Search

The advent of the modern high-speed digital computer has tremendously enhanced the utility of Monte Carlo methods for evaluating complex environmental simulation models. In particular, random searching is becoming popular, as thousands of model runs can now be executed quickly and with minimal effort. Indeed, the issues of computational burden and inefficiency, hitherto the bane of random searching, are now receding. This paper presents one such method, uniform covering by probabilistic rejection (UCPR), which combines a pure random search with a probabilistic rejection algorithm that significantly enhances its efficiency. Using nearest-neighbor distances, an ensemble of points in a predefined parameter sampling domain migrates to locate and define a final distribution of optimal parameter vectors, thus providing a realistic depiction of parameter uncertainty. In a prototypical case study of the Oconee River (Georgia, USA), UCPR and regionalized sensitivity analysis, are employed for identifying the parameters of sediment-transport-associated nutrient dynamics, a dynamic river water quality model. Results indicate the existence of a complex interactive parameter structure, evidenced by multiple sets of optimal points widely dispersed over a broad domain of feasible parameter values. [ABSTRACT FROM AUTHOR]

A mathematical model (Sediment-Transport-Associated Nutrient Dynamics-STAND) has been developed for the study of sediment-associated water quality issues. The model is intended to simulate changes of water composition associated with sediment behavior. It has a 3-level structure. The first level accounts for the hydraulics of open-channel flow. The second computes sediment transport potential and actual rates based on the information provided by the first level. A non-equilibrium approach is used. In the third level, changes of nutrient concentrations along a studied river are computed with the consideration of nutrient transport, adsorption/desorption, and release. In order to calibrate the model, field data were collected from the Oconee River, a major tributary of the Altamaha River in Georgia, USA. Two stations, approximately 17 km distant from each other, were established along the river for the purpose of data collection. Observations of the river's hydraulics, suspended sediment, and water quality (mainly orthophosphate, nitrate, temperature, specific conductivity, oxidation-reduction potential, dissolved oxygen, and pH) were collected at the two stations. Another data set collected along a major tributary of the Yellow River in China was also used for calibration of the model's hydraulics and sediment transport parts. Calibration and validation results are encouraging, which suggests STAND may be a useful tool for the thorough study and understanding of nutrient dynamics associated with sediment behaviour.