{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T08:10:45Z","timestamp":1777623045973,"version":"3.51.4"},"reference-count":80,"publisher":"Walter de Gruyter GmbH","issue":"4","license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The aim of the study is to analyse changes and predict the course of mean monthly water temperatures of the Danube River at various locations for the future. The first part of the study involves conducting a statistical analysis of the annual and monthly average air temperatures, water temperatures, and discharges along the Danube River. The study examines long-term trends, changes in the trends, and multiannual variability in the time series. The second part of the study focuses on simulating the average monthly water temperatures using Seasonal Autoregressive Integrated Moving Average (SARIMA) models and nonlinear regression models (NonL), based on two RCP based incremental mean monthly air temperature scenarios. To assess the impact of future climate on stream temperatures, the historical long-term average of the monthly water temperature (1990\u20132020) was compared with scenarios S1 (2041\u20132070) and S2 (2071\u20132100). The simulation results from the two stochastic models, the SARIMA and NonL, showed that in scenario S1, the Danube River\u2019s average monthly water temperature is projected to increase by 0.81\/0.82\u00b0C (Passau), 0.55\/0.71\u00b0C (Bratislava), and 0.68\/0.56\u00b0C (Reni). In scenario S2, the models predict higher increases: 2.83\/2.50\u00b0C (Passau), 2.06\/2.46\u00b0C (Bratislava), and 2.52\/1.90\u00b0C (Reni). Overall, the SARIMA model proved to be more stable and effective in simulating the increase in monthly water temperatures in the Danube River.<\/jats:p>","DOI":"10.2478\/johh-2023-0028","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T14:31:15Z","timestamp":1699972275000},"page":"382-398","source":"Crossref","is-referenced-by-count":14,"title":["Monthly stream temperatures along the Danube River: Statistical analysis and predictive modelling with incremental climate change scenarios"],"prefix":"10.2478","volume":"71","author":[{"given":"Pavla","family":"Pek\u00e1rov\u00e1","sequence":"first","affiliation":[{"name":"Slovak Academy of Sciences, Institute of Hydrology , D\u00fabravsk\u00e1 cesta 9 , Bratislava , Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zbyn\u011bk","family":"Bajtek","sequence":"additional","affiliation":[{"name":"Slovak Academy of Sciences, Institute of Hydrology , D\u00fabravsk\u00e1 cesta 9 , Bratislava , Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e1n","family":"Pek\u00e1r","sequence":"additional","affiliation":[{"name":"Comenius University in Bratislava, Faculty of Mathematics, Physics, and Informatics , Department of Applied Mathematics and Statistics , Mlynsk\u00e1 dolina , Bratislava , Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roman","family":"V\u00fdleta","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Department of Land and Water Resources Management , Radlinsk\u00e9ho 11, 810 05 Bratislava , Slovak University of Technology in Bratislava , Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ognjen","family":"Bonacci","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Architecture and Geodesy , University of Split , Matice hrvatske 15 , , Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pavol","family":"Mikl\u00e1nek","sequence":"additional","affiliation":[{"name":"Slovak Academy of Sciences, Institute of Hydrology , D\u00fabravsk\u00e1 cesta 9 , Bratislava , Slovakia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg Uwe","family":"Belz","sequence":"additional","affiliation":[{"name":"Federal Institute of Hydrology , Am Mainzer Tor 1 , Koblenz , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liudmyla","family":"Gorbachova","sequence":"additional","affiliation":[{"name":"Ukrainian Hydrometeorological Institute , 37 Nauki Prospect , Kyiv-28 , Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"2026042818410095725_j_johh-2023-0028_ref_001","doi-asserted-by":"crossref","unstructured":"Abdi, R., Rust, A., Hogue, T.S., 2021. development of a multilayer deep neural network model for predicting hourly river water temperature from meteorological data. Front. Environ. Sci., 9., 