Displays

The recent Australian summer witnessed bushfire at a scale that is without historical precedent. We analysed the scale and severity of the fires, the main processes contributing to their scale, and environmental consequences that have already become apparent.  We did this by combining satellite-derived information of vegetation cover, biomass and history, of soil and vegetation moisture content, and of fire extent and severity. More than 80,000 km2 was burnt, much of it native forest. Fire severity varied, but was overall greater than in preceding years. A critical factor contributing to fire conditions was a multi-year drought in Eastern Australia, which culminated in 2019 with the hottest and driest year in more than a century. During the fire season, fire danger conditions were further exacerbated by oceanic modes in the Indian and Southern Oceans, which limited circulation and caused excessive heating of the Australian land mass. Fuel availability in forests was unusually high. Reasons for this were several, including afforestation and regrowth as well as effective fire suppression in preceding years, while a contributing role for CO₂ fertilisation is also plausible. Combined with the drought and associated vegetation mortality, this created a high and flammable fuel load. The fires strongly affected Australia’s total living carbon pool, which was already depleted by several years of below-average rainfall. Greenhouse gas releases associated with drought and bushfires are not considered in official emission accounts, but are of comparable magnitude. The smoke emissions also caused direct health impacts, affecting cities like Sydney, Melbourne and Canberra for prolonged periods. Most of the burnt forests are resilient to fire and will regenerate, assuming rainfall conditions improve. The severity, scale and connectedness of some of the fire complexes suggest ecological recolonization may be very slow, while a number of threatened species may not recover. Perhaps most concerning, some of the forests affected had burnt only years before, whereas other areas contained vegetation communities not experiencing fire for centuries, raising questions about their ability to regenerate and possibly permanent ecological regime shifts.

How to cite: van Dijk, A. and Yebra, M.: The extraordinary 2019/20 Australian bushfire season: contributing processes and environmental impacts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3804, https://doi.org/10.5194/egusphere-egu2020-3804, 2020.

The 2019/2020 fire season in Australia has been unusually energetic since early spring. In the last days of December and early January an unprecedented number of pyrocumulonimbus (pyroCb) storms erupted in New South Wales and Victoria, creating a seemingly unrivaled stratospheric smoke plume as well as devastation on the ground. Preliminary indications from satellite remote sensing are that the clustering of active pyroCbs and smoke injection heights exceeded all previous Australian pyroCb events, and perhaps pyroCb events worldwide. Similar to another extraordinary pyroCb event, the so-called Pacific Northwest Event in 2017, the Australian smoke plume has been observed to rise above its injection altitude by several kilometers. We report on the active blowups and quantify the impact on stratospheric composition using satellite remote sensing. Our analysis also consists of a quantitative comparison of the 2019/20 Australian pyrocb event with other major pyroCb events such as Black Saturday, Victoria, Australia in 2009. At the time of submission of this abstract, this is an unfolding episode; our report will characterize the unusual nature of this pyroCb event as the evolving plume and satellite remote sensing data permit.

How to cite: Fromm, M. and Kablick III, G.: The Massive New Year 2020 pyroCb Event in Australia: Observations of Unprecedented Stratospheric Smoke, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20366, https://doi.org/10.5194/egusphere-egu2020-20366, 2020.

Wildfires are generating higher concern worldwide, especially in the Mediterranean regions. Fire season severity and total annual burnt area strongly depend on weather conditions and climate variability.

The first objective of this work was to analyse Fire Weather Indexes (FWI) in the Iberian Peninsula for the present-day conditions and future climate scenarios, using reanalysis data from ERA-Interim (for 1980-2014) and an ensemble of 11 models from EURO-CORDEX, with high spatial (12 km) and daily resolution. FWI were computed for historical (1976 – 2005) and three future periods (2011-2040, 2041 – 2070 and 2071-2100), using maximum temperature, precipitation, relative humidity and wind speed data simulated for two future scenarios (RCP4.5 and RCP8.5). The second objective was to use the Iberian Pyro-Regions and an analysis of the Number of Extreme Days (NED), using previously published methods, to apply on the future scenarios and assess the intra-annual pattern of NED; and, subsequently, to assess if the pyro-regions will change in a future climate, by taking into account the link between monthly burnt area and extreme days found in previous work.

The results anticipate a progressive growth of the SW pyro-region throughout the NW pyro-region, and a shift of the present-day NW pyro-region to most of the provinces occupying the N pyro-region, with exception of those north of the Cantabrian Mountains, in effect moving the present-day pattern northwards. This is driven by the large increase of the NED in summer months and eventually a decrease in March and April. Projections alto point to FWI values increasing considerably when comparing the historical and the future scenarios, especially in late spring and early autumn. These results anticipate a higher fire weather risk in the future, with a larger and stronger fire season.

References:

Calheiros, T., Pereira, M. G and Nunes, J. P. (2020, in press) ‘Recent evolution of spatial and temporal patterns of burnt areas and fire weather risk in the Iberian Peninsula’, Agricultural and Forest Meteorology.

How to cite: Calheiros, T., Pereira, M., and Nunes, J.: Iberian Fire Regimes for Future Climate Scenarios using a Climate Ensemble, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11610, https://doi.org/10.5194/egusphere-egu2020-11610, 2020.

Fire modeling needs timely fuel information.  Land cover and land use data are often used for fuel type mapping.  Existing large scale mapping efforts do not provide frequent land cover information, due partly to the lack of frequent raw data, and partly to the huge computational cost.  In this presentation, we will report our latest land cover and land use mapping efforts toward mapping global land cover at seasonal steps while mapping land use at annual intervals.  We report a data-cube approach applied to over 20-year Landsat and Terra and Aqua data (2000-2019) that made it convenient to experiment with various land cover and land use mapping procedures.  

With a data cube, time series analysis can be easily done that allows not only fuel type mapping but also fire event detection.  We report the use of multiple season land cover samples collected in a specific year at the global scale to map seasonal land cover.  We also report the use of historical land use for annual land use mapping. In addition, we report burnt area detection results from the using selected data from historical burnt area maps in training machine learning algorithms based on the data cube.  Land cover and land use data are cross-walked to fuel type data. This approach provide more accurate fuel type data for fire emission estimation and fire behavior modeling.

How to cite: Gong, P., Liu, H., and Bai, Y.: Seasonal land cover and annual land use mapping for fire modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12161, https://doi.org/10.5194/egusphere-egu2020-12161, 2020.

Iceland is well known for its harsh weather, long winters and frequent geologic activity impacting on the build and natural environment. Although wildfires are rather rare in Iceland, their occurrence might reach a disastrous extent as revealed during the wildfire in 2006. Today, one of the main challenges consist of optimisation landscape planning, disaster and risk management by integrating state-of-art fire models, knowledge in geographical information systems (GIS) and remote sensing. In this study, we present the implementation of simulation results in a decision support system for fire protection. For this purpose, the area of Laugarvatn in South Iceland was selected as a pilot area. Lagarvatn is an ideal pilot area with a high concentration of summer houses and camp caravans surrounded by large-scale natural birch forests. The core of the study is forest fire spread analysis using simulation models and the identification of accessible water sources for firefighting. The input parameters were generated from remote sensing data and GIS databases. Forest types, canopy cover, wind direction and speed and other meteorological variables, topographic feature accelerating forest fire were crucial parameters for producing fire spread probability maps. The fire spread scenario maps, water source maps and road network analysis is one of the critical elements in the decision support system.

How to cite: Hrabalikova, M., Traustason, B., and Finger, D. C.: Integration of spatial fire risk model results into a decision support system – A case study at Laugarvatn, South Iceland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16805, https://doi.org/10.5194/egusphere-egu2020-16805, 2020.

Biomass burning on natural and agricultural lands has profound effects on atmospheric chemistry, climate, and air quality. Over the past decade, a number of global fire emissions inventories have been developed based on near-real time detection of actively burning fires by the MODIS instruments. However, the MODIS instruments provide variable and incomplete global sampling of fire activity, resulting in large uncertainty in the spatiotemporal accuracy of daily emissions inventories. Here, we compared active fire products from MODIS and VIIRS to characterize product-specific shortcomings of each system with the goal to develop a new, more accurate, global emissions inventory. The VIIRS 375m product was most sensitive to global fire activity and detected up to 55% more energy release from fires than the comparable 1km MODIS product in the tropics. Differences originated from improved coverage, sensitivity to low energy fires, and a more consistent cross-track spatial resolution. Nevertheless, both MODIS and VIIRS instruments showed reduced sensitivity to low energy fires at larger off-nadir angles, resulting in a cyclical pattern of daily fire detections and an underestimate of low energy fires, the dominant fire-type in shoulder seasons and more densely populated regions. Starting in 2018, the constellation of VIIRS instruments aboard NOAA-20 and SNPP provide improved near-nadir coverage, largely eliminating issues originating from incomplete sampling of low-energy fires at the edge of the VIIRS image swath. Based these findings, we developed a new near-real time emissions inventory that is spatially consistent with the GFED4s data record (1997-2016). Spatial allocation of emissions in the new GFED near-real time product differ considerably from existing daily emissions inventories, highlighting how different methodologies redistribute emissions across natural and human dominated landscapes based on daily active fire detections. Using column observations of NO₂ and daily fire expansion rates from USGS, we demonstrate that the new, VIIRS-based daily fire emissions product provides more consistent spatial and temporal distribution of fire emissions compared to systems based on MODIS active fire detections. Improved accuracy is critical for air quality forecasts, source attribution, and the development of management strategies to minimize impacts on society.

