Transcript Development and Land Use: Colorado Case Studies

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
Project context
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Challenges of STARMAP (EPA STAR):
Addressing science needs Clean Water Act
 Integrate science with states/tribes needs
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Assisting statisticians to test tenable hypotheses
generated using understanding of ecological
processes
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.
Landscape Context of Metrics
1.
Co-variate(s) at spatial location, site context
- E.g., geology, elevation, population density at a point
2.
Co-variate(s) within some distance of a location
- Housing density at multiple scales
3.
Watershed-based variables
- Proportion of urbanized area
4.
Spatial relationships between locations
- Euclidean (as the crow flies) distance between points
- Euclidean (as the fish swims) hydrologic network distance between
points
5.
Functional interaction between locations
- Directed process (flow direction), anisotropic, multiple scales
- How to develop spatial weights matrix?
- Not symmetric, stationary  violate traditional geostatistical
assumptions!?
From watersheds/catchments as
hierarchical, overlapping regions…
River continuum concept (Vannote et al. 1980)
… to network of catchments
Network Dynamics Hypothesis - Benda et al. BioScience 2004
SCALE: Grain
Terrestrial
Aquatic
Landscape
River Network
COARSE
Climate
Atmospheric deposition
Geology
Topography
Soil Type
Network Connectivity
Nested Watersheds
Land Use
Topography
Stream Network
Connectivity
Drainage Density
Flow Direction
Confluence Density
Network Configuration
Vegetation Type
Basin Shape/Size
Segment
Contributing Area
Segment
Tributary Size Differences
Network Geometry
Localized Disturbances
Land Use/ Land Cover
Reach
Riparian Zone
Riparian Vegetation Type
& Condition
Floodplain / Valley Floor Width
Microhabitat
Cross Sectional Area
Channel Slope, Bed Materials
Large Woody Debris
Substrate
FINE
Shading
Detritus Inputs
Peterson 2005
Overhanging
Vegetation
Biotic
Condition
Microhabitat
Biotic Condition, Substrate Type,
Overlapping Vegetation
Detritus, Macrophytes
Pre-processing segment contributing
areas (SCAs)
Automated delineation
 Inputs:
 stream
network (from
USGS NHD 1:100K)
 topography (USGS
NED, 30 m)
 Process:
 “Grow”
contributing
area away from segment
until ridgeline
 Uses WATERSHED
command
“true”
“adjoint”
catchments
catchments
Segments
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
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% 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.
Example: Human Urban Index
Local
Accumulated
Accumulated
Generating RCAs
1.) Filled DEM
2.) Flow Direction
Generating RCAs
3.) Stream Reaches 
4.) RCAs (Yellow)
Landscape Network
Landscape network features and associated relationships table
From graph theory perspective,
reaches are nodes, confluences are
edges
Landscape networks with Python
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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
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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)
USGS
NHD,
NED
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FLoWS v1 tools for
ArcGIS v9.0…
Will migrate to v9.1
Next steps
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Attach additional datasets to SCA database
Land cover (urban, ag, “natural”)
 Historical, current, future housing density
 Road density
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From segments to geomorphological reaches,
gradient
Project/tool website:
www.nrel.colostate.edu/projects/starmap
 Email: [email protected]
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Thanks!
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Comments? Questions?
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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.
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FLoWS:
www.nrel.colostate.edu/projects/
starmap
[email protected]
CR - 829095