433. https:\/\/doi.org\/10.3389\/fenvs.2021.738322","DOI":"10.3389\/fenvs.2021.738322"},{"key":"2026042818410095725_j_johh-2023-0028_ref_002","doi-asserted-by":"crossref","unstructured":"Ahmadi-Nedushan, B., St-Hilaire, A., Ouarda, T.B.M.J., Bilodeau, L., Robichaud, \u00c9., Thi\u00e9monge, N., Bob\u00e9e, B., 2007. Predicting river water temperatures using stochastic models: case study of the Moisie River (Quebec, Canada). Hydrol. Process., 21, 1, 21\u201334. https:\/\/doi.org\/10.1002\/hyp.6353","DOI":"10.1002\/hyp.6353"},{"key":"2026042818410095725_j_johh-2023-0028_ref_003","doi-asserted-by":"crossref","unstructured":"Asarian, J.E., Robinson, C., Genzoli, L., 2023. Modeling seasonal effects of river flow on water temperatures in an agriculturally dominated California River. Water Resour. Res., 59, 3, e2022WR032915. https:\/\/doi.org\/10.1029\/2022WR032915","DOI":"10.1029\/2022WR032915"},{"key":"2026042818410095725_j_johh-2023-0028_ref_004","doi-asserted-by":"crossref","unstructured":"Azad, A.S., Sokkalingam, R., Daud, H., Adhikary, S.K., Khurshid, H., Mazlan, S.N.A., Rabbani, M.B.A., 2022. Water level prediction through Hybrid SARIMA and ANN models based on time series analysis: Red Hills Reservoir Case Study. Sustainability, 14, 3, 1843. https:\/\/doi.org\/10.3390\/su14031843","DOI":"10.3390\/su14031843"},{"key":"2026042818410095725_j_johh-2023-0028_ref_005","doi-asserted-by":"crossref","unstructured":"Bacova Mitkov\u00e1, V., Halmova, D., Pekarova, P., Mikl\u00e1nek, P., 2023. The Copula application for analysis of the flood threat at the river confluences in the Danube River Basin in Slovakia. Water, 15, 984. https:\/\/doi.org\/10.3390\/w15050984","DOI":"10.3390\/w15050984"},{"key":"2026042818410095725_j_johh-2023-0028_ref_006","doi-asserted-by":"crossref","unstructured":"Bahari, M., Hamid, N.Z.A., 2019. Analysis and prediction of temperature time series using chaotic approach. IOP Conf. Ser. Earth Environ. Sci., 286, 012027. https:\/\/doi.org\/10.1088\/1755-1315\/286\/1\/012027","DOI":"10.1088\/1755-1315\/286\/1\/012027"},{"key":"2026042818410095725_j_johh-2023-0028_ref_007","doi-asserted-by":"crossref","unstructured":"Belotti, J., Mendes, J.J., Jr., Leme, M. Trojan, F., Stevan, S.L. Jr., Siqueira, H., 2021. Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models. J. Hydrol. Hydromech., 69, 2, 150\u2013195. https:\/\/doi.org\/10.2478\/johh-2021-0001","DOI":"10.2478\/johh-2021-0001"},{"key":"2026042818410095725_j_johh-2023-0028_ref_008","doi-asserted-by":"crossref","unstructured":"Benyahya, L., Caissie, D., St-Hilaire, A., Ouarda, T.B.M.J., Bob\u00e9e, B., 2007a. A review of statistical water temperature models. Can. Water Resour. J. Rev. Can. Ressour. Hydr., 32, 3, 179\u2013192. https:\/\/doi.org\/10.4296\/cwrj3203179","DOI":"10.4296\/cwrj3203179"},{"key":"2026042818410095725_j_johh-2023-0028_ref_009","doi-asserted-by":"crossref","unstructured":"Benyahya, L., St-Hilaire, A., Quarda, T.B.M.J., Bob\u00e9e, B., Ahmadi-Nedushan, B., 2007b. Modeling of water temperatures based on stochastic approaches: case study of the Deschutes River. J. Environ. Eng. Sci., 6, 4, 437\u2013448. https:\/\/doi.org\/10.1139\/s06-067","DOI":"10.1139\/s06-067"},{"key":"2026042818410095725_j_johh-2023-0028_ref_010","doi-asserted-by":"crossref","unstructured":"Bisselink, B., Roo, A., Bernhard, J., Gelati, E., 2018. Future projections of water scarcity in the Danube River basin due to land use, water demand and climate change. J. Environ. Geogr., 11, 25\u201336. https:\/\/doi.org\/10.2478\/jengeo-2018-0010","DOI":"10.2478\/jengeo-2018-0010"},{"key":"2026042818410095725_j_johh-2023-0028_ref_011","doi-asserted-by":"crossref","unstructured":"Bonacci, O., \u0110urin, B., Bonacci, T.R., Bonacci, D., 2022. The influence of reservoirs on water temperature in the downstream part of an open watercourse: A case study at Botovo Station on the Drava River. Water, 14, 21, 3534. https:\/\/doi.org\/10.3390\/w14213534","DOI":"10.3390\/w14213534"},{"key":"2026042818410095725_j_johh-2023-0028_ref_012","doi-asserted-by":"crossref","unstructured":"Bonacci, O., Oskoru\u0161, D., 2010. The changes in the lower Drava River water level, discharge and suspended sediment regime. Environ. Earth Sci., 59, 8, 1661\u20131670. https:\/\/doi.org\/10.1007\/s12665-009-0148-8","DOI":"10.1007\/s12665-009-0148-8"},{"key":"2026042818410095725_j_johh-2023-0028_ref_013","doi-asserted-by":"crossref","unstructured":"Bonacci, O., Patekar, M., Pola, M., Roje-Bonacci, T., 