How to cite: Andela, N., Morton, D. C., van der Werf, G. R., Schroeder, W., Giglio, L., Chen, Y., and Randerson, J. T.: Improved daily accuracy from a new VIIRS-based, near-real time GFED emissions product, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12237, https://doi.org/10.5194/egusphere-egu2020-12237, 2020.

Wildfire smoke presents a growing threat in the Western U.S.; and human health, transportation, and economic systems in growing western communities suffer due to increasingly severe and widespread fires. While modelling wildfire activity and associated wildfire smoke distributions have substantially improved, understanding how people perceive and respond to emerging smoke hazards has received little attention. Understanding and incorporating human perceptions of threats from wildfire smoke is critical, as decision-makers need such information to mitigate smoke-related hazards. We surveyed 614 randomly selected people (in-person) across the Boise Metropolitan Area in Idaho and 1,623 Boise State University affiliates (online), collecting information about their level of outside activity during smoke event(s), knowledge about the source of air quality information and effective messaging preference, perception of wildfire smoke as a hazard, and smoke-related health experiences. This relatively large dataset provides a novel perspective of people’s perception of smoke hazards and provides crucial policy-relevant information to decision-makers. Dataset is available to the public and can be used to address a wide range of research questions.

How to cite: Fowler, M. and Sadegh, M.: Human Perception of and Response to Wildfire Smoke, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21099, https://doi.org/10.5194/egusphere-egu2020-21099, 2020.

The Mediterranean region is characterized by frequent summer wildfires, which represent an environmental and socioeconomic burden [1]. Some Mediterranean countries (or provinces) are particularly prone to Large Fires (LF), namely Portugal, Galicia (Spain), Greece, and southern France [1,2]. Moreover, the Mediterranean basin corresponds to a major hotspot of climate change, and anthropogenic warming is expected to increase the total burned area due to wildfires in Iberian Peninsula (IP) [3].

Here, we propose to classify summer LF (June-September) for fifty-four provinces of the IIP according to their local-scale weather conditions (i.e. temperature, relative humidity, wind speed) and to fire danger weather conditions as measured by two fire weather indices (Duff Moisture Code and Drought Code). A cluster analysis was applied to identify a limited set of Fire Weather Types (FWT), each characterized by a combination of meteorological conditions leading to a better understanding of the relationship between meteorological drivers and fire occurrence. For each of the provinces, two significant FWT were identified with different characteristics, one dominated by high positive temperature anomalies and negative humidity anomalies (FWT1), and the other by intense zonal wind anomalies (FWT2) with two distinct subtypes in Iberia (FWT2_E and FWT2_W). Consequently, three distinct regions in the IP are identified: 1) dominated by FWT1, which is responsible for the largest amount of area burned in most of central-West provinces of Iberia; 2) the regions where the FWT2_E, associated with east winds is predominant, which are concentrated in the Northwest regions of the IP and the 3) regions where second subtype dominates, related with west winds (FWT2_W) in the easternmost provinces of the peninsula. Additionally, it was possible to verify that for each of the three regions the influence of the variables under study varies at different timescales. We reinforce the importance of studying the problem associated with LF for regions where similar conditions were verified regardless national borders.

[1] Trigo, R. M., Sousa, P. M., Pereira, M. G., Rasilla, D., & Gouveia, C. M. (2013). “Modelling wildfire activity in Iberia with different atmospheric circulation weather types”. International Journal of Climatology 36(7), 2761–2778. https://doi.org/10.1002/joc.3749.

[2] Ruffault, J., Moron, V., Trigo, R. M., & Curt, T. (2016). “Objective identification of multiple large fire climatologies: An application to a Mediterranean ecosystem”. Environmental Research Letters 11(7). https://doi.org/10.1088/1748-9326/11/7/075006.

[3] Sousa, P. M., Trigo, R. M., Pereira, M. G., Bedia, J., & Gutiérrez, J. M. (2015).”Different approaches to model future burnt area in the Iberian Peninsula”. Agricultural and Forest Meteorology 202, 11–25. https://doi.org/10.1016/j.agrformet.2014.11.018.

Acknowledgements: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project IMPECAF (PTDC/CTA-CLI/28902/2017). The authors also thank Miguel M. Pinto for extracting the ERA-Interim reanalysis, the MSG and the FWI data used in this study.

How to cite: Vieira, I., Russo, A., and M. Trigo, R.: Identification of favourable local-scale weather forcing conditions to Iberia’s largest fires, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5773, https://doi.org/10.5194/egusphere-egu2020-5773, 2020.

High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction modelling to evaluate the effects of fuel reduction treatments in preventing soil runoff in Mediterranean ecosystems. The study was carried out in a 68,000-ha forest area located in Northern Sardinia, Italy. We treated 15% of the study area, and compared no-treatment conditions vs alternative strategic fuel treatments. We estimated pre- and post-treatment fire behaviour by using the Minimum Travel Time (MTT) fire spread algorithm. For each fuel treatment scenario, we simulated 25,000 wildfires replicating the historic weather conditions associated with severe wildfires in the area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within the fuel treatment scenarios tested. The treatments realized nearby roads were the most efficient. We also evaluated the effects of other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimize proactive risk management activities aimed at reducing post-fire erosion in Mediterranean areas.

How to cite: Del Giudice, L., Arca, B., Robichaud, P., Ager, A., Canu, A., Duce, P., Pellizzaro, G., Ventura, A., Alcasena-Urdiroz, F., Spano, D., and Salis, M.: Coupling wildfire spread and erosion models to quantify post-fire erosion in Northern Sardinia, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5872, https://doi.org/10.5194/egusphere-egu2020-5872, 2020.

This paper addresses the product consistency in a cross-ECV model space driven ECV’s to estimate the radiative forcing (RF) due to the direct effect of fire- driven surface albedo change. Monthly radiative forcing’s are modeled using three Earth Observation land surface albedo (MCD43C3, GlobAlbedo and Copernicus Global Land Services) and five burnt area (FireCCIv4, FireCCIv5, MCD45C5, MCD64C6 and Copernicus Global Land Services) products, and the ERA5 downward Solar radiation at the Surface. The ensemble consistency is analyzed spatially and seasonally by vegetation cover type using the Land Cover CCI product, and using four spatial resolutions (0.05°, 0.10°, 025° and 0.5°). Results show that depending on the combined products and spatial resolution, estimates can differ significantly. In general, higher estimates result at coarser resolutions and variation between product combinations can differ between 26% to 46%, depending on the type of vegetation. In addition, significant temporal trends of opposing signs can be detected. This study presents an example of cross-ECV modelling. Due to the increasing number, and coverage, of Earth Observation satellite programs, these results highlight the need to assess the fitness for purpose of the derived products.

How to cite: Mota, B., Gobron, N., Lanconelli, C., and Capucci, F.: Product dependency of Fire-driven surface albedo radiative forcing global estimates: a spatial and temporal consistency analysis , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9222, https://doi.org/10.5194/egusphere-egu2020-9222, 2020.

Fire is the primary driving factor of the ecosystem dynamics of many forests, directly affecting the global carbon balance and atmospheric concentrations of the trace gases including carbon dioxide. Recent anthropogenic influence has led to an increase in frequency and impact of wild fires. Hence, it is of vital importance to predict forest fire risk at monthly and seasonal time scales in order to mitigate its impacts, including fire driven dynamics of ecosystem and socio-economic services.

Resilience of the ocean–atmosphere system provides potential for early detection of upcoming fire season intensity. Here, we report on the development of a probabilistic empirical prediction system for forest fire risk on monthly to seasonal timescales across the Northern Hemisphere, using local and large scale climate information as predictors for future fire weather. The fire risk is quantified by the monthly drought code (MDC), which is an established indicator for seasonal fire activity.

The forecasts are disseminated through the KNMI climate explorer, using an interactive online Python application, in order to convey forecast information in a simple and digestible manner. A forecasting page allows for end-users to assess local seasonal fire weather risk, associated forecast skill, and the relation between historical MDC and observed fires. The forecasts are updated monthly throughout the fire season. A research page allows for local and global analysis of the sources of predictability, and characterization of the patterns of spatial and temporal variability of fire weather risk.

How to cite: Krikken, F., Eden, J., and Drobyshev, I.: Dissemination of seasonal fire weather information for stakeholders and researchers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15263, https://doi.org/10.5194/egusphere-egu2020-15263, 2020.

Fire activity has decreased in the last decades in Spain, as a whole and in most regions. However, little is known about the changes in the fire season peak, timing, and length. Here we studied the temporal variation in the fire season since the 1970’s for different Spanish regions. We analyzed weekly time series of annually burned area by fitting GAMs (Generalized Additive Models) models in R. Area burned was log transformed and smoothing P-splines were fit to study weekly seasonality. GAMS allowed us to model spring, summer, and autumn fire seasons. Changes in the sign of the smoothing parameter determined the timing (onset/end dates) of each fire season, while the maximum value of the parameter established the peak of the fire season. We applied trend analysis to study inter-annual variation in fire season timing, length, and amplitude. We found temporal and spatial differences in the fire season across regions. In the northern Atlantic regions, models performed better, and captured a bimodal fire season (spring-summer). Nonetheless, the bimodal fire-season structure is no longer distinguishable in recent years, since both are increasing in duration. In the Mediterranean regions, larger peaks of burned areas occur in shorter time spans. The amplitude and duration of the summer season is decreasing, probably due to the increase in fire suppression during the summer. The summer season is starting earlier, while, in general, no trend was found for the end of the season. Furthermore, spring fire peaks in Mediterranean regions are becoming more frequent, suggesting that more attention should be paid to these out-of-season conditions.

