Articles | Volume 25, issue 11
https://doi.org/10.5194/hess-25-5937-2021
© 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/hess-25-5937-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2021
Research article |
| 15 Nov 2021
| 15 Nov 2021
Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
Elena Leonarduzzi
CORRESPONDING AUTHOR
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Brian W. McArdell
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Peter Molnar
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Related authors
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.
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.
Jacob Hirschberg, Alexandre Badoux, Brian W. McArdell, Elena Leonarduzzi, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 21, 2773–2789, https://doi.org/10.5194/nhess-21-2773-2021, https://doi.org/10.5194/nhess-21-2773-2021, 2021
Short summary
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.
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.
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.
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.
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.
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.
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.
Jacob Hirschberg, Alexandre Badoux, Brian W. McArdell, Elena Leonarduzzi, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 21, 2773–2789, https://doi.org/10.5194/nhess-21-2773-2021, https://doi.org/10.5194/nhess-21-2773-2021, 2021
Short summary
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.
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
Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review and
new perspectives, B. Eng. Geol. Environ., 58,
21–44, 1999. a
Anagnostopoulos, G. G., Fatichi, S., and Burlando, P.: An advanced
process-based distributed model for the investigation of rainfall-induced
landslides: The effect of process representation and boundary conditions,
Water Resour. Research, 51, 7501–7523, https://doi.org/10.1002/2015WR016909, 2015. a, b
Anderson, S. A. and Sitar, N.: Analysis of rainfall-induced debris flows,
J. Geotechn. Eng., 121, 544–552, 1995. a
Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic regression
for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central
Japan, Geomorphology, 65, 15–31, 2005. a
Baum, R. L., Savage, W. Z., and Godt, J. W.: TRIGRS – a Fortran program for
transient rainfall infiltration and grid-based regional slope-stability
analysis, US geological survey open-file report, 424, 38, https://doi.org/10.3133/ofr02424, 2002. a
Baum, R. L., Savage, W. Z., and Godt, J. W.: TRIGRS-A Fortran program for
transient rainfall infiltration and grid-based regional slope-stability
analysis, version 2.0, Tech. rep., US Geological Survey, https://doi.org/10.3133/ofr20081159, 2008. a, b
Beven, K.: Topmodel, in: Computer Models of Watershed Hydrology, Water Resour.
Pub., edited by : Singh, V. P., 627–668, ISBN 0-918334-91-8, 1995. a
Bodeneignungskarte der Schweiz, Geodaten, 13147140,
available at: https://www.bfs.admin.ch/bfs/en/home/services/geostat/swiss-federal-statistics-geodata/land-use-cover-suitability/derivative-complementary-data/swiss-soil-suitability-map.assetdetail.13147140.html, last access: July 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, b
Brocca, L., Ponziani, F., Moramarco, T., Melone, F., Berni, N., and Wagner, W.:
Improving landslide forecasting using ASCAT-derived soil moisture data: A
case study of the Torgiovannetto landslide in central Italy, Remote Sens.,
4, 1232–1244, 2012. a
Casadei, M., Dietrich, W., and Miller, N.: Testing a model for predicting the
timing and location of shallow landslide initiation in soil-mantled
landscapes, Earth Surf. Proc. Landf., 28, 925–950, 2003. a
Chung, C.-J. F., Fabbri, A. G., and Van Westen, C. J.: Multivariate regression
analysis for landslide hazard zonation, in: Geographical information systems
in assessing natural hazards, Springer, Dordrecht, 5, 107–133, https://doi.org/10.1007/978-94-015-8404-3_7, 1995. a
Cohen, D., Lehmann, P., and Or, D.: Fiber bundle model for multiscale modeling
of hydromechanical triggering of shallow landslides, Water Resour.
