Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift

An Author Correction to this article was published on 22 September 2021

This article has been updated

Abstract

Human activity influences both the occurrence and impact of landslides in mountainous environments. Population pressure and the associated land-use changes are assumed to exacerbate landslide risk, yet there is a lack of statistical evidence to support this claim, especially in the Global South where historical records are scarce. In this work, we explore the interactions between population, deforestation and landslide risk in the Kivu Rift in Africa. To do so, we develop a holistic landslide risk model that evaluates 58 years of population and forest-cover trends. We show that the current landslide risk in the eastern Democratic Republic of the Congo (DRC) is twice as high as in neighbouring Rwanda and Burundi. Congolese households, on average, populate more hazardous terrain, probably as a result of conflicts and economic pull factors such as mining. Moreover, the recent large-scale deforestation of primary rainforest in the DRC has considerably exacerbated the landslide risk. Our analysis demonstrates how the legacy of deforestation, conflicts and population dynamics is reflected in the landslide risk in the Kivu Rift.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Overview of the Kivu Rift.
Fig. 2: The 1958 orthomosaic and forest cover for the Kivu Rift.
Fig. 3: Conflicts, forest dynamics and demography in the Kivu Rift.
Fig. 4: Landslide hazard trends in the Kivu Rift and the link with landslide susceptibility.
Fig. 5: Landslide exposure and risk in the Kivu Rift.
Fig. 6: Conceptual overview of the key processes affecting shallow landslide risk in the Kivu Rift.

Data availability

The 1958 forest-cover data can be accessed at: https://doi.org/10.5281/zenodo.5027117. The 1958 panchromatic orthomosaics will become available at the end of the PAStECA project in March 2022 (http://pasteca.africamuseum.be/data). The landslide inventory is provided by Depicker et al.20 (https://doi.org/10.5194/esurf-9-445-2021) and can be downloaded at: https://doi.org/10.5281/zenodo.5027004. The land-cover data for 1988 and 2001 are provided by Basnet and Vodacek66 (https://doi.org/10.3390/rs70606683). The 2016 land-cover data are provided by ESA and can be accessed at: http://2016africalandcover20m.esrin.esa.int/. The population-density data are derived from the Global Human Settlement Layer that can be accessed at: http://ghsl.jrc.ec.europa.eu/. The Shuttle Radar Topography Mission digital elevation model is provided by the US Geological Survey (https://earthexplorer.usgs.gov/). The seismic data (Peak Ground Acceleration) are provided by Delvaux et al.83 (https://doi.org/10.1016/j.jafrearsci.2016.10.004) upon contacting the corresponding author. The road data can be downloaded from OpenStreetMap (https://www.openstreetmap.org/#map=7/50.510/4.475). The Global Forest Change 2000–2019 data from Hansen et al.71 can be found at: https://data.globalforestwatch.org/documents/14228e6347c44f5691572169e9e107ad/explore. The lithology data are retrieved from the work of Depicker et al.29 (https://doi.org/10.1016/j.geomorph.2019.106886) and can be requested from the author. The raw data used for Figs. 3–5 in this work can be accessed at: https://doi.org/10.6084/m9.figshare.14838825. Source data are provided with this paper.

Code availability

The Python code used to derive the forest cover from aerial photographs and reconstruct the forest-cover changes can be requested from the corresponding authors.

Change history

References

  1. McBean, G. A. Integrating disaster risk reduction towards sustainable development. Curr. Opin. Environ. Sustain. 4, 122–127 (2012).

    Google Scholar 

  2. Kelman, I. Linking disaster risk reduction, climate change, and the sustainable development goals. Disaster Prev. Manag. 26, 254–258 (2017).

    Google Scholar 

  3. United Nations Department of Economic and Social Affairs World Population Prospects 2019 (United Nations, accessed 16 July 2020); population.un.org/wpp/

  4. Ehrlich, D. et al. Remote sensing derived built-up area and population density to quantify global exposure to five natural hazards over time. Remote Sens. 10, 1378 (2018).

    Google Scholar 

  5. Lambin, E. F. & Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl Acad. Sci. USA 108, 3465–3472 (2011).

    CAS  Google Scholar 

  6. Chamberlin, J., Jayne, T. S. & Headey, D. Scarcity amidst abundance? Reassessing the potential for cropland expansion in Africa. Food Policy 48, 51–65 (2014).

