四川雅江县火后泥石流易发性与冲出规模预测模型

龚学强; 周永豪; 何坤; 胡卸文; 罗刚; 杨东强; 马洪生

doi:10.3799/dqkx.2025.094

Volume 50 Issue 10

Oct. 2025

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Article Navigation > Earth Science > 2025 > 50(10): 4096-4110

Gong Xueqiang, Zhou Yonghao, He Kun, Hu Xiewen, Luo Gang, Yang Dongqiang, Ma Hongsheng, 2025. Prediction Models for Post-Fire Debris Flow Susceptibility and Debris Flow Volume in Yajiang County, Sichuan, China. Earth Science, 50(10): 4096-4110. doi: 10.3799/dqkx.2025.094

Citation:

Gong Xueqiang, Zhou Yonghao, He Kun, Hu Xiewen, Luo Gang, Yang Dongqiang, Ma Hongsheng, 2025. Prediction Models for Post-Fire Debris Flow Susceptibility and Debris Flow Volume in Yajiang County, Sichuan, China. Earth Science, 50(10): 4096-4110. doi: 10.3799/dqkx.2025.094

Citation:

Gong Xueqiang, Zhou Yonghao, He Kun, Hu Xiewen, Luo Gang, Yang Dongqiang, Ma Hongsheng, 2025. Prediction Models for Post-Fire Debris Flow Susceptibility and Debris Flow Volume in Yajiang County, Sichuan, China. Earth Science, 50(10): 4096-4110. doi: 10.3799/dqkx.2025.094

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Prediction Models for Post-Fire Debris Flow Susceptibility and Debris Flow Volume in Yajiang County, Sichuan, China

doi: 10.3799/dqkx.2025.094

Gong Xueqiang^{1
,
,},
Zhou Yonghao¹,
He Kun^{1, 2},
Hu Xiewen^{1, 2
,
,},
Luo Gang^{1, 2},
Yang Dongqiang³,
Ma Hongsheng⁴

1.
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
2.
Sichuan Province Engineering Technology Research Center of Ecological Mitigation of Geohazards in Tibet Plateau Transportation Corridors, Chengdu 611756, China
3.
Nuclear Industry Southwest Geotechnical Investigation & Design Institute Co., Ltd., Chengdu 610000, China
4.
Sichuan Highway Planning, Survey, Design and Research Institute Ltd., Chengdu 610056, China

Received Date: 2025-03-23
Publish Date: 2025-10-25

Abstract

Abstract

On March 15, 2024, an extensive forest fire occurred in Yajiang County, Sichuan Province. In the first post-fire rainy season, hundreds of post-fire debris flows (PFDFs) were triggered, providing a valuable dataset for studying PFDFs in southwestern mountainous regions. This study developed PFDF susceptibility and volume prediction models based on field investigations, UAV imagery, satellite remote sensing, and rainfall data. The models were constructed using the PFDF event database from the burned area of Yajiang County on March 15, 2024, and were subsequently applied to hazard prediction for two burned areas: Chengxiang Village (December 9, 2024) and Muzexi Village (February 2, 2025). The results show follows. (1) The optimal Random Forest susceptibility model achieved an AUC of 0.905 and an accuracy of 0.950. For the Chengxiang and Muzexi burned areas, 10 and 22 catchments, respectively, were classified as extremely high or high susceptibility, accounting for 48.57% and 73.68% of their total watersheds. (2) The optimal factor combination for the volume prediction model included hourly rainfall intensity, percentage of catchment area with slopes exceeding 30°, soil clay content, gully density, normalized difference vegetation index (NDVI), and the moderate and severe burned area. The generalized additive model for volume prediction achieved an R² of 0.65. Under Q25%, Q75%, and P20% rainfall scenarios, the proportion of catchments in Chengxiang with debris flow volumes exceeding 200 m³ was 2.86%, 25.72%, and 34.29%, respectively, while in Muzexi, the proportion of catchments with volumes exceeding 1 000 m³ was 0%, 15.79%, and 63.16%, respectively. Debris flow volumes in Chengxiang are generally smaller, but the area contains a high density of vulnerable elements, with catchments CX05, CX08, CX13, and CX25 posing significant hazards. In contrast, debris flows in Muzexi tend to have larger volumes, with catchments MZX02 and MZX04 identified as high-risk areas.
- Yajiang,
- post-fire debris flow,
- susceptibility,
- debris flow volume,
- prediction model,
- engineering geology

