A Watershed Classification System for Improved Monitoring and Restoration: Landscape Indicators of Watershed ImpairmentEPA Grant Number: R831369
Title: A Watershed Classification System for Improved Monitoring and Restoration: Landscape Indicators of Watershed Impairment
Investigators: Prince, Stephen D. , Goetz, Scott J. , Jordan, Thomas E. , Weller, Donald E.
Institution: University of Maryland , Smithsonian Environmental Research Center , Woods Hole Research Center
EPA Project Officer: Hiscock, Michael
Project Period: February 1, 2004 through January 31, 2007
Project Amount: $896,497
RFA: Development of Watershed Classification Systems for Diagnosis of Biological Impairment in Watersheds and Their Receiving Water Bodies (2003) RFA Text | Recipients Lists
Research Category: Water and Watersheds , Water
To develop a watershed classification scheme based on recent, much improved, comprehensive watershed data sets to diagnose aquatic ecosystem impairment and to target resource management. To use hydrologic metrics, nutrient budgets incorporating point and non-point source/sinks, and landscape function metrics to provide indicators of aquatic ecosystem condition (hydrology, plant, fish, macroinvertebrates, water quality) in reference watersheds. To identify the watershed variables most relevant to prediction of impairment of the receiving water bodies by developing a set of empirical classification models for multiple scales. To develop classifications for mid-Atlantic training watersheds, test them in the mid-Atlantic, apply the entire methodology in southern New England (MA, RI, CT), and to generalize the methods for future national application.
We will quantify aquatic health using flow metrics, water quality metrics (such as sediment and nutrient concentrations), and biological indicators. We have developed land use, land cover, other geospatial variables related to watershed function, anthropogenic influences, and landscape metrics from multitemporal Landsat ETM+ data. The relationships between watershed attributes, flow and water quality, and biological indicators will be formalized as statistical models with associated significance and confidence metrics for each scale. The sensitivity of ecological response variables to natural and anthropogenic variations in watershed properties will be assessed. The models will be used to develop hierarchical decision trees that specify a set of binary splits leading to a finite set of impairment categories. The rules will be tested and errors in the independent variables (e.g., land cover misclassification) will be quantified to measure classification accuracy. The definition of impairment categories, derivation of decision rules, and assessment will be undertaken with the help of representative managers.
As a result of the much-improved landscape data inputs and the use of metrics of watershed function, we expect to be able to predict watershed impairment and trends toward impairment. The hierarchical structure of the classification, with explicit decision rules, will be accessible to managers. The classifications will diagnose impairment of watersheds, assess ecosystem vulnerability, and provide for monitoring and prioritization for restoration activities, all at multiple scales. The methods will be tested with resource managers in two EPA regions (1 and 3), providing an assessment in a wide variety of physical, biological and anthropogenic conditions.