2020. Analyses of climate variations at four meteorological stations on remote islands in the Croatian part of the Adriatic Sea. Atmosphere, 11, 10, 1044. https:\/\/doi.org\/10.3390\/atmos11101044","DOI":"10.3390\/atmos11101044"},{"key":"2026042818410095725_j_johh-2023-0028_ref_014","doi-asserted-by":"crossref","unstructured":"Bonacci, O., Trnini\u0107, D., Roje-Bonacci, T., 2008. Analysis of the water temperature regime of the Danube and its tributaries in Croatia. Hydrol. Process., 22, 7, 1014\u20131021. https:\/\/doi.org\/10.1002\/hyp.6975","DOI":"10.1002\/hyp.6975"},{"key":"2026042818410095725_j_johh-2023-0028_ref_015","doi-asserted-by":"crossref","unstructured":"Boudreault, J., Bergeron, N.E., St-Hilaire, A., Chebana, F., 2019. Stream temperature modeling using functional regression models. JAWRA J. Am. Water Resour. Assoc., 55, 6, 1382\u20131400. https:\/\/doi.org\/10.1111\/1752-1688.12778","DOI":"10.1111\/1752-1688.12778"},{"key":"2026042818410095725_j_johh-2023-0028_ref_016","doi-asserted-by":"crossref","unstructured":"Brilly, M., 2010. Danube River basin coding. In: Brilly, M. (Ed:) Hydrological Processes of the Danube River Basin. Springer, Dordrecht. https:\/\/doi.org\/10.1007\/978-90-481-3423-6_4","DOI":"10.1007\/978-90-481-3423-6"},{"key":"2026042818410095725_j_johh-2023-0028_ref_017","doi-asserted-by":"crossref","unstructured":"Caissie, D., 2006. The thermal regime of rivers: a review. Freshw. Biol., 51, 8, 1389\u20131406. https:\/\/doi.org\/10.1111\/j.1365-2427.2006.01597.x","DOI":"10.1111\/j.1365-2427.2006.01597.x"},{"key":"2026042818410095725_j_johh-2023-0028_ref_018","doi-asserted-by":"crossref","unstructured":"Caissie, D., El-Jabi, N., St-Hilaire, A., 1998. Stochastic modelling of water temperatures in a small stream using air to water relations. Can. J. Civ. Eng., 25, 2, 250\u2013260. https:\/\/doi.org\/10.1139\/l97-091","DOI":"10.1139\/l97-091"},{"key":"2026042818410095725_j_johh-2023-0028_ref_019","unstructured":"Caissie, D., El-Jabi, N., Turkkan, N., 2014. Stream water temperature modeling under climate change scenarios B1 & A2. Canadian Technical Report of Fisheries and Aquatic Sciences."},{"key":"2026042818410095725_j_johh-2023-0028_ref_020","doi-asserted-by":"crossref","unstructured":"Chang, X., Gao, M., Wang, Y., Hou, X., 2013. Seasonal autoregressive integrated moving average model for precipitation time  series. J.  Math. Stat., 8, 4, 500\u2013505. https:\/\/doi.org\/10.3844\/jmssp.2012.500.505","DOI":"10.3844\/jmssp.2012.500.505"},{"key":"2026042818410095725_j_johh-2023-0028_ref_021","doi-asserted-by":"crossref","unstructured":"Chen, P., Niu, A., Liu, D., Jiang, W., Ma, B., 2018. Time series forecasting of temperatures using SARIMA: An example from Nanjing. IOP Conf. Ser. Mater. Sci. Eng., 394, 5, 052024. https:\/\/doi.org\/10.1088\/1757-899X\/394\/5\/052024","DOI":"10.1088\/1757-899X\/394\/5\/052024"},{"key":"2026042818410095725_j_johh-2023-0028_ref_022","doi-asserted-by":"crossref","unstructured":"DeWeber, J.T., Wagner, T., 2014. A regional neural network ensemble for predicting mean daily river water temperature. J. Hydrol., 517, 187\u2013200. https:\/\/doi.org\/10.1016\/j.jhydrol.2014.05.035","DOI":"10.1016\/j.jhydrol.2014.05.035"},{"key":"2026042818410095725_j_johh-2023-0028_ref_023","unstructured":"Dokulil, M.T., 2018. Climate warming affects water temperature in the river Danube and tributaries \u2013 present and future perspectives. Geomorphologica Slovaca et Bohemica, 18, 57\u201363."},{"key":"2026042818410095725_j_johh-2023-0028_ref_024","doi-asserted-by":"crossref","unstructured":"Dugdale, S.J., Hannah, D.M., Malcolm, I.A., 2017. River temperature modelling: A review of process-based approaches and future directions. Earth-Sci. Rev., 175, 97\u2013113. https:\/\/doi.org\/10.1016\/j.earscirev.2017.10.009","DOI":"10.1016\/j.earscirev.2017.10.009"},{"key":"2026042818410095725_j_johh-2023-0028_ref_025","doi-asserted-by":"crossref","unstructured":"\u0110urin, B., Kranj\u010di\u0107, N., Kanga, S., Singh, S.K., Saka\u010d, N., Pham, Q.B., Hunt, J., Dogan\u010di\u010d, D., Di Nunno, F., 2022. Application of Rescaled Adjusted Partial Sums (RAPS) method in hydrology\u2013an overview. Advances in Civil and Architectural Engineering, 13, 25, 