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Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models. R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2 1 Michigan State Univ., East Lansing, MI 2 Univ. Michigan, Ann Arbor, MI - PowerPoint PPT Presentation
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Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-
stressor modelsR. Jan Stevenson1, M. J. Wiley2
D. Hyndman1, B. Pijanowski3, P. Seelbach2
1Michigan State Univ., East Lansing, MI 2Univ. Michigan, Ann Arbor, MI
3Purdue University, West Lafayette, IN
Project Period: 5/1/2003-4/30/2006; NCX 4/30/2007
Project Cost: $748,527Stevenson et al.
Natural Ecosystems Are Complex but can be Organized for Management
Septic Systems
SilvicultureLivestockGrazing
IrrigationCrop & Lawn
FertilizersConstruction
Organic/Part PNC
PO4NOxNH3 Heat SedimentsHydrologicVariability
NitrifyingBacteria
PeriphyticMicroalgae
BenthicMacroalgae
OtherBacteria
BenthicInvertebrates Fish
DissolvedOxygen
Sewers &Treatment
Herb BufferStrips
TreeCanopy
LivestockFences
Ret. Basins& Wetlands
Other BMPs
Light
Hu
ma
n A
cti
vit
ies
Str
ess
ors
En
dp
oin
ts
Ecosystem ServicesValued Ecological Attributes – Management Targets
Understanding how it all works:Complicating Issues
• Non-linearity and thresholds: – graded responses may be rare in complex systems. – thresholds complicate management choices.
• Complex causation: – multiple actions simultaneously shape biological responses. – issues of direct and indirect causation (effects): spurious
correlations
• Scale and dynamics: – Potential stressors operate at different spatial and dynamic
scales– Scales complicate the diagnosis of stressor-response
relationships• obscure causal dependencies through time lags, ghosts of past
events, and misidentification of natural spatial/temporal variability.
Stevenson et al.
Goals• Relate patterns of human landscape activity to commonly
co–varying stressors (nutrients, temperature, sediment load, DO, and hydrologic alterations)
• Relate those stressors to valued fisheries capital and ecological integrity of stream ecosystems
• Link empirical and mechanistic modeling approaches as a means to improving understanding and prediction
Stevenson et al.
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
Approach1. Building on other regional
assessment & modeling by our team (MI, IN, KY, OH, IL, WI)
2. Focus on basic interactions between landuse, hydrology, nutrients (CNP), and DO
3. Multi-scale Analysis:– Regional (Michigan)– (6) Focal Watersheds– Detailed Site monitoring
4. Modeling1. empirical (statistical)2. process-based (mechanistic)3. hybrids ( a little of both!)
using existing platforms and an integrated modeling system
Ecological significance• Our project is focused on the streams and rivers of the
Lower Michigan Peninsula.
• These are the veins and arteries of the Laurentian Great Lakes, the largest and most complex river-lake ecosystem in the world.
• What we learn here about multiple stressors is applicable in fluvial ecosystems anywhere.
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
Key findings1. Urban land use is a stronger stressor than agricultural land
use but agricultural impacts are more widespread.
2. Legacy impacts of landuse can be as important as current impacts.
3. Agricultural impacts appear to occur through a complex but tractable interaction of nutrient, hydrologic and metabolic stressors.
4. Impacts of specific stressors and their interaction varies with ecological setting in general; and specific hydraulic setting in particular.
5. Management expectations (ecological targets and assessment scoring criteria) need to be conditioned by ecological context of the site in question.
