NH7.1

Wildfires are uncontrolled fires that can burn the forest and agricultural parcels, semi-natural areas and wildlands (e.g., forests, scrublands and abandoned agricultural areas). The wildfire-prone regions in the world range from tropical savannahs to boreal forests, characterized by factors and conditions required for fire activity. In the last 20 years, wildfires were the 5th costliest and the 6th most frequent type of disaster in the world. In the same period, Europe experienced a high number of wildfires and burnt areas, mainly concentrated in the Mediterranean basin and with increasing trends in countries such as Portugal. The fire incidence presents high spatial and temporal (intra- and inter-annual) variability patterns associated with human activities, including land use/land cover changes, extreme weather conditions, climate variability and climate changes.

The objectives of this study include the identification and characterization of the spatial and temporal variability of wildfire incidence, as well as its main drivers, in Portugal. The study uses the most recent fire and environmental databases, analyses wildfires with different causes (e.g., negligent and intentional wildfires) and, in particular, the influence of weather and climate variability and extremes, namely heat waves (HW) and droughts (D) on the occurrence of large fires.

Obtained results comprise the spatial and temporal patterns of wildfires and the assessment of the main drivers of wildfires. We conclude that extreme weather conditions (HW and D) can explain the spatiotemporal patterns of large wildfires, which are responsible for the vast majority of the total burned area. Nevertheless, other factors (such as vegetation type, topography, distance to roads) can explain part of the variability patterns. Our findings are fundamental for forest, landscape, and wildfire management, as they include the identification and characterization of the areas and frame periods where fires are more frequent and have a greater impact. Additionally, this information, together with the identification of the nature of the main danger factors, can support monitoring and forecasting systems, aiming at the development of strategies for wildfire prevention, preparedness and response activities, as well as adaptation to climate change.

Acknowledgements:

This work was financed by the National Funds through FCT - Foundation for Science and Technology under the project UIDB/04033/2020. This work was also supported by the project FRISCO - managing Fire-induced RISks of water quality Contamination (PCIF/MPG/0044/2018), and funding attributed to the CE3C research center (UIDB/00329/2020).

How to cite: Pereira, M., Parente, J., and Tonini, M.: Spatial and temporal characterization of wildfires, human and biophysical factors in Portugal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9277, https://doi.org/10.5194/egusphere-egu22-9277, 2022.

Wildfires are fundamental for maintaining ecosystem structure and functioning, and thus it is important to know how projected climate and land use changes will affect wildfire regimes globally. Fire-enabled vegetation models can be used to predict changes in fire regime but are still far from perfect since many of the processes that control different aspects of the fire regime are still relatively poorly understood. In this work, we investigated the underlying relationships between potential controls of different fire properties, including fire size, intensity and burnt area, at a global scale. We fitted three generalized linear models (GLM) to monthly data from 2010 to 2015 for fractional burnt area from the Global Fire Emissions Database version 4.1 (GFEDv4), fire size from the Global Fire Atlas database and median fire radiative power divided by square root of median fire size (as a proxy for fire intensity) from the MODIS MCD14ML dataset. We used partial residual plots between each predictor and each response variable to show the underlying linear relationships fitted by each model. We show that there are different controls on burnt area, on fire size and on fire intensity. Specifically, whilst burnt area is driven mainly by fuel availability and dryness, fire size is driven primarily by wind speed and fire intensity by tree cover and road density. Land fragmentation was highly limiting for fire size and burnt area whereas dryness was limiting for fire intensity. These findings suggest that it is possible to develop empirical models of multiple aspects of fire regimes which could be used to predict how these will change in the future. Furthermore, they highlight the importance of including landscape fragmentation as a control on fire explicitly within process-based fire models. Additionally, the limiting nature of dryness on fire intensity could be due to antecedent vegetation conditions and highlights the need for better representation of these conditions and their effect on fuel load. Finally, these results also suggest that the current treatment of ignition sources as an important driver in these models is unnecessary.

How to cite: Haas, O., Prentice, C., and P. Harrison, S.: Exploring Independent Spatial Controls on the Global Distribution of Burnt Area, Fire Size and Fire Intensity through Generalized Linear Modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3628, https://doi.org/10.5194/egusphere-egu22-3628, 2022.