58\u201372. https:\/\/doi.org\/10.13167\/2022.25.6","DOI":"10.13167\/2022.25.6"},{"key":"2026042818410095725_j_johh-2023-0028_ref_026","doi-asserted-by":"crossref","unstructured":"Feigl, M., Lebiedzinski, K., Herrnegger, M., Schulz, K., 2021. Machine learning methods for stream water temperature prediction (preprint). Rivers and lakes\/modelling approaches. https:\/\/doi.org\/10.5194\/hess-2020-670","DOI":"10.5194\/hess-2020-670"},{"key":"2026042818410095725_j_johh-2023-0028_ref_027","doi-asserted-by":"crossref","unstructured":"Ficklin, D.L., Hannah, D.M., Wanders, N., Dugdale, S.J., England, J., Klaus, J., Kelleher, C., Khamis, K., Charlton, M.B., 2023. Re-thinking river water temperature in a changing, human-dominated  world.  Nat. Water, 1, 2, 125\u2013128. https:\/\/doi.org\/10.1038\/s44221-023-00027-2","DOI":"10.1038\/s44221-023-00027-2"},{"key":"2026042818410095725_j_johh-2023-0028_ref_028","doi-asserted-by":"crossref","unstructured":"Garbrecht, J., Fernandez, G.P., 1994. Visualization of trends and fluctuations in climatic records 1. JAWRA Journal of the American Water Resources Association, 30, 2, 297\u2013306. https:\/\/doi.org\/10.1111\/j.1752-1688.1994.tb03292.x","DOI":"10.1111\/j.1752-1688.1994.tb03292.x"},{"key":"2026042818410095725_j_johh-2023-0028_ref_029","doi-asserted-by":"crossref","unstructured":"Garner, G., Hannah, D., Watts, G., 2017. Climate change and water in the UK: Recent scientific evidence for past and future change. Prog. Phys. Geogr., 41, 2, 030913331667908. https:\/\/doi.org\/10.1177\/0309133316679082","DOI":"10.1177\/0309133316679082"},{"key":"2026042818410095725_j_johh-2023-0028_ref_030","doi-asserted-by":"crossref","unstructured":"Gizinska, J., Sojka, M., 2023. How climate change affects river and lake water temperature in Central-West Poland \u2013 A case study of the Warta River Catchment. Atmosphere 14, 330. https:\/\/doi.org\/10.3390\/atmos14020330","DOI":"10.3390\/atmos14020330"},{"key":"2026042818410095725_j_johh-2023-0028_ref_031","doi-asserted-by":"crossref","unstructured":"Graf, R., Aghelpour, P., 2021. Daily river water temperature prediction: A comparison between neural network and stochastic techniques. Atmosphere, 12, 9, 1154. https:\/\/doi.org\/10.3390\/atmos12091154","DOI":"10.3390\/atmos12091154"},{"key":"2026042818410095725_j_johh-2023-0028_ref_032","doi-asserted-by":"crossref","unstructured":"Graf, R., Zhu, S., Sivakumar, B., 2019. Forecasting river water temperature time series using a wavelet\u2013neural network hybrid modelling approach. J. Hydrol., 578, 124115. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.124115","DOI":"10.1016\/j.jhydrol.2019.124115"},{"key":"2026042818410095725_j_johh-2023-0028_ref_033","doi-asserted-by":"crossref","unstructured":"Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Ehalt Macedo, H., Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M.E., Meng, J., Mulligan, M., Nilsson, C., Olden, J.D., Opperman, J.J., Petry, P., Reidy Liermann, C., S\u00e1enz, L., Salinas-Rodr\u00edguez, S., Schelle, P., Schmitt, R.J.P., Snider, J., Tan, F., Tockner, K., Valdujo, P.H., van Soesbergen, A., Zarfl, C., 2019. Mapping the world\u2019s free-flowing rivers. Nature, 569, 7755, 215\u2013221. https:\/\/doi.org\/10.1038\/s41586-019-1111-9","DOI":"10.1038\/s41586-019-1111-9"},{"key":"2026042818410095725_j_johh-2023-0028_ref_034","unstructured":"Hannah, D.M., Garner, G., 2015. A climate change report card for water Working Technical Paper. University of Birmingham, UK."},{"key":"2026042818410095725_j_johh-2023-0028_ref_035","doi-asserted-by":"crossref","unstructured":"Hebert, C., Caissie, D., Satish, M., El-Jabi, N., 2015. Predicting hourly stream temperatures using the equilibrium temperature model. J. Water Resour. Prot., 7, 322\u2013338. https:\/\/doi.org\/10.4236\/jwarp.2015.74026","DOI":"10.4236\/jwarp.2015.74026"},{"key":"2026042818410095725_j_johh-2023-0028_ref_036","doi-asserted-by":"crossref","unstructured":"Heggenes, J., Stickler, M., Alfredsen, K., Brittain, J. E., Adeva-Bustos, A., Huusko, A., 2021. Hydropower-driven thermal changes, biological responses and mitigating measures in northern river systems. River Res. Appl., 37, 5, 743\u2013765. https:\/\/doi.org\/10.1002\/rra.3788","DOI":"10.1002\/rra.3788"},{"key":"2026042818410095725_j_johh-2023-0028_ref_037","unstructured":"HISTALP, n.d. URL http:\/\/www.zamg.ac.at\/histalp\/ (accessed Febr. 24, 