    Google Scholar 

  7. Nkonya, E., Mirzabaev, A. & von Braun, J. (eds) Economics of Land Degradation and Improvement—A Global Assessment for Sustainable Development (Springer, 2015).

  8. Young, A. Poverty, hunger and population policy: linking Cairo with Johannesburg. Geogr. J. 171, 83–95 (2005).

    Google Scholar 

  9. Mugagga, F., Kakembo, V. & Buyinza, M. Land use changes on the slopes of Mount Elgon and the implications for the occurrence of landslides. Catena 90, 39–46 (2012).

    Google Scholar 

  10. Sidle, R. C. & Bogaard, T. A. Dynamic Earth system and ecological controls of rainfall-initiated landslides. Earth Sci. Rev. 159, 275–291 (2016).

    Google Scholar 

  11. Kirschbaum, D., Stanley, T. & Zhou, Y. Spatial and temporal analysis of a global landslide catalog. Geomorphology 249, 4–15 (2015).

    Google Scholar 

  12. Froude, M. J. & Petley, D. N. Global fatal landslide occurrence from 2004 to 2016. Nat. Hazard Earth Syst. Sci. 18, 2161–2181 (2018).

    Google Scholar 

  13. Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M. & Guzzetti, F. A review of statistically-based landslide susceptibility models. Earth Sci. Rev. 180, 60–91 (2018).

    Google Scholar 

  14. Guzzetti, F., Reichenbach, P., Cardinali, M. & Ardizzone, A. Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72, 272–299 (2005).

    Google Scholar 

  15. Corominas, C. et al. Recommendations for the quantitative analysis of landslide risk. Bull. Eng. Geol. Environ. 73, 209–263 (2014).

    Google Scholar 

  16. Ray, R. L., Jacobs, J. M. & Ballestero, T. P. Regional landslide susceptibility: spatiotemporal variations under dynamic soil moisture conditions. Nat. Hazards 59, 1317–1337 (2011).

    Google Scholar 

  17. Reichenbach, P., Busca, C., Mondini, A. C. & Rossi, M. The influence of land use change on landslide susceptibility zonation: the Briga Catchment test site (Messina, Italy). Environ. Manage. 54, 1372–1384 (2014).

    CAS  Google Scholar 

  18. Hua, Y., Wang, X., Li, Y., Xu, P. & Xia, W. Dynamic development of landslide susceptibility based on slope unit and deep neural networks. Landslides 18, 281–302 (2021).

    Google Scholar 

  19. Montgomery, D., Schmidt, K., Greenberg, H. & Dietrich, W. Forest clearing and regional landsliding. Geology 28, 311–314 (2000).

    Google Scholar 

  20. Depicker, A. et al. Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika–Kivu Rift region, Africa. Earth Surf. Dyn. 9, 445–462 (2021).

    Google Scholar 

  21. Knapen, A. et al. Landslides in a densely populated country at the footslopes of Mount Elgon (Uganda): characteristics and causal factors. Geomorphology 73, 149–165 (2006).

    Google Scholar 

  22. Kucsicsa, G. et al. Assessing the potential future forest-cover change in Romania, predicted using a scenario-based modelling. Environ. Model. Assess. 25, 471–491 (2020).

    Google Scholar 

  23. Dolidon, N., Hofer, T., Jansky, L. & Sidle, R. in Landslides: Disaster Risk Reduction (eds Sasa, K. & Canuti, P.) 633–649 (Springer, 2009).

  24. Moos, C. et al. Ecosystem-based disaster risk reduction in mountains. Earth Sci. Rev. 177, 497–513 (2018).

    Google Scholar 

  25. de Jesús Arce-Mojica, T., Nehren, U., Sudmeier-Rieux, K., Miranda, P. J. & Anhuf, D. Nature-based solutions (NbS) for reducing the risk of shallow landslides: where do we stand? Int. J. Disaster Risk Reduct. 41, 101293 (2019).

    Google Scholar 

  26. Maes, J. et al. Landslide risk reduction measures: a review of practices and challenges for the tropics. Prog. Phys. Geogr. 41, 191–221 (2017).