In the last decades, Mediterranean Europe has been highly affected by wildfires. Larger wildfires and impacts occurred during and as a result of extreme fire weather, as observed in recent years. In the Iberian Peninsula, the influence of the fire weather on the fire incidence is particularly important, and the purpose of this study was to investigate in detail this relationship and its influence on the current and future fire regime.

The Daily Severity Rating (DSR) and the other indices of the Canadian Forest Fire Weather Index (FWI) System were computed using the ECMWF Reanalysis v5 (ERA5) and CORDEX atmospheric datasets. The meteorological variables needed to compute the FWI indices were the air temperature, relative humidity, wind speed and daily accumulated precipitation, at 12 UTC. We defined the Number of Extreme Days (NED) using extreme values of DSR and Drought Code and related them with the Normalized Burnt Area (NBA), loaded from Portuguese and Spanish wildfire official datasets. A cluster analysis was performed on NBA, revealing four pyro-regions characterized by different intra-annual variability of NBA. The strong link between the NED and the NBA intra-annual patterns was used to project the future pyro-regions, using a climate ensemble for two future scenarios.

Finally, we investigate the relationship between extreme wildfires and fire weather at a finer spatial scale in Continental Portugal, namely between extreme DSRp and large wildfires at the municipal level. We used weather data from ERA5 to compute DSR percentiles (DSRp) for an extended summer period (defined between 15th May and 31st October) and combine it with large (>100 ha) burnt areas (BA), with the purpose to identify the DSRp value responsible of a large amount of BA (80 or 90%) at the municipality level. A cluster analysis was performed using the relationship between DSRp and BA, in each municipality of Continental Portugal. Obtained clusters are distinguished by differences in land cover, revealing that higher (lower) DSRp is needed to explain the same high percentage of total BA when forest (scrublands) is the predominant affected vegetation type.

Our findings include recent changes in fire regimes in the recent past, a strong relationship between NED and NBA, that explain those observed changes and can be used to anticipate future fire regimes. Projected changes in NED suggest different future pyro-regions mapping in the Iberian Peninsula. Forest or shrublands prevalence has a significant influence on the spatial variability of the relationship between the extreme DSR threshold and most of total BA at the municipality level, particularly in Portugal.

How to cite: Calheiros, T., Pereira, M., Silva, J., Benali, A., and Nunes, J.: Present and future fire regime in Iberia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10173, https://doi.org/10.5194/egusphere-egu22-10173, 2022.

Records from the Forestry Bureau of Taiwan show that dozens of wildfire events occur every year in Taiwan. Furthermore, it is known that with climate change-induced extreme weather events, e.g., heatwaves and droughts, occurring more frequently, the wildfire occurrences are consequently increasing. Although the scale of wildfires in Taiwan is much smaller than in other places around the world, the potential harm caused by Taiwan’s wildfires is worth investigating due to the potential health issue and the safety concerns of wildfires.

Unfortunately, little has been done on the issue of wildfires in Taiwan. One of the difficulties of wildfire research in Taiwan can be attributed to the lack of forest areas data due to the limited number of observation stations. As a result, satellite data with more information on forest areas are used in this investigation to supplement the missing data and to complete the time series and variables. Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) is applied to identify the spatiotemporal distribution of variables, the meteorological factors affecting wildfires, and the wildfire influences on the vegetation of forests detected by satellite image time-series data. In the meantime, a time-frequency tool, Complementary Ensemble Empirical Mode Decomposition (CEEMD), is conducted to evaluate the trend and the variability of the time series of wildfire occurrences.

Wildfires can be lightning-caused and anthropogenic-caused. Therefore, to verify the intrinsic correlation between meteorological variables and wildfire occurrences, a scale- and time-dependent correlation approach, time-dependent intrinsic correlation (TDIC), is used. On the other hand, to estimate the impacts of wildfire, which may include air pollution, water quality, and health issues, time-dependent intrinsic cross-correlation (TDICC) is applied by considering the time lag effect.

