Fire Drivers Research Articles

Community-Based Water Management in Thailand Toward Wildland Fire and Smoke Haze Issue in the Upper Asean

Community-based Water Management (CBWM) in Thailand is fundamentally grounded in the Sufficiency Economy Philosophy (SEP), a concept introduced by King Rama IX in 1974. This approach highlights the importance of self-reliance, active community participation, and the development of locally adapted solutions to foster disaster resilience and promote environmental sustainability. Community-based Fire and Water Management (CBFWM) represents an integrated strategy designed to address environmental challenges, particularly wildland fires and smoke haze, which are prevalent in the Upper ASEAN region, specifically within the Lower Mekong River Basin. By combining water and fire management, this holistic approach not only strengthens community resilience to climate change but also enhances local livelihoods in a sustainable manner. Given that social issues often serve as the primary drivers of wildland fires, this approach provides strategies focused on building community capacity to independently maintain ecosystem sustainability while addressing economic and environmental challenges. Keywords: Community-based, Sufficiency Economy Philosophy (SEP), holistic approach, Upper ASEAN.

Disentangling the Roles of Climate Variables in Forest Fire Occurrences in China

In the context of global warming, climate strongly affects forest fires. With long-term and strict fire prevention policies, China has become a unique test arena for comprehending the role of climatic variables in affecting forest fires. Here, using GIS spatial analysis, Pearson correlation, and geographical detector, the climate drivers of forest fires in China are revealed using the 2003–2022 active fire data from the MODIS C6 and climate products from CHELSA (Climatologies at high resolution for the Earth’s land surface areas). The main conclusions are as follows: (1) In total, 82% of forest fires were prevalent in the southern and southwestern forest regions (SR and SWR) in China, especially in winter and spring. (2) Forest fires were mainly distributed in areas with a mean annual temperature and annual precipitation of 14~22 °C (subtropical) and 800~2000 mm (humid zone), respectively. (3) Incidences of forest fires were positively correlated with temperature, potential evapotranspiration, surface downwelling shortwave flux, and near-surface wind speed but negatively correlated with precipitation and near-surface relative humidity. (4) Temperature and potential evapotranspiration dominated the roles in determining spatial variations of China’s forest fires, while the combination of climate variables complicated the spatial variation. This paper not only provides new insights on the impact of climate drives on forest fires, but also offers helpful guidance for fire management, prevention, and forecasting.

Assessing drivers of vegetation fire occurrence in Zimbabwe - Insights from Maxent modelling and historical data analysis

Assessing drivers of vegetation fire occurrence in Zimbabwe - Insights from Maxent modelling and historical data analysis

Protected areas, drought, and grazing regimes influence fire occurrence in a fire-prone Mediterranean region

BackgroundExtreme fire seasons in the Mediterranean basin have received international attention due to the damage caused to people, livelihoods, and vulnerable ecosystems. There is a body of literature linking increasingly intense, large fires to a build-up of fuel from rural land abandonment exacerbated by climate change. However, a better understanding of the complex factors driving fires in fire-prone landscapes is needed. We use a global database based on the MODIS Fire CCI51 product, and the Greater Côa Valley, a 340,000-ha area in Portugal, as a case study, to investigate the environmental drivers of fire and potential tools for managing fires in a landscape that has undergone changing agricultural and grazing management.ResultsBetween 2001 and 2020, fires burned 32% (1881.45 km2) of the study area. Scrublands proportionally burnt the most, but agricultural land and forests were also greatly impacted. The risk of large fires (> 1 km2) was highest in these land cover types under dry conditions in late summer. Areas with higher sheep densities were more likely to burn, while cattle density had no apparent relationship with fire occurrence. There was also a 15% lower probability of a fire occurring in protected areas.ConclusionFuture climatic changes that increase drought conditions will likely elevate the risk of large fires in the Mediterranean basin, and abandoned farmland undergoing natural succession towards scrubland will be at particularly high risk. Our results indicate that livestock grazing does not provide a simple solution to reducing fire risk, but that a more holistic management approach addressing social causes and nature-based solutions could be effective in reducing fire occurrence.

