Strategic River Water Quality Planning Using Calibrated Stochastic Simulation

Abstract

Coordinated application of empirical and physically based stochastic models, based upon diverse and variable field data, creates a method for river water quality planning that is calibrated to account for parameter uncertainty. The models are applied to assess interventions for improving the quality of river flows diverted for irrigation along a 190-km segment of the South Platte River in Colorado. Eleven different pollution control strategies are considered, alone and in combination. Statistics of total dissolved solids (TDS), dissolved nitrogen (N) species, and fecal coliform concentrations are modeled, along with associated probabilities of violation of recommended criteria and the spatial and temporal uniformity of these probabilities. Over a 25-year horizon, for example, combined pollution control measures are predicted to achieve average reductions from 27 to 36% and from 31 to 39% in mean concentrations of TDS and N, respectively, for the 11 irrigation diversions located along the 53-km stretch just downstream of Denver. Uncertainty in predicted concentrations, as measured by the coefficient of variation, is considerable. An average decrease of 18 percentage points in the probability of violation for TDS is predicted, while the estimated average decrease for N and fecal coliform is a modest 6 to 8 percentage points.