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
Lessons Learned
• Where exactly you look (sample locale), and at what scale you look (sample extent and frequency), affects what you can see (and model)
• We need more concise language to talk about multiple stressors and stresses [incorporate concepts of frequency, duration, co-variation and interaction, contingency]
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
Interactions & Users
• MDEQ nutrient criteria development• MDNR groundwater protection criteria• EPA nutrient criteria workgroups • MDNR Ecoregional management teams• GLFT Lake Michigan Tributary Assessments• Local watershed groups (MWA, HRWC, MiCORP)
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
Graduate students supported: total of 10 across all 3 institutions
M.S. theses developed/completed: 4
Extensive linkage with other EPA-Star, NSF, Great Lakes Fisheries Trust,
and Great Lakes Fisheries Commission projects
G2M104070
Developing relations among human activities, stressors, and stream ecosystem responses for integrated regional, multi-stressor models
2006a Progress Report1. Late start first year, 2004 first
extensive field year, NCX to 2007
2. Analyses of regional, aggregated data sets underway! {first looks}
3. Analysis of 2004 and 2005 focal basin surveys continues {some highlights}
4. intensive hydrologic and WQ monitoring continues in Cedar and Crane Creeks
5. Integrated process modeling running for Cedar, underway for Brooks, Bigelow, & Crane {description and early results}
Large, Regional-Scale Statistical Large, Regional-Scale Statistical ModelingModeling • Urban and agricultural land use as key multiple stressorsUrban and agricultural land use as key multiple stressors
– Relative impacts?Relative impacts?– Direct and indirect effects? {watersheds and riparian buffers}Direct and indirect effects? {watersheds and riparian buffers}– Causal relationships? Intervening variables?Causal relationships? Intervening variables?
• Data assembled from MDEQ, Michigan Rivers Inventory, Data assembled from MDEQ, Michigan Rivers Inventory, previous EPA-STAR, NSF, Muskegon River Assessment; previous EPA-STAR, NSF, Muskegon River Assessment; registered on attributed NHD database (EPA-STAR/USGS registered on attributed NHD database (EPA-STAR/USGS AQGAP product)AQGAP product)
• Used regional Normalization approach to standardize Used regional Normalization approach to standardize datasets and metrics (fish and invertebrate)datasets and metrics (fish and invertebrate)
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$N
EW
S
MDEQ siteResearch site
N
EW
S
N
EW
S
MDEQ siteResearch siteMDEQ siteResearch site
Interpolation of normalized overall fish and invertebrate score
Interpolation of normalized overall fish score
Interpolation of normalized overall
invertebrate score
Tamarack Creek watershed
Interpolation of normalized overall fish and invertebrate score
Interpolation of normalized overall fish score
Interpolation of normalized overall
invertebrate score
Tamarack Creek watershed
Legend
Bad
Very poorPoorThreatenedAcceptableGood
Legend
Bad
Very poorPoorThreatenedAcceptableGood
Regional modeling ofMultiple-source assessment datasets:Patterns of human activitiesand fluvial ecosystem response
Data coverage
Table 6. Impairment classifications (% of total sites in basin) based on fish
and invertebrate assemblage summary score (average of normalized scores
for fish and invertebrates) in the five Great lakes basins. Normalized scores
were classified as good (>0.5), acceptable > -0.5 and <0.5), threatened (< -
0.5 and > -1.0), poor (< -1.0) and very poor (< 2.0).
