A rangeland watershed management spatial decision support system: Design, implementation, and sensitivity analysis
AuthorMiller, Ryan Craig
AdvisorGuertin, D. Phillip
MetadataShow full item record
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.
AbstractA prototype spatial decision support system for rangeland watershed management was developed to simplify the process of incorporating advances in technology into the decision process. The application utilizes an open framework by using Web services that are components that communicate using text-based messages, thus eliminating proprietary protocols. This new framework provides an extensible, accessible, and interoperable approach for spatial decision support systems. An important input into the SDSS is digital elevation data where data are produced using different methods, and with different accuracies and resolutions. Six digital elevation models were compared with survey data to evaluate accuracies at different locations in the Walnut Gulch Experimental Watershed. The sensitivity of the SDSS was evaluated using six management systems that were ranked based on minimizing sediment yield. The sensitivity of the DEM, contributing source area value, and precipitation event size on management system rankings was evaluated. Results provide assistance for users in selecting these data and modeling values. This research illustrated that recent advances in information technology can be effectively utilized in watershed decision support technology. The Internet-based SDSS provides core functionality required for rangeland watershed management education and decision-making. In comparing digital elevation data of different sources and resolutions with survey data, the DEM data approximated surfaces well, with the higher resolution data producing lower root mean square error values. And finally, different digital elevation models, contributing source area values, and precipitation event sizes produced different management system rankings. (Abstract shortened by UMI.)
Degree ProgramGraduate College
Renewable Natural Resources