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The impacts of digital elevation model data type and resolution on hydrologic modeling

This dissertation examines the variations in the results of a physically-based kinematic routing rainfall-runoff model (KINEROS2) in response to variations in the geometric model definitions caused by grid size and accuracy of Digital Elevation Model (DEM) data sets. A range of independently acquired DEM data resolutions within the 148 km2 USDA-ARS Walnut Gulch Experimental Watershed provided a sound basis for this analysis. Analysis was conducted over a range of watershed scales and DEM effects were studied in relation to other model parameters. Emphasis was placed on identification of dominant processes most influenced by topographic representation. It was evident from the analysis that the topographic algorithms have their limitations and an insufficient resolution of DEM data results in gross misrepresentation of actual drainage network leading to serious modeling errors. It was also observed that geometric representation of the watershed is inherently linked to the other soils and hydrologic parameter definition. As a result, a variation in geometric representation results in variation of other model parameters. This analysis has illustrated that infiltration dynamics have a dominant control on the model results. Further, a change in geometric configuration due to variation of DEM grid size influences the hydrologic model more through their indirect impact on other parameter definitions than the direct effects due to pure geometric changes. A major problem with using fine resolution DEM is the increased storage requirements and enhanced computational burden. On the basis of this research some guidelines are framed for the user to facilitate a choice of DEM data depending on the modeling requirements and the computational resources. There is clear evidence from this research that an extremely fine and accurate DEM does not necessarily add further accuracy to the modeling results. Therefore a moderate resolution and fair accuracy DEM may be chosen safely for a given modeling task given that a few simple checks on the data are performed by the user.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/288972
Date January 1999
CreatorsSyed, Kamran Haider, 1962-
ContributorsSorooshian, Soroosh
PublisherThe University of Arizona.
Source SetsUniversity of Arizona
Languageen_US
Detected LanguageEnglish
Typetext, Dissertation-Reproduction (electronic)
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.

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