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References(50)

References

Austin, M. P., 2002. Spatial Prediction of Species Distribution: An Interface between Ecological Theory and Statistical Modelling. Ecological Modelling, 157(2/3): 101-118. https://doi.org/10.1016/S0304-3800(02)00205-3

Bordoni, M., Galanti, Y., Bartelletti, C., et al., 2020. The Influence of the Inventory on the Determination of the Rainfall-Induced Shallow Landslides Susceptibility Using Generalized Additive Models. CATENA, 193: 104630. https://doi.org/10.1016/j.catena.2020.104630

Cannon, S. H., Gartner, J. E., Rupert, M. G., et al., 2010. Predicting the Probability and Volume of Postwildfire Debris Flows in the Intermountain Western United States. Geological Society of America Bulletin, 122(1-2): 127-144. https://doi.org/10.1130/B26459.1

Cannon, S. H., Gartner, J. E., Wilson, R. C., et al., 2008. Storm Rainfall Conditions for Floods and Debris Flows from Recently Burned Areas in Southwestern Colorado and Southern California. Geomorphology, 96(3-4): 250-269. https://doi.org/10.1016/j.geomorph.2007.03.019

Cannon, S. H., Powers, P. S., Savage, W. Z., 1998. Fire-Related Hyperconcentrated and Debris Flows on Storm King Mountain, Glenwood Springs, Colorado, USA. Environmental Geology, 35(2): 210-218. https://doi.org/10.1007/s002540050307

Chen, S. Q., Sun, Y. H., Ding, G. Q., et al., 2025. Holocene Dynamics of Vegetation Cover and Their Driving Mechanisms in Asian Drylands. Journal of Earth Science, 36(2): 839-842. https://doi.org/10.1007/s12583-025-0173-x

de Jesus Pugliese Viloria, A., Folini, A., Carrion, D., et al., 2024. Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review. Remote Sensing, 16(18): 3374. https://doi.org/10.3390/rs16183374

DeGraff, J. V., Cannon, S. H., Gartner, J. E., 2015. The Timing of Susceptibility to Post-Fire Debris Flows in the Western United States. Environmental & Engineering Geoscience, 21(4): 277-292. https://doi.org/10.2113/gseegeosci.21.4.277

Dormann, C. F., Elith, J., Bacher, S., et al., 2013. Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance. Ecography, 36(1): 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x

Esposito, G., Gariano, S. L., Masi, R., et al., 2023. Rainfall Conditions Leading to Runoff-Initiated Post-Fire Debris Flows in Campania, Southern Italy. Geomorphology, 423: 108557. https://doi.org/10.1016/j.geomorph.2022.108557

Fang, Z. C., Wang, Y., van Westen, C., et al., 2024. Landslide Hazard Spatiotemporal Prediction Based on Data-Driven Models: Estimating Where, When and How Large Landslide may Be. International Journal of Applied Earth Observation and Geoinformation, 126: 103631. https://doi.org/10.1016/j.jag.2023.103631

Gartner, J. E., Cannon, S. H., Santi, P. M., 2014. Empirical Models for Predicting Volumes of Sediment Deposited by Debris Flows and Sediment-Laden Floods in the Transverse Ranges of Southern California. Engineering Geology, 176: 45-56. https://doi.org/10.1016/j.enggeo.2014.04.008

Gartner, J. E., Cannon, S. H., Santi, P. M., et al., 2008. Empirical Models to Predict the Volumes of Debris Flows Generated by Recently Burned Basins in the Western U. S.. Geomorphology, 96(3-4): 339-354. https://doi.org/10.1016/j.geomorph.2007.02.033

Gorr, A., McGuire, L., Youberg, A., 2024. Empirical Models for Postfire Debris-Flow Volume in the Southwest United States. Journal of Geophysical Research: Earth Surface, 129(11): e2024JF007825. https://doi.org/10.1029/2024jf007825

Green, S. B., 1991. How Many Subjects does It Take to do a Regression Analysis. Multivariate Behavioral Research, 26(3): 499-510. https://doi.org/10.1207/s15327906mbr2603_7

He, K., Hu, X. W., Wu, Z. L., et al., 2024. Preliminary Analysis of the Wildfire on March 15, 2024, and the Following Post-Fire Debris Flows in Yajiang County, Sichuan, China. Landslides, 21(12): 3179-3189. https://doi.org/10.1007/s10346-024-02364-z

Hou, Z. F., Li, J. J., Song, C. H., et al., 2014. Understanding Miocene Climate Evolution in Northeastern Tibet: Stable Carbon and Oxygen Isotope Records from the Western Tianshui Basin, China. Journal of Earth Science, 25(2): 357-365. https://doi.org/10.1007/s12583-014-0416-8

Hu, X. W., Jin, T., Yin, W. Q., et al., 2020. The Characteristics of Forest Fire Burned Area and Susceptibility Assessment of Post-Fire Debris Flow in Jingjiu Township, Xichang City. Journal of Engineering Geology, 28(4): 762-771(in Chinese with English abstract).