2023)"},{"key":"2026042818410095725_j_johh-2023-0028_ref_038","doi-asserted-by":"crossref","unstructured":"Hrdinka, T., Vlas\u00e1k, P., Havel, L., Mlejnsk\u00e1, E., 2015. Possible impacts of climate change on water quality in streams of the Czech Republic. Hydrol. Sci.J., 60, 2, 192\u2013201. https:\/\/doi.org\/10.1080\/02626667.2014.889830","DOI":"10.1080\/02626667.2014.889830"},{"key":"2026042818410095725_j_johh-2023-0028_ref_039","doi-asserted-by":"crossref","unstructured":"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\u2019s Atlantic salmon rivers under climate change. Sci. Total Environ., 612, 1543\u20131558. https:\/\/doi.org\/10.1016\/j.scitotenv.2017.09.010","DOI":"10.1016\/j.scitotenv.2017.09.010"},{"key":"2026042818410095725_j_johh-2023-0028_ref_040","doi-asserted-by":"crossref","unstructured":"Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., D\u00e9qu\u00e9, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kr\u00f6ner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., Yiou, P., 2014. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg. Environ. Change, 14, 2, 563\u2013578. https:\/\/doi.org\/10.1007\/s10113-013-0499-2","DOI":"10.1007\/s10113-013-0499-2"},{"key":"2026042818410095725_j_johh-2023-0028_ref_041","doi-asserted-by":"crossref","unstructured":"Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R.M., Casanueva, A., Christensen, O.B., Christensen, J.H., Coppola, E., De Cruz, L., Davin, E.L., Dobler, A., Dom\u00ednguez, M., Fealy, R., Fernandez, J., Gaertner, M.A., Garc\u00eda-D\u00edez, M., Giorgi, F., Gobiet, A., Goergen, K., G\u00f3mez-Navarro, J.J., Alem\u00e1n, J.J.G., Guti\u00e9rrez, C., Guti\u00e9rrez, J.M., G\u00fcttler, I., Haensler, A., Halenka, T., Jerez, S., Jim\u00e9nez-Guerrero, P., Jones, R.G., Keuler, K., Kjellstr\u00f6m, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Mont\u00e1vez, J.P., Navarra, A., Nikulin, G., de Noblet-Ducoudr\u00e9, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietik\u00e4inen, J.-P., Prein, A.F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., S\u00e1nchez, E., Sieck, K., Soares, P.M. M., Somot, S., Srnec, L., S\u00f8rland, S.L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., Wulfmeyer, V., 2020. Regional climate downscaling over Europe: perspectives from the EUROCORDEX community. Reg. Environ. Change, 20, 2, 51. https:\/\/doi.org\/10.1007\/s10113-020-01606-9","DOI":"10.1007\/s10113-020-01606-9"},{"key":"2026042818410095725_j_johh-2023-0028_ref_042","doi-asserted-by":"crossref","unstructured":"Jeong, D.I., Daigle, A., St-Hilaire, A., 2013. Development of a stochastic water temperature model and projection of future water temperature and extreme events in the Ouelle River Basin in Quebec, Canada. River Res. Appl., 29, 7, 805\u2013821. https:\/\/doi.org\/10.1002\/rra.2574","DOI":"10.1002\/rra.2574"},{"key":"2026042818410095725_j_johh-2023-0028_ref_043","doi-asserted-by":"crossref","unstructured":"Keszeliov\u00e1, A., V\u00fdleta, R., Dan\u00e1\u010dov\u00e1, M., Hlav\u010dov\u00e1, K., Sleziak, P.,Gribovszki, Z., Szolgay, J., 2022. Detection of changes in evapotranspiration on a catchment scale under changing climate conditions in selected river basins of Slovakia. Slovak Journal of Civil Engineerin., 30, 4, 55\u201363. https:\/\/doi.org\/10.2478\/sjce-2022-0029","DOI":"10.2478\/sjce-2022-0029"},{"key":"2026042818410095725_j_johh-2023-0028_ref_044","doi-asserted-by":"crossref","unstructured":"Kwak, J., St-Hilaire, A., Chebana, F., 2016. A comparative study for water temperature modelling in a small basin, the Fourchue River, Quebec, Canada. Hydrol. Sci. J., 62. https:\/\/doi.org\/10.1080\/02626667.2016.1174334","DOI":"10.1080\/02626667.2016.1174334"},{"key":"2026042818410095725_j_johh-2023-0028_ref_045","doi-asserted-by":"crossref","unstructured":"Leach, J.A., Moore, D., 2017. Insights on stream temperature processes through development of a coupled hydrologic and stream temperature model for forested coastal headwater catchments. Hydrol. Process., 31, 18, 3160\u20133177. https:\/\/doi.org\/10.1002\/hyp.11190","DOI":"10.1002\/hyp.11190"},{"key":"2026042818410095725_j_johh-2023-0028_ref_046","doi-asserted-by":"crossref","unstructured":"Letcher, B.H., Hocking, D.J., O\u2019Neil, K., Whiteley, A.R., Nislow, K.H., O\u2019Donnell, M.J., 2016. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ, 4, 10, e1727. https:\/\/doi.org\/10.7717\/peerj.1727","DOI":"10.7717\/peerj.1727"},{"key":"2026042818410095725_j_johh-2023-0028_ref_047","doi-asserted-by":"crossref","unstructured":"Liptay, Z., 2022. Neurohydrological prediction of water temperature and runoff time series. Acta Hydrologica Slovaca, 23, 2, 190\u2013196. https:\/\/doi.org\/10.31577\/ahs-2022-0023.02.0021","DOI":"10.31577\/ahs-2022-0023.02.0021"},{"key":"2026042818410095725_j_johh-2023-0028_ref_048","doi-asserted-by":"crossref","unstructured":"Li, Y.-T., Li, Y., Song, J.-M., Guo, Q.-H., Yang, C., Zhao, W.-J., Wang, J.-Y., Luo, J., Xu, Y.-N., Zhang, Q., Ding, X.-Y., Liang, Y., Li, Y.-N., Feng, Q.-L., Liu, P., Gao, H.-Y., Li, G., Zhao, S.-J., Zhang, Z.-S., 2022. Has breeding altered the light environment, photosynthetic apparatus, and photosynthetic capacity of wheat leaves? J. Exp. Bot., 73, 10, 3205\u20133220. https:\/\/doi.org\/10.1093\/jxb\/erab495","DOI":"10.1093\/jxb\/erab495"},{"key":"2026042818410095725_j_johh-2023-0028_ref_049","doi-asserted-by":"crossref","unstructured":"Malki, A., Atlam, E.-S., Hassanien, A.E., Ewis, A., Dagnew, G., Gad, I., 2022. SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples\u2019 view towards the vaccines. Alex. Eng. J., 61, 12, 12091\u201312110. https:\/\/doi.org\/10.1016\/j.aej.2022.05.051","DOI":"10.1016\/j.aej.2022.05.051"},{"key":"2026042818410095725_j_johh-2023-0028_ref_050","doi-asserted-by":"crossref","unstructured":"Mohseni, O., Stefan, H.G., Erickson, T.R., 1998. A nonlinear regression model for weekly stream temperatures. Water Resour. Res., 34, 10, 2685\u20132692. https:\/\/doi.org\/10.1029\/98WR01877","DOI":"10.1029\/98WR01877"},{"key":"2026042818410095725_j_johh-2023-0028_ref_051","doi-asserted-by":"crossref","unstructured":"Okhravi, S., Sok\u00e1\u010d, M., Vel\u00edskov\u00e1, Y., 2022. Three-dimensional numerical modeling of water temperature distribution in the Rozgrund Reservoir, Slovakia. Acta Hydrologica Slovaca, 23, 2, 305\u2013316. https:\/\/doi.org\/10.31577\/ahs-2022-0023.02.0035","DOI":"10.31577\/ahs-2022-0023.02.0035"},{"key":"2026042818410095725_j_johh-2023-0028_ref_052","doi-asserted-by":"crossref","unstructured":"Oktaviani, F., Miftahuddin, Setiawan, I., 2021. Forecasting sea surface temperature anomalies using the SARIMA ARCH\/GARCH model. J. Phys.: Conf. Ser., 1882, 012020. https:\/\/doi.org\/10.1088\/1742-6596\/1882\/1\/012020","DOI":"10.1088\/1742-6596\/1882\/1\/012020"},{"key":"2026042818410095725_j_johh-2023-0028_ref_053","doi-asserted-by":"crossref","unstructured":"O\u2019Sullivan, A.M., Devito, K.J., Ogilvie, J., Linnansaari, T., Pronk, T., Allard, S., Curry, R.A., 2020. Effects of topographic resolution and geologic setting on spatial statistical river temperature models. Water Resour. Res., 56, 12, e2020WR028122. https:\/\/doi.org\/10.1029\/2020WR028122","DOI":"10.1029\/2020WR028122"},{"key":"2026042818410095725_j_johh-2023-0028_ref_054","doi-asserted-by":"crossref","unstructured":"Ouellet, V., St-Hilaire, A., Dugdale, S.J., Hannah, D.M., Krause, S., Proulx-Ouellet, S., 2020. River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems. Sci. Total Environ., 736, 139679. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.139679","DOI":"10.1016\/j.scitotenv.2020.139679"},{"key":"2026042818410095725_j_johh-2023-0028_ref_055","unstructured":"Pek\u00e1rov\u00e1, P., 2009. Multiannual runoff variability in the upper Danube region. Doctoral (DrSc.) Thesis. IH SAS, Bratislava, 151 p. https:\/\/inis.iaea.org\/collection\/NCLCollectionStore\/_Public\/41\/084\/41084384.pdf"},{"key":"2026042818410095725_j_johh-2023-0028_ref_056","unstructured":"Pek\u00e1rov\u00e1, P., Ba\u010dov\u00e1 Mitkova, V., Pekar, J., Garaj, M., 2022. Analysis and design values of minimum daily flows in the Ipe\u013e river basin. In: Interdisciplinary Approach in Current Hydrological Research. IH SAS, Bratislava, pp. 109\u2013121."