This study aims at quantifying the time-lag correlation between wildfires and their potential effects on air pollutions, water quality, and health issues. Furthermore, the high-risk areas of wildfires in Taiwan are also identified. Meanwhile, the classical wildfire study case, California, USA, will be studied as a comparison case due to its large-scale wildfire, unique climate, and wildfire research. The results can serve as a reference for the Taiwan government to make decisions on management strategies referring to wildfire occurrences and livelihood problems.

How to cite: Tung, Y. and Tsai, C. W.: Spatiotemporal Analysis of the Causes and Effects of Wildfire by Landsat Imagery and in situ Data: Case studies of Taiwan and California, USA, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6999, https://doi.org/10.5194/egusphere-egu22-6999, 2022.

Despite recent climate change producing more favourable conditions for landscape fire in many regions, studies of remote sensing data have suggested that global burned area is declining. The reasons for this are poorly understood but land use change, landscape fragmentation and CO₂ fertilisation have all been suggested as contributing factors. Understanding human-fire interactions has been hampered by fragmentation of work across multiple disciplines – including geography, anthropology, land economics and ecology – and much case-study work in specific local locations. Consequently, coherent understanding of how contemporary anthropogenic land use and associated fire management strategies influence spatial and temporal patterns of fire globally has not yet been established.

To address this challenge, we have developed WHAM! – the Wildfire Human Agency Model - parameterised using the global empirical Database of Anthropogenic Fire Impacts (DAFI, [1]). This new model is driven by explicit representations of human behaviour, drawing on agent functional types to capture categorical differences in anthropogenic approaches to fire management globally. We present initial results and evaluate WHAM! using land management data based on the Human Appropriation of Net Primary Production (HANPP) and find good agreement between model outputs and these independent data. Further, to enable a like-for-like comparison with moderate resolution remote sensing products, we present a model emulator to screen model outputs of small agricultural fires (0.5-21 ha). 

We discuss how WHAM! shows land use intensification in South America, itself driven by increases in global demand for meat, has led to a substantial decline in anthropogenic fire use. This provides a partial process-based explanation of declines in global burned area observed from remote sensing. We discuss implications for understanding global spatio-temporal patterns of wildfire and share how fellow modellers can access model data and code. 

[1] Perkins and Millington (2021) DAFI: a global database of Anthropogenic Fire. Figshare. https://doi.org/10.6084/m9.figshare.c.5290792.v1 

How to cite: Perkins, O., Millington, J., Matej, S., and Erb, K.: Modelling spatial and temporal patterns of fire due to human activity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2462, https://doi.org/10.5194/egusphere-egu22-2462, 2022.

Fires can have anthropogenic or lightning origins. The spatiotemporal niches of anthropogenic and lightning fires are different. Lightning fires usually occur during a discrete apex in seasonal lightning occurrence. Conversely, anthropogenic fires have an expanded temporal niche and occur throughout the year. In addition, lightning and anthropogenic fires occupy different parts of the landscape. While human accessibility is a key determinant of anthropogenic ignitions, lightning ignitions prevail in remote landscapes.

We used these differing temporal and spatial niches between anthropogenic and lightning fires to construct random forest models that attribute causes, lightning vs. anthropogenic, to global fire activity. We built two separate models. The first model predicts the fraction of lightning fires, whereas the second model predicts the fraction of burned area from lightning. Our model ingests two geospatial predictor variables that quantify the differences between the temporal and spatial niches of lightning and anthropogenic fires. The first predictor is the seasonal correlation between lightning and burned area. The second predictor is the fraction of low impact land. These fire cause predictors capture 47 % of the spatial variability in ignition cause, and 40 % of the spatial variability in burned area cause, compared to reference data from six different parts of the world.

Our global fire cause attribution contrasts savannas and agricultural lands with human-dominated fire regimes from temperate and boreal forests with lightning-dominated fire regimes. Our global fire cause attribution can be implemented in fire and Earth system models to further optimize projections of future fire activity under changing socio-economic and climatological conditions.

How to cite: Veraverbeke, S., Finney, D., van der Werf, G., van Wees, D., Xu, W., and Jones, M.: Global attribution of anthropogenic and lightning fires, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1160, https://doi.org/10.5194/egusphere-egu22-1160, 2022.