Good Acceptable Threatened Poor Very poor
Erie (n=458) 5% 21% 22% 31% 21%
St Claire (n=89) 2% 26% 31% 27% 13%
Michigan (n=1359) 11% 36% 15% 28% 10%
Huron (n=665) 15% 40% 17% 22% 6%
Superior (n=139) 19% 40% 12% 22% 6%
Statewide (n=2765) 11% 34% 17% 27% 11%
Fish & Invertebrate Multi-Metric
5%, 50% 1%, 8%
r= -.36r= -.20
r= -.29
Regional “~dose-response” relationships to Land use StressorsIndicator: normalized EPT score [(obs-exp)/sd]
%Urban in riparian buffer%Ag in riparian buffer
Urb and Ag: geom. meanNoiseyLinear(izable)Urb > Agthresholds
.02
xWT_agxWT_urb
.85
xRT_urb
.81
xRT_ag
.18
avgJntN_EPT
er3
er2
er4
er1
.92.78
-.24-.02
.04
.32
.07
-.15
-.36
Standardized Total Effects - Estimates
xWT_urb xWT_ag xRT_ag xRT_urb
xWT_ag -0.152 0.000 0.000 0.000
xRT_ag -0.118 0.776 0.000 0.000
xRT_urb 0.923 0.000 0.000 0.000
nEPT -0.354 -0.189 -0.244 -0.023
Issues of direct and indirect effects: •Urbanization of Ag areas•Multiple ways to represent land use/cover
Structural Equation Modeling tosort out direct, indirect and total effects
VEA:EPT score
watershed
Riparian buffer
Results:Overall Urban stronger than AgRiparian Ag > than Basin AGBasin Urban > Riparian Urban
Best fitting, structurally plausible model
TerminalNode 1
Class = 1-2Class Cases %
1-2 780 56.43 225 16.34 296 21.45 82 5.9
N = 1383
TerminalNode 2
Class = 3Class Cases %
1-2 138 38.43 88 24.54 104 29.05 29 8.1
N = 359
TerminalNode 3
Class = 4Class Cases %
1-2 63 25.63 41 16.74 112 45.55 30 12.2
N = 246
Node 3WT_AGR <= 48.500
N = 605
Node 2WT_URBAN <= 5.500
N = 1988
TerminalNode 4
Class = 5Class Cases %
1-2 14 6.73 18 8.64 75 35.95 102 48.8
N = 209
Node 1WT_URBAN <= 22.500
N = 2197
Training data Predicted
Observed N % Correct 1-2 3 4 5
1-2 995 78.392 780 138 63 14
3 372 23.656 225 88 41 18
4 587 19.08 296 104 112 75
5 243 41.975 82 29 30 102
Test (%20 withheld from training) Predicted
Observed N % Correct 1-2 3 4 5
1-2 248 77.016 191 31 20 6
3 105 16.19 68 17 15 5
4 145 13.103 79 26 19 21
5 58 36.207 16 12 9 21
Attainment class thresholdsBasin Urban <= 5.5% or > 22.5%Basin Ag <=48.5%
CART model fish & invert based Attainment Class
Interpolation of normalized overall fish and invertebrate score
Interpolation of normalized overall fish score
Interpolation of normalized overall
invertebrate score
Tamarack Creek watershed
Interpolation of normalized overall fish and invertebrate score
Interpolation of normalized overall fish score
Interpolation of normalized overall
invertebrate score
Tamarack Creek watershed
Legend
Bad
Very poorPoorThreatenedAcceptableGood
Legend
Bad
Very poorPoorThreatenedAcceptableGood
CART of normalized overall fish and invert multi-metric
Statistical Modeling of Focal Basin dataset Agricultural impacts on Stream Ecosystems (6 )100-300 mi2 systems representing a targeted gradient of agricultural land cover
• Cedar Creek– hIgh value fishery with Ag impacts, threatened by development
• Bigelow– Pristine high value fishery resource
• Mill Creek• Brooks Creek
– threatened by developmentcurrently with signif agricultural
• Crane Creek • Sycamore Creek
– intensive agricultural impacts
What is the nature of biological responses to agricultural land use?
