Operational algorithm for generation of snow depth maps from discrete data in Indian Western Himalaya

- An algorithm for snow depth estimation in Western Himalaya is proposed.
- Proposed algorithm improves upon the limitations of earlier published snow depth interpolation algorithm.
- It also has advantages over the previous models for estimation of snow depth in Western Himalaya.
- Snow depth maps generated from the algorithm are being used operationally in Western Himalaya.

In this paper, an algorithm is proposed for generation of snow depth maps. The efficacy of the algorithm has been established through a case study in lower and middle Himalayas, India. The algorithm is a modified version of the spatial interpolation method proposed earlier in Swiss Alps. The method uses discrete point data supplemented with remotely sensed derived information data to create snow depth maps at spatial resolution of 0.5 km. In situ snow depth observations from 14 locations, automatic weather station (AWS) recorded snow depth from 9 locations, moderate-resolution imaging spectroradiometer (MODIS) images and shuttle radar topographic mission (SRTM) DEM form the database. The algorithm is based on the dependency of snow depth on elevation above mean sea level, which is later adjusted through the in situ snow depth observations to represent the local and regional characteristics of the snow distribution. The algorithm has been validated for different days of the winter season 2012-2013 using leave-one-out station cross-validation method. The mean absolute error (MAE) and root mean square error (RMSE) in estimation of snow depth have been observed as ~ 34 cm and ~ 42 cm respectively during the season. The snow depth maps generated from the proposed algorithm may be useful in assessment of snow avalanche hazards as well as in various hydrological and glaciological studies in the inaccessible cryospheric region of the Western Himalaya.