Articles | Volume 26, issue 6
https://doi.org/10.5194/nhess-26-2561-2026
© Author(s) 2026. 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-26-2561-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Capturing the complete landslide–debris-rich flood continuum for accurate inventory, susceptibility and exposure mapping – lessons from Cyclone Idai
Antoine Dille
CORRESPONDING AUTHOR
Department of Earth Sciences, Royal Museum for Central Africa, Tervuren, Belgium
Olivier Dewitte
Department of Earth Sciences, Royal Museum for Central Africa, Tervuren, Belgium
Jente Broeckx
Vlaamse Instelling voor Technologisch Onderzoek (VITO), Mol, Belgium
Koen Verbist
UNESCO, Intergovernmental Hydrological Programme, Paris, France
Andile Sindiso Dube
School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland
Jean Poesen
Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
Faculty of Earth Sciences and Spatial Management, Maria-Curie Sklodowska University, Lublin, Poland
Matthias Vanmaercke
Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
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Cited articles
Amatya, P., Scheip, C., Déprez, A., Malet, J. P., Slaughter, S. L., Handwerger, A. L., Emberson, R., Kirschbaum, D., Jean-Baptiste, J., Huang, M. H., Clark, M. K., Zekkos, D., Huang, J. R., Pacini, F., and Boissier, E.: Learnings from rapid response efforts to remotely detect landslides triggered by the August 2021 Nippes earthquake and Tropical Storm Grace in Haiti, Nat. Hazards, https://doi.org/10.1007/s11069-023-06096-6, 2023.
Arango-Carmona, M. I., Voit, P., Hürlimann, M., Aristizábal, E., and Korup, O.: Hillslope torrential hazard cascades in tropical mountains, Nat. Hazards Earth Syst. Sci., 25, 3641–3663, https://doi.org/10.5194/nhess-25-3641-2025, 2025.
Bennett, G. L., Panici, D., Rengers, F. K., Kean, J. W., and Rathburn, S. L.: Landslide-channel feedbacks amplify channel widening during floods, npj Nat. Hazards, 2, 7, https://doi.org/10.1038/s44304-025-00059-6, 2025.
Bhuyan, K., Rana, K., Ozturk, U., Nava, L., Rosi, A., Meena, S. R., Fan, X., Floris, M., van Westen, C., and Catani, F.: Towards automatic delineation of landslide source and runout, Eng. Geol., 345, 107866, https://doi.org/10.1016/j.enggeo.2024.107866, 2025.
Brenna, A., Surian, N., Ghinassi, M., and Marchi, L.: Sediment–water flows in mountain streams: Recognition and classification based on field evidence, Geomorphology, 371, https://doi.org/10.1016/j.geomorph.2020.107413, 2020.
Brenna, A., Marchi, L., Borga, M., Zaramella, M., and Surian, N.: What drives major channel widening in mountain rivers during floods? The role of debris floods during a high-magnitude event, Geomorphology, 430, 108650, https://doi.org/10.1016/j.geomorph.2023.108650, 2023.
Brenning, A.: Improved Spatial Analysis and Prediction of Landslide Susceptibility: Practical Recommendations, in: Landslides and Engineered Slopes: Protecting Society Through Improved Understanding, edited by: Eberhardt, E., Froese, C., Turner, K., and Leroueil, S., CRC Press, 789–795, ISBN 0415621232, 2012.
Broeckx, J., Vanmaercke, M., Duchateau, R., and Poesen, J.: A data-based landslide susceptibility map of Africa, Earth-Science Rev., 185, 102–121, https://doi.org/10.1016/j.earscirev.2018.05.002, 2018.
Chanza, N., Siyongwana, P. Q., Williams-Bruinders, L., Gundu-Jakarasi, V., Mudavanhu, C., Sithole, V. B., and Manyani, A.: Closing the Gaps in Disaster Management and Response: Drawing on Local Experiences with Cyclone Idai in Chimanimani, Zimbabwe, Int. J. Disaster Risk Sci., 11, 655–666, https://doi.org/10.1007/s13753-020-00290-x, 2020.