    Google Scholar 

  27. Monsieurs, E. et al. Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment. Landslides 15, 2195–2209 (2018).

    Google Scholar 

  28. Delvaux, D. & Barth, A. African stress pattern from formal inversion of focal mechanism data. Tectonophysics 482, 105–128 (2010).

    Google Scholar 

  29. Depicker, A. et al. The added value of a regional landslide susceptibility assessment: the western branch of the East African Rift. Geomorphology 353, 106886 (2020).

    Google Scholar 

  30. Dewitte, O. et al. 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 (2021).

    Google Scholar 

  31. Emberson, R., Kirschbaum, D. & Stanley, T. New global characterisation of landslide exposure. Nat. Hazards Earth Syst. Sci. 20, 3413–3424 (2020).

    Google Scholar 

  32. National Contingency Plan for Floods and Landslides (Ministry in Charge of Emergency Management of the Republic of Rwanda, 2018); https://www.minema.gov.rw/fileadmin/user_upload/Minema/Publications/Contingency_Plans/Contingency_Plan_for_Floods_nd_Landslides.pdf

  33. Aleman, J. C., Jarzyna, M. A. & Staver, A. C. Forest extent and deforestation in tropical Africa since 1900. Nat. Ecol. Evol. 2, 26–33 (2018).

    Google Scholar 

  34. Ellis, E. C., Goldewijk, K. K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).

    Google Scholar 

  35. Stebbing, E. P. Forests of the Belgian Congo. Nature 172, 1177 (1953).

    Google Scholar 

  36. Kleinschroth, F., Laporte, N., Laurance, W. F., Goetz, S. J. & Ghazoul, J. Road expansion and persistence in forests of the Congo Basin. Nat. Sustain. 2, 628–634 (2019).

    Google Scholar 

  37. Gachuruzi, S. B. The impact of refugees on the environment: the case of Rwandan refugees in Kivu, Zaire. Refuge 15, 24–26 (1996).

    Google Scholar 

  38. Huggins, C. & Clover, J. (eds) From the Ground Up: Land Rights, Conflict and Peace in Sub-Saharan Africa (ISS, 2005).

  39. Garrett, N., Sergiou, S. & Vlassenroot, K. Negotiated peace for extortion: the case of Walikale territory in eastern DR Congo. J. East. Afr. Stud. 3, 1–21 (2009).

    Google Scholar 

  40. Butsic, V., Baumann, M., Shortland, A., Walker, S. & Kuemmerle, T. Conservation and conflict in the Democratic Republic of Congo: the impacts of warfare, mining, and protected areas on deforestation. Biol. Conserv. 191, 266–273 (2015).

    Google Scholar 

  41. Muñoz-Torrero Manchado, A. et al. Three decades of landslide activity in western Nepal: new insights into trends and climate drivers. Landslides 18, 2001–2015 (2021).

    Google Scholar 

  42. Chaney, N. W., Sheffield, J., Villarini, G. & Wood, E. F. Development of a high-resolution gridded daily meteorological dataset over sub-Saharan Africa: spatial analysis of trends in climate extremes. J. Clim. 27, 5815–5835 (2014).

    Google Scholar 

  43. Omondi, P. A. O. et al. Changes in temperature and precipitation extremes over the Greater Horn of Africa Region from 1961 to 2010. Int. J. Climatol. 34, 1262–1277 (2014).

    Google Scholar 

  44. Leurquin, P. P. Agricultural change in Ruanda–Urundi: 1945–1960. Food Res. Inst. Stud. 4, 1–51 (1963).

    Google Scholar 

  45. Meditz, S. W., Merrill, T. & Library of Congress, Federal Research Division (eds) Zaire: A Country Study 4th edn (Library of Congress, 1994).

  46. GHS Population Grid, Derived from GPW4, Multitemporal (1975, 1990, 2000, 2015) (European Commission, JRC, accessed 12 August 2020); http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a

  47. Kanyamibwa, S. Impact of war on conservation: Rwandan environment and wildlife in agony. Biodivers. Conserv. 7, 1399–1406 (1998).

    Google Scholar 

  48. Ordway, E. M. Political shifts and changing forests: effects of armed conflict on forest conservation in Rwanda. Glob. Ecol. Conserv. 3, 448–460 (2015).

