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The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results. [ABSTRACT FROM AUTHOR]

The article presents the results of an analysis of the impact of cumulative negative air temperature degree-days (CDD) on formation of ice cover on the typical lowland river in the moderate climate zone (River Noteć, western Poland). During the period 1987–2013 the occurrence of a series of 306 consecutive days of negative air temperatures was identified in relation to atmospheric circulation in the winter seasons, with a duration of 1–53 days. The longest CDD series occurred in 2010, while low air temperatures were particularly frequent in the year 1996 (−600 °C·d). The analysis showed that this phenomenon was most identifiable in December. Research made it possible to distinguish two types of circulation, which were conducive to the cumulation of CDD. Using the logistic regression model it was determined that the probability of ice cover occurrence increased most rapidly in the lower course (−70 °C·d), while the weakest interdependence was observed along the other sections. On average, a one-degree-day increase of CDD was connected with an increase in the probability of a permanent ice cover appearing of approximately 1.2–5.7%. The results obtained are of particular significance for identifying the thermal and circulatory factors determining the appearance of ice cover on the rivers of the Polish Lowlands. [ABSTRACT FROM AUTHOR]