Refined Meteorological Early Warning for Rainfall-Induced Landslide Based on Probabilistic Rainfall threshold

The landslide meteorological early warning model based on empirical rainfall thresholds(ERT) always has a low warning accurate, and the temporal probability model(TPM) is expected to make up forthis shortcoming. In order to verify this idea, this research conducted a comparative experiment. First, we used accumulated effective rainfall-duration(EE-D) and rainfall on the day-accumulated effective rainfall in the previous 4 days(R0-AE4) as variables to construct two sets of TPM models, the receiver operating characteristic(ROC) curve and correlation coefficient were then used to evaluate the discriminative and predictive abilities of ERT/TPM. Then，the conditional probability formula was used to couple the spatiotemporal probability of landslides, and a probabilistic landslide meteorological early warning model(P-LEWM) was proposed. Finally, through the way of simulated warning, P-LEWM was compared with the matrix-based landslide early warning model(M-LEWM), which was constructed with ERT, the results show that: (1) The ERT/TPM constructed by R0-AE4 is more accurate in judging the hazard level of rainfall to trigger landslides, the area under the ROC curve increased by 6.8% to 12.5% compared to EE-D, (2) The TPM proposed in this paper can predict the probability of rainfall triggering landslides accurately, the correlation coefficient between the predicted amount of triggering-rainfall and the recorded amount is above 0.83，moreover, the EE-D type TPM is more accurate for heavy rainfall prediction, while the R0-AE4 is more suitable for regular rainfall events, (3) The EE-D type ERT will underestimates the hazard level of long-lasting heavy rainfall triggering landslide, which caused M-LEWM missed lots of landslides which happened in two typical rainfall events in 2018, with an missed rate of more than 50%, while P-LEWM constructed with TPM has a correct alert rate of over 90%, (4) Because of the accurate TPM and reasonable spatiotemporal model coupling method, the correct alert rate of the P-LEWM proposed in this article has been significantly improved compared to M-LEWM, the correct alert rate increased by 20.7% to 26%, the reasonable correct alert rate increased by 15.6% to 28.6%, and the missed alert rate decreased by more than 20.5%.