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Using automatic snow depth and snow water equivalent data to minimize the error in winter precipitation measurements
Peňáz, Štěpán ; Jeníček, Michal (advisor) ; Jirák, Jan (referee)
The measurement of winter precipitation represents one of the largest uncertainties in the calculation of the hydrological balance. Manual measurement of winter precipitation is time- consuming, costly in terms of personnel and money, and is for this reason not often carried out. The measurement of winter precipitation using a heated rain gauge is in turn affected by wind and partly by the increased evaporation caused by heating. Therefore, automatic snow depth measurement is increasingly used and is becoming more affordable over time. This paper deals with the analysis of automatically measured data from a heated rain gauge, from ultrasonic or laser sensors for snow depth measurements, and from sensors for snow water equivalent measurements in order to find the dependence of new snow density on air temperature and on air humidity. Subsequently, a multiple linear regression equation was derived to calculate the density of new snow, which, together with the new snow accretion, was used to calculate snowfall totals. In addition, alternative winter precipitation was calculated and measured by sensors to calculate the snow water value as well as reference winter precipitation measured by a rain gauge from a manned station. The accuracy of these three precipitation alternatives was assessed using the...
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Using ultrasonic snow depth data to minimaze the error in winter precipitaton measurements
Peňáz, Štěpán ; Jeníček, Michal (advisor) ; Šípek, Václav (referee)
Winter precipitation measurement is more problematic in terms of accuracy than measurement of rainfall precipitation that occur during the rest of the year. The results of rainfall measurements during the winter are significantly influenced by wind, both during the accumulation itself and subsequent redistribution of the snow cover. The aim of this work is to analyze automatically measured data from a heated tipping bucket, ultrasonic snow depth sensor for snow measurement and sensors for measuring snow water equivalent, which aims to determine the dependence of the density of new snow on temperature or humidity and consequently to derive the relation that will serve to refine the quantum estimation of winter precipitation. Based on the correlation and regression analysis of the data, the direct dependence between the density of the new snow and air humidity has not been proven, but the dependence between the density of the new snow and the air temperature has already been proven, though not very significant (Spearman's rank corelation rs = 0.39), this dependence explains approximately 9 % of data variability. The resulting calculated snowfall values in the winter seasons of 2016 and 2017 indicate that the heated tipping bucket has underestimated the amount of precipitation by 50% (2016) and 59%...
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Local scale variability of new snow depth
Valdhansová, Klára ; Jeníček, Michal (advisor) ; Matějka, Ondřej (referee)
Vegetation has an important effect on the catchment water balance, especially due to its influence on snow acccumulation and snow melt. The aim of this bachelor thesis is to evaluate how the canopy structure influences the new snow depth variability, while emphasis is put on the interception process. The snow depth data obtained by field measurements in the Ptačí brook experimental catchment in the Šumava Mountains were analysed using the Natural Neighbor interpolation method and regression analysis. The thesis is focused on evaluating the new snow depth variability in relation to differences in the canopy structure described by the Leaf Area Index. The measurements were carried out in four study plots covered with healthy forest, disturbed forest affeted by the bark beetle, open area and mixed area, which consists of all three types. The results showed, that average snow depth is by 45% lower in healthy forest and by 18% lower in disturbed forest compared to open area. The variability of new snow depth was largest in plot with mixed vegetation during maximum snow accumulation. On the contrary, the snow depth variability was lowest in open area. key words: interception, snow depth, Leaf Area Index, linear regression
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