Natural hazards often result from interacting physical processes across a wide range of spatial and temporal scales. Namely, the occurrence of heatwaves and droughts increases the risk of wildfires, with recurrent extensive human, ecological and economic losses being reported throughout the world. In Australia, several extreme bushfires have occurred following severe droughts and heatwaves, namely the Black Saturday bushfires in 2009 and the extreme bushfire season of 2019-2020.

In this work, we analyze the relation between fire occurrence, drought conditions, and temperature extremes in southeastern Australia for the period 1982-2018 and considering the months between December and February. Monthly burned area (BA) was retrieved from the FireCCILT11 dataset, with a spatial resolution of 0.25˚. The drought index SPEI was used to assess the drought conditions and was computed using monthly precipitation and temperature data from the CRU TS 4.05 database. The occurrence of temperature extremes was assessed using the index Number of Hot Days (NHD), which was computed using daily maximum temperature obtained from the ERA5 dataset.

The study area comprises pixels that have burned at least 25 times on these months. A correlation analysis was performed between BA and SPEI at time scales of 1, 3, and 6 months, and between BA and NHD. The influence of current and previous conditions on BA was assessed, by correlating BA with SPEI and NHD at the current month, and in the previous 1 to 3 months. The joint probability of BA, drought, and temperature extremes was also assessed, using copula functions.

The results show a negative correlation between BA and SPEI, and a positive correlation between BA and NHD. For previous months, the correlation is stronger between BA and SPEI, than for BA and NHD, pointing to an effect of the drought conditions on previous months, whereas the effect of temperature on BA is seen instantaneously. The probabilistic analysis shows a clear increase in the probability of BA exceeding the 80th percentile, given drought conditions, compared to non-drought conditions. A similar result was obtained for the case of extreme temperature.

Acknowledgements: This study was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under projects FIRECAST (PCIF/GRF/0204/2017) and Floresta Limpa (PCIF/MOG/0161/2019); and the 2021 FirEUrisk project, funded by the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement no. 101003890.

How to cite: Páscoa, P., Gouveia, C., Russo, A., and Ribeiro, A.: The effect of drought and temperature extremes on burned area in Southeastern Australia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8295, https://doi.org/10.5194/egusphere-egu22-8295, 2022.

Fire is a major disturbance mechanism in arctic-boreal ecosystems and results in warming and cooling feedbacks to the climate system. Greenhouse gas emissions from fires are a major positive feedback, yet post-fire carbon sequestration in recovering ecosystems partly offsets this. In addition, fire removes part of the organic soil layer and may result in permafrost thaw and consequent greenhouse gas emissions. Yet, fire-induced changes in ecosystem structures result in a larger spring-time snow cover compared to unburned areas, and this imposes a negative climate feedback through increased surface albedo. These various climate forcings are spatially and temporally heterogeneous and depend on various landscape components and fire regime characteristics. Understanding the net climate forcing effect is crucial in managing and mitigating climate change impacts on carbon cycling. We applied the concept of radiative forcing in a quantitative spatial assessment of the net climate feedbacks induced by arctic-boreal North American fires. We capitalize upon the state-of-the-art carbon combustion estimates by the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED) and a novel climate forcing framework to predict fire-driven changes in net forcing under historical and future climate scenarios. In our analyses we incorporated all fires between 2001 and 2019, evaluating the net fire-induced forcing over the regrowth successional phase (at 20-years after fire) and after full succession (at 80-years after fire). Our results highlight the spatial and temporal heterogeneity in climate forcings from arctic-boreal fires, and in future work we plan to characterize spatiotemporal patterns of the net climate feedback.

How to cite: van Gerrevink, M. J., Veraverbeke, S., Cooperdock, S., Potter, S., Moubarak, M., Goetz, S. J., Mack, M. C., Randerson, J. T., Turetsky, M. R., and Rogers, B. M.: Integrated Climate Radiative Forcing from Arctic-Boreal Fires, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4696, https://doi.org/10.5194/egusphere-egu22-4696, 2022.

