An HGM approach for assessing wetland functions in central Oklahoma: Hydrogeomorphic classification and functional attributes

Scope and Method of Study: The main objectives of this study were to: (1) identify the hydrogeomorphic (HGM) wetland subclasses within the Cross Timbers and Central Great Plains Ecoregions of central Oklahoma, (2) develop a spatial inventory of the number of wetlands within each subclass, (3) assess if natural variability of wetland assessment variables was reduced by HGM subclassification, and (4) determine if landscape disturbance could be correlated with assessment variables. The identification of HGM subclasses was completed by conducting field verification of 190 wetlands throughout the study area. Wetlands were placed in subclasses based on visual assessments of water source, hydrodynamics and geomorphology. The HGM inventory was calculated by reclassifying National Wetlands Inventory (NWI) polygons into HGM classes in Geographic Information Systems using spatial and attribute queries. The variability within the two dominant HGM riverine subclasses was calculated for 21 vegetation physiognomy, soil structure and water chemistry variables using redundancy analysis (RDA), principal components analysis (PCA) and forward stepwise regression. The effects of HGM subclass, precipitation, stream order, and landscape disturbance were assessed on all the response variables.;Findings and Conclusions: In the study area, 16 HGM subclasses in 4 HGM classes were identified. Using NWI polygons that were mapped over 30 years ago introduced over 20% error to the reclassification. As a result, it is essential that HGM inventories developed from NWI include accuracy assessments in the field. Wetland subclass explained 14.2% of the variability among site metrics using RDA. However, within subclasses several hydrological and climactic factors caused natural variation among the assessment variables. The effects of landscape disturbance on assessment variables were minimal. Natural variability within subclasses may be too high to create assessment models that are responsive to disturbance. Alternatively, disturbance factors not included may have a greater impact on assessment variables. It is essential for others developing HGM assessment tools to calibrate assessment variables to disturbance, so assessment models can be developed that accurately assess wetland health.