different climate scenarios, they largely disregard the impacts of land use change, or the interaction with adaptation through changes in vegetation management. This creates uncertainties regarding the role of anthropogenic processes and the reliability of projections under various policy scenarios. Next to the interaction with wildfire hazard, socioeconomic processes are shaping all dimensions of wildfire risk. Building on the IPCC notion that risk arises at the intersection of hazard, exposure and vulnerability, we screen the relevant empirical literature to identify key socioeconomic drivers of wildfire risk in a European context and bring this together with the Shared Socioeconomic Pathways (SSP) perspectives on plausible socioeconomic futures. The resulting wildfire risk scenario space serves two main purposes: (i) providing a qualitative navigator for incorporating socioeconomic uncertainty in model-based wildfire risk assessments and (ii) establishing boundary conditions for evaluating the feasibility of management strategies. Applying the SSP framework for envisioning plausible development trajectories, we systematically investigate the role of socioeconomic dynamics in determining future wildfire risk. Sustainable land use practices and profitable agricultural value chains reduce hazard (e.g. SSP1), while factors like poor environmental regulation (e.g. SSP5) and increasing pressure on land abandonment as competitive value chains disappear (e.g. SSP4), increase this dimension of wildfire risk. Exposure remains high across scenarios for different reasons. Ineffective land use planning contributes to the expansion of human settlements in areas dominated by unmanaged flammable vegetation (e.g. SSP3, SSP5), with a further escalation of livelihood exposure on poorly managed agricultural land (e.g. SSP4). Vulnerability becomes a distinctive driver of wildfire risk in scenarios with low rates of economic development and poor investment in human capital (e.g. SSP3, SSP4). While increased socioeconomic welfare may enhance coping capacities in the context of wildfires (e.g. SSP1, SSP5), the prioritization of business-related objectives in institutional risk management (e.g. SSP5) poses a risk of neglecting other critical aspects. As wildfires transition from a climate hazard into a potential disaster at the intersection with exposure and differential vulnerabilities, we emphasize the importance of addressing all three dimensions of risk. By expanding the view of future wildfire risk, we show that challenges to wildfire risk management differ significantly between scenarios. Social, economic and socioecological challenges may lead to paradoxical situations in managing wildfire risk. In scenarios, where vulnerability reduction has maximum leverage in reducing risk, socioeconomic challenges hinder the feasibility of implementing the measures necessary to achieve it. Similar dilemmas may arise in the context of hazard and exposure. By considering multiple plausible futures, this work stresses the importance of considering socioeconomic dynamics in shaping wildfire risk and keeping the design of risk management strategies open and flexible to adapt to changing circumstances.  

How to cite: Preinfalk, E. and Handmer, J.: Fuelling the fires - An exploration of the drivers and the scope for management of European wildfire risk under the Shared Socioeconomic Pathways , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10384, https://doi.org/10.5194/egusphere-egu24-10384, 2024.

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (CNN-LSTM) produces favorable predictive results. This approach effectively tackles certain drawbacks of fire weather indices (FWI), notably the insufficient consideration of surface coverage and coarse resolution. However, previous research inadequately explored variations in the impact of influencing factors across different categories and spatial orientations, neglecting the internal structural features within the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with terrestrial ecology, terrain, and simulated meteorological elements. By introducing channel and spatial attention mechanisms, high-resolution imagery, and visual transformer model, this research optimizes the CNN-LSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 93.30%, 85.63%, and 98.15%, respectively. This refined model not only refines high-risk prevention areas highlighted by FWI but also enhances understanding of mountain trails in hilly terrains. Consequently, it reduces false alarms in regions such as non-harvesting agricultural fields, reinforcing predictive risk assessment concerning potential human activities within forested areas. Sensitivity analysis reveals that while the impact of internal sample structural features on wildfire risk prediction is lower than meteorological elements, it surpasses the influence of terrain and terrestrial ecology elements. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, furnishing high-precision daily kilometer-level wildfire risk prediction products. This approach holds substantial promise for the precise prevention and control of regional wildfires.

