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This study presents an analysis of the influence of climatic conditions and the operation of a dam reservoir on the occurrence of ice cover and water temperature in two rivers (natural and transformed by reservoir operations) located in the Carpathian Mountains (central Europe). The analyses are based on data obtained from four hydrological and two climatological stations. The Extreme Gradient Boosting (XGBoost) machine learning model was used to quantitatively separate the effects of climate change from the effects arising from the operation of the dam reservoir. An analysis of the effects of reservoir operation on the phase synchronization between air and river water temperatures based on a continuous wavelet transform was also conducted. The analyses showed that there has been an increase in the average air temperature of the study area in November by 1.2 °C per decade (over the period 1984–2016), accompanied by an increase in winter water temperature of 0.3 °C per decade over the same period. As water and air temperatures associated with the river not influenced by the reservoir increased, there was a simultaneous reduction in the duration of ice cover, reaching nine days per decade. The river influenced by the dam reservoir showed a 1.05 °C increase in winter water temperature from the period 1994–2007 to the period 1981–1994, for which the operation of the reservoir was 65% responsible and climatic conditions were 35% responsible. As a result of the reservoir operation, the synchronization of air and water temperatures was disrupted. Increasing water temperatures resulted in a reduction in the average annual number of days with ice cover (by 27.3 days), for which the operation of the dam reservoir was 77.5% responsible, while climatic conditions were 22.5% responsible. [ABSTRACT FROM AUTHOR]

The establishment and operation of cascade reservoirs in the mainstream of the upper and middle reaches of the Yangtze River have changed the river's thermal regimes. This study analyzed the correlation between water temperature and its influencing factors and employed various evaluation indexes—including ∆ T (the temperature-increasing index, °C/100 km), I E C (the extreme fluctuation index), I B D (the baseline deviation index), and I P O (the phase offset time index). The aim was to uncover the variation characteristics and influencing factors of water temperature and quantify the impact of cascade reservoir construction on annual and seasonal water temperature rhythms. Our findings show that the construction and operation of cascade reservoirs weaken the synchronization of water temperature and air temperature downstream. The construction and operation of cascade reservoirs in the middle and lower reaches of the Jinsha River led to obvious homogenization, baseline deviation, and lagging effects on water temperature downstream, which intensified with the increase in storage capacity. These effects were more pronounced in colder months compared to warmer months. Additionally, the influence of tributaries and water–air heat exchange on these effects is alleviated to different degrees. These results are significant for assessing river ecological health in the context of cascade hydropower development. [ABSTRACT FROM AUTHOR]

Rivers worldwide are warming due to the impact of climate change and human interventions. This study investigated river heatwaves in the Vistula River Basin, one of the largest river systems in Europe using long-term observed daily river water temperatures from the past 30 years (1991–2020). The results showed that river heatwaves are increased in frequency and intensity in the Vistula River Basin. The total number of river heatwaves showed clear increasing trend with an average rate of 1.400 times/decade, the duration of river heatwaves increased at an average rate of 14.506 days/decade, and the cumulative intensity of river heatwaves increased at an average rate of 53.169 °C/decade. The Mann-Kendall (MK) test was also employed, showing statistically significant increasing trends in the total number, duration, and intensity of heatwaves for all rivers, including the main watercourse of the Vistula River and its tributaries, with few exceptions. Air temperature is the major controller of river heatwaves for each hydrological station, and with the increase of air temperatures, river heatwaves will increase in frequency and intensity. Another impacting factor is flow, and with the increase of flow, river heatwaves tend to decrease in number, duration and intensity. The results suggested that mitigation measures shall be taken to reduce the effect of climate change on river systems.

The paper concentrates on seasonal and spatial variations of sub-daily water temperature dynamics in lowland agricultural streams. Temperature monitoring was carried out in 24 sampling sites distributed along the tributaries of the Wkra River during the hydrological year 2021. Statistical analysis of the obtained data documented the highest water temperature dynamics in the morning, from 5:00 to 9:00 CEST, while the lowest – from 14:00 to 18:00 CEST. Seasonally, greater water dynamics were noted in the winter, expressed by a coefficient of variation reaching up to 100%. Spatially, the highest dynamics occurred in sites with the lowest proportion of riparian vegetation, while the lowest dynamics was related to higher catchment area. In the winter, the minimum daily values were recorded most frequently in the morning hours, while maximum values in the afternoon. A similar pattern was observed in the summer, but with much lower dispersion of the relative frequencies. It was found that in the winter, the dominant influence on temperature dynamics was exerted in the upstream catchment area, while in the summer, a negative relationship with riparian shade was marked. The findings suggest that the presence of riparian vegetation reduces diurnal dynamics of water temperature and is simultaneously extremely important in prolonging the duration of optimum fluctuation, responsible for the proper development of poikilothermic organisms.

