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A framework for landscape indicators for measuring aquatic responses

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. Context.

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A framework for landscape indicators for measuring aquatic responses

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  1. 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

  2. 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

  3. Constraints Landscape processes • Spatial & temporal scales, processes Poff, N.L. 1997

  4. 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 • Amount of contributing area, flow volume, etc. • 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!?

  5. Challenges: conceptual & practical • Definition of a watershed • Overland surface process vs. in-stream flow process • Scale/resolution issues • E.g., different answers at 1:500K vs. 1:100K vs. 1:24K • 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

  6. “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.

  7. Watersheds/catchments as hierarchical, overlapping regions River continuum concept (Vannote et al. 1980)

  8. Dominant downstream process Upper and lower Colorado Basin Flows to downstream HUCs

  9. “true” catchments “adjoint” catchments Reaches (segments) Reach Contributing Areas (RCAs) Automated delineation • Inputs: • stream network (from USGS NHD 1:100K) • topography (USGS NED, 30 m or 90 m) • Process: • “Grow” contributing area away from reach segment until ridgeline • Uses WATERSHED command

  10. Watershed – Stream Process/Functional Zonal Accumulate Up/down (net.) Watersheds HUCs/WBD Reach Contributing Areas (RCAs) Grain (Resolution)

  11. Reaches are linked to catchments • 1 to 1 relationship • Properties of the watershed can be linked to network for accumulation operation

  12. RCA example • US ERF1.2 & 1 km DEM: 60,833 RCAs

  13. 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

  14. Feature to Feature Relationships via Relationship Table

  15. Up Down

  16. RCAs are linked together – but spatial configuration within an RCA? 1. Ignore variability 2. Buffer streams 3. Buffer outlet

  17. 2 major hydro. processes w/in RCA 1. Overland (hillslope): Distance (A to A’) 2. Instream flow: Distance (A’ to O)

  18. Flow distance: overland + instream • Hydro-conditioned DEM (e.g., EDNA) • FLOWDIRECTION • FLOWLENGTH

  19. Flow distance: overland • Hydro-conditioned DEM (e.g., EDNA) • Burn stream into FLOWDIRECTION • FLOWLENGTH

  20. Flow distance: instream • Hydro-conditioned DEM (e.g., EDNA) • FLOWDIRECTION • FLOWLENGTH from outline – overland FLOWLENGTH

  21. 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

  22. FLOWS v0.1: ArcGIS v9 tools • Higher-level objects  faster coding! • Open source • Integrated development for documentation

  23. Laramie Foothills Study Area and Sample Points

  24. Accessibility:travel time along roads from urban areas

  25. 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

  26. 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?

  27. CR - 829095 This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 # CR - 829095 # CR - 829095 # CR - 829095 # CR - 829095 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@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

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