Chatiza, K.: Cyclone Idai in Zimbabwe – An analysis of policy implications for post-disaster institutional development to strengthen disaster risk management, 30 pp., https://doi.org/10.21201/2019.5273, 2019.
Church, M. and Jakob, M.: What Is a Debris Flood?, Water Resour. Res., 56, 1–17, https://doi.org/10.1029/2020WR027144, 2020.
Croissant, T., Lague, D., Steer, P., and Davy, P.: Rapid post-seismic landslide evacuation boosted by dynamic river width, Nat. Geosci., 10, 680–684, https://doi.org/10.1038/ngeo3005, 2017.
Cutter, S. L.: Compound, Cascading, or Complex Disasters: What's in a Name?, Environ. Sci. Policy Sustain. Dev., 60, 16–25, https://doi.org/10.1080/00139157.2018.1517518, 2018.
Dahal, A. and Lombardo, L.: Explainable artificial intelligence in geoscience: a glimpse into the future of landslide susceptibility modeling [Preprint], Comput. Geosci., 176, 105364, https://doi.org/10.1016/j.cageo.2023.105364, 2023.
Das, R. and Wegmann, K. W.: Evaluation of machine learning-based algorithms for landslide detection across satellite sensors for the 2019 Cyclone Idai event, Chimanimani District, Zimbabwe, Landslides, 19, 2965–2981, https://doi.org/10.1007/s10346-022-01912-9, 2022.
De Angeli, S., Malamud, B. D., Rossi, L., Taylor, F. E., Trasforini, E., and Rudari, R.: A multi-hazard framework for spatial-temporal impact analysis, Int. J. Disaster Risk Reduct., 73, 102829, https://doi.org/10.1016/j.ijdrr.2022.102829, 2022.
Depicker, A., Jacobs, L., Mboga, N., Smets, B. B., Van Rompaey, A., Lennert, M., Wolff, E., Kervyn, F. F., Michellier, C., Dewitte, O., Govers, G., Rompaey, A. Van, Lennart, M., Kervyn, F. F., Michellier, C., Dewitte, O., and Govers, G.: Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift, Nat. Sustain., 4, 965–974, https://doi.org/10.1038/s41893-021-00757-9, 2021a.
Depicker, A., Govers, G., Jacobs, L., Campforts, B., Uwihirwe, J., and Dewitte, O.: Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika–Kivu rift region, Africa, Earth Surf. Dyn., 9, 445–462, https://doi.org/10.5194/esurf-9-445-2021, 2021b.
Deprez, A., Marc, O., Malet, J.-P., Stumpf, A., and Michéa, D.: ALADIM – A change detection on-line service for landslide detection from EO imagery, in: EGU General Assembly 2022, https://doi.org/10.5194/egusphere-egu22-3536, 2022.
Devi, S.: Cyclone Idai: 1 month later, devastation persists, Lancet, World Rep., 393, 1585, https://doi.org/10.1016/S0140-6736(19)30892-X, 2019.
de Vilder, S., Kelly, S., Buxton, R., Allan, S., and Glassey, P.: Landslide planning guidance reducing landslide risk through land-use planning, GNS Scienc., GNS Science, Lower Hutt, 77 pp., https://doi.org/10.21420/R2X8-FJ49, 2024.
Dewitte, O., Dille, A., Depicker, A., Kubwimana, D., Maki Mateso, J. -C., Mugaruka Bibentyo, T., Uwihirwe, J., and Monsieurs, E.: Constraining landslide timing in a data-scarce context: from recent to very old processes in the tropical environment of the North Tanganyika-Kivu Rift region, Landslides, 18, 161–177, https://doi.org/10.1007/s10346-020-01452-0, 2021.
Dewitte, O., Depicker, A., Moeyersons, J., and Dille, A.: Mass Movements in Tropical Climates, Treatise Geomorphol., 338–349, https://doi.org/10.1016/B978-0-12-818234-5.00118-8, 2022.