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

The paper concentrates on seasonal and spatial variations of sub-daily water temperature dynamics in lowland agricultural streams. Temperature monitoring was carried out in 24 sampling sites distributed along the tributaries of the Wkra River during the hydrological year 2021. Statistical analysis of the obtained data documented the highest water temperature dynamics in the morning, from 5:00 to 9:00 CEST, while the lowest -- from 14:00 to 18:00 CEST. Seasonally, greater water dynamics were noted in the winter, expressed by a coefficient of variation reaching up to 100%. Spatially, the highest dynamics occurred in sites with the lowest proportion of riparian vegetation, while the lowest dynamics was related to higher catchment area. In the winter, the minimum daily values were recorded most frequently in the morning hours, while maximum values in the afternoon. A similar pattern was observed in the summer, but with much lower dispersion of the relative frequencies. It was found that in the winter, the dominant influence on temperature dynamics was exerted in the upstream catchment area, while in the summer, a negative relationship with riparian shade was marked. The findings suggest that the presence of riparian vegetation reduces diurnal dynamics of water temperature and is simultaneously extremely important in prolonging the duration of optimum fluctuation, responsible for the proper development of poikilothermic organisms. [ABSTRACT FROM AUTHOR]

The water temperature in the Yangtze River has been found altering over the last decades. Although these alterations are expected to occur over multiple timescales and with multiple factors, including both reservoir operation and climate change, few efforts have been made to quantify the alterations and conduct attribution analysis on multiple timescales. It hinders the deep understanding of the thermal regime in the Yangtze River and how it responses to the driving forces. Here, a multiresolution analysis based on the maximal overlap discrete wavelet analysis is applied to quantify the multi-timescale alterations of the water temperature, and to distinguish the impacts caused by reservoirs and climate change. The results indicate that the water temperature displays a significant upward trend in past three decades, which is mostly driven by the climate change. Reservoir operation has reduced the intra-annual variability of the water temperature, especially for the short-term and annual scales, resulting in a smoother and flatter water temperature process. The correlation between the water and air temperature is also investigated on multiple timescales based on the wavelet cross-correlation coefficients. It is found that the correlation between the air and water temperature in the natural state shows an overall increasing trend with the increasing timescale and peaks on annual scale. Due to the reservoir operation, the correlation on the moderate scales (1–4 months) has been notably weakened. While for the longer scales, the correlation between air and water temperature remains high, although their phases difference on some scales may be extended by months due to the reservoir operation.

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for seven major polluted river catchments of India. The hybrid kNN‐LSTM has effectively predicted RWT at monthly time scales under data limitations and outperformed the standalone LSTM, WT‐LSTM, and hybrid three‐parameter version air2stream models. The RWT increase for Tunga‐Bhadra, Musi, Ganga, and Narmada basins is predicted as 3.0, 4.0, 4.6, and 4.7°C, respectively, for 2071–2100. Key Points: Hybrid deep learning models coupled with resampling algorithms and wavelet transforms to predict river water temperatures under sparse dataThe hybrid models effectively predicted river water temperatures at monthly time scales under data limitationsAssessed the climate change impacts on river water temperatures in seven major polluted river catchments of India [ABSTRACT FROM AUTHOR]

[Display omitted] • Daily water temperatures in the Odra River Basin reconstructed. • Annual water temperatures exhibited a clear warming trend. • Summer warms faster, followed by autumn and spring. • River heatwaves increased in frequency and intensity in the Odra River Basin. Water temperature is a critical indicator and weathervane of aquatic ecosystems. However, the vast majority of rivers lack long-term continuous and complete water temperature datasets. In this study, ensemble models by combining NARX (nonlinear autoregressive network with exogenous inputs) and air2stream were used to reconstruct daily river water temperatures for 27 hydrological stations in the Odra River Basin, one of the largest river systems in Europe. For each hydrological station, both the NARX and air2stream models were calibrated and validated, and the better-performed model was selected to reconstruct daily river water temperatures from 1985 to 2022. The results showed that hybrid modeling by combining NARX and air2stream is promising for reconstructing daily river water temperatures. Based on the reconstructed dataset, annual and seasonal trends of water temperature and characteristics of river heatwaves were evaluated. The results indicated that annual river water temperatures showed a consistent warming trend over the past 40 years with an average warming rate of 0.315 °C/decade. Seasonal river water temperatures indicated that summer warms faster, followed by autumn and spring, and winter river water temperatures showed an insignificant warming trend. River heatwaves are increased in frequency, duration, and intensity in the Odra River Basin, and 6 out of 27 hydrological stations have river heatwaves categorized as 'severe' and 'extreme', suggesting that mitigation measures are needed to reduce the impact of climate warming on aquatic systems. Moreover, results showed that air temperature is the major controller of river heatwaves, and river heatwaves tend to intensify with the warming of air temperatures. [ABSTRACT FROM AUTHOR]

