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Acceleration of a large deep-seated tropical landslide due to urbanization feedbacks

Abstract

The movement of large, slow-moving, deep-seated landslides is regulated principally by changes in pore-water pressure in the slope. In urban areas, drastic reorganization of the surface and subsurface hydrology—for example, associated with roads, housings or storm drainage—may alter the subsurface hydrology and ultimately the slope stability. Yet our understanding of the influence of slope urbanization on the dynamics of landslides remains elusive. Here we combined satellite and (historical) aerial images to quantify how 70 years of hillslope urbanization changed the seasonal, annual and multi-decadal dynamics of a large, slow-moving landslide located in the tropical environment of the city of Bukavu, Democratic Republic of the Congo. Analysis of week-to-week landslide motion over the past 4.5 years reveals that it is closely tied to pore-water pressure changes, pointing to interacting influences from climate, weathering, tectonics and urban development on the landslide dynamics. Over decadal timescales, we find that the sprawl of urbanized areas led to the acceleration of a large section of the landslide, which was probably driven by self-reinforcing feedbacks involving slope movement, rerouting of surface water flows and pipe ruptures. As hillslopes in many tropical cities are being urbanized at an accelerating pace, better understanding how anthropogenic activity influences surface processes will be vital to effective risk planning and mitigation.

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Fig. 1: Landslides in the city of Bukavu.
Fig. 2: Landslide motion and surface strains.
Fig. 3: Landslide displacement, pore pressure and rainfall time series.
Fig. 4: Surface drainage.
Fig. 5: Urban growth and slope instability from 1947 to 2018.

Data availability

Data used in this study are available for download from https://doi.org/10.5281/zenodo.7118267. The satellite imagery that supports the findings of this study is available from the space agencies and satellite operators (ESA/Copernicus, ASI, CNES/Airbus) but restrictions apply to the availability of some of these data, which were used under license for the current study and so are not systematically available publicly. Sentinel data are made available by ESA: https://scihub.copernicus.eu/. Source data are provided with this paper.

Code availability

All computer codes used in this work are available from the authors upon reasonable request.

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Acknowledgements

This article is a contribution in the framework of the projects RESIST funded by the Belgian Science Policy (BELSPO), Belgium (SR/00/305) and the Fonds National de la Recherche, Luxembourg (INTER/STEREOIII/13/05/RESIST/d’Oreye); MODUS (SR/00/358), AfReSlide (BR/ 121/A2/AfReSlide) and PAStECA (BR/165/A3/PASTECA) research projects funded by BELSPO and RA_S1_RGL_GEORISK and HARISSA funded by Development Cooperation programme of the Royal Museum for Central Africa, which is supported directly by the Directorate-General Development Cooperation and Humanitarian Aid of Belgium. E.M. benefited from an F.R.S.–FNRS PhD scholarship. Part of this research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). COSMO-SkyMed images were acquired through RESIST and MODUS projects as well as the CEOS Landslide Pilot. The images are under an Italian Space Agency (ASI) licence. Special thanks go to Université Officielle de Bukavu, and particularly to the members of the Department of Geology. Together with the support of the Civil Protection of South Kivu, they made it possible to execute fieldwork in the study area and provided crucial help for the dGNSS acquisition campaigns and the many discussions on landslide processes in the area. We thank D. Delvaux for sharing field pictures and discussions on the tectonics and geology of the area. We further thank G. Bennett, P. Gonzalez, J.-P. Malet and M. Rutzinger for their insightful discussions and recommendation regarding this research.

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Authors and Affiliations

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Contributions

A.D. and O.D. conceived the study with inputs from F.K. and M.K.. A.D. processed and analysed the data and created the figures. A.D. wrote the manuscript, with main inputs from O.D. and key contribution from M.K. and A.L.H. A.D., O.D., F.K., G.B.G., G.I.M., E.M. and T.M.B. participated in the field-data acquisition and interpretation. C.M. and J.M. participated in the interpretation of the field data. N.d‘O., D.D., S.S. and B.S. assisted with the processing of SAR and UAS data. A.L.H. assisted with the processing of slope pore-water simulations. All the authors contributed to the final version of the paper. O.D. and F.K. coordinated and designed this collaborative study in the frame of the RESIST and MODUS projects.

Corresponding author

Correspondence to Antoine Dille.

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Nature Geoscience thanks Joanne Wood and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Regional context and overview of the city of Bukavu.

a, Location of Bukavu, DR Congo. b, The Kivu Rift, showing main fault lineaments, the South Kivu Volcanic Province (SKVP) and the location of the main earthquakes that occurred during the period 2015-2019. c, Outlines of Funu landslide (in yellow) and other deep-seated landslides10,11 (in red) mapped in the area. Background digital elevation model is obtained from photogrammetric processing of stereo Pléiades images from July 2013 (see Methods).

