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This study analyzes the uncertainties in numerical simulations of river flow and solute transport, considering spatial and temporal variabilities in flow and solute conditions. Field measurements and modeling techniques are used to simulate uncertain open channel flow fields and solute transport along a river reach. The ensemble average solute concentration is computed using different approaches, and the results are compared. The study concludes that the ensemble average transport equation effectively replicates the Monte Carlo method results and that technology is available to determine the macrodispersion coefficient for transport by unsteady river flow.
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MACRODISPERSION AND DISPERSIVE TRANSPORT BY UNSTEADY RIVER FLOW UNDER UNCERTAIN CONDITIONS M.L. Kavvas and L.Liang UCD J.Amorocho Hydraulics Laboratory
Uncertainties existing in the numerical simulations of river flow and solute transport 1. Spatial and temporal variability in flow conditions -geometry (channel width, elevation, length, roughness) -initial and boundary conditions of flow field -net lateral inflow/outflow from/to surrounding landscape as sinks/sources 2. Spatial and temporal variability in solute conditions -chemical and biological reaction rates -solute concentration in the point and non-point sinks/sources 3. Field measurement -measurement errors -limited Sampling
Modeling the open channel flow within the framework of uncertain parameters • Generate 700 realizations of the uncertain flow parameters, such as channel width, bed slope, lateral inflow, and Manning’s roughness, used in the one-dimensional open channel flow simulation. • All the uncertain parameters are assumed to be normal distributions or log-normal distributions. Their CVs (absolute value of the ratio of the standard deviation to the mean) are set between 0.25 to 1.0 based on literature review. • Simulate the uncertain open channel flow fields by Monte Carlo method. One realization of the flow field is obtained by the numerical solution of de Saint Venant’s equation with a randomly generated set of flow parameter values. By randomly generating 700 sets of parameter values and solving de Saint Venant’s equation 700 times with each of these generated sets of parameter values, 700 realizations of the open channel flow field is obtained.
Parameter/Flow Condition Mean CV Distribution Case 1 Case 2 Case 3 Lateral inflow ql (m3/s/m) 0.006 1 0.5 1 Normal Lateral solute concentration cl (mg/m3) 9.0 1 0.5 1 Normal Channel bed slope So 0.001 0.5 0.25 0.5 Normal Manning's roughness n 0.037 0.25 0.125 0.25 Log Normal Channel width B (m) 8 0.25 0.125 0.25 Normal Diffusion at the local scale D (m2/s) 0 0 0 0 N/A Reaction rate k (1/day) 1.0* 0 0 1 Normal
Solute transport equation with uncertain parameters: where U’, D’, K’, S’ are uncertain. The values of U’ are generated from the ensemble of flow fields.
Modeling the uncertain solute concentration within the framework of uncertain parameters • Corresponding to each set of flow parameters, also generate a set of uncertain transport parameters, such as lateral solute concentration of non-point sources, reaction rate, and point source solute, used in the transport equation. • As such, generate 700 realizations of solute transport along a river channel reach, by means of the solution of the solute transport equation 700 times, each solution corresponding to one set of realized flow and transport parameters. • Compute the ensemble average solute concentration along the river reach with four approaches: • a) Ensemble average of the 700 realizations, simulated by the Monte Carlo method; • b) Deterministic solution of the transport equation by average flow and transport parameters used in de Saint Venant and solute transport equations; • c) Ensemble average transport equation with complete 2nd order closure (all seven covariance integral terms accounted for); • d) Ensemble average transport equation with 2nd order closure where only the macrodispersion term is accounted for.
The ensemble of solute concentration realizations along the channel reach
Ensemble average form of the equation for transport by unsteady river flow under uncertain conditions (exact 2nd order closure): Macro-dispersion coefficient 1st convection-correction coefficient 2nd convection-correction coefficient 1st reaction-correction coefficient 2nd reaction-correction coefficient 1st source-correction coefficient 2nd source-correction coefficient * The Lagrangian trajectory of the flow field from the initial time to the time of interest used in the covariance integral terms is expressed as:
Computational results Nonzero Covariance integral terms along the mean velocity trajectory at the computational node with x = 20 km and t = 5 hr
Macrodispersion coefficient for transport by unsteady flow (Kavvas and Karakas, 1996; Kavvas, 2001) The Lagrangian trajectory of the flow field from the initial time to the time of interest used in the covariance integral terms is expressed as:
3. Spatial variations of solute concentration for five time periods * CIT – covariance integral term
CONCLUSIONS • The solute transport simulation results by the Monte Carlo method are well-replicated by the ensemble average transport equation. • 2. Technology is available to determine the macrodispersion coefficient for transport by unsteady river flow. The magnitude of the macrodispersion term is much larger than of those terms that quantify fluctuations in the solute concentration due to other causes (such as the effect of nonuniformity of the flow field on convective motion, the uncertainty in the reaction rate, the uncertainty in the solute sources/sinks).
