Hydrologic modeling as a development tool for HGM functional assessment models

Abstract

The hydrogeomorphic (HGM) approach to wetland classification and functional assessment was developed to facilitate the rapid assessment of wetland functions and support implementation of the 404 regulatory program in the U.S. This paper describes development of a functional assessment model for the hydrologic regime function of depressional wetlands on the Tennessee Highland Rim. Model output is a functional capacity index that ranges from 0 to 1 and indicates the level of alteration to the wetland hydrologic regime relative to the unaltered condition. The model initially includes four variables, each corresponding to an alteration type common in the study area. One variable reflects alterations to wetland morphology that increase or decrease storage capacity. Two variables reflect alterations to the wetland watershed that result in increased surface runoff. The remaining variable reflects changes in evapotranspiration losses resulting from timber harvesting. Model variable sensitivity and interaction are examined by simulating 47 years of hydrology for a single depressional wetland site. Based on the simulation of hypothetical alterations representing the range of conditions anticipated in the reference domain, three important variable interactions were identified and one variable eliminated from consideration. Several forms of the aggregation equation relating a functional capacity index to three model variables were tested by comparison to index values based on an independent quantitative measure that was simulated with a hydrologic model. Initial model forms, similar in form to many existing models, are improved by the addition of interaction terms and modification of variable weighting. This study demonstrates the utility of hydrologic modeling as a development tool for HGM functional assessment models. Even when limited field measurements are available for a rigorous calibration and validation, hydrologie modeling provides valuable insight into model variable sensitivity and interaction.