Res., 45, 1–20, https://doi.org/10.1029/2009WR007889, 2009. a, b
Dietrich, W. E. and Montgomery, D. R.: SHALSTAB: a digital terrain model for
mapping shallow landslide potential, University of California, available at: http://calm.geo.berkeley.edu/geomorph/shalstab/index.htm (last access: June 2020), 1998. a
Dorren, L. and Schwarz, M.: Quantifying the stabilizing effect of forests on
shallow landslide-prone slopes, in: Ecosystem-Based Disaster Risk Reduction
and Adaptation in Practice, edited by: Renaud, F., Sudmeier-Rieux, K., Estrella, M., and Nehren, U., Springer, Cham, 42, 255–270, https://doi.org/10.1007/978-3-319-43633-3_11, 2016. a
Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to
landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005. a
Fan, L., Lehmann, P., and Or, D.: Effects of soil spatial variability at the
hillslope and catchment scales on characteristics of rainfall-induced
landslides, Water Resour. Res., 52, 1781–1799,
https://doi.org/10.1002/2015WR017758, 2016. a
Formetta, G., Capparelli, G., and Versace, P.: Evaluating performance of simplified physically based models for shallow landslide susceptibility, Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, 2016. 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
Frei, C., Schöll, R., Fukutome, S., Schmidli, J., and Vidale, P. L.:
Future change of precipitation extremes in Europe: Intercomparison of
scenarios from regional climate models, J. Geophys. Res.-Atmos., 111, D06105, https://doi.org/10.1029/2005JD005965, 2006. a
Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J., and
Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems
climatology from a physically consistent simulation, Sci. Data, 6,
1–9, 2019. a
Geotechdata.info, Angle of Friction:
http://geotechdata.info/parameter/angle-of-friction, last access:
14 December 2013. a
Glade, T., Crozier, M., and Smith, P.: Applying probability determination to
refine landslide-triggering rainfall thresholds using an empirical
“Antecedent Daily Rainfall Model”, Pure Appl. Geophys., 157,
1059–1079, 2000. a
Godt, J. W., Baum, R. L., and Chleborad, A. F.: Rainfall characteristics for
shallow landsliding in Seattle, Washington, USA, Earth Surf. Proc.
Landf., 31,
97–110, 2006. a
Griffiths, D., Huang, J., and Fenton, G. A.: Probabilistic infinite slope
analysis, Comput. Geotech., 38, 577–584, 2011. 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
Hammond, C. J., Prellwitz, R. W., and Miller, S. M.: Landslide hazard
assessment using Monte Carlo simulation, in: Proceedings of 6th international
symposium on landslides, Christchurch, New Zealand, Balkema, 2,
251–294, 1992. a
Heimsath, A. M., Dietrich, W. E., Nishiizumi, K., and Finkel, R. C.: Stochastic
processes of soil production and transport: Erosion rates, topographic
variation and cosmogenic nuclides in the Oregon Coast Range, Earth Surf.
Proc. Landf., 26, 531–552, 2001. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Antonio Guevara, M., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., Kempen, B.: SoilGrids250m: Global gridded soil
information based on machine learning, PLoS one, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. 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
Kjekstad, O. and Highland, L.: Economic and Social Impacts of Landslides, in: Landslides – Disaster Risk Reduction, edited by: Sassa, K. and Canuti, P., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-69970-5_30, 2009. a
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and Hendricks Franssen, H.-J.: TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model, Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, 2016. a
Lee, S. and Pradhan, B.: Landslide hazard mapping at Selangor, Malaysia using
frequency ratio and logistic regression models, Landslides, 4, 33–41, 2007. a
Leonarduzzi, E. and Molnar, P.: Deriving rainfall thresholds for landsliding at the regional scale: daily and hourly resolutions, normalisation, and antecedent rainfall, Nat. Hazards Earth Syst. Sci., 20, 2905–2919, https://doi.org/10.5194/nhess-20-2905-2020, 2020. a
Leonarduzzi, E., Maxwell, R. M., Mirus, B. B., and Molnar, P.: Numerical
Analysis of the Effect of Subgrid Variability in a Physically Based
Hydrological Model on Runoff, Soil Moisture, and Slope Stability, Water
Resour. Res., 57, e2020WR027326,
https://doi.org/10.1029/2020WR027326, 2021. a
Lu, N. and Godt, J.: Infinite slope stability under steady unsaturated seepage
conditions, Water Resour. Res., 44, W11404, https://doi.org/10.1029/2008WR006976, 2008. a
Lu, N., Kaya, B. S., and Godt, J. W.: Direction of unsaturated flow in a
homogeneous and isotropic hillslope, Water Resour. Res., 47, W02519,
https://doi.org/10.1029/2010WR010003, 2011. a
Marc, O., Gosset, M., Saito, H., Uchida, T., and Malet, J.-P.: Spatial patterns
of storm-induced landslides and their relation to rainfall anomaly maps,
Geophys. Res. Lett., 46, 11167–11177, 2019. a
Marino, P., Peres, D. J., Cancelliere, A., Greco, R., and Bogaard, T. A.: Soil
moisture information can improve shallow landslide forecasting using the
hydrometeorological threshold approach, Landslides, 17, 2041–2054, 2020. a
Mathew, J., Babu, D. G., Kundu, S., Kumar, K. V., and Pant, C.: Integrating
intensity–duration-based rainfall threshold and antecedent rainfall-based
probability estimate towards generating early warning for rainfall-induced
landslides in parts of the Garhwal Himalaya, India, Landslides, 11,
575–588, 2014. a
Mirus, B. B., Jones, E. S., Baum, R. L., Godt, J. W., Slaughter, S., Crawford, M. M., Lancaster, J., Stanley, T., Kirschbaum, D. B., Burns, W. J., Schmitt, R. G., Lindsey, K. O., and McCoy, K. M.:
Landslides across the USA: occurrence, susceptibility, and data limitations,
Landslides, 17, 2271–2285, https://doi.org/10.1007/s10346-020-01424-4, 2020. a
Ohlmacher, G. C. and Davis, J. C.: Using multiple logistic regression and GIS
technology to predict landslide hazard in northeast Kansas, USA, Eng.