How to cite: Urbieta, I. R., Arellano, G., and Moreno, J. M.: Changes in the timing and length of the fire season in Spain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20346, https://doi.org/10.5194/egusphere-egu2020-20346, 2020.

The Wildland-Urban Interface (WUI) is the spatial manifestation of the coupling of human communities and ecosystems, and wildfire is the most prominent issue. The WUI accounts for large percentages of fire prevention and suppression expenditures because it is where most human fatalities and structure losses occur. Therefore a fire-risk based definition of the spatial delimitation of the WUI may be critical to properly distributing prevention action and management investments to obtain the maximum social return. We present the first methodological approach that can be used to delineate the WUI based on a fire risk assessment. To accomplish this, we developed a geographical framework to model fire risk with the most prominent drivers and their interactions to define spatial explicit thresholds of the WUI. We built a Bagged Decision Tree (BDT) model to quantify fire risk based on Human Activity, Geographic and Topographic, and Land Cover variable interaction with fire ignition. For national and subnational threshold definition, we used Partial Dependence Plots (PDP) to analyze relationships between individual variables and predicted responses. A PDP can show the inflection point where a management action could potentially attain the best social return for decreasing fire risk. We find that the spatial threshold can vary more than double between subnational areas using the local fire risk-based approach. Subnational threshold definition accounts for 52% of fires in 3.4% of the national territory where lives 63% of the human population versus the conventional threshold or even nationally defined threshold that accounts for 36% and 54.4% of fires but in 3.3% and 4.3% of the land respectively. This multi-scale approach can be used to identify both general thresholds for large-scale applications as well as local thresholds for defining the WUI both operationally and empirically to determine optimal management areas.

How to cite: Miranda, A., Carrasco, J., González, M., Pais, C., Lara, A., Weintraub, A., Altamirano, A., and Syphard, A.: Multiscale local definition of the wildland-urban interface to mitigate fire risk: an evidence-based approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21068, https://doi.org/10.5194/egusphere-egu2020-21068, 2020.

Peatland ecosystems provide critical ecosystem-services such as water and carbon storage and harbor unique biodiversity. Once ignited, peat fires may burn uncontrollably for weeks or months resulting in rapid ecosystem degradation and excessive CO₂- Emissions. Despite the impact of peat fires on ecosystem services and climate, peatland fire regimes remain poorly characterized for many parts of the world. Here we investigate the global occurrence of peatland fires over the last two decades.

We estimate the global extent of peatland fires from 2009 to 2018 and identify drivers of variability and trends using a global peatland map (Global Peatland Database /Greifswald Mire Centre 2019), active fire detections from the Moderate Resolution imaging Spectroradiometer (MODIS), and several fire regime and climate anomaly-datasets. The data were used to delineate 14 ‘Peatland Fire Regions’ (PFR).

Our results indicate that between 2009 and 2018 globally 553,950 km² of peatland have been affected by fire (7.88 % of the global peatland area), whereas patterns and trends are widely differing. The extent of fire-affected area in the PFRs of Boreal North America and Boreal Eurasia both exceeded 80,000 km², which for both areas accounts for ~3.5 % of the peatland area. In the same time, over 120,000 km² were affected in both Central Asia and Equatorial Asia, i.e. ~23 % of their respective peatland area.

Northern peatlands are rather subject to natural fires and fire incidence is mostly driven by climate anomalies like droughts. Large peaks in fire occurrence in Boreal North America and Boreal Eurasia were correlated with higher temperatures and less rain. The strong linkage of inter-annual fire variability to temperature anomalies suggests that in these regions fire frequency and intensity may increase in future.

In tropical regions, particularly those of Africa and Asia, peatland fires tended to occur on degraded peatlands and fires occurred often multiple times on the same site during our study period. While inter-annual variability in fire occurrence was strongly determined by climate, the long term trends in these regions are dominated by human land management. In Africa the fire affected peatland area was rather constant over the years and fires had the highest return frequency, which reflects the widespread culture of burning in land reclamation and agriculture.

Southern/Equatorial Asia and to some extent South America showed peaks correlated with ENSO associated drought events, leading to the largest fire-affected peatland area in just one year in the Equatorial Asia region of 50,900 km² (in 2015).

How to cite: de Waard, F., Barthelmes, A., and Joosten, H.: Global distribution and temporal patterns of fire on peatlands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21761, https://doi.org/10.5194/egusphere-egu2020-21761, 2020.

Effective monitoring of global wildfire activity requires comprehensive knowledge of changing environmental (including atmospheric and hydrological) conditions, fuel availability and routine observations of fire locations and intensity. The European Centre for Medium-Range Weather Forecasts (ECMWF) through its operation of, and contribution to, different Copernicus Services is in a unique position to provide detailed information on the conditions leading to wildland fire activity, the evolution of wildfires, and their potential impacts, when they occur. Fire weather forecasts from the Copernicus Emergency Management Service, and surface climate anomalies from the Copernicus Climate Change Service both provide context to the environmental conditions required for wildfires to persist. Analyses based on observations of fire radiative power, along with analyses and forecasts of associated atmospheric pollutants, from the Copernicus Atmosphere Monitoring Service aid in quantifying the scale and intensity in near-real-time and the subsequent atmospheric impacts. During 2019, regions of anomalously hot and dry surface conditions in Arctic Siberia and southeast Australia experienced large-scale, long-duration wildfires which burned thousands of square kilometres with a total intensity that was significantly above the average of the previous 16 years of data in those regions. We present an overview of the evolution of fire activity in Siberia between June-August 2019, and Australia between September 2019-January 2020, based on ECMWF/Copernicus data for fire weather, climate anomalies and active fires. We will show that the different datasets, while being relatively independent, show a strong correspondence and provide a wealth of information vital to understanding global wildfires, their underlying causes and environmental impacts.

How to cite: Parrington, M., Di Giuseppe, F., Smith, T., Vitolo, C., Garrigues, S., Wooster, M., Zhang, T., Kaiser, J., Ades, M., Agusti-Panareda, A., Barre, J., Bousserez, N., Engelen, R., Flemming, J., Inness, A., Kipling, Z., Peuch, V.-H., Vamborg, F., and Coughlan, R.: Wildfire weather, intensity and smoke emissions of large-scale fire events in 2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11786, https://doi.org/10.5194/egusphere-egu2020-11786, 2020.

Wildfire (WF) has the potential to occur in more than 30% of the worldwide land area, in many different biomes/ecosystems/land cover types, where it is controlled mainly by the environmental drivers such as vegetation structure, meteorological/climate conditions, and human activities. On the other hand, land use/land cover changes (LULCC) are one of the most important global alterations of the environment. In the last decades, Europe registered significant-high fire incidence and LULCC between all land cover classes. In the 2000 – 2018 period, according to the European Forest Fire Information System (EFFIS), Europe was affected by 18 882 WFs which burned 6 887,713 ha. According to CORINE land cover maps, the observed LULCC area in Europe for the same period was of 23,510,075 ha. Recent studies suggested that regional LULCC in the last decades promoted the occurrence of more and larger WF, in some European regions. Therefore, the main objectives of this study were to assessed the LULCC in and around burnt areas (BAs) during the 2000–2018 period. This study benefits from the use of reliable CORINE inventories and EFFIS BA product. A geospatial methodological approach was implemented to identify and quantify LULCC and to characterize the relationship between LULCC and WFs in Europe. This research provides a detailed characterization of the LULCC in and around BAs in Europe, and attempts to contribute to a better management of the landscape, urbanization and wildland-urban interface to reduce related losses in the natural and human system including losses of life, property and assets.

How to cite: Parente, J., Tonini, M., Stamou, Z., Koutsias, N., and Pereira, M.: Wildfires in Europe: the role of land use/land cover changes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12920, https://doi.org/10.5194/egusphere-egu2020-12920, 2020.

A statistical analysis on the wildfire events, that have taken place in Greece during the period 1985-2007, for the assessment of the extremes has been performed. The total burned area of each fire was considered here as a key variable to express the significance of a given event. The data have been analyzed through the extreme value theory, which has been in general proved a powerful tool for the accurate assessment of the return period of extreme events. Both frequentist and Bayesian approaches have been used for comparison and evaluation purposes. Precisely, the Generalized Extreme Value (GEV) distribution along with Peaks over Threshold (POT) have been compared with the Bayesian Extreme Value modelling. Furthermore, the correlation of the burned area with the potential extreme values for other key parameters (e.g. wind, temperature, humidity, etc.) has been also investigated.

How to cite: Koutsias, N. and Coutelieris, F. A.: Frequentist and Bayesian extreme value analysis on the wildfire events in Greece , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13014, https://doi.org/10.5194/egusphere-egu2020-13014, 2020.

Extreme fire danger is expected in certain regions at summer times. However in some cases (e.g. Australian fire in 2019) possibly because of  early onsets or  prolonged conditions,  fires lead to catastrophic outcomes. These events are often referred as "anomalous" without a quantification. At the European Centre for Medium-Range Weather Forecasts (ECMWF), one tool that could aid pinpointing how uncommon these  situations  are  is the extreme forecast index (EFI), an index that highlights regions that are forecast to substantial diverege  from  to the local climate. 
The EFI concept has been in the past applied to meteorological fields such as temperature and precipitation. In this work we build on  previous findings by undertaking a global verification out to 15 days forecast  on the ability of the EFI for the Fire Weather Index (FWI)to capture extreme observed fire. Using the ECMWF ensemble prediction system and probabilistic skills score we analyse the fire season in  2019. In most case the  EFI is more skillful than the simple FWI to detect anomalous conditions for fire danger.

