Possibilities of River Water Temperature Reconstruction Using Statistical Models in the Context of Long-Term Thermal Regime Changes Assessment

Abstract

Water temperature in rivers is the key property determining the biotic and abiotic processes occurring in these ecosystems. In many regions of the world, the significant lack of measurement data is a serious problem. This paper presents reconstruction of water temperature for selected Polish rivers with monitoring discontinued in the period 2015–2020. Information regarding air temperature and water temperature in lakes provided the basis for the comparison of three models: multiple linear regression, random forest regression, and multilayer perceptron network. The results show that the best reconstruction results were obtained with a multilayer perceptron network model based on water temperatures in the lake and air temperatures from three meteorological stations. The average values of mean error, root mean square error and standard error were for the rivers in Poland: 1.52 °C, 5.03%, and 0.47 °C. The course of mean yearly water temperature in the years 1987–2020 showed a statistically significant increase from 0.18 to 0.49 °C per decade. The results show that the largest increases occurred in June, August, September, November, and December.

Keywords

• artificial neural network
• multiple linear regression
• random forest regression
• multilayer perceptron network
• Mann–Kendall test
• Sen test