    Google Scholar 

  49. Michellier, C., Pigeon, P., Kervyn, F. & Wolff, E. Contextualizing vulnerability assessment: a support to geo-risk management in central Africa. Nat. Hazards 82, S27–S42 (2016).

    Google Scholar 

  50. Banerjee, O. et al. Economic, land use, and ecosystem services impacts of Rwanda’s Green Growth Strategy: an application of the IEEM + ESM platform. Sci. Total Environ. 729, 138779 (2020).

    CAS  Google Scholar 

  51. Vervisch, T., Titeca, K., Vlassenroot, K. & Braeckman, J. Social capital and post-conflict reconstruction in Burundi: the limits of community-based reconstruction. Dev. Change 44, 147–174 (2013).

    Google Scholar 

  52. Reyntjens, F. The Great African War—Congo and Regional Geopolitics, 1996–2006 (Cambridge Univ. Press, 2009).

  53. De Putter, T. & Delvaux, C. Certifier les ressources minérales dans la région des Grands Lacs. Polit. Etrang. 2, 99–112 (2013).

    Google Scholar 

  54. Nassar, N. T. Shifts and trends in the global anthropogenic stocks and flows of tantalum. Resour. Conserv. Recycl. 125, 233–250 (2017).

    Google Scholar 

  55. Maps of Conflict Minerals in Eastern DRC—A0 Posters (International Peace Information Service, accessed 25 September 2020); https://ipisresearch.be/publication/map-conflict-minerals-eastern-drc-a0-posters/

  56. van Acker, F. Where did all the land go? Enclosure & social struggle in Kivu (D.R. Congo). Rev. Afr. Polit. Econ. 32, 79–98 (2005).

    Google Scholar 

  57. Luethje, F., Kranz, O. & Schoepfer, E. Geographic object-based image analysis using optical satellite imagery and GIS data for the detection of mining sites in the Democratic Republic of the Congo. Remote Sens. 6, 6636–6661 (2014).

    Google Scholar 

  58. Tyukavina, A. et al. Congo Basin forest loss dominated by increasing smallholder clearing. Sci. Adv. 4, eaat2993 (2018).

    Google Scholar 

  59. Günter, S., Weber, M., Erreis, R. & Aguirre, N. Influence of distance to forest edges on natural regeneration of abandoned pastures: a case study in the tropical mountain rain forest of southern Ecuador. Eur. J. For. Res. 126, 67–75 (2007).

    Google Scholar 

  60. Pollock, W. & Wartman, J. Human vulnerability to landslides. GeoHealth 4, e2020GH000287 (2020).

    Google Scholar 

  61. Vlassenroot, K. & Raeymaekers, T. Conflict and Social Transformation in Eastern DR Congo (Academia Press, 2005).

  62. Lehmann, P., Von Ruette, J. & Or, D. Deforestation effects on rainfall-induced shallow landslides: remote sensing and physically-based modelling. Water 55, 9962–9976 (2019).

    Google Scholar 

  63. Meyfroidt, P. & Lambin, E. F. Global forest transition: prospects for an end to deforestation. Annu. Rev. Environ. Resour. 36, 343–371 (2011).

    Google Scholar 

  64. Willemen, L. et al. How to halt the global decline of lands. Nat. Sustain. 3, 164–166 (2020).

    Google Scholar 

  65. Corbane, C. et al. A global cloud free pixel-based image composite from Sentinel-2 data. Data Brief 31, 105737 (2020).

    CAS  Google Scholar 

  66. Basnet, B. & Vodacek, A. Tracking land use/land cover dynamics in cloud prone areas using moderate resolution satellite data: a case study in Central Africa. Remote Sens. 7, 6683–6709 (2015).

    Google Scholar 

  67. ESA Climate Change Initiative–Land Cover Project 2017. 20m Resolution (European Space Agency, 2016).

  68. He, F., Li, S. & Zhang, X. A spatially explicit reconstruction of forest cover in China over 1700–2000. Glob. Planet. Change 131, 73–81 (2015).

    Google Scholar 

  69. Bolliger, J., Schmatz, D., Pazúr, R., Ostapowicz, K. & Psomas, A. Reconstructing forest-cover change in the Swiss Alps between 1880 and 2010 using ensemble modelling. Reg. Environ. Change 17, 2265–2277 (2017).

