Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?
Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the to...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-03-01
|
Series: | Forests |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4907/12/4/392 |
_version_ | 1797539862703767552 |
---|---|
author | Zige Lan Zhangwen Su Meng Guo Ernesto C. Alvarado Futao Guo Haiqing Hu Guangyu Wang |
author_facet | Zige Lan Zhangwen Su Meng Guo Ernesto C. Alvarado Futao Guo Haiqing Hu Guangyu Wang |
author_sort | Zige Lan |
collection | DOAJ |
description | Understanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development. |
first_indexed | 2024-03-10T12:51:56Z |
format | Article |
id | doaj.art-0e9c47b849bb4ae6947cb9db4544ef02 |
institution | Directory Open Access Journal |
issn | 1999-4907 |
language | English |
last_indexed | 2024-03-10T12:51:56Z |
publishDate | 2021-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Forests |
spelling | doaj.art-0e9c47b849bb4ae6947cb9db4544ef022023-11-21T12:10:29ZengMDPI AGForests1999-49072021-03-0112439210.3390/f12040392Are Climate Factors Driving the Contemporary Wildfire Occurrence in China?Zige Lan0Zhangwen Su1Meng Guo2Ernesto C. Alvarado3Futao Guo4Haiqing Hu5Guangyu Wang6College of Forestry, Northeast Forestry University, Harbin 150040, ChinaCollege of Forestry, Northeast Forestry University, Harbin 150040, ChinaKey Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, ChinaSchool of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USACollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaCollege of Forestry, Northeast Forestry University, Harbin 150040, ChinaAsia Forest Research Centre, Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaUnderstanding the drivers of wildfire occurrence is of great value for fire prevention and management, but due to the variation in research methods, data sources, and data resolution of those studies, it is challenging to conduct a large-scale comprehensive comparative qualitative analysis on the topic. China has diverse vegetation types and topography, and has undergone rapid economic and social development, but experiences a high frequency of wildfires, making it one of the ideal locations for wildfire research. We applied the Random Forests modelling approach to explore the main types of wildfire drivers (climate factors, landscape factors and human factors) in three high wildfire density regions (Northeast (NE), Southwest (SW), and Southeast (SE)) of China. The results indicate that climate factors were the main driver of wildfire occurrence in the three regions. Precipitation and temperature significantly impacted the fire occurrence in the three regions due to the direct influence on the moisture content of forest fuel. However, wind speed had important influence on fire occurrence in the SE and SW. The explanation power of the landscape and human factors varied significantly between regions. Human factors explained 40% of the fire occurrence in the SE but only explained less than 10% of the fire occurrence in the NE and SW. The density of roads was identified as the most important human factor driving fires in all three regions, but railway density had more explanation power on fire occurrence in the SE than in the other regions. The landscape factors showed nearly no influence on fire occurrence in the NE but explained 46.4% and 20.6% in the SE and SW regions, respectively. Amongst landscape factors, elevation had the highest average explanation power on fire occurrence in the three regions, particularly in the SW. In conclusion, this study provides useful insights into targeted fire prediction and prevention, which should be more precise and effective under climate change and socio-economic development.https://www.mdpi.com/1999-4907/12/4/392wildfire driversclimate changerandom forestsfire modellingChina |
spellingShingle | Zige Lan Zhangwen Su Meng Guo Ernesto C. Alvarado Futao Guo Haiqing Hu Guangyu Wang Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? Forests wildfire drivers climate change random forests fire modelling China |
title | Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? |
title_full | Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? |
title_fullStr | Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? |
title_full_unstemmed | Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? |
title_short | Are Climate Factors Driving the Contemporary Wildfire Occurrence in China? |
title_sort | are climate factors driving the contemporary wildfire occurrence in china |
topic | wildfire drivers climate change random forests fire modelling China |
url | https://www.mdpi.com/1999-4907/12/4/392 |
work_keys_str_mv | AT zigelan areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT zhangwensu areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT mengguo areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT ernestocalvarado areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT futaoguo areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT haiqinghu areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina AT guangyuwang areclimatefactorsdrivingthecontemporarywildfireoccurrenceinchina |