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 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 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 trigger-cause pairs and the use of suitable thresholds in bilinear format improved the prediction capacity of the real trigger-based thresholds.

Satellite rainfall products outperform ground observations for landslide prediction in India

“…Rainfall thresholds all over the world are derived from the statistical analysis of rainfall conditions that have resulted in past landslides (e.g. Guzzetti et al, 2007Guzzetti et al, , 2008Cepeda et al, 2010;Sengupta et al, 2010;Berti et al, 2012;Staley et al, 2013;Zêzere et al, 2015;Segoni et al, 2018;Gariano et al, 2019;Valenzuela et al, 2019;Jordanova et al, 2020;Leonarduzzi and Molnar, 2020;Salinas-Jasso et al, 2020;Uwihirwe et al, 2020). Defining rainfall thresholds over large and diverse areas needs statistically significant information on the occurrence of past landslides and a large amount of rainfall data.…”

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

“…Others used empirical field tests [39,40] or numerical modelling [3,41], and various studies conducted on LEWS have generally used historical rainfall data together with landslide occurrence records to determine the rainfall threshold inducing landslides [34,[42][43][44][45][46][47][48]. The studies conducted in [22,[49][50][51][52][53][54][55][56] have shown that they have used antecedent rainfall as a key parameter that has a great impact on landslides' occurrence. In this research paper, the parameters considered are rainfall intensity together with soil moisture content (SMC) that can help to achieve a better landslide prediction modelling tool that might be used for LEWS.…”

Satellite rainfall products outperform ground observations for landslide forecasting in India

“…The use of rainfall thresholds, combined with susceptibility maps, makes it possible to estimate the probability of landslide triggering (Rossi et al, 2018). Rainfall thresholds all over the world are derived from the statistical analysis of rainfall conditions that have resulted in past landslides (e.g., Guzzetti et al, 2007Guzzetti et al, , 2008Cepeda et al 2010;Sengupta et al, 2010;Ruiz-Villanueva et al 2011;Berti et al, 2012;Staley et al, 2013;Zêzere et al, 2015;Rosi et al, 2016;Peruccacci et al, 2017;Segoni et al, 2018;Gariano et al, 2019;Valenzuela et al, 2019;Jordanova et al, 2020;Leonarduzzi and Molnar, 2020;Salinas-Jasso et al, 2020;Uwihirwe et al, 2020). Defining rainfall thresholds over large and diverse areas needs statistically significant information on the occurrence of past landslides and a large amount of rainfall data.…”