The modelling of solute transport in rivers is usually based on simulating the physical processes of advection, dispersion and transient storage, which requires the modeller to specify values of corresponding model parameters for the particular river reach under study. In recent years it has become popular to combine a numerical solution scheme of the governing transport equations with a parameter optimisation technique. However, there are several numerical schemes and optimisation techniques to choose from. The chapter addresses a very simple question, namely, do we get the same, or do we get different, parameter values from the application of two independent solute transport models/parameter optimisation techniques to the same data? Results from seven different cases of observed solute transport suggest the latter, which implies that parameter values cannot be transferred between modelling systems.
Main Channel Solute Transport Dead Zone River Reach Space Step
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Bencala KE, Walters RA (1983) Simulation of solute transport in a mountain pool-and-rifle stream: a transient storage model. Water Resour Res 19:718–724CrossRefGoogle Scholar
Bottacin-Busolin A, Marion A, Musner T, Tregnaghi M, Zaramella M (2011) Evidence of distinct contaminant transport patterns in rivers using tracer tests and a multiple domain retention model. Adv Water Resour 34:737–746CrossRefGoogle Scholar
Deng Z-Q, Jung H-S, Ghimire B (2010) Effect of channel size on solute residence time distributions in rivers. Adv Water Resour 33:1118–1127CrossRefGoogle Scholar
Fischer HB (1967) The mechanics of dispersion in natural streams. J Hydraul Div Proc Am Soc Civ Eng 93:187–216Google Scholar
Fischer HB (1968) Dispersion predictions in natural streams. J Sanit Eng Div Proc Am Soc Civ Eng 94:927–943Google Scholar
Hart DR (1995) Parameter estimation and stochastic interpretation of the transient storage model for solute transport. Water Resour Res 31:323–328CrossRefGoogle Scholar
Manson JR, Wallis SG, Hope D (2001) A conservative semi-Lagrangian transport model for rivers with transient storage zones. Water Resour Res 37:3321–3330CrossRefGoogle Scholar
Mrokowska MM, Osuch M (2011) Assessing validity of the dead zone model to characterize transport of contaminants in the River Wkra. In: Rowinski P (ed) Experimental methods in hydraulic research. Springer-Verlag, Berlin, pp 235–245CrossRefGoogle Scholar
Price KH, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer-Verlag, BerlinGoogle Scholar
Romanowicz RJ, Osuch M, Wallis SG (2013) Modelling of solute transport in rivers under different flow rates: a case study without transient storage. Acta Geophys (In Press)Google Scholar
Runkel RL, Broshears RE (1991) One-dimensional transport with inflow and storage (OTIS): a solute transport model for small streams. CADSWES Department of Civil, Environmental and Architectural Engineering, University of ColoradoGoogle Scholar
Runkel RL, Chapra SC (1993) An efficient numerical solution of the transient storage equations for solute transport in small streams. Water Resour Res 29:211–215CrossRefGoogle Scholar
Scott DT, Gooseff MN, Bencala KE, Runkel RL (2003) Automated calibration of a stream solute transport model: implications for interpretation of biogeochemical parameters. J N Am Benthol Soc 22:492–510CrossRefGoogle Scholar
Seo IW, Cheong TS (2001) Moment-based calculation of parameters for the storage zone model for river dispersion. J Hydrol Eng Am Soc Civ Eng 127:453–465CrossRefGoogle Scholar
Singh SK, Beck MB (2003) Dispersion coefficient of streams from tracer experiment data. J Environ Eng Proc Am Soc Civ Eng 129:539–546Google Scholar
Storn RM, Price KH (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:41–359CrossRefGoogle Scholar
Taylor GI (1954) The dispersion of matter in turbulent flow through a pipe. Proc R Soc Lond A 233:446–468Google Scholar
Wagener T, Camacho LA, Wheater HS (2002) Dynamic identifiability analysis of the transient storage model for solute transport in rivers. J Hydroinform 4:199–211Google Scholar
Wagner BJ, Gorelick SM (1986) A statistical methodology for estimating transport parameters: theory and applications to one-dimensional advective-dispersive systems. Water Resour Res 22:1303–1315CrossRefGoogle Scholar
Wagner BJ, Harvey JW (1997) Experimental design for estimating parameters of rate-limited mass transfer: analysis of stream tracer studies. Water Resour Res 33:1731–1741CrossRefGoogle Scholar