Articles | Volume 21, issue 9
https://doi.org/10.5194/nhess-21-2773-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-2773-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment
Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Alexandre Badoux
Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Brian W. McArdell
Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Elena Leonarduzzi
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Peter Molnar
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Related authors
Paul Emil Schmid, Jacob Hirschberg, Raffaele Spielmann, and Jordan Aaron
EGUsphere, https://doi.org/10.5194/egusphere-2025-743, https://doi.org/10.5194/egusphere-2025-743, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Debris flows are fast-moving water-sediment mixtures in steep mountain channels, posing risks to infrastructure and lives. Traditional analysis is slow and labor-intensive. This study presents a method using 3D LiDAR and AI to detect and track moving objects like rocks and wood during events. By converting 3D data into 2D images, it enables fast, accurate measurement of object speed and size, even at night. This improves debris-flow monitoring, enhancing hazard understanding and mitigation.
Amber van Hamel, Peter Molnar, Joren Janzing, and Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 29, 2975–2995, https://doi.org/10.5194/hess-29-2975-2025, https://doi.org/10.5194/hess-29-2975-2025, 2025
Short summary
Short summary
Suspended sediment is a natural component of rivers, but extreme suspended sediment concentrations (SSCs) can have negative impacts on water use and aquatic ecosystems. We identify the main factors influencing the spatial and temporal variability of annual SSC regimes and extreme SSC events. Our analysis shows that different processes are more important for annual SSC regimes than for extreme events and that compound events driven by glacial melt and high-intensity rainfall led to the highest SSCs.
Chantal Schmidt, David Mair, Naki Akçar, Marcus Christl, Negar Haghipour, Christof Vockenhuber, Philip Gautschi, Brian McArdell, and Fritz Schlunegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3055, https://doi.org/10.5194/egusphere-2025-3055, 2025
This preprint is open for discussion and under review for Earth Surface Dynamics (ESurf).
Short summary
Short summary
Our study examines erosion in a small, pre-Alpine basin by using cosmogenic nuclides in river sediments. Based on a dense measuring network we were able to distinguish two main zones: an upper zone with slow erosion of surface material, and a steeper, lower zone where faster erosion is driven by landslides. The data suggests that sediment has been constantly produced over thousands of years, indicating a stable, long-term balance between contrasting erosion processes.
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025, https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Short summary
The experimentally based μ(I) rheology, widely used for gravitational mass flows, is reinterpreted as a Voellmy-type relationship to highlight its link to grain flow theory. Through block modeling and case studies, we establish its equivalence to μ(R) rheology. μ(I) models shear thinning but fails to capture acceleration and deceleration processes and deposit structure. Incorporating fluctuation energy in μ(R) improves accuracy, refining mass flow modeling and revealing practical challenges.
Paul Emil Schmid, Jacob Hirschberg, Raffaele Spielmann, and Jordan Aaron
EGUsphere, https://doi.org/10.5194/egusphere-2025-743, https://doi.org/10.5194/egusphere-2025-743, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Debris flows are fast-moving water-sediment mixtures in steep mountain channels, posing risks to infrastructure and lives. Traditional analysis is slow and labor-intensive. This study presents a method using 3D LiDAR and AI to detect and track moving objects like rocks and wood during events. By converting 3D data into 2D images, it enables fast, accurate measurement of object speed and size, even at night. This improves debris-flow monitoring, enhancing hazard understanding and mitigation.
Mosisa Tujuba Wakjira, Nadav Peleg, Johan Six, and Peter Molnar
Hydrol. Earth Syst. Sci., 29, 863–886, https://doi.org/10.5194/hess-29-863-2025, https://doi.org/10.5194/hess-29-863-2025, 2025
Short summary
Short summary
In this study, we implement a climate, water, and crop interaction model to evaluate current conditions and project future changes in rainwater availability and its yield potential, with the goal of informing adaptation policies and strategies in Ethiopia. Although climate change is likely to increase rainfall in Ethiopia, our findings suggest that water-scarce croplands in Ethiopia are expected to face reduced crop yields during the main growing season due to increases in temperature.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Zheng Chen, Siming He, Alexandre Badoux, and Dieter Rickenmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2525, https://doi.org/10.5194/egusphere-2024-2525, 2024
Short summary
Short summary
We developed a novel bedload monitoring system, which integrates phased microphone arrays and an accelerometer for enhanced performance. This monitoring system can be used to identify bedload particle impact locations on the system plate with precision using beamforming techniques applied to the generated microphone signals. Optimal use of multiple types of signals recorded by the monitoring system improves the accuracy of bedload size prediction.