The canopy of trees plays a very important role in forest ecosystems and acts as a regulator, as it is a factor that affects the microclimate and the soil conditions. The density of the forest canopy is associated with forest development, and it is a factor that can indicate the degree of forest degradation. Additionally, forest density is one of the most important parameters, used in the design and implementation of programs for forest restoration, especially in cases of areas affected by fires. The main objective of this study is to determine the disturbance that occurred in the canopy density after the fire events that occurred in Argaka and Solea villages in June 2016 at Paphos forest and Adelfoi forest respectively, in Cyprus. For the purposes of this study, the Forest Canopy Density model (FCD model) was estimated using the Landsat-8 satellite data. Moreover, this study aimed to evaluate the FCD using Sentinel-2 data. The results obtained from Sentinel-2 seem to be very promising and the calculation of the canopy density through this study is achieved in a better resolution, in contrast to the analysis available by the Landsat-8 satellite. This work has been supported by the project ‘ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment-EXCELSIOR’ (https://excelsior2020.eu/) that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857510 (Call: WIDESPREAD-01-2018-2019 Teaming) and from the  Government of the  Republic of  Cyprus through the Directorate-General for the  European  Programmes,  Coordination, and  Development.

How to cite: Prodromou, M., Gitas, I., Themistocleous, K., and Hadjimitsis, D.: The implementation of the Forest Canopy Density (FCD) model for Coniferous ecosystems in Cyprus forests, using Landsat-8 and Sentinel-2 satellite data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9865, https://doi.org/10.5194/egusphere-egu22-9865, 2022.

Wildfire propagation is a non-linear and multiscale phenomenon in which there are involved multiple physical and chemical processes. One critical mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon which occurs when embers are transported by wind causing the start of new spotting ignitions. Due to its nature, fire-spotting is usually implemented into the fire spread models as a pure probabilistic process regardless the existing physical conditions when the phenomenon occurs. In this work, we have implemented the physical parametrization of fire-spotting RandomFront (Trucchia et al., 2019) into the stochastic operational fire spread software PROPAGATOR (Trucchia et al., 2020), based on cellular automata approach. The research has been conducted in two objetives: (i) To study the impact of macroscale (Egorova et al., 2020) and mesoscale factors (Egorova et al., 2022) over the spot fires generation and its influence over the Rate of Spread within the cellular automaton framework and (ii) compare these results against those by the pure probabilistic model of fire-spotting previously used in literature (Alexandridis et al., 2008), which was explicitly developed in the framework of wildfire spread simulators based on cellular automata. The preliminary results show how the RandomFront parameterization can reproduce the same areas of maximum probability as the model we are comparing but is able to assign a non-zero burning probability to larger areas. The observed long-range fluctuations of the burning probability within RandomFront parametrization create a complex pattern of fire spread for middle and low burning probability areas which is not observed within the Alexandridis et al. (2008) parametrization.

Refrerences:

Alexandridis, A., Vakalis, D., Siettos, C. I., and Bafas, G. V.: A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990, Appl. Math. Comput., 204, 191–201, https://doi.org/10.1016/j.amc.2008.06.046, 2008.

Egorova, V. N., Trucchia, A., and Pagnini, G.: Fire-spotting generated fires. Part I: The role of atmospheric stability, Appl. Math. Model., 84, 590–609, https://doi.org/10.1016/j.apm.2019.02.010, 2020.

Egorova, V. N., Trucchia, A., and Pagnini, G.: Fire-spotting generated fires. Part II: The role of flame geometry and slope, Appl. Math. Model., 104, 1–20, https://doi.org/10.1016/j.apm.2021.11.010, 2022.

Trucchia, A., Egorova, V., Butenko, A., Kaur, I., and Pagnini, G.: RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models – implementation in WRF-SFIRE and response analysis with LSFire+, Geosci. Model Dev., 12, 69–87, https://doi.org/10.5194/gmd-12-69-2019, 2019.

Trucchia, A., D’Andrea, M., Baghino, F., Fiorucci, P., Ferraris, L., Negro, D., Gollini, A., and Severino, M.: PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator, Fire, 3, 26, https://doi.org/10.3390/fire3030026, 2020.

How to cite: López-De-Castro, M., Trucchia, A., Fiorucci, P., and Pagnini, G.: A comparison study between fire-spotting models by a wildfire simulator based on a cellular automata approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4600, https://doi.org/10.5194/egusphere-egu22-4600, 2022.

In recent decades, the frequency of wildfire incidents has increased worldwide. These ecological disasters, often triggered by natural and/or anthropogenic factors, can have long-lasting effects on the hydrologic systems, ecosystems, environment, and biodiversity. They alter the land use conditions and thereby increase the chances of floods, soil and nutrient loss, and groundwater deficit. Wildfires are not uncommon in India, whose total forest cover exceeds 0.7 million km², about 21.67% of its total geographical area.  This study is motivated to identify the extreme events whose concurrence has led to forest fires in different parts of the Indian peninsula and to assess their impact on hydrological processes in river basins. The latter is demonstrated by analyzing a recent (Feb 2021) fire event in Simlipal National Park (Odisha). The park is a part of the UNESCO world network of biosphere reserves, and a major part of it lies in Budhabalanaga river basin. The wildfire event was preceded by a prolonged dry spell and a below-average monsoon in the year 2020. Two widely used hydrological models, SWAT (Soil Water Assessment Tool) and HEC-HMS are considered for simulating streamflows in the study area. The models are calibrated and validated using a variety of statistical performance measures. Furthermore, hydrological processes in the study area are simulated, corresponding to two different post-wildfire scenarios (optimistic and non-optimistic). A significant rise in streamflow is observed in both cases, indicating the possibility of flash floods in the downstream areas. It is concluded that the conjunctive use of models for wildfire prediction and hydrological simulation provides information to policize better fire risk mitigation strategies.

How to cite: L V Prasad, B. and V Vemavarapu, S.: Impact of Wildfire-related Compound extreme events on hydrology - A case study of Simlipal National Park, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-441, https://doi.org/10.5194/egusphere-egu22-441, 2022.

Wildfire events are increasing globally due to climate change, with significant adverse impacts on regional air quality and global climate. In the middle of September 2020, a wildfire event occurred in Souesmes (Loir-et-Cher, France), and its plume spread out to 200 km around in the following day as observed by the MODIS satellite. Based on comprehensive field measurements at a suburban atmospheric observation site (~50 km northwest from the wildfire location) in Orléans, young fire plumes were identified. Significant increases in trace gases (CO, CH₄, N₂O, VOCs, etc.) and particles (including black carbon) were found within the BB plumes. Molar enhancement ratios, defined as EF (X) = ∆X/∆CO (where X represents the target species), of various trace gases and black carbon within young plumes were determined accordingly and compared with previous studies. Changes in the ambient ions (ammonium, sulfate, nitrate, chloride, nitrite, etc. in the particle- and gas-phase) and aerosol properties (e.g., aerosol water content, pH) were also quantified and discussed. Furthermore, along with trajectory model (FLEXPART) simulations, we found that the Global Fire Assimilation System (GFAS) may underestimate emissions (e.g., CO) of this small wildfire while other inventories (GFED, FINN) showed significant overestimation. Estimation of emissions of this fire event was conducted and compared with GFAS emissions. Related atmospheric implications are also presented and discussed.

How to cite: Xue, C., Krysztofiak, G., Ren, Y., Cai, M., Grosselin, B., Daële, V., Mellouki, A., and Catoire, V.: Wildfire Emissions and Air Quality: A Case Study on Forest Fires in Southern Orléans, France, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1674, https://doi.org/10.5194/egusphere-egu22-1674, 2022.

Wildfires are a serious social and environmental issue in the Mediterranean basin, menacing human lives, infrastructures and ecosystems. Italy, due to land cover, orography  and climate, expresses a complex wildfire regime that is worth investigating. Static maps, such as susceptibility, hazard and risk maps, are valid allies for wildfire management and land use planning. In particular, the wildfire susceptibility is defined as the spatially distributed probability of experiencing wildfire at a certain point, depending only on  the intrinsic characteristics of the territory. In the presented work, a Machine Learning  (ML) model  is built following a similar approach of [1], to produce different National Scale susceptibility maps for Italy. The adopted algorithm is Random Forest, an ensemble ML method.