Di Napoli, M., Di Martire, D., Bausilio, G., Calcaterra, D., Confuorto, P., Firpo, M., Pepe, G., and Cevasco, A.: Rainfall-induced shallow landslide detachment, transit and runout susceptibility mapping by integrating machine learning techniques and gis-based approaches, Water (Switzerland), 13, 11–14, https://doi.org/10.3390/w13040488, 2021.
Dubey, S., Sattar, A., Goyal, M. K., Allen, S., Frey, H., Haritashya, U. K., and Huggel, C.: Mass Movement Hazard and Exposure in the Himalaya, Earth's Futur., 11, 1–18, https://doi.org/10.1029/2022EF003253, 2023.
Emberson, R., Kirschbaum, D., and Stanley, T.: New Global Characterization of Landslide Exposure, Nat. Hazards Earth Syst. Sci., 30, 1–21, https://doi.org/10.5194/nhess-2019-434, 2020.
Emberson, R., Kirschbaum, D. B., Amatya, P., Tanyas, H., and Marc, O.: Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories, Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022, 2022.
Fan, L., Lehmann, P., McArdell, B., and Or, D.: Linking rainfall-induced landslides with debris flows runout patterns towards catchment scale hazard assessment, Geomorphology, 280, 1–15, https://doi.org/10.1016/j.geomorph.2016.10.007, 2017.
Fan, X., Juang, C. H., Wasowski, J., Huang, R., Xu, Q., Scaringi, G., van Westen, C. J., and Havenith, H. B.: What we have learned from the 2008 Wenchuan Earthquake and its aftermath: A decade of research and challenges, Eng. Geol., 241, 25–32, https://doi.org/10.1016/j.enggeo.2018.05.004, 2018.
Gill, J. C. and Malamud, B. D.: Reviewing and visualizing the interactions of natural hazards, Rev. Geophys., 52, 680–722, https://doi.org/10.1002/2013RG000445, 2014.
Gill, J. C., Malamud, B. D., Barillas, E. M., and Guerra Noriega, A.: Construction of regional multi-hazard interaction frameworks, with an application to Guatemala, Nat. Hazards Earth Syst. Sci., 20, 149–180, https://doi.org/10.5194/nhess-20-149-2020, 2020.
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R. G.: High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Highland, L. M. and Bobrowsky, P.: The Landslide Handbook – A Guide to Understanding Landslides, Landslides, 129, ISBN 978-141132226-4, 2008.
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.
IFRC: Zimbabwe: Tropical Cycle Idai – Final Report, International Federation of Red Cross and Red Crescent Societies, 1–13, https://reliefweb.int/report/zimbabwe/zimbabwe-tropical-cyclone-idai-final-report-dref-operation (last access: 2 June 2026), 2020.
Iverson, R. M. and Ouyang, C.: Entrainment of bed material by Earth-surface mass flows: Review and reformulation of depth-integrated theory, Rev. Geophys., 53, 27–58, https://doi.org/10.1002/2013RG000447, 2015.
Iverson, R. M., Reid, M. E., and LaHusen, R. G.: Debris-flow mobilization from landslides, Annu. Rev. Earth Planet. Sci., 25, 85–138, 1997.
Iverson, R. M. M., George, D. L. L., Allstadt, K., Reid, M. E. E., Collins, B. D. D., Vallance, J. W., Schilling, S. P., Godt, J. W. W., Cannon, C. M. M., Magirl, C. S. S., Baum, R. L. L., Coe, J. a. A., Schulz, W. H. H., and Bower, J. B. B.: Landslide mobility and hazards: Implications of the 2014 Oso disaster, Earth Planet. Sci. Lett., 412, 197–208, https://doi.org/10.1016/j.epsl.2014.12.020, 2015.
Jacobs, L., Maes, J., Mertens, K., Sekajugo, J., Thiery, W., van Lipzig, N., Poesen, J., Kervyn, M., and Dewitte, O.: Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda, Nat. Hazards, 84, 851–876, https://doi.org/10.1007/s11069-016-2458-y, 2016.
