Landslide precipitation thresholds as a first step towards robust landslide early warning systems in Rwanda

15 Jul 2020

Landslide precipitation thresholds as a first step towards robust landslide early warning systems in Rwanda


Global landslide fatalities have been increasing following the rise in population density, changes in seasonal rainstorm pattern and human activities. Rainfall is likely to be one of the major triggers of landslide-induced fatalities in mountainous areas as it is the case in many tropical African countries including Rwanda. One of the solution to minimize landslide fatalities is to avail landslide early warning system to warn people located in the danger zone. Regional empirical-statistical thresholds indicating the precipitation conditions initiating landslides are of crucial importance for landslide early warning system development. The objectives of this research were to use landslide and precipitation data in an empirical-statistical approach to (1) identify precipitation-related variables with the highest explanatory power for landslide occurrence and (2) define both trigger and trigger-cause based thresholds for landslides in Rwanda, Central-East Africa. Receiver operating characteristics (ROC) and area under the curve (AUC) metrics were used to test the suitability of a suite of precipitation-related explanatory variables. A Bayesian probabilistic approach, maximum true skill statistics and the minimum radial distance were used to determine the most informative threshold levels above which landslide are high likely to occur. The results indicated that the event precipitation volumes E, cumulative one day rainfall (RD1) that coincide with the day of landslide occurrence and 10 days antecedent precipitation are variables with the highest discriminatory power to distinguish landslide from no landslide conditions. The highest landslide prediction capability in terms of true positive alarms was obtained from single rainfall variables based on trigger-based thresholds. However, that predictive capability was constrained by the high rate of false positive alarms and thus the elevated probability to neglect the contribution of additional causal factors that lead to the occurrence of landslides and which can partly be accounted for by the antecedent precipitation indices. Further combination of different variables into trigger-cause pairs and the use of suitable thresholds in bilinear format improved the prediction capacity of the real trigger-based thresholds.

Example of a landslide event in Rubazo cell Rwankuba sector, Karongi district_Rwanda (left photo taken in Sept. 2018, right photo taken on 7th May 2018). About 1000 debris slides and debris avalanches were triggered by an intense rainfall on 6 May 2018 which caused fatalities and was highly reported in the news papers

Rock avalanche at a road cut Mukamira-Ngororero (Left side photo) and mud-flow in Kabaya sector Ngororero district (Right side photo) in Rwanda, photos taken in September and May 2018.

Livelong with landslides


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