An application of multiple‐point statistics downscaling approach over North‐West Himalayas in avalanche‐prone areas

Abstract

AbstractWeather data at fine spatial resolution in the Himalayan region are important for assessing various hydrological events such as avalanches. In recent years, national agencies have been exploring the use of Weather Research and Forecasting (WRF) model in this region. Setting up a WRF model with fine spatial grids in the Himalayan region is challenging due to large topographical variations and associated computational cost. In this study, we use High Asia Refined analysis (HAR) dataset to set up a Multiple‐Point Statistics (MPS) based downscaling approach using the hindcast data of years 2001–2012 at both coarse and fine spatial grids. A total of 11 covariates are used in the study belonging to various environmental factors such as weather, location, topography and vegetation. We generate multiple realizations of precipitation and temperature for the year 2013. The efficacy of the MPS model is tested in a gridbox consisting of 61 × 61 grids, in 10 districts and at sites prone to avalanches and extreme precipitation events. Results show that the downscaled precipitation and temperature have reasonable accuracy. Mean monthly root mean square error and mean absolute error values for precipitation range between 2.87–8.0 mm and 1.45–3.27 mm, respectively. For temperature, both the error statistics range between 0.65–1.13 K and 0.51–0.83 K, respectively. Overall, the study shows that MPS is an effective tool for generating reliable fine‐resolution weather information over multiple geographical locations, overcoming the diverse challenges of the Northwest Himalayas. Furthermore, it is capable of providing consistent fine‐resolution weather data at point locations. Such data may be highly sensitive for hydrological studies, such as avalanche forecasting in the Himalayas.