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Ecohydrological model parameter selection for stream health evaluation.

Sean A Woznicki1, A Pouyan Nejadhashemi1, Dennis M Ross2

  • 1Department of Biosystems and Agricultural Engineering, 524 S. Shaw Lane, Room 216, Michigan State University, East Lansing, MI 48824, USA.

The Science of the Total Environment
|January 2, 2015
PubMed
Summary

Selecting key stream variables improves ecological health models. Grouping streams and using Bayesian methods identified important flow and nitrate data, enhancing predictions for biological integrity measures.

Keywords:
Biological IntegrityClusteringFishFuzzy logicMacroinvertebrateSWAT

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Area of Science:

  • Environmental Science
  • Ecology
  • Water Resource Management

Background:

  • Accurate stream health prediction models require careful selection of in-stream variables.
  • Existing methods may not fully capture complex ecological relationships influencing biological integrity.

Purpose of the Study:

  • To develop and evaluate a framework for selecting critical in-stream variables for predicting biological integrity.
  • To compare different stream grouping and variable selection methods for improving model performance.
  • To identify key environmental factors influencing stream health in the River Raisin watershed.

Main Methods:

  • Calculated over 200 flow regime and water quality variables using Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT).
  • Grouped streams using Strahler stream order, k-means clustering, and a single group approach.
  • Employed Bayesian variable selection, principal component analysis, and Spearman's rank correlation for variable selection, followed by adaptive-neuro fuzzy inference systems (ANFIS) modeling.

Main Results:

  • Identified multiple unique variable sets, with Bayesian selection and stream grouping (k-means, stream order) generally outperforming other methods.
  • Commonly selected variables included streamflow magnitude, rate of change, and seasonal nitrate concentration.
  • Models demonstrated high predictive power, with validation R2 values ranging from 0.49 to 0.99 across different biological integrity measures.

Conclusions:

  • The proposed comprehensive framework for variable selection and modeling enhances understanding of watershed-scale stream health.
  • Stream grouping significantly improves the accuracy of biological integrity predictions.
  • The study highlights the importance of flow dynamics and nitrate levels in assessing stream health.