1. The case for chronic metabolic stresses– Agricultural land use and nutrients– Agricultural land use and dissolved oxygen dynamics
2. Highly variable response tied to variation in hydrologic/hydraulic/DO regime
Meso-scale empirical modeling(6) stream systems sampled across Ag and Hydrologic gradients
Organic Carbon (COD) Inorg Nitrogen (ppm) Phosphorus (ppm) PM oxygen (ppm)
%
met
abo
lic
con
form
ers
EP
T T
axa
%
surf
ace
bre
ath
ers
% Riparian Buffer area in Ag % Riparian Buffer area in Ag % Watershed area in Ag
% Watershed in Ag % Watershed Ag % Watershed in Ag
Multiple Local (direct) Stressors response to Agriculture (indirect stressor)
% Riparian Buffer area in Ag
Biological response to indirect Landscape stressors
Early Morning D.O. levels
Site-Intensive data collection &Site-Intensive data collection &Integrated Mechanistic Integrated Mechanistic ModelingModeling• Test hypothesis that cause-effect relations in Test hypothesis that cause-effect relations in
regional statistical models are plausibleregional statistical models are plausible
• Understand how multiple stressors interact to Understand how multiple stressors interact to cause biological responsecause biological response
– Cedar Creek **Cedar Creek **– Mill Creek*Mill Creek*– Brooks Creek*Brooks Creek*– Crane Creek *Crane Creek *– Sycamore CreekSycamore Creek– Bigelow*Bigelow*
SepticSystems
SilvicultureLivestockGrazing
IrrigationCrop & Lawn
FertilizersConstruction
Organic/Part PNC
PO4NOxNH3 Heat SedimentsHydrologicVariability
NitrifyingBacteria
PeriphyticMicroalgae
BenthicMacroalgae
OtherBacteria
BenthicInvertebrates Fish
DissolvedOxygen
Sewers &Treatment
Herb BufferStrips
TreeCanopy
LivestockFences
Ret. Basins& Wetlands
Other BMPs
Light
Hu
man
Act
ivit
ies
Str
esso
rs
Integrated Modeling of Cedar Creek
Q(cfs) Conductivity (uS) NOx-N (pbb) TP (pbb)0.0 824 101 1201.0 670 102 901.1 521 522 121
15.9 278 197 5318.4 293 209 4324.4 293 156 4824.5 300 150 10
- Spatially & temporally intensive water chemistry and biological sampling
Holten
River Rd.
Holten to River Rd. RatiosCatchment area ratio= 26%Typical storm peak ratio = 80%Average flow ratio= 3%
Max Q = 250 cfs
Mean Q =2cfs
groundwaterRunoff [ 67%]
Max Q = 200 cfs
Mean Q =46cfs
Groundwater [95%]
Runoff [ 5%]75
1
Qr
1.223 1042 r
75
1
Qr
4412 r
Holten Gage
River Rd. Gage
Poor
Below expectation
Acceptable
Excellent
Biological Quality
Cedar Creek BasonMulti-Stressor Project
Site Name Fish score EPT score AverageReeman Road 0.00 0.00 0.00Brickyard N 0.29 0.00 0.14Holten 1.00 0.93 0.96Ryerson Road 0.50 0.93 0.71Sweeter Road 0.83 1.00 0.92River Road 1.67 0.50 1.08Below River Rd 0.58 0.55 0.57
Observed/Expected diversity
Habitat stressoxygen
temperaturebed transport
Surface abstraction
Weather model*
Groundwater Model
BasinRouting transforms
ChannelRouting transforms
Channel hydraulicswidthdepth
velocityshear
Thermograph
HEC-HMSum HEC-HMSum
HEC-RASum
MODFLOWmsu
KendallPREPmsu
DOSMOSCum
SRTMum
Landcover model*
0 50 100 150 200 250 300 35002468
1012141618202224
24
0
O2j
SAT j
tempj
daz0 hourj
24
* or historical data
Model accumulates hrs [or relative freq] of oxygen and bed mobilization stress over long period runs (e.g. 1-2 years)
LTM2purdue
Linking local-scale mechanistic models forCausal evaluation and modeling experiments
MT3DmsuQUAL2Kmsu
Or Water Quality
Data
Hydrologic Modeling:Simulate Transient Fluxes to SW
• Preprocessor & MODFLOW– Inputs:
