Modeling the spatial snow-water equivalent using NOAA-AVHRR data for mesoscale catchments

Stefan Taschner; Ulrich Strasser; Wolfram Mauser

doi:10.1117/12.332736

11 December 1998 Modeling the spatial snow-water equivalent using NOAA-AVHRR data for mesoscale catchments

Stefan Taschner, Ulrich Strasser, Wolfram Mauser

Proceedings Volume 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology; (1998) https://doi.org/10.1117/12.332736
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

The spatially distributed, physically based snowsubmodel ESCIMO (Energy Balance Snow Cover GIS Integrated Model) is presented which is integrated in the multiscale modeling shell PROMET. ESCIMO calculates the spatial snow cover distribution and snowmelt runoff by an energy and mass balance assertion. The meteorological input parameter fields are derived from generally available measurements of the German Weather Service (DWD) climatic station network ensuring the transferability of the model. These measurements of precipitation, temperature, wind speed and cloudiness are temporally and spatially interpolated. To take the influences of different vegetation into account spatial landuse information is derived by a multitemporal spectral unmixing procedure of NOAA-AVHRR satellite data from the vegetation period 1995. Necessary model parameters are taken from literature and no calibration is conducted. The temporal distribution of the snow cover and snow water equivalent was modeled for the winter season of 1993/94 in the Weser catchment. The modeled snow water equivalent is verified with measurements. The resulting coefficient of determination between model and measurement is 0.62. The spatial performance of ESCIMO in the Weser catchment is validated by a comparison of the modeled with the snow cover derived from a NOAA-AVHRR image. The correspondence between the two images is 86.3%.

Citation Download Citation

Stefan Taschner, Ulrich Strasser, and Wolfram Mauser "Modeling the spatial snow-water equivalent using NOAA-AVHRR data for mesoscale catchments", Proc. SPIE 3499, Remote Sensing for Agriculture, Ecosystems, and Hydrology, (11 December 1998); https://doi.org/10.1117/12.332736

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