Preprints
https://doi.org/10.5194/nhess-2022-72
https://doi.org/10.5194/nhess-2022-72
 
12 Apr 2022
12 Apr 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

Model Comparisons for Predicting Grassland Fire Occurrence Probability in Inner Mongolia Autonomous Region, China

Chang Chang1,2, Yu Chang1, Zaiping Xiong1, Xiaoying Ping1,2, Heng Zhang3, Meng Guo4, and Yuanman Hu1,5 Chang Chang et al.
  • 1CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China
  • 4School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
  • 5E’erguna Wetland Ecosystem National Research Station, Inner Mongolia, 022250, China

Abstract. Grassland fires threaten grassland ecosystem, human life and economic development greatly. However, there are limited researches focusing on grassland fire prediction, thereby it is necessary to find a better method to predict probability of grassland fire occurrence. Here we selected 16 environmental variables that may have impacts on fire occurrence, then built regression models of grassland fire probability based on historical fire points and variables in Inner Mongolia by three methods to identify the grassland fire drivers and to predict fire probability. The three methods were global logistic regression, geographically weighted logistic regression and random forest. The results showed that random forest model had the best predictive effect. The influence of 9 variables selected by geographically weighted logistic regression model on grassland fire was unbalanced spatially. The three models all showed that meteorological factors were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurred in different areas had various responses to the influencing drivers, and the areas with different natural and geographical condition had different fire prevention periods. Thus, the grassland fire management strategy based on local conditions should be advocated.

Chang Chang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-72', Anonymous Referee #1, 27 Jul 2022
    • AC1: 'Reply on RC1', Chang Chang, 27 Jul 2022
  • RC2: 'Comment on nhess-2022-72', Anonymous Referee #2, 29 Jul 2022
    • AC2: 'Reply on RC2', Chang Chang, 01 Aug 2022
  • RC3: 'Comment on nhess-2022-72', Anonymous Referee #3, 22 Aug 2022
    • AC3: 'Reply on RC3', Chang Chang, 08 Sep 2022

Chang Chang et al.

Data sets

China monthly Vegetation index (NDVI) spatial distribution dataset Xinliang Xu https://doi.org/10.12078/2018060602

Model code and software

Windows Application for Geographically Weighted Regression Modelling Tomoki Nakaya https://download.csdn.net/download/xiaodongfly/7027693

Chang Chang et al.

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Short summary
Random forest model had the highest fitting goodness to Inner Mongolia grassland fires from 2000 to 2018. The influence of 9 drivers on grassland fire was spatially unbalanced. Meteorological factors were of great importance to grassland fire. In Inner Mongolia, different areas had different sensitivities to different drivers. Thus, the grassland fire management strategy based on local conditions should be advocated.
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