Show simple item record

An integral assessment of landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain

dc.contributor.authorOrtiz-Giraldo L
dc.contributor.authorBotero B.A
dc.contributor.authorVega J.
dc.date.accessioned2023-10-24T19:23:53Z
dc.date.available2023-10-24T19:23:53Z
dc.date.created2023
dc.identifier.issn22966463
dc.identifier.urihttp://hdl.handle.net/11407/7880
dc.description.abstractLandslides caused by rainfall are one of the most frequent causes of disasters in tropical countries and mountainous terrain and can block rivers generating landslide dams. This paper presents a methodology for the estimation of the obstruction of water streams generated by rainfall-induced shallow landslides. The spatial distribution of the landslide hazard was estimated in terms of the Factor of Safety (FoS) values using the deterministic method with physical basis SLIDE (Slope - Infiltration - Distributed Equilibrium). The rainfall regimes of the study area were estimated by means of a simple scaling Log Normal Model. Subsequently, the resulting areas with a high hazard level that could detach and reach the riverbed were identified as sources for the simulation of the debris flow runout using the Rapid Mass Movement Simulation model with its debris flow module, (i.e., RAMMS-DF), estimating zones of the riverbed that should be analyzed in detail. Finally, the effects of river channel obstructions generated after debris flow movement were analyzed by means of the Iber, a well-known, physically based 2D hydraulic model and their possible changes on the river hydraulic. In order to generate a workflow that allows the application of the SLIDE methodology and the preparation of inputs for the subsequent processes of debris flow propagation and hydraulic modeling of the river corridor of analysis, a Python-based toolbox was created. Our results highlight the changes in the fluvial dynamics in the corridor of the river of analysis after the landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chain for the different return periods. In all cases, the material deposited in the river channel was sufficient to change the hydraulic regime of the river corridor, showing longer delay times in the transit of the flow, in addition to the decrease in the specific flow. This would imply a water shortage in the study basin of the hydroelectric project; however, in the scope of this project it is not possible to really determine the real effects that could be generated by this event. Copyright © 2023 Ortiz-Giraldo, Botero and Vega.eng
dc.language.isoeng
dc.publisherFrontiers Media S.A.
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161964026&doi=10.3389%2ffeart.2023.1157881&partnerID=40&md5=b53600cf631a8ec3c1437544206ac5c1
dc.sourceFront. Earth Sci.
dc.sourceFrontiers in Earth Scienceeng
dc.subjectDebris floweng
dc.subjectHazard chaineng
dc.subjectLandslide dameng
dc.subjectRainfall-induced landslideeng
dc.subjectRAMMS modeleng
dc.subjectSLIDE modeleng
dc.titleAn integral assessment of landslide dams generated by the occurrence of rainfall-induced landslide and debris flow hazard chaineng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería Ingeniería Civilspa
dc.type.spaArtículo
dc.identifier.doi10.3389/feart.2023.1157881
dc.relation.citationvolume11
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationOrtiz-Giraldo, L., Facultad de Ingeniería, Universidad de Medellín, Medellín, Colombia
dc.affiliationBotero, B.A., Facultad de Ingeniería, Universidad de Medellín, Medellín, Colombia
dc.affiliationVega, J., Facultad de Ingeniería, Universidad de Medellín, Medellín, Colombia
dc.relation.referencesAbraham, M.T., Satyam, N., Pradhan, B., Tian, H., Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters (2022) J. Rock Mech. Geotechnical Eng, 14, pp. 1747-1760
