CR 33:257-270 (2007)  -  doi:10.3354/cr033257

Mapping snowpack distribution over large areas using GIS and interpolation techniques

Juan I. López-Moreno1,2,*, Sergio M. Vicente-Serrano1, Siham Lanjeri3

1Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (CSIC), Campus de Aula Dei, PO Box 202, Zaragoza 50080, Spain
2Climate Change and Climate Impacts Group, University of Geneve, 7 Route de Drize, Geneve 1227, Switzerland
3Departamento de Geografía y Ordenación del Territorio, Universidad de Zaragoza, Campus de San Francisco, Zaragoza 50009, Spain

ABSTRACT: The objective of this study was to provide a simple and accurate method for mapping long-term averages of monthly snowpack (from 1986 to 2003) over large regions at a resolution suitable for management purposes (cell size 100 m2). The proposed method requires few data and overcomes the problem of the limited availability of meteorological information in mountainous areas. In a case study, the proposed method is applied to the Aragón region, NE Spain. Distributed layers of monthly temperature (maximum and minimum) and precipitation are combined to compile maps of the potential magnitude of snowpack at a monthly timescale over the entire study region. Temperature and precipitation grids were obtained using interpolation techniques and data from several weather stations. Maps of snowpack magnitude were obtained for January, March, and April. For these months, it was possible to verify the results in the north of the study area using in situ snow-depth measurements for the Central Pyrenees. The results demonstrate that the maps of potential snowpack magnitude provide a reliable estimate of the observed snow-depth distribution over the study region. Calibration between the observed and predicted values enabled us to convert the potential snowpack magnitude (in dimensionless units) into real snowpack values in absolute units (snow depth, cm). The addition of a model of incoming solar radiation in the calculation procedure provided better results in terms of the final predictions because it captured local variations in snowpack related to variable relief.

KEY WORDS: Snowpack prediction · Geographical Information Systems · Temperature · Precipitation · Incoming solar radiation · Pyrenees · Aragón · Spain

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