Model for Determination of Water Temperature in Rivers
Keywords:
River, Temperature, Influence, Model, Method EMDAbstract
Seasonal and daily variations in water temperatures determine the distribution of aquatic species and water chemistry. Some authors were able to estimate the water temperature using only the air temperature. A well-performed sensitivity analysis is able to assess river temperature variations in response to changes in hydraulic and meteorological conditions. In this sense, water quality modeling involves the prediction of parameters using mathematical simulation techniques. For this purpose computational models of river water temperature prediction can be used. The empirical modal decomposition (EMD) method is ideal for analyzing data sets such as temperature, which are oscillatory. Among the river water temperature models, the simple linear regression models that use as input only the air temperature applied to weekly and/or monthly data demonstrate high efficiency.References
Ahmadi-Nedushan, B., André St-Hilaire, Ouarda, T. B. M. J. O., Bilodeau, L., Robichaud E., Thiémonge N., Bobée, B. (2007). Predicting river water temperatures using stochastic models: case study of the moisie river (québec, canada). Hydrological Processes, 21(1),29 21-34. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.6353 doi: https://doi.org/ 10.1002/hyp.635332
Bartholow, J. M. (2005). Recent water temperature trends in the lower klamath river, california. North American Journal of Fisheries Management, 25(1), 152–162.
Caissie, D. (2006). The thermal regime of rivers: a review. Freshwater biology, 51(8), 1389-1406.
Caissie, D., Satish, M. G., & El-Jabi, N. (2005). Predicting river water temperature susing the equilibrium temperature concept with application on miramichi river catchments (new brunswick, canada). Hydrological Processes: An International Journal, 19(11), 2137–2159.
Georges, B. (2022). Characterization of the river thermal regime in relation to its environment: a regional approach using in situ sensors in a temperate region (wallonia, belgium).
Graf, R., Zhu, S. & Sivakumar, B. (2019). Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. Journal of Hydrology, 578,48 124115.
Gresselin, F., Dardaillon, B., Bordier, C., Parais, F. & Kauffmann, F. (2022). Use of statistical methods to characterize the influence of groundwater on the thermal regime of rivers in normandy, france: comparison between the highly permeable, chalk catchment of the touques river and the low permeability, crystalline rock catchment of the orne river. Geological Society, London, Special Publications, 517(1), SP517–56 2020.
Gu, R. R., & Li, Y. (2002). River temperature sensitivity to hydraulic and meteorological parameters. Journal of Environmental Management, 66(1), 43-56. Retrieved from https://www.sciencedirect.com/science/article/pii/S0301479702905657 doi: https://doi.org/0.1006/jema.2002.0565
Huang, N., et al. (1998). The empirical mode decomposition method and the hilbert spectrum for non-stationary time series. Proc Roy Soc London 45AA, 703–775.
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, 612, 1543 - 1558. Retrieved from https://www.sciencedirect.com/science/article/pii/S0048969717323525 doi: https://doi.org/10.1016/j.scitotenv.2017.09.010
Letcher, B. H., Hocking, D. J., O’neil1, K., Whiteley, A. R., Nislow, K. h., & O’donnell, M. J. (2016). A hierarchical model of daily stream temperature using air water temperature synchronization, autocorrelation, and time lags. PeerJ, 4, 1727.
O’Sullivan, 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 Resources Research, 56(12), e2020WR028122. Retrieved from https://agupubs.onlinelibrary.wiley.com/doi/abs/ 85 10.1029/2020WR028122 (e2020WR0281222020WR028122) doi: https://doi.org/10.1029/2020WR028122
Pero, E. J. I., Georgieff, S. M., de Lourdes Gultemirian, M., Romero, F., Hankel, G.E., & Domínguez, E. (2020). Ecore gions, climate, topography, physicochemical, or a combination of all: Which criteria are the best to define river types based on abiotic variables and macroinvertebrates in neotropical rivers? Science of The Total Environment, 738,140303. Retrievedfrom https://www.sciencedirect.com/science/article/pii/S0048969720338250 doi: https://doi.org/10.1016/j.scitotenv.2020.140303.