dc.relation.references(2015) Plan de Ordenación y Manejo de la Cuenca Hidrográfica del Río Aburrá, , https://www.corantioquia.gov.co/planes-de-ordenacion-y-manejo-de-la-cuenca-hidrografica-pomca/, (Accessed May 7, 2023
dc.relation.referencesArghya, A.B., Hawlader, B., Guthrie, R.H., A comparison of two runout programs for debris flow assessment at the Solalex-Anzeindaz region of Switzerland (2022) Géorisques - VIII - Geohazards, , https://www.stantec.com/en/ideas/a-comparison-of-two-runout-programs-for-debris-flow-assessment-solalex-anzeindaz-region-switzerland, Quebec, Canada, :, Accessed May 1, 2023, in
dc.relation.referencesAristizábal, E., Garcia, E.F., Marin, R.J., Gómez, F., Guzmán-Martínez, J., Aristizábal, E., (2022) Rainfall-intensity effect on landslide hazard assessment due to climate change in north-western Colombian Andes, pp. 51-66. , Medellín, Colombia, Revista Facultad de Ingeniería Universidad de Antioquia
dc.relation.referencesAristizábal, E., Martínez-Carvajal, H., García-Aristizábal, E., Modelling shallow landslides triggered by rainfall in tropical and mountainous basins (2017) Adv. Cult. Living Landslides, 207, pp. 207-212
dc.relation.referencesArmaș, I., Gheorghe, M., Silvaș, G.C., Shallow landslides physically based susceptibility assessment improvement using InSAR. Case study: Carpathian and subcarpathian prahova valley, Romania (2021) Remote Sens, 13, p. 2385
dc.relation.references(2015) ALOS PALSAR_Radiometric_Terrain_Corrected_high_res, , https://asf.alaska.edu/data-sets/derived-data-sets/alos-palsar-rtc/alos-palsar-radiometric-terrain-correction/, (Accessed November 11, 2021
dc.relation.referencesBaggio, T., Mergili, M., D’Agostino, V., Advances in the simulation of debris flow erosion: The case study of the Rio Gere (Italy) event of the 4th August 2017 (2021) Geomorphology, 381, p. 107664
dc.relation.referencesBladé, E., Cea, L., Corestein, G., Escolano, E., Puertas, J., Vázquez-Cendón, E., Iber — river modelling simulation tool (2014) Rev. Int. Métodos Numéricos Cálculo Diseño Ing, 30, pp. 1-10
dc.relation.referencesBomers, A., (2020) Hydraulic modelling approaches to decrease uncertainty in flood frequency relations, , PhD Thesis, University of Twente, Enschede, Netherlands
dc.relation.referencesCalcaterra, D., Martire, D.D., Guerriero, L., Tomás, R., Košová, V., Molokáč, M., Avalanche hazard modelling within the kráľova hoľa area in the low tatra mountains in Slovakia (2022) Land, 11, p. 766
dc.relation.referencesChae, B.G., Park, H.J., Catani, F., Simoni, A., Berti, M., Landslide prediction, monitoring and early warning: A concise review of state-of-the-art (2017) Geosciences J, 21 (6), pp. 1033-1070
dc.relation.referencesChen, W., Tsai, F., Kainz, W., Thomas, J., Gupta, M., Srivastava, P.K., Assessment of a dynamic physically based slope stability model to evaluate timing and distribution of rainfall-induced shallow landslides (2023) ISPRS Int. J. Geo-Information, 12, p. 105
dc.relation.referencesChen, Y., Zhao, L., Wang, Y., Jiang, Q., Qi, D., Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models (2019) Front. Earth Sci, 13, pp. 695-704
dc.relation.referencesChen, Z., He, S., Shen, W., Wang, D., Effects of defense-structure system for bridge piers on two-phase debris flow wakes (2022) Acta Geotech, 17, pp. 1645-1665
dc.relation.referencesChikalamo, E.E., (2018) Comparing modelling approaches for landside early warning: A case study of bogowonto catchment, central java, Indonesia, , Thesis, Enschede, The Netherlands, ITC Faculty Geo-Information Science and Earth Observation