Measurement by a unmanned aerial vehicles (UAV) equipped with thermal infrared sensors has great potential as a new technique for understanding the spatial heterogeneity of river water temperature; however, the measurement technique has not been established yet. Therefore, this study measured surface and water temperatures using two uncooled thermal infrared sensors, which are commercially available, for 7 months, to obtain knowledge to establish a method for predicting the river water temperature using uncooled thermal infrared sensors mounted on UAVs. The survey covered a 500 m section upstream from the confluence of the Inukai River in the Katsura River of the Yodo River system, in Japan. DJI Zenmuse XT2 and MicaSense Altum were mounted on a DJI Matrice 300 at the same time and took images seven times at intervals of approximately 1 month between June 2021 and January 2022. The water temperature and depth were measured at several locations within the study area within 1.5 hr after the UAV acquisition. Then, the relationship between the measured water temperature and the surface temperature extracted from the thermal infrared orthorectified mosaic image was analyzed. Altum was more suitable than XT2 for obtaining thermal infrared orthorectified mosaic images with less inconsistencies among courses in the surface temperature range, and the difference in water temperature was also smaller, making it suitable for understanding the river water temperature. To predict water temperature from surface temperature, actual measured data of water temperature is necessary, but I demonstrated that it is possible to measure the river water temperature in a spatially continuous manner. [ABSTRACT FROM AUTHOR]

Study region: The study region comprises 125 water gauges from 70 rivers covering the whole territory of Poland. Study focus: The air2stream model was used to reconstruct daily river water temperature (RWT) from 125 water gauges in 70 Polish rivers for the period 1966–2020 (55 years). Then, the annual and seasonal warming trends of RWT, and a biologically relevant metric (the annual number of days when water temperature exceeded 20 °C) were evaluated. New hydrological insights for the region: The modelling results showed that the air2stream model performed well for RWT modeling (the averaged Root Mean Square Error (RMSE) values for the calibration and validation periods are 1.21 and 1.32 °C, and Nash-Sutcliffe efficiency coefficient (NSE) for the two periods are 0.96 and 0.95 respectively). Annual averaged RWT of 121 gauges (96.8 % of the 125 gauges) showed clear warming trend in the past 55 years with the warming rate ranging between 0.08 and 0.44 °C/decade (average: 0.25 °C/decade). The warming trends of river waters on the main rivers and their tributaries are spatially differentiated. Seasonal RWT trends showed that summer is warming fastest, followed by spring and then by autumn and winter. Long-term increases in RWT were typically correlated with increases in air temperatures. For the three large rivers (Vistula, Odra, and Warta), the biologically relevant metric (the annual number of days when water temperature exceeded 20 °C) increased synchronously with the warming of RWT. The results reported in this study will be useful to stakeholders and the reconstructed dataset will be useful to the community for the further study of river thermal dynamics and aquatic habitats in Poland.

River water temperature measurement networks suffer from an inadequate spatial coverage and a lack of data. No methods exist for the regional estimation of river water temperature at ungauged sites based on data series from gauged sites. The development of such methods is hence of significant importance. It is proposed in this study to develop a Temperature-Duration-Curve (TDC) based method to estimate river water temperature at ungauged sites on a real-time basis. A Generalised Additive Model (GAM) based method is used to estimate TDCs at ungauged sites. The estimated TDCs are then used in combination with a spatial interpolation method to obtain daily temperature estimates at ungauged sites. Results are compared with a simple method based on the geographical distance weighted average of neighboring stations. The approaches are applied to 126 river thermal stations located on Atlantic salmon rivers in eastern Canada. Leave-one-out cross validation results indicate that the TDC based methods are robust and outperform the geographical distance weighted method.

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.