• Land Use (historical & LTM2)• Regional Geology• NEXRAD Precipitation• NOAA Snow Depth• MODIS LAI• DEM• Solar radiation
• HEC-HMS– Surface Water and channel routing
NEXRAD for Expanded Muskegon
Mukegon Expanded watershed boundary with NEXRAD gridcells used for extracting spatially variable precipitation overlaied
10 yrs + 10 synth
Monthly Vegetation Density Distribution in Expanded Muskegon and Cedar Creek
1km resolution MODIS LAI grids showing vegetation density over the expanded Muskegon and Cedar Creek watersheds
Leaf Area Index (LAI)
<1
1-2
2-3
3-4
4-5
5-6
6-7
Cedar Creek watershed
Expanded Muskegon watershed
Weekly Leaf Area Index ModelBased on MODIS coverage
Results
• % of precipitation that becomes recharge
• Landuse effects
Recharge
Cedar Creek well recharge monitoring
Regional analyses indicate reduced recharge in agricultural vs forest watersheds
Results
– Observations
MODFLOW
180
190
200
210
220
230
180 190 200 210 220 230
Simulated Head, m
Ob
se
rve
d h
ea
d, m
Pre-1988 Observations
1988-2004 Observations
All head observations:
R2 = 0.81
Pre-1988:
R2= 0.79
1988-2004:
R2=0.89
Results
MODFLOW
0
50000
100000
150000
200000
1/1/2003 1/1/2004
Q,
m3
/d
Actual Streamflow
Extracted Baseflow
Simulated Baseflow
0
20000
40000
60000
80000
1/1/2003 1/1/2004
Q,
m3
/d
Actual Streamflow
Extracted Baseflow
Simulated Baseflow
Upper Cedar Creek
Lower Cedar Creek
Nitrate Transport Simulation (MT3D)
• Used GW model fluxes
• Nitrate sources– Atmosphere– Agricultural lands– CAFOs– Septic systems
• Nitrate fluxes exported to stream ecohydrology model
NO3, mg/L
Simulating Water Chemistry and Biological Response in Cedar Creek
• Using nitrate fluxes to Cedar Creek calculated in transport model
• QUAL2K
8
9
10
11
12
13
14
0 5 10 15 20Distance Downstream (km)
Wat
er T
empe
ratu
re (
°C)
Simulated Water Temperature
Observed Water Temperature
4
6
8
10
12
0 5 10 15 20
Distance Downstream (km)
Dis
solv
ed O
xyge
n (m
g/L
)
0
40
80
120
160
Simulated Dissolved Oxygen
Observed Dissolved Oxygen
Simulated Dissolved Oxygen Saturation
Observed Chlorophyll
0
500
1000
1500
2000
0 5 10 15 20
Distance Downstream (km)
Nitr
ate
+ N
itrite
(ug
N/L
)
Observed Nitrate
Simulated Nitrate
Coupling models to generate realistic processes
Recharge Model
MODFLOW
MT3D
QUAL2Kw
Site Biological response(annual)
Recharge
Groundwater fluxes
Nitrate fluxes
Stream concentrations
Recharge Model
MODFLOW
HEC-HMS
HEC-RAS
MRI-DOHSAM
Recharge
Groundwater fluxes
Watershed hydrology
Channel hydraulics
Cum metabolic stress
(hr)(hr)
(hr)
(hr)
(day)
(day) (day)
(day)
20 40 60 80 100 120 140 160 1800123456789
101112
12
0
O2 j
SAT j
daz 2410 hour j
20 40 60 80 100 120 140 160 1800
0.5
1
diffcoef j
1
data floor hourj 1 ddepth
speed floor hourj
hour j
0.01 0.1 10.01
0.1
1
10
100100
.01
SortO2i
1.01 exceedFreqi
0.01 0.1 11 10
3
0.01
0.1
1
10
100
SortSheari
exceedFreqi
1 1041 10
30.01 0.1 1 10
2
1
0
1
stressthreshold O2
shear
D84
0.01 0.1 10.01
0.1
1
10
100
SortO2i
exceedFreqi
0.01 0.1 11 10
3
0.01
0.1
1
10
100max shear( )
.001
SortSheari
1.01 exceedFreqi
Exceedence frequencies forDissolved oxygen and bed mobilization
Specified stress thresholds:O2 : 4 ppmIncipient Bed mobilization : ratio of ave. shear to D84critical shear/5
Stress summary: as % of periodScour_stress = 56.8O2 stress = 2.5Combined = 59.1Simultaneous = <.1CMSI
MRI_DOHSAMcumulative DO & Hydraulic Stress
Assessment Model
8 day simulation for Crane Creek Outlet channel using observed flow temp, depth and velocity data from an up-looking doppler sensor.
