Prediction of soil moisture and rainfall induced landslides: A comparison of several PBL parameters in the WRF model

Abstract

Abstract Rainfall intensity thresholds only do not take advantage of the awareness of the slope's hydrological processes, so they appear to produce large false and missed alert rates, decreasing the credibility of early warning systems for landslides. This study analyzes this dilemma by modeling the behavior of slopes to precipitation, including the potential effect of soil moisture uncertainty given by numerical modeling. For the simulation of soil moisture during the study period and event rainfall thresholds of an extreme event used to describe the intensity of a rainfall event, the Weather Research and Forecasting (WRF) model is used. The three days simulation conducted during a landslide event in Samigaluh, Kulon Progo on 28 November 2018. The four Planetary Boundary Layer (PBL) parameters in the WRF model are compared to understand each character, i.e., Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ), Shin-Hong (SH), and Bougeault-Lacarrère (BL). To evaluate the precipitation as simulated by WRF, we use observation data from rain gauge and the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). In general, all parameters have an underestimation of precipitation. Each PBL parameter's response to rainfall is different. Both MYJ and SH schemes are closer to observation than others for day 1 and day 2 of simulation, daily precipitation. For all PBL schemes, increased soil moisture is seen, suggesting that the soil is wetter and more vulnerable to landslide events. As an early warning predictor of landslides in terms of rainfall parameters, the SH method is very useful in this analysis. For early warning of landslides, a short period (<6 hours) of precipitation with a high accumulation of precipitation would be very beneficial.