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. HIGHLIGHTS Machine learning models coupled with global sensitivity analysis to predict RWT.; Ridge regression, KNN, random forest, SVR, along with Sobol’ sensitivity analysis were explored.; Maximum AT as the most sensitive variable in RWT prediction.; The SVR as the most robust ML model to predict RWT at monthly time scale.; Application on a tropical river system of India.;

Monthly quantitative sampling of caridean shrimps and prawns, and logging of river water temperatures were undertaken over 13 months in the Kawazu-yatsu and Ogamo Rivers, Izu Peninsula, and the Morito and Shimoyama Rivers, Miura Peninsula, Japan in order to clarify the overwintering status. Low water temperature tolerances of 9 major species were also examined using culture experiments. Water temperatures in the Kawazu-yatsu River were highest throughout the year due to the inflow of hot spring water. Fifteen shrimp and prawn species were collected, the number of individuals tending to decrease in winter in most. In particular, two species, Macrobrachium australe and M.lar, which have low tolerance to low water temperature were found only in autumn in three rivers except Kawazu-Yatsu River, and it was confirmed that they died due to low water temperature in winter. The rate of decline from autumn to winter for the species established in the study area was the lowest in Paratya compressa, which has the highest tolerance to low water temperatures, and in the Kawazu-Yatsu River, which has the highest water temperatures. The decrease in the populations of M. formosense, M japonicum, Caridina leucosticta and C. typus occurred earlier than the decline in river water temperatures, suggesting that they moved to places where it is difficult to collect them, such as deep pools. [ABSTRACT FROM AUTHOR]

River water temperature is known to be important for water quality and ecosystem processes. We quantified the degree to which lower river flows are associated with warmer river water, after accounting for seasonality and meteorological variability. We applied a systematic methodology to analyse observed mean daily river water temperature and mean daily river flow from 47 sites draining mountain, hill, and lowland catchments across the Canterbury region of Aotearoa New Zealand. We fitted regression models to remove seasonal patterns from all variables, then removed correlations between water temperature and each of three meteorological variables (solar radiation, air temperature, and earth temperature) before quantifying water temperature‐river flow relationships. Strong seasonal patterns were present in water temperature and each meteorological variable across all sites. Many sites also showed strong seasonal patterns in river flows. We demonstrated that seasonal patterns must be accounted for before day‐to‐day associations between water temperature and meteorological variables or river flow can be characterised. Higher water temperatures were associated with lower flows for 46 of 47 sites, even after having accounted for seasonality and associations with each meteorological variable. Increases in water temperature associated with a hypothetical reduction in river flow from the median to the fifth percentile varied with the site but were 0.5°C on average. This finding has important implications for river flow management because it indicates that increased river water temperatures would accompany reduced river flows regardless of site or catchment characteristics. [ABSTRACT FROM AUTHOR]

Rivers worldwide are warming due to the impact of climate change and human interventions. This study investigated river heatwaves in the Vistula River Basin, one of the largest river systems in Europe using long-term observed daily river water temperatures from the past 30 years (1991-2020). The results showed that river heatwaves are increased in frequency and intensity in the Vistula River Basin. The total number of river heatwaves showed clear increasing trend with an average rate of 1.400 times/decade, the duration of river heatwaves increased at an average rate of 14.506 days/decade, and the cumulative intensity of river heatwaves increased at an average rate of 53.169 °C/decade. The Mann-Kendall (MK) test was also employed, showing statistically significant increasing trends in the total number, duration, and intensity of heatwaves for all rivers, including the main watercourse of the Vistula River and its tributaries, with few exceptions. Air temperature is the major controller of river heatwaves for each hydrological station, and with the increase of air temperatures, river heatwaves will increase in frequency and intensity. Another impacting factor is flow, and with the increase of flow, river heatwaves tend to decrease in number, duration and intensity. The results suggested that mitigation measures shall be taken to reduce the effect of climate change on river systems., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:The corresponding author Senlin Zhu is the Associate Editor of Heliyon, Earth Science Section. All the other authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