How to cite: Fan, G. and He, Z.: Deep Learning Modeling of Human Activity Affected Wildfire Risk by Incorporating Structural Features: A Case Study in Eastern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1627, https://doi.org/10.5194/egusphere-egu24-1627, 2024.

Wildfires are becoming increasingly frequent and devastating in many tropical forests. In India, nearly 6% of forest cover is highly fire-prone, and 36% is prone to frequent fires. These frequent wildfires have compound impacts on forests, which include changes in biodiversity and, forest functionality. In the short term, the burnt ecosystem cannot have the same functionality as that of a pre-fire situation. Understanding forest ecosystem health status after a fire event can help increase our ability to manage the fire seasons. 
In the current study, time series data from optical remote sensing is used to assess the forest ecosystem resilience. The study area chosen is the forest region in Uttarakhand, India, including Jim Corbett National Park, that witnessed a severe forest fire in 2016. The active fire pixels during the 2016 fire event in the area were identified, and resilience over the identified pixels was measured based on the concept of engineering resilience.  Results are presented as resilience quantified for all burnt pixels based on three different indices which represent resistance and recovery namely, time taken for recovery, maximum impact of fire event, and cumulative impact. Further, we identified how resilience differs with the type of forest cover in the study area and how well each type of forest recovers from the fire event. The findings suggest that in the Indian scenario, deciduous broadleaf forests have a longer recovery followed by evergreen broadleaf and evergreen needleleaf, while grasslands and broadleaf cropland have shorter recovery times and impacts. From this work, we aim to study forest resilience in the Indian scenario and how well this can be compared with other areas where similar climatic conditions exist. The current work has potential applications in risk governance, ecosystem management, etc. and in evaluating the post-fire processes and primary factors driving the processes. Extensive data feeds available from current satellite platforms enable the post-fire dynamics study to be more accurate, thus more informed, and faster choices by stakeholders.

How to cite: Madhukumar, A., Indu, J., and Karthikeyan, L.: Indicator-based measurement of resilience and analysis of spatial trend in resilience to forest fire in Uttarakhand, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14466, https://doi.org/10.5194/egusphere-egu24-14466, 2024.

Wildfires represent a significant threat to natural ecosystems, biodiversity, and communities worldwide. Disruption in precipitation regimes and temperature rise caused by climate change are key factors that worsen and increase wildfire incidents. In Brazil, recent studies have shown the majority of fire incidents are initiated by anthropogenic action, as a consequence of agricultural expansion, deforestation and land disputes. Although the human use of fire as an illegal tool is difficult to predict, the occurrence of dry meteorological conditions, prone to uncontrolled spreading of fires, can be studied employing climate modeling, providing a useful instrument to aid authorities in preventive measures and improved responses to mitigate these impacts, contributing to more efficient and sustainable management of fire-related risks. The Standardized Precipitation Index (SPI) is a useful tool for assessing precipitation variability, allowing the analysis of drought period duration, distribution, and severity. The SPI uses precipitation data to standardize the deviation of accumulated precipitation from the historical average in each location. This process yields negative or positive values, which correspond to water deficits or surpluses, respectively. Aiming to identify areas in Brazil where predicted disruption in rainfall patterns, in face of climate change, may create drier conditions and increase vulnerability to fire incidents, we evaluated precipitation trends, comparing historical simulations from the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and future scenarios data from the Intergovernmental Panel on Climate Change (IPCC). We focused our analysis on 3 climate change scenarios, referred to as Shared Socioeconomic Pathways: SSP2-4.5, SSP3-7.0, and SSP5-8.5. These scenarios encompass anticipated global socioeconomic transformations up to the year 2100, based on different projections of greenhouse gas emissions, and offer an assessment of the climate outlook for current society. Thus, we calculated SPI indexes for the time spans 1960-1990 and 2020-2050, examining the variations in rainfall patterns across the country during both periods. Using SPI derived from MIROC6 climatological data, it is possible to identify past patterns that are the basis for understanding future changes' impact. The results from SPI climatological data are consistent with the climate and seasonal rainfall patterns historically observed in Brazil, where Northeast and Central Brazil exhibit greater water deficits. The scenarios employed suggested that the historical patterns of droughts would be worsened in severity in central Brazil and the areas of influence would be extrapolated, creating drier meteorological conditions to the Southern and East portions of Amazonia and the Southeast of Brazil. The SPI indexes calculated to the projected scenarios reinforce the understanding of the impacts of climate change, suggesting the pathway SSP55-8.5, with higher emission of CO2, implicates in increased occurrences of extreme events, particularly prolonged and severe droughts in regions that suffer from wildfires. Identifying regions with an increased likelihood of prolonged drought events in the projected future is a valuable instrument for examining fire hazard and mitigation plans within a country such as Brazil, which encompasses diverse climates and biomes across its territory with resources of significant conservation value.

