## 2 Materials and Methods  
### Study Area  
This paper employs data regarding water temperature in rivers and lakes, as well as air temperatures provided by the Institute of Meteorology and Water Management--National Research Institute. The provided data cover mean monthly water temperatures for 10 hydrological stations in rivers and 10 hydrological stations in lakes, and mean monthly air temperatures for 15 meteorological stations (Figure 1). The obtained data cover the period from 1987 to 2020. Complete data were obtained for five rivers. For the remaining five rivers in the period 2015-2020, the data are incomplete. The period of water temperature monitoring in rivers is presented in Table 1. The following method of coding the stations was adopted: hydrological stations on rivers were marked with capital letters from A to J; hydrological stations on lakes were marked with digits from 0 to 9, and meteorological stations were marked with lower case letters from the top (Figure 1; Table 1). Moreover, the table presents distances of hydrological stations on rivers (marked in capital letters) from meteorological stations (marked in small letters) and hydrological stations on lakes (marked by numbers). Development of Models Multiple Linear Regression, Multilayer Perceptrone Network, and Random Forest Regression for the Reconstruction of Water Temperature in Rivers  
Due to the limitation of observations of water temperature in rivers, the analysis of trends first requires the reconstruction of the missing data. This paper implements such an approach based on mean monthly air and water temperatures in lakes. Although the relationship of water temperatures in rivers with water temperatures in lakes and air temperatures is obvious, the following research questions appeared: (1) What is the necessary set of data for the reconstruction of water temperature in rivers? (2) What  
Figure 1: Locations of the analyzed river hydrological stations against the background of meteorological stations and lake stations [28] (descriptions of the stations are provided in Table 1). Developed in ArcGIS software [28]. statistical method will allow obtaining the best results? (3) How will the reconstructed data affect the directions and values of trends? The answer to the first question was based on the assumption that the reconstruction of mean monthly water temperatures in rivers would employ mean monthly air temperatures from a maximum of three meteorological stations located in different directions from the point of measurement of river water temperature and lake water temperature from the closest hydrological station. In order to answer the second research question, it was assumed that the reconstruction of mean monthly water temperatures in lakes would employ three statistical methods with different degrees of advancement, namely, multiple linear regression, multilayer perceptrone network, and random forest regression. Answering the third question involved the comparison of the directions and values of trends in the mean monthly and annual water temperatures in rivers obtained for use as measurement data with those obtained for use as sets with reconstructed data. The research material covers data for five rivers for which complete data were obtained in the period from 1987 to 2020. The data series was divided into two parts: the first one from 1987 to 2014, and the second one from 2015 to 2020. The first series (from 1987 to 2014) was used for the development and testing of the models of multiple linear regression, multilayer perceptrone network, and random forest regression. The second series (from 2015 to 2020) comprised independent