dc.relation.referencesChristen, M., Kowalski, J., Bartelt, P., Ramms: Numerical simulation of dense snow avalanches in three-dimensional terrain (2010) Cold Reg. Sci. Technol, 63, pp. 1-14
dc.relation.referencesCui, P., Guo, J., Evolution models, risk prevention and control countermeasures of the valley disaster chain (2021) Gongcheng Kexue Yu Jishu/Advanced Eng. Sci, 53, pp. 5-18
dc.relation.referencesDash, R.K., Samanta, M., Kanungo, D.P., Debris flow hazard in India: Current status, research trends, and emerging challenges (2023) Landslides: Detection, Prediction and Monitoring, pp. 211-231. , Thambidurai P., Singh T.N., (eds), Manhattan, NY, USA, Springer, Cham, Editors
dc.relation.referencesDhanai, P., Singh, V.P., Soni, P., Rainfall triggered slope instability analysis with changing climate (2022) Indian Geotechnical J, 52, pp. 477-492
dc.relation.referencesDíaz-Salas, A.M., Guevara-Pérez, E., Vidal-Moreno, J.D., Modelamiento numérico de un flujo de escombros asociado a una rotura de presa en la subcuenca Quillcay, Áncash, Perú (2021) Rev. Ing. UC, 28, pp. 35-46
dc.relation.referencesDo, H.M., Long, K., Zi, Y., Guo, Z., Yin, K.L., Guo, Z.Z., A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment (2020) Geomatics, Nat. Hazards Risk, 11 (1), pp. 2449-2485
dc.relation.referencesDysarz, T., Development of RiverBox—an ArcGIS toolbox for river bathymetry reconstruction (2018) Water, 10, p. 1266
dc.relation.referencesFernandes Azevedo, G., Montoya Botero, E., Martínez, H.E., García, E., Newton, M.S., Estimativa da profundidade do solo pelo uso de técnicas de geoprocessamento, estudo de caso: Setor Pajarito, Colômbia (2015) XVII Brazilian symposium on remote sensing, , João pessoa, Brazil, April 2015, in
dc.relation.referencesFranco-Ramos, O., Ballesteros-Cánovas, J.A., Figueroa-García, J.E., Vázquez-Selem, L., Stoffel, M., Caballero, L., Modelling the 2012 lahar in a sector of jamapa gorge (pico de Orizaba volcano, Mexico) using RAMMS and tree-ring evidence (2020) Water, 12, p. 333
dc.relation.referencesFustos-Toribio, I.J., Morales-Vargas, B., Somos-Valenzuela, M., Moreno-Yaeger, P., Muñoz-Ramirez, R., Rodriguez Araneda, I., Debris flow event on Osorno volcano, Chile, during summer 2017: New interpretations for chain processes in the southern Andes (2021) Nat. Hazards Earth Syst. Sci, 21, pp. 3015-3029
dc.relation.referencesGan, J., Zhang, Y.X., Numerical simulation of debris flow runout using ramms: A case study of luzhuang gully in China (2019) Comput. Model Eng. Sci, 121, pp. 981-1009
dc.relation.referencesGarcía, J.V.G., Panta, J.E.R., Reynoso, D.S.F., Ayala, C.R., Hidalgo, R.R., García, F.G.G., Modelación hidráulica en Iber para prevención de inundaciones en la cuenca Tesechoacán (2022) Rev Mex Cienc, 13, pp. 159-181
dc.relation.referencesGarcía-Alén, G., González-Cao, J., Fernández-Nóvoa, D., Gómez-Gesteira, M., Cea, L., Puertas, J., Analysis of two sources of variability of basin outflow hydrographs computed with the 2D shallow water model Iber: Digital Terrain Model and unstructured mesh size (2022) J. Hydrol. (Amst), 612, p. 128182
dc.relation.referencesGarcía-Delgado, H., Villamizar-Escalante, N., Bermúdez, M.A., Bernet, M., Velandia, F., Climate or tectonics? What controls the spatial-temporal variations in erosion rates across the eastern cordillera of Colombia? (2021) Glob. Planet Change, 203, p. 103541
dc.relation.referencesGonzález-Cao, J., García-Feal, O., Fernández-Nóvoa, D., Domínguez-Alonso, J.M., Gómez-Gesteira, M., Towards an automatic early warning system of flood hazards based on precipitation forecast: The case of the miño river (NW Spain) (2019) Nat. Hazards Earth Syst. Sci, 19, pp. 2583-2595
