Grantee Research Project Results
2004 Progress Report: Model-based Clustering for Classification of Aquatic Systems and Diagnosis of Ecological Stress
EPA Grant Number: R831368Title: Model-based Clustering for Classification of Aquatic Systems and Diagnosis of Ecological Stress
Investigators: Smith, Eric , Orth, Donald J. , Yagow, Gene , Berkson, Jim , Brannan, Kevin , Mostaghimi, Saied , Bates, Samantha
Institution: Virginia Tech
EPA Project Officer: Packard, Benjamin H
Project Period: November 10, 2003 through November 9, 2006
Project Period Covered by this Report: November 10, 2003 through November 9, 2004
Project Amount: $843,771
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: Watersheds , Water
Objective:
The objectives of this research are to develop methodologies for classifying watersheds and to evaluate the ability of this classification system to delineate areas of biological stress. Using a clustering approach based on the classification likelihood, we have developed an algorithm that can cluster regression relationships. The method was applied to data from Ohio, using the Index of Biotic Integrity (IBI) as the biological response and several environmental measurements as stressors. The result of the application was two main clusters, one associated with the northwest region of the state and a second with the remaining locations. The clusters primarily differentiate based on the average value of IBI and a habitat metric. A region associated with a single basin was found to form a separate cluster. The clustering program is part of an EXCEL macro that allows a user to make use of the power of this program for data entry, manipulation, and graphics. The algorithm uses a Monte Carlo Markov Chain approach that allows for the estimation of uncertainty associated with the clustering.
Progress Summary:
In another study we investigated impacts of alternative land use sources, reference watersheds, and the water quality model used on the final Total Maximum Daily Load (TMDL) for watersheds with benthic impairments. Questions considered in this research included: Do the different land use sources (D igital O rthophoto Q uarter Q uadrangle [DOQQ] and National Land Cover Database [NLCD]) result in different stressor loadings? Does the use of alternative water quality models (Generalized Watershed Loading Functions [GWLF] and Soil and Water Assessment Tool [SWAT]) result in different stressor loadings? Is there a difference in stressor loadings when different reference watersheds are used? Stroubles Creek, a benthically impaired waterbody on Virginia’s 1998 303d list, was selected for study. Sediment is the primary benthic stressor and therefore the target for stressor reductions. Study results showed that the land use source used for determining land use parameters, the model used to determine sediment loads, and the reference watershed selected to determine the target load all have marked effects on resulting stressor load reduction requirements. Using different land use sources, regardless of the reference watershed, resulted in required stressor reductions that were different by greater than 10%. With respect to water quality model selection, in two of the three scenarios considered, a difference in stressor load reduction requirements of greater than 10% resulted from using different water quality models. In one scenario, 2.8 times greater reductions were required with GWLF modeling than with SWAT modeling. Finally, different reference watersheds resulted in a difference of as much as 73% in required reductions of sediment in the impaired watershed. Since TMDL reports become legal documents, it is crucial to be able to consistently and scientifically determine the required reductions of stressor loading in an impaired watershed.
Future Activities:
The next year will involve the following activities:
- Apply the method to the data set from the Mid Atlantic Highlands. We have completed a preliminary evaluation of data quality for this data set and will apply the classification likelihood approach to analyze the data.
- Extend the clustering approach by developing a finite mixture likelihood approach. The finite mixture approach has better statistical properties and may be more useful for estimation of parameters of the model.
- Extend the methods to allow for nonparametric regression analysis. The nonparametric approach may improve the strength of the relationships.
- Extend the methods to allow for multivariate regression approaches. The extension will allow for the analysis of sets of metrics rather than just a single metric. Also, methods such as canonical correspondence analysis will be implemented to allow for the inclusion of counts as response variables as well as metrics.
- Evaluate the ability of various water quality models for assessing the impact of land use activities on downstream water quality.
Journal Articles:
No journal articles submitted with this report: View all 3 publications for this projectSupplemental Keywords:
RFA, Scientific Discipline, Water, ECOSYSTEMS, Ecosystem Protection/Environmental Exposure & Risk, Water & Watershed, Aquatic Ecosystems & Estuarine Research, Monitoring/Modeling, Aquatic Ecosystem, Terrestrial Ecosystems, Environmental Monitoring, Ecological Risk Assessment, Ecology and Ecosystems, Watersheds, risk assessment, ecosystem modeling, anthropogenic stress, watershed classification, watershed, ecosystem monitoring, decision making, water quality, model based cluster anaysis, ecological risk, aquatic ecosystems, environmental stress, stressor effect relationships, ecological indicators, ecology assessment models, ecosystem stress, watershed assessment, ecological models, water monitoring, adaptive implementation modeling, stress responseProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.