Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda
Mount Elgon in Eastern Uganda is one of the most landslide-prone regions in Africa. This extinct shield volcano is characterized by steep slopes, intense precipitation, and fertile lands supporting a dense human population. As a result, landslides frequently cause damage and many fatalities. Apart from the need for a landslide susceptibility assessment, insight into the landslide mobilization rates [ton/km2/y] is required to assess the geomorphological importance of landslides. Such quantitative information is scarce for many regions around the world and particularly for Africa. We therefore compiled a calibration dataset of 653 landslides (514 slides and 139 rockfalls). Additionally, a second dataset of over 400 landslides was independently collected for validation purposes. To determine a logistic landslide susceptibility model, we used Monte Carlo simulations that selected different subsets of the calibration dataset to test the significance of the considered environmental variables. Susceptibility maps for all landslide types and for rockfalls were constructed for the Mount Elgon region in Uganda. In both maps, topography is by far the most significant factor controlling landslide susceptibility. Including lithology and soil moisture, further improved the model predictions. The models explain about 55% and 85% of the observed variance in landslide occurrence for all landslide types and for rockfalls respectively. The average calculated landslide frequency and mobilization rate for the landslide affected area are respectively 0.04 landslides/km2/y and 750 ton/km2/y. Landslide size is only weakly positively correlated with landslide susceptibility. Therefore, observed larger landslide mobilization rates correlating with higher landslide susceptibilities result from larger landslide numbers rather than from larger landslides. Our research highlights the relevance of detailed and long-term landslide mapping at the regional scale in data-scarce areas for regional planning and risk reduction strategies, as an improvement to continental and global susceptibility models, but also to assess the geomorphological importance of landslides as an erosion process.
KeywordsMass movement Rockfall Landslide inventory Landslide susceptibility Landslide mobilization rate Landslide risk East Africa
Akaike information criterion
Area under the ROC curve
Landslide mobilization rate
Receiver operating characteristic
The authors acknowledge the cooperation of all local communities in the Mt. Elgon region. We are also very grateful to Lawrence Guloba for facilitating our field work with his excellent car driving skills in challenging conditions.
This research received financial support of VLIR-UOS (Surelive project), which supports partnerships between universities in Flanders and in the Global South, and BELSPO Brain.be AfReSlide project (BR/121/A2/AfReSlide).
- Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Catani F, van den Eeckhaut M, Mavrouli O, Agliardi F, Pitilakis K, Winter MG, Pastor M, Ferlisi S, Tofani V, Hervás J, Smith JT (2014) Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73:209–263. https://doi.org/10.1007/s10064-013-0538-8 Google Scholar
- Jacobs L, Maes J, Mertens K, Sekajugo J, Thiery W, van Lipzig N, Poesen J, Kervyn M, Dewitte O (2016b) 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 CrossRefGoogle Scholar
- Jacobs L, Dewitte O, Poesen J, Sekajugo J, Nobile A, Rossi M, Thiery W, Kervyn M (2018) Field-based landslide susceptibility assessment in a data-scarce environment: the populated areas of the Rwenzori Mountains. Nat Hazards Earth Syst Sci 18:105–124. https://doi.org/10.5194/nhess-18-105-2018 CrossRefGoogle Scholar
- Mertens K, Jacobs L, Maes J, Kabaseke C, Maertens M, Poesen J, Kervyn M, Vranken L (2016) The direct impact of landslides on household income in tropical regions: a case study from the Rwenzori Mountains in Uganda. Sci Total Environ 550:1032–1043. https://doi.org/10.1016/j.scitotenv.2016.01.171 CrossRefGoogle Scholar
- Oleson KW, Niu GY, Yang ZL, Lawrence DM, Thornton PE, Lawrence PJ, Stöckli R, Dickinson RE, Bonan GB, Levis S, Dai A, Qian T (2008) Improvements to the community land model and their impact on the hydrological cycle. J Geophys Res Biogeosci 113. https://doi.org/10.1029/2007JG000563
- Scott P, IUCN FCP, IUCN RO for EA (1998) From conflict to collaboration: people and forests at Mount Elgon, Uganda. IUCN: International Union for Conservation of Nature, GlandGoogle Scholar
- Steger S, Glade T (2017) The challenge of “trivial areas” in statistical landslide susceptibility modelling. In: Mikos M, Tiwari B, Yin Y, Sassa K (eds) Advancing culture of living with landslides. WLF 2017. Springer, Cham, p 1148Google Scholar
- UBOS (2017) The population of the regions and districts of Uganda according to census results. Citypopulation.de Quoting Uganda Bureau of Statistics (UBOS). http://citypopulation.de/php/uganda-admin.php. Accessed 26 Feb 2018
- UNISDR (2015) Sendai Framework for Disaster Risk Reduction 2015–2030. In: A/CONF.224/CRP.1. Sendai, Miyagi, Japan: UNISDR, 1–25Google Scholar
- Vlaeminck P, Maertens M, Isabirye M, Vanderhoydonks F, Poesen J, Deckers S, Vranken L (2016) Coping with landslide risk through preventive resettlement. Designing optimal strategies through choice experiments for the Mount Elgon region, Uganda. Land Use Policy 51:301–311. https://doi.org/10.1016/j.landusepol.2015.11.023 CrossRefGoogle Scholar
- Westerhof ABP, Härmä P, Isabirye E et al (2014) Geology and geodynamic development of Uganda with Explanation of the 1:1,000,000 -Scale Geological Map. Geol Surv Finland, Spec Pap 55Google Scholar