<abstract xmlns="http://www.w3.org/1999/xhtml">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 (<italic>Ta</italic>), flow discharge (<italic>Q</italic>) and the day of year (<italic>DOY</italic>) as predictors. Results showed that compared to the individual variable alone with <italic>Ta</italic> as input, combining <italic>Ta</italic> and <italic>Q</italic> in the MLPNN and ANFIS models explained temporal variations of daily RWT more accurately. The best accuracy was achieved when the three inputs (<italic>Ta</italic>, <italic>Q</italic> and the <italic>DOY</italic>) 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.</abstract>

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.

Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia

State Key Laboratory of Hydrology-Water resources and Hydraulic Engineering

Faculty of Civil Engineering and Architecture

Josip Juraj Strossmayer University of Osijek

Faculty of Civil Engineering and Architecture Osijek

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which therefore affects the overall health of aquatic...

Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia

Publicado en línea: 15 nov 2019

Páginas: 305 - 313

Recibido: 17 ene 2019

Aceptado: 13 mar 2019

DOI: https://doi.org/10.2478/johh-2019-0019

Palabras clave
Climate change, Machine learning models, River water temperature

© 2019 Senlin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia

Publicado en línea: 15 nov 2019

Páginas: 305 - 313

Recibido: 17 ene 2019

Aceptado: 13 mar 2019

DOI: https://doi.org/10.2478/johh-2019-0019

Palabras claveClimate change, Machine learning models, River water temperature

© 2019 Senlin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Climate change, Machine learning models, River water temperature