Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 100-109).

Thesis (M.S.)--Western Washington University, 2010. / Includes bibliographical references (leaves 55-61). Also issued online.

Thesis (Ph.D.)--Technische Universiteit Delft, 2006. / Vita. Includes bibliographical references (p. 189-206).

The research assesses the value of forecast information in operating a hydro-electric project with a storage reservoir. The benefits are the increased hydro power production, when forecasts are available. The value of short term forecasts is determined by comparing results obtained with the use of one month ahead perfect predictions to those obtained without forecasts but a knowledge of the statistics of the possible flows. The benefits with perfect forecasts provide an upper limit to the benefits which could be obtained with actual less than perfect forecasts. The effects of generating capacity and flow patterns are also discussed. The operation of a hypothetical but typical project is modelled using stochastic dynamic programming. A simple model of streamflow is formulated based on the historical statistics ( means and deviations). The conclusions are: The inflow forecasts can improve the operational efficiency of the reservoir considerably because of the reduction in forecasting uncertainty. The maximum release constraints affect the additional expected values. The benefits from the forecasts increase as the discharge limits reduce. Flow predictions in the high flow season are most valuable when the runoff in that time period dominates the annual flow pattern. However flow predictions at other times of the year also have value. / Applied Science, Faculty of / Civil Engineering, Department of / Graduate

Thesis (M. Phil.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 148-160).

Thesis (Ph. D. - Renewable Natural Resources)--University of Arizona, 1989. / Includes bibliographical references (leaves 188-193).

Thesis (M.S.)--Ohio University, August, 1996. / Title from PDF t.p.

Thesis (Ph. D.)--University of California, Los Angeles, 1997. / Vita. Includes bibliographical references (leaves 200-208).

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

A methodology is presented for forecasting the probabilistic response of salinity movement in an estuary to seasonal rainfall and freshwater inflows. The Gambia River basin in West Africa is used as a case study in the research. The rainy season is from approximately July to October. Highest flows occur in late September and early October. Agriculturalists are interested in a forecast of the minimum distance that occurs each year at the conclusion of the wet season between the mouth of the river and the 1 part per thousand (ppt) salinity level. They are also interested in the approximate date that the minimum distance will occur. The forecasting procedure uses two approaches. The first uses a multisite stochastic process to generate long-term synthetic records (100 to 200 years) of 10-day rainfall for two stations in the upper basin. A long-term record of 10-day average flow is then computed from multiple regression models that use the generated rainfall records and real-time initial flow data occurring on the forecast date as inputs. The flow series is then entered into a one-dimensional finite element salt intrusion model to compute the movement of the 1 ppt salinity level for each season. The minimum distances between the mouth of the river and the 1 ppt salinity front that occurred for each season in the long-term record are represented in a cumulative probability distribution curve. The curve is then used to assign probability values of the occurrence of the 1 ppt salinity level to various points along the river. In the second approach, instead of generating a rainfall series and computing flow from regression models, a long-term flow record was generated using a stochastic first-order Markov process. Probability curves were made for three forecast dates: mid- July, mid-August, and mid-September using both approaches. With the first approach, the initial conditions at the time of the forecast had a greater influence on the flow series than the second approach.