Predicting future forest fire occurrence probability based on drought characteristics at various temporal scales in P. R. China.
Uloženo v:
| Název: | Predicting future forest fire occurrence probability based on drought characteristics at various temporal scales in P. R. China. |
|---|---|
| Autoři: | Shao X; Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, P. R. China.; Colledge of Geography and Environment, Shandong Normal University, Jinan, P. R. China., Li C; Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, P. R. China., Chang Y; Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, P. R. China., Xiong Z; Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, P. R. China., Chen H; School of Life Sciences and Engineering, Shenyang University, Shenyang, P. R. China., Li R; Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, P. R. China. |
| Zdroj: | PloS one [PLoS One] 2025 Dec 02; Vol. 20 (12), pp. e0337473. Date of Electronic Publication: 2025 Dec 02 (Print Publication: 2025). |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Výrazy ze slovníku MeSH: | Droughts* , Forests* , Wildfires*/statistics & numerical data , Fires*, China ; Climate Change ; Probability ; Forecasting ; Principal Component Analysis ; Logistic Models |
| Abstrakt: | Future climate change will lead to extreme weather events, such as droughts, which may exacerbate forest fire regimes. However, the impact of future drought characteristics on forest fire regimes has rarely been reported in China. Here, we employed principal component analysis to reduce the dimensionality of drought characteristics, and then used geographically weighted logistic regression models to develop predictive models. These models were applied to future climate simulations under different scenarios to provide projections for different periods, which were then compared with the historical period (2000-2019) to assess the relative changes. We found that the model performed well in its predictions (AUC > 0.75). By comparing the Brier scores, it was found that the models with better predictive performance were those using the SPEI-1 and SPEI-12 timescales. We also found that in the near and medium term of the future, with climate change, the forest fire occurrence probability in most forest land of northern China (NWC, NC, and NEC), especially in Northeast China (NEC), shows an increasing trend, but a decreasing trend in most forest land of southern China (SC, SWC, and EC). Our research can provide a scientific basis for the development of future forest fire management practices that mitigate drought stress according to local conditions. (Copyright: © 2025 Shao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| References: | Glob Chang Biol. 2015 Aug;21(8):2861-80. (PMID: 25752680) Sci Data. 2024 Oct 24;11(1):1167. (PMID: 39448662) Nature. 2024 Nov;635(8040):898-905. (PMID: 39537920) Nat Commun. 2018 Oct 2;9(1):3821. (PMID: 30279564) Sci Rep. 2019 Jul 11;9(1):10073. (PMID: 31296883) Sci Rep. 2017 Dec 21;7(1):17966. (PMID: 29269737) Sci Total Environ. 2022 Mar 1;810:152002. (PMID: 34856282) PLoS One. 2025 Dec 2;20(12):e0337473. (PMID: 41329716) Proc Natl Acad Sci U S A. 2018 Oct 16;115(42):10600-10605. (PMID: 30275323) Sci Total Environ. 2018 Aug 1;631-632:534-549. (PMID: 29529441) Sci Total Environ. 2019 May 15;665:338-346. (PMID: 30772564) Sci Total Environ. 2020 Feb 20;704:135245. (PMID: 31818549) Sci Total Environ. 2022 Jan 10;803:150018. (PMID: 34525734) PeerJ. 2022 Mar 30;10:e13203. (PMID: 35378927) Nature. 2020 Jan;577(7792):618-620. (PMID: 31996825) Sci Total Environ. 2024 Mar 15;916:170133. (PMID: 38242467) Nature. 2019 Aug;572(7770):520-523. (PMID: 31435055) Sci Total Environ. 2021 Feb 25;757:143702. (PMID: 33310580) Sci Data. 2024 Feb 21;11(1):223. (PMID: 38383611) Sci Total Environ. 2018 May 15;624:1523-1538. (PMID: 29929262) Sci Rep. 2019 Oct 10;9(1):13886. (PMID: 31601820) Data Brief. 2015 Jul 29;4:563-5. (PMID: 26288802) Philos Trans R Soc Lond B Biol Sci. 2016 Jun 5;371(1696):. (PMID: 27216510) Sci Rep. 2024 Jun 21;14(1):14299. (PMID: 38906958) Nat Commun. 2021 Mar 19;12(1):1764. (PMID: 33741924) Sci Total Environ. 2024 Sep 15;943:173886. (PMID: 38857791) Nat Clim Chang. 2021 Aug;11(8):689-695. (PMID: 39650282) Environ Monit Assess. 2022 Oct 7;194(12):853. (PMID: 36203117) Nat Commun. 2018 Jul 13;9(1):2718. (PMID: 30006529) Sci Total Environ. 2024 Feb 20;912:168641. (PMID: 38007112) |
| Entry Date(s): | Date Created: 20251202 Date Completed: 20251202 Latest Revision: 20251205 |
| Update Code: | 20251205 |
| PubMed Central ID: | PMC12671787 |
| DOI: | 10.1371/journal.pone.0337473 |
| PMID: | 41329716 |
| Databáze: | MEDLINE |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science Buďte první, kdo okomentuje tento záznam!