Articles | Volume 22, issue 5
https://doi.org/10.5194/nhess-22-1627-2022
https://doi.org/10.5194/nhess-22-1627-2022
Research article
 | 
17 May 2022
Research article |  | 17 May 2022

Variable hydrograph inputs for a numerical debris-flow runout model

Andrew Mitchell, Sophia Zubrycky, Scott McDougall, Jordan Aaron, Mylène Jacquemart, Johannes Hübl, Roland Kaitna, and Christoph Graf

Related authors

Tailings-flow runout analysis: examining the applicability of a semi-physical area–volume relationship using a novel database
Negar Ghahramani, Andrew Mitchell, Nahyan M. Rana, Scott McDougall, Stephen G. Evans, and W. Andy Take
Nat. Hazards Earth Syst. Sci., 20, 3425–3438, https://doi.org/10.5194/nhess-20-3425-2020,https://doi.org/10.5194/nhess-20-3425-2020, 2020
Short summary

Related subject area

Landslides and Debris Flows Hazards
Brief communication: Monitoring impending slope failure with very high-resolution spaceborne synthetic aperture radar
Andrea Manconi, Yves Bühler, Andreas Stoffel, Johan Gaume, Qiaoping Zhang, and Valentyn Tolpekin
Nat. Hazards Earth Syst. Sci., 24, 3833–3839, https://doi.org/10.5194/nhess-24-3833-2024,https://doi.org/10.5194/nhess-24-3833-2024, 2024
Short summary
Size scaling of large landslides from incomplete inventories
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024,https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024,https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024,https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024,https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary

Cited articles

Aaron, J., Stark, T. D., and Baghdady, A. K.: Closure to “Oso, Washington, Landslide of March 22, 2014: Dynamic Analysis” by Jordan Aaron, Oldrich Hungr, Timothy D. Stark, and Ahmed, K. Baghdady, J. Geotech. Geoenviron., 144, 07018023, https://doi.org/10.1061/(ASCE)GT.1943-5606.0001748, 2018. 
Arai, M., Hübl, J., and Kaitna, R.: Occurrence conditions of roll waves for three grain-fluid models and comparison with results from experiments and field observations, Geophys. J. Int., 195, 1464–1480, https://doi.org/10.1093/gji/ggt352, 2013. 
Bennett, G. L., Molnar, P. McArdell, B. W., and Burlando, P.: A probabilistic sediment cascade model of sediment transfer in the Illgraben, Water Resour. Res., 50, 1225–1244, https://doi.org/10.1002/2013WR013806, 2014. 
Berti, M., Bernard, M., Gregoretti, C., and Simoni, A.: Physical interpretation of rainfall thresholds for runoff-generated debris flows, J. Geophys. Res.-Earth, 125, e2019JF005513, https://doi.org/10.1029/2019JF005513, 2020. 
Bovis, M. J. and Jakob, M.: The role of debris supply conditions in predicting debris flow activity, Earth Surf. Proc. Land., 24, 1039–1054, https://doi.org/10.1002/(SICI)1096-9837(199910)24:11<1039::AID-ESP29>3.0.CO;2-U, 1999. 
Download
Short summary
Debris flows are complex, surging movements of sediment and water. Discharge observations from well-studied debris-flow channels were used as inputs for a numerical modelling study of the downstream effects of chaotic inflows. The results show that downstream impacts are sensitive to inflow conditions. Inflow conditions for predictive modelling are highly uncertain, and our method provides a means to estimate the potential variability in future events.
Altmetrics
Final-revised paper
Preprint