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Pregled bibliografske jedinice broj: 1006543

Long Term Variations of River Temperature and the Influence of Atmospheric and Hydrological Factors: Case Study of Kupa River Watershed in Croatia


Zhu, Senlin; Bonacci, Ognjen; Oskoruš, Dijana; Hadzima-Nyarko, Marijana
Long Term Variations of River Temperature and the Influence of Atmospheric and Hydrological Factors: Case Study of Kupa River Watershed in Croatia // Journal of Hydrology and Hydromehanics, March 2019 (2019) (znanstveni, poslan)


Naslov
Long Term Variations of River Temperature and the Influence of Atmospheric and Hydrological Factors: Case Study of Kupa River Watershed in Croatia

Autori
Zhu, Senlin ; Bonacci, Ognjen ; Oskoruš, Dijana ; Hadzima-Nyarko, Marijana

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
Journal of Hydrology and Hydromehanics, March 2019 (2019)

Status rada
Poslan

Ključne riječi
Air temperature, climate change, flow discharge, machine learning models, river water temperature

Sažetak
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 0.0232-0.0796 ºC 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 nd ANFIS models explained temporal variations of daily RWT more accurately. Including of the three inputs as predictors (Ta, Q and the DOY) yielded the best accuracy among all the developed models. 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. Keywords: air temperature, climate change, flow discharge, machine learning models, river water temperature

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Interdisciplinarne prirodne znanosti, Interdisciplinarne tehničke znanosti