1. Functional Linkage of Water Basins and Streams: FLoWS v1 ArcGIS tools David Theobald, John Norman, Erin Peterson Natural Resource Ecology Lab, Dept of Recreation & Tourism, Colorado State University Fort Collins, CO 80523 USA 17 May 2006

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 Develop landscape-based indicators to assist in testing tenable hypotheses generated using understanding of ecological processes Develop landscape-based indicators to assist in testing tenable hypotheses generated using understanding of ecological processes

3. Premise Challenges to develop improved landscape-scale indicators (Fausch et al. 2002; Gergel et al. 2002; Allan 2004) are: - clearer representation of watersheds & hierarchical relationship; - incorporate nonlinearities of condition among different watersheds and along a stream segment Need to characterize spatial heterogeneity & scaling of watersheds when developing indicators of biological condition Goal: to develop indicators that more closely represent our understanding of how ecological processes are operating

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

5. “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.

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

7. Reaches 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

8. Covariates: landscape context 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!?

9. Local vs. accumulated (e.g., Human Urban Index)

10. Local

11. Accumulated

12. Accumulated

14. USGS NHD, NED

15. USGS NHD, NED

16. Pre-processing Generating reach contributing areas (RCAs) Automated delineation Inputs: Inputs: stream network (from USGS NHD or other) stream network (from USGS NHD or other) topography (USGS NED, 30 m) topography (USGS NED, 30 m)Processes: 1. traditional WATERSHED command requires FILLed DEM – “hydro-conditioned” 1. traditional WATERSHED command requires FILLed DEM – “hydro-conditioned” 2. Cost-distance using Topographic Wetness & Position Indices 2. Cost-distance using Topographic Wetness & Position Indices “true” catchments “adjoint” catchments Segments

17. Generating RCAs: FILLed 1.) Filled DEM 2.) Flow Direction

18. Artifacts?

19. Generating RCAs: cost-distance 1.) DEM

20. 2.) Topographic Wetness Index 3.) Topographic Position Index

21. Generating RCAs 4) Stream Reaches  5.) RCAs (Yellow)

22. Evaluation of RCAs “Truth” “Truth” Hand-delineated from 1:24K Hand-delineated from 1:24K Modeled (1:100K, 30 m DEM): Modeled (1:100K, 30 m DEM): A. traditional (FILL-ing) A. traditional (FILL-ing) B. cost-distance B. cost-distance Measure: Jaccard’s similarity coefficient: Measure: Jaccard’s similarity coefficient: b / (a + b + c) b / (a + b + c) a b c

23. Preliminary results FILLed DEM 50 m/WATERSHED Mean accuracy: 78% Cost-distance RCAs Mean accuracy: 85%

24. Within RCA hydro-weighting Overland flow (hydro distance to stream) Instream flow (hydro network distance to outlet)

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

26. Network connectivity errors

27. Selections User-defined field User-defined field Polylines or RCAs Polylines or RCAs Cumulative (distance from selected feature) Cumulative (distance from selected feature)

28. Analysis

29. Estimated discharge Average annual precipitation & temperature, basin area Average annual precipitation & temperature, basin area Vogel et al. 1999 Vogel et al. 1999 Vogel

31. Analysis

32. Export to distance matrices Straight-line Instream distance

33. Distance matrices (cont.) Downstream onlyUpstream only

34. Distance matrices (cont.) Proportion upstreamProportion downstream

35. Distance matrices (cont.) Downstream portion dist onlyNumber of confluences

36. Example: Coho salmon distances

37. Summary River Continuum to Network River Continuum to Network From overlapping waterbasins to network spatial structure From overlapping waterbasins to network spatial structure Open Open Simple data structure Simple data structure Python linked to GeoProcessing object Python linked to GeoProcessing object Non-GIS (thru Access, SQL, etc.) Non-GIS (thru Access, SQL, etc.) Flexible Flexible User-defined variables to accumulate, navigate network User-defined variables to accumulate, navigate network Different selection sets, combinations Different selection sets, combinations Compute framework once, use with many point configurations (samples) Compute framework once, use with many point configurations (samples) Robust Robust Flow-based vs. Strahler stream order Flow-based vs. Strahler stream order Cost-weighted methods Cost-weighted methods Developed, tested (broken), refined Developed, tested (broken), refined E.g, Mid-Atlantic Highlands; Oregon; Central Shortgrass Prairie; Alaska; E.g, Mid-Atlantic Highlands; Oregon; Central Shortgrass Prairie; Alaska;

38. Next steps Project/tool website: Project/tool website: www.nrel.colostate.edu/projects/starmap www.nrel.colostate.edu/projects/starmap www.nrel.colostate.edu/projects/starmap FLoWS, FunConn, RRQRR FLoWS, FunConn, RRQRR FLoWS database to complement tools FLoWS database to complement tools Attach additional attributes to FLoWS database Attach additional attributes to FLoWS database Land cover (urban, ag, “natural”) Land cover (urban, ag, “natural”) Historical, current, future housing density Historical, current, future housing density Hydro & slope weighted road density Hydro & slope weighted road density Human accessibility Human accessibility Within reach/segment Within reach/segment Streams as 2D features Streams as 2D features

39. 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

40. Example: 2D stream in Virginia

44. Thanks! Comments? Questions? Comments? Questions? Thanks to K. Verdin at USGS EROS Data Center for sharing EDNA datasets Thanks to K. Verdin at USGS EROS Data Center for sharing EDNA datasets 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 davet@nrel.colostate.edu davet@nrel.colostate.edu CR - 829095

45. Water basin - 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 ? Watershed HUCs/WBDReach Contributing Areas (RCAs) Grain (Resolution) Process/Functional Zonal Accumulate Up/down (net.)