Ríos-Villamizar, E. A., Piedade, M. T., Junk, W. J., & Waichman, A.V. (2017). Surface water quality and deforestation of the purus river basin, brazilian amazon. International Aquatic Research, 9(1), 81–88.
Sang, Y.-F. (2012). Spatial and temporal variability of daily temperature in the yangtze river delta, China. Atmospheric Research, 112, 12-24. Retrieved from https://www.sciencedirect.com/science/article/pii/S0169809512001123 doi: https://doi.org/10510.1016/j.atmosres.2012.04.006
St-Hilaire, A., Ouarda, T. B. M. J., Bargaoui, Z., Daigle, A. & Bilodeau, L. (2012). Daily river water temperature forecast model with a k-nearest neighbour approach. Hydrological Processes, 26(9),1302-1310. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.8216 doi: https://doi.org/10.1002/hyp.8216
Syvitski, J., Cohen, S., Miara, A., & Best, J. (2019). River temperature and the thermal-dynamic transport of sediment. Global and Planetary Change, 178, 168-183. Retrieved from https://www.sciencedirect.com/science/article/pii/S0921818118306805 doi: https://doi.org/10.1016/j.gloplacha.2019.04.011.
Toffolon, M., & Piccolroaz, S. (2015). A hybrid model for river water temperature as a function of air temperature and discharge. Environmental Research Letters, 10(11), 114011.
Torregroza-Espinosa, A. C., Restrepo, J. C., Escobar, J., Pierini, J., & Newton, A. (2021). Spatial and temporal variability of temperature, salinity and chlorophyll a in the magdalena river mouth, caribbean sea. Journal of South American Earth Sciences, 105, 102978. Retrieved from https://www.sciencedirect.com/science/6article/pii/S0895981120305216 doi: https://doi.org/710.1016/j.jsames.2020.1029788
van Vliet, M. T., Franssen, W. H., 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(2), 450-12 464. Retrieved from https://www.sciencedirect.com/science/article/pii/S0959378012001331 doi: https://doi.org/10.1016/j.gloenvcha.2012.11.002
Wan Mohd Jaafar, W. S., Abdul Maulud, K. N., Muhmad Kamarulzaman, A. M., Raihan, A., Md Sah, S., Ahmad, A., Razzaq Khan, W. (2020). The influence of deforestation on land surface temperaturea case study of perak and kedah, malaysia. Forests, 11(6), 670.
Webb, B. W., & Walling, D. (1993). Temporal variability in the impacto friver regulation on thermal regime and some biological implications. Freshwater Biology, 29, 167-182. Fuel, 90(8), 2738–2745. https://doi.org/10.1016/j.fuel.2011.03.017
Wilby, R., Johnson, M., & Toone, J. (2014). Nocturnal river water temperatures: Spatial and temporal variations. Science of The Total Environment, 482-483, 157-173. Retrieved from https://www.sciencedirect.com/science/383article/pii/S0048969714003131 doi: https://doi.org/38410.1016/j.scitotenv.2014.02.123
Woltemade, C. J., & Hawkins, T. W. (2016). Stream temperature impacts because of changes in air temperature, land cover and stream discharge: Navarro river watershed, california, usa. River Research and Applications, 32(10), 2020 - 2031. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1002/rra.3043
Zeiler, A., Faltermeier, R., Keck, I. R., Tomé, A. M., Puntonet, C. G., & Lang, E. W. (2010). Empirical mode decomposition an introduction. In The 2010 international joint conference on neural networks (IJCNN) (pp. 1–8).
Published
How to Cite
Issue
Section
Copyright (c) 2023 Carmen Gonçalves de Macedo e Silva
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Engineering & Technology Scientific Journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Disclaimer: Articles published by Engineering & Technology Scientific Journal have been pre-viewed and authenticated by the Authors before publication. The Journal, Editor and the editorial board are not entitled or liable to either justify or responsible for inaccurate and misleading data if any. It is the sole responsibility of the Author concerned. Read our Plagiarism Policy and use of this site signifies your agreement to the Terms of Use.
