|Guertin, D.P. - UNIV. OF ARIZ.|
|Fiedler, Reno H. - UNIV. OF ARIZ.|
|Miller, S.N. - UNIV. OF ARIZ.|
Submitted to: American Society Of Civil Engineers Watershed Management Conference
Publication Type: Proceedings
Publication Acceptance Date: June 1, 2000
Publication Date: June 20, 2000
Interpretive Summary: In order to achieve successful land management, managers rely heavily on information collected in the field regarding the current status of the area being managed, such as ownership status, grazing intensity, and logging. This data is often synthesized using tools that assume that the information that has been collected is very accurate and comprehensive. These tools are often limited by the fact that they cannot account for the range of expected results that may happen because of management decisions. This paper presents a technique, known as fuzzy analysis, that attempts to account for both the error in input data and the range in expected results. This technique is tested on five watersheds in central Arizona and the results are compared to a conventional tool, the Riparian Restoration Ranking System, for assessing the health of a watershed.
Technical Abstract: Watershed assessment requires techniques capable of synthesizing large quantities of spatial information. Fuzzy logic is one approach for addressing this problem. Geographic information systems (GIS) have the capacity to represent and integrate several levels of information on watershed characteristics and condition. Spatial analyses can show the relative condition of a watershed or delineate zones requiring different levels of protection related to a set of activities. One of the disadvantages of techniques commonly used in these analyzes (e.g. Boolean, Weighted Index, Analytical Hierarchy Process) is that they do not include the uncertainty of a given factor or in the final results, which can be addressed with a fuzzy logic approach. With fuzzy logic a fuzzy membership function is defined for each environmental factor, which defines a region where the inclusion or exclusion of the factor is unclear. This paper briefly reviews the approach and application of fuzzy logic to the Riparia Restoration Ranking (R3) System on several montane watersheds in Arizona.