R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2

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


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


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


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


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


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


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




invertebrate score


Legend

Bad

Very poorPoorThreatenedAcceptableGood

Legend

Bad


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


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


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




invertebrate score





invertebrate score


Legend

Bad


Legend

Bad


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


Given current BOD and NH4 stressorsHow do the sites respond to a TP gradient?


[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]



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?

R. Jan Stevenson 1 , M. J. Wiley 2 D. Hyndman 1 , B. Pijanowski 3 , P. