Articles | Volume 21, issue 8
https://doi.org/10.5194/nhess-21-2543-2021
© 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-21-2543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2021
Research article |
| 25 Aug 2021
| 25 Aug 2021
Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Robin Kohrs
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
Eric Parra Hormazábal
Institute of Geosciences, University of Potsdam, Potsdam, Germany
Manuel Bustos Morales
Instituto de Geografía, Pontificia Universidad Católica de
Chile, Chile, Centre for Sustainable Urban Development, CEDEUS, Centro de Cambio Global UC, Santiago, Chile
María Belén Araneda Riquelme
Instituto de Geografía, Pontificia Universidad Católica de
Chile, Chile, Centre for Sustainable Urban Development, CEDEUS, Centro de Cambio Global UC, Santiago, Chile
Cristián Henríquez
Instituto de Geografía, Pontificia Universidad Católica de
Chile, Chile, Centre for Sustainable Urban Development, CEDEUS, Centro de Cambio Global UC, Santiago, Chile
Alexander Brenning
Department of Geography, Friedrich Schiller University Jena, Jena, Germany
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Cited articles
Aaron, J., McDougall, S., and Nolde, N.: Two methodologies to calibrate landslide runout models, Landslides, 16, 907–920, https://doi.org/10.1007/s10346-018-1116-8, 2019.
Aleotti, P. and Chowdhury, R.: Landslide hazard assessment: summary review and new perspectives, B. Eng. Geol. Environ., 58, 21–44, https://doi.org/10.1007/s100640050066, 1999.
Angillieri, M. Y. E.: Debris flow susceptibility mapping using frequency ratio and seed cells, in a portion of a mountain international route, Dry Central Andes of Argentina, CATENA, 189, 104504, https://doi.org/10.1016/j.catena.2020.104504, 2020.
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002.
ASF DAAC: ALOS PALSAR Radiometric Terrain Corrected high resolution digital elevation model: Includes Material © JAXA/METI, https://doi.org/10.5067/Z97HFCNKR6VA, 2011.
Bathurst, J. C., Burton, A., and Ward, T. J.: Debris Flow Run-Out and Landslide Sediment Delivery Model Tests, J. Hydraul. Eng., 123, 410–419, https://doi.org/10.1061/(ASCE)0733-9429(1997)123:5(410), 1997.
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012.
Bivand, R. and Rundel, C.: rgeos: Interface to Geometry
Engine – Open Source (`GEOS'), R package version 0.5-2, available at:
https://CRAN.R-project.org/package=rgeos (last access: 3 June 2021), 2019.
Bivand, R., Keitt T., and Rowlingson B.: rgdal: Bindings for the `Geospatial' Data Abstraction Library, R package version 1.4-8, available at: https://CRAN.R-project.org/package=rgdal (last access: 3 June 2021), 2019.
Blahut, J., Horton, P., Sterlacchini, S., and Jaboyedoff, M.: Debris flow hazard modelling on medium scale: Valtellina di Tirano, Italy, Nat. Hazards Earth Syst. Sci., 10, 2379–2390, https://doi.org/10.5194/nhess-10-2379-2010, 2010a.
Blahut, J., van Westen, C. J., and Sterlacchini, S.: Analysis of landslide inventories for accurate prediction of debris-flow source areas, Geomorphology, 119, 36–51, https://doi.org/10.1016/j.geomorph.2010.02.017, 2010b.
Bordoni, M., Galanti, Y., Bartelletti, C., Persichillo, M. G., Barsanti, M., Giannecchini, R., Avanzi, G. D.'A., Cevasco, A., Brandolini, P., Galve, J. P., and Meisina, C.: 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, 2020.
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Syst. Sci., 5, 853–862, https://doi.org/10.5194/nhess-5-853-2005, 2005.
Brenning, A.: Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest, in: 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 2012-07-22–2012-07-27, 5372–5375, 2012.
Brenning, A., Bangsm D., and Becker, M.: RSAGA: SAGA Geoprocessing and Terrain Analysis, R package version 1.3.0, available at: https://CRAN.R-project.org/package=RSAGA (last access: 3 June 2021), 2018.
Brock, J., Schratz, P., Petschko, H., Muenchow, J., Micu, M., and Brenning, A.: The performance of landslide susceptibility models critically depends on the quality of digital elevation models, Geomat. Nat. Haz. Risk, 11, 1075–1092, https://doi.org/10.1080/19475705.2020.1776403, 2020.
Cama, M., Lombardo, L., Conoscenti, C., and Rotigliano, E.: Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy), Geomorphology, 288, 52–65, https://doi.org/10.1016/j.geomorph.2017.03.025, 2017.
Campitelli, E.: metR: Tools for Easier Analysis of Meteorological Fields, R package version 0.7.0, available at: https://CRAN.R-project.org/package=metR (last access: 3 June 2021), 2020.
Carrara, A., Guzzetti, F., Cardinali, M., and Reichenbach, P.: Use of GIS technology in the prediction and monitoring of landslide hazard, Nat. Hazards, 20, 117–135, https://doi.org/10.1023/A:1008097111310, 1999.
Cepeda, J., Chávez, J. A., and Cruz Martínez, C.: Procedure for the selection of runout model parameters from landslide back-analyses: application to the Metropolitan Area of San Salvador, El Salvador, Landslides, 7, 105–116, https://doi.org/10.1007/s10346-010-0197-9, 2010.
Chung, C. F. J. and Fabbri, A. G.: Probabilistic prediction models for landslide hazard mapping, Photogramm. Eng. Rem. S., 65, 1389–1399, 1999.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Dong, J.-J., Lee, C.-T., Tung, Y.-H., Liu, C.-N., Lin, K.-P., and Lee, J.-F.: The role of the sediment budget in understanding debris flow susceptibility, Earth Surf. Proc. Land., 34, 1612–1624, https://doi.org/10.1002/esp.1850, 2009.
Fabbri, A. G., Chung, C.-J. F., Cendrero, A., and Remondo, J.: Is Prediction of Future Landslides Possible with a GIS?, Nat. Hazards, 30, 487–503, https://doi.org/10.1023/B:NHAZ.0000007282.62071.75, 2003.
Galas, S., Dalbey, K., Kumar, D., Patra, A., and
Sheridan, M.: Benchmarking TITAN2D mass flow model against a sand
flow experiment and the 1903 Frank Slide, in: Proceedings of the
2007 International Forum on Landslide Disaster Management, Hong
Kong, 10–12 December 2007, Ho, K. and Li, V. (Eds.), Geotechnical Division, Hong Kong, 899–918, 2007.
Gamma, P.: dfwalk – Ein Murgang-Simulationsprogramm zur Gefahrenzonierung, Geographica Bernensia, University of Bern, Switzerland, G66, 14pp, 2000.
Goetz, J.: runoptGPP – an R package for optimizing mass movement runout models (v0.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.4428050, 2021.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and empirical landslide susceptibility models using generalized additive models, Geomorphology, 129, 376–386, https://doi.org/10.1016/j.geomorph.2011.03.001, 2011.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada, Nat. Hazards Earth Syst. Sci., 15, 1311–1330, https://doi.org/10.5194/nhess-15-1311-2015, 2015a.
Goetz, J. N., Brenning, A., Petschko, H., and Leopold, P.: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling, Comput. Geosci., 81, 1–11, https://doi.org/10.1016/j.cageo.2015.04.007, 2015b.
Guthrie, R. H.: The effects of logging on frequency and distribution of landslides in three watersheds on Vancouver Island, British Columbia, Geomorphology, 43, 273–292, https://doi.org/10.1016/S0169-555X(01)00138-6, 2002.
Guthrie, R. H., Deadman, P. J., Cabrera, A. R., and Evans, S. G.: Exploring the magnitude–frequency distribution: a cellular automata model for landslides, Landslides, 5, 151–159, https://doi.org/10.1007/s10346-007-0104-1, 2008.
Hauser, A.: Rock avalanche and resulting debris flow in Estero Parraguirre and Río Colorado, Region Metropolitana, Chile, in: Catastrophic Landslides: Effects, Occurrence, and Mechanisms, Geological Society of America, Boulder, Colorado, USA, 135–148, https://doi.org/10.1130/REG15-p135, 2002.
Heckmann, T. and Schwanghart, W.: Geomorphic coupling and sediment connectivity in an alpine catchment – Exploring sediment cascades using graph theory, Geomorphology, 182, 89–103, https://doi.org/10.1016/j.geomorph.2012.10.033, 2013.
