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Abstract

Data from the climate stations was on the one hand used as an input source for the temporal and spatial interpolation of meteorological data by the model AtmoStations (see Chaps. 1.5, 1.6, and 1.7) and on the other hand to validate the results of the component Rivernetwork (Chap. 2.3.1) and the models Snow (Chap. 2.4.1) and AtmoMM5 (Chap. 2.5.1). Data from all available climate stations (totalling 377) were obtained from the respective German and Austrian national meteorological agencies for the modelling period between 1960 and 2004. The analysis of the source data showed differences in the measurement procedures over time (change to automated data collection) and between the two national agencies (time of measurement and measurement units). However, these differences are taken into account in the model AtmoStations as much as possible, so that the degree of inhomogeneity in the data is kept as low as possible. The individual stations cover different time periods. Only a quarter of the stations (approx. 100) have a complete record for the modelling period. Therefore, the number of stations per time increment available for spatial interpolation varies considerably. The altitude of the climate stations varies from 96 m above sea level (Mannheim) to 3,105 m above sea level (Sonnblick). The analysis of the altitude distribution of the climate stations shows that the vast majority of stations are below 1,000 m above sea level. However, station density approximately corresponds to the area-altitude distribution of the catchment area.

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Reference

  • Gattermayr W (2001) Hydrometeorologische Erhebungen am Mühleggerköpfl / Nordtiroler Kalkalpen. In: Herman F, Smidt S, Englisch M (eds) Stickstoffflüsse am Mühleggerköpfl in den Nordtiroler Kalkalpen, FBVA-Berichte, No 119. Bundesministerium für Land- und Forstwirtschaft, Wiien, pp 53–59

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Correspondence to Ruth Weidinger .

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© 2016 Springer International Publishing Switzerland

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Colgan, A., Weidinger, R. (2016). Climate Stations. In: Mauser, W., Prasch, M. (eds) Regional Assessment of Global Change Impacts. Springer, Cham. https://doi.org/10.1007/978-3-319-16751-0_10

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