Application of Hidden Markov Model for avalanche danger simulations for road sectors in North-West Himalaya

Abstract

Hidden Markov Model (HMM) has been developed for avalanche warning on 10 different road sectors in Pir-Panjal and Great Himalayan mountain ranges of North-West Himalaya. The model uses a data set of nine snow and meteorological variables—average air temperature, snow temperature index, snow drift index, snowfall in 24 h, snowfall in 48 h, snow water equivalent, snowfall intensity, standing snow and snowpack settlement collected during past 20 winters (1992–2012). The HMM is composed of four observations derived from the model input variables and four state variables. The state variables of the model are four levels of avalanche danger (No, Low, Medium and High). Single HMM has been developed to provide avalanche warning for both direct and delayed/wet avalanches with a lead time of two days. The HMM has been validated with (Case-1) and without (Case-2) incorporating delayed/wet avalanches using data collected during four winters (2012–2016) and compared with official Avalanche Warning Bulletin issued by Snow and Avalanche Study Establishment during these winters. The model has been validated through computation of accuracy measures such as percent correct (PC), bias, false alarm rate, probability of detection and Heidke Skill Score. The PC of the HMM for different stations for Case-1 varies from 80.1 to 98.6% for day-1 and 81.2 to 98.3% for day-2 and that for Case-2 from 82.2 to 98.6% for day-1 and 83.3 to 98.3% for day-2.