The role of data sources and simulation model complexity in using a prototype decision support system
AdvisorStone, Jeffry J.
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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.
AbstractMultiobjective decision support systems (DSS) are gaining acceptance as tools to evaluate resource management systems. Before applying a DSS, a matrix of decision criteria and alternative management systems is populated using information from measured data, expert opinion or simulation models. As each information source exhibits differences in data availability and accuracy, the extent to which outcomes from the DSS are influenced by the source of information remains an important issue. A conceptual framework links the Prototype Decision Support System (P-DSS) developed by the USDA-ARS Southwest Watershed Research Center in Tucson, Arizona, to a conservation practice physical effects matrix. Four rangeland practices of yearlong (YL) and rotation (ROT) grazing, with mesquite trees retained (+M) and removed (-M), are evaluated against eight decision variables that consider soil, water, plants and wildlife habitat. Each decision variable is quantified using data from four experimental watersheds on the Santa Rita Experimental Range, expert opinions, and two simulation modeling approaches. The simple approach uses the Curve Number method, RUSLE and MUSLE, while the complex approach uses the CREAMS hydrology and erosion models. Outcomes from the P-DSS are sensitive to the source of information. When measured data and complex models quantify the decision variables, the YL-M and ROT-M management systems dominate the current system of YL+M. The simple modeling approach identifies ROT+M in addition to YL-M and ROT-M. However, when a frequency of rank methodology is used, the simple and complex modeling approaches identify ROT-M as the preferred system, while the measured data and expert opinion identify YL-M. Ranking the four management systems quantified by simple models matches the ranking obtained from the expert survey. Rank ordering using the complex models agrees with the opinion of the most knowledgeable expert. Simple and complex modeling estimates of sediment yield are significantly different, as are estimates of peak runoff rate. The results suggest that model complexity improved information accuracy but had limited effect on the outcomes from the P-DSS. The effect of information sources on the outcomes from the P-DSS may become more pronounced if the evaluation changes from a relative assessment to one involving quality standards.
Degree ProgramGraduate College
Renewable Natural Resources