A mathematical model was developed to simulate the performance of a prototype wind-powered reverse osmosis desalination system. The model consists of two sub-models operated in a series. The first sub-model is the wind-energy conversion sub-model, which has wind energy and feed water as its input and pressurized feed water as its output. The second sub-model is a reverse osmosis (RO) process sub-model, with pressurized feed water as its input and the flow and salinity of the product water or permeate as its output. Model coefficients were determined based on field experiments of a prototype wind powered RO desalination system of the University of Hawaii, from June to December 2001. The mathematical model developed by this study predicts the performance of wind-powered RO desalination systems under different design conditions. The system optimization is achieved using a linear programming approach. Based on the results of system optimization, a design guide is prepared, which can be used by both manufacturer and end-user of the wind-driven reverse osmosis system.

TMDL development and implementation have great potential fur use in efforts to improve water quality management, but the TMDL approach still has several difficulties to overcome in terms of cost, time requirements, and suitable methodologies. A well-defined prioritization approach for identifying watersheds of concern among several tar-get locations that would benefit from TMDL development and implementation, based on a simple screening approach, could be a major step in solving some of these difficulties. Therefore, a web-based decision support system (DSS) was developed to help identify areas within watersheds that might be priority areas for TMDL development. The DSS includes a graphical user interface based on the HTML protocol, hydrological models, databases, and geographic information system (GIS) capabilities. The DSS has a hydrological model that can estimate non-point source pollution loading based on over 30 years of daily direct runoff using the curve number method and pollutant event mean concentration data. The DSS provides comprehensive output analysis tools using charts and tables, and also provides probability analysis and best management practice cost estimation. In conclusion, the DSS is a simple, affordable tool for the preliminary study of TMDL development via the Internet, and the DSS web site can also be used as an information web server for education related to TMDL.

The impacts of climate change on soil moisture in sub - Arctic watershed simulated by using the hydrologic model. A range of arbitrary changes in temperature and precipitation are applied to the runoff model to study the sensitivity of soil moisture due to potential changes in precipitation and temperature. The sensitivity analysis indicates that changes in precipitation are always amplified in soil moisture with the amplification factor for flow. The change in precipitation has effect on the soil moisture in the catchment. The percentage change in soil moisture levels can be greater than the percentage change in precipitation. Compared to precipitation, temperature increases or decreases alone have impacts on the soil moisture. These results show the potential for climate change to bring about soil moisture that may require a significant planning response. They are also indicative of the fact that hydrological impacts affecting water supply may be important in consider-ing the cost and benefits of potential climate change.

Two-dimensional depth-averaged advection-dispersion equation was simulated using FEM. In the straight rectangular channel, the advection-dispersion processes are simulated so that these results can be compared with analyti-cal solutions for the transverse line injection and the point injection. In the straight domain the standard Galerkin method with the linear basis function is found to be inadequate to the advection-dispersion analysis compared to the upwind finite element scheme. The experimental data in the S-curved channel were compared with the result by the numerical model using SUPG(Streamline upwind Petrov-Galerkin) method.