Evaluation of Algorithms for Calculating Forest Micrometeorological Variables Using an Extensive Dataset of Paired Station Recordings

Abstract:

Forests represent significant areas of subalpine environments and their influence is crucial for the snow cover dynamics on the ground. Since measurements of major micrometeorological variables are usually lacking for forested sites, physically based or empirical parameterizations are usually applied to calculate the beneath-canopy micrometeorological conditions for snow hydrological modeling. Most of these parameterizations have been developed from observations at selected long-term climate stations. Consequently, the high spatial variability of the micrometeorological variables is usually not taken into account.

The goal of this study is to evaluate existing approaches using an extensive dataset collected during five winter seasons using a stratified sampling design with pairs of snow monitoring stations (SnoMoS) at open/forested sites in three study areas (Black Forest region of SW Germany, Brixenbach catchment in the Austrian Alps and the Berchtesgadener Ache catchment in the Berchtesgaden Alps of SE Germany). In total, recordings from 110 station pairs were available for analysis. The measurements of air temperature, relative humidity, wind speed and global radiation from the open field sites were used to calculate the adjacent inside forest conditions. Calculation results are compared to the respective beneath-canopy measurements in order to evaluate the applied model algorithms. The results reveal that the algorithms surprisingly well reproduced the inside canopy conditions for wind speed and global radiation. However, air temperature and relative humidity are not well reproduced. Our study comes up with a modification of the two respective parameterizations developed from the paired measurements.