As the second largest river in the U.S., the entrance to the Columbia River is home to some of the most extreme wave conditions on the Pacific Coast. Winter storms commonly generate waves 6-8 m in height, which in combination with strong tidal currents, can produce dangerous navigation conditions. To improve understanding of the wave dynamics in this complex setting, the SWAN model is applied; 2 hindcasts are conducted and an operations forecast is developed. The model is forced with offshore wave heights obtained from a buoy located in 134 m water depth (for the hindcasts) and a specialized WaveWatchIII forecast (for the forecast). In both cases tidal currents are obtained from SELFE, a circulation model of the Columbia River. The hindcasts are validated through measurements obtained from an inshore buoy located in 25 m water depth, a 4-week field experiment and remote sensing methods. The model performs best at the location of the buoy, with a normalized root-mean-squared error (NRMSE) of 11%, primarily because it is outside the area of strong tidal currents. Within the river mouth, the model is able to predict the changes in the wave field due to currents, but its performance is limited by errors in velocity estimates and strong shears in the tidal current profile. From the modeling work, it is evident that wave transformations at the mouth of the river are dominated by the tidal currents. The forecast has been operational since August 2011 and provides 45-hours of predictive wave information. In comparison with measured wave heights at the buoy, the forecast performs well, with a NRMSE of 16%. The majority of errors are caused by errors in the input conditions, since they themselves are forecasted. Additional errors arise from phase-resolved properties in the wave field that the model is unable to produce; these errors are also present in the hindcasts. Despite the limitations, this forecast provides valuable information to bar pilots since it includes the effects of the tidal currents. / Graduation date: 2013

A Disaggregation model was tested for arid land stream flow generating. The test was performed on data from Black River, near Fort Apache, Arizona. The model was tested in terms of preserving the relevant historical statistics on both monthly and daily levels, the monthly time series were disaggregated to a random observation of their daily components and the daily components were then reaggregated to yield monthly values. A computer model (DSGN) was developed to perform the model implementation. The model was written and executed on the Macintosh plus personal computer Data from two months were studied; the October data represented the low flow season, while the April data represented the high flow season. Twenty five years of data for each month was used. The generated data for the two months was compared with the historical data.