Physical Sciences and Mathematics Commons^™

Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani

Civil and Environmental Engineering Faculty Publications and Presentations

Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate …

Amaila Falls Hydroelectric Project Model Development And Scenarios, Chris Berger, Scott A. Wells, Vanessa Wells

Civil and Environmental Engineering Faculty Publications and Presentations

The focus of this present study is to perform the following tasks:

* Develop a hydrodynamic and water quality model of the reservoir formed by the Amaila Falls Hydroelectric Project

* Develop and run modeling scenarios

Water quality model simulations of the 23.3 km² reservoir for Amaila Falls Hydroelectric Project were conducted for low, average, and high flow years. A scenario with no vegetation removed from the reservoir for an average flow was also simulated. Conditions downstream of the reservoir were also modeled using a river model.

The model used for the reservoir formed by Amaila Falls Hydroelectric Project …