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Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Comparing hydrogeomorphic approaches to lake classification.

Sherry L Martin¹, Patricia A Soranno, Mary T Bremigan

¹Department of Geological Sciences, Michigan State University, East Lansing, MI 48824, USA. marti686@msu.edu

Environmental Management

|August 23, 2011

Summary

Hydrogeomorphic (HGM) classifications effectively group lakes by water chemistry and clarity. However, no single HGM classification can capture all lake responses, necessitating variable-specific models incorporating multiple spatial scales.

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Area of Science:

Ecology
Limnology
Environmental Science

Background:

Ecosystem classification aids governmental agencies in monitoring and managing diverse environments.
Hydrogeomorphic (HGM) classifications offer a framework for grouping similar lake ecosystems.

Purpose of the Study:

To evaluate the effectiveness of different HGM-based classifications for grouping lakes based on water chemistry and clarity.
To identify key HGM features influencing lake class distinctions.
To determine if a single classification system can successfully group lakes for all tested water chemistry/clarity variables.

Main Methods:

Utilized classification and regression tree (CART) and multivariate CART (MvCART) analyses on HGM features.
Classified 151 minimally disturbed Michigan lakes based on alkalinity, water color, Secchi depth, total nitrogen, total phosphorus, and chlorophyll a.
Compared models using local HGM characteristics alone versus combined with regionalizations and landscape position.

Main Results:

Combined CART models demonstrated high strength of evidence (ω(i) 0.92-1.00) and maximized homogeneity (ICC 36-66%) for most water chemistry/clarity variables.
No single classification successfully accounted for all tested water chemistry/clarity variables, with the best model being ~20% less effective for other variables.
Key HGM features strongly related to lake classes were identified.

Conclusions:

The most successful lake classification approach is specific to individual response variables.
Effective lake classification requires integrating information from multiple spatial scales, including regionalization and local HGM variables.
A single, universal HGM classification is insufficient for comprehensive lake management and monitoring.