Keck, J., Istanbulluoglu, E., Campforts, B., Tucker, G., and Horner-Devine, A.: A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications, Earth Surf. Dynam., 12, 1165–1191, https://doi.org/10.5194/esurf-12-1165-2024, 2024.
Kritikos, T. and Davies, T.: Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand, Landslides, 12, 1051–1075, https://doi.org/10.1007/s10346-014-0533-6, 2015.
Lee, R., White, C. J., Adnan, M. S. G., Douglas, J., Mahecha, M. D., O'Loughlin, F. E., Patelli, E., Ramos, A. M., Roberts, M. J., Martius, O., Tubaldi, E., van den Hurk, B., Ward, P. J., and Zscheischler, J.: Reclassifying historical disasters: From single to multi-hazards, Sci. Total Environ., 912, 169120, https://doi.org/10.1016/j.scitotenv.2023.169120, 2024.
Legros, F.: The mobility of long-runout landslides, Eng. Geol., 63, 301–331, https://doi.org/10.1016/S0013-7952(01)00090-4, 2002.
Lin, Q., Steger, S., Pittore, M., Zhang, Y., Zhang, J., Zhou, L., Wang, L., Wang, Y., and Jiang, T.: Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change, Earth's Futur., 11, 1–21, https://doi.org/10.1029/2023EF003741, 2023.
Maki Mateso, J.-C., Bielders, C. L., Monsieurs, E., Depicker, A., Smets, B., Tambala, T., Bagalwa Mateso, L., and Dewitte, O.: Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo), Nat. Hazards Earth Syst. Sci., 23, 643–666, https://doi.org/10.5194/nhess-23-643-2023, 2023.
Marc, O. and Hovius, N.: Amalgamation in landslide maps: effects and automatic detection, Nat. Hazards Earth Syst. Sci., 15, 723–733, https://doi.org/10.5194/nhess-15-723-2015, 2015.
Marc, O., Stumpf, A., Malet, J.-P., Gosset, M., Uchida, T., and Chiang, S.-H.: Initial insights from a global database of rainfall-induced landslide inventories: the weak influence of slope and strong influence of total storm rainfall, Earth Surf. Dynam., 6, 903–922, https://doi.org/10.5194/esurf-6-903-2018, 2018.
McGuire, L. A., Ebel, B. A., Rengers, F. K., Vieira, D. C. S., and Nyman, P.: Fire effects on geomorphic processes, Nat. Rev. Earth Environ., 41–79, https://doi.org/10.1038/s43017-024-00557-7, 2024.
Melo, R., Zêzere, J. L., Rocha, J., and Oliveira, S. C.: Combining data-driven models to assess susceptibility of shallow slides failure and run-out, Landslides, 16, 2259–2276, https://doi.org/10.1007/s10346-019-01235-2, 2019.
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., Avtar, R., and Abderrahmane, B.: Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance, Earth-Science Rev., 207, 103225, https://doi.org/10.1016/j.earscirev.2020.103225, 2020.
Mergili, M., Emmer, A., Juřicová, A., Cochachin, A., Fischer, J. T., Huggel, C., and Pudasaini, S. P.: How well can we simulate complex hydro-geomorphic process chains? The 2012 multi-lake outburst flood in the Santa Cruz Valley (Cordillera Blanca, Perú), Earth Surf. Process. Landforms, 43, 1373–1389, https://doi.org/10.1002/esp.4318, 2018.
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.
Merz, B., Blöschl, G., Vorogushyn, S., Dottori, F., Aerts, J. C. J. H., Bates, P., Bertola, M., Kemter, M., Kreibich, H., Lall, U., and Macdonald, E.: Causes, impacts and patterns of disastrous river floods, Nat. Rev. Earth Environ., 2, 592–609, https://doi.org/10.1038/s43017-021-00195-3, 2021.
