The National Wetlands Inventory (NWI) is the most extensive inventory of wetland resources in the U.S., but it has limited ability to contribute to characterizations of wetland functions. We provide a methodology for reclassifying NWI polygons into Hydrogeomorphic (HGM) classes to facilitate monitoring wetland functions. We conducted this reclassification using spatial and attribute queries within Geographic Information Systems (GIS) for wetlands in central Oklahoma. Once classified, 149 randomly selected wetlands in four HGM classes (depressional, lacustrine fringe, riverine, and impounded depressional) were field verified. The overall accuracy of the GIS classification was 60%. Inherent issues with NWI due to attribute accuracy, spatial accuracy, and map age accounted for >50% of the misclassified sites in this analysis. The results from this analysis were also used to provide an inventory of wetlands in each HGM class and subclass based on user's accuracy metrics. Reclassifying NWI polygons into HGM classes can assist with determining the spatial distribution and relative abundance of specific wetland classes which allows for more focused wetland restoration and monitoring efforts. However, the error rate associated with reclassification should be calculated to ensure that incorrect conclusions are not drawn regarding the abundance of HGM wetland classes and their associated functions.

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The hydrogeomorphic approach (HGM) to wetland classification and functional assessment has been applied regionally throughout the United States, but the ability of HGM functional assessment models to reflect wetland condition has limited verification. Our objective was to determine how variability derived from anthropogenic effects and natural variability impacted site assessment variables within regional wetland subclasses in central Oklahoma. We collected data for nine potential assessment variables including vegetation physiognomy (e.g., tree basal area, herbaceous cover, canopy cover, etc.) and soil organic matter at wetlands of two HGM riverine subclasses (oxbow and riparian) in May and June, 2010. Using Akaike Information Criteria, we identified limited relationships between landscape disturbance metrics and assessment variables within subclasses. The high degree of natural variability from climatic and hydrologic factors within both subclasses may be masking the impact of landscape disturbance on the other measured assessment variables. Precipitation had significant effects on assessment variables within each of the subclasses. To reduce natural climatic variability, the reference domain may need to be further subdivided. The approach used in this study provides fairly rapid and quantitative methods for evaluating the effectiveness of using HGM assessment variables in assessing wetland condition regionally.

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