Quantifying the impacts of climate variation, damming, and flow regulation on river thermal dynamics: a case study of the Włocławek Reservoir in the Vistula River, Poland

Background River damming inevitably reshapes water thermal conditions that are important to the general health of river ecosystems. Although a lot of studies have addressed the damming’s thermal impacts, most of them just assess the overall effects of climate variation and human activities on river thermal dynamics. Less attention has been given to quantifying the impact of climate variation, damming and flow regulation, respectively. In addition, for rivers that have already faced an erosion problem in downstream channels, an adjustment of the hydroelectric power plant operation manner is expected, which reinforces the need for understanding of flow regulation’s thermal impact. To fill this gap, an air2stream-based approach is proposed and applied at the Włocławek Reservoir in the Vistula River in Poland. Results In the years of 1952–1983, downstream river water temperature rose by 0.31 ℃ after damming. Meanwhile, the construction of dam increased the average annual water temperature by 0.55 ℃, while climate change oppositely made it decreased by 0.26 ℃. In addition, for the seasonal impact of damming, autumn was the most affected season with the warming reached 1.14 ℃, and the least affected season was winter when water temperature experienced a warming of 0.1 ℃. The absolute values of seasonal average temperature changes due to flow regulation were less than 0.1 ℃ for all the seasons. Conclusions The impacts of climate variation, damming, and flow regulation on river water temperatures can be evaluated reasonably on the strength of the proposed methodology. Climate variation and damming led to general opposite impacts on the downstream water temperature at the Włocławek Reservoir before 1980s. It is noted that the climate variation impact showed an opposite trend compared to that after 1980s. Besides, flow regulation below dam hardly affected downstream river water temperature variation. This study extends the current knowledge about impacts of climate variation and hydromorphological conditions on river water temperature, with a study area where river water temperature is higher than air temperature throughout a year.

Development of a Multilayer Deep Neural Network Model for Predicting Hourly River Water Temperature From Meteorological Data

Water temperature is a vital attribute of physical riverine habitat and one of the focal objectives of river engineering and management. However, in most rivers, there are not enough water temperature measurements to characterize thermal regimes and evaluate its effect on ecosystem functions such as fish migration. To aid in river restoration, machine learning-based algorithms were developed to predict hourly river water temperature. We trained, validated, and tested single-layer and multilayer linear regression (LR) and deep neural network (DNN) algorithms to predict water temperature in the Los Angeles River in southern CA, United States. For the single-layer models, we considered air temperature as the predictive feature, and for the multilayer models, relative humidity, wind speed, and barometric pressure were included in addition to air temperature as the considered features. We trained the LR and DNN algorithms on Google’s TensorFlow model using Keras artificial neural network library on Python. Results showed that multilayer predictions performed better compared to single-layer models by producing mean absolute errors (MAEs), that were 20% smaller (1.05°C), on average, compared to the single-layer models (1.3°C). The multilayer DNN algorithm outperformed the other model where the model’s coefficient of determination was 26 and 12% higher compared to the single-layer LR (the base model) and multilayer LR model, respectively. The multilayer machine learning algorithms, under proper data preparation protocols, may be considered useful tools for predicting water temperatures in sampled and unsampled rivers for current conditions and future estimations affected by different stressors such as climate and land-use change. River temperature predictions from the developed models provide valuable information for evaluating sustainability of river ecosystems and biota.

The influence of treated wastewater from the Lodz city agglomeration on the ice regime and water temperature of the Ner river

The study attempts to answer the following question: Does human impact contribute to changes recorded in the Ner river ice regime? In replying to this question, data on water consumption in Lodz (Łódź) (a city in central Poland) in 1951–2017 were used, as well as observations of ice cover and all ice phenomena for the same period. The ice regime and water temperature of the river have changed over the past 70 years. The changes result not only from changes caused by global warming but also from additional fluctuations in this temperature as determined by changes in the quantity and quality of wastewater discharged into the river from the Lodz city agglomeration. The frequency of ice phenomena in the river decreased, and their duration dropped by almost half. This tendency was compounded by a decrease in number of days with ice phenomena, which in turn was caused by a rapid increase in the amount of waste and thermally polluted waters supplied from Lodz. The river water temperature has now stopped increasing. The course of the river ice regime now resembles that of a natural watercourse again.

Prediction of river water temperature using machine learning algorithms: a tropical river system of India

Machine learning (ML) has been increasingly adopted due to its ability to model complex and non-linearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling framework's effectiveness is demonstrated with a tropical river system of India, Tunga-Bhadra River, as a case study. The SVR has been noted as the most robust ML model to predict RWT at a monthly time scale compared with daily and seasonal. The study demonstrates how ML methods can be coupled with a global sensitivity algorithm and DA techniques to generate accurate RWT predictions in river water quality modelling.

Investigation on River Thermal Regime under Dam Influence by Integrating Remote Sensing and Water Temperature Model

River water temperature (RWT), a primary parameter for hydrological and ecological processes, is influenced by both climate change and anthropogenic intervention. Studies on such influences have been severely restricted due to the scarcity of river temperature data. This paper proposed a three-stage method to obtain long-term daily water temperature for rivers and river-type reservoirs by integrating remote sensing technique and river water temperature modelling. The proposed three-stage method was applied to the Three Gorges Reservoir (TGR) and validated against in situ measured RWTs in the two study sites, Cuntan and Huanglingmiao. The result showed improvements in the method: the quadrate window selection and RWT correction jointly reduce RMSE from 1.8 to 0.9 °C in Cuntan and from 2.1 to 1.2 °C in Huanglingmiao. As a whole, the estimated daily RWT has a consistent RMSE of 1.1–1.9 °C. Meanwhile, by analysing the Landsat-derived daily RWT, we demonstrated that the TGR had a significant impact on the outflow’s thermal regime. At the downstream reach of TGR, an apparent increase in RWT in the cold season and interannual thermal regime delay compared to inflow were found with the increasing water level after the dam construction. All the results and analyses indicate that the proposed three-stage method could be applied to obtain long time series of daily RWT and provide a promising approach to qualitatively analyse RWT variation in the poorly gauged catchment for river water quality monitoring and management.