Milledge, D. G., Densmore, A. L., Bellugi, D., Rosser, N. J., Watt, J., Li, G., and Oven, K. J.: Simple rules to minimise exposure to coseismic landslide hazard, Nat. Hazards Earth Syst. Sci., 19, 837–856, https://doi.org/10.5194/nhess-19-837-2019, 2019.
Nohrstedt, D., Mazzoleni, M., Parker, C. F., and Di Baldassarre, G.: Exposure to natural hazard events unassociated with policy change for improved disaster risk reduction, Nat. Commun., 12, https://doi.org/10.1038/s41467-020-20435-2, 2021.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., and Vanderplas, J.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 1, 2815–2830, 2011.
Pudasaini, S. P. and Krautblatter, M.: The mechanics of landslide mobility with erosion, Nat. Commun., 12, https://doi.org/10.1038/s41467-021-26959-5, 2021.
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F.: A review of statistically-based landslide susceptibility models, Earth-Science Rev., 180, 60–91, https://doi.org/10.1016/j.earscirev.2018.03.001, 2018.
Roback, K., Clark, M. K., West, A. J., Zekkos, D., Li, G., Gallen, S. F., Chamlagain, D., and Godt, J. W.: The size, distribution, and mobility of landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal, Geomorphology, 301, 121–138, https://doi.org/10.1016/j.geomorph.2017.01.030, 2018.
Royal Museum for Central Africa: Landslide Susceptibility and Exposure Assesment – Chimanimani and Chipinge districts, Zimbabwe, UNESCO [data set], https://doi.org/10.63253/nii4g2ac, 2026.
Scheip, C. M. and Wegmann, K. W.: HazMapper: a global open-source natural hazard mapping application in Google Earth Engine, Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, 2021.
Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Mubiru, D., Kanyiginya, V., Vranken, L., Jacobs, L., Dewitte, O., and Kervyn, M.: Exposure and physical vulnerability to geo-hydrological hazards in rural environments: A field-based assessment in East Africa, Int. J. Disaster Risk Reduct., 102, 104282, https://doi.org/10.1016/j.ijdrr.2024.104282, 2024.
Sharma, S., Talchabhadel, R., Nepal, S., Ghimire, G. R., Rakhal, B., Panthi, J., Adhikari, B. R., Pradhanang, S. M., Maskey, S., and Kumar, S.: Increasing risk of cascading hazards in the central Himalayas, Nat. Hazards, 119, 1117–1126, https://doi.org/10.1007/s11069-022-05462-0, 2023.
Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y. S. E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J.: Continental-Scale Building Detection from High Resolution Satellite Imagery, 1–15, https://doi.org/10.48550/arXiv.2107.12283, 2021.
Slater, L. J., Singer, M. B., and Kirchner, J. W.: Hydrologic versus geomorphic drivers of trends in flood hazard, Geophys. Res. Lett., 42, 370–376, https://doi.org/10.1002/2014GL062482, 2015.
Stanley, T. and Kirschbaum, D. B.: A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 87, 145–164, https://doi.org/10.1007/s11069-017-2757-y, 2017.
Tanyaş, H., van Westen, C. J., Allstadt, K. E., Anna Nowicki Jessee, M., Görüm, T., Jibson, R. W., Godt, J. W., Sato, H. P., Schmitt, R. G., Marc, O., and Hovius, N.: Presentation and Analysis of a Worldwide Database of Earthquake-Induced Landslide Inventories, J. Geophys. Res. Earth Surf., 122, 1991–2015, https://doi.org/10.1002/2017JF004236, 2017.
Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Kilic, T., Murray, S., Blankespoor, B., Prydz, E. B., and Dang, H.-A. H.: Mapping the World Population One Building at a Time. World Bank, https://doi.org/10.1596/33700, 2017.
Tilloy, A., Malamud, B. D., Winter, H., and Joly-Laugel, A.: A review of quantification methodologies for multi-hazard interrelationships, Earth-Science Rev., 196, 102881, https://doi.org/10.1016/j.earscirev.2019.102881, 2019.