Hu, X. W., Wang, Y., Yang, Y., 2018. Research Actuality and Evolution Mechanism of Post-Fire Debris Flow. Journal of Engineering Geology, 26(6): 1562-1573(in Chinese with English abstract).

Hu, X. W., Zhou, Y. H., He, K., et al., 2024. Post-Fire Debris Flow Mechanisms in Different Lithological Zones. Mountain Research, 42(4): 535-545(in Chinese with English abstract).

Jin, T., Hu, X. W., Liu, B., et al., 2022. Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective. Remote Sensing, 14(6): 1306. https://doi.org/10.3390/rs14061306

Kean, J. W., Staley, D. M., Cannon, S. H., 2011. In Situ Measurements of Post-Fire Debris Flows in Southern California: Comparisons of the Timing and Magnitude of 24 Debris-Flow Events with Rainfall and Soil Moisture Conditions. Journal of Geophysical Research, 116(F4): F04019. https://doi.org/10.1029/2011jf002005

Kern, A. N., Addison, P., Oommen, T., et al., 2017. Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States. Mathematical Geosciences, 49(6): 717-735. https://doi.org/10.1007/s11004-017-9681-2

Lombardo, L., Tanyas, H., Huser, R., et al., 2021. Landslide Size Matters: A New Data-Driven, Spatial Prototype. Engineering Geology, 293: 106288. https://doi.org/10.1016/j.enggeo.2021.106288

Long, Y. J., Li, W. L., Huang, R. Q., et al., 2023. A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin, China. Journal of Earth Science, 34(2): 316-329. https://doi.org/10.1007/s12583-021-1525-9

Merghadi, A., Yunus, A. P., Dou, J., et al., 2020. Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance. Earth-Science Reviews, 207: 103225. https://doi.org/10.1016/j.earscirev.2020.103225

Nikolopoulos, E. I., Destro, E., Bhuiyan, M. A. E., et al., 2018. Evaluation of Predictive Models for Post-Fire Debris Flow Occurrence in the Western United States. Natural Hazards and Earth System Sciences, 18(9): 2331-2343. https://doi.org/10.5194/nhess-18-2331-2018

Pourghasemi, H. R., Kariminejad, N., Amiri, M., et al., 2020. Assessing and Mapping Multi-Hazard Risk Susceptibility Using a Machine Learning Technique. Scientific Reports, 10: 3203. https://doi.org/10.1038/s41598-020-60191-3

Riley, K. L., Bendick, R., Hyde, K. D., et al., 2013. Frequency-Magnitude Distribution of Debris Flows Compiled from Global Data, and Comparison with Post-Fire Debris Flows in the Western U. S.. Geomorphology, 191: 118-128. https://doi.org/10.1016/j.geomorph.2013.03.008

Galiano, V. R., Castillo, M. S., Olmo, M. C., et al., 2015. Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines. Ore Geology Reviews, 71: 804-818. https://doi.org/10.1016/j.oregeorev.2015.01.001

Shen, T., Deng, C. Y., Ding, X. H., et al., 2023. Analysis of Influencing Factors of Shanghai University Students' Self-Study Efficiency Based on Multiple Linear Stepwise Regression. Statistics and Application, 12(1): 100-109(in Chinese with English abstract). doi: 10.12677/SA.2023.121012

Staley, D. M., Negri, J. A., Kean, J. W., et al., 2017. Prediction of Spatially Explicit Rainfall Intensity-Duration Thresholds for Post-Fire Debris-Flow Generation in the Western United States. Geomorphology, 278: 149-162. https://doi.org/10.1016/j.geomorph.2016.10.019

Tan, L., Zhang, L. L., Wei, X., et al., 2025. Study on Regional Landslide Susceptibility Assessment Method Based on U-Net Semantic Segmentation Network and Its Cross-Generalization Ability. China Civil Engineering Journal, 58(6): 103-116 (in Chinese with English abstract).

