Analysis and Modelling of Temperature at the Water – Atmosphere Interface of a Lake by Energy Budget and ANNs Models

Abstract

Different processes of lake surface water temperature (Tsw) are considered in this work. The Tsw estimation is based on the energy budget method and Artificial Neural Networks (ANNs models).These processes were applied to Vegoritis Lake in northern Greece. For the analysis, daily meteorological data, lake characteristics, as well as simulation results from an integrated heat transfer model were used for two complete years. The simulation results from the heat transfer model are considered as the reference and more accurate procedure to estimate Tsw. These results are used to compare the performance of the other processes. The examined processes include:(a) models of heat storage changes in relation to net radiation values Qt(Rn); (b) net radiation estimation using different approaches, such as the process of Slob’s equation with adjusted coefficients to lake data; and (c) ANN models with various architectures and input variables. The results show that the surface water energy budget model accurately describes the temperature (r2 = 0.916, RMSE = 2.422 °C). The ANN(5,6,1) model in which Tsw(i-1) is incorporated in the input variables was considered the best performing compared to all other ANN structures (r2 = 0.995, RMSE = 0.490 °C). Using different approaches for simulating net radiation (Rn) and Qt(Rn) in the equation of surface water temperature estimation provides results with lower accuracy.