Following these results, the operational implementation of the FWI EFI  is currently being planned.

How to cite: Di Giuseppe, F., Vitolo, C., and Krzeminski, B.: How unusual are fire conditions ?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18654, https://doi.org/10.5194/egusphere-egu2020-18654, 2020.

Crop residue and rangeland burning is a common practice in the United States but verified ground-based estimates for the frequency of these fires is sparse. We present a comparison between known fire locations collected during the summer 2019 NOAA/NASA FIREX-AQ field campaign with several satellite-based active fire detections to estimate the occurrence of small-scale fires in agroecosystems. Many emissions inventories at the state-, country-, and global-level are driven by active fire detections and not burned area estimates for small fires in agroecosystems. The study area is focused on the southern Great Plains and Mississippi Delta of the United States. We combined fire occurrence data from 375 m Visible Infrared Imaging Spectrometer (VIIRS), 1 km Moderate Resolution Imaging Spectroradiometer (MODIS), and 2 km Geostationary Operational Environmental Satellite (GOES) active fires with 30 m land use data from U.S. Department of Agriculture Cropland Data Layer (CDL). The detections were compared to fires and land use validated in the field during the NOAA/NASA FIREX-AQ mission. GOES detected these fires at a higher frequency than MODIS or VIIRS. For example, MODIS detected 873 active fires and VIIRS detected 2,859, while GOES detected 13,634 active fires. Additionally, a large amount of the fires documented in the field, approximately 41%, were not detected by any satellite instrument used in the study. If GOES detections are excluded, approximately 5% of the documented fires were detected. This suggests that a large amount of cropland and rangeland burning are not detected by current active fire products from polar orbiting satellites like MODIS and VIIRS, with implications for regional air pollution monitoring, emissions inventories, and climate impacts of open burning.  

How to cite: Rintsch, E. and McCarty, J. L.: Where’s the fire? Using in-situ observations from the NOAA/NASA FIREX-AQ campaign to validate small fire in the central and southern U.S. , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3076, https://doi.org/10.5194/egusphere-egu2020-3076, 2020.

Analyzing trends of changes in fire regimes on a global scale

Wildfire is a dominant factor for shaping the landscape ecology in many parts of the world. It also poses an enormous threat to human lives and property. Climate change is expected to influence historical fire patterns, e.g., to intensify the occurrence of fire in already fire-prone ecosystems. This work is an attempt to investigate trends of changes in fire regimes on a global scale, regarding seasonality, intensity, and distribution of fire activity.

Thermal remote sensing allows the monitoring of wildfire activity worldwide. Data from several satellite sensors featuring varying spatial/temporal resolutions and radiometric sensitivities have been used towards that purpose, allowing for a combined temporal resolution of only a few hours between satellite overpasses (in the case of geostationary satellites, such as MSG or GOES, data is even gathered every 15 minutes). The combination of the acquired data therefore allows a fairly seamless monitoring timespan of several decades.

Due to the differences in utilized systems and methodologies, however, these data collections are highly heterogeneous regarding spatial/temporal resolution, utilized data formats, naming conventions, data types and comprised information. In preparation for this work, available datasets have been collected and harmonized, e.g. fire radiative power (FRP) has been corrected to account for the respective spatial resolution. By that, a comprehensive, decade-scale data basis was generated, which is used to derive fire related trends.

This study uses data from AQUA/TERRA MODIS, SUOMI-NPP VIIRS, MSG SEVIRI (covering Europe, South America and Africa), ENVISAT AATSR as well as ERS-2 ATSR-2. The generated data basis covers the time span from June 1995 to October 2019 and contains a collection of several billion active fire locations together with radiated power. The data was transferred into a uniform grid of 1x1 degrees, which was then analyzed regarding year-to-year developments.

How to cite: Nolde, M., Plank, S., Friedemann, M., and Riedlinger, T.: Analyzing trends of changes in fire regimes on a global scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3632, https://doi.org/10.5194/egusphere-egu2020-3632, 2020.

Evidence of wildfire activity in deep time is preserved in the rock record as fossilised charcoal. Modern wildfire temperature is often a function of fuel type, structure and availability. These three factors are reliant upon climatic conditions and offer a potential insight into palaeoenvironmental conditions through geothermometric analysis of preserved charcoals. Much like the analysis of vitrinite reflectance as an assessor of thermal maturity, similar methodology has been applied historically to charcoal in order to obtain palaeowildfire temperatures.  Raman spectroscopy has similarly been applied to organic material as an identifier of thermal maturity, via the analysis of carbon microstructure changes with increasing temperature – however very little palaeocharcoal has been analysed via Raman spectroscopy, with no apparent application to palaeowildfire geothermometry. Through the application of Raman spectroscopy, we present the first comparison of modern pyrolyzed plant material with spectra of early Danian palaeocharcoals, associated with wildfire activity. These results indicate that Raman spectroscopy of modern wildfire charcoal facilitates a correlation between charcoal microstructure change and temperature of formation. This in turn has enabled comparison with palaeocharcoal, and the generation of reliable wildfire geothermometry. With this new methodology, we intend to further the understanding of (1) changes in palaeowildfire regimes and intensity through time (2) the interaction between climate, plant community composition and structure, and palaeowildfires  (3) correlation and comparison with existing palaeowildfire interpretive approaches. Further analysis and experimentation is required to identify the impact of fire determining factors on observed spectra to target the new approach towards interpreting current and future wildfire behaviour under climatic stress. 

How to cite: Theurer, T., Muirhead, D., and Jolley, D.: Applying Raman Spectroscopy to Modern and Palaeocharcoals Associated with Wildfire Activity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3727, https://doi.org/10.5194/egusphere-egu2020-3727, 2020.

Forest fires are naturals in the Mediterranean ecosystems. However, in the last decade, the number of wildfires has significantly increased in the Mediterranean basin along with climate change. Therefore, forecasts of this region by using fire indices are crucial to take necessary precautions. In the present study, the projected changes for the period 2070 - 2099 concerning the control period 1971 - 2000 were used to estimate forest fire risk by the Canadian Fire Weather Index (FWI). RCP4.5 and RCP8.5 emission scenarios (IPCC) outputs of MPI-ESM-MR and HadGEM2-ES dynamically downscaled to 50 km for the CORDEX-MENA domain with the use of the RegCM4 were utilized. ERA-Interim observational data from ECMWF covering the period 1980-2012 were also used to test the performances of models. The output of MPI-ESM-MR gave more similar fire risk prediction with the reforecast of observational data (ERA-Interim). Thus, the MPI-ESM-MR model could be more suitable to estimate fire risk by FWI. According to future projection, forest fire risk will significantly increase throughout the region for the last 30 years of this century.

How to cite: Calda, B., Collu, K., Pacal, A., and Kurnaz, M. L.: Estimation of Forest Fire Risk by Using Fire Weather Index in the MENA Region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4593, https://doi.org/10.5194/egusphere-egu2020-4593, 2020.

The present work deals with the time series analysis of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). While many works have been published concerning the trends of nighttime and daytime LST at the regional or local scale, little attention has been paid to structural changes observed within the LST time series in various sub-periods. This could be of much interest not only for climate studies but also for unveiling the possible relation between natural disasters such as wildfires and global changes. In this work we tested the hypothesis of a constant trend in LST time series from 2000 to 2019 and highlighted the existence of periods with changing trends. The methodology was applied in an area of approximately 17.000 km² located in NE Greece and South Bulgaria. The nighttime and daytime LST time series data were initially subjected to a gap filling algorithm to account for missing values and were then aggregated at the catchment level. Furthermore, LST time series were analyzed using the Breaks For Additive Season and Trend (BFAST) method. Results indicated that an abrupt change in both nighttime and daytime LST trends was observed in all examined time series, indicating a transition from a decreasing LST regime from 2002 to 2006 to an abrupt increasing thereafter until today. An initial comparison with the existing inventory of wildfires in the area for the last 20 years indicated an increase of wildfire events which coincides with the LST breakpoint, indicating thus possible connections between rising LST and wildfire events.

How to cite: Gemitzi, A. and Falalakis, G.: Analyzing trends in Land Surface Temperature using remotely sensed time series data and the BFAST method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6732, https://doi.org/10.5194/egusphere-egu2020-6732, 2020.

Wildfires risk in the Amazonian forest will probably increases in the future as a consequence of the predicted increased frequency of droughts combined with the growing rate of deforestation. The main cause of fire ignition in the Amazon tropical rainforest is anthropogenic (human-made). Indeed, burning is the easiest and cheaper way to clear the land, especially in the absence of transport or roads. This practice, known as slash-and-burn, consist on cutting trees and low vegetation/agricultural residuals and finally burning the biomass. The purpose is to make way for agriculture, livestock, logging, or simply to clear the agricultural land for new cultivations. In Bolivia this centuries-old practice of burning portions of tropical forest to prepare fields for the next year’s crop is called chaqueo. This practice can get out of control and initiate large fires, burning hectares and hectares of forest. Moreover, in September 2019 a controversial national decree, allowed the use of chaqueo in forestry areas to promote the expansion of the agricultural frontier, triggered an unprecedented situation. Although it is evident that fires in Bolivia, mainly cause by the practice of chaqueo, and land use and land cover change (LULCC), mainly the deforestation, are related, the spatio-temporal association among these two elements has not been deeply investigated jet.