Geol., 69, 331–343, 2003. a
OpenLandMap: OpenLandMap, available at: http://www.openlandmap.org, last access: 30 April 2020. a
Petley, D.: Global patterns of loss of life from landslides, Geology, 40,
927–930, 2012. a
Ponziani, F., Pandolfo, C., Stelluti, M., Berni, N., Brocca, L., and Moramarco,
T.: Assessment of rainfall thresholds and soil moisture modeling for
operational hydrogeological risk prevention in the Umbria region (central
Italy), Landslides, 9, 229–237, https://doi.org/10.1007/s10346-011-0287-3, 2012. a
re3data.org: Data Publication Server Forschungszentrum Jülich, editing status 2020-09-02, re3data.org – Registry of Research Data Repositories [data set], https://doi.org/10.17616/R31NJMGR, 2021. a
Saito, H., Nakayama, D., and Matsuyama, H.: Comparison of landslide
susceptibility based on a decision-tree model and actual landslide
occurrence: the Akaishi Mountains, Japan, Geomorphology, 109, 108–121, 2009. a
Salvati, P., Bianchi, C., Rossi, M., and Guzzetti, F.: Societal landslide and flood risk in Italy, Nat. Hazards Earth Syst. Sci., 10, 465–483, https://doi.org/10.5194/nhess-10-465-2010, 2010. a
Schmidt, J., Turek, G., Clark, M. P., Uddstrom, M., and Dymond, J. R.: Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions, Nat. Hazards Earth Syst. Sci., 8, 349–357, https://doi.org/10.5194/nhess-8-349-2008, 2008. a, b
Schwarz, M., Cohen, D., and Or, D.: Spatial characterization of root
reinforcement at stand scale: theory and case study, Geomorphology, 171,
190–200, 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,
2018. a
Shepard, D. S.: Computer Mapping: The SYMAP Interpolation Algorithm, in: Spatial Statistics and Models. Theory and Decision Library (An International Series in the Philosophy and Methodology of the Social and Behavioral Sciences), edited by: Gaile, G. L. and Willmott, C. J., Springer, Dordrecht, 40, https://doi.org/10.1007/978-94-017-3048-8_7,
1984. a
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
Thomas, M. A., Mirus, B. B., and Collins, B. D.: Identifying physics-based
thresholds for rainfall-induced landsliding, Geophys. Res. Lett.,
45, 9651–9661, 2018. a
Thomas, M. A., Collins, B. D., and Mirus, B. B.: Assessing the feasibility of
satellite-based thresholds for hydrologically driven landsliding, Water
Resources Research, 55, 9006–9023, 2019. a
Trezzini, F., Giannella, G., and Guida, T.: Landslide and Flood: Economic and
Social Impacts in Italy, Springer, 2, 171–176,
https://doi.org/10.1007/978-3-642-31313-4_22, 2013. a
von Ruette, J., Papritz, A., Lehmann, P., Rickli, C., and Or, D.: Spatial
statistical modeling of shallow landslides–validating predictions for
different landslide inventories and rainfall events, Geomorphology, 133,
11–22, 2011. a
Wang, S., Zhang, K., van Beek, L. P., Tian, X., and Bogaard, T. A.:
Physically-based landslide prediction over a large region: Scaling
low-resolution hydrological model results for high-resolution slope stability
assessment, Environ. Model. Softw., 124, 104607, https://doi.org/10.1016/j.envsoft.2019.104607, 2020. a, b
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, 2020. a
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.
Landslides are a dangerous natural hazard affecting alpine regions, calling for effective...
