Comparing Ecological Models for Assessing Rio Grande Silvery Minnow Response to Environmental Flows

Abstract

The proliferation of continuous streamflow monitoring and spatial data suitable for hydraulic modeling is increasing opportunities to use hydraulic habitat analysis to inform ecological models. However, species population and streamflow data exhibit high variability, making it challenging to identify hydrologic and hydraulic metrics that effectively correlate with ecological outcomes. Metric selection presents a challenge for informing environmental flow decisions and adaptive management of water infrastructure. This study applies models to characterize environmental flows with in-creasing model complexity, including the use of hydraulic models to estimate suitable habitat areas at a given flow. The results are compared to field-measured fish outcomes over the same period using functional data analysis. The variance in model correlation with ecological outcomes aids in identifying the most effective environmental flow parameters while also indicating potential pitfalls from increasing model complexity. This analysis demonstrates techniques that synthesize environmental flows with available habitat analysis and validates the approach. The case study is based on the Rio Grande silvery minnow (Hybognathus amarus, minnow), an endangered fish species in the Middle Rio Grande. Analysis focused on different methods to quantify spring runoff coinciding with the inundation of floodplain nursery habitat necessary for the minnows larval and juvenile life stages.

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Document Details

Document Type
Technical Report
Publication Date
May 29, 2024
Accession Number
AD1228843

Entities

People

  • Aubrey E. Harris
  • Jonathan S. Aubuchon
  • Michael D. Porter

Organizations

  • Engineer Research and Development Center
  • United States Army Corps of Engineers

Tags

Fields of Study

  • Environmental science

Readers

  • Archaeological Resource Survey
  • Computational Modeling and Simulation
  • Wetland-Land-Environmental Management.