},{"key":"2026042818410095725_j_johh-2023-0028_ref_057","doi-asserted-by":"crossref","unstructured":"Pek\u00e1rov\u00e1, P., Halmova, D., Miklanek, P., Onderka, M., Pekar, J., Skoda, P., 2008. Is the water temperature of the Danube River at Bratislava, Slovakia, rising? J. Hydrometeorol., 9, 5, 1115\u20131122. https:\/\/doi.org\/10.1175\/2008JHM948.1","DOI":"10.1175\/2008JHM948.1"},{"key":"2026042818410095725_j_johh-2023-0028_ref_058","doi-asserted-by":"crossref","unstructured":"Piotrowski, A., Napi\u00f3rkowski, M., Napi\u00f3rkowski, J., Osuch, M., 2015. Comparing various artificial neural network types for water temperature prediction in rivers. J. Hydrol., 529, 302\u2013315. https:\/\/doi.org\/10.1016\/j.jhydrol.2015.07.044","DOI":"10.1016\/j.jhydrol.2015.07.044"},{"key":"2026042818410095725_j_johh-2023-0028_ref_059","doi-asserted-by":"crossref","unstructured":"Piotrowski, A.P., Napiorkowski, J.J., 2018. Performance of the air2stream model that relates air and stream water temperatures depends on the calibration method. J. Hydrol., 561, 395\u2013412. https:\/\/doi.org\/10.1016\/j.jhydrol.2018.04.016","DOI":"10.1016\/j.jhydrol.2018.04.016"},{"key":"2026042818410095725_j_johh-2023-0028_ref_060","doi-asserted-by":"crossref","unstructured":"Probst, E., Mauser, W., 2023. Climate change impacts on water resources in the Danube River Basin: A hydrological modelling study using EURO-CORDEX Climate Scenarios. Water, 15, 1, 8. https:\/\/doi.org\/10.3390\/w15010008","DOI":"10.3390\/w15010008"},{"key":"2026042818410095725_j_johh-2023-0028_ref_061","doi-asserted-by":"crossref","unstructured":"Rahmani, F., Lawson, K., Ouyang, W., Appling, A., Oliver, S., Shen, C., 2021. Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data. Environ. Res. Lett., 16, 2, 024025. https:\/\/doi.org\/10.1088\/1748-9326\/abd501","DOI":"10.1088\/1748-9326\/abd501"},{"key":"2026042818410095725_j_johh-2023-0028_ref_062","doi-asserted-by":"crossref","unstructured":"Romanova, Y., Shakirzanova, Z., Ovcharuk, V., Todorova, O., Medvedieva, I., Ivanchenko, A., 2019. Temporal variation of water discharges in the lower course of the Danube River across the area from Reni to Izmail under the influence of natural and anthropogenic factors. Energetika, 65, 2\u20133. https:\/\/doi.org\/10.6001\/energetika.v65i2-3.4108","DOI":"10.6001\/energetika.v65i2-3.4108"},{"key":"2026042818410095725_j_johh-2023-0028_ref_063","doi-asserted-by":"crossref","unstructured":"Sapin, J., Rajagopalan, B., Saito, L., Caldwell, R.J., 2017. A K-Nearest neighbor based stochastic multisite flow and stream temperature generation technique. Environ. Model. 91, 87\u201394. https:\/\/doi.org\/10.1016\/j.envsoft.2017.02.005","DOI":"10.1016\/j.envsoft.2017.02.005"},{"key":"2026042818410095725_j_johh-2023-0028_ref_064","unstructured":"Schiemer, F., Guti, G., Keckeis, H., Staras, M., 2004. Ecological status and problems of the Danube River and its fish fauna: A review. In: Proc. Symposium on the management of large rivers for fisheries, 1, p. 273, FAO."},{"key":"2026042818410095725_j_johh-2023-0028_ref_065","doi-asserted-by":"crossref","unstructured":"Stagl, J.C., Hattermann, F.F., 2015. Impacts of climate change on the hydrological regime of the Danube River and its tributaries using an ensemble of climate scenarios. Water, 7, 11, 6139\u20136172. https:\/\/doi.org\/10.3390\/w7116139","DOI":"10.3390\/w7116139"},{"key":"2026042818410095725_j_johh-2023-0028_ref_066","doi-asserted-by":"crossref","unstructured":"Stagl, J., Hattermann, F.F., 2016. Impacts of climate change on riverine ecosystems: Alterations of ecologically relevant flow dynamics in the Danube River and its major tributaries. Water, 8, 12, 566. https:\/\/doi.org\/10.3390\/w8120566","DOI":"10.3390\/w8120566"},{"key":"2026042818410095725_j_johh-2023-0028_ref_067","doi-asserted-by":"crossref","unstructured":"Stan\u010d\u00edkov\u00e1, A., 2010. Thermal and ice regimes of the Danube River and its tributaries. In: Hydrological Processes of the Danube River Basin: Perspectives from the Danubian Countries, pp. 259\u2013291. https:\/\/doi.org\/10.1007\/978-90-481-3423-6_8","DOI":"10.1007\/978-90-481-3423-6_8"},{"key":"2026042818410095725_j_johh-2023-0028_ref_068","doi-asserted-by":"crossref","unstructured":"Sutadian, A., Muttil, N., Yilmaz, A., Perera, B., 2016. Development of river water quality indices \u2013 a review. Environ. Monit. Assess., 188, 58. https:\/\/doi.org\/10.1007\/s10661-015-5050-0","DOI":"10.1007\/s10661-015-5050-0"},{"key":"2026042818410095725_j_johh-2023-0028_ref_069","doi-asserted-by":"crossref","unstructured":"Tang, Ch., Garcia, V., 2023. Identifying stream temperature variation by coupling meteorological, hydrological, and water temperature models. Journal of the American Water Resources Association (JAWR), 59, 4, 665\u2013680. https:\/\/doi.org\/10.1111\/1752-1688.13113","DOI":"10.1111\/1752-1688.13113"},{"key":"2026042818410095725_j_johh-2023-0028_ref_070","doi-asserted-by":"crossref","unstructured":"Tavares, M.H., Cunha, A.H.F., Motta-Marques, D., Ruhoff, A. L., Fragoso, C.R., Munar, A.M., Bonnet, M.