AuthorBoyle, Douglas Patrick
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThe level of spatial and vertical detail of important hydrologic processes within a watershed that needs to be represented by a conceptual rainfall-runoff (CRR) model in order to accurately simulate the streamflow is not well understood. The paucity of high-resolution hydrologic information in the past guided the direction of model development to more accurately represent processes directly related to the vertical movement of moisture within the watershed rather than the spatial variability of these processes. As a result, many of the CRR models currently available are so complex (vertically), that expert knowledge of the model and watershed system is required to successfully estimate values for model parameters using manual methods. Automatic parameter estimation procedures, developed to reduce the time and effort required with manual methods, do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting. Newly available, high-resolution hydrologic information may provide insight to the spatial variability of important rainfall-runoff processes. However, effective and efficient methods to incorporate the data into the current modeling strategies need to be developed. This work describes a new hybrid multicriteria calibration approach that combines the strength of automatic and manual calibration methods. The new approach was used to investigate the benefits of different levels of spatial and vertical representation of important watershed hydrologic variables with conceptual rainfall runoff models.
Degree ProgramGraduate College
Hydrology and Water Resources