    Google Scholar 

  70. Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression 2nd edn (John Wiley & Sons, 2000).

  71. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    CAS  Google Scholar 

  72. Rosa, I. M., Purves, D., Souza, C. & Ewers, R. M. Predictive modelling of contagious deforestation in the Brazilian Amazon. PLoS ONE 8, e77231 (2013).

    CAS  Google Scholar 

  73. Poor, E. E., Jati, V. I., Imron, M. A. & Kelly, M. J. The road to deforestation: edge effects in an endemic ecosystem in Sumatra, Indonesia. PLoS ONE 14, e0217540 (2019).

    CAS  Google Scholar 

  74. USGS Shuttle Radar Topography Mission, Global Land Cover Facility. 1 Arc-Second (Univ. of Maryland, 2006).

  75. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006).

    Google Scholar 

  76. Brenning, A. Improved spatial analysis and prediction of landslide susceptibility: practical recommendations. In Landslides and Engineered Slopes: Protecting Society Through Improved Understanding. Proc. 11th International and 2nd North American Symposium on Landslides and Engineered Slopes (eds Eberhardt, E. et al.) 789–794 (CRC Press/Balkema, 2012).

  77. McFadden, D. in Frontiers in Econometrics (ed. Zarembka, P.) 105–142 (Academic Press, 1973).

  78. McFadden, D. in Behavioural Travel Modelling (eds Hensher, D. & Stopher, P.) 279–318 (Croom Helm, 1978).

  79. Rosa, I. M., Smith, M. J., Wearn, O. R., Purves, D. & Ewers, R. M. The environmental legacy of modern tropical deforestation. Curr. Biol. 26, 2161–2166 (2016).

    CAS  Google Scholar 

  80. Broeckx, J. et al. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda. Landslides 16, 571–584 (2019).

    Google Scholar 

  81. Hungr, O., Lerouel, S. & Picarelli, L. The Varnes classification of landslide types, an update. Landslides 11, 167–194 (2014).

    Google Scholar 

  82. Harrell, F. E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis 2nd edn (Springer, 2003).

  83. Delvaux, D. et al. Seismic hazard assessment of the Kivu Rift segment based on a new seismotectonic zonation model (western branch, East African Rift system). J. Afr. Earth Sci. 134, 831–855 (2017).

    Google Scholar 

Download references

Acknowledgements

This study was supported by the Belgium Science Policy Office (BELSPO) through the PAStECA project (BR/165/A3/PASTECA) entitled ‘Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa’ (http://pasteca.africamuseum.be/). We further wish to thank F. Canters, F. Makanzu Imwangana, A. M. C. Umutoni, G. Sakindi, J. van Vliet and T. De Putter for their insightful discussions and recommendations regarding this research.

Author information

Authors and Affiliations

Authors

Contributions

A.D., L.J., G.G. and O.D. designed the study. A.D. developed the models and carried out the analyses. A.D. wrote the paper and designed the figures, with input from L.J., G.G. and O.D. B.S. and F.K. organized and processed the historical aerial photographs. B.S. and A.D. created the panchromatic orthomosaic. N.M., M.L. and E.W. developed the algorithms to classify the orthomosaic into forest. C.M. contributed to the historical analysis of the societal drivers. A.V.R. contributed to the design of the land-cover methodology. All authors proofread and commented on the manuscript.

Corresponding authors

Correspondence to Arthur Depicker or Olivier Dewitte.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Sustainability thanks Fritz Kleinschroth, Faith E. Taylor and Anthony Vodacek for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–5, and Tables 1 and 2.

Reporting Summary

Source data

Source Data Fig. 3

Raw data used for the graphs in Fig. 3.

Source Data Fig. 4

Raw data used for the graphs in Fig. 4.

Source Data Fig. 5

Raw data used for the graphs in Fig. 5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Depicker, A., Jacobs, L., Mboga, N. et al. Historical dynamics of landslide risk from population and forest-cover changes in the Kivu Rift. Nat Sustain 4, 965–974 (2021). https://doi.org/10.1038/s41893-021-00757-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-021-00757-9

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing