On the basis of the results of calibration of current meters at water of varying temperatures, a hypothesis that water temperature influences measured water velocities was formulated. The analysis of our long-term data showed that the water temperature does have an influence on measured water velocity. This influence can be taken into account for practical purposes as a contribution to the uncertainty of measurements. The influence depends on the type of current meter propeller. This paper presents results obtained for the Ott C-2 current meter with propellers of the types 1, 2, 3, 5 and 6. Our analysis showed that the uncertainty is equal or less than 5% for measurements carried out in water with temperatures above 8°C. The differences between measured water velocities for water temperatures 5°C and 20°C reached maximum 6% (depending on the propeller) in a slowly flowing water (rotational frequency n = 1 s-1 ). For rotational velocity n ≥ 2 s-1 the differences between velocities measured at water temperatures 5 and 20°C were mostly under 3%. The less influenced propeller is of type 3 for which the uncertainty of measurement does not reach 5% even for water temperature 1ºC if the rotational frequency is bigger than 0.7 s-1 .
The most frequently used instrument for measuring velocity distribution in the cross-section of small rivers is the propeller-type current meter. Output of measuring using this instrument is point data of a tiny bulk. Spatial interpolation of measured data should produce a dense velocity profile, which is not available from the measuring itself. This paper describes the preparation of interpolation models. Measuring campaign was realized to obtain operational data. It took place on real streams with different velocity distributions. Seven data sets were obtained from four cross-sections varying in the number of measuring points, 24-82. Following methods of interpolation of the data were used in the same context: methods of geometric interpolation arithmetic mean and inverse distance weighted, the method of fitting the trend to the data thin-plate spline and the geostatistical method of ordinary kriging. Calibration of interpolation models carried out in the computational program Scilab is presented. The models were tested with error criteria by cross-validation. Ordinary kriging was proposed to be the most suitable interpolation method, giving the lowest values of used error criteria among the rest of the interpolation methods.