• Nonparametric copula density was introduced to model extreme river water temperature and low flow for the Swiss River. • Beta kernel estimator, Bernstein estimator and Transformation kernel estimator were employed and compared to approximate copula density. • Estimated bivariate exceedance probabilities and joint and conditional return periods benefit aquatic and freshwater ecosystems. This study proposed a nonparametric copula hazard framework in the joint risk of river water temperature (RWT) and low flow (LF) events for aquatic ecosystems, specifically ectotherm fish. This nonparametric copula density can adapt to any mutual dependence structure, providing greater flexibility. This can reduce the risk of misspecification if the underlying assumption is violated compared to conventional parametric or semiparametric copula settings. The analysis uses nonparametric copulas densities like Beta kernel copula estimator (BKCE), Bernstein copula estimator (BCE), and Transformation kernel estimator (TKE), conjoined with Gaussian Kernel density estimations (GKDEs) and parametric marginals for joint annual maximum RWT (AMRWT) and LF. The study compares different models for analyzing five Swiss river basins: parametric copulas with best-fitted GKDE and parametric margins, versus nonparametric copula. Six bandwidth selectors are estimated to fit GKDE. BKCE with GKDE margins outperformed most stations, while TKE and BCE density with GKDE margins work best for only one station. However, at one station, BKCE with best-fitted parametric margins is outperformed. All stations except Station 2473 are characterized by temperature and low flows that may be conducive to stress of a number of aquatic species, causing high AMRWT (exceeding 19 °C) and minimum LF or specific discharge, SD) quantiles at low AND-joint return periods (RP). AND (i.e. low flow AND high temperature) hazard events are less likely to occur together than OR hazard events, while univariate RPs happen more often than OR-joint RPs. In addition, the joint RP of AMRWT, given LF at different percentiles, significantly affected AMRWT for various LF conditions. Higher AMRWT with low flow conditions results in lower joint RP (except station 2473), and this stress level would be reduced when conditioned with high LF events at the same AMRWT. Station 2473 has high LF even at low percentiles, and with low AMRWT makes it very less stressful than other station under different LF conditions. Summer river flow maintenance can improve aquatic environments with high RWT. Analyzing joint statistics is crucial to understanding mutual risk in freshwater ecosystems. [ABSTRACT FROM AUTHOR]

[Display omitted] • The model performs good in capturing the seasonal pattern and peak values of river water temperature. • Air temperature and day of year are the major factorson modeling river water temperature. • The model is a promising tool to investigate the impact of climate change on river thermal dynamics. Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics. [ABSTRACT FROM AUTHOR]

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which therefore affects the overall health of aquatic ecosystems. In this study, long term variations of river water temperatures (RWT) in Kupa River watershed, Croatia were investigated. It is shown that the RWT in the studied river stations increased about 0.0232–0.0796ºC per year, which are comparable with long term observations reported for rivers in other regions, indicating an apparent warming trend. RWT rises during the past 20 years have not been constant for different periods of the year, and the contrasts between stations regarding RWT increases vary seasonally. Additionally, multilayer perceptron neural network models (MLPNN) and adaptive neuro-fuzzy inference systems (ANFIS) models were implemented to simulate daily RWT, using air temperature (Ta), flow discharge (Q) and the day of year (DOY) as predictors. Results showed that compared to the individual variable alone with Ta as input, combining Ta and Q in the MLPNN and ANFIS models explained temporal variations of daily RWT more accurately. The best accuracy was achieved when the three inputs (Ta, Q and the DOY) were included as predictors. Modeling results indicate that the developed models can well reproduce the seasonal dynamics of RWT in each river, and the models may be used for future projections of RWT by coupling with regional climate models.

Water temperature is an important habitat factor in river ecosystems that exhibits the characteristics of continuous change. Dam construction disrupts the continuity of river water temperature and reset it, thus exerting sharp rise/decrease on the characteristics of water temperature change. The effect of a dam on river continuity is directly related to the dam size. To explain this relationship, two rivers in China were selected: one river without reservoirs and one river with cascade reservoirs. Through the analysis of the longitudinal change of water temperature in free‐flowing rivers, we found that water temperature changes continuously and steadily in the longitudinal direction. Based on this, a temperature trend hypothesis in river was proposed, and the discontinuity of the water temperature in the reservoir section was evaluated. The results are as follows: (1) In mixed reservoirs, river water temperature remained as continuous as free‐flowing rivers. However, the river water temperature had a large discontinuity in the stratified reservoir. (2) Water residence time was used as an indicator of the continuity of reservoir water temperature. (3) Selective withdrawal of stratified reservoirs in January could not remove the discontinuity caused by itself, but it worked in June. [ABSTRACT FROM AUTHOR]

Water temperature in the Yangtze River mainstream has been experiencing significant changes due to the climate change and the operation of a series of world-class large dams, i.e. the Three Gorges Dam (TGD) and upstream cascade dams (CDs). However, quantitative effects of these factors are not fully known, which hinders our understanding on the thermal regime alterations and further prediction in ecosystem response. Here, we will simulate the riverine water temperature (RWT) variations by building a physics-based model, and quantify the respective impacts from TGD, CDs and climate change through a model-based framework. In the framework, both the dam-regulated hydro-thermodynamic processes and the spatial heterogeneity of the meteorological condition in this large river-reservoir system are thoroughly considered. The results show a fluvial warming of 0.31 °C–0.56 °C/10a in recent three decades, mainly attributed to climate change (44%–80% for different reaches). The dam has caused a substantial seasonal thermal lag, e.g. ∼40 d near the TGD in the dry season, and accompanying severe alterations in the monthly RWT. A reduction of 10% in seasonal RWT range is identified, which is attributed to both dam and climate change.