How to cite: Guida Barroso, A., Michel, G. P., Zanandrea, F., Vinicius Aguiar Soares, M., Ferreira Subtil de Almeida, G., Cataldi, M., Esposte Coutinho, P., and Sancho, L.: Standard Precipitation Index (SPI) applied to Socioeconomic Pathway Scenarios (SSPs) as a tool to map the distribution of droughts and potential fire hazard areas in Brazil in the face of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3221, https://doi.org/10.5194/egusphere-egu24-3221, 2024.

Changes in temperature and precipitation in Austria due to climate change are expected to increase the days of fire weather in the near future. Extreme wildfire events are not common in Austria, nevertheless, given climate change and an increase in the number of events in the last years, authorities and decision-makers require tools to identify vulnerable hotspots and to reduce the upcoming risk in the Wildland Urban Interface. A number of projects ran by the University of Natural Resources and Life Sciences in Vienna focus on the assessment of vulnerability at different levels (national and local) and different elements at risk (industrial and residential buildings, free spaces and infrastructure). We present herein the current research landscape on the field in Austria and more specifically the projects PHLoX (StartClim), REVEAL (Waldfonds), and FIREPRIME (DG ECHO). Each project is based on expert knowledge and data-driven approaches and deals with different elements at risk and vulnerability indicators emphasising the need for participatory methods for wildfire risk management. We demonstrate how these projects can serve as a blueprint for increased wildfire resilience in Europe and beyond.

How to cite: Fuchs, S., Echtler, P., Hausharter, D., Schlögl, M., and Papathoma-Köhle, M.: The research landscape of wildfire vulnerability in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6060, https://doi.org/10.5194/egusphere-egu24-6060, 2024.

Changes in temperature and precipitation in the European Alps are reflected in an increasing number of wildfire events and burnt areas. Therefore, apart from conducting research on the behaviour of wildfires in regions with high fire danger, it is important to analyse the vulnerability of settlements, buildings, and infrastructure also in areas with less experience with the impacts of an increasing wildfire hazard. Studies focusing on the vulnerability of the built environment do exist, but they are mostly limited to the interaction of buildings with fire, rather than offering a tool to measure this vulnerability for planning and conducting risk reduction measures. We attempt to close this gap by assessing the physical vulnerability of elements at risk located at the Wildland Urban Interface (WUI) in several case study areas in the Austrian Alps. In the absence of empirical data and by using a co-creation approach, we engage experts from various domains (firefighters, managers, planners, and government officials) to develop a tool for wildfire risk management. The tool is based on indicators to assess the vulnerability of different characteristics of elements at risk, including residential buildings, hotels, industry, critical infrastructure, and cultural heritage. Applications in each case study area are designed to demonstrate the usability of the tool in various disaster risk reduction activities and different contexts with a high or low wildfire danger. A final workshop is planned to ensure the dissemination of the results in the entire wildfire community. The resulting REVEAL decision support tool should be applicable not only in other regions of Austria, but also in other European regions that do not have experience and empirical data related to the impacts of wildfire events on infrastructure.

