IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i11d10.1007_s11069-025-07289-x.html
   My bibliography  Save this article

Integration of Morris and GLUE methods for improving massflow-based debris flow simulation

Author

Listed:
  • Haibo Yang

    (Zhengzhou University
    State Key Laboratory of Tunnel Boring Machine and Intelligent Operation and Maintenance)

  • Jiaqi Huang

    (Zhengzhou University)

  • Xinyi Liu

    (POWERCHINA GUIYANG Engineering Corporation Limited)

  • Peng Xu

    (POWERCHINA GUIYANG Engineering Corporation Limited)

  • Bo Yu

    (POWERCHINA GUIYANG Engineering Corporation Limited)

  • Jinjun Guo

    (Zhengzhou University)

  • Junhua Li

    (Yellow River Institute of Hydraulic Research)

  • Yuhang Zhou

    (Yellow River Institute of Hydraulic Research)

  • Xiaosong Shu

    (Zhengzhou University
    State Key Laboratory of Tunnel Boring Machine and Intelligent Operation and Maintenance)

Abstract

Debris flow is a common geologic hazard in mountainous areas. It is crucial to simulate its initiation, flow, and accumulation efficiently and accurately for disaster prevention and control. Parameter determination in simulations is quite subjective. To address this issue, the paper presents a simulation approach using the Morris and GLUE methods. The Morris sensitivity analysis is used to identify the sensitive parameters of the Massflow model. The GLUE method is used to calibrate the sensitive parameters and determine the optimal parameter set. Finally, the determined parameter set is employed to simulate the debris flow in Massflow. To validate the effectiveness of this method, Huangyang gully is selected as the case study. The Results show that (1) Rainfall is the most significant factor affecting Massflow simulation results, followed by the channel bed roughness coefficient. (2) The calibrated parameter set improves simulation accuracy from 64 to 76.22%. (3) As the rainfall probabilities decrease, the debris flow accumulation area expands significantly. The largest increase occurs when the area grows from 30,712 m2 for the AEP of P = 2% to 46,272 m2 for the AEP of P = 1%, a 22.7% increase. The simulation results show that the debris flow simulation method based on Morris and GLUE significantly enhances model accuracy. It provides technical support and a scientific foundation for debris flow disaster simulation and prevention.

