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Simple snow cover model for agrometeorological applications
- Trnka, M., Kocmánková, E., Balek, J., Eitzinger, J., Ruget, F., Formayer, H., Hlavinka, P., Schaumberger, A., Horáková, V., Možný, M., Žalud, Z.
- Agricultural and forest meteorology 2010 v.150 no.7-8 pp. 1115-1127
- snow, climate models, simulation models, agrometeorology, air temperature, night temperature, precipitation, altitude, geographical variation, accuracy, seasonal variation, data analysis, frost injury, frost resistance, Czech Republic, Austria
- This study was aimed to develop, test and provide access to a snow cover model for agrometeorological use (snowMAUS) that would rely strictly on the weather data used by all crop simulation models, i.e., diurnal temperature extremes and total daily precipitation. Such snow model can easily be used to preprocess input data in order to account for the presence or absence of snow cover whenever required by the crop modeler, without necessitating the acquisition of additional data. The snow cover model was tested across 65 sites across Austria with considerable variability in elevation (155-3111m a.s.l.). In addition 7 sites in the Czech Republic were used to evaluate snowMAUS reliability in assessing frost damage to winter wheat crop. For illustration a case study documenting the benefit of coupling snowMAUS with process based crop model (STICS) was also included in the study. The presented work complements previous snow cover modeling studies with its focus on the development of a simple snow cover model for areas under intensive agricultural use (mostly lowlands) with 65% of stations providing weather data being located at altitudes below 800m. The presented results proved that SnowMAUS can estimate with reasonable precision snow cover presence/absence, and to a large extent snow cover depth. The model also accurately represented seasonal variations in the number of snow days or in the volume of precipitation in the form of snow. The simplicity of the model and the fact that it relies only on the daily data is a great advantage. We have demonstrated that in areas with a high probability of winter temperatures dropping below frost-tolerance thresholds and that tend to have considerable snow cover (e.g., Central Europe), the information about snow cover presence/absence is essential for estimating potential frost damage to winter crops. The snow cover model tested in this study could be easily implemented in most crop growth models and would enhance their performance in the Central European region. The up-to-date version of the model is freely available to users in the scientific community, which should allow for model testing in different climate conditions and application with various types of agrometeorological models.