Uncertainty and Forecasting of Water Quality

Abstract

One: Introduction.- Uncertainty, system identification, and the prediction of water quality.- The validity and credibility of models for badly defined systems.- Two: Uncertainty and Model Identification.- An approach to the analysis of behavior and sensitivity in environmental systems.- Distribution and transformation of fenitrothion sprayed on a pond: modeling under uncertainty.- Input data uncertainty and parameter sensitivity in a lake hydrodynamic model.- Maximum likelihood estimation of parameters and uncertainty in phytoplankton models.- Model identification methods applied to two Danish lakes.- Analysis of prediction uncertainty: Monte Carlo simulation and nonlinear least-squares estimation of a vertical transport submodel for Lake Nantua.- Multidimensional scaling approach to clustering multivariate data for water-quality modeling.- Nonlinear steady-state modeling of river quality by a revised group method of data handling.- Three: Uncertainty, Forecasting, and Control.- Parameter uncertainty and model predictions: a review of Monte Carlo results.- A Monte Carlo approach to estimation and prediction.- The need for simple approaches for the estimation of lake model prediction uncertainty.- Statistical analysis of uncertainty propagation and model accuracy.- Modeling and forecasting water quality in nontidal rivers: the Bedford Ouse study.- Adaptive prediction of water quality in the River Cam.- Uncertainty and dynamic policies for the control of nutrient inputs to lakes.- Four: Commentary.- Uncertainty and forecasting of water quality: reflections of an ignorant Bayesian.