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In this study, we use the "Bremen composite" Mg II index (Snow et al. 2014) for January 1992 to October 2015 and compare them with the Geotail/EPIC observations.
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Two models were tested to reproduce the measured discharge dynamics: (1) a detailed energy-balance model (ALPINE3D) primarily designed for snow simulations; (2) a conceptual runoff model system (PREVAH), including a distributed temperature-index snow and ice melt model.
The spatio-temporal variability of snow cover in the Hengduan Mountains was investigated with two indices: snow cover days (SCD) and snow fraction.
Among many different products from satellite data available in IMWM, the following are of high importance: Baltic Sea monitoring (SST, ice cover, suspended matter), land surface of Poland monitoring (vegetation indices, temperature, snow cover), atmosphere monitoring (cloud classification, precipitation estimation, retrieval of temperature, moisture, wind fields and ozone content).
This is an effective index for mapping snow cover in rugged terrain (Hall et al., 1995).
The approach identifies possible cloud pixels through the use of the normalized difference snow index (NDSI).
The normalized difference snow index maps were prepared starting from 2002 to 2012 for four different months, viz.
Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence).
The normalized difference snow index map is then used to obtain the areal extent of snow in the Brahmaputra catchment area.
This method is known as Normalize Difference Snow Index (NDSI) method and is one of the very potential methods to map snow cover of an area.
MODIS image MOD09A1.5 (MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid) of 500 m resolution consisting of seven bands has been taken to prepare the normalized difference snow index maps of the study area.
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