Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato

Farfán, Michelle; Domínguez, Christian; Espinoza, Alejandra; Jaramillo, Alejandro; Alcántara, Camilo; Maldonado, Victor; Tovar, Israel; Flamenco, Alejandro

doi:10.1007/s10661-021-09494-0

Cited by 16 publications

(12 citation statements)

References 61 publications

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“…Thereafter, the data decreased following a trajectory fitted to a positive quadratic function, limited to the right in P/T equal to 120.53, and statistically validated (p-value = 0.0046) (Figure 4). This means that, if temperature increases and humidity decreases, plants become stressed, generating conditions conducive to a forest fire (Farfán et al, 2021). Although 99% of forest fires in Nariño are caused by burning crop residues (Alcaldía de Pasto, 2014c).…”

Section: Resultsmentioning

confidence: 99%

Correlative analysis of climate impacts in an Andean municipality of Colombia

Chamorro

Barriga

2022

rcia

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The municipality of Pasto (Colombia) is an intermediate, emerging city of the Colombian Andes, which is vulnerable to the impacts of climate change due to its environmental conditions, mountainous morphology, moderately humid climate, and socio-economic structure. This research aims at identifying the impacts of historical trends in climate variables on the main urban elements of the municipality of Pasto. The study uses the correlational method to determine the relationships between climate variables and some impacted urban elements between 2004 and 2019. The adjusted models allow identifying possible trajectories in the evolution of urban variables affected by climate change, population growth, and culture. Results show that climate variability produces negative quadratic trajectories in crop yields and flood events; positive quadratic patterns in landslides, forest fires, the prevalence of acute diarrheal diseases (ADD), and acute respiratory infections (ARI); and linear patterns in water availability, livestock production and food security. In general, the urban variables show a departure from equilibrium when exposed to higher temperatures and precipitation, which affect the reliability of crop yields, food security, water availability, natural disasters, and public health in the municipality of Pasto.

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Section: Resultsmentioning

confidence: 99%

Correlative analysis of climate impacts in an Andean municipality of Colombia

Chamorro

Barriga

2022

rcia

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“…For several states, the developed models showed some skill in relating the higher observed fire activity (e.g., years 2011-2013 for Chihuahua, Figure 4a) to corresponding higher accumulated fuel dryness in those periods (corresponding years and state in Figure 4a) and, conversely, in explaining the lower fire activity observed in wetter years (e.g., year 2015 for Chihuahua or Mexico, Figures 4a and 5a). This interannual variability in fire activity has been previously documented to be related to annual ENSO indices for Mexico (e.g., [89][90][91]) and other countries (e.g., [92][93][94]), although the country-and region-specific effects of Niño/Niña are still not fully understood for many areas of Mexico (e.g., [95][96][97]). The temporal patterns of observed fire activity (Figure 4 and Supplementary Figures S1-S5) and fuel dryness (Figure 5 and Supplementary Figures S6-S10) point that, for several states in the analysis, particularly in the North of Mexico, higher fire activity is observed for La Niña years, such as 2011, which had been documented to be a record for both number of fires and burned area in Mexico [64].…”

Section: Discussionmentioning

confidence: 97%

Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Vega-Nieva,

Briseño-Reyes,

López-Serrano

et al. 2023

Forests

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There is a need to convert fire danger indices into operational estimates of fire activity to support strategic fire management, particularly under climate change. Few studies have evaluated multiple accumulation times for indices that combine both dead and remotely sensed estimates of live fuel moisture, and relatively few studies have aimed at predicting fire activity from both such fuel moisture estimates and autoregressive terms of previous fires. The current study aimed at developing models to forecast the 10-day number of fires by state in Mexico, from an accumulated Fuel Dryness Index (FDI) and an autoregressive term from the previous 10-day observed number of fires. A period of 50 days of accumulated FDI (FDI50) provided the best results to forecast the 10-day number of fires from each state. The best predictions (R2 > 0.6–0.75) were obtained in the largest states, with higher fire activity, and the lower correlations were found in small or very dry states. Autoregressive models showed good skill (R2 of 0.99–0.81) to forecast FDI50 for the next 10 days based on previous fuel dryness observations. Maps of the expected number of fires showed potential to reproduce fire activity. Fire predictions might be enhanced with gridded weather forecasts in future studies.

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“…The periodic occurrence of ENSO events and their significant impact on climate play a crucial role in the occurrence and spread of forest fires. This provides a deeper understanding and hopes to optimize fire management strategies through improved prediction models [59][60][61]. While this study has made progress in developing a forest fire prediction model, we recognize that obtaining long time series, high-precision, and consistent spatial and temporal resolution data sources remains a challenge for large-scale study areas, potentially affecting the accuracy of the research.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Occurrence of Forest Fire in the Central-South Region of China

Hai,

Han,

Vandansambuu

et al. 2024

Preprint

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Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

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Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato

Cited by 16 publications

References 61 publications

Correlative analysis of climate impacts in an Andean municipality of Colombia

Correlative analysis of climate impacts in an Andean municipality of Colombia

Autoregressive Forecasting of the Number of Forest Fires Using an Accumulated MODIS-Based Fuel Dryness Index

Predicting the Occurrence of Forest Fire in the Central-South Region of China

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