Daniel Bolliger, Fritz Schlunegger, and Brian W. McArdell
Nat. Hazards Earth Syst. Sci., 24, 1035–1049, https://doi.org/10.5194/nhess-24-1035-2024, https://doi.org/10.5194/nhess-24-1035-2024, 2024
Short summary
Short summary
We analysed data from the Illgraben debris flow monitoring station, Switzerland, and we modelled these flows with a debris flow runout model. We found that no correlation exists between the grain size distribution, the mineralogical composition of the matrix, and the debris flow properties. The flow properties rather appear to be determined by the flow volume, from which most other parameters can be derived.
Jessica Droujko, Srividya Hariharan Sudha, Gabriel Singer, and Peter Molnar
Earth Surf. Dynam., 11, 881–897, https://doi.org/10.5194/esurf-11-881-2023, https://doi.org/10.5194/esurf-11-881-2023, 2023
Short summary
Short summary
We combined data from satellite images with data measured from a kayak in order to understand the propagation of fine sediment in the Vjosa River. We were able to find some storm-activated and some permanent sources of sediment. We also estimated how much fine sediment is carried into the Adriatic Sea by the Vjosa River: approximately 2.5 Mt per year, which matches previous findings. With our work, we hope to show the potential of open-access satellite images.
Nicolas Steeb, Virginia Ruiz-Villanueva, Alexandre Badoux, Christian Rickli, Andrea Mini, Markus Stoffel, and Dieter Rickenmann
Earth Surf. Dynam., 11, 487–509, https://doi.org/10.5194/esurf-11-487-2023, https://doi.org/10.5194/esurf-11-487-2023, 2023
Short summary
Short summary
Various models have been used in science and practice to estimate how much large wood (LW) can be supplied to rivers. This contribution reviews the existing models proposed in the last 35 years and compares two of the most recent spatially explicit models by applying them to 40 catchments in Switzerland. Differences in modelling results are discussed, and results are compared to available observations coming from a unique database.
Tobias Siegfried, Aziz Ul Haq Mujahid, Beatrice Sabine Marti, Peter Molnar, Dirk Nikolaus Karger, and Andrey Yakovlev
EGUsphere, https://doi.org/10.5194/egusphere-2023-520, https://doi.org/10.5194/egusphere-2023-520, 2023
Preprint archived
Short summary
Short summary
Our study investigates climate change impacts on water resources in Central Asia's high-mountain regions. Using new data and a stochastic soil moisture model, we found increased precipitation and higher temperatures in the future, leading to higher water discharge despite decreasing glacier melt contributions. These findings are crucial for understanding and preparing for climate change effects on Central Asia's water resources, with further research needed on extreme weather event impacts.
