A multi-scale, hierarchical approach to map the location and condition of riparian zones in the southern Rockies ecoregion
Date
2014
Authors
Salo, Jessica Ann, author
Theobald, David, advisor
Bledsoe, Brian, committee member
Brown, Thomas, committee member
Kampf, Stephanie, committee member
Merritt, David, committee member
Journal Title
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Volume Title
Abstract
Riparian zones are important for their contribution to biodiversity and ecosystem services, especially in the western United States where riparian zones occupy a small proportion of the landscape but support a majority of the biodiversity. Riparian zones are currently threatened by multiple stressors, and will likely face further stresses associated with climate change and additional water withdrawals due to population growth particularly in the western United States and other arid regions. Consequently, it is imperative to understand the current location and extent of riparian zones. Although many agencies and organizations are concerned with the location, condition, and benefits of these ecosystems, few accurate datasets of riparian zone are available over broad spatial extents, and cost-effective methods to map riparian zones at fine spatial resolutions do not currently exist. My dissertation research develops a more comprehensive understanding of the location and condition riparian ecosystems in a semi-arid, mountainous region by developing methods that can be applied to other geographic regions. To do this, I took a three pronged approach to mapping riparian zone location and condition. First, I identify and evaluate existing GIS-based methods that have been previously used to map riparian zones in order to determine how accurately the methods are in a semi-arid, mountainous watershed. Second, I create a multi-scale, hierarchal method to map riparian zones by capturing the dominant physical processes to map the location of current and potential riparian zones using readily available, national datasets and demonstrate the approach for the Southern Rockies Ecoregion. Third, I estimate riparian condition using a straightforward, cost-effective approach at management relevant scales (i.e. reach) and evaluate the dominant ecological and physical processes and anthropogenic stressors that impact riparian ecosystems. Results from my dissertation indicate that existing methods to map potential riparian zones are not very accurate, having only a maximum accuracy of kappa coefficient of 0.38. The most appropriate existing method for mapping potential riparian zones in semi-arid mountainous regions incorporates upstream drainage area and valley topography. I develop a multi-scale, hierarchical, process guided model to map riparian zones and found that the Southern Rockies Ecoregion is composed of 3.2% (± 0.3%) potential and 2.5 (± 0.3%) current riparian zones, indicating that 20.3% (± 1.1%) of riparian zones have been removed by human activities. Based on field verification/validation, my new method has an overall accuracy of 92% for potential riparian zones and 91% in the current riparian zones. Finally, the method I developed to predict riparian condition indicated that riparian zones in the Southern Rockies Ecoregion are comprised of 7.2% low condition, 15.2% medium condition, and 77.7% high condition and that the most important variables in predicting riparian condition in the Southern Rockies Ecoregion are human modification in riparian zones, the number of upstream transportation crossings, human modification within the upstream watershed, and the proportion of the upstream watershed that is protected by GAP Status 1 management plans. The overall accuracy of my riparian condition model was 60.5%. The model could be improved though the use of higher resolution predictor variables. If fine grain (< 5 m) terrain data were available for the study area, additional geomorphic variables, such as valley width to channel width ratio, could be developed and should enhance model performance.
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Subject
GIS
valley bottom topography
riparian ecosystems
mapping riparian ecosystems
spatial modeling