1. Hydrologic network metrics based on functional distance and stream discharge David Theobald & Mary Kneeland Natural Resource Ecology Lab Dept of Recreation & Tourism Colorado State University Fort Collins, CO 80523 USA May 16, 2003

2. Goal: develop approaches for spatio-temporal design and modeling in order to further our understanding of aquatic resources Objectives, to develop: 1. spatio-temporal models for a continuous response, 2. spatio-temporal models for count and/or categorical data, 3. design and analysis methods for data collected at different scales.

3. STARMAP Projects 1. Combining environmental datasets (Hoeting) 2. Local inferences (Briedt) 3. Development and evaluation of landscape indicators (Theobald) 4. Extension and outreach (Urquhart) 5. Integration and coordination (Urquhart)

4. Big questions: Broad-scale processes (e.g., acid deposition in Mid-Atlantic region) to watershed processes Probability-based sampling for state compliance to CWA Sampling perennial/intermittent streams (I.e. flow all year for most years) –What is perennial and shouldn’t be? (~24%) –What is not included and should be? (~18%) Fragmentation of hydrologic regime on biodiversity

5. Goals of indicator development Develop and evaluate landscape-level indicators suitable for spatial and temporal analyses of EMAP data Investigate limitations of currently- available data and offer new, robust methodologies

6. Overview of presentation Link watershed and hydrologic network: “…in every respect, the valley rules the stream.” – Hynes 1975 From surrogates to direct measures Towards network-based metrics

7. Indicators that measure watershed characteristics and aquatic ecology: Reviews 1. Land use in entire watershed vs. riparian buffer (IBI): -watershed better: Richards et al. 1996 -buffer better: Arya (1999); Lammert and Allan (1999) 2. Other indicators: - road density (Bolstad and Swank) - dam density (Moyle and Randall 1998) - amount of roads near streams (Moyle & Randall) and Arya (1999)

8. Key: measuring watershed- stream linkage? 1. Lumped measures - %, #, density 2. Spatially-explicit - Euclidean distance 3. Network-based (directional, cumulative) - Strahler stream order - Length of stream line - Watershed area 4. Direct network-based - Discharge?!

9. 1. Lumped % agricultural, % urban Ave road density Dam density (Moyle and Randall 1998) # mines Road length w/in riparian zone EPA. 1997. An ecological assessment of the US Mid-Atlantic Region: A landscape atlas. Southern Rockies Ecosystem Project. 2000.

10. 1. Lumped (cont.) ArcINFO, Basinsoft (Harvey and Eash 1996): –Drainage area, shape, relief –# O 1 streams, main channel length, stream density

11. 2. Spatially-explicit, Distance: As the crow flies (Euclidean)

12. 3. Network-based Distance: As the seed floats (downstream)

13. Distance: As the fish swims (down & up stream)

14. Distance: Upstream length - mainstem (2) - arbolate (1+2+3+4)

15. Upstream 66 km Downstream 298 km Mainstem Upstream 37 km Network 16 km (down) 6 km (up) RWTools ArcView v3 extension

16. Direct measures Surrogate, e.g. Strahler order: The usefulness of stream order assumes, with a sufficiently large sample, that order is proportional to stream discharge – Strahler 1957 Ordinal data Not robust to data artifacts

18. Link watershed and network 1 to 1 relationship between stream reach and catchment Need robust method of delineation for large extents

19. Pilot area: Colorado, Yampa

20. “Smart bump” delineation 1. Reach catchment - flowdirection 30 m DEM - watershed from buffered hydrology (USGS NHD 1:100K) 2. Differentiate local ridges (artifacts) from true catchment boundary - “smart bump” using ZONALMIN 3. Remove conversion slivers at shared boundaries - regiongroup - if <10 cells, NIBBLE Currently, 1-2 days processing time per basin

21. Comparison of automated vs. hand-delineated 1.Randomly selected 111 (out of 2151 watersheds) 2.Computed area of automated vs. hand-delineated (“truth”) 3.RMSE = 204.39 (in ha) 4.Mean error 2.4% 5.Challenges in defining commensurate watersheds

22. Hand- delineated “truth” watersheds 11% error

23. Reaches are linked to catchments 1 to 1 relationship Properties of the watershed can be linked to network for accumulation and networking operations Ordinal value (order) to real value (length, area, etc.)

