The Role of El Nino Variability and Peatland in Burnt Area and Emitted Carbon in Forest Fire Modeling

This study was conducted to model fire occurrence within El Nino variability and peatland distribution. These climate and geographical factors have a significant impact on forest fires in tropical areas such as Indonesia. The re-analysis dataset from ECMWF was observed with respect to climate characteristics in Indonesian El Nino events. The INFERNO (INteractive Fire and Emission algoRithm for Natural envirOnments) was utilized to simulate fires over Borneo Island due to its capability to simulate large-scale fires with simplified parameters. There were some adjustments in this INFERNO model, especially for peat fire as peatland has a significant impact on fires. The first was the contribution of climate to the peat fire which is represented by long-term precipitation. The second was the combustion completeness of peat fire occurrence that is mainly affected by human-induced peat drainage. The result of the model shows that El Nino variability mainly affected peat fires but was unable to well simulate the above-ground fire. It increased the burnt area during strong El Nino but overestimated the fires during low/no El Nino season due to lack of peat fire ignition in the calculation. Moreover, as the model did not provide peat drainage simulation, it underestimated the carbon emission. This model has shown promising results by addressing key features in limited input data, but improving some simulations is necessary for regulating weak/no El Nino conditions and carbon combustion of peat fire.

A data-driven prediction model for Fennoscandian wildfires

Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia, and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly 2001–2019 satellite-based fire occurrence dataset at a 0.25° spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian fire weather index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This improvement shows the potential of developing reliable data-driven prediction models for regions with a high quality fire occurrence record, and the limitation of using satellite-based fire occurrence data in regions subject to small fires not picked up by satellites. We conclude that data-driven fire prediction models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and its compound predictors can further be used to assess potential changes in fire danger probability under future climate scenarios.

Methodology for mapping the probability of fire occurrence in the Brazilian cerrado biome based on the danger of fire propagation variables

The area covered by the Brazilian cerrado biome has been greatly reduced in recent years due to the expansion of agricultural land and the increased number of fire outbreaks. The objective of this paper is to propose a methodology based on geospatial analysis and logistic regression analysis (LRA) for mapping the probability of fire occurrence in Brazilian cerrado conservation units. This model was applied in the Serra da Canastra National Park (SCNP) in the Southeast of Brazil. The methodology uses the maps of the following environmental variables, which are related to the danger of fire propagation: wind effect (WIN), terrain convexity (CVX), slope (SLO), drainage density (DRD), altitude (ELV), vegetation index (NDVI), and road density (ROD). The results of the LRA showed that the variables SLO, ELV, NDVI, ROD (p<0.0001), DRD (p=0.0005) and WIN (p=0.0007) contributed significantly to the occurrence of fire outbreaks. The model correctly classified 94.26% of cases. We conclude that this methodology can be used to inform the planning of firefighting actions in the Brazilian cerrado biome.

Fire hazard forecast by the regional climate change projection using the ETA model: a case study in Bahia, Brazil

This article proposes a method for predicting fire occurrence, considering regional climate change projection using the Eta model, with a 20 km resolution, for the RCP4.5 and RCP8.5 scenarios. Fire occurrence in the state of Bahia was calculated as a function of the three main sensitivity factors on a daily time-scale: days without precipitation, precipitation, and maximum temperature. Historical fire occurrences from 1998 to 2018 and meteorological data from 1960 to 2018 were obtained from official institutes, and weather forecast parameters from 2018 to 2050 were downscaled from the web platform PROJETA. The correlations between the meteorological factors and fire occurrence were calculated for the historical data and a weight factor corresponding to a control simulation. Afterwards, a correction factor was determined, based on the historical fire occurrence data used for the forecast in the two scenarios. The results indicate that between 2018 and 2050, risk of fire will have an average increase of 27% at the RCP4.5 and 38% at the RCP8.5 scenario.

Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness.

Assessment of fire hazard weather indices in the eastern Amazon: a case study for different land uses

ABSTRACT The fire frequency in the Amazon increased rapidly after the 1990s due to deforestation and forest degradation, and it is expected to increase in response to climate change. We analyzed the fire occurrence and assessed seven fire hazard indices in the municipality of Canaã dos Carajás, in the eastern Amazon, for different land use and land cover (LULC) types. We used data from three weather stations located at different heights to compare the performance of the indices using skill scores and success percentages for each LULC. Overall most hotspots occurred in deforested areas and native forests, which were the main LULC types, while few were observed in rupestrian fields, urban areas, and mining areas. However, forests presented the lowest number of hotspots per unit area, especially inside protected areas, and all hotspots in forest areas were observed after a severe drought in 2015. The performance of the fire indices varied as a function of the LULC class and the weather station considered, which indicates the importance of choosing the most appropriate location of the station according to the purpose of the monitoring. The Keetch-Byram Drought Index showed the best performance for predicting fire occurrence for all LULC classes, and forests and deforested areas individually. Despite its simplicity, the Angstrom index stood out due to its good performance in the prediction of days with more than six hotspots.

Developing fire danger models using logistic regression analysis for mid-hills of Himachal Pradesh

The present study intended to develop a climatic fire danger model for mid-hills zone of Himachal Pradesh using ten years weather data in relation with forest fire occurrence (2007-2016). Logistic regression technique was used to determine the relationship between fire occurrence and weather parameters viz., maximum temperature (°C), relative humidity (%), and wind speed (ms-1). The model was validated by calculating area under curve (AUC), coefficient of determination (R2) and root mean square Error (RMSE), with estimated values of 88.90%, 0.705 and 0.247, respectively. The fire danger model was verified with actual fire incidences in the study area during the year 2017. Wald's test was carried out to quantify impact climatic parameters on forest fire. Wald's test value was highest for maximum temperature (40.07) followed by relative humidity (1.15) and wind speed (0.75), respectively. In future such model can be utilized for prevention of forest fire hazards in the study area.

Risk-Oriented Model of the Distribution of Buildings (Structures) by Categories of Risk of Causing Harm (Damage) as the Result of Fire to Substantiate the Frequency of Scheduled Inspections of the Buildings (Structures)

Normative legal acts of the Russian Federation establish that the criteria for assigning control objects to the categories of risk of causing harm should be formed based on the results of the assessment of the risk of causing harm. In the developed countries of the world, as a rule, the distribution of objects of control by risk categories and the substantiation of the frequency of their inspections are carried out depending either on the point risk assessment, or on the number and importance of the violations of mandatory fire safety requirements revealed during the inspection of the object of control. The purpose of this work is to substantiate the frequency of scheduled inspections of the objects of control by the state fire supervision bodies depending on whether the objects of protection belong to a particular category of risk of causing harm. As a criterion for assigning control objects to various categories of risk of causing harm, it is proposed to use the risk of causing harm (damage) as the result of fire in the buildings of various classes of functional fire hazard, which is understood as the product of the probability of fire occurrence, the probability of causing socio-economic harm (damage) as the result of fire and the value terms of socio-economic harm (damage). A mathematical model was developed to determine the risk of causing harm (damage) as the result of a fire in a building, and an assessment of its values for the buildings of various classes of functional fire hazard is given. Distribution of the buildings by categories of risk of causing harm (damage) was carried out depending on the calculated value of the risk of causing harm. It is shown that the distribution of control objects by risk categories significantly depends on the degree of detail in the fire record card of the characteristics of the building in which the fire occurred. The optimal terms for carrying out scheduled inspections of the objects of control are proposed depending on the category of risk to which they are assigned. Proposals are formulated concerning the improvement of the risk-oriented approach in the activities of the state fire supervision bodies of the EMERCOM of Russia.