2024, Amnuaylojaroen, Teerachai, Ptak, Mariusz, Sojka, Mariusz

The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites.

2023, Gizińska, Joanna, Sojka, Mariusz

Climate change has a significant impact on the abiotic and biotic environment. An increase in air temperatures translates into higher temperatures of water constituting the habitat of a wide range of species. The purpose of this study is to present the direction and extent of water temperature increases in eight rivers and three lakes on a monthly and annual basis. The analysis of river water temperatures used both measured data and data reconstructed using artificial neural networks from the period of 1984–2020. The analysis of the direction and extent of changes in air and water temperatures was performed using Mann-Kandall tests and a modified Sen test. The analysis of water temperature changes was conducted against the background of climatic conditions and catchment characteristics. The results indicate that in the Warta River basin in the period of 1984–2020, the average annual temperature rise reached 0.51 °C decade−1, ranging from 0.43 to 0.61 °C decade−1. This translated into an increase in mean annual water temperatures in lakes in a range from 0.14 to 0.58 °C decade−1, and for rivers in a range from 0.10 to 0.54 °C decade−1. The greatest changes in air temperature occurred in April, June, August, September, and November. It was reflected in an increase in water temperature in lakes and rivers. However, these changes did not occur in all rivers and lakes, suggesting the role of local factors that modify the effect of climate change. The study showed that the extent of air temperature changes was significantly higher than the extent of water temperature changes in rivers.