Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China

San, Wang Joong; Li, Hongli; Niu, Shukui

doi:10.3390/su13147773

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…Climate change and human activities have altered peat fire regimes, which will be realized through changes in atmospheric dynamics [14], [15], [16]. Peat fire smoke on the other hand affected in reduction of droplets (hygroscopic particles) forming in the clouds, subsequently causing fewer raindrop occurrences [14].…”

Section: Peat Firesmentioning

confidence: 99%

Weather Modification Technology as an Engineering Solution for Reducing Peat Fire Disasters

Sandhyavitri¹,

Widodo²,

Sasmita³

et al. 2023

eer

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The objectives of this research are to investigate the effectiveness of an application of weather modification technology (WMT) in reducing peat fire disasters; and to generate trends and correlations between the implementation of WMT with 3 variables, improvement of precipitation rates, reduction of hotspot cases, and reducing the size of burnt areas. A cloud seeding method was applied in the WMT processes by spraying ultra-giant nucleic (10-50 microns) of NaCl throughout airplanes within the Pelalawan Regency, Indonesia, 2020. The target-only method was applied for evaluating an increase in the precipitation rates during the TMC period of 2017-2020. Three satellites (TRMM, Landsat, and MODIS) data and geographical information system (GIS) were applied in the calculation of precipitation rates, hotspots, and peat fire (burnt) areas. The results showed that during 4 years of the observation period, the implementation of WMT in this research location was considered effective in improving the precipitation rates as PCH = 1.33 >1. There was also a trend that the higher the precipitation rates were, the lower the size of burnt areas would be (R 2 > 80%). This article is expected to be a reference for further research and development in peat fire mitigation and control.

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Section: Peat Firesmentioning

confidence: 99%

Weather Modification Technology as an Engineering Solution for Reducing Peat Fire Disasters

Sandhyavitri¹,

Widodo²,

Sasmita³

et al. 2023

eer

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“…Tan et al believed that human factors and drought were important causes of forest fires in Indonesia [ 10 ]. Wang et al [ 11 ] analyzed forest fire risk areas in Sichuan and believed that climate warming would lead to increased forest fire risk. The scholars studied the main causes of forest fires from social, man-made and weather perspectives.…”

Section: Introductionmentioning

confidence: 99%

Remote monitoring, personnel extinguishment or helicopter extinguishment? How to control forest fires more effectively

Bai

Yuan

2023

PLoS ONE

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Forest fires have a great impact on the ecological environment. The government needs to take measures to control forest fires. Forest fires are easily affected by wind speed and other weather conditions, and the difficulty of extinguishing forest fires is easily affected by terrain complexity. Effective management methods can protect the important ecological functions of forests, thereby maintaining long-term development and economic efficiency. The government can put out the fire by remote monitoring, personnel extinguishing and helicopter extinguishing. Different from most articles on how to eliminate forest fires from the technical point of view, this article mainly analyzes from the two aspects of optimizing resource allocation and guiding policy formulation. This article constructs the differential game model under these three modes, and then obtains the equilibrium result. And the comparative analysis. Finally, the conclusion is drawn. The stronger the wind, the more residents need to flee. However, strong winds are not conducive to crews and helicopters fighting the fire. Rather than fighting fires, residents are more inclined to detect forest fires in time through remote monitoring. When the personnel can effectively control the fire, the personnel fire extinguishing mode is preferentially selected. Otherwise, helicopter firefighting mode should be selected.

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“…In recent years, research on wildfires in Sichuan Province has focused on the spatial and temporal distribution characteristics of wildfire [49], the impact of climate change on wildfire [51][52][53], the spatial distribution of fuels [54], and regional fire forecast [55,56]. The majority of the research examined the relationship between wildfires and meteorological elements on an annual scale, suggesting that meteorological factors have a significant impact on wildfires in Sichuan Province [57]. They neglected to consider the impact of monthly climatic conditions and human activities on the occurrence of wildfires in Sichuan Province.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Peng,

Wei,

Chen

et al. 2023

Forests

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Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.

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Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China

Cited by 10 publications

References 28 publications

Weather Modification Technology as an Engineering Solution for Reducing Peat Fire Disasters

Weather Modification Technology as an Engineering Solution for Reducing Peat Fire Disasters

Remote monitoring, personnel extinguishment or helicopter extinguishment? How to control forest fires more effectively

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

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