Loading parameters BOD = 8 ppm, NH4=.2 ppm
d84 4 ppm
%MC
cum O2 stress 1: .533 .0 .153 .00 .00 .031cum bed mobil 2: .00 .003 .01 .02 .06 .00 % Ag in Basin 57% 42% 37% 18% 18% 15%% Ag in RT 41% 33% 29% 21% 21% 14%
Integrated Modeling of Cedar CreekStress Assessment: year 2003 NexRAD with 1998 Landcover
%MC %MC%MC EPT EPT EPTEPT
%MC = % of taxa that are Metabolic ConformersEPT = count (# species) of EPT Taxa
Field data from our Biological Assessment
0
5
10
15
1 2 3 4 5 6
Modeling Multiple stressors: hydraulics, temp, NH4, TP, BOD
Sensitive taxa
EPT
Metabolic conformers
Num
ber
of g
ener
a
@Brickyard @Crystal @M-120 @ Ryerson @Sweeter @River Rd
2
4
6
8
10
12
14
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6
Cedar_metrics
EPT Taxa
Metabolic Conformers
y = 12.094 - 16.126x R= 0.96021
y = 9.9731 - 13.722x R= 0.9414
Obs
erve
d N
umbe
r of
gen
era
Modeled cumulative oxygen stress
COD
TP
NH4
Temp
Hydraulics
Relative effect{as % reduction}in total stress score
-53%
-0%
-4%
-73%
-81%
Cedar Creeke.g. Model “experiment” 1Cedar@Brickyard site
What are the individualeffects of each stressorOn cumulative stress?
•Sum >100%•Hydraulics>temp>WQ
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0 50 100 150 200 250 300 350
[simulating simple response to a single stressor]
@brickyard@crystal lake rd@m-120@ryerson rd@sweeteer rdBelow river rd
Cum
ulat
ive
Met
abol
ic S
tres
s In
dex
TP ppb
e.g. Cedar Creek Modeling “experiment” 2
eliminating BOD and NH4 effectsHow do the sites respond to a TP gradient?
@brickyard
@m-120
Below river rd
All others
How spatially variable is Cedar Creeks response to TP loading?
C&N set lowBOD=1NH4=.02 ppm
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0 50 100 150 200 250 300 350
Cum
ulat
ive
Met
abol
ic S
tres
s In
dex
TP ppb
@brickyard@crystal lake rd@m-120@ryerson rd@sweeteer rdBelow river rd
Given current BOD and NH4 stressorsHow do the sites respond to a TP gradient?
e.g. Cedar Creek Modeling “experiment” 3
[simulating response to a single stressor in a Multi-Stressor setting]
@brickyard
@m-120
Below river rd
All others
Current concs
How spatially variable is Cedar Creeks response to TP loading?
Current elevatedC and Nconcs
-120
-100
-80
-60
-40
-20
0
20
40
0 50 100 150 200 250 300 350
Cum
ulat
ive
Met
abol
ic S
tres
s In
dex
TP ppb
[simulating response to a single stressor in a multi-stressor setting]
@brickyard@crystal lake rd@m-120@ryerson rd@sweeteer rdBelow river rd
e.g. Cedar Creek Modeling “experiment” 3
Response to TP relative to current conditions
@brickyard
@m-120
Below river rd
All others
Final Steps• Model refinements
– Regional & focal watersheds
• Complete model integration for focal watersheds• Validate using bio-assessment data• Re-visit regional empirical models based on mechanistic
model insights; improve with stratification?