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.

In this study, different versions of feedforward neural network (FFNN), Gaussian process regression (GPR), and decision tree (DT) models were developed to estimate daily river water temperature using air temperature (Ta), flow discharge (Q), and the day of year (DOY) as predictors. The proposed models were assessed using observed data from eight river stations, and modelling results were compared with the air2stream model. Model performances were evaluated using four indicators in this study: the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicated that the three machine learning models had similar performance when only Ta was used as the predictor. When the day of year was included as model input, the performances of the three machine learning models dramatically improved. Including flow discharge instead of day of year, as an additional predictor, provided a lower gain in model accuracy, thereby showing the relatively minor role of flow discharge in river water temperature prediction. However, an increase in the relative importance of flow discharge was noticed for stations with high altitude catchments (Rhône, Dischmabach and Cedar) which are influenced by cold water releases from hydropower or snow melting, suggesting the dependence of the role of flow discharge on the hydrological characteristics of such rivers. The air2stream model outperformed the three machine learning models for most of the studied rivers except for the cases where including flow discharge as a predictor provided the highest benefits. The DT model outperformed the FFNN and GPR models in the calibration phase, however in the validation phase, its performance slightly decreased. In general, the FFNN model performed slightly better than GPR model. In summary, the overall modelling results showed that the three machine learning models performed well for river water temperature modelling.

River water temperature (RWT) forecasting is important for the management of stream ecology. In this paper, a new method based on coupling of wavelet transformation (WT) and artificial intelligence (AI) techniques, including multilayer perceptron neural network (MLPNN) and adaptive neural-fuzzy inference system (ANFIS) for RWT prediction is proposed. The performances of the hybrid models are compared with regular MLPNN and ANFIS models and multiple linear regression (MLR) models for RWT forecasting in two river stations in the Drava River, Croatia. Model performance was evaluated using the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicate that the combination of WT and AI models (WTMLPNN and WTANFIS) yield better models than the conventional forecasting models for RWT simulation for both regular periods and heatwave events. The MLPNN and ANFIS models outperform the MLR models for RWT simulation for the studied river stations. RMSE values of WTMLPNN2 and WTANFIS2 models range from 1.127 to 1.286 °C, and 1.216 to 1.491 °C for the Botovo and Donji Miholjac stations respectively. Additionally, modeling results further confirm the importance of the day of year (DOY) on the thermal dynamics of the river. The results of this study indicate the potential of coupling of WT and MLPNN, ANFIS models in forecasting RWT. [ABSTRACT FROM AUTHOR]

In this study, we developed a global river water temperature model that explicitly represents water and heat budgets. The model considers fluvial dynamics such as floodplain inundation and as well simulates seasonal freeze‐thaw cycles for temperate and arctic rivers, and it was found that proposed physically based parameterizations appropriately reproduced in situ observed seasonal variations in 12 global rivers. Theoretically, floodplain inundation has two major effects on water temperature variability: (1) "broadening" of water surfaces, which increases heat exchange with the atmosphere and friction with river bed, and (2) "shallowing" mean water depth, which increases the absorption rate of shortwave radiation per unit volume. In contrast to the broadening which affects both warming and cooling processes, the shallowing causes warming only and has dominant impacts mainly during the ice melting season in Arctic regions and the rainy season in the tropics. Furthermore, it was revealed that lateral mixing between channel and floodplain has a significant impact on determination of river water temperature. Although water temperature plays an important role in water quality and riverine material circulation linking terrestrial and coastal environments, it has not been considered in most Earth system models. The improvements in global‐scale river water temperature modeling demonstrated in this study will benefit for future Earth systems research. Key Points: A global model was developed to simulate river water temperature considering fluvial and thermodynamics, including floodplain inundationFloodplain inundation increases the temperature of the river water mainly due to an increase in the absorption of shortwave radiationRefinement of a topographic data and water distribution representation potentially improves understanding of the riverine environment [ABSTRACT FROM AUTHOR]

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Ta), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Ta, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Ta is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation. [ABSTRACT FROM AUTHOR]

