Landslide precipitation thresholds in Rwanda
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Judith Uwihirwe I Markus Hrachowitz I Thom A. Bogaard
Landslide precipitation thresholds in Rwanda
Abstract 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 precipitationrelated 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 precipitationrelated 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 1-day rainfall (RD1) that coincide with the day of landslide occurrence and 10-day 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 triggercause pairs and the use of suitable thresholds in bilinear format improved the prediction capacity of the real trigger-based thresholds. Keywords Landslide . Precipitation thresholds . Trigger-based thresholds . Trigger-cause-based thresholds Introduction Landslides are one of the world’s most widely occurring natural hazards, causing high number of human casualties and considerable economic damage (Nadim et al., 2006; Petley 2012; Froude and Petley 2018). Global landslide fatalities have been increasing following the increase in population density (Petley 2012), changes in seasonal rainstorm pattern and human activities (Froude and Petley 2018). 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 (Monsieurs et al. 2018b, c). However, a bias in geographical distribution of studies on landslides induced by climate factors including but not limited to changes in seasonal rainfall was found with a major gap in Africa (Gariano and Guzzetti 2016). To reduce rainfall-induced landslide casualties, empirical and physically based dynamic models to forecast landslide hazards have been proposed and adopted to define rainfall induced-landsli
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