Numerical weather prediction model outputs define intensity-duration thresholds of extreme-precipitation-induced sediment disasters

Rainfall intensity-duration (ID) thresholds are helpful to estimate the likelihood of natural hazards during extreme precipitation events. Sub-daily time-series of weather data is necessary to define precise ID thresholds of sediment disasters. The Himalayas, vulnerable to extreme precipitation events, experience large-scale sediment disasters, i.e., landslides, debris flows, and flash floods. Present early warning systems currently in operation encounter difficulties forecasting sub-daily time-series of weather due to instrumental and operational challenges. Here, we present a new framework to analyse and predict extreme rainfall-induced landslides using a weather research and forecasting model (WRF) followed by a spatially distributed numerical model. The operational framework starts with the WRF model running at 1.8 km × 1.8 km resolution. Then, the spatiotemporal numerical model for landslide forecasting at the same resolution uses the WRF model outputs. We calibrate the models using Uttarakhand, India's 2013 heavy rainfall-induced landslide events. We perform parametric numerical simulations to identify critical ID thresholds of landslides under different precipitation intensities, i.e., moderate rain, rather heavy, heavy rain, very heavy rain, and extremely heavy rain according to the India Meteorological Department (IMD) glossary. Our analysis opens avenues for integrating the WRF model with rainfall ID threshold-based territorial early warning of landslides.