Qinggang Gao, Christian Zeman, Jesus Vergara-Temprado, Daniela C. A. Lima, Peter Molnar, and Christoph Schär
Weather Clim. Dynam., 4, 189–211, https://doi.org/10.5194/wcd-4-189-2023, https://doi.org/10.5194/wcd-4-189-2023, 2023
Short summary
Short summary
We developed a vortex identification algorithm for realistic atmospheric simulations. The algorithm enabled us to obtain a climatology of vortex shedding from Madeira Island for a 10-year simulation period. This first objective climatological analysis of vortex streets shows consistency with observed atmospheric conditions. The analysis shows a pronounced annual cycle with an increasing vortex shedding rate from April to August and a sudden decrease in September.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
Short summary
Short summary
Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Dieter Rickenmann, Lorenz Ammann, Tobias Nicollier, Stefan Boss, Bruno Fritschi, Gilles Antoniazza, Nicolas Steeb, Zheng Chen, Carlos Wyss, and Alexandre Badoux
Earth Surf. Dynam., 10, 1165–1183, https://doi.org/10.5194/esurf-10-1165-2022, https://doi.org/10.5194/esurf-10-1165-2022, 2022
Short summary
Short summary
The Swiss plate geophone system has been installed and tested in more than 20 steep gravel-bed streams. It is an indirect bedload transport measuring system. We compare the performance of this system with three alternative surrogate measuring systems, using calibration measurements with direct bedload samples from three field sites and an outdoor flume facility. Three of the four systems resulted in robust calibration relations between signal impulse counts and transported bedload mass.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-345, https://doi.org/10.5194/hess-2022-345, 2022
Publication in HESS not foreseen
Short summary
Short summary
As the stress on water resources from climate change grows, we need models that represent water processes at the scale of counties, states, and even countries in order to make viable predictions about things will change. While such models are powerful, they can be cumbersome to deal with because they are so large. This research explores a novel way of increasing the efficiency of large-scale hydrologic models using an approach called Simulation-Based Inference.
Silvan Ragettli, Tabea Donauer, Peter Molnar, Ron Delnoije, and Tobias Siegfried
Earth Surf. Dynam., 10, 797–815, https://doi.org/10.5194/esurf-10-797-2022, https://doi.org/10.5194/esurf-10-797-2022, 2022
Short summary
Short summary
This paper presents a novel methodology to identify and quantitatively analyze deposition and erosion patterns in ephemeral ponds or in perennial lakes with strong water level fluctuations. We apply this method to unravel the water and sediment balance of Lac Wégnia, a designated Ramsar site in Mali. The study can be a showcase for monitoring Sahelian lakes using remote sensing data, as it sheds light on the actual drivers of change in Sahelian lakes.
Zheng Chen, Siming He, Tobias Nicollier, Lorenz Ammann, Alexandre Badoux, and Dieter Rickenmann
Earth Surf. Dynam., 10, 279–300, https://doi.org/10.5194/esurf-10-279-2022, https://doi.org/10.5194/esurf-10-279-2022, 2022
Short summary
Short summary
Bedload flux quantification remains challenging in river dynamics due to variable transport modes. We used a passive monitoring device to record the acoustic signals generated by the impacts of bedload particles with different transport modes, and established the relationship between the triggered signals and bedload characteristics. The findings of this study could improve our understanding of the monitoring system and bedload transport process, and contribute to bedload size classification.
Elena Leonarduzzi, Brian W. McArdell, and Peter Molnar
Hydrol. Earth Syst. Sci., 25, 5937–5950, https://doi.org/10.5194/hess-25-5937-2021, https://doi.org/10.5194/hess-25-5937-2021, 2021
Short summary
Short summary
Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open-access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
Georgios Maniatis, Trevor Hoey, Rebecca Hodge, Dieter Rickenmann, and Alexandre Badoux
Earth Surf. Dynam., 8, 1067–1099, https://doi.org/10.5194/esurf-8-1067-2020, https://doi.org/10.5194/esurf-8-1067-2020, 2020
Short summary
Short summary
One of the most interesting problems in geomorphology concerns the conditions that mobilise sediments grains in rivers. Newly developed
smartpebbles allow for the measurement of those conditions directly if a suitable framework for analysis is followed. This paper connects such a framework with the physics used to described sediment motion and presents a series of laboratory and field smart-pebble deployments. Those quantify how grain shape affects the motion of coarse sediments in rivers.