UNESCO Intergovernmental Hydrological Program: Comprehensive resilience building in the Chimanimani and Chipinge Districts, Paris, France, https://ihp-wins.unesco.org/dataset/comprehensive-resilience-building-in-the-chimanimani (last access: 2 June 2026), 2021.
van den Bout, B., Tang, C., van Westen, C., and Jetten, V.: Physically based modeling of co-seismic landslide, debris flow, and flood cascade, Nat. Hazards Earth Syst. Sci., 22, 3183–3209, https://doi.org/10.5194/nhess-22-3183-2022, 2022.
Vanmaercke, M., Chen, Y., Haregeweyn, N., De Geeter, S., Campforts, B., Heyndrickx, W., Tsunekawa, A., and Poesen, J.: Predicting gully densities at sub-continental scales: a case study for the Horn of Africa, Earth Surf. Process. Landforms, 45, 3763–3779, https://doi.org/10.1002/esp.4999, 2020.
van Westen, C. J., Van Asch, T. W. J. J., and Soeters, R.: Landslide hazard and risk zonation – why is it still so difficult?, Bull. Eng. Geol. Environ., 65, 167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006.
Wallace, C. S., Santi, P. M., and Walton, G.: Scoring system to predict landslide runout in the Pacific Northwest, USA, Landslides, 19, 1449–1461, https://doi.org/10.1007/s10346-021-01839-7, 2022.
Weiss, A.: Topographic position and landforms analysis, ESRI User Conference, 227–245, https://env761.github.io/assets/files/tpi-poster-tnc_18x22.pdf (last access: last access: 2 June 2026), 2000.
Wohl, E., Brierley, G., Cadol, D., Coulthard, T. J., Covino, T., Fryirs, K. A., Grant, G., Hilton, R. G., Lane, S. N., Magilligan, F. J., Meitzen, K. M., Passalacqua, P., Poeppl, R. E., Rathburn, S. L., and Sklar, L. S.: Connectivity as an emergent property of geomorphic systems, Earth Surf. Process. Landforms, 44, 4–26, https://doi.org/10.1002/esp.4434, 2019.
Yanites, B. J., Clark, M. K., Roering, J. J., West, A. J., Zekkos, D., Baldwin, J. W., Cerovski-Darriau, C., Gallen, S. F., Horton, D. E., Kirby, E., Leshchinsky, B. A., Mason, H. B., Moon, S., Barnhart, K. R., Booth, A., Czuba, J. A., McCoy, S., McGuire, L., Pfeiffer, A., and Pierce, J.: Cascading land surface hazards as a nexus in the Earth system, Science, 388, https://doi.org/10.1126/science.adp9559, 2025.
Zêzere, J. L., Pereira, S., Melo, R., Oliveira, S. C., and Garcia, R. A. C.: Mapping landslide susceptibility using data-driven methods, Sci. Total Environ., 589, 250–267, https://doi.org/10.1016/j.scitotenv.2017.02.188, 2017.
Zhou, W., Qiu, H., Wang, L., Pei, Y., Tang, B., Ma, S., Yang, D., and Cao, M.: Combining rainfall-induced shallow landslides and subsequent debris flows for hazard chain prediction, Catena, 213, 106199, https://doi.org/10.1016/j.catena.2022.106199, 2022.
Zimbabwe National Statistics Agency: Population and housing census 2022, 163 pp., http://www.zimstat.co.zw/wp-content/uploads/Census/Zimbabwe_2022_PHC_Gender_thematic_FINAL_DRAFT_Jan_25.pdf (last access: 2 June 2026), 2022.
Short summary
In mountain regions, intense rainfall can trigger thousands of landslides within hours. Yet, while most efforts focus on where landslides start, the worst impacts often occur far downstream because slope material can mix with large runoffs. Studying Cyclone Idai’s impacts in eastern Zimbabwe, we found that landslide sources explain only one-fifth of total population exposure, highlighting the need to consider the full landslide–flood continuum to better protect people and plan safer landscapes.
In mountain regions, intense rainfall can trigger thousands of landslides within hours. Yet,...
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