-P., 2020. Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models. Remote Sens. Environ., 241, 111721. https:\/\/doi.org\/10.1016\/j.rse.2020.111721","DOI":"10.1016\/j.rse.2020.111721"},{"key":"2026042818410095725_j_johh-2023-0028_ref_071","doi-asserted-by":"crossref","unstructured":"Vyshnevskyi, V., Shevchuk, S., 2021. Thermal regime of the Dnipro Reservoirs. J. Hydrol. Hydromech., 69, 3, 300\u2013310. https:\/\/doi.org\/10.2478\/johh-2021-0016","DOI":"10.2478\/johh-2021-0016"},{"key":"2026042818410095725_j_johh-2023-0028_ref_072","doi-asserted-by":"crossref","unstructured":"Vyshnevskyi, V., Shevchuk, S., 2023. Thermal regime of the Danube Delta and the adjacent lakes. J. Hydrol. Hydromech., 71, 3, 283\u2013292. https:\/\/doi.org\/10.2478\/johh-2023-0015","DOI":"10.2478\/johh-2023-0015"},{"key":"2026042818410095725_j_johh-2023-0028_ref_073","doi-asserted-by":"crossref","unstructured":"Wanders, N., van Vliet, M.T.H., Wada, Y., Bierkens, M.F.P., van Beek, L.P.H.R., 2019. High-resolution global water temperature modeling. Water Resour. Res., 55, 4, 2760\u20132778. https:\/\/doi.org\/10.1029\/2018WR023250","DOI":"10.1029\/2018WR023250"},{"key":"2026042818410095725_j_johh-2023-0028_ref_074","unstructured":"WWF, 2002. Waterway Transport on Europe\u2019s Lifeline, The Danube: Impacts, Threats and Opportunities. World Wide Fund for Nature, Vienna."},{"key":"2026042818410095725_j_johh-2023-0028_ref_075","doi-asserted-by":"crossref","unstructured":"Webb, B.W., 1996. Trends in stream and river temperature. Hydrol. Process., 10, 2, 205\u2013226. https:\/\/doi.org\/10.1002\/(SICI)1099-1085(199602)10:2<205::AID-HYP358>3.0.CO;2-1","DOI":"10.1002\/(SICI)1099-1085(199602)10:2<205::AID-HYP358>3.3.CO;2-T"},{"key":"2026042818410095725_j_johh-2023-0028_ref_076","doi-asserted-by":"crossref","unstructured":"Webb, B.W., Hannah, D.M., Moore, R.D., Brown, L.E., Nobilis, F., 2008. Recent advances in stream and river temperature research. Hydrol. Process., 22, 7, 902\u2013918. https:\/\/doi.org\/10.1002\/hyp.6994","DOI":"10.1002\/hyp.6994"},{"key":"2026042818410095725_j_johh-2023-0028_ref_077","doi-asserted-by":"crossref","unstructured":"Webb, B.W., Nobilis, F., 2007. Long-term changes in river temperature and the influence of climatic and hydrological factors. Hydrol. Sci. J., 52, 1, 74\u201385. https:\/\/doi.org\/10.1623\/hysj.52.1.74","DOI":"10.1623\/hysj.52.1.74"},{"key":"2026042818410095725_j_johh-2023-0028_ref_078","doi-asserted-by":"crossref","unstructured":"Yang, D., Peterson, A., 2017. River water temperature in relation to local air temperature in the Mackenzie and Yukon Basins. ARCTIC, 70, 1, 47\u201358. https:\/\/doi.org\/10.14430\/arctic4627","DOI":"10.14430\/arctic4627"},{"key":"2026042818410095725_j_johh-2023-0028_ref_079","doi-asserted-by":"crossref","unstructured":"Zhu, S., Bonacci, O., Oskoru\u0161, D., Hadzima-Nyarko, M., Wu, S., 2019. Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia. J. Hydrol. Hydromech., 67, 4, 305\u2013313. https:\/\/doi.org\/10.2478\/johh-2019-0019","DOI":"10.2478\/johh-2019-0019"},{"key":"2026042818410095725_j_johh-2023-0028_ref_080","doi-asserted-by":"crossref","unstructured":"Zhu, S., Nyarko, E.K., Hadzima-Nyarko, M., 2018. Modelling daily water temperature from air temperature for the Missouri River. PeerJ, 6, e4894. https:\/\/doi.org\/10.7717\/peerj.4894","DOI":"10.7717\/peerj.4894"}],"container-title":["Journal of Hydrology and Hydromechanics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/reference-global.com\/pdf\/10.2478\/johh-2023-0028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T22:35:32Z","timestamp":1777415732000},"score":1,"resource":{"primary":{"URL":"https:\/\/reference-global.com\/article\/10.2478\/johh-2023-0028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"references-count":80,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,11,14]]},"published-print":{"date-parts":[[2023,12,1]]}},"alternative-id":["10.2478\/johh-2023-0028"],"URL":"https:\/\/doi.org\/10.2478\/johh-2023-0028","relation":{},"ISSN":["1338-4333"],"issn-type":[{"value":"1338-4333","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,14]]}}}