Since Italy exhibits two different wildfire seasons, the summer and the winter one, two maps are produced, to identify the different regimes. The presented analysis at the national scale allows the experts and the decision makers to have a deep understanding on the wildfire regimes, and may constitute a solid paradigm for wildfire risk management. The Random Forest associated  a data-set of geographic (orography, land cover), anthropic (distance from crops, roads and urban features) and climatic information (mean precipitation and temperature) to the database of ground-retrieved burned area polygons.  The classifier is then employed to evaluate each pixel of the study area, producing the susceptibility map. The performance of the adopted frameworks are evaluated via spatial cross validation and the evaluation of mean squared error and Area Under the RoC curve  on a test dataset.  A subsequent analysis of the importance of each input factor through the Gini impurity method allows to spot the most important variables, paving the way for further improvements in the dataset.

References

[1] Tonini, M.; D’Andrea, M.; Biondi, G.; Degli Esposti, S.; Trucchia, A.; Fiorucci, P. A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences 2020, 10, 105. https://doi.org/10.3390/geosciences10030105 

How to cite: Trucchia, A., Meschi, G., Tonini, M., and Fiorucci, P.: Computing wildfire Susceptibility Maps at the national level in Italy: a Machine Learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3981, https://doi.org/10.5194/egusphere-egu22-3981, 2022.

Large landscape fires in 2015 and 2020 in the Chornobyl Exclusion Zone (CEZ), that burnt in total more than 82 thousand ha of highly radioactive forest lands all over the territory, including Red Forest, located near the Unit 4 Confinement, posed a significant threat for health of fire fighters who participated in the suppression and other personnel of the Zone. Burning of forest fuel contaminated with six radionuclides generated smoke that migrated far beyond borders of the Exclusion Zone with prevailing winds towards populated areas. Future uncertainties caused by climate change require risk assessment for development of fire resilient landscape and risk-based integrated fire management system.            

To improve fire prevention in CEZ we have developed a web-based framework for assessing the potential risk of a wildfire that integrates weather data, ignition likelihood, models burn probability, contamination by radionuclides, and available firefighting resources. We combined available field sampling and forest inventory data to parametrize our fuel models. Landsat time series were used for mapping the seasonal pattern of fuels distribution, which conforms to landscape flammability. Canopy fuels were predicted using machine learning models and remote sensing data. We calibrated surface and canopy fuel metrics so that the perimeters of the largest wildfires matched those simulated using the FARSITE fire modelling system based on hourly weather data (i.e., wind speed, wind direction, precipitation etc.).

For modelling of the current risk of fires according to fire weather parameters, the relations of the area and number of fires (according to the MODIS MCD64A1 product) and the modified for Ukraine PORTU fire weather index were calculated on the basis of historical meteorological data for the period from 2010 to 2020 for CEZ. Python scripts have been developed, in order to automatically download fire weather data several times per day and calculate PORTU index in 16 km grid cells.

The research in CEZ funded by European Union’s Horizon 2020 Program within the project FirEUrisk “Development a holistic, risk-wise strategy for European wildfire management” (GA 101003890).      

How to cite: Myroniuk, V., Zibtsev, S., G. Goldammer, J., Bogomolov, V., Borsuk, O., Soshenskii, O., Gumeniuk, V., and Zibtseva, E.: Fire risk assessment for prevention improvement in the Chornobyl exclusion zone, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13368, https://doi.org/10.5194/egusphere-egu22-13368, 2022.