Forest Fire Prediction: A Spatial Machine Learning and Neural Network Approach

The study of forest fire prediction holds significant environmental and scientific importance, particularly in regions like South Carolina (SC) with a high incidence rate of forest fires. Despite the limited existing research on forest fires in this area, the application of machine learning and neural network techniques presents an opportunity to enhance forest fire prevention and control efforts. Utilizing data of forest fire from the SC Forestry Commission for the year 2023, prediction models were developed incorporating various factors such as meteorology, terrain, vegetation, and infrastructure—key drivers of forest fires in SC. Feature importance analysis was employed to construct the final fire prediction model using different machine learning and neural network approaches including Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Correlation coefficients analysis was employed to construct the final fire hazard map using a correlation test. The evaluation of predictive performance based on accuracy scores revealed that the DT model achieved the highest accuracy of 90.58%, surpassing other models. However, based on the kernel density map of the fire data from 2000 to 2023, the correlation test gave the better fire hazard map compared to any other machine learning or neural network approach that utilized feature importance. Nonetheless, all models achieved prediction accuracies exceeding 80%. This finding directed us to the approach based on the correlation coefficients rather than to those just based on feature importance. The overlap between fire locations and carbon hotspots provided the immediate need to mitigate the carbon loss due to fire in those locations. These results serve as a valuable resource for forest fire prediction in SC, demonstrating the efficacy of the correlation test, providing a theoretical foundation and data support for future forestry applications in the region, and showing the outperforming capability of this method compared to other approaches based on feature importance and the importance to prioritize areas to mitigate the climate change impact based upon fire prediction.

A global behavioural model of human fire use and management: WHAM! v1.0

Abstract. Fire is an integral ecosystem process and a major natural source of vegetation disturbance globally. Yet at the same time, humans use and manage fire in diverse ways and for a huge range of purposes. Therefore, it is perhaps unsurprising that a central finding of the first Fire Model Intercomparison Project was simplistic representation of humans is a substantial shortcoming in the fire modules of dynamic global vegetation models (DGVMs). In response to this challenge, we present a novel, global geospatial model that seeks to capture the diversity of human–fire interactions. Empirically grounded with a global database of anthropogenic fire impacts, WHAM! (the Wildfire Human Agency Model) represents the underlying behavioural and land system drivers of human approaches to fire management and their impact on fire regimes. WHAM! is designed to be coupled with DGVMs (JULES-INFERNO in the current instance), such that human and biophysical drivers of fire on Earth, and their interactions, can be captured in process-based models for the first time. Initial outputs from WHAM! presented here are in line with previous evidence suggesting managed anthropogenic fire use is decreasing globally and point to land use intensification as the underlying reason for this phenomenon.

Examining the effects of climate change and human impacts on a high-resolution, late Holocene paleofire record from South Africa's winter rainfall zone

Examining the effects of climate change and human impacts on a high-resolution, late Holocene paleofire record from South Africa's winter rainfall zone

Natural or man-made? Finding the drivers of forest fires within the protected area habitats: a case study from the lower Shivalik Landscape, Western Himalaya

Natural or man-made? Finding the drivers of forest fires within the protected area habitats: a case study from the lower Shivalik Landscape, Western Himalaya

Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm

Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted regression (GTWR) method in conjunction with a refined continuous invasive weed optimization (CIWO) algorithm to assess certain spatially relevant drivers of forest fires, encompassing both biophysical and anthropogenic influences. Our proposed approach demonstrates theoretical utility in addressing the spatial regression problem by meticulously accounting for the autocorrelation and non-stationarity inherent in spatial data. We leverage tricube and Gaussian kernels to weight the GTWR for two distinct temporal datasets, yielding coefficients of determination (R2) amounting to 0.99 and 0.97, respectively. In contrast, traditional geographically weighted regression (GWR) using the tricube kernel achieved R2 values of 0.87 and 0.88, while the Gaussian kernel yielded R2 values of 0.8138 and 0.82 for the same datasets. This investigation underscores the substantial impact of both biophysical and anthropogenic factors on forest fires within the study areas.

Unrecorded Tundra Fires in Canada, 1986–2022

Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30%. The median fire size was small, with roughly 3/4 of the fires being <100 ha in size. The majority of newly detected fires (69%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.

Peat fires in Brunei Darussalam: considerations for ASEAN haze cooperation and emerging regional infrastructure development

This paper sheds light on the extent of the haze problem in Brunei Darussalam and on Brunei's unique position in contributing to the haze through fires occurring in disturbed parts of its peatlands. Brunei's peatland fires, which have their roots in infrastructure development, juxtapose drastically with the drivers of peat fires in other parts of southern Southeast Asia, which are mainly due to small‐ or large‐scale agriculture development. Our discussion highlights how Brunei's status as both a small state in ASEAN and a minor producer of smoke haze has resulted in Brunei remaining at the sidelines of haze diplomacy and cooperation at the ASEAN level. Further, the paper points out a lack of attention to the role of infrastructure development on peatlands in driving fires and haze in the country and how this is also increasingly becoming an issue in neighbouring countries, where massive infrastructure projects are underway, cutting through Borneo's peatlands.