The present study aims at investigating the spatio-temporal evolution of wildfires in the Chiquitania region, Bolivia, and its relationship with LULCC, particularly with regards to the deforestation, in the last three decades. The investigated region is located in the Department of Santa Cruz and is part of the Chiquitano dry forest ecoregion - spreading over Bolivia, Brazil and Paraguay - connecting the Gran Chaco shrublands to the south with the Amazon rainforests to the north. The characteristic tropical forest biome combined with the the near future drier and more seasonally extreme climatic conditions will increase the risk of wildfires in the Chiquitania region. Changes are also driven by the actual policies supporting the settlement of new farming communities and the expansion of the agricultural frontier and the road network in the region.

The investigation methods are based on a geospatial statistical approaches allowing to: 1) explore LULCC within the entire the study period and elaborate the map of changes showing the transitions among different classes; 2) quantify gained and lost areas for the classes forest, urban and croplands; 3) investigate the evolution in space and in time of fires and map local over-densities; 4) asses the main drivers for fire risk in the region.

How to cite: Bustillo Sanchez, M., Poffet, B., Tonini, M., and Fiorucci, P.: Wildfires risk and spatio-temporal dynamic in the Chiquitania region (Bolivia), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7594, https://doi.org/10.5194/egusphere-egu2020-7594, 2020.

Fire remains the main natural disturbance factor in the European boreal zone (EBZ), which exhibits strong gradients in climate conditions, modern and historical patterns of forest use, and the modern human infrastructure density. Understanding climatic forcing on fire activity is important for projecting effects of climate change on multiple ecosystem services in this region. Here we analyzed available records of annually burned areas (ABA) in 16 administrative regions of EBZ (countries or sub-country units) and fire weather variability to test for their spatio-temporal patterns over 1901-2017. To define sub-regions of EBZ with similar fire activity we compiled 30-60 year long ABA chronologies and clustered them in Euclidian space to identify regions of EBZ with temporally synchronous fire activity. We then reconstructed 100-year long ABA chronologies for each cluster, capitalizing on its member with the highest correlation between observational fire record and climatological fire weather proxy (MDC, monthly drought code). The 100-year chronologies helped identified large fire years (LFY), i.e. years with the ABA being above 10% of its long-term distribution. The climatic forcing of these events was tested in superposed epoch analysis operated with gridded 500 hPa pressure fields. Finally, we tested trends in (a) synchrony of LFY's across clusters, (b) MDC values over the EBZ, and (c) spatial variability in July MDC over the EBZ geographic domain during 1901-2017.

EBZ exhibits large variability in forest fire activity with the fire cycles varying from ~10⁴ (Scandinavia) to 3*10² years (Russian Republic of Komi). Clustering of administrative units in respect to their ABA suggested the presence of sub-regions with synchronous dynamics of ABA, located  along W-E and S-N gradients. LFYs in each of the cluster was associated with the development of the high pressure cell over the regions in question in July, indicating climatic forcing of LFYs. Contingency analysis indicated no long-term trend in the synchrony of LFYs observed simultaneously in several administrative units. We observed a trend towards higher values of MDC for the months of April and May in the western section of EBZ (April) and southern-eastern sections of the Baltic sea region and North sections of EBZ in Russia (May). Trends in MDC during the summer months were largely absent. We discuss teleconnections of fire activity in the EBZ with Atlantic SST.

How to cite: Drobyshev, I., Kitenberga, M., Ryzhkova, N., Eden, J., Krikken, F., and Pinto, G.: Trends and patterns in annually burned forest areas and fire weather across the European boreal zone in the 20th and early 21st centuries, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8364, https://doi.org/10.5194/egusphere-egu2020-8364, 2020.

Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.

How to cite: Liu, J.: Assessment of different indices from Landsat time series in burned area mapping , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8397, https://doi.org/10.5194/egusphere-egu2020-8397, 2020.

Wildfires can inflict serious damage to forest ecosystems, agricultural areas and often endanger human settlements and lives. Rising global temperatures and changes in precipitation pattern increase the risk of severe fires. In Croatia, the areas currently most affected with high risk of forest fires are located in the Mediterranean region. Due to climate change the risk will likely increase and further strain the available fire-fighting resources. The situation could be even more alarming in Continental parts of the country where forest fires were not common in the past, but may become increasingly likely in the near future. Therefore, accurately assessing the wildfire risk is increasingly important in implementing fire-avoidance activities and optimizing the management of country’s fire-fighting resources.

The aim of our study is to assess the change in the spatio-temporal distribution of the fire Daily Severity Rating (DSR) and the Seasonal Severity Rating (SSR) in the last two decades, with respect to the reference period 1961–1990. We present a spatial analysis of SSR for the period 1989–2018 in Croatia based on the Croatian Meteorological and Hydrological Service (DHMZ) data and compare it with the one of European Forest Fire Information System (EFFIS). The relation between the SSR and the burned area, estimated from MODIS MCD64A1 Version 6 Burned Area data product, during 2001–2018 is investigated with the aim to facilitate locally optimized model for the assessment of the expected burned area associated with a given SSR. The results should contribute to improved understanding of the near-future risk of severe fires in Croatia related to possible future climate scenarios.

How to cite: Marjanovic, H., Kern, A., Ostrogovic Sever, M. Z., Vucetic, V., and Anic, M.: Relations and trends of Fire Weather Severity and MODIS Burned Area in Croatia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8502, https://doi.org/10.5194/egusphere-egu2020-8502, 2020.

Africa is the biggest continental source of biomass burning emissions. The emissions result in regional to transcontinental air pollution. Atmospheric model studies that address the linkage between fires and air quality have to cope with substantial uncertainties in fire emission inventories. All contemporary fire emission inventories build upon satellite information to quantify the spatial and temporal occurrence of fires, but they use different satellite sensors, detection algorithms, and estimation methods. Large discrepancies between inventories in terms of emission totals and spatial and temporal patterns are the consequence.

Most satellite products employed for fire emission estimations across Africa cannot resolve small fires, and the omission of thereof strongly lowers the accuracy of the estimated emission fluxes. The ESA Fire_cci project has recently released the first burned area product for Africa that builds upon high resolution optical imagery from Sentinel-2. By resolving small fires, it detects 60 to 110% more burned area in 2016 than estimated by the widely used, MODIS-based products GFED4s, GFED4, MCD64A1 Collection 6, FireCCI51 and FINN.

We inter-compare biomass burning emission inventories computed from these products and analyse potential sources of discrepancies. Sensitivity simulations with the WRF-Chem atmospheric chemistry model using the inventories as boundary condition complement the analysis. Modelled concentrations of atmospheric trace species are evaluated against a set of satellite observations that act as top-down constraint on the fire emission estimates.

How to cite: Heil, A., Bouarar, I., and Brasseur, G.: Uncertainties in estimating biomass burning emissions for Africa: implications for atmospheric modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9808, https://doi.org/10.5194/egusphere-egu2020-9808, 2020.

The frequently wildfire-prone Dadu Terrace is located on the outskirts of a densely populated city. According to the Taichung Fire Department’s 2013-2017 statistics, there are more than 400 wildfires a year. In particular, hundreds of wildfires occur each month during the dry season, and the mobilization of firefighters will increase the burden of social resources. Wildfire damage and smoke can also endanger protected objects nearby. Combined with the characteristics of agricultural farming, the seasonal variation of NDVI extracted from the satellite images can reflect the land cover category. Wildfires in the Dadu Terrace often accidentally caused by human interference, vegetation change over a short time can be as the factor of artificial interference for analyzing the time-based wildfire frequency. Results show that March to April is the peak period of wildfire occurrence, which consistent with the historic records of wildfire reporting. In addition to the temporal distribution of wildfire occurrence, this study also established a model for estimating the spatial distribution of wildfire risk at the mostly occurring period using the concepts of risk analysis. The model can effectively reflect the distribution of hotspots where wildfires occur, and can be the reference for the relevant authorities on the countermeasures of wildfire disaster prevention and control.

Keywords: Wildfire, Environmental indicators, Risk assessment

How to cite: Wu, S.-W. and Lin, C.-Y.: A study of wildfire risk using environmental indicators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9827, https://doi.org/10.5194/egusphere-egu2020-9827, 2020.

In recent years, large forest fires in Fennoscandia have shown that wildfires can have a strong impact on society also in northern Europe. In the future, meteorological conditions are expected to become increasingly favorable for wildfires due to climate change. An important aspect in fire management are the national forest management strategies that play a crucial role in controlling e.g. fuel availability in forests, and further areal coverage of burned area. In addition, the effectiveness of rescue services is crucial. Thus, the development of fire risk prediction and fire detection systems, as well as, modeling of spread of fires and emissions of harmful ingredients, such as black carbon are urgently required to improve the societies preparedness to the increasing thread. In this presentation we synthetize the current state-of-the-art understanding of wildfires in Fennoscandia from a wide range of key perspectives: historical fire regimes, monitoring using in-situ and remote-sensing technologies, integrated modeling (e.g. climate models, spatial fire propagation models forced with operational weather forecast model) and fire suppression. In addition, we assess the amount of black carbon emissions released from recent wildfires in Fennoscandia. These results will help northern societies to tackle against the negative impacts of climate change and to support the development of efficient mitigation strategies. In the upcoming decades the effective management of wildfires is especially relevant, as wildfires greatly affect regional carbon budgets and mitigation efforts. 

How to cite: Aalto, J., Backman, L., Virtanen, T., Partanen, T., Lehtonen, I., Kolstela, J., Aalto, T., Vanha-Majamaa, I., Shorokhova, E., Kokki, E., Lindberg, H., Tolppi, R., and Venäläinen, A.: Wildfires in Fennoscandia under changing climate and forest cover, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10648, https://doi.org/10.5194/egusphere-egu2020-10648, 2020.

