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

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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.

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  • Albek M. Albek E. 2009. Stream temperature trends in Turkey. Clean Soil Air & Water 37 142–149.

  • Ayllón D. Almodóvar A. Nicola G.G. Parra I. Elvira B. 2012. A new biological indicator to assess the ecological status of Mediterranean trout type streams. Ecological Indicators 20 295–303.

  • Bonacci O. Andrić I. 2010. Impact of an inter-basin water transfer and reservoir operation on a karst open streamflow hydro-logical regime: an example from the Dinaric karst (Croatia). Hydrological Processes 24 3852–3863.

  • Bonacci O. Trninić D. Roje-Bonacci T. 2008. Analysis of the water temperature regime of the Danube and its tributaries in Croatia. Hydrological Processes 22 1014–1021.

  • Chen D. Hu M. Guo Y. Dahlgren R.A. 2016. Changes in river water temperature between 1980 and 2012 in Yongan water-shed eastern China: magnitude drivers and models. Journal of Hydrology 533 191–199.

  • Cingi S. Keinänen M. Vuorinen P.J. 2010. Elevated water temperature impairs fertilization and embryonic development of whitefish Coregonus lavaretus. Journal of Fish Biology 76 502–521.

  • Cox B.A. Whitehead P.G. 2009. Impacts of climate change scenarios on dissolved oxygen in the River Thames UK. Hydrology Research 40 138–152.

  • DeWeber J.T. Wagner T. 2014. A regional neural network ensemble for predicting mean daily river water temperature. Journal of Hydrology 517 187–200.

  • Feng M. Zolezzi G. Pusch M. 2018. Effects of thermopeaking on the thermal response of alpine river systems to heatwaves. Science of the Total Environment 612 1266–1275.

  • Frančišković-Bilinski S. Bhattacharya A.K. Bilinski H. Bhattacharya B.D. Mitra A. Sarkar S.K. 2012. Fluvial geo-morphology of the Kupa River drainage basin Croatia: a perspective of its application in river management and pollution studies. Zeitschrift für Geomorphologie 56 93–119.

  • Fullerton A.H. Torgersen C.E. Lawler J.J. Steel E.A. Eber-sole J.L. Lee S.Y. 2018. Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: effects of scale and climate change. Aquatic Sciences 80 3.

  • Gooseff M.M. Strzepek K. Chapra S.C. 2005. Modeling the potential effects of climate change on water temperature downstream of a shallow reservoir lower Madison River MT. Climatic Change 68 331–353.

  • Hadzima-Nyarko M. Rabi A. Šperac M. 2014. Implementation of artificial neural networks in modeling the water-air temperature relationship of the river Drava. Water Resources Management 28 1379–1394.

  • Hardenbicker P. Viergutz C. Becker A. Kirchesch V. Nilson E. Fischer H. 2017. Water temperature increases in the river Rhine in response to climate change. Regional Environmental Change 17 299–308.

  • Heddam S. 2016. New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the Gregorian calendar as inputs: case study of Clackamas River Oregon USA. Modeling Earth Systems & Environment 2 1–5.

  • Heddam S. Kisi O. 2017. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environmental Science and Pollution Research 24 16702–16724.

  • Isaak D.J. Wollrab S. Horan D. Chandler G. 2012. Climate change effects on stream and river temperatures across the northwest U.S. from 1980–2009 and implications for salmonid fishes. Climatic Change 113 499–524.

  • Jackson F.L. Fryer R.J. Hannah D.M. Millar C.P. Malcolm I.A. 2018. A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland’s Atlantic salmon rivers under climate change. Science of the Total Environment 621 1543–1558.

  • Kim J.H. Park H.J. Hwang I.K. Han J.M. Kim D.H. Oh C.W. Lee J.S. Kang J.C. 2017. Toxic effects of juvenile sablefish Anoplopoma fimbria by ammonia exposure at different water temperature. Environmental Toxicology and Pharmacology 54 169–176.

  • Leblanc R.T. Brown R.D. Fitzgibbon J.E. 1997. Modeling the effects of land use change on the water temperature in unregulated urban streams. Journal of Environmental Management 49 445–469.

  • Lepori F. Pozzoni M. Pera S. 2014. What drives warming trends in streams? A case study from the Alpine Foothills. River Research and Applications 31 663–675.

  • Markovic D. Scharfenberger U. Schmutz S. Pletterbauer F. Wolter C. 2013. Variability and alterations of water temperatures across the Elbe and Danube River Basins. Climatic Change 119 375–389.

  • Moatar F. Gailhard J. 2006. Water temperature behaviour in the River Loire since 1976 and 1881. Comptes Rendus Geoscience 338 319–328.

  • Null S.E. Viers J.H. Deas M.L. Tanaka S.K. Mount J.F. 2013. Stream temperature sensitivity to climate warming in California’s Sierra Nevada: impacts to coldwater habitat. Climatic Change 116 149–170.

  • Orr H.G. Simpson G.L. des Clers S. Watts G. Hughes M. Hannaford J. Dunbar M.J. Laizé C.L.R. Wilby R.L. Battarbee R.W. Evans R. 2015. Detecting changing river temperatures in England and Wales. Hydrological Processes 29 752–766.

  • Pekárová P. Miklánek P. Halmová D. Onderka M. Pekár J. Kučárová K. Liová S. Škoda P. 2011. Long-term trend and multi-annual variability of water temperature in the pristine Bela River basin (Slovakia). Journal of Hydrology 400 333–340.

  • Piotrowski A.P. Napiorkowski M.J. Napiorkowski J.J. Osuch M. 2015. Comparing various artificial neural network types for water temperature prediction in rivers. Journal of Hydrology 529 302–315.

  • Rice K.C. Jastram J.D. 2015. Rising air and stream-water temperatures in Chesapeake Bay region USA. Climatic Change 128 127–138.

  • Schär C. Vidale P.L. Lüthi D. Frei C. Häberli C. Liniger M.A. Appenzeller C. 2004. The role of increasing temperature variability in European summer heatwaves. Nature 427 332–336.

  • Sohrabi M.M. Benjankar R. Tonina D. Wenger S.J. Isaak D.J. 2017. Estimation of daily stream water temperatures with a Bayesian regression approach. Hydrological Processes 31 1719–1733.

  • Temizyurek M. Dadaser-Celik F. 2018. Modelling the effects of meteorological parameters on water temperature using artificial neural networks. Water Science and Technology 77 1724–1733.

  • Toffolon M. Piccolroaz S. 2015. A hybrid model for river water temperature as a function of air temperature and discharge. Environmental Research Letters 10 114011.

  • van Vliet M.T.H. Ludwig F. Zwolsman J.J.G. Weedon G.P. Kabat P. 2011. Global river temperatures and sensitivity to atmospheric warming and changes in river flow. Water Resources Research 47 247–255.

  • van Vliet M.T.H. Franssen W.H.P. Yearsley J.R. Ludwig F. Haddeland I. Lettenmaier D.P. Kabat P. 2013. Global river discharge and water temperature under climate change. Global Environmental Change 23 450–464.

  • Webb B.W. Clack P.D. Walling D.E. 2003. Water–air temperature relationships in a Devon river system and the role of flow. Hydrological Processes 17 3069–3084.

  • Žganec K. 2012. The effects of water diversion and climate change on hydrological alteration and temperature regime of karst rivers in central Croatia. Environmental Monitoring and Assessment 184 5705–5723.

  • Zhu S. Heddam S. Nyarko E.K. Hadzima-Nyarko M. Piccolroaz S. Wu S. 2019. Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environmental Science and Pollution Research 26 402–420.

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