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): Challenges of STARMAP (EPA): Addressing science needs Clean Water Act Addressing science needs Clean Water Act Integrate science with states/tribes needs Integrate science with states/tribes needs From correlation to causation From correlation to causation Tenable hypotheses generated using understanding of ecological processes 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. Landscape processes Spatial & temporal scales, processes Spatial & temporal scales, processes Poff, N.L. 1997 Constraints

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

5. Challenges: conceptual & practical Definition of a watershed Definition of a watershed Overland surface process vs. in-stream flow process Overland surface process vs. in-stream flow process Scale/resolution issues Scale/resolution issues E.g., different answers at 1:500K vs. 1:100K vs. 1:24K E.g., different answers at 1:500K vs. 1:100K vs. 1:24K Artifacts in data Artifacts in data Attribute errors, flow direction, braided streams Attribute errors, flow direction, braided streams Linking locations/points/events to stream network Linking locations/points/events to stream network Reach-indexing gauges, dams? Reach-indexing gauges, dams? Very large databases Very large databases GIS technology innovations and changes GIS technology innovations and changes

6. “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. Southern Rockies Ecosystem Project. 2000.

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

10. 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 ? Watersheds HUCs/WBDReach Contributing Areas (RCAs) Grain (Resolution) Process/Functional Zonal Accumulate Up/down (net.)

11. Reaches 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

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

13. Key  GeoNetworks! 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 Implementation in ArcGIS Implementation in ArcGIS GeometricNetworks (ESRI – complicated, slow) GeometricNetworks (ESRI – complicated, slow) GeoNetworks: Open, simple, fast GeoNetworks: Open, simple, fast

14. Feature to Feature Relationships via Relationship Table

17. UpDown

18. RCAs are linked together – but spatial configuration within an RCA? 1. Ignore variability2. Buffer streams3. Buffer outlet

19. 2 major hydro. processes w/in RCA 1. Overland (hillslope): Distance (A to A’) 2. Instream flow: Distance (A’ to O)

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

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

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

23. Why are functional metrics important? Clearer relationship between assumption of ecological (aquatic, terrestrial) process, potential effects (e.g., land use change) and response 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 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 More direct relationship between process and measure, biologically meaningful

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

25. Laramie Foothills Study Area and Sample Points

26. Accessibility: travel time along roads from urban areas

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

28. Possible future activities Dataset development Dataset development RCA nationwide with involvement for USGS NHD program RCA nationwide with involvement for USGS NHD program Reach indexing dams (for EPA, Dewald) Reach indexing dams (for EPA, Dewald) Discharge volume Discharge volume Symposium: “At the interface of GIS and statistics for ecological applications” (~January 2005) 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? 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 statisticians respond, direct, and utilize GIS-based types of tools? How can/should statistical tools be best integrated with GIS? 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? 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? How can these two approaches best complement one another?

29. 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. STARMAP: www.stat.colostate.edu/~nsu/starmap STARMAP: www.stat.colostate.edu/~nsu/starmap www.stat.colostate.edu/~nsu/starmap RWTools: email davet@nrel.colostate.edu 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. This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 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. This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 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. This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 This research is funded by U.S.EPA – Science To Achieve Results (STAR) Program Cooperative Agreement # CR - 829095 CR - 829095