Scots pine forests that make up 33% of the total forest area of Ukraine (9,4 million ha) that 
are represented mostly by single species and planted stands, have low resilience to climate 
change, fires and insects. More than 180 000 ha of pine forests were burned within 5 fire 
episodes in northern and south-eastern regions of the country during extremely dry fire 
season of 2020. In Luhansk oblast 16 civilians died, 54 were injured, 22 villages and hundreds 
of houses were burned or damaged because of July and October 2020 fires. Climate change 
uncertainties and numerous ignition sources in the landscapes require development and 
implementation of long-term strategy towards building fire resilient landscapes and fire 
resilient communities. 
National Strategy of Integrated Landscape Fire Management in Ukraine was developed by 
joint research team of the Regional Eastern Europe Fire Monitoring Europe and the Global 
Fire Monitoring Center for defining approaches and stakeholders as well as institutional 
arrangement of fire resilient landscape and community concept implementation. The 
Strategy was approved by the Ministry of Environmental Protection and Natural Resource 
Management of Ukraine and publicly discussed. 
Silvicultural intervention and fuel treatment methods were tested experimentally in pine 
forests of Ukraine within implementation of the RESILPINE project supported by the German 
Federal Ministry for Food and Agriculture (BMEL) / German Federal Agency for Agriculture 
and Food (BLE). In particular, fire resilient forest edges on territories with high ignition 
probability near agricultural fields and lowlands were established via planting birch, apple 
tree, pear, lime tree in Boyarka Forest Experimental Station, Osterskii Military Forestry and 
Teteriv Forestry Enterprise. Formation of open fire resilient structure in 60-year-old pine 
forests via heavy thinning (40%) of overcrowded stands and prescribed burning of ground fuel 
on southern and south-eastern vicinity of villages Kudriashivka and Varvarivka of Luhansk 
Oblast that were threatened by fires in 2020. Oblasts were justified and prepared for spring 
2022. Preliminary recommendations for state forest enterprises on increasing fire resilience 
of pine forests were presented and approved by scientific-technical council of the State 
Agency of Forest Resources of Ukraine.

How to cite: Zibtsev, S., Georg Goldammer, J., Soshenskii, O., and Gumeniuk, V.: Transformation of Forests to Close-to-Nature Forest Management in Ukraine: Nature-based silvicultural and fire management methods for increasing the resilience of pine stands to drought and wildfire, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13361, https://doi.org/10.5194/egusphere-egu22-13361, 2022.

The devastating fires that in 2020 burned more than 3.9 million hectares in the Brazilian Pantanal, the largest tropical wetland in the world, were the result of a complex interplay among human activity, landscape characteristics, and meteorological conditions, and have deepened the concerns about the future of that unique region of the world.

The meteorological component has played a prominent role in 2020, Pantanal having been affected by the most extreme drought since 1950 and by long periods of extremely high temperature. Both factors, combined with fire ignitions, mostly related to human activities, have contributed to the onset of large fire events that spread over water-stressed vegetation.

The aim of the present work is to set up a statistical model that is able to provide reliable forecast of probability of occurrence of a wildfire, taking into consideration both the longer and shorter effects of atmospheric conditions on vegetation stress and, provided an ignition has occurred, on the building up and spreading of a wildfire.

Fire data cover the period 2001-2020 and consist of Fire Radiative Power (FRP) as acquired by the MODIS instrument on-board Aqua and Terra Satellites. Meteorological fire danger was characterized by the Fire Weather (FWI) data covering the same period from the Copernicus Emergency Management Service.

Statistical models used in this study combine a lognormal distribution central body with a lower and an upper tail, both consisting of Generalized Pareto (GP) distributions, and daily FWI is used as a covariate of the parameters of the lognormal and the two GP distributions. First a base model (with fixed parameters) is fitted to the decimal logarithm of FRP, and the quality of fit is assessed using an Anderson-Darling test. Then the model is improved using FWI as a covariate, and performances of models without and with covariate are compared by computing the Bayes Factor as well as by applying the Vuong’s closeness test.

Statistical models were developed for the nine hydrological subregions of Pantanal using data for the period 2001-2019. Five classes of meteorological fire danger were then defined based on probabilities of exceedance of predefined values of FRP. The procedure was then separately applied to the extreme year of 2020.

The developed procedure is on the basis of an operational early warning system of fire danger in Pantanal that is currently being set up.

This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017), and by the State Public Prosecutor's Office of Mato Grosso do Sul.

How to cite: Nunes, S. A., Belém, L. B. C., Libonati, R., and DaCamara, C. C.: An early warning system of fire danger for the Brazilian Pantanal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12293, https://doi.org/10.5194/egusphere-egu22-12293, 2022.