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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