Marius G. Floriancic, Wouter R. Berghuijs, Tobias Jonas, James W. Kirchner, and Peter Molnar
Hydrol. Earth Syst. Sci., 24, 5423–5438, https://doi.org/10.5194/hess-24-5423-2020, https://doi.org/10.5194/hess-24-5423-2020, 2020
Short summary
Short summary
Low river flows affect societies and ecosystems. Here we study how precipitation and potential evapotranspiration shape low flows across a network of 380 Swiss catchments. Low flows in these rivers typically result from below-average precipitation and above-average potential evapotranspiration. Extreme low flows result from long periods of the combined effects of both drivers.
Elena Leonarduzzi and Peter Molnar
Nat. Hazards Earth Syst. Sci., 20, 2905–2919, https://doi.org/10.5194/nhess-20-2905-2020, https://doi.org/10.5194/nhess-20-2905-2020, 2020
Short summary
Short summary
Landslides are a natural hazard that affects alpine regions. Here we focus on rainfall-induced shallow landslides and one of the most widely used approaches for their predictions: rainfall thresholds. We design several comparisons utilizing a landslide database and rainfall records in Switzerland. We find that using daily rather than hourly rainfall might be a better option in some circumstances, and mean annual precipitation and antecedent wetness can improve predictions at the regional scale.
Cited articles
Abancó, C., Hürlimann, M., Moya, J., and Berenguer, M.: Critical
rainfall conditions for the initiation of torrential flows. Results from the
Rebaixader catchment (Central Pyrenees), J. Hydrol., 541,
218–229, https://doi.org/10.1016/j.jhydrol.2016.01.019, 2016. a, b, c
Badoux, A., Graf, C., Rhyner, J., Kuntner, R., and McArdell, B. W.: A
debris-flow alarm system for the Alpine Illgraben catchment: Design and
performance, Nat. Hazards, 49, 517–539, https://doi.org/10.1007/s11069-008-9303-x,
2009. a, b, c, d
Badoux, A., Turowski, J. M., Mao, L., Mathys, N., and Rickenmann, D.: Rainfall intensity–duration thresholds for bedload transport initiation in small Alpine watersheds, Nat. Hazards Earth Syst. Sci., 12, 3091–3108, https://doi.org/10.5194/nhess-12-3091-2012, 2012. a, b
Badoux, A., Andres, N., Techel, F., and Hegg, C.: Natural hazard fatalities in Switzerland from 1946 to 2015, Nat. Hazards Earth Syst. Sci., 16, 2747–2768, https://doi.org/10.5194/nhess-16-2747-2016, 2016. a
Bennett, G. L., Molnar, P., Eisenbeiss, H., and Mcardell, B. W.: Erosional
power in the Swiss Alps: Characterization of slope failure in the Illgraben,
Earth Surf. Proc. Land., 37, 1627–1640,
https://doi.org/10.1002/esp.3263, 2012. a
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. a
Berger, C., McArdell, B. W., and Schlunegger, F.: Sediment transfer patterns
at the Illgraben catchment, Switzerland: Implications for the time scales of
debris flow activities, Geomorphology, 125, 421–432,
https://doi.org/10.1016/j.geomorph.2010.10.019, 2011. a, b
Berti, M. and Simoni, A.: Experimental evidences and numerical modelling of
debris flow initiated by channel runoff, Landslides, 2, 171–182,
https://doi.org/10.1007/s10346-005-0062-4, 2005. a
Berti, M., Bernard, M., Simoni, A., and Gregoretti, C.: Physical
interpretation of rainfall thresholds for runoff-generated debris flows,
J. Geophys. Res.-Earth, 125, 1–25,
https://doi.org/10.1029/2019JF005513, 2020. a
Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a, b, c
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010. a, b
Caine, N.: The Rainfall Intensity -Duration Control of Shallow Landslides and
Debris Flows, Geogr. Ann. A, 62, 23–27,
https://doi.org/10.1080/04353676.1980.11879996, 1980. a, b, c
Cannon, S. H., Gartner, J. E., Rupert, M. G., Michael, J. A., Rea, A. H., and
Parrett, C.: Predicting the probability and volume of postwildfire debris
flows in the intermountain western United States, Bull. Geol.