· Mean annual stream temperature was found to warm by 1.00 °C over 1992–2021. · Increased air temperatures and urbanization are key drivers of warming stream temperatures. · Air temperature and urbanization together warm streams more than their individual effects alone. · Both local and global solutions are needed to reduce future increases to stream temperature. Climate change and urbanization threaten streams and the biodiversity that rely upon them worldwide. Emissions of greenhouse gases are causing air and sea surface temperatures to increase, and even small areas of urbanization are degrading stream biodiversity, water quality and hydrology. However, empirical evidence of how increasing air temperatures and urbanization together affect stream temperatures over time and their relative influence on stream temperatures is limited. This study quantifies changes in stream temperatures in a region in South-East Australia with an urban-agricultural-forest landcover gradient and where increasing air temperatures have been observed. Using Random Forest models we identify air temperature and urbanization drive increasing stream temperatures and that their combined effects are larger than their individual effects occurring alone. Furthermore, we identify potential mitigation measures useful for waterway managers and policy makers. The results show that both local and global solutions are needed to reduce future increases to stream temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

• Random forest models reveal seasonal signals in controls on river temperatures. • River temperatures are closely linked to climate but influenced by basin features. • Sensitivity of water temperatures to climate is strongly impacted by dams. Water temperature is vitally important to the health of rivers and streams, influencing the integrity of ecosystems, aquatic biogeochemistry, and the habitability of waterways for a variety of species. While climate is often regarded as the primary driver of stream temperature regimes, other factors - including hydrology, watershed characteristics, and human impacts - add substantial complexity to the variability of water temperatures. However, it remains challenging to disentangle the influence of these drivers through time and across rivers spanning diverse settings. To quantify the underlying controls on river thermal regimes, we applied conditional inference random forest models to predict maximum monthly stream temperatures and thermal sensitivities, averaged across a 4-year period (2016 to 2020), at 410 watersheds spanning the conterminous United States. Maximum stream temperatures were selected given their ecological relevance, while thermal sensitivity, which measures the relationship between air and water temperatures, was used to assess the responsiveness of stream temperatures to climate forcings. We interpreted these random forest models using variable importance rankings, describing seasonal and spatial variability in the dominant controls on water temperatures. Although our empirical results confirm that climate is indeed a primary control on temperature magnitude, our models highlight the diversity in drivers of water temperature variability across seasons, hydrologic regions, and between metrics. By combining random forest models with process-based understanding of stream thermal regimes, we provide new insights on the dynamic controls of water temperature variability across broad geographical domains, informing region- and season-specific controls for tailored thermal watershed management and guiding the framing of future water temperature modeling. [ABSTRACT FROM AUTHOR]

A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on ak-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat. [ABSTRACT FROM AUTHOR]

Changes in river water temperatures are anticipated to have direct effects on thermal habitat and fish population vital rates, and therefore, understanding temporal trends in water temperatures may be necessary for predicting changes in thermal habitat and how species might respond to such changes. However, many investigations into trends in water temperatures use regression methods that assume long-term monotonic changes in temperature, when in fact changes are likely to be nonmonotonic. Therefore, our objective was to highlight the need and provide an example of an analytical method to better quantify the short-term, nonmonotonic temporal changes in thermal habitat that are likely necessary to determine the effects of changing thermal conditions on fish populations and communities. To achieve this objective, this study uses Bayesian dynamic linear models (DLMs) to examine seasonal trends in river water temperatures from sites located in the eastern and western United States, regions that have dramatically different riverine habitats and fish communities. We estimated the annual rate of change in water temperature and found little evidence of seasonal changes in water temperatures in the eastern U.S.We found more evidence of warming for river sites located in the western U.S., particularly during the fall and winter seasons. Use of DLMs provided a more detailed view of temporal dynamics in river thermal habitat compared to more traditional methods by quantifying year-to-year changes and associated uncertainty, providing managers with the information needed to adapt decision making to short-term changes in habitat conditions that may be necessary for conserving aquatic resources in the face of a changing climate. [ABSTRACT FROM AUTHOR]

• RWT changes due to dam and climate are distinguished on multiple timescales. • Long-term atmospheric warming dominates the water warming in the Yangtze River. • RWT variability on short and annual scales is significantly reduced by TGR. • Dam reduces the correlation between the RWT and AT on moderate scales. • TGR leads to significant thermal lag in the annual and semiannual RWT components. The water temperature in the Yangtze River has been found altering over the last decades. Although these alterations are expected to occur over multiple timescales and with multiple factors, including both reservoir operation and climate change, few efforts have been made to quantify the alterations and conduct attribution analysis on multiple timescales. It hinders the deep understanding of the thermal regime in the Yangtze River and how it responses to the driving forces. Here, a multiresolution analysis based on the maximal overlap discrete wavelet analysis is applied to quantify the multi-timescale alterations of the water temperature, and to distinguish the impacts caused by reservoirs and climate change. The results indicate that the water temperature displays a significant upward trend in past three decades, which is mostly driven by the climate change. Reservoir operation has reduced the intra-annual variability of the water temperature, especially for the short-term and annual scales, resulting in a smoother and flatter water temperature process. The correlation between the water and air temperature is also investigated on multiple timescales based on the wavelet cross-correlation coefficients. It is found that the correlation between the air and water temperature in the natural state shows an overall increasing trend with the increasing timescale and peaks on annual scale. Due to the reservoir operation, the correlation on the moderate scales (1–4 months) has been notably weakened. While for the longer scales, the correlation between air and water temperature remains high, although their phases difference on some scales may be extended by months due to the reservoir operation. [ABSTRACT FROM AUTHOR]

Seasonal and multi-annual variability of river water temperature has been analysed based on data from 24 gauge stations of the IMGW network. It has been characterised by means of several values of the thermal regime parameters: mean annual, semi-annual (November-April, May-October) and amplitude. The variability of water temperature in the catchment and in the stream network has been estimated. Measurement data included seven stations at the Vistula river. On the background of natural variability, rivers or their segments have been distinguished where water temperature is impacted by anthropopressure.

This study examines the impact of the world’s largest dam, the Three Gorges Dam (TGD), on the thermal dynamics of the Yangtze River (China). The analysis uses long-term observations of river water temperature (RWT) in four stations and reconstructs the RWT that would have occurred in absence of the TGD. Relative to pre-TGD conditions, RWT consistently warmed in the region due to air temperature (AT) increase. In addition, the analysis demonstrates that the TGD significantly affected RWT in the downstream reach. At the closest downstream station (Yichang) to the TGD, the annual cycle of RWT experienced a damped response to AT and a marked seasonal alteration: warming during all seasons except for spring and early summer which were characterized by cooling. Both effects were a direct consequence of the larger thermal inertia of the massive water volume stored in the TGD reservoir, causing the downstream reach to be more thermally resilient. The approach used here to quantify the separate contributions of climate and human interventions on RWT can be used to set scientific guidelines for river management and conservation planning strategies.

The thermal regime of rivers is threatened by anthropogenic stresses at a large variety of timescales. We focus on sub-daily thermal alterations induced by the release of hypolimnetic water for hydropower production (thermopeaking). We analyse the thermal signal focusing on the following characteristics that are potentially affected by hypolimnetic releases: (i) sub-daily thermal rate of change and (ii) oscillation frequencies contained in the thermal signal. Through a proper scaling, we derive two dimensionless at-a-station indicators to compare alterations among stations with different locations and physiographic characteristics of the basins. Then we analyse the data from two different thermal datasets (Italy/Switzerland) for a total of 48 stations with 10 min time resolution of temperature data. The stations are grouped according to the absence of upstream hydropeaking releases (29 stations, reference group) and the existence of upstream hydropeaking, hence potentially impacted by thermopeaking (19 stations, altered group). Using a simple statistical approach, based on a non-parametric definition of outliers, we identify the range of variability of the two indicators for the reference, unaltered group. This range measures the 'natural' sub-daily thermal variability of the proposed indicators. Finally, we investigate the seasonality effects on the two proposed indicators and it results, that sub-daily alterations mostly occur during summer. The two indicators represent a novel tool for the assessment of river thermal regime alterations and can be easily included in existing methodologies to assess river quality. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Climate change and urbanisation of watercourses affect water temperatures and current flow velocities in river systems on a global scale. This represents a particularly critical issue for migratory fish species with complex life histories that use rivers to reproduce. Salmonids are migratory keystone species that provide substantial economical value to ecosystems and human societies. Consequently, a comprehensive understanding of the effects of environmental stressors on their reproductive success is critical in order to ensure their continued abundance during future climatic change. Salmonids are capital breeders, relying entirely on endogenous energy stores to fuel return migration to their natal spawning sites and reproduction upon arrival. Metabolic rates and cost of transport en-route increase with temperature and at extreme temperatures, swimming is increasingly fuelled anaerobically, resulting in an oxygen debt and reduced capacity to recover from exhaustive exercise. Thermally challenged salmonids also produce less viable gametes, which themselves are affected by water temperature after release. Passage through hydrological barriers and temperature changes both affect energy expenditure. As a result, important energetic tradeoffs emerge between extra energy used during migration and that available for other facets of the reproductive cycle, such as reproductive competition and gamete production. However, studies identifying these tradeoffs are extremely sparse. This review focuses on the specific locomotor responses of salmonids to thermal and hydrological challenges, identifying gaps in our knowledge and highlighting the potential implications for key aspects of their reproduction. [ABSTRACT FROM AUTHOR]