1. 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

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

3. 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.

4. 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!?

5. From watersheds/catchments as hierarchical, overlapping regions… River continuum concept (Vannote et al. 1980)

6. … to network of catchments Network Dynamics Hypothesis - Benda et al. BioScience 2004

7. SCALE: Grain Substrate Biotic Condition Overhanging Vegetation Segment River Network Network Connectivity Tributary Size Differences Network Geometry Stream Network Connectivity Flow Direction Network Configuration Drainage Density Confluence Density Cross Sectional Area Channel Slope, Bed Materials Large Woody Debris Biotic Condition, Substrate Type, Overlapping Vegetation Detritus, Macrophytes Microhabitat Segment Contributing Area Riparian Vegetation Type & Condition Floodplain / Valley Floor Width Localized Disturbances Land Use/ Land Cover Landscape Climate Atmospheric deposition Geology Topography Soil Type Microhabitat Shading Detritus Inputs Riparian Zone Nested Watersheds Land Use Topography Vegetation Type Basin Shape/Size COARSE FINE Reach Terrestrial Aquatic Peterson 2005

8. Pre-processing segment contributing areas (SCAs) Automated delineation Inputs: Inputs: stream network (from USGS NHD 1:100K) stream network (from USGS NHD 1:100K) topography (USGS NED, 30 m) topography (USGS NED, 30 m) Process: Process: “Grow” contributing area away from segment until ridgeline “Grow” contributing area away from segment until ridgeline Uses WATERSHED command Uses WATERSHED command “true” catchments “adjoint” catchments Segments

9. Segments are linked to catchments 1 to 1 relationship 1 to 1 relationship Properties of the watershed can be linked to network for accumulation operation Properties of the watershed can be linked to network for accumulation operation

10. “Lumped” or watershed-based analyses % agricultural, % urban (e.g., ATtILA) % agricultural, % urban (e.g., ATtILA) Average road density (Bolstad and Swank) Average road density (Bolstad and Swank) Dam density (Moyle and Randall 1998) Dam density (Moyle and Randall 1998) Road length w/in riparian zone (Arya 1999) Road length w/in riparian zone (Arya 1999) But ~45% of HUCs are not watersheds But ~45% of HUCs are not watersheds EPA. 1997. An ecological assessment of the US Mid-Atlantic Region: A landscape atlas. EPA ATtILA 2002.

11. Example: Human Urban Index

12. Local

13. Accumulated

14. Accumulated

18. Generating RCAs 1.) Filled DEM 2.) Flow Direction

19. Generating RCAs 3.) Stream Reaches  4.) RCAs (Yellow)

20. Landscape Network Landscape network features and associated relationships table From graph theory perspective, reaches are nodes, confluences are edges

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

22. USGS NHD, NED

23. FLoWS v1 tools for ArcGIS v9.0… FLoWS v1 tools for ArcGIS v9.0… Will migrate to v9.1 Will migrate to v9.1

27. Next steps Attach additional datasets to SCA database Attach additional datasets to SCA database Land cover (urban, ag, “natural”) Land cover (urban, ag, “natural”) Historical, current, future housing density Historical, current, future housing density Road density Road density From segments to geomorphological reaches, gradient From segments to geomorphological reaches, gradient Project/tool website: Project/tool website: www.nrel.colostate.edu/projects/starmap www.nrel.colostate.edu/projects/starmap www.nrel.colostate.edu/projects/starmap Email: starmap@nrel.colostate.edu Email: starmap@nrel.colostate.edustarmap@nrel.colostate.edu

28. Thanks! Comments? Questions? 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. 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 FLoWS: www.nrel.colostate.edu/projects/ starmap www.nrel.colostate.edu/projects/ starmap www.nrel.colostate.edu/projects/ starmap davet@nrel.colostate.edu davet@nrel.colostate.edu CR - 829095