Soc. Am., 122, 127–144, https://doi.org/10.1130/B26459.1, 2010. a, b
Chmiel, M., Walter, F., Wenner, M., Zhang, Z., McArdell, B. W., and Hibert, C.:
Machine Learning Improves Debris Flow Warning, Geophys. Res.
Lett., 48, e2020GL090874, https://doi.org/10.1029/2020GL090874, 2021. a
Coe, J. A., Kinner, D. A., and Godt, J. W.: Initiation conditions for debris
flows generated by runoff at Chalk Cliffs, central Colorado, Geomorphology,
96, 270–297, https://doi.org/10.1016/j.geomorph.2007.03.017, 2008. a, b, c
de Haas, T., Nijland, W., de Jong, S. M., and Mcardell, B. W.: How memory
effects, check dams, and channel geometry control erosion and deposition by
debris flows, Sci. Rep.-UK, 10, 1–8,
https://doi.org/10.1038/s41598-020-71016-8, 2020. a
Domènech, G., Fan, X., Scaringi, G., van Asch, T. W., Xu, Q., Huang, R.,
and Hales, T. C.: Modelling the role of material depletion, grain coarsening
and revegetation in debris flow occurrences after the 2008 Wenchuan
earthquake, Eng. Geol., 250, 34–44,
https://doi.org/10.1016/j.enggeo.2019.01.010, 2019. a
Dowling, C. A. and Santi, P. M.: Debris flows and their toll on human life: A
global analysis of debris-flow fatalities from 1950 to 2011, Nat.
Hazards, 71, 203–227, https://doi.org/10.1007/s11069-013-0907-4, 2014. a
Dunkerley, D.: Identifying individual rain events from pluviograph records: A
review with analysis of data from an Australian dryland site, Hydrol.
Process., 22, 5024–5036, https://doi.org/10.1002/hyp.7122, 2008. a
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett.,
27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006. a
Frei, C. and Schär, C.: A precipitation climatology of the Alps from
high-resolution rain-gauge observations, Int. J.
Climatol., 18, 873–900,
https://doi.org/10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9, 1998. a
Fryxwell, F. M. and Horberg, L.: Alpine mudflows in Grand Teton National Park,
Wyoming, GSA Bulletin, 54, 457–472, https://doi.org/10.1130/GSAB-54-457, 1943. a
Gaál, L., Molnar, P., and Szolgay, J.: Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland, Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, 2014. a, b, c
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S.,
Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of
rainfall thresholds for shallow landslide forecasting in Sicily, southern
Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019,
2015. a
Gariano, S. L., Melillo, M., Peruccacci, S., and Brunetti, M. T.: How much
does the rainfall temporal resolution affect rainfall thresholds for
landslide triggering?, Nat. Hazards, 100, 655–670,
https://doi.org/10.1007/s11069-019-03830-x, 2020. a
Gregoretti, C.: The initiation of debris flow at high slopes: Experimental
results, J. Hydraul. Res., 38, 83–88,
https://doi.org/10.1080/00221680009498343, 2000. a
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds
for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, https://doi.org/10.1007/s00703-007-0262-7, 2007. a
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I.,
Rossi, M., and Melillo, M.: Geographical landslide early warning systems,
Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973,
2020. a
Hanssen, A. W. and Kuipers, W. J. A.: On the relationship between the frequency of rain and various meteorological parameters, Meded. Verh, 81, 2–15, 1965. a
Hilker, N., Badoux, A., and Hegg, C.: The Swiss flood and landslide damage database 1972–2007, Nat. Hazards Earth Syst. Sci., 9, 913–925, https://doi.org/10.5194/nhess-9-913-2009, 2009. a, b
Hirschberg, J.: Source code for: Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment, EnviDat [code], https://doi.org/10.16904/envidat.240, 2021. a
Hirschberg, J., McArdell, B. W., Badoux, A., and Molnar, P.: Analysis of
rainfall and runoff for debris flows at the Illgraben catchment,
Switzerland, in: Debris-Flow Hazards Mitigation: Mechanics, Monitoring,
Modeling, and Assessment – Proceedings of the 7th International Conference on
Debris-Flow Hazards Mitigation, 693–700, 2019. a, b
Hirschberg, J., Fatichi, S., Bennett, G. L., McArdell, B. W., Peleg, N., Lane,
S. N., Schlunegger, F., and Molnar, P.: Climate Change Impacts on Sediment
Yield and Debris-Flow Activity in an Alpine Catchment, J.
Geophys. Res.-Earth, 126, e2020JF005739, https://doi.org/10.1029/2020JF005739, 2021. a, b, c, d
Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update, Landslides, 11, 167–194,
https://doi.org/10.1007/s10346-013-0436-y, 2014. a
Hürlimann, M., Rickenmann, D., and Graf, C.: Field and monitoring data
of debris-flow events in the Swiss Alps, Can. Geotech. J., 40,
161–175, https://doi.org/10.1139/t02-087, 2003. a, b, c
Hydrological Atlas of Switzerland: Mean Precipitation, [data set], available at:
https://hydrologicalatlas.ch (last access: 4 September 2020-09-), 2015. a
Iverson, R. M.: The Physics of Debris Flows, Rev. Geophys., 35,
245–296, https://doi.org/10.1029/97RG00426, 1997. a
Iverson, R. M., Reid, M. E., Logan, M., LaHusen, R. G., Godt, J. W., and
Griswold, J. P.: Positive feedback and momentum growth during debris-flow
entrainment of wet bed sediment, Nat. Geosci., 4, 116–121,
https://doi.org/10.1038/ngeo1040, 2011. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: Resampling Methods,
Springer New York, New York, NY, 175–201,
https://doi.org/10.1007/978-1-4614-7138-7_5, 2013a. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: Tree-Based Methods,
Springer New York, New York, NY, 303–335,
https://doi.org/10.1007/978-1-4614-7138-7_8, 2013b. a
Johnson, A. M. and Rodine, J. D.: Debris flow, in: Slope instability, edited
by: Brunsden, D. and Prior, D., Wiley and Sons, Chichester, England, 1984. a
Kern, A. N., Addison, P., Oommen, T., Salazar, S. E., and Coffman, R. A.:
Machine Learning Based Predictive Modeling of Debris Flow Probability
Following Wildfire in the Intermountain Western United States, Math.
Geosci., 49, 717–735, https://doi.org/10.1007/s11004-017-9681-2, 2017. a, b
Lichtenhahn, C.: Zwei Betonmauern: die Geschieberückhaltesperre am Illgraben [Wallis] und die Staumauer des Hochwasserschutzbeckens an der Orlegna im Bergell [Graubünden], Internationales Symposium Interpraevent, Villach, Austria: F.f.v. Hochwasserbekämpfung, 451–456, 1971. a
Marra, F.: Rainfall thresholds for landslide occurrence: systematic
underestimation using coarse temporal resolution data, Nat. Hazards,
95, 889–890, https://doi.org/10.1007/s11069-018-3508-4, 2019. a
Marra, F., Nikolopoulos, E. I., Creutin, J. D., and Borga, M.: Space–time
organization of debris flows-triggering rainfall and its effect on the
identification of the rainfall threshold relationship, J. Hydrol.,
541, 246–255, https://doi.org/10.1016/j.jhydrol.2015.10.010, 2016. a, b, c
McArdell, B. W.: Field Measurements of Forces in Debris Flows at the
Illgraben: Implications for Channel-Bed Erosion, International Journal of
Erosion Control Engineering, 9, 194–198, https://doi.org/10.13101/ijece.9.194, 2016. a
McArdell, B. W. and Badoux, A.: Influence of rainfall on the initiation of
debris flows at the Illgraben catchment, canton of Valais, Switzerland, in:
Geophysical Research Abstracts, 9, 8804, 2007. a
McArdell, B. W. and Hirschberg, J.: Debris-flow volumes at the Illgraben
2000–2017, EnviDat [data set], https://doi.org/10.16904/envidat.173, 2020. a, b, c
McArdell, B. W. and Sartori, M.: The Illgraben Torrent System, 367–378,
Springer International Publishing, Cham, https://doi.org/10.1007/978-3-030-43203-4_25,
2021. a
McArdell, B. W., Bartelt, P., and Kowalski, J.: Field observations of basal
forces and fluid pore pressure in a debris flow, Geophys. Res.
Lett., 34, 2–5, https://doi.org/10.1029/2006GL029183, 2007. a
McCoy, S. W., Kean, J. W., Coe, J. A., Tucker, G. E., Staley, D. M., and
Wasklewicz, T. A.: Sediment entrainment by debris flows: In situ
measurements from the headwaters of a steep catchment, J.
Geophys. Res.-Earth, 117, F03016, https://doi.org/10.1029/2011JF002278, 2012. a
Mirus, B., Morphew, M., and Smith, J.: Developing Hydro-Meteorological
Thresholds for Shallow Landslide Initiation and Early Warning, Water, 10,
1274, https://doi.org/10.3390/w10091274, 2018. a, b
Mostbauer, K., Kaitna, R., Prenner, D., and Hrachowitz, M.: The temporally varying roles of rainfall, snowmelt and soil moisture for debris flow initiation in a snow-dominated system, Hydrol. Earth Syst. Sci., 22, 3493–3513, https://doi.org/10.5194/hess-22-3493-2018, 2018. a
Nikolopoulos, E. I., Destro, E., Bhuiyan, M. A. E., Borga, M., and Anagnostou, E. N.: Evaluation of predictive models for post-fire debris flow occurrence in the western United States, Nat. Hazards Earth Syst. Sci., 18, 2331–2343, https://doi.org/10.5194/nhess-18-2331-2018, 2018. a, b, c, d
Nikolopoulos, E. I., Crema, S., Marchi, L., Marra, F., Guzzetti, F., and Borga,
M.: Impact of uncertainty in rainfall estimation on the identification of
rainfall thresholds for debris flow occurrence, Geomorphology, 221,
286–297, https://doi.org/10.1016/j.geomorph.2014.06.015, 2014. a, b
Papa, M. N., Medina, V., Ciervo, F., and Bateman, A.: Derivation of critical rainfall thresholds for shallow landslides as a tool for debris flow early warning systems, Hydrol. Earth Syst. Sci., 17, 4095–4107, https://doi.org/10.5194/hess-17-4095-2013, 2013. a
Pastorello, R., Hürlimann, M., and D'Agostino, V.: Correlation between
the rainfall, sediment recharge, and triggering of torrential flows in the
Rebaixader catchment (Pyrenees, Spain), Landslides, 15, 1921–1934,
https://doi.org/10.1007/s10346-018-1000-6, 2018. a
Pedregosa, F., Michel, V., Varoquaux, G., Thirion, B., Dubourg, V., Passos, A.,
Perrot, M., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Vanderplas,
J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B.,
Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand,
M., and Duchesnay, É.: Scikit-learn: Machine Learning in Python,
J. Machine Learn. Res., 12, 2825–2830,
2011. a
Peirce, C. S.: The numerical measure of the success of predictions, Science,
4, 453–454, https://doi.org/10.1126/science.ns-4.93.453-a, 1884. a
Peruccacci, S., Brunetti, M. T., Luciani, S., Vennari, C., and Guzzetti, F.:
Lithological and seasonal control on rainfall thresholds for the possible
initiation of landslides in central Italy, Geomorphology, 139–140, 79–90,
https://doi.org/10.1016/j.geomorph.2011.10.005, 2012. a
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., and
Guzzetti, F.: Rainfall thresholds for possible landslide occurrence in
Italy, Geomorphology, 290, 39–57, https://doi.org/10.1016/j.geomorph.2017.03.031,
2017. a
Postance, B., Hillier, J., Dijkstra, T., and Dixon, N.: Comparing threshold
definition techniques for rainfall-induced landslides: A national assessment
using radar rainfall, Earth Surf. Proc. Land., 43, 553–560,
https://doi.org/10.1002/esp.4202, 2018. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Rickenmann, D., Hürlimann, M., Graf, C., Näf, D., and Weber, D.:
Murgang-Beobachtungsstationen in der Schweiz, in: Wasser Energie Luft,
93, 1–8, 2001. a
Schlunegger, F., Badoux, A., McArdell, B. W., Gwerder, C., Schnydrig, D.,
Rieke-Zapp, D., and Molnar, P.: Limits of sediment transfer in an alpine
debris-flow catchment, Illgraben, Switzerland, Quaternary Sci. Rev.,
28, 1097–1105, https://doi.org/10.1016/j.quascirev.2008.10.025, 2009. a, b
Schneuwly-Bollschweiler, M. and Stoffel, M.: Hydrometeorological triggers of
periglacial debris flows in the Zermatt valley (Switzerland) since 1864,
J. Geophys. Res.-Earth, 117, 1–12,
https://doi.org/10.1029/2011JF002262, 2012. a
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent
literature on rainfall thresholds for landslide occurrence, Landslides, 15,
1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018. a, b, c
Stähli, M., Sättele, M., Huggel, C., McArdell, B. W., Lehmann, P., Van Herwijnen, A., Berne, A., Schleiss, M., Ferrari, A., Kos, A., Or, D., and Springman, S. M.: Monitoring and prediction in early warning systems for rapid mass movements, Nat. Hazards Earth Syst. Sci., 15, 905–917, https://doi.org/10.5194/nhess-15-905-2015, 2015. a
Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., and Laber, J. L.:
Objective definition of rainfall intensity-duration thresholds for the
initiation of post-fire debris flows in southern California, Landslides, 10,
547–562, https://doi.org/10.1007/s10346-012-0341-9, 2013. a, b, c, d
Staley, D. M., Negri, J. A., Kean, J. W., Laber, J. L., Tillery, A. C., and
Youberg, A. M.: 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, 2017. a, b
Takahashi, T.: Mechanical Characteristics of Debris Flow, J. Hydr. Eng. Div.-ASCE, 104, 1153–1169, 1978. a
Takahashi, T.: Debris Flow, Annu. Rev. Fluid Mech., 13,
57–77, https://doi.org/10.1146/annurev.fl.13.010181.000421, 1981. a
Tang, H., McGuire, L. A., Rengers, F. K., Kean, J. W., Staley, D. M., and
Smith, J. B.: Developing and Testing Physically Based Triggering Thresholds
for Runoff-Generated Debris Flows, Geophys. Res. Lett., 46,
8830–8839, https://doi.org/10.1029/2019GL083623, 2019. a
Tognacca, C.: Beitrag zur Untersuchung der Entstehungsmechanismen von
Murgangen, Phd thesis, ETH Zürich, Zürich, https://doi.org/10.3929/ethz-a-010025751,
1999. a
Wenner, M., Hibert, C., van Herwijnen, A., Meier, L., and Walter, F.: Near-real-time automated classification of seismic signals of slope failures with continuous random forests, Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, 2021. a
Wicki, A., Lehmann, P., Hauck, C., Seneviratne, S. I., Waldner, P., and
Stähli, M.: Assessing the potential of soil moisture measurements for
regional landslide early warning, Landslides, 17, 1881–1896,
https://doi.org/10.1007/s10346-020-01400-y, 2020.
a
Youden, W. J.: Index for rating diagnostic tests, Cancer, 3, 32–35,
https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3, 1950. a
Short summary
Debris-flow prediction is often based on rainfall thresholds, but uncertainty assessments are rare. We established rainfall thresholds using two approaches and find that 25 debris flows are needed for uncertainties to converge in an Alpine basin and that the suitable method differs for regional compared to local thresholds. Finally, we demonstrate the potential of a statistical learning algorithm to improve threshold performance. These findings are helpful for early warning system development.
Debris-flow prediction is often based on rainfall thresholds, but uncertainty assessments are...
Altmetrics
Final-revised paper
Preprint