Functional Linkage of Water Basins and Streams: FLoWS v1 ArcGIS tools

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): • Addressing science needs Clean Water Act • Integrate science with states/tribes needs • 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) • 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.

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

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

8. Covariates: landscape context • 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 - Proportion of urbanized area • 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!?

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

10. Local

11. Accumulated

12. Accumulated

14. USGS NHD, NED

15. USGS NHD, NED

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

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

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

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

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 • Polylines or RCAs • Cumulative (distance from selected feature)

28. Analysis

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

31. Analysis

32. Export to distance matrices Instream distance Straight-line

33. Distance matrices (cont.) Downstream only Upstream only

34. Distance matrices (cont.) Proportion upstream Proportion downstream

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

36. Example: Coho salmon distances

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

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

39. SCALE: Grain Landscape River Network Microhabitat Microhabitat COARSE Climate Atmospheric deposition Geology Topography Soil Type Network Connectivity Stream Network Nested Watersheds Connectivity Flow Direction Network Configuration Drainage Density Confluence Density Vegetation Type Basin Shape/Size Land Use Topography 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 Cross Sectional Area Channel Slope, Bed Materials Large Woody Debris Overhanging Vegetation Substrate FINE Biotic Condition, Substrate Type, Overlapping Vegetation Detritus, Macrophytes Shading Detritus Inputs Biotic Condition Terrestrial Aquatic Peterson 2005

40. Example: 2D stream in Virginia

41. Example: 2D stream in Virginia

42. Example: 2D stream in Virginia

43. Example: 2D stream in Virginia

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

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