A copula model to identify the risk of river water temperature stress for meteorological drought.

Drought is a natural phenomenon that can occur in all climatic zones, and is persistent and regionally widespread. Extreme drought caused by climate change can have serious consequences for freshwater ecosystems, which can have significant social and economic impacts. In this study, the effect of meteorological drought on river water temperature was analyzed probabilistically in order to identify the risk of river water temperature stress experienced by the aquatic ecosystem when a meteorological drought occurs. Meteorological drought is divided into a situation in which moisture is insufficiently supplied from the atmosphere and a situation in which the atmosphere requires excessive moisture from the earth's surface. Using the copula theory, a joint probabilistic model between the river water temperature and each meteorological drought caused by two causes is proposed. In order to consider the propagation time from meteorological drought to river water temperature, the optimal time-scale meteorological drought index is adopted through correlation analysis between the meteorological drought index calculated at various time-scales and the river water temperature. The optimal copula function of the drought index and river water temperature is determined using AIC analysis. Using the proposed model, a risk map is drawn for the river water temperature stress experienced by the aquatic ecosystem under the user-defined meteorological drought severity. The risk map identifies the stream sections where the river water temperature is relatively more sensitive to meteorological drought. The identified stream sections appear differently depending on the cause of the meteorological drought, the region, and the season.

The potential impacts of migratory difficulty, including warmer waters and altered flow conditions, on the reproductive success of salmonid fishes.

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.

Characteristics of river heatwaves in the Vistula River basin, Europe.

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.

Empirical evidence of climate change and urbanization impacts on warming stream temperatures.

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.

Two hybrid data-driven models for modeling water-air temperature relationship in rivers.

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.

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

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.

Assessing the performance of a suite of machine learning models for daily river water temperature prediction.

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.