Discharge‐dependent pollutant dispersion in rivers: Estimation of aggregated dead zone parameters with surrogate data

Abstract

[1] Much has been done to mitigate the effects of intermittent discharges of pollutants; however, pollution incidents still occur, and the downstream transport and dispersion of pollutants must be predicted. The application of transient storage models based on the advection dispersion equation is often limited by the strong dependence of the parameters on changes in discharge. In this paper a methodology is outlined for estimating the parameters of the simple aggregated dead zone model using surrogate data derived from continuous water quality measurements such as conductivity, including a full treatment of the errors and prediction uncertainties within a Bayesian framework. This methodology is demonstrated in the prediction of a tracer experiment on a reach of the river Elbe in the Czech Republic.

A method of computing uncertain nitrogen and phosphorus loads in a small stream from an agricultural catchment using continuous monitoring data

“…Uncertainty about the values of the explanatory variables could also be incorporated. In both cases an increased computational burden may result (see for example Smith et al, 2006). Analytical derivation of pðŷ f x f ; y; X Þ for the prior distribution and Gaussian error assumptions given above is given in a number of statistical texts, for example Section 6.7 of Lee, 1989.…”

Predicting Longitudinal Dispersion Coefficient in Natural Streams Using M5′ Model Tree

“…Considerable research efforts have been devoted to the modeling of dead/storage zones in the last decade. More details in this regard can be found in Seo and Cheong (2001), Singh (2003), Smith et al (2006), Cheong et al (2007) and Marion et al (2008). Caplow et al (2004) suggested that dams (as one of the hydraulic structures) reduce the longitudinal dispersion coefficient below the expected value in a natural channel with the same discharge.…”

Applications of the concept of river tracer data similarity

“…; Smith et al . ; Camacho & Gonzalez ). Figure shows the travel times of the leading edge (t L ), peak (t P ) and trailing edge (t T ) of the temporal concentration profiles at Burnsall plotted against river flow rate.…”