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

Relation of Physical Measures of Streamflow Conditions to Ecological Effects of Urbanization in Streams

USGSJeffrey Steuer; Middleton, WI

Krista Stensvold; Middleton, WI

Elise Giddings; Raleigh, NC

Jerad Bales; Raleigh, NC

National Water-Quality Assessment (NAWQA)

Program Effects of urbanization on

stream ecology

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

Milwaukee-Green Bay 30 Watersheds

Milwaukee-Green Bay – range of urbanization30 Watersheds

Watershed Size Range = 5 – 39 mi2

Urban land cover Range = 3- 99 percent

Proportion population change 1990 – 2000 Range = -0.16 – 1.38

Problem review

• Compare two time series data foundations for response to urbanization and association to stream biology.

1. Hydraulic variables (HEC); simulated hydraulic variables estimate direct stream conditions such as velocity, depth, shear stress, turbulence, bed exposure.

2. Hydrologic condition metrics (HCM); measures patterns of flow conditions during different time periods (magnitude, duration and freq of high, low flow and flow change).

Eleven transects per ~150 m reach…..

15m

Rio (UII=10)Mapped imp = 1%

Hoods (UII=31)Mapped imp = 6%

Jambo (UII = 0)Mapped imp = 1%

Lincoln (UII=100)Mapped imp = 45%

Garners (UII=60)Mapped imp = 26%

Fox (UII=40)Mapped imp = 6%

Transect measurements of instream and channel

conditions……

1.Bankfull width

2.Wetted channel width

3.Depth & velocity 1

2

3ThalwegFitzpatrick; modified

Transect measurements of streambank characteristics…..

4.Bank angle

5.Bank height

(bankfull

depth)

4

5

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)

Energy balance – transect to transect

Unsteady –storage, mass balance

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)

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

Summer POR

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

Maximum shear stress at transects for 25 streams two transect refuge denoted in red

Summer POR

Transects

Hydraulic model time series variables (continued…)

1. Refuge concept (shear) – Minimum shear stress in a “refuge” (2,3,4,5,6 adjacent transects) for a range of sizes

2. Exceed a threshold (shear)- Duration and integration of shear stress above a threshold (1, 2, 5, 20, 100 dyne/cm2)

3. Fraction exposed bed – estimated from wetted perimeter time series and fixed bed width (photos/survey)

Additional variables derived from the HEC generated time series

Example hydraulic (HEC) variable relations

Invertebrate - Filter collector richness increased with minimum shear stress

March 2004

8

10

12

14

16

18

20

0 10 20 30 40 50

Minimum shear stress; reach averaged (min.tau; dyn/cm2)

Filt

er C

olle

ctor

Ric

hnes

s (Q

_FC

) .

R2 = 0.58

fall (hourly)

R2 = 0.35

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.00 0.05 0.10 0.15 0.20Fraction of exposed bed

R_

Gu

ild 8

_rA

.

R_Guild 8

Log.(R_Guild 8)

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).

Example hydraulic (HEC) variable relations continued..

Fall (hourrly)

0

10

20

30

40

50

60

0 20 40 60 80 100 120 140 160 180 Two transect refuge (refuge.2; dyne/cm2)

Fis

h IB

I

Fish IBI

Log. (FishIBI)

R2 = 0.46

Fish IBI had negative association with increased shear stress in the two transect refuge

Refuge.2 bottom shear stress increased with urban intensity index

Fall (hourly)

Fish IBI – Lyons et al., 1992

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)]

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

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

Hydrologic condition metric (HCM) – biologic relations

Invertebrate - EPT abundance had negative correlation with flow variation

Diatom richness decreased with the duration of low flow during fall POR

Fall (hourly)

R2 = 0.46

30

35

40

45

50

55

60

65

70

75

80

0 5 10 15 20 25 30Median duration of low flow (hrs; MDL_5)

D.d

tm.R

ich

.

D.dtm.Rich

Log.(D.dtm.Rich)

Hydrologic condition metric (HCM) relations continued..

Fish IBI decreased with stream flashiness in the fall POR.

Fall (hourly)

R2 = 0.63

0

10

20

30

40

50

60

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Richard_Baker flashiness index (rb_flash)

Fis

h IB

I .

Fish IBI

Linear(FishIBI)

With increased urbanization the duration of high flow (exceeded 10% of the time) was shorter.

Hourly based metrics (HCM and HEC) computed over 3 intervals (hourly data) and annual POR (daily data)

Low UII

0

100

200

300

400

500

600

700

800

900

1,000

10/01/03 12/01/03 01/31/04 04/01/04 06/01/04 08/01/04 10/01/04

Black Creek (hr)

Black (daily)

Hourly data- missing

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

10/1/03 12/1/03 1/31/04 4/1/04 6/1/04 8/1/04 10/1/04

Lincoln Creek (hr)

Lincoln (daily)

High UII

Spring 2004

summer 2004fall 2003

Flo

w/a

rea

Flo

w/a

rea

Mean spearman correlation coefficients (absolute value) for 37 biologic endpoints

[Hydrologic condition metric (HCM); Hydraulic model variable (HEC)]

Overall… HCM relations ~15% stronger than HEC

Blue value is maximum correlation within a group

CHANGE HYD to HEC…

Fish IBI regression tree model build on hydraulic variable data foundation (daily data, annual POR)

Fish IBI regression tree model build on hydrologic condition metric foundation (daily data, annual POR)

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

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.

Invertebrate drift became exponential at shear stress of 9 dyne/cm2 …..

Gibbins et al.; 2007

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

0

5

10

15

20

25

30

35

40

45

100 600

R_S

C

Summer; maximum shear in refug.2

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50 60 70 80 90 100Shear stress (dyn/cm2)

R_S

C_

pa .

refuge (2)

9 dyn/cm2

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.

Acknowledgements…..many, many people

• 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

Many skill sets/backgrounds required…..

USGS/NAWQA able to provide framework