USGS Jeffrey Steuer; Middleton, WI Krista Stensvold; Middleton, WI Elise Giddings; Raleigh, NC

1. Relation of Physical Measures of Streamflow Conditions to Ecological Effects of Urbanization in Streams USGS Jeffrey Steuer; Middleton, WI Krista Stensvold; Middleton, WI Elise Giddings; Raleigh, NC Jerad Bales; Raleigh, NC

2. Explanaton WMIC EUSE Study Area WMIC Study Unit Water Streams Final watersheds Urban Index I 80-100 60-80 40-60 20-40 0-20 National Water-Quality Assessment (NAWQA) ProgramEffects of urbanization on stream ecology Milwaukee-Green Bay 30 Watersheds

3. Milwaukee-Green Bay – range of urbanization 30 Watersheds Watershed Size Range = 5 – 39 mi2 Urban land cover Range = 3- 99 percent Proportion population change 1990 – 2000 Range = -0.16 – 1.38

4. Problem review • Compare two time series data foundations for response to urbanization and association to stream biology. • Hydraulic variables (HEC); simulated hydraulic variables estimate direct stream conditions such as velocity, depth, shear stress, turbulence, bed exposure. • Hydrologic condition metrics (HCM); measures patterns of flow conditions during different time periods (magnitude, duration and freq of high, low flow and flow change).

5. 15m Eleven transects per ~150 m reach….. Jambo (UII = 0) Mapped imp = 1% Fox (UII=40) Mapped imp = 6% Rio (UII=10) Mapped imp = 1% Garners (UII=60) Mapped imp = 26% Hoods (UII=31) Mapped imp = 6% Lincoln (UII=100) Mapped imp = 45%

6. Transect measurements of instream and channel conditions…… 1.Bankfull width 2.Wetted channel width 3.Depth & velocity 1 2 3 Thalweg Fitzpatrick; modified

7. 4 5 Transect measurements of streambank characteristics….. 4.Bank angle 5.Bank height (bankfull depth)

8. First data foundation – Hydraulic (HEC) variable time series…. • Build upon habitat geometry, reach map, photographs, reach gradient (water surface slope at low Q) • Hydrograph (daily and hourly)

9. Energy balance – transect to transect Unsteady –storage, mass balance

10. Hydraulic model (Hec-Rasv3.1.2) • Limitations/assumptions: • Crude cross section data, estimated overbank slope • Rough elevation data • One dimensional (no lateral velocity gradient) • HEC model output - hydraulic variable time series for 11 transects – annual period of record (POR) and three seasons. • Flow • Wetted perimeter • Depth • Velocity • Stream power • Froude number • Water column Reynolds number • Bottom shear stress ~ 10,000 time series (30 sites, 8 variables, 11 transects, 4 POR)

11. Bottom shear stress for 11 transects at OAK Creek Each reach aggregated into a max, min, average value Summer POR

12. Maximum shear stress at 11 Oak Creek transects; two adjacent transects with lowest peak shear - variable “refug.2”

13. Maximum shear stress at transects for 25 streams two transect refuge denoted in red Summer POR Transects

14. Hydraulic model time series variables (continued…) Additional variables derived from the HEC generated time series • Refuge concept (shear) – Minimum shear stress in a “refuge” (2,3,4,5,6 adjacent transects) for a range of sizes • Exceed a threshold (shear)- Duration and integration of shear stress above a threshold (1, 2, 5, 20, 100 dyne/cm2) • Fraction exposed bed – estimated from wetted perimeter time series and fixed bed width (photos/survey)

15. Example hydraulic (HEC) variable relations Decreased motile algae with increase amount of exposed bed • Biological metrics we’ve selected - • Not assemblages in a multivariate fashion but do represent measures of communities which are meaningful (metrics that could be measured in a biomonitoring program). Invertebrate - Filter collector richness increased with minimum shear stress

16. Example hydraulic (HEC) variable relationscontinued.. Fall (hourly) Fish IBI had negative association with increased shear stress in the two transect refuge Fish IBI – Lyons et al., 1992 Refuge.2 bottom shear stress increased with urban intensity index

17. Second data foundation….. Hydrologic Condition Metrics (HCM)- measure patterns of flow conditions during different time periods (magnitude, duration and freq of high, low flow and flow change). [modified from Nature Conservancy indicators of hydrologic alteration (IHA)]

18. Examples of hydrologic condition metrics (HCMs). Area normalized hydrographs for a low and high UII site. Arrows indicate rises that area (flow) was seven times the median rise (PERIODR7). Pigeon had seven rises; Pokeberry had three rises. Fm Giddings; in review

19. Hydrologic condition metrics (HCMs) continued...Example of duration metric Storm hydrograph for a low and high UII site. Shaded area is portion of hydrograph above the 90 percent flow value (MDH90). Pigeon was 11 hrs; Pokeberry was 43 hrs. Fm Giddings; in review

20. Hydrologic condition metric (HCM) – biologic relations Diatom richness decreased with the duration of low flow during fall POR Invertebrate - EPT abundance had negative correlation with flow variation

21. Hydrologic condition metric (HCM) relations continued.. Fish IBI decreased with stream flashiness in the fall POR. With increased urbanization the duration of high flow (exceeded 10% of the time) was shorter.

22. Hourly based metrics (HCM and HEC) computed over 3 intervals (hourly data) and annual POR (daily data) Flow/area Flow/area

23. Mean spearman correlation coefficients (absolute value) for 37 biologic endpoints [Hydrologic condition metric (HCM); Hydraulic model variable (HEC)] CHANGE HYD to HEC… Blue value is maximum correlation within a group Overall… HCM relations ~15% stronger than HEC

24. Fish IBI regression tree modelbuild on hydraulic variable data foundation (daily data, annual POR)

25. Fish IBI regression tree modelbuild on hydrologic condition metric foundation (daily data, annual POR)

26. Hydrologic condition metric (HCM) tree regression models ~ 8% stronger (lower deviation) than hydraulic (HEC) variable models….. consistent with correlation results. • However hydraulic variables offer potential link between reach scale change and biologic endpoint…. BLOT

27. And a possible understanding to biologic mechanism….. Field experiment in 27 patches in 150 m reach – northeastern Spain. Examined invertebrate loss from bed with shear stress. Gibbins et al.; 2007.

28. Invertebrate drift became exponential at shear stress of 9 dyne/cm2 ….. Gibbins et al.; 2007

29. Our hydraulic modeling of refuge bottom shear stress at 25 sites is consistent with that finding….. Scraper abundance decreased with increasing shear stress in refug.2

30. findings to date • A 1- dimensional hydraulic model, based on the NAWQA habitat data and flow record, allowed us to examine hydraulic and habitat conditions throughout the water year. • Time series based hydrologic condition metrics (HCM) and hydraulic variables (HEC) had numerous significant biologic relations (algae, invertebrates, fish) across all POR. • HCM data foundation had stronger association with biology than the hydraulic data foundation. • Both foundations may provide link between watershed scale change and stream biology. • Hydraulic variables may provide mechanistic insight and provide a link between reach scale change (restoration) and biology.

31. Acknowledgements…..many, many people Many skill sets/backgrounds required….. USGS/NAWQA able to provide framework • Study design and management • Cathy Tate, Jerry McMahon, Tom Cuffney • Site installations and hydrology data collectors • Habitat and biology data collectors • Data processing – biology, hydrology, habitat • Hec-Ras model (30) construction and output processing