Functional linkage of watersheds and streams using landscape networks of reach contributing areas

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Functional linkage of watersheds and streams
using landscape networks of reach contributing
areas

• David Theobald, John Norman, Erin Peterson,
  Silvio Ferraz
• Natural Resource Ecology Lab, Dept of Recreation
  Tourism, Colorado State University
• Fort Collins, CO 80523 USA 26
  July 2005

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Project context

• Challenges of STARMAP (EPA STAR)
• Addressing science needs Clean Water Act
• Integrate science with states/tribes needs
• Assisting statisticians to test tenable
  hypotheses generated using understanding of
  ecological processes

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Premise

• Goal to find measures that more closely
  represent our understanding of how ecological
  processes are operating
• Challenges to develop improved landscape-scale
  indicators (Fausch et al. 2002 Gergel et al.
  2002 Allan 2004) are
• - a clearer representation watersheds and their
  hierarchical relationship
• - to incorporate nonlinearities of condition
  among different watersheds and along a stream
  segment
• Ignoring the spatial heterogeneity and scaling of
  watersheds has led to somewhat equivocal
  conclusions regarding general proportions of land
  use in a watershed as an overall indicator of
  biological condition.

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Landscape Context of Metrics

• Co-variate(s) at spatial location, site context
• - E.g., geology, elevation, population density at
  a point
• Co-variate(s) within some distance of a location
• - Housing density at multiple scales
• Watershed-based variables
• - Proportion of urbanized area
• Spatial relationships between locations
• - Euclidean (as the crow flies) distance between
  points
• - Euclidean (as the fish swims) hydrologic
  network distance between points
• Functional interaction between locations
• - Directed process (flow direction), anisotropic,
  multiple scales
• - How to develop spatial weights matrix?
• - Not symmetric, stationary ? violate traditional
  geostatistical assumptions!?

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From watersheds/catchments as hierarchical,
overlapping regions
River continuum concept (Vannote et al. 1980)
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to network of catchments
Network Dynamics Hypothesis - Benda et al.
BioScience 2004
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Terrestrial
Aquatic
Peterson 2005
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Pre-processing segment contributing areas (SCAs)

• Automated delineation
• Inputs
• stream network (from USGS NHD 1100K)
• topography (USGS NED, 30 m)
• Process
• Grow contributing area away from segment until
  ridgeline
• Uses WATERSHED command

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Segments are linked to catchments

• 1 to 1 relationship
• Properties of the watershed can be linked to
  network for accumulation operation

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Lumped or watershed-based analyses

• agricultural, urban (e.g., ATtILA)
• Average road density (Bolstad and Swank)
• Dam density (Moyle and Randall 1998)
• Road length w/in riparian zone (Arya 1999)
• But 45 of HUCs are not watersheds

EPA. 1997. An ecological assessment of the US
Mid-Atlantic Region A landscape atlas.
EPA ATtILA 2002.
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Example Human Urban Index
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Local
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Accumulated
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Accumulated
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Generating RCAs
1.) Filled DEM
2.) Flow Direction
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Generating RCAs
3.) Stream Reaches ?
4.) RCAs (Yellow)
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Landscape Network
Landscape network features and associated
relationships table
From graph theory perspective, reaches are nodes,
confluences are edges
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Landscape networks with Python

• Need to represent relationships between features
• Using graph theory, networks
• Retain tie to geometry of features
• Flow relationships table (like NHD, but
  flow-sorted!)
• Implementation in ArcGIS
• Geometric Networks (ESRI complicated, slow)
• Landscape Networks Open, simple, fast
• Began with VBA (1.5 years), moved to Python (2
  months)
• Working on integration with PySal (Python Spatial
  Library)

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USGS NHD, NED
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• FLoWS v1 tools for ArcGIS v9.0
• Will migrate to v9.1

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Next steps

• Attach additional datasets to SCA database
• Land cover (urban, ag, natural)
• Historical, current, future housing density
• Road density
• From segments to geomorphological reaches,
  gradient
• Project/tool website
• www.nrel.colostate.edu/projects/starmap
• Email starmap_at_nrel.colostate.edu

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• Thanks!
• Comments? Questions?
• Funding/Disclaimer The work reported here was
  developed under the STAR Research Assistance
  Agreement CR-829095 awarded by the U.S.
  Environmental Protection Agency (EPA) to Colorado
  State University. This presentation has not been
  formally reviewed by EPA.  The views expressed
  here are solely those of the presenter and
  STARMAP, the Program (s)he represents. EPA does
  not endorse any products or commercial services
  mentioned in this presentation.
• FLoWS www.nrel.colostate.edu/projects/starmap
• davet_at_nrel.colostate.edu