Spatial interpolation of sub‐daily air temperatures for snow and hydrologic applications in mesoscale Alpine catchments

Abstract

AbstractAir temperature represents a key parameter for snow hydrology, as it controls the precipitation phase, as well as snow accumulation and snowmelt. Hydrological modelling in mountain regions like the Alps needs high‐resolution temperature fields as input, preferably at sub‐daily time steps. The estimation of such temperature fields is challenging due to the spatio‐temporal variability of environmental lapse rates (i.e. the decreasing of temperature with altitude) associated to complex topography. In this study, 10 years (2000–2009) of data from about 200 temperature stations were interpolated at 0, 6, 12 and 18 h Universal Time Coordinated (UTC) over a 1‐km resolution grid covering a window of 71 500 km2in the Northern French Alps. Three different kriging methods were tested. Kriging with elevation as external drift gave the best results in terms of mean absolute error, root mean square error and kriging standard deviation. Adding potential solar radiation as an additional external variable did not improve significantly the interpolation results. Prediction errors showed dependence on elevation and season, as well as on the time of interpolation, with globally better results in summer and daytime than in winter and night‐time. Despite some shortcomings that are discussed in the paper, the interpolated temperature fields look promising for further snowmelt and snow cover dynamics modelling studies. Copyright © 2012 John Wiley & Sons, Ltd.