## 2 Materials and Methods
### ERA-5 Land Reanalysis Data  
The number of missing observation data in the early period of automated station operation is high. Therefore, different methods for the gap-filling procedure were tested. Continuous weather data on various meteorological characteristics are required to produce a continuous gap-free data set for calculation of correct monthly or yearly averaged data [35].  
The fifth-generation of atmospheric reanalysis (ERA5-Land) from the European Centre for Medium-range Weather Forecasts (ECMWF) was chosen as a source of continuous meteorological data describing the general synoptic situation but not the local meteorological features. The ERA5 dataset showed the best performance with NASA's most recent satellite-based dataset [51]. ERA5 updates ERA-Interim using the most recent ECMWF model [52], adopting a four-dimensional variational data assimilation system (4D-VAR). It improves the correction of satellite observations and ground-based radar [53]. Hourly data for 47 meteorological parameters (Table 2) were provided by the ECMWF, downloaded from the Climate Data Store [54] for the period from 1 January 2010 to 31 December 2021.  
The dataset has a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\), which approximately corresponds to 11.1 km in latitude and 5.4 km in longitude for the study area. The ERA5-Land time series for a grid point with coordinates 60.9\({}^{\circ}\) N 68.7\({}^{\circ}\) E was used as a reference of continuous meteorological data. It is clear that direct comparison of local observation data with the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**N** & **Variable, Unit** & **N** & **Variable, Unit** \\ \hline
1 & 2 m temperature, \({}^{\circ}\)C (**Ta**) & 25 & ST level 1, \({}^{\circ}\)C (**Ts**) \\
2 & 2 m dewpoint temperature, \({}^{\circ}\)C & 26 & ST level 2, \({}^{\circ}\)C \\
3 & 10 m U wind component, m/s & 27 & ST level 3, \({}^{\circ}\)C \\
4 & 10 m V wind component, m/s & 28 & ST level 4, \({}^{\circ}\)C \\
5 & EV, mwe/s & 29 & Sub-surface runoff, m/s \\
6 & EV from bare soil, mwe/s & 30 & Surface latent heat flux, W/m\({}^{2}\) \\
7 & EV from open water surfaces, & 31 & Surface net solar radiation, W/m\({}^{2}\) \\  & mwe/s & & W/m\({}^{2}\) \\
8 & EV from the top of canopy, & 32 & Surface net thermal radiation, W/m\({}^{2}\) \\
9 & EV from vegetation & 33 & Surface pressure, hPa (**Pa**) \\  & transpiration, mwe/s & & W/m\({}^{2}\) \\
10 & Forecast albedo & 34 & Surface runoff, m/s \\
11 & LAI, high vegetation, m\({}^{2}\)/m\({}^{2}\) & 35 & Surface sensible heat flux, W/m\({}^{2}\) \\
12 & LAI, low vegetation, m\({}^{2}\)/m\({}^{2}\) & 36 & Surface solar radiation \\  & & & downwards, W/m\({}^{2}\) (**IR**) \\
13 & Potential EV, mwe/s & 37 & Surface thermal radiation \\
14 & Runoff, m/s & 38 & Temperature of snow layer, \({}^{\circ}\)C \\
15 & Skin reservoir content, mwe & 39 & Total precipitation, mm/h \\
16 & Skin temperature, \({}^{\circ}\)C & 40 & VSW layer 1, \% \\
17 & Snow albedo & 41 & VSW layer 2, \% \\
18 & Snow cover, \% & 42 & VSW layer 3, \% \\
19 & Snow density, kg/m\({}^{3}\) & 43 & VSW layer 4, \% \\
20 & Snow depth, m & 44 & Relative air humidity, \% (**RH**) \\
21 & Snow depth water equivalent, & 45 & Wind speed at 10 m, m/s (**WS**) \\  & mwe & & Wind direction at 10 m, deg \\
22 & Snow evaporation, mwe/s & 46 & **(WD)** \\
23 & Snowfall, mwe/s & 47 & Net radiation, W/m\({}^{2}\) (**Rn**) \\
24 & Snowmelt, mwe/s & & \\ \hline \hline \end{tabular}  
\end{table}
Table 2: List of meteorological variables from ERA5-Land reanalysis used for gap-filling. Corresponding target variables from Table 1 are indicated in bold and in brackets.  
global reanalysis product is senseless, because the data sets have completely different origins and purposes. Nevertheless, ERA5-Land reanalysis reproduce regional weather conditions with reasonable accuracy. The differences in the time series of observed and reanalysis data are high, but the linear correlation is good [4; 21; 36; 55].