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EN
A methodology to derive solute transport models at any flow rate is presented. The novelty of the proposed approach lies in the assessment of uncertainty of predictions that incorporate parameterisation based on flow rate. A simple treatment of un certainty takes in to account hetero- scedastic modelling errors related to tracer experiments performed over a range of flow rates, as well as the uncertainty of the observed flow rates themselves. The proposed approach is illustrated using two models for the transport of a conservative solute: a physically based, deterministic, advection-dispersion model (ADE), and a stochastic, transfer function based, active mixing volume model (AMV). For both models the uncertainty of any parameter increases with increasing flow rate (reflecting the heteroscedastic treatment of modelling errors at different observed flow rates), but in contrast the uncertainty of travel time, computed from the predicted model parameters, was found to decrease with increasing flow rate.
EN
The simulation of solute transport in rivers is frequently based on numerical models of the Advection-Dispersion Equation. The construction of reliable computational schemes, however, is not necessarily easy. The paper reviews some of the most important issues in this regard, taking the finite volume method as the basis of the simulation, and compares the performance of several types of scheme for a simple case of the transport of a patch of solute along a uniform river. The results illustrate some typical (and well known) deficiencies of explicit schemes and compare the contrasting performance of implicit and semi-Lagrangian versions of the same schemes. It is concluded that the latter have several benefits over the other types of scheme.
EN
This paper describes the main features of the DISCUS model for one-dimensional advection-dispersion computations in rivers, and describes its application to a short reach of The Murray Burn (a small stream in Edinburgh). DISCUS was calibrated using tracer data and an optimisation technique that uses a genetic algorithm. The optimised dispersion coefficients were found to increase from 0.25 to 2 m2/s in the flow range 16-261 l/s. The model was validated using tracer data not used in the calibration stage. It appears that transient storage does not play a major role in the transport of solutes in the reach that was modeled.
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