Probabilistic rainfall thresholds for debris flows occurred after the Wenchuan earthquake using a Bayesian technique

Empirically derived rainfall thresholds of debris flows are used for regional-scale early warning. However, triggering rainfall intensities of post-seismic debris flows evolve with time, causing high false alarms since thresholds estimated by conventional methods ignore the uncertainty of non-triggering rainfall events. Based on 172 triggering rainfalls and 2396 non-triggering rainfalls from 2008 to 2013 after the Wenchuan earthquake, we analyzed the evolution of probabilistic rainfall thresholds for post-seismic debris flows using a Bayesian technique. We found, rainfall thresholds significantly decrease compared with pre-earthquake events initially and later tend to increase annually. Meanwhile, the triggering rainfall characteristics tend to gradually change from a short-duration high-intensity pattern to a long-duration and low-intensity pattern. We also checked the effect of antecedent precipitation on debris flows by defining an IET (inter-event time). Our results suggest the antecedent precipitation plays an important role in low-intensity long-duration rainfall-induced debris flows and has little effect on the short-duration high-intensity rainfall-induced debris flows. The characteristics of triggering rainfall for debris flows after the Wenchuan earthquake can be best reflected by IET = 7 h. By employing a Naïve Bayes algorithm, uncertainties of three rainfall threshold models, I-D (mean intensity-duration), IT-D (triggering intensity-duration), and IT-E (triggering intensity-cumulative rainfall) were investigated, and the IT-D model was found to perform best. The characteristics and evolution of debris flow rainfall thresholds over the years after the Wenchuan earthquake was best presented by the Bayesian probabilistic rainfall thresholds. We believe the results can be used to improve the precision of an early warning system for post-seismic debris flows.