Modelling the dead fuel moisture content in a grassland of Ergun City,China

作     者：CHANG Chang CHANG Yu GUO Meng HU Yuanman CHANG Chang;CHANG Yu;GUO Meng;HU Yuanman

作者机构：CAS Key Laboratory of Forest Ecology and ManagementInstitute of Applied EcologyChinese Academy of SciencesShenyang 110016China University of Chinese Academy of SciencesBeijing 100049China School of Geographical SciencesNortheast Normal UniversityChangchun 130024China E'erguna Wetland Ecosystem National Research StationHulunbuir 022250China 

出 版 物：《Journal of Arid Land》 (干旱区科学（英文版）)

年 卷 期：2023年第15卷第6期

页      面：710-723页

核心收录：

学科分类：090503[农学-草业科学] 0909[农学-草学] 0905[农学-畜牧学] 09[农学] 0903[农学-农业资源与环境] 

基　　金：funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800) the National Natural Science Foundation of China (31971483) 

主　　题：dead fuel moisture content(DFMC) random forest(RF)model extreme gradient boosting(XGB)model boosted regression tree(BRT)model grassland Ergun City 

摘      要：The dead fuel moisture content(DFMC)is the key driver leading to fire *** estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression *** this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in *** chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data *** ensure accuracy,we added time-lag variables of 3 d to the *** results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three *** accuracies of the models in spring and autumn were higher than those in the other two *** addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time ***,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the *** study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.