Fires have become a major concern worldwide because of their serious effects, such as economic losses, alteration of ecosystems often leading to enhanced soil erosion, air pollution, and contribution to global warming through releasing CO2. In Mongolia, the dry climate with strong winds together with the low population number resulting in weak firefighting capabilities forces the generation of fires which are therefore considered the main natural disaster seriously affecting ecosystems and producing dramatic economic damages. Due to the advantages of remote sensing, i.e. wide coverage, high spatio-temporal resolution, easy access, and relatively low expense (or free), satellite data has been widely used for fire studies from local to regional and global scales. Depending on the study area scale, various fire products from different sensors have been used, e.g. the Landsat – TM/ETM+/OLI sensor; the Moderate Resolution Imaging Spectroradiometer (MODIS), the Fire_CCI 5.1 (developed by the European Spatial Agency); and the fire products from the AVHRR sensor. To date, among all the fire products, MODIS data is most widely used in fire-related studies. The new sensor onboard the geostationary Himawari satellite (AHI-8), is providing a new level of data (i.e. very high temporal resolution - 10 minute, along with a high spatial resolution - 0.5 to 2.0 km) for monitoring fires. Since available it has received much attention from the remote sensing application community. However, because this is still a new satellite data, it has not been popularized in applications and research. More studies of assessments and evaluations of this data are needed in various fields, particularly in fire research. In addition, the MODIS instruments were only designed with six years of operating lifetime in mind, therefore both instruments (the Terra and Aqua satellites) are expected to only last until 2020. This makes it necessary to implement a study to evaluate the existing MODIS data, as well as the potential replacement data for fire detection in Mongolia. This motivates us to implement the present study, for which our goals are: (i) to compare the MODIS (MCD64A1) and AHI-8 products in their effectiveness for detecting fires in Mongolia, and (ii) to test the plausibility of the detected fires based on changes in multivariate satellite data before and after the fire events. In order to achieve these goals, we use data from the last five years from July 2015 to July 2019 over the entire Mongolian country. Our results reveal that there is a difference between MODIS and AHI-8 products in detecting fires in Mongolia.

How to cite: Lehnert, L. and Phan, T. N.: Comparison of fire products in Mongolia reveals contrasting results, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11064, https://doi.org/10.5194/egusphere-egu2020-11064, 2020.

The municipalities of Qeqertalik and Qeqqata in western Greenland experienced two wildfires in July 2017 and July 2019, respectively. Both fires occurred near Sisimiut, the second largest city in Greenland, with the ignition site of the July 2019 wildfire along the Arctic Circle Trail. These Arctic fires vary in fuels and burning behaviour from other high northern latitude fires due to unique flora, specifically the lack of extensive grasses, shrubbery, and more vascular vegetation, and presence of deep vertical beds of carbon-rich humus. The purpose of this research was to create wildfire risk models scalable across the Arctic landscapes of Greenland. We test multiple wildfire risk models based on expert-derived weighted matrix and four geostatistical techniques: Equal Influence (eq_infl), Multiple Logistic Regression (MLR), Geographically Weighted Regression (GWR) and Generalized Geographically Weighted Regression.The eq_infl model applied an even influence of each landscape characteristics. Two MLR models were developed, one using all the available data for the peninsula where the wildfire occurred (MLR_full) and the other which used an equal randomly chosen 50,000 pixel subset of both the burned area and unburned area (MLR_sub) immediately surrounding the 2017 Qeqertalik wildfire.The optimum model was selected in a stepwise fashion for both MLR models using AIC. GWR and GGWR models were derived from the MLR_sub, to avoid multicollinearity. Landscape characteristics for the wildfire risk models relied on open source remotely sensed data like ~20 m synthetic aperture radar imagery from the European Space Agency Sentinel-1 for soil moisture; elevation, slope, and aspect derived from the 10 m Arctic DEM provided by the U.S. National Geospatial Intelligence Agency (NGA) and National Science Foundation (NSF); vegetation fuel beds from the Global Fuelbed Dataset; normalized difference vegetation indices (NDVI) from 20 m Sentinel-2 served as proxies for vegetation condition; and soil carbon information from the 250 m SoilsGrid product was used to indicate likelihood of humus combustion. The nominal spatial resolution of each wildfire risk model was 20 m, after resampling of data. The optimum wildfire risk model was the model that displayed the greatest fire risk within the 2017 burned area. The average fire risks for each model were compared for significant difference in the mean fire risk using an ANOVA and Tukey's Post hoc. Average predicted fire risks by our models were compared to 2017 and 2019 burned areas visually digitized from 10 m Sentinel-2 data. The MLR_full model best represented the burned area of the 2017 Qeqertalik wildfire, though with an R² of 0.232, this leaves large amounts of variation unexplained. This is not surprising as wildfires in Greenland are uncommon and applying traditional fire risk approaches may not accurately represent the real-world. We can interpret from the results of the MLR_full model that landscapes across western Greenland have the potential to burn in a similar manner to the 2017 and 2019 wildfires.

How to cite: McCarty, J., Francis, R., Fain, J., and Haynes, K.: Wildfire Risk Models for western Greenland: Geostatistical Considerations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12660, https://doi.org/10.5194/egusphere-egu2020-12660, 2020.

Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the phenology patterns and the vegetation recovery pattern of various wildfires occurred in Greece during the period 2000-2020. Satellite remote sensing data from MODIS satellites in the period from 2000 to 2020 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas inside and outside the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles for some years before and some years after the fire. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we explore the strength and the use of these time series satellite data to characterize vegetation phenology as an aid to assess the fire-affected areas and to monitor their vegetation recovery.

How to cite: Tsipoka, K. and Koutsias, N.: Monitoring fire-affected areas using temporal profiles of spectral signal from time series MODIS satellite images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13136, https://doi.org/10.5194/egusphere-egu2020-13136, 2020.

This study presents an improvement of an old rule-based semi-automatic method to map burned areas by using multi-temporal Landsat and Seninel-2 images. The rule-based approach consists of a set of rules developed based on spectral properties of burned areas as compared to the pre-fire unburned vegetation and to the spectral signatures of other land cover types found in post-fire satellite scene. Actually, the spectral properties based on which the rules have been developed are presented in two graphs, one that corresponds to spectral signatures plots and the second that corresponds to the histogram data plots. The spectral patterns based on which the rule-based approach has been developed are not always the same. For example, depending on the type of the fire-affected vegetation (e.g. dry vegetation instead of green) the spectral pattern of the SWIR channel that correspond to channel 7 in Landsat 4-7 and 8 is not valid. Instead, there is a similar spectral behaviour but in the SWIR channel that correspond to channel 5 in Landsat 4-7, or channel 6 in Landsat 8. Additionally, the threshold value of 0.10-0.25 of the second rule seems not to be sufficient to cover all variability since there are cases that this value should be higher. Two characteristic examples of the insufficiencies found on the old-rules are concerned in the current analysis, one that presents limitations concerning the rule 5 (Serifos) and one that represents limitations concerning the rule 2 (Portugal). In this study we present a further improvement of the method and also its application to several cases spread out in Greek islands using both Landsat and Sentinel-2 images.

How to cite: Kanaropoulos, S. and Koutsias, N.: An improved rule-based approach to map burned areas using Landsat and Sentinel-2 images, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13168, https://doi.org/10.5194/egusphere-egu2020-13168, 2020.

Risk and susceptibility analyses for  natural hazards are of great importance for the sake of  civil protection, land use planning  and risk reduction programs. Susceptibility maps are based on the assumption that future events are expected to occur under similar conditions as the observed ones. Each unit area is assessed in term of relative spatial likelihood, evaluating the potential to experience a particular hazard in the future based solely on the intrinsic local characteristics. These concept is well-consolidated in the research area related with the risk assessment, especially for landslides. Nevertheless, the need exist for developing new quantitative and robust methods allowing to elaborate susceptibility  maps and to apply this tool to the study of other natural hazards.  In  the presented work, such  task is pursued for the specific  case of wildfires in Italy. The  two main approaches for such studies are the adoption  of physically based models and the data driven methods. In  the presented work, the latter  approach is  pursued, using  Machine Learning techniques in order to learn  from and make prediction  on the available information (i.e. the observed burned area and the predisposing factors) . Italy is severely affected by wildfires due to the high topographic and vegetation heterogeneity of its territory  and  to  its   meteorological conditions. The present study has as its main objective the  elaboration of a wildfire susceptibility map for Liguria region (Italy) by making use of Random Forest, an ensemble ML algorithm based on decision trees. The quantitative evaluation of susceptibility is carried out considering two different aspects: the location of past  wildfire occurrences, in terms of burned area, and the related anthropogenic and geo-environmental  predisposing factors that may favor fire spread. Different implementation of the model  were performed and compared. In  particular,  the effect of  a pixel's  neighboring land cover (including the type of vegetation and no-burnable area) on the output susceptibility map is investigated. In order to assess the  performance  of the model, the spatial-cross validation has been carried  out, trying  out different  number of folders. Susceptibility maps for the two fire seasons (the  summer  and  the winter  one) were finally computed  and validated. The  resulting  maps show  higher susceptibility zones , developing closer to the coast in summer and along the interior part of  the region in winter. Such zones matched well with the testing burned area, thus  proving the  overall  good performance of the proposed method.

