A framework for landscape indicators for measuring aquatic responses

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A framework for landscape indicators for
measuring aquatic responses

• David Theobald,
• John Norman, Erin Poston, Silvio Ferraz
• Natural Resource Ecology Lab
• Dept of Recreation Tourism
• Colorado State University
• Fort Collins, CO 80523 USA
• 11 September 2004

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Context

• Challenges of STARMAP (EPA)
• Addressing science needs Clean Water Act
• Integrate science with states/tribes needs
• From correlation to causation
• Tenable hypotheses generated using understanding
  of ecological processes

Goal to find measures that more closely
represent our assumptions of how ecological
processes are operating
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Landscape processes

• Spatial temporal scales, processes

Poff, N.L. 1997
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Landscape Context of Metrics

1. Co-variate(s) at spatial location, site context
2. E.g., geology, elevation, population density at a
   point
3. Co-variate(s) within some distance of a location
4. Housing density at multiple scales
5. Watershed-based variables
6. Amount of contributing area, flow volume, etc.
7. Spatial relationships between locations
8. Euclidean (as the crow flies) distance between
   points
9. Euclidean (as the fish swims) hydrologic network
   distance between points
10. Functional interaction between locations
11. Directed process (flow direction), anisotropic,
    multiple scales
12. How to develop spatial weights matrix?
13. Not symmetric, stationary ? violate traditional
    geostatistical assumptions!?

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Challenges conceptual practical

• Definition of a watershed
• Overland surface process vs. in-stream flow
  process
• Scale/resolution issues
• E.g., different answers at 1500K vs. 1100K vs.
  124K
• Artifacts in data
• Attribute errors, flow direction, braided streams
• Linking locations/points/events to stream network
• Reach-indexing gauges, dams?
• Very large databases
• GIS technology innovations and changes

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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

Southern Rockies Ecosystem Project. 2000.
EPA. 1997. An ecological assessment of the US
Mid-Atlantic Region A landscape atlas.
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Watersheds/catchments as hierarchical,
overlapping regions
River continuum concept (Vannote et al. 1980)
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Dominant downstream process
Upper and lower Colorado Basin Flows to
downstream HUCs
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Reach Contributing Areas (RCAs)

• Automated delineation
• Inputs
• stream network (from USGS NHD 1100K)
• topography (USGS NED, 30 m or 90 m)
• Process
• Grow contributing area away from reach segment
  until ridgeline
• Uses WATERSHED command

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Watershed Stream
Hydrologic distance Instream Up vs. down? FLOWS
Overlapping watersheds Accumulate downstream FLOWS (and SPARROW)
Stand-alone watershed Watershed-based analyses (HUCs) Tesselation of true, adjoint catchments ?
Process/Functional Zonal
Accumulate Up/down (net.)
Watersheds HUCs/WBD Reach
Contributing Areas (RCAs) Grain (Resolution)
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Reaches are linked to catchments

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

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RCA example

• US ERF1.2 1 km DEM 60,833 RCAs

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Key ? GeoNetworks!

• Need to represent relationships between features
• Using graph theory, networks
• Retain tie to geometry of features
• Implementation in ArcGIS
• GeometricNetworks (ESRI complicated, slow)
• GeoNetworks Open, simple, fast

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Feature to Feature Relationships via Relationship
Table
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RCAs are linked together but spatial
configuration within an RCA?
1. Ignore variability 2. Buffer streams 3.
Buffer outlet
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2 major hydro. processes w/in RCA

• 1. Overland (hillslope) Distance (A to A)
• 2. Instream flow Distance (A to O)

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Flow distance overland instream

• Hydro-conditioned DEM (e.g., EDNA)
• FLOWDIRECTION
• FLOWLENGTH

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Flow distance overland

• Hydro-conditioned DEM (e.g., EDNA)
• Burn stream into FLOWDIRECTION
• FLOWLENGTH

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Flow distance instream

• Hydro-conditioned DEM (e.g., EDNA)
• FLOWDIRECTION
• FLOWLENGTH from outline overland FLOWLENGTH

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Why are functional metrics important?

• Clearer relationship between assumption of
  ecological (aquatic, terrestrial) process,
  potential effects (e.g., land use change) and
  response
• Huge (insurmountable?) challenge is that we
  cannot develop traditional experimental design
  (manipulated vs. controlled) because landscapes
  are so large and human activities so dominant
• More direct relationship between process and
  measure, biologically meaningful

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FLOWS v0.1 ArcGIS v9 tools

• Higher-level objects ? faster coding!
• Open source
• Integrated development for documentation

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Laramie Foothills Study Area and Sample Points
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Accessibilitytravel time along roads from urban
areas
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Planned future activities

• Papers
• Completing draft manuscripts on GIS-GRTS, RCAs,
  overland/instream flow, dam fragmentation,
  GeoNetworks
• Presentations
• Theobald GRTS Sept. 23
• Poston
• Products
• FLOWS tools
• Datasets RCAs (ERF1.2)
• Education/outreach
• Training session for FLOWS tools

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Possible future activities

• Dataset development
• RCA nationwide with involvement for USGS NHD
  program
• Reach indexing dams (for EPA, Dewald)
• Discharge volume
• Symposium At the interface of GIS and
  statistics for ecological applications (January
  2005)
• What are the strengths and weaknesses of
  GIS-based and statistical-based tools?
• How can/should statisticians respond, direct, and
  utilize GIS-based types of tools?
• How can/should statistical tools be best
  integrated with GIS?
• What are the needs of agencies if
  statistical-based tools are to be used? When
  should GIS-based tools be used?
• How can these two approaches best complement one
  another?

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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.
• STARMAP www.stat.colostate.edu/nsu/starmap
• RWTools email davet_at_nrel.colostate.edu

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.
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.
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.
CR - 829095
CR - 829095
CR - 829095