Thomas, M. A., Kean, J. W., McCoy, S. W., et al., 2023. Postfire Hydrologic Response along the Central California (USA) Coast: Insights for the Emergency Assessment of Postfire Debris-Flow Hazards. Landslides, 20(11): 2421-2436. https://doi.org/10.1007/s10346-023-02106-7

Wall, S. A., Roering, J. J., Rengers, F. K., 2020. Runoff-Initiated Post-Fire Debris Flow Western Cascades, Oregon. Landslides, 17(7): 1649-1661. https://doi.org/10.1007/s10346-020-01376-9

Wall, S., Murphy, B. P., Belmont, P., et al., 2023. Predicting Post-Fire Debris Flow Grain Sizes and Depositional Volumes in the Intermountain West, United States. Earth Surface Processes and Landforms, 48(1): 179-197. https://doi.org/10.1002/esp.5480

Wang, L., Zhang, Y. K., Shu, B., et al., 2023. Improved Method for Fusion of Loess Landslide Monitoring Data Based on Feature Selection and Stepwise Regression. Journal of Earth Sciences and Environment, 45(3): 511-521 (in Chinese with English abstract).

Wang, Y., Hu, X. W., Wu, L. J., et al., 2022. Evolutionary History of Post-Fire Debris Flows in Ren'e Yong Valley in Sichuan Province of China. Landslides, 19(6): 1479-1490. https://doi.org/10.1007/s10346-022-01867-x

Wang, Y. H., Shen, H. W., Xie, W. Y., et al., 2024. Analysis of the Rainfall Threshold for Post-Fire Debris Flow Initiation: A Case Study of the Debris Flow at Ren'eyong Gully in Xiangcheng County, Sichuan Province. The Chinese Journal of Geological Hazard and Control, 35(1): 108-115 (in Chinese with English abstract).

Weiss, K., Khoshgoftaar, T. M., Wang, D. D., 2016. A Survey of Transfer Learning. Journal of Big Data, 3(1): 9. https://doi.org/10.1186/s40537-016-0043-6

Yang, Y., Hu, X. W., Han, M., et al., 2022. Post-Fire Temporal Trends in Soil Properties and Revegetation: Insights from Different Wildfire Severities in the Hengduan Mountains, Southwestern China. CATENA, 213: 106160. https://doi.org/10.1016/j.catena.2022.106160

Zhou, R. C., Hu, X. W., Jin, T., et al., 2024. Characteristics of Burned Area and Susceptibility Assessment of Post-Fire Debris Flow of Chongqing Wildfire in August, 2022. Hydrogeology & Engineering Geology, 51(5): 150-160 (in Chinese with English abstract).

胡卸文, 金涛, 殷万清, 等, 2020. 西昌市经久乡森林火灾火烧区特点及火后泥石流易发性评价. 工程地质学报, 28(4): 762-771.

胡卸文, 王严, 杨瀛, 2018. 火后泥石流成灾特点及研究现状. 工程地质学报, 26(6): 1562-1573.

胡卸文, 周永豪, 何坤, 等, 2024. 不同岩性区火后泥石流成灾机理. 山地学报, 42(4): 535-545.

沈婷, 邓辰钰, 丁小荷, 等, 2023. 基于多元线性逐步回归的上海大学学生自习效率影响因素分析. 统计学与应用, 12(1): 100-109.

谭林, 张璐璐, 魏鑫, 等, 2025. 基于U-Net语义分割网络的区域滑坡易发性评价方法和跨地区泛化能力研究. 土木工程学报, 58(6): 103-116.

王利, 张懿恺, 舒宝, 等, 2023. 基于特征优选和逐步回归的黄土滑坡监测数据融合改进方法. 地球科学与环境学报, 45(3): 511-521.

王元欢, 沈昊文, 谢万银, 等, 2024. 火后泥石流启动降雨阈值分析: 以四川乡城县仁额拥沟泥石流为例. 中国地质灾害与防治学报, 35(1): 108-115.

周瑞宸, 胡卸文, 金涛, 等, 2024. 重庆市2022年8月森林火灾火烧区特点及火后泥石流易发性评价. 水文地质工程地质, 51(5): 150-160.

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Prediction Models for Post-Fire Debris Flow Susceptibility and Debris Flow Volume in Yajiang County, Sichuan, China

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