Search

The aim of the study was to establish the strength and direction of the relationship between daily temperature of river water and air with the use of selected estimation methods. The relationship was assessed for the River Noteć and its tributaries (Western Poland), using the cross-correlation function and Granger causality. The study established cause-and-effect relations for "water–air" and "air–water" directions of influence. It was confirmed that forecasting the pattern of flowing water temperature from changes in air temperature yields better results when done based on data from the previous day. Results of modelling the relationship between data series with the use of the linear and natural cubic splines models confirmed the presence of a nonlinear relation. It was also established that there is a statistically significant correlation of random fluctuations for both temperature series on the same days. This made it possible to confirm the occurrence of short-term connections between water and air temperature. The results can be used to determine the qualities of thermal regimes and to predict temperature of river waters in the conditions of climate change. [ABSTRACT FROM AUTHOR]

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]

• A new hybrid model by coupling WT and ANN is developed to forecast water temperature in rivers. • Four mother wavelets (Daubechies, Symlet, discrete Meyer and Haar) are considered to develop the WT-ANN hybrid model. • The hybrid models perform much better than the regular ANN model in both normal and heat wave conditions. • The hybrid model with discrete Meyer mother wavelet performs the best, superior to the others. Accurate and reliable water temperature forecasting models can help in environmental impact assessment as well as in effective fisheries management in river systems. In this paper, a hybrid model that couples discrete wavelet transforms (WT) and artificial neural networks (ANN) is proposed for forecasting water temperature. Four mother wavelets, including Daubechies, Symlet, discrete Meyer and Haar, are considered to develop the WT-ANN hybrid model. The hybrid model is applied to forecast daily water temperature on the Warta River in Poland. Time series of daily water temperatures in eight river gauges as well as daily air temperatures of seven meteorological stations are used for forecasting daily water temperature. The performance of this WT-ANN hybrid model is evaluated by comparing the results with those obtained from linear and non-linear regression models as well as a traditional ANN model. The results show that the WT-ANN models perform well in simulating and forecasting river water temperature time series, and outperform the linear, non-linear and traditional ANN models. The superior performance of the WT-ANN models is particularly observed for extreme weather conditions, such as heat waves and drought. Among the four mother wavelets applied, the discrete Meyer performs the best, slightly better than the Daubechies at level 10 and Symlet, while the Haar mother wavelet has the lowest accuracy. In addition, the model performance improves with an increase in the decomposition level, indicating the importance of the choice of decomposition level. The outcomes of this study have important implications for water temperature forecasting and ecosystem management of rivers. [ABSTRACT FROM AUTHOR]