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Batch cultures of

Recently propsed algal uptake and growth models dependent on the cell quote (Q), the intracellular limiting nutrient to cell population quotient, were analyze and applied to experimental data. The data base used for comparing the models consisted of

This report demonstrates the feasibility of applying stochastic techniques to linear water quality models. The Monte Carlo, First Order, and Generation of Moment Equation techniques are applied to a long term phosphorus model of Lake Washington. The effect of uncertainty of the phosphours loading term on simulated phosphous levels is analyzed. All three stochastic techniques produced the same results. The simulated concentrations of phosphorus in the water column are very responsive to uncertainty in annual phosphorus loading, the sediment concentrations relatively insensitive. The Monte Carlo technique is shown to require the most computation time of the three stochastic techniques applied. The First Order and Generation of Moment Equation techniques are shown to be precise and efficent methods of stochastic analysis. In this application they required less than one thousandth the computation time of the Monte Carlo technique. The Generation of Moment Equations technique is also applied to a steady state salinity model of the Colorado River system. Two sources of uncertainty are considered: 1) the estimation of "steady state" values of salinity loading from a limited historic data base and 2) the estimation of salinity loading from irrigated land by a semi-empirical approach. Six stochastic simulations of the Colorado River system are presented. Coefficients of variations of simulated salinities at Imperial Dam are shown to vary from 5.7 to 10.3 percent. The major source of uncertainty in all simulations is the estimation of the steady state salinity loading with the agricultural loading term becoming important in some simulated management alternatives.