REFERENCE

Tonini M., D’Andrea M., Biondi G., Degli Esposti S.; Fiorucci P., A machine learning based approach for wildfire susceptibility mapping. The case study of Liguria region in Italy. Geosciences (2020, submitted)

How to cite: Fiorucci, P., D'Andrea, M., Trucchia, A., and Tonini, M.: Wildfire susceptibility mapping via machine learning: the case study of Liguria Region, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18802, https://doi.org/10.5194/egusphere-egu2020-18802, 2020.

Wildfires represent a major threat to Mediterranean ecosystems and are responsible for relevant impacts to environmental, economic and social values. In the period 2010-2016, the cross-border Interreg Italy-France Maritime territory, which includes Sardinia, Corsica, Tuscany, Liguria and PACA Regions, had about 20,000 wildfire ignitions and a total burned area of about 122,000 ha. In the face of social and environmental conditions and risks of the Maritime Regions, strengthening and developing innovative common guidelines and systems of wildfire management, from the monitoring and forecast to suppression, can provide more effective solutions to the wildfire problem, and can help strengthen cross-border cooperation in case of days with high risk. This work is devoted to introduce the MED-Star project, and to describe his main activities and results, with a focus on the tasks and activities coordinated by the National Research Council of Italy, Institute of BioEconomy (CNR-IBE) of Sassari. MED-Star is a 3-years strategic project supported by the Interreg Italy-France Maritime Program 2014-2020, which is co-financed by the European Regional Development Fund (ERDF). MED-Star is closely linked to 4 joint simple projects (Intermed; Med-Coopfire; Med-Foreste; Med-PSS), which mainly focus on investments in small infrastructures for wildfire risk prevention and support to wildfire suppression operations. The MED-Star project aims to share and discuss fire management policies and the most advanced strategies that can reduce the risk associated with wildfires, also through the combination of joint action plans and pilot / demonstration actions. The partnerships of MED-Star and the related 4 simple projects include the main actors competent at the administrative, technical and scientific level on the wildfire topic in the Maritime area of cooperation, and are able to meet the abovementioned challenges, contributing to 1) the reduction of wildfire risk in the five Regions involved, 2) the definition of strategic and operational solutions, 3) the implementation of operational actions and investments for wildfire prevention, monitoring, forecast and suppression, and 4) the strengthening of joint early warning and risk monitoring systems.

How to cite: Salis, M., Arca, B., Pellizzaro, G., Ventura, A., Canu, A., Casula, M., Del Giudice, L., Scarpa, C., Schirru, M., and Duce, P.: MED-Star: Strategies and measures to reduce wildfire risk in the Mediterranean area , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18892, https://doi.org/10.5194/egusphere-egu2020-18892, 2020.

Wildfires are becoming an increasing threat to human health, infrastructure, forestry, agriculture and biodiversity. In Alpine regions, fires are often at the start of cascading risks including avalanches, mudslides or rock fall due to the loss of forest and vegetation layers. Additionally, wildfires are expected to occur more frequently in the future as a result of a warming climate, which is estimated to affect alpine regions in particular.

Fire danger forecasts, such as the commonly used Fire Weather indices, indicate the danger of forest fires based on numerical weather forecasts. Such indices are typically available at coarse spatial resolutions and, hence, have limited use in mountainous regions with their highly variable weather and other environmental conditions. Stakeholders, such as fire departments and forest managers, require more detailed forecasts in order to make robust decisions and efficiently plan their resources. The CONFIRM project, which started in December 2019 with funding from the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP), addresses this gap by using high-resolution earth observation data provided by the European Copernicus programme to develop a pre-operational fire danger forecast system.

Data from both optical and microwave sensors aboard satellites are known to be sensitive to changes in soil and vegetation water content. Exploiting this sensitivity, satellite data with high temporal and spatial resolutions from the Copernicus Sentinel-1 and Sentinel-2 missions will be used to estimate fuel moisture state. The estimates will be integrated with airborne Laser-scanning (LiDAR) data, high-resolution weather forecasts, socioeconomic and topographic data to develop a novel, high-resolution integrated forest fire danger system (IFDS) for Austria. The project team will apply its expertise in forest management, remote sensing, fire science and machine learning to estimate fire danger using the Austrian fire database, an extensive record of historic fire events, as a training dataset. Key stakeholders from national weather services (ZAMG, DWD), fire brigades, state forest administrations and infrastructure providers (Austrian Railways ÖBB) are continuously involved in the project to develop the IFDS according to their requirements. They will evaluate the prototype of the system during the fire season of 2021.

How to cite: Zotta, R.-M., Atzberger, C., Degenhart, J., Hollaus, M., Immitzer, M., Krajnz, H., Lick, H., Müller, M. M., Oblasser, H., Schaffhauser, A., Schlaffer, S., Vacik, H., and Dorigo, W.: CONFIRM – Copernicus Data for Novel High-Resolution Wildfire Danger Services in Mountain Regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19288, https://doi.org/10.5194/egusphere-egu2020-19288, 2020.

Medium and long-term forecasts for assessing the danger of fires.

V.Santarsiero^1,2, A. Lanorte¹, G. Nolè¹, B. Murgante², B. Tucci¹ e P. Baldantoni²

Seasonal fire forecasts are a challenge made possible in recent years thanks to availability of better time series of climatic data and wider statistical databases on fires. In addition, the long-term fire risk estimate is considered an element crucial for the preparation of prevention activities (Mavsar et al., 2013). Many of the studies related to seasonal fire forecasts follow an approach empirical based on statistical correlations between fires and climatic variables antecedents. All relevant changes in local and / or weather conditions changes in the local socio-environmental context can influence the regimes of the climate-related fires. Recent advances in seasonal climate forecasting systems based on the analysis of ocean-atmosphere-earth processes make it possible to use prediction models for fire hazard prediction. Such models based on physical processes use models global climate together with human factors to predict the fire hazard on a scale monthly or seasonal (Roads et al. 2005, 2010; Spessa et al. 2015; Field et al. 2015). Seasonal fire hazard predictions in the USA (Roads et al., 2010) are recorded on the NCEP-CFS (National Center for Environmental Prediction's Coupled Forecasting System) (Saha et al., 2006, 2014). The NCEP-CFS system generates forecasts for ensembles of global and regional spectral models over a period of 3 to 7 months.
The forecasts generated by the NCEP-CFS system were used to derive precipitation and temperature anomaly maps. Forecasts are made starting from the initial conditions of the last 30 days, with four runs per day. The Forecast ensembles are made up of 40 members from an initial period of 10 days. To provide high resolution seasonal forecasts has been developed and a generalized empirical statistical downscaling system is applied. On this basis precipitation and temperature anomaly maps were extrapolated. The values ​​of the precipitation and temperature anomalies in the various decades have been integrated in order to develop a meteorological index capable of highlighting the areas where these anomalies affect the increase or decrease in the fire hazard in relation to the average conditions for each specific decade. The index is built in a way such as to attribute a greater weight to the precipitation anomalies (70%) than the temperature anomalies (30%).

Keywords: fire hazard prediction, long-term forecasting.

¹ IMAA-CNR C.da Santa Loja, zona Industriale, Tito Scalo, Potenza 85050, Italy;

²School of Engineering, University of Basilicata, Viale dell'Ateneo Lucano 10, Potenza 85100, Italy.

How to cite: Santarsiero, V.: Medium and long-term forecasts for assessing the danger of fires., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21571, https://doi.org/10.5194/egusphere-egu2020-21571, 2020.

Copernicus is the European Union’s Earth Observation programme aiming at monitoring and forecasting the state of the environment on land, sea and in the atmosphere, in order to support climate change mitigation and adaptation strategies, the efficient management of emergency situations and improve the security of every citizen.

Copernicus has created a wealth of datasets related to the forecasting of wildfire danger as well as the detection of wildfire events and related emissions in the atmosphere. These products contribute to the operational services provided by the Copernicus Emergency Management Service (CEMS) and the Copernicus Atmosphere Monitoring Service (CAMS) and consists of real time forecasts as well as historical datasets based on ECMWF reanalysis database ERA5. Most of these data are available through the Copernicus Climate Data Store (CDS) and the Global Wildfire Information System (GWIS).

We will present the complete wildfire-related data offering under the Copernicus CDS and GWIS and showcase how data can be post-processed and visualised using the caliver R package.

How to cite: Vitolo, C., Di Giuseppe, F., and Parrington, M.: Analysis and forecast of wildfires using Copernicus data and services, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21781, https://doi.org/10.5194/egusphere-egu2020-21781, 2020.

Vegetation fires contribute to 38% to the emission of CO₂ into the atmosphere, against 62% caused by the combustion of fossil fuels. Further, it could approach levels of anthropogenic carbon emissions, especially in years of extreme fire activity (e.g. 2003, 2017). According to the equation first proposed by Seiler and Crutzen (1980), fire emission estimation use information on the amount of burned biomass, the emission factors associated with each specific chemical species, the burned area, and the combustion efficiency. Still, simulating emission from forest fires is affected by several errors and uncertainties, due to the different assessment approach to characterize the various parameters involved in the equation. For example, regional assessment relied on fire-activity reports from forest services, with assumptions regarding the type of vegetation burned, the characteristics of burning, and the burned area. Improvements and new advances in remote sensing, experimental measurements of emission factors, fuel consumption models, fuel load evaluation, and spatial and temporal distribution of burning are a valuable help for predicting and quantifying accurately the source and the composition of fire emissions.