Extended Data Fig. 2 Surface deformation maps over the city of Bukavu.

a, East-west, b, vertical and c, north-south surface velocities measured from combined interferometric processing of CSK and Sentinel 1 images. Background digital elevation model is obtained from photogrammetric processing of stereo Pléiades images from July 2013 (see Methods).

Extended Data Fig. 3 Urban density and landslide impacts.

a, Very-high resolution UAS-SfM orthomosaic (Oct. 2018) of a section the landslide that includes the 14-ha ‘fastest unit’ (in red). Note the very-high density of individual building (the landslide population is estimated to ~55 000 inhab./km²4). In blue is highlighted the extent of the two gullies that partially delimit the toe of the fastest unit. b, View of the landslide taken from the headscarp. Lake Kivu is visible in the background. c, and d, damages to a road at the border of the fastest unit, where velocity gradients are the highest. e, damages to a house within the landslide. Extent of a, is shown in Extended Data Fig. 4.

Extended Data Fig. 4 Morphological landslide units.

Very-high resolution shaded relief of the landslide obtained from UAS-SfM (Oct. 2017) on which are outlined the different landslide morphologic units. In green and red are shown the ‘fastest unit’ and the ‘active toe’ units, grouped based on their kinematic behaviour. Note the dense urban fabric.

Extended Data Fig. 5 Sites of displacement time series.

Location of the sites where InSAR displacement time series were extracted. In red are shown sites located in and grouped as ‘fastest unit’ (8 sites), in green the ‘active toe’ (3 sites) and in grey the ‘central units’ (13 sites).

Extended Data Fig. 6 Seasonal deformation pattern.

East-west surface velocities are measured by InSAR for each season (defined as dry from June to September and wet from October to May). The ‘fastest unit’ is outlined in red and the ‘active toe’ in green.

Extended Data Fig. 7 Monthly landslide velocities.

2D InSAR velocities (east-west and vertical) averaged by months for the period 2015-2019. Thick lines represent mean 2D velocity time series from all sites in landslide ‘active toe’, ‘fastest unit’ and the landslide ‘central units’ (see Extended Data Fig. 5 for sites locations). The 2015-2019 mean monthly rainfall and simulated pore water pressure in the slope are displayed. A detailed analysis of the different seasonal velocity patterns for the different landslide units is provided as supplementary discussion (1.1).

Extended Data Fig. 8 Simulations of slope surface drainage.

a, Slope surface drainage over Funu landslide simulated on very-high-resolution UAS-SfM DSM. Arrows highlight zones outside the natural catchment of the ‘fastest unit’ (in red) from which water is conveyed by man-made infrastructure. Blue diamonds show the location of springs. b, Slope surface drainage simulated on a Digital Terrain Model (where the influence of artificial infrastructures is assumed negligeable).

Extended Data Fig. 9 Progressive urbanisation of Funu landslide.

Aerial and satellite images of Funu landslide for the period 1947 until 2018. 1947, 1959 and 1974 are derived from historical aerial images available at the Royal Museum for Central Africa (Belgium). 2001 is an Ikonos satellite orthomosaic and 2013 and 2018 are very-high resolution Pléiades orthomosaics. These images were used in the evaluation of the changes in landslide motion and urban fabric over the last 70 years (Fig. 5).

Extended Data Fig. 10 Urban development at Funu landslide.

a, Very-high resolution shaded relief of the landslide from UAS-SfM (Oct. 2017). The resolution stresses the very-high density of individual building (the population is estimated to ~55 000 inhab./km²4). b, Funu landslide fastest unit, where characteristic landslide features (such as surface discontinuities, tension cracks, marked steps in slope profile) translating important internal deformation are the most visible. Two gullies (also shown in c,) delimit a sub-unit where velocities are the highest (up to 3 m/yr). d, Historical picture showing part of Funu landslide in 1959. It highlights the low housing density at that time. e, Illustrates work on the drainage systems in Bukavu (photo taken in 1959). © Royal Museum for Central Africa.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Tables 1 and 2, Discussions1 and 2, Methods and references.

Source data

Source Data Fig. 5

Data points used to create Fig. 5. Landslide velocity (1959–2018) and evolution of urban footprint (1947–2018).

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Dille, A., Dewitte, O., Handwerger, A.L. et al. Acceleration of a large deep-seated tropical landslide due to urbanization feedbacks. Nat. Geosci. 15, 1048–1055 (2022). https://doi.org/10.1038/s41561-022-01073-3

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