dc.relation.referencesGraf, C., McArdell, B., Simulation of debris flow runout before and after construction of mitigation measures: an example from the Swiss Alps (2008) Debris Flows: Disasters, Risk, Forecast, Protection, pp. 233-236. , https://elibrary.ru/item.asp?id=37236604, International Conference, Pyatigorsk, Russia, :, Accessed May 7, 2023
dc.relation.referencesGuo, J., Cui, Y., Xu, W., Yin, Y., Li, Y., Jin, W., Numerical investigation of the landslide-debris flow transformation process considering topographic and entrainment effects: A case study (2022) Landslides, 19, pp. 773-788. , a
dc.relation.referencesGuo, Z., Torra, O., Hürlimann, M., Abancó, C., Medina, V., Fslam: A qgis plugin for fast regional susceptibility assessment of rainfall-induced landslides (2022) Environ. Model. Softw, 150, p. 105354. , b
dc.relation.referencesHafnaoui, M.A., Debabeche, M., Numerical modeling of the hydraulic jump location using 2D Iber software (2021) Model Earth Syst. Environ, 7, pp. 1939-1946
dc.relation.referencesHamdan, A.N.A., Almuktar, S., Scholz, M., Rainfall-runoff modeling using the HEC-HMS model for the Al-adhaim river catchment, northern Iraq (2021) Hydrology, 8, p. 58
dc.relation.referencesHidalgo, C.A., Vega, J.A., Probabilistic landslide risk assessment in water supply basins: La liboriana River Basin (salgar-Colombia) (2021) Nat. Hazards, 109, pp. 273-301
dc.relation.referencesHoyos, H., Botero, B.A., Vulnerability assessment with scarce information for a quantitative flood risk model. Case study monteria-Colombia (2019) IOP Conf. Ser. Mater Sci. Eng, 471, p. 102005
dc.relation.referencesHu, H., Zhou, G.G.D., Song, D., Cui, K.F.E., Huang, Y., Choi, C.E., Effect of slit size on the impact load against debris-flow mitigation dams (2020) Eng. Geol, 274, p. 105764
dc.relation.referencesHungr, O., Leroueil, S., Picarelli, L., The Varnes classification of landslide types, an update (2014) Landslides, 11, pp. 167-194
dc.relation.referencesHürlimann, M., Guo, Z., Puig-Polo, C., Medina, V., Impacts of future climate and land cover changes on landslide susceptibility: Regional scale modelling in the val d’Aran region (pyrenees, Spain) (2022) Landslides, 19, pp. 99-118
dc.relation.referencesKun-Ting, C., Chia-Hsing, L., Xiao-Qing, C., Gui-Sheng, H., Xiao-Jun, G., Chjeng-Lun, S., An assessment method for debris flow dam formation in taiwan (2018) Earth Sci. Res. J, 22, pp. 37-43
dc.relation.referencesLi, Y., Chen, J., Zhou, F., Song, S., Zhang, Y., Gu, F., Identification of ancient river-blocking events and analysis of the mechanisms for the formation of landslide dams in the Suwalong section of the upper Jinsha River, SE Tibetan Plateau (2020) Geomorphology, 368, p. 107351. , a
dc.relation.referencesLi, Y., Chen, J., Zhou, F., Song, S., Zhang, Y., Gu, F., Identification of ancient river-blocking events and analysis of the mechanisms for the formation of landslide dams in the Suwalong section of the upper Jinsha River, SE Tibetan Plateau (2020) Geomorphology, 368, p. 107351. , b
dc.relation.referencesLiao, Z., Hong, Y., Adler, R.F., Bach, D., A physically based SLIDE model for landslide hazard assessments using remotely sensed data sets (2011) Geomechanics and geotechnics, pp. 807-813. , Boca raton, FL, USA, CRC Press
dc.relation.referencesLiao, Z., Hong, Y., Wang, J., Fukuoka, H., Sassa, K., Karnawati, D., Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets (2010) Landslides, 7, pp. 317-324
dc.relation.referencesLiu, T., Wang, Y., Yu, H., Chen, Y., Using statistical functions and hydro-hydraulic models to develop human vulnerability curves for flash floods: The flash flood of the Taitou catchment (China) in 2016 (2022) Int. J. Disaster Risk Reduct, 73, p. 102876
dc.relation.referencesLiu, X., Zhao, C., Zhang, Q., Lu, Z., Li, Z., Yang, C., Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China (2021) Eng. Geol, 284, p. 106033
dc.relation.referencesMarin, R.J., Velásquez, M.F., Sánchez, O., Applicability and performance of deterministic and probabilistic physically based landslide modeling in a data-scarce environment of the Colombian Andes (2021) J. South Am. Earth Sci, 108, p. 103175
dc.relation.referencesMedina, V., Hürlimann, M., Guo, Z., Lloret, A., Vaunat, J., Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale (2021) Catena (Amst), 201, p. 105213
dc.relation.referencesMikoš, M., Bezak, N., Debris flow modelling using RAMMS model in the alpine environment with focus on the model parameters and main characteristics (2021) Front. Earth Sci. (Lausanne), 8, p. 732
dc.relation.referencesMontoya Botero, E., (2018) Metodologia para aplicação de redes neurais artificiais para sistemas de alerta de escorregamentos deflagrados por chuvas em regiões montanhosas, , https://repositorio.unb.br/handle/10482/33056, (Accessed May 7, 2023
dc.relation.referencesMontrasio, L., Valentino, R., Losi, G.L., Corina, A., Rossi, L., Rudari, R., Space-time hazard assessment of rainfall-induced shallow landslides (2013) Landslide Sci. Pract. Glob. Environ. Change, 4, pp. 283-293
dc.relation.referencesMoreira Melo, C., Kobiyama, M., Paulo Michel, G., Madruga de Brito, M., Lu, Z., Miguel Ferreira, T., The relevance of geotechnical-unit characterization for landslide-susceptibility mapping with SHALSTAB (2021) GeoHazards, 2, pp. 383-397
dc.relation.referencesNáquira Bazán, M.V., (2009) Susceptibilidad de Remociones en Masa en las Costas de Fiordos Cercanos a Hornopirén, X Región, , https://repositorio.uchile.cl/handle/2250/103473, (Accessed May 7, 2023
dc.relation.referencesNian, T., Li, D., Liang, Q., Wu, H., Guo, X., Multi-phase flow simulation of landslide dam formation process based on extended coupled DEM-CFD method (2021) Comput. Geotech, 140, p. 104438
dc.relation.referencesOliveira, S.C., Zêzere, J.L., Lajas, S., Melo, R., Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale (2017) Nat. Hazards Earth Syst. Sci, 17, pp. 1091-1109
dc.relation.referencesPérez-Montiel, J.I., Cardenas-Mercado, L., Nardini, A.G.C., Flood modeling in a coastal town in northern Colombia: Comparing MODCEL vs. IBER (2022) Water, 14, p. 3866
dc.relation.referencesPulgarín Dávila, E.G., (2009) Fórmulas regionales para la estimación de curvas intensidad-frecuencia-duración basadas en las propiedades de escala de la lluvia (región andina colombiana), , https://repositorio.unal.edu.co/bitstream/handle/unal/70277/98671272.2009_1.pdf?sequence=3, (Accessed May 1, 2023
dc.relation.referencesPulgarín, E., Poveda, G., Estimación de curvas IDF basadas en las propiedades de escala de la lluvia (2008) Proceedings of the XVIII Seminario Nacional de Hidráulica e Hidrología, , Bogotá, Colombia, May 2008, in
dc.relation.referencesQiang, X., Dalei, P., Chaoyang, H., Xing, Q., Kuanyao, Z., Dehao, X., Theory and method of monitoring and early warning for sudden loess landslide—A case study at heifangtai terrace (2020) J. Eng. Geol, 28 (1), pp. 111-121
dc.relation.referencesRana, H., Babu, G.L.S., Regional back analysis of landslide events using TRIGRS model and rainfall threshold: An approach to estimate landslide hazard for kodagu, India (2022) Bull. Eng. Geol. Environ, 81, pp. 160-216
dc.relation.referencesReddy, B.S.N., Pramada, S.K., A hybrid artificial intelligence and semi-distributed model for runoff prediction (2022) Water Supply, 22, pp. 6181-6194
dc.relation.referencesRoldán, F., Salazar, I., González, G., Roldán, W., Toro, N., Flow-type landslides analysis in arid zones: Application in La chimba basin in antofagasta, atacama desert (Chile) (2022) Water, 14, p. 2225
dc.relation.referencesRuiz-Villanueva, V., Gamberini, C., Bladé, E., Stoffel, M., Bertoldi, W., Numerical modeling of instream wood transport, deposition, and accumulation in braided morphologies under unsteady conditions: Sensitivity and high-resolution quantitative model validation (2020) Water Resour. Res, 56. , e2019WR026221
dc.relation.referencesSahu, S.A.L.I.L., Pyasi, S.K., Galkate, R.V., A review on the HEC-HMS rainfall–runoff simulation model (2020) Int. J. Agric. Sci. Res, 10 (4), pp. 183-190
dc.relation.referencesThomas, J., Gupta, M., Srivastava, P.K., Petropoulos, G.P., Assessment of a dynamic physically based slope stability model to evaluate timing and distribution of rainfall-induced shallow landslides (2023) ISPRS Int. J. Geoinf, 12, p. 105
dc.relation.referencesTrujillo-Vela, M.G., Ramos-Cañón, A.M., Escobar-Vargas, J.A., Galindo-Torres, S.A., An overview of debris-flow mathematical modelling (2022) Earth Sci. Rev, 232, p. 104135
dc.relation.referencesTyagi, A., Kamal Tiwari, R., James, N., A review on spatial, temporal and magnitude prediction of landslide hazard (2022) J. Asian Earth Sci. X, 7, p. 100099
dc.relation.references(2017) Economic losses, poverty and disasters 1998-2017, , Geneva, Switzerland, UNDRR
dc.relation.referencesVega, J.A., Hidalgo, C.A., Methodology for landslides assessment causing river channel obstructions and the consequent water shortage in rural communities (2021) ICL contribution to landslide disaster risk reduction, , Manhattan, NY, USA, Springer, Cham
dc.relation.referencesVega, J.A., Hidalgo, C.A., Quantitative risk assessment of landslides triggered by earthquakes and rainfall based on direct costs of urban buildings (2016) Geomorphology, 273, pp. 217-235
dc.relation.referencesVon Boetticher, A., Turowski, J.M., Mcardell, B.W., Rickenmann, D., Hürlimann, M., Scheidl, C., DebrisInterMixing-2.3: A finite volume solver for three-dimensional debris-flow simulations with two calibration parameters-Part 2: Model validation with experiments (2017) Geosci. Model Dev, 10, pp. 3963-3978
dc.relation.referencesWalczak, N., Walczak, Z., Nieć, J., Influence of debris on water intake gratings in small hydroelectric plants: An experimental study on hydraulic parameters (2021) Energies, 14, p. 3248
dc.relation.references(2022) RAMMS
dc.relation.referencesZeng, P., Wang, S., Sun, X., Fan, X., Li, T., Wang, D., Probabilistic hazard assessment of landslide-induced river damming (2022) Eng. Geol, 304, p. 106678
dc.relation.referencesZhang, J., Qiu, H., Tang, B., Yang, D., Liu, Y., Liu, Z., Accelerating effect of vegetation on the instability of rainfall-induced shallow landslides (2022) Remote Sens. (Basel), 14, p. 5743. , a
dc.relation.referencesZhang, X., Li, L., Xu, C., Large-scale landslide inventory and their mobility in lvliang city, shanxi province, China (2022) Nat. Hazards Res, 2, pp. 111-120. , b
dc.relation.referencesZhang, Y., Xu, X., Li, Z., Yi, R., Xu, C., Luo, W., Modelling soil thickness using environmental attributes in karst watersheds (2022) Catena (Amst), 212, p. 106053. , c
dc.relation.referencesZhou, W., Qiu, H., Wang, L., Pei, Y., Tang, B., Ma, S., Combining rainfall-induced shallow landslides and subsequent debris flows for hazard chain prediction (2022) Catena (Amst), 213, p. 106199
dc.relation.referencesZhu, L., He, S., Qin, H., He, W., Zhang, H., Zhang, Y., Analyzing the multi-hazard chain induced by a debris flow in Xiaojinchuan River, Sichuan, China (2021) Eng. Geol, 293, p. 106280
dc.relation.referencesZimmermann, F., McArdell, B.W., Rickli, C., Scheidl, C., 2D runout modelling of hillslope debris flows, based on well-documented events in Switzerland (2020) Geosciences, 10, p. 70
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record