With the aim to contribute to a better estimation of biomass burning emission, in this work we compared fire emission estimations using two different types of burned area products and combustion efficiency approaches in the framework of the recently developed system for modeling fire emission in Italy (Bacciu et al., 2012). This methodology combines a fire emission model (FOFEM - First Order Fire Effect Model, Reinhardt et al., 1997) with spatial and non-spatial inputs related to fire, vegetation, and weather conditions. The perimeters and burned area of selected large fires that occurred in 2017 in Italy were obtained by the former Corpo Forestale dello Stato (actually Carabinieri C.U.F.A.A.) and by the Copernicus Emergency Management Service (EMS). The vegetation types were derived from CORINE LAND COVER (2012). For each vegetation type, fuel loading was assigned using a combination of field observations and literature data (e.g., Mitsopoulos and Dimitrakopoulos 2007; Ascoli et al., 2019). Fuel moisture conditions, influencing the combustion efficiency, were derived from the daily Canadian Fine Fuels Moisture Code (FFMC), calculated from MARS interpolated weather data (25km x 25km). The daily FFMC was then associated with the two types of fire data with the aim of group fires in function of their relative ease of ignition and flammability of fine fuel (burning conditions, from low to extreme). For the EMS fire, it was also possible to further define fire severity and thus the percentage of combusted crown through the assessed fire damage grade.

The results showed differences in the total emissions according to the fire product and the approach to estimate the combustion efficiency. Furthermore, it seems that the difference in the evaluation of severity - and therefore in the degree of combustion of the canopy- affects more than the differences in terms of area burned. Overall, the results pointed out the crucial role of appropriate fuel, fire, and weather data and maps to attain reasonable simulations of fuel consumption and smoke emissions.

How to cite: Bacciu, V., Scarpa, C., Sirca, C., and Donatella, S.: Comparison of burned area mapping products and combustion efficiency approaches for estimating GHG and particulate emissions from Italian fires, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21939, https://doi.org/10.5194/egusphere-egu2020-21939, 2020.

Wind profile observations are used to estimate turbulent properties in the atmospheric boundary layer from 1 m up to 300 m height above north Florida pine woods. Basic turbulence characteristics of the lower boundary layer are presented. Together with theoretical models for the mean horizontal velocity we derive the lateral diffusivity using Taylor's frozen turbulence hypothesis in the surface fuel layer (tens of centimeters). This parameter is used to predict the spread of surface fires in a simple 1D model. Initial assessments of sensitivity of the fire spread rates to the lateral diffusivity are made. Estimated lateral diffusivity with and without fire are made and associated fire spread rates are explored. Our results support the conceptual framework that eddy dynamics in the fuel layer is set by larger eddies developed in the canopy layer aloft. The presence of fire modifies the eddy structure depending on the fire intensity.

How to cite: Bebieva, Y. and Speer, K.: Role of horizontal eddy diffusivity within the canopy on fire spread, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5934, https://doi.org/10.5194/egusphere-egu2020-5934, 2020.

In China, coal spontaneous combustion (CSC) is seriously disasters in gobs during coal seam groups mining, the secondary or multiple oxidation processes of residual coal occur inevitably, severely increasing the risk of coal fires. This paper focused on the thermal reaction behavior of two samples of raw coal and degrees of pre-oxidized (Oxi-80 °C, Oxi-130 °C, and Oxi-180 °C), we determined their characteristics of physical and chemical via thermogravimetric-Fourier transform infrared spectroscopy (TG-FTIR) with the heating rates being 1.0, 2.0, 5.0, and 10.0 °C min⁻¹. According to the characteristic temperature, the CSC process could be divided into three stages of oxidation (stage I), combustion (stage II), and thermal residual (stage III). The results indicated that for pre-oxidized coal the length of aliphatic side chains was shorter, and the number of branched aliphatic side chains was lower than that of the raw coal. The kinetic models revealed the mechanism category was changed between raw coal and pre-oxidized coal. However, the heating rate exerted little influence on the mechanism category of each stage, particularly in stage I. The average values of apparent activation energy for the pre-oxidized coal samples were lower than that of raw coal. Therefore, the pre-oxidized coal samples required less energy to activate and more readily caused spontaneous combustion than raw coal at certain stages.

How to cite: Deng, J., Lv, H.-F., Bai, L., and Li, D.: Comparative of thermokinetic behaviors and four functional groups variations from spontaneous combustion of coal and its pre-oxidized, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6645, https://doi.org/10.5194/egusphere-egu2020-6645, 2020.

The Third United Nations World Conference on Disaster Risk Reduction, held in Sendai in 2015, has defined a global strategy directed at enhancing risk-exposed communities’ resilience. In line with those needs, the study intends to improve and optimize decision-making processes in wildfire risk management by implementing predictive spatially distributed models of wildfire behaviour.

The proposed methodology has been applied to simulate some large and fully documented wildfire events in Umbria and Sardinia regions, in Central and Southern Italy respectively. The predictive model for wildfire behaviour is based on the reviewed Rothermel’s quasi-empirical mathematical model, which investigates propagation-driving parameters, i.e. the local geomorphometrical and meteorological parameters along with the pyrological and phenological characteristics of the local plant communities, to estimate the rate of spread of the fire. Propagation-driving parameters and their spatiotemporal variability have been estimated in the pre-fire environment by applying and adapting empirical relationships well-established in literature. Remote sensing-derived data have been analysed over phenologically distinct periods, along with ancillary data, to elicit information necessary to distinguish the mosaic of fuel model types and to monitor spatiotemporal variations in either live or dead fuel moisture content. According to input data availability, the methodology has been adapted to different case studies, focusing major attention on MODIS instrument by NASA on board the Terra satellite as well as on Sentinel constellations of satellites of the ESA Copernicus programme due to their accessibility and to their medium-high spatial and temporal resolution. A two-dimensional Agent-Based Model with a hexagonal grid, which, given a map of the rate of spread and an ignition point as inputs, returns a map of the cumulative propagation time, has been developed in order to simulate the wildland surface fire behaviour.

Satellite estimated propagation-driving parameters have been compared with information collected in the field and recorded by the regional annual reports on wildfire events, revealing a good predictive ability. Likewise, the wildfire behaviour model has provided accurate predictions, up to 70% in terms of morphological matching between obtained simulations and respective documented historical events boundaries, also if compared with results from other well-known wildfire simulation toolset and software. Obtained results suggest the developed wildfire behaviour model could represent a promising tool in prioritizing firefighting interventions in near-real time.

How to cite: Voltolina, D., Sterlacchini, S., Cappellini, G., Zazzeri, M., and Apuani, T.: Agent-based modelling for wildfire behaviour prediction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17948, https://doi.org/10.5194/egusphere-egu2020-17948, 2020.

The aim of this study is to assess wildland fire selectivity patterns in respect to topography in selected places in South Greece including eleven NUTS-3 counties of which two are islands, from 1984 to 2015. Fire scar perimeters within the time window 1984-2015 were delineated from freely available Landsat images from USGS and ESA archives and maps of fire frequency and fire return interval were finally created. Derived from eight different Landsat scenes (path/row), almost six thousands satellite images processed and more than five thousand and eight hundred fire perimeters were extracted, in order to reconstruct the fire history of South Greece, in a thirty two years’ period. Fireperimeters within each year of fire occurrence were compared against the available to burn under complete random processes to identify selectivity patterns in respect to topography.
It is clear that even though there is a decreasing trend in east, north east and south east facing aspects, fire selectivity in these areas is higher as compared to the available to burn. On the other hand there is a considerable rising in the trend of fire selectivity on west, southwest and northwest facing aspects. In terms of slope, lower- and mid-slopes tend to burn more than the available, opposite to upper- and higher –slopes. In addition, upper-elevation areas (over 800 meters), are negative related to wildfires while most of wildfires occur in altitude from 100 to 600 meters.

How to cite: Stamou, Z.: Forest fire selectivity patterns in respect to topography during the period 1984-2015 in selected places in Greece, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19642, https://doi.org/10.5194/egusphere-egu2020-19642, 2020.

Heterogeneous patterns of trees and openings in forests create a patchy fuel matrix that may burn with different fire severity, which can affect post-fire regeneration. Understanding how forest structures determines fire severity and whether fire severity metrics entails variability in such structures within a given category is important to improve our ability to assess post-fire forest development. Here, we assessed how fire severity changes the vertical and horizontal structure of trees and forest stands, and what are the main post-fire tree/forest structures associated with different fire severities. The study site was a large and mixed severity fire (3,217 ha) occurred in southeast Spain (Yeste, Albacete) in the summer of 2017. Pre-fire forest structures were estimated from LiDAR data (sensor ALS 50 – II) collected in 2016, with a theoretical laser pulse density between 0.5-2 returns m². Post-fire forest structures were estimated from LidarPod data (sensor Velodyne HDL-32e), with a laser pulse density of 312 returns m², collected in April 2018 (8 months after fire) at 3 burned sites plus one unburned control. Fire severity was estimated from the post-fire NBR (Normalized Burn Ratio) and other similar indices derived from Sentinel 2. We found that up to 5 post-fire tree classes and up to 4 forest stand structures were separable, each characterized by different heights, gap fractions, crown properties and fire intensities. There was not a one-to-one relationship between tree/forest structures and standard fire severity levels. The main changes in height, crown and other tree properties were highly correlated with post-fire tree structures and fire severity indices. Accordingly, the trees more severely burned were those with higher losses in height and crown area. Our results indicate that satellite fire severity metrics were highly related to biomass consumption; nonetheless, standard fire severity classifications included several tree/forest structures that without Lidar data it would be impossible to differentiate.

How to cite: Viedma, O. and Moreno, J. M.: Fire severity and tree/forest structures derived from pre- and post-fire LiDAR data in a large forest fire in SE Spain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21867, https://doi.org/10.5194/egusphere-egu2020-21867, 2020.