Distribution Properties of a Measurement Series of River Water Temperature at Different Time Resolution Levels (Based on the Example of the Lowland River Noteć, Poland)

The paper investigates the distribution properties of measurement series of river water temperatures for the lowland River Noteć and its tributaries (Western Poland), as well as air temperatures at different data time resolution levels (1987–2013). The aspect of distribution normality was examined in quantile plots, the series’ stationarity was assessed with an augmented Dickey-Fuller test, while autocorrelation was studied using an Autoregressive Integrated Moving Average (ARIMA) model. It was demonstrated that distributions of river water and air temperature series at different levels of analyses are generally close to normal but also display a certain skewness. Both daily temperature measurement series are stationary series. The periodic component accounts for about 93% (water temperature) and 77% (air temperature) of the daily variability of the variable, while the random factor equals 6–7% and 22%, respectively. The Autoregressive Integrated Moving Average (ARIMA) model confirmed a clear annual seasonality in temperature distribution and indicated the long memory of the autoregressive process AR (2–4). The temperature prediction performed on the basis of a 4th-order Fourier series is consistent with the course of historical data. In the multiannual period 1987–2013, particularly high maximum temperatures were recorded for the Upper Noteć in the summer half-years (28.4 °C); these are related to anthropogenic factors and increase the threat to the existence of cyprinids and salmonids. The thermal anomalies identified in the River Noteć clearly point to the necessity of intensifying the monitoring of its waters