Suggested Citation

  • Haibo Yang & Jiaqi Huang & Xinyi Liu & Peng Xu & Bo Yu & Jinjun Guo & Junhua Li & Yuhang Zhou & Xiaosong Shu, 2025. "Integration of Morris and GLUE methods for improving massflow-based debris flow simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(11), pages 12589-12612, June.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:11:d:10.1007_s11069-025-07289-x
    DOI: 10.1007/s11069-025-07289-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-025-07289-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-025-07289-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Heredia, María Belén & Prieur, Clémentine & Eckert, Nicolas, 2022. "Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Missaghi, Shahram & Hondzo, Miki, 2010. "Evaluation and application of a three-dimensional water quality model in a shallow lake with complex morphometry," Ecological Modelling, Elsevier, vol. 221(11), pages 1512-1525.
    3. Arhonditsis, George B. & Qian, Song S. & Stow, Craig A. & Lamon, E. Conrad & Reckhow, Kenneth H., 2007. "Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake," Ecological Modelling, Elsevier, vol. 208(2), pages 215-229.
    4. Victor Carvalho Cabral & Fábio Augusto Gomes Vieira Reis & Fernando Mazo D’Affonseca & Ana Lucía & Claudia Vanessa Corrêa & Vinicius Veloso & Marcelo Fischer Gramani & Agostinho Tadashi Ogura & Andrea, 2021. "Characterization of a landslide-triggered debris flow at a rainforest-covered mountain region in Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 3021-3043, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lindim, C. & Pinho, J.L. & Vieira, J.M.P., 2011. "Analysis of spatial and temporal patterns in a large reservoir using water quality and hydrodynamic modeling," Ecological Modelling, Elsevier, vol. 222(14), pages 2485-2494.
    2. Xu, Yanhong & Peng, Hong & Yang, Yinqun & Zhang, Wanshun & Wang, Shuangling, 2014. "A cumulative eutrophication risk evaluation method based on a bioaccumulation model," Ecological Modelling, Elsevier, vol. 289(C), pages 77-85.
    3. Niu, Zhiguang & Gou, Qianqian & Wang, Xiujun & Zhang, Ying, 2016. "Simulation of a water ecosystem in a landscape lake in Tianjin with AQUATOX: Sensitivity, calibration, validation and ecosystem prognosis," Ecological Modelling, Elsevier, vol. 335(C), pages 54-63.
    4. Gökçe Koç & Ayşe Uzmay, 2024. "Construction of a Farm-Level Food Security Index: Case Study of Turkish Dairy Farms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(2), pages 687-714, November.
    5. Zhang, Weitao & Arhonditsis, George B., 2009. "A Bayesian hierarchical framework for calibrating aquatic biogeochemical models," Ecological Modelling, Elsevier, vol. 220(18), pages 2142-2161.
    6. Vinicius Queiroz Veloso & Fabio Augusto Vieira Gomes Reis & Victor Cabral & José Eduardo Zaine & Claudia Vanessa Santos Corrêa & Marcelo Fischer Gramani & Caiubi Emmanuel Kuhn, 2023. "Hazard assessment of debris-flow-prone watersheds in Cubatão, São Paulo State, Brazil," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3119-3138, April.
    7. McDonald, C.P. & Bennington, V. & Urban, N.R. & McKinley, G.A., 2012. "1-D test-bed calibration of a 3-D Lake Superior biogeochemical model," Ecological Modelling, Elsevier, vol. 225(C), pages 115-126.
    8. Yang, Likun & Zhao, Xinhua & Peng, Sen & Li, Xia, 2016. "Water quality assessment analysis by using combination of Bayesian and genetic algorithm approach in an urban lake, China," Ecological Modelling, Elsevier, vol. 339(C), pages 77-88.
    9. Muhammad Mazhar Iqbal & Muhammad Shoaib & Hafiz Umar Farid & Jung Lyul Lee, 2018. "Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia," IJERPH, MDPI, vol. 15(10), pages 1-26, October.
    10. Chung, S.W. & Imberger, J. & Hipsey, M.R. & Lee, H.S., 2014. "The influence of physical and physiological processes on the spatial heterogeneity of a Microcystis bloom in a stratified reservoir," Ecological Modelling, Elsevier, vol. 289(C), pages 133-149.
    11. Bojun Liu & Jun Xia & Feilin Zhu & Jin Quan & Hao Wang, 2021. "Response of Hydrodynamics and Water-quality Conditions to Climate Change in a Shallow Lake," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4961-4976, November.
    12. Katin, Alexey & Giudice, Dario Del & Hall, Nathan S. & Paerl, Hans W. & Obenour, Daniel R., 2021. "Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity," Ecological Modelling, Elsevier, vol. 447(C).
    13. Aalirezaei, Armin & Kabir, Dr. Golam & Khan, Md Saiful Arif, 2023. "Dynamic predictive analysis of the consequences of gas pipeline failures using a Bayesian network," International Journal of Critical Infrastructure Protection, Elsevier, vol. 43(C).
    14. Ramin, Maryam & Labencki, Tanya & Boyd, Duncan & Trolle, Dennis & Arhonditsis, George B., 2012. "A Bayesian synthesis of predictions from different models for setting water quality criteria," Ecological Modelling, Elsevier, vol. 242(C), pages 127-145.
    15. Mata Almonacid, Pablo & Medel, Carolina, 2022. "A structure-preserving model for the dynamics of estuarine ecosystems and its application in western Patagonia fjords," Ecological Modelling, Elsevier, vol. 466(C).
    16. Alessio C. Spassiani & Matthew S. Mason & Vincent Y. S. Cheng, 2023. "An Australian convective wind gust climatology using Bayesian hierarchical modelling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2037-2067, September.
    17. Shimoda, Yuko & Arhonditsis, George B., 2016. "Phytoplankton functional type modelling: Running before we can walk? A critical evaluation of the current state of knowledge," Ecological Modelling, Elsevier, vol. 320(C), pages 29-43.
    18. Zhang, Xiaodong & Dimitrov, Nikolay, 2024. "Variable importance analysis of wind turbine extreme responses with Shapley value explanation," Renewable Energy, Elsevier, vol. 232(C).
    19. Zhang, Weitao & Watson, Sue B. & Rao, Yerubandi R. & Kling, Hedy J., 2013. "A linked hydrodynamic, water quality and algal biomass model for a large, multi-basin lake: A working management tool," Ecological Modelling, Elsevier, vol. 269(C), pages 37-50.
    20. Ramin, Maryam & Perhar, Gurbir & Shimoda, Yuko & Arhonditsis, George B., 2012. "Examination of the effects of nutrient regeneration mechanisms on plankton dynamics using aquatic biogeochemical modeling," Ecological Modelling, Elsevier, vol. 240(C), pages 139-155.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:121:y:2025:i:11:d:10.1007_s11069-025-07289-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.