Shoot flammability differences between forest and savanna trees are driven by leaf dry matter content

Shoot flammability differences between forest and savanna trees are driven by leaf dry matter content

Effects of Bush Fires on Biodiversity in West Africa Sahel: A Review

Bush fire is one of the drivers of biodiversity loss worldwide. However, the impact of bush fires on biodiversity in the West Africa Sahel is not well documented. Therefore, this study reviewed the effects of bush fires on biodiversity, the typology of the bush fire drivers and bush fires solutions in the West Africa Sahel via a systematic review. The authors used many research engines such as Google Scholar and Mendeley from 2010 to 2022 with some keywords in French and English. It comes from the analysis of the data that Mali is the most country affected by bush fires with an average of 35,000,000 ha burned. In Burkina Faso, bush fires burned more than 2 million hectares each year. The analysis showed also a loss of 1,675,157 ha in Niger and 56,568.10 ha in Senegal. The study recommends that climate actions should target bush fires prevention and fighting as climate response in order to promote sustainable biodiversity managementin the West Africa Sahel. The study recommends also that West Africa Sahel countries develop bushfire community education programs for fire prevention.

System-level feedbacks of active fire regimes in large landscapes

BackgroundClimate is a main driver of fire regimes, but recurrent fires provide stabilizing feedbacks at several spatial scales that can limit fire spread and severity—potentially contributing to a form of self-regulation. Evaluating the strength of these feedbacks in wildland systems is difficult given the spatial and temporal scales of observation required. Here, we used the REBURN model to directly examine the relative strengths of top-down and bottom-up drivers of fire over a 3000-year simulation period, within a 275,000-ha conifer-dominated landscape in north central Washington State, USA.ResultsWe found strong support for top-down and bottom-up spatial and temporal controls on fire patterns. Fire weather was a main driver of large fire occurrence, but area burned was moderated by ignition frequencies and by areas of limited fuels and fuel contagion (i.e., fire fences). Landscapes comprised of >40% area in fire fences rarely experienced large fire years. When large fires did occur during the simulation period, a recovery time of 100–300 years or more was generally required to recover pre-fire vegetation patterns.ConclusionsSimulations showed that interactions between fire weather, fuel contagion, topography, and ignitions manifest variability in fire size and severity patch size distributions. Burned and recovering vegetation mosaics provided functional stabilizing feedbacks, a kind of metastability, which limited future fire size and severity, even under extreme weather conditions. REBURN can be applied to new geographic and physiographic landscapes to simulate these interactions and to represent natural and culturally influenced fire regimes in historical, current, or future climatic settings.

Restoring fire‐degraded social forests via biocultural approaches: a key strategy to safeguard the Amazon legacy

Wildfires have emerged as an escalating source of tropical forest degradation with a cascade of negative consequences for the sustainable development of tropical regions. This is particularly the case in the Amazon forest region as wildfires have already degraded a considerable extension of both secondary and old‐growth forests, including “social forests” managed by traditional communities into protected areas. Here, we advocate in favor of a neglected perspective recognizing wildfires as an escalating threat of unanticipated but certainly devasting impacts on social forests, communal territories, the social reproduction of local/traditional forest‐based communities, and the network of protected areas devoted to safeguard the Amazon forest legacy, including ecosystem services of global relevance such as climate regulation. In response to this prognosis, we call for a research agenda focused on fire drivers, impacts, management, and mitigation, including the establishment of biocultural restoration initiatives/pilot programs in communal territories. Initiatives should integrate co‐produced goals, approaches, and technology to economically, socially, and politically empower those stakeholders with an effective interest in forest persistence although they are already vulnerable. Finally, we propose a governance model connecting key stakeholders to scale up fire‐related biocultural restoration in the Amazon region with the potential to produce timely and globally relevant knowledge and lessons. We hope our ideas stimulate key stakeholders to promote biocultural initiatives able to guarantee the emergence of sustainable territories and thus safeguard the climate‐buffering Amazon forest and its legacy for the sake of both local and global society.

Deforestation as the Prominent Driver of the Intensifying Wildfire in Cambodia, Revealed through Geospatial Analysis

Cambodia has the most fires per area in Southeast Asia, with fire activity have significantly increased since the early 2000s. Wildfire occurrences are multi-factorial in nature, and isolating the relative contribution of each driver remains a challenge. In this study, we quantify the relative importance of each driver of fire by analyzing annual spatial regression models of fire occurrence across Cambodia from 2003 to 2020. Our models demonstrated satisfactory performance, explaining 69 to 81% of the variance in fire occurrence. We found that deforestation was consistently the dominant driver of fire across 48 to 70% of the country throughout the study period. Although the influence of low precipitation on fires has increased in 2019 and 2020, the period is not long enough to establish any significant trends. During the study period, wind speed, elevation, and soil moisture had a slight influence of 6–20% without any clear trend, indicating that deforestation continues to be the main driver of fire. Our study improves the current understanding of the drivers of biomass fires across Cambodia, and the methodological framework developed here (quantitative decoupling of the drivers) has strong potential to be applied to other fire-prone areas around the world.

Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

Combining Field Observations and Remote Sensing to Forecast Fine Fuel Loads

Effective wildfire management requires accurate information about the spatial distribution of fuels. While static maps work well for coarse forest fuels with slow turnover, fine fuels—the driver of rangeland fires—often vary dramatically from year to year. Our goal was to develop a fine fuel forecast to help managers optimally allocate fire suppression resources and funding before the start of the fire season and to identify critical gaps in understanding or data that limit forecast skill. We compiled a historical record of fine fuel loads collected in the Great Basin and combined it with remotely sensed data on herbaceous productivity. Using a Bayesian State-Space approach to account for both process and observation error, we built a “Fuels Model” in which the predicted fuel load at a location depends on the fuel load in the previous year and productivity of the current year. Next, we built a “productivity model” to predict current year production based on remotely sensed data available in early spring. Finally, we combined these two models to generate early spring forecasts for summer fuel loads from 1987 to 2020 and quantified the associated uncertainty. We found that current year productivity contributed twice as much as the previous year's fuel load to the current fuel load. Our productivity predictions reduced mean absolute predictive error by 11% compared with a strong null model without early-season weather covariates. However, when we fed the productivity predictions into our fuel model, the resulting fuel load forecasts had too much uncertainty to inform management decisions, with most uncertainty coming from the process error of the Fuels Model. Reducing this uncertainty will require higher-quality observations of fuel loads. Until those are available, our results suggest that managers could rely on productivity forecasts as a reasonable proxy for fuel load forecasts.

Active governance of agro-pastoral, forest and protected areas mitigates wildfire impacts in Italy

Wildfire regimes affected by global change have been the cause of major concern in recent years. Both direct prevention (e.g., fuel management planning) and land governance strategies (e.g., agroforestry development) can have an indirect regulatory effect on wildfires. Herein, we tested the hypothesis that active land planning and management in Italy have mitigated wildfire impacts in terms of loss of ecosystem services and forest cover, and burned wildland-urban interface, from 2007 to 2017. At the national scale, we assessed the effect size of major potential fire drivers such as climate, weather, flammability, socio-economic descriptors, land use changes, and proxies for land governance (e.g., European funds for rural development, investments in sustainable forest management, agro-pastoral activities), including potential interactions, on fire-related impacts via Random Forest modelling and Generalized Additive Mixed Model. Agro-forest districts (i.e., aggregations of neighbouring municipalities with homogeneous forest and agricultural characteristics) were used as spatial units of analysis. Our results confirm that territories with more active land governance show lower wildfire impacts, even under severe flammability and climatic conditions. This study supports current regional, national, and European strategies towards "fire resistant and resilient landscapes" by fostering agro-forestry, rural development, and nature conservation integrated policies.

Landscape fires disproportionally affect high conservation value temperate peatlands, meadows, and deciduous forests, but only under low moisture conditions

Landscape fires are a natural component of the Earth System. However, they are of growing global concern due to climate change exacerbating their multiple impacts on biodiversity, ecosystems, carbon storage, human health, economies, and wider society. Temperate regions are predicted to be at greatest risk of increasing fire activity due to climate change, where fires can seriously impact important ecosystems for biodiversity and carbon storage, such as peatlands and forests. There is insufficient literature on the background prevalence, distribution, and drivers of fires in these regions, especially within Europe, to assess and mitigate their risks. Using a global database of fire patches based on the MODIS FireCCI51 product, we address this knowledge gap by quantifying the current prevalence and size of fires in Polesia, a 150,000km2 area comprising a mosaic of peatland, forest, and agricultural habitats in northern Ukraine and southern Belarus. Between 2001 and 2019, fires burned 31,062km2 of land, and were most frequent in spring and autumn. Although most fires started in agricultural land, fires disproportionately affected natural and semi-natural land cover types, particularly in protected areas. Over one fifth of protected land burned. Coniferous forests were the most common land cover type in protected areas, but fires mostly occurred in meadows, open peatlands (especially fen and transition mires), and native deciduous forests. These land cover types were highly susceptible to fires under low soil moisture conditions, but the risk of fire was low under average or higher soil moisture conditions. Restoring and maintaining natural hydrological regimes could be an effective nature-based solution to increase the resilience of fire-vulnerable ecosystems and support global biodiversity and carbon storage commitments under the United Nations Framework Conventions on Climate Change and Convention on Biological Diversity.