How to cite: Papathoma-Koehle, M., Echtler, P., Fuchs, S., Schlögl, M., Müller, M., and Vacik, H.: Assessing Wildfire Vulnerability in the absence of empirical data: the REVEAL Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6539, https://doi.org/10.5194/egusphere-egu24-6539, 2024.

Abstract

Wildfire Susceptibility Assessment (WSA) is one of the critical approaches to wildfire risk management. In this study, we employed a hybrid approach by integrating two distinct statistical models, namely Frequency Ratio (FR), Weight of Evidence (WoE), with Shannon Entropy (SE) (i.e., FR-SE and WoE-SE) for WSA. To meet the aim, 18538 historical wildfire data were collected and separated into two sections for model development and validation. Next, 13 wildfire-influencing parameters, including slope degree, aspect, topographic wetness index, elevation, evapotranspiration, land use/land cover, normalized differences vegetation index, distance from the lake, precipitation, distance from the rivers, distance from the roads, soil moisture, and mean annual maximum temperature were prepared and feed the models. Finally, model performance were evaluated using the validation data set and receiver operating characteristic (ROC) curve technique. Findings shows that the integration of models has improved the modeling performance, as WOE-SE model has the highest performance (96.5%), followed by WoE (96.3%), SE-RF (95.9%) and RF (95.2%) model respectively. Result of SE model showed that mean annual maximum temperature has the highest impact on the wildfire occurrence across Canada, while topographic wetness index is the lowest effective parameter.

Keywords: Wildfire, statistical models, Canada, Shannon Entropy, Frequency ratio, Weight of Evidence.

How to cite: Khosravi, K. and Farooque, A.: Canada’s Wildfire Susceptibility Assessment Using Statistical Data-Driven Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6775, https://doi.org/10.5194/egusphere-egu24-6775, 2024.

How to cite: Zahoor, Z., Eden, J., Blackett, M., and Chen, Y.-F.: Spatiotemporal analysis and projections of wildfire risk across Pakistan under different climate change scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-614, https://doi.org/10.5194/egusphere-egu24-614, 2024.

In an era of increasing wildfire incidents worldwide, fire risk mapping has emerged as a crucial tool for ecosystem management and environmental safeguarding against the significant loss of socio-ecological value. Our research introduces a novel daily global fire risk model, combining the probability of fire ignition as a fire hazard model with an analysis of exposure and vulnerability. This model was calculated with over 4 million historical fire and non-fire ignitions recorded between 2000 and 2020 and tested with more than 24 million ignition points. It integrates key explanatory variables encompassing climatic conditions, agro-environmental factors, terrain, and social drivers at the time of fire ignition, processed through advanced machine learning techniques, such as the XBoost Random Forest algorithm.

Further enhancing our model's robustness, we incorporate a suite of socio-ecological models, previously developed using machine reasoning, an AI algorithm based on semantics, through the k.LAB platform. These models cover critical areas such as vegetation carbon mass, pollination, outdoor recreation, and soil retention, enabling us to identify regions where humans and nature are most vulnerable to fire hazards.

Adhering to FAIR principles, our approach ensures that our data and models are findable, accessible, interoperable, and reusable. This commitment not only advances scientific research but also promotes broader application and collaboration. The global fire risk model provides temporally and spatially explicit results on a daily basis, offering a dynamic and precise tool for understanding and preventing fire risks.

This research has significant implications for policymaking and emergency response planning. By offering a detailed and dynamic understanding of fire risks, stakeholders can make informed decisions that can mitigate the impact of wildfires. The combination of diverse datasets, advanced analytical techniques, and a focus on practical applications makes this model a valuable resource in the global effort to address the increasing challenges of wildfires.

How to cite: Marquez Torres, A. and Bengochea Paz, D.: Advanced Wildfire Risk Mapping: A Novel Global Approach Using AI and Socio-Ecological Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16436, https://doi.org/10.5194/egusphere-egu24-16436, 2024.