This report presents an evaluation of three different methods for estimation of areal precipitation and temperature, with special emphasis on their applicability for runoff modelling in the Scandinavian mountains. All three methods estimate the areal values as a weighted mean of the observations at nearby meteorological stations. The weights are determined by:
a) a manual subjective selection of the most representative stations
b) inverse square distance weighting
c) optimal interpolation
The methods were tested for a mountainous region in the north-western part of Sweden, which is an area with few meteorological stations and complex precipitation gradients. The elevation range is some 1500m, but meteorological stations are normally located at low altitudes in the valleys. For the subjective and inverse distance weighting methods, precipitation was extrapolated to higher elevations by assuming a linear increase with elevation. For the optimal interpolation method the climatological spatial variation in precipitation was described by means of the standard deviation, related to topographical features. Temperature was extrapolated using the wet adiabatic lapse rate. The evaluation included comparison of areal estimates, verification against point observations and the water balance equation and sensitivity analyses with respect to method parameters and network changes.
For operational runoff modelling in Sweden, areal precipitation and temperature have traditionally been estimated by the subjective weighting method. This evaluation showed that for routine applications this time-consuming method can be replaced by optimal interpolation. Inverse-distance weighting can not be recommended in areas with few stations and complex gradients.
The evaluation also showed that none of the methods correctly described the spatial variation in precipitation and temperature in the investigated region. They are thus not directly applicable for non-routine modelling applications where the estimation of runoff is not the sole objective. All methods also proved to be sensitive to at least some of the necessary parameters like, e.g., elevation dependency. This pointed to possible improvements of the estimates, as the parameters for the evaluation were selected without special consideration to local conditions. The optimal interpolation method seemed to be the least sensitive to changes in the meteorological network.