Heckmann, T., Gegg, K., Gegg, A., and Becht, M.: Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows, Nat. Hazards Earth Syst. Sci., 14, 259–278, https://doi.org/10.5194/nhess-14-259-2014, 2014.
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, R package version 3.0-12, available at: https://CRAN.R-project.org/package=raster (last access: 3 June 2021), 2020.
Holmgren, P.: Multiple flow direction algorithms for runoff modelling in grid based elevation models: An empirical evaluation, Hydrol. Process., 8, 327–334, https://doi.org/10.1002/hyp.3360080405, 1994.
Horton, P., Jaboyedoff, M., Rudaz, B., and Zimmermann, M.: Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale, Nat. Hazards Earth Syst. Sci., 13, 869–885, https://doi.org/10.5194/nhess-13-869-2013, 2013.
Hungr, O.: A model for the runout analysis of rapid flow slides, debris flows, and avalanches, Can. Geotech. J., 32, 610–623, https://doi.org/10.1139/t95-063, 1995.
Hungr, O., Morgan, G. C., and Kellerhals, R.: Quantitative analysis of debris torrent hazards for design of remedial measures, Can. Geotech. J., 21, 663–677, https://doi.org/10.1139/t84-073, 1984.
Ikeya, H.: Debris flow and its countermeasures in Japan, B. Eng. Geol. Environ., 40, 15–33, https://doi.org/10.1007/BF02590339, 1989.
Knevels, R., Petschko, H., Proske, H., Leopold, P., Maraun, D., and Brenning, A.: Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover, Geosciences, 10, 217, https://doi.org/10.3390/geosciences10060217, 2020.
Lombardo, L., Cama, M., Maerker, M., and Rotigliano, E.: A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster, Nat. Hazards, 74, 1951–1989, https://doi.org/10.1007/s11069-014-1285-2, 2014.
Lombardo, L., Opitz, T., and Huser, R.: Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster, Stoch. Env. Res. Risk A., 32, 2179–2198, https://doi.org/10.1007/s00477-018-1518-0, 2018.
Lorente, A., Beguería, S., Bathurst, J. C., and García-Ruiz, J. M.: Debris flow characteristics and relationships in the Central Spanish Pyrenees, Nat. Hazards Earth Syst. Sci., 3, 683–691, https://doi.org/10.5194/nhess-3-683-2003, 2003.
Loye, A., Jaboyedoff, M., and Pedrazzini, A.: Identification of potential rockfall source areas at a regional scale using a DEM-based geomorphometric analysis, Nat. Hazards Earth Syst. Sci., 9, 1643–1653, https://doi.org/10.5194/nhess-9-1643-2009, 2009.
Marchi, L. and D'Agostino, V.: Estimation of debris-flow magnitude in the Eastern Italian Alps, Earth Surf. Proc. Land., 29, 207–220, https://doi.org/10.1002/esp.1027, 2004.
McDougall, S.: 2014 Canadian Geotechnical Colloquium: Landslide runout analysis – current practice and challenges, Can. Geotech. J., 54, 605–620, https://doi.org/10.1139/cgj-2016-0104, 2017.
Mergili, M., Fellin, W., Moreiras, S. M., and Stötter, J.: Simulation of debris flows in the Central Andes based on Open Source GIS: possibilities, limitations, and parameter sensitivity, Nat. Hazards, 61, 1051–1081, https://doi.org/10.1007/s11069-011-9965-7, 2012.
Mergili, M., Krenn, J., and Chu, H.-J.: r.randomwalk v1, a multi-functional conceptual tool for mass movement routing, Geosci. Model Dev., 8, 4027–4043, https://doi.org/10.5194/gmd-8-4027-2015, 2015.
Mergili, M., Schwarz, L., and Kociu, A.: Combining release and runout in statistical landslide susceptibility modeling, Landslides, 16, 2151–2165, https://doi.org/10.1007/s10346-019-01222-7, 2019.
Microsoft and Weston, S.: foreach: Provides Foreach Looping Construct, R package version 1.4.8, available at: https://CRAN.R-project.org/package=foreach (last access: 12 April 2021), 2020.
Moreiras, S., Lisboa, M. S., and Mastrantonio, L.: The role of snow melting upon landslides in the central Argentinean Andes, Earth Surf. Proc. Land., 37, 1106–1119, https://doi.org/10.1002/esp.3239, 2012.
Moreiras, S. M. and Sepúlveda, S. A.: Megalandslides in the Andes of central Chile and Argentina (32∘–34∘ S) and potential hazards, Geol. Soc. Spec. Publ., 399, 329–344, https://doi.org/10.1144/SP399.18, 2015.
Niculiţa, M.: Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs, Nat. Hazards Earth Syst. Sci., 16, 2021–2030, https://doi.org/10.5194/nhess-16-2021-2016, 2016.
Parra-Hormazábal, E., Calabrese-Fernández, J., Bustos-Morales, M., Araneda-Riquelme, M. B., Goetz, J., Kohrs, R., Brenning, A., and Henríquez, C.: Debris flow inventory and data for regionally modelling runout in the upper Maipo river basin, Chile (v0.0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.4428080, 2021.
Pawley, S.: Rsagacmd: Linking R with the Open-Source `SAGA-GIS' Software, R package version 0.0.2, available at: https://CRAN.R-project.org/package=Rsagacmd (last access: 3 June 2021), 2019.
Pebesma, E.: Simple Features for R: Standardized Support for Spatial Vector Data, R J., 10, 439, https://doi.org/10.32614/RJ-2018-009, 2018.
Pebesma, E. and Bivand, R.: Classes and methods for spatial data in R, R News, 5, available at: https://cran.r-project.org/doc/Rnews/ (last access: 3 June 2021), 2005.
Perla, R., Cheng, T. T., and McClung, D. M.: A Two–Parameter Model of Snow–Avalanche Motion, J. Glaciol., 26, 197–207, https://doi.org/10.3189/S002214300001073X, 1980.
Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.: Assessing the quality of landslide susceptibility maps – case study Lower Austria, Nat. Hazards Earth Syst. Sci., 14, 95–118, https://doi.org/10.5194/nhess-14-95-2014, 2014.
Planchon, O. and Darboux, F.: A fast, simple and versatile algorithm to fill the depressions of digital elevation models, CATENA, 46, 159–176, https://doi.org/10.1016/S0341-8162(01)00164-3, 2002.
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org/ (last access: 3 June 2021), 2019.
Rickenmann, D. and Zimmermann, M.: The 1987 debris flows in Switzerland: documentation and analysis, Geomorphology, 8, 175–189, https://doi.org/10.1016/0169-555x(93)90036-2, 1993.
Rudy, A. C. A., Lamoureux, S. F., Treitz, P., and van Ewijk, K. Y.: Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models, Geomorphology, 264, 95–108, https://doi.org/10.1016/j.geomorph.2016.04.011, 2016.
Ruß, G. and Brenning, A.: Data Mining in Precision
Agriculture: Management of Spatial Information, in: Computational
Intelligence for Knowledge-Based Systems Design, Hutchison, D.,
Kanade, T., Kittler, J., Kleinberg, J. M., Mattern, F., Mitchell,
J. C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Steffen, B.,
Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M. Y., Weikum, G.,
Hüllermeier, E., Kruse, R., and Hoffmann, F. (Eds.), Springer, Berlin, Heidelberg, 350–359, https://doi.org/10.1007/978-3-642-14049-5_36, 2010.
Sattler, K., Keiler, M., Zischg, A., and Schrott, L.: On the Connection between Debris Flow Activity and Permafrost Degradation: A Case Study from the Schnalstal, South Tyrolean Alps, Italy, Permafrost Periglac., 22, 254–265, https://doi.org/10.1002/ppp.730, 2011.
Schraml, K., Thomschitz, B., McArdell, B. W., Graf, C., and Kaitna, R.: Modeling debris-flow runout patterns on two alpine fans with different dynamic simulation models, Nat. Hazards Earth Syst. Sci., 15, 1483–1492, https://doi.org/10.5194/nhess-15-1483-2015, 2015.
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., and Brenning, A.: Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data, Ecol. Model., 406, 109–120, https://doi.org/10.1016/j.ecolmodel.2019.06.002, 2019.
Sepúlveda, S. A., Rebolledo, S., and Vargas, G.: Recent catastrophic debris flows in Chile: Geological hazard, climatic relationships and human response, Quatern. Int., 158, 83–95, https://doi.org/10.1016/j.quaint.2006.05.031, 2006.
Sepúlveda, S. A., Moreiras, S. M., Lara, M., and Alfaro, A.: Debris flows in the Andean ranges of central Chile and Argentina triggered by 2013 summer storms: characteristics and consequences, Landslides, 12, 115–133, https://doi.org/10.1007/s10346-014-0539-0, 2015.
Serey, A., Piñero-Feliciangeli, L., Sepúlveda, S. A., Poblete, F., Petley, D. N., and Murphy, W.: Landslides induced by the 2010 Chile megathrust earthquake: a comprehensive inventory and correlations with geological and seismic factors, Landslides, 16, 1153–1165, https://doi.org/10.1007/s10346-019-01150-6, 2019.
Servicio Nacional de Geología y Minería de Chile: Geología de Chile Escala 1:1 000 000 (v.1.0), Publicacion Geologica Digital, no. 4.
Sing, T., Sander, O., Beerenwinkel, N., and Lengauer, T.: ROCR: visualizing classifier performance in R, Bioinformatics (Oxford, England), 21, 3940–3941, https://doi.org/10.1093/bioinformatics/bti623, 2005.
Stevenson, J. A., Sun, X., and Mitchell, N. C.: Despeckling SRTM and other topographic data with a denoising algorithm, Geomorphology, 114, 238–252, https://doi.org/10.1016/j.geomorph.2009.07.006, 2010.
Sun, X., Rosin, P., Martin, R., and Langbein, F.: Fast and effective feature-preserving mesh denoising, IEEE T. Vis. Comput. Gr., 13, 925–938, https://doi.org/10.1109/TVCG.2007.1065, 2007.
Taylor, F. E., Malamud, B. D., Witt, A., and Guzzetti, F.: Landslide shape, ellipticity and length-to-width ratios, Earth Surf. Proc. Land., 43, 3164–3189, https://doi.org/10.1002/esp.4479, 2018.
Tuanmu, M.-N., Viña, A., Roloff, G. J., Liu, W., Ouyang, Z., Zhang, H., and Liu, J.: Temporal transferability of wildlife habitat models: implications for habitat monitoring, J. Biogeogr., 38, 1510–1523, https://doi.org/10.1111/j.1365-2699.2011.02479.x, 2011.
van Westen, C. J., van Asch, T. W. J., and Soeters, R.:
Landslide hazard and risk zonation – why is it still so difficult?, B. Eng. Geol. Environ., 65, 167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Wenger, S. J. and Olden, J. D.: Assessing transferability of ecological models: an underappreciated aspect of statistical validation, Methods Ecol. Evol., 3, 260–267, https://doi.org/10.1111/j.2041-210X.2011.00170.x, 2012.
Wichmann, V.: The Gravitational Process Path (GPP) model (v1.0) – a GIS-based simulation framework for gravitational processes, Geosci. Model Dev., 10, 3309–3327, https://doi.org/10.5194/gmd-10-3309-2017, 2017.
Wichmann, V. and Becht, M.: Rockfall modelling: methods
and model application in an alpine basin (Reintal, Germany), in:
SAGA – Analysis and Modelling Applications, edited by: Böhner, J., McCloy, K. R., and Strobl, J., Goltze, Göttingen, 105–116, 2006.
Wickham, H.: ggplot2: Elegant graphics for data analysis, Use R!, viii, Springer, New York, p. 213, 2009.
Wood, S. N.: Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, J. R. Stat. Soc. B, 73, 3–36, https://doi.org/10.1111/j.1467-9868.2010.00749.x, 2011.
Zweig, M. H. and Campbell, G.: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine, Clin. Chem., 39, 561–577, https://doi.org/10.1093/CLINCHEM/39.4.561, 1993.
Short summary
Debris flows are fast-moving landslides that can cause incredible destruction to lives and property. Using the Andes of Santiago as an example, we developed tools to finetune and validate models predicting likely runout paths over large regions. We anticipate that our automated approach that links the open-source R software with SAGA-GIS will make debris-flow runout simulation more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.
Debris flows are fast-moving landslides that can cause incredible destruction to lives and...
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