Estimation of Longitudinal Dispersion Coefficient Using Field Experimental Data and 1D Numerical Model of Solute Transport

  • Hata MilišićEmail author
  • Emina Hadžić
  • Suvada Jusić
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 83)


The use of water quality models in natural environments is a very useful tool for the management of water resources. In the case of the transport of pollutants into natural watercourses, the advection-dispersion equation is widely used in its one-dimensional form to predict the spatial and temporal distribution of the dissolved substance, whether the release has occurred intentionally or accidentally. Among the important parameters of these models is the longitudinal dispersion coefficient. The objectives of this paper are: (1) the evaluation of dispersion coefficients using salt dilution method experiment and (2) the development, calibration and evaluation of numerical model for an instantaneous pollutant release in the Neretva River. In this study, field techniques are used to determine the longitudinal dispersion coefficient in the Neretva River (Bosnia and Herzegovina) using salt tracer test. Experiments are performed in order to corroborate the numerical predictions of the spatial and temporal distribution of the dissolved substance. A one-dimensional numerical model MIKE 11 is used for numerical simulation in this study. Using salt tracer data and hydrodynamic data collected from ADCP measurements for the Neretva River a dispersion coefficient was determined.


Transport processes Longitudinal dispersion coefficient Salt tracer test MIKE 11 


  1. 1.
    Fischer, H.B.: Longitudinal Dispersion in Laboratory and Natural Streams. Rept. H-4–12, California Institute of Technology, Keck Lab., Pasadena, CA, USA (1966)Google Scholar
  2. 2.
    Bajraktarević-Dobran, H.: Dispersion in mountainous natural streams. J. Environ. Eng. Div., ASCE 108(EE3), 502–514 (1982)Google Scholar
  3. 3.
    Day, T.J.: Field procedures and evaluation of a slug dilution gauging method in mountain streams. J. Hydrol. (NZ) 16(2), 113–133 (1977)Google Scholar
  4. 4.
    Heron, A.J.: Pollutant transport in rivers: estimating dispersion coefficients from tracer experiments. Master Thesis, School of Energy, Geoscience, Infrastructure and Society, Heriot-Watt University (2015)Google Scholar
  5. 5.
    Jobson, H.E., Sayre, W.W.: Predicting concentration profiles in open channel. Am. Soc. Civ. Eng. 96(10), 1983–1986 (1970)Google Scholar
  6. 6.
    Rutherford, J.C.: River Mixing. Wiley, New York (1994)Google Scholar
  7. 7.
    Kilpatrick, F.A.: Techniques of water-resources investigations of the United States geological survey, Simulation of soluble waste transport and buildup in surface waters using tracers. USGS, Denver, CO (1993)Google Scholar
  8. 8.
    Singh, S.K., Beck, M.B.: Dispersion coefficient of streams from tracer experiment data. J. Environ. Eng. 129(6), 539–546 (2003)CrossRefGoogle Scholar
  9. 9.
    Socolofsky, S.A., Jirka, G.H.: Special topics in mixing and transport processes in the environment engineering - lectures. Coastal and ocean engineering division, Texas (2005)Google Scholar
  10. 10.
    Chapra, S.C.: Surface Water Quality Modelling. McGraw-Hill, New York (1997)Google Scholar
  11. 11.
    Deng, Z.Q., Singh, V.P., Bengtsson, L.: Longitudinal dispersion coefficient in straight rivers. J. Hydraul. Eng., ASCE 127(11), 919–927 (2001)CrossRefGoogle Scholar
  12. 12.
    Kashefipour, S.M., Falconer, R.A.: Longitudinal dispersion coefficients in natural channels. Water Res. 36, 1596–1608 (2002)CrossRefGoogle Scholar
  13. 13.
    Seo, I.W., Cheong, T.S.: Predicting longitudinal dispersion coefficient in natural streams. J. Hydraul. Eng., Am. Soc. Civ. Eng. 124(1), 25–32 (1998)CrossRefGoogle Scholar
  14. 14.
    Parsaie, A., Hamzeh, H.A.: Computational modeling of pollution transmission in rivers. Appl. Water Sci. 2017(7), 1213–1222 (2017). Scholar
  15. 15.
    Duarte, A.A.L.S., Pinho, J.L.S., Vieira, J.M.P., Boaventura, R.A.R.: Comparison of numerical techniques solving longitudinal dispersion problems in the River Mondego. In: Bento, J., et al. (eds.) EPMESCVII: Computational Methods in Engineering and Science, vol. 2, pp. 1197–1206. Elsevier Science, Ltd., Oxford (1999)Google Scholar
  16. 16.
    Duarte, A.B.R.: Pollutant dispersion modelling for Portuguese river water uses section linked to tracer dye experimental data, vol. 4, ISSN: 1790–5079 1047, Issue 12 (2008)Google Scholar
  17. 17.
    Milišić, H.: Field and numerical investigations of the coefficient of longitudinal turbulent dispersion in the transport processes of open watercourses. Doctoral Thesis, Faculty of Civil Engineering, University of Sarajevo (2017)Google Scholar
  18. 18.
    Milišić, H., et al.: Mathematical modeling of surface water quality. Advanced technologies, systems and applications III. In: Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT), vol. 2, Springer (2019)Google Scholar
  19. 19.
    MIKE bay DHI- MIKE 11: User Guide, A modelling system for Rivers and Channels (2008)Google Scholar
  20. 20.
    Launay, M., Le Coz, J., Camenen, B., Walter, C., Angot, H., et al.: Calibrating pollutant dispersion in 1-D hydraulic models of river networks. J. Hydro-Environ. Res., Elsevier, 9(1), 120–132 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Civil Engineering, Department of Water Resources and Environmental EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina

Personalised recommendations