24. Networking Import into ArcGIS Geometric Network Use networking tools, e.g. 1. Set flag 2. Trace upstream 3. Trace downstream

25. 4. Direct metric: stream discharge Physical-based model: Q = Precipitation – Evapotranspiration Q is VMAD (Virgin Mean Annual Discharge)

26. USGS Stream Gauges

27. R 2 =0.7282 P-value=3.407e-006

28. Surrogate  Direct metric Order Area Discharge

29. Fragmentation and flow regulation Deynesius and Nilsson, Science (1994) – 77% of upper 1/3 of northern hemisphere rivers are strongly or moderately affected - F = regulated/total channel length - R = % of VMAD (cumulative reservoir live, gross capacity) RCL TCL

30. Alteration of natural flow regime Accumulation of dam storage Tributaries below dams mediating flow modification?

31. Flow modification How to measure relative modification of hydrologic regime? 1. Degree of modification to flow = cumulative annual flow – cum. dam max. storage: Q’ = Q-S 2. Proportion of modified to VMAD ( “natural”) flow: F = Q’/Q

32. High Dam “shadow” Reservoirs

35. Or/CO Table of output data Expand this to other factors: e.g., geology, vegetation, etc. Linked to rest of data

36. EMAP sites

37. Oregon + dam accumulation + overlap of catchment area

38. Within catchment hydrologic distance Moved from basins, HUCs and watersheds to stream reach catchments Within catchment: –Distance along hydro network distance (distance along the network upstream of pour point) –Allocation (using flat weight surface) 1

39. Challenges Data NHD 1:100K Dams – NID Processes natural flow diversions ET

40. Data: attribute errors Irrigation canals and pipelines incorrectly attributed as river/stream

41. Data: positional error Spatial location of dam locations is imprecise ? ?

42. Data: duplicates Stagecoach reservoir is duplicated – Challenges of understanding diverse datasets

43. Data: missing data? ?

44. Scale Dam on tributary that is not in 1:100K network

45. NID dams (red) > 50’ high, many other dams (in yellow) and other structures!

46. Dam data

47. Western Water Assessment, Figure 7

48. Network metrics Have foundation – direct measure Build on/refine existing metrics: –# first order streams –Main-channel length –Total stream length –Drainage density = stream length / catchment area Examine location within network and make available to statistical models

49. EMAP sites

50. Euclidean distance 1 2 Use x,y to create distance matrix Reasonable for broad-scale processes

51. Hydrologic distance 1 2 Follows stream network 3 4

52. Spatial weights 11 2 3 5 4 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 W =

53. Functional distance Reflect distance weighted by: -Stream gradient -Geology -Land use -Etc. 1.7 1.2 1.9 1.0 A B C

54. Functional weighting 1 1 2 3 5 4 0 0 0 0 0 0 0 0.7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0 0 0 0.2 0.8 0 0.2 0.8 0 0 0.1 0.2 1.0 0.1 0.2 1.0 0 W = 6 7 E.g., downstream hydrology

55. Connectivity matrix To/ from 1234567 1 2 311 4111 5 6 7111111

56. Functional spatial weights StationDischarge (kacft) 11501 24 31515 49651 584 682 79972 1 2 3 4 5 6 7 StationOrderArea overlap (%, km2) Length (m) Discharge 1  35  5 98%=2900/2952 453299.00% 1  45  5 11%=2900/25316 4256815.00% 1  75  5 11%=2900/26001 5838915.00% 2  31  5 0.4%=14/2952 231210.20% 2  41  5 0.05%=14/25316 597150.04% 2  71  5 11%=14/26001 755360.04% 3  45  5 11%=2952/25316 3810515.00% 3  75  5 11%=2952/26001 5392515.00% 4  75  5 97%=25316/26001 1582096.00% 5  72  5 0.5%=145/26001 549640.80% 6  74  5 0.5%=140/26001 309330.80%

57. Incorporate watershed conditions? 1 1 2 3 5 4 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 W = 6 7 E.g., macroinvertebrates

58. Challenges Generating spatial weights matrix –O(n 2 )  O(n)? Functional (cost-weighted) spatial weights table

59. Products Watershed-reach network database GIS-based tool to develop functional spatial weights matrix ArcGIS extension for hydrologic network metrics

60. Thanks! Comments? Questions? Work funded by: US-EPA STAR Cooperative agreement CR829095 awarded to CSU STARMAP: www.stat.colostate.edu/~nsu/starmap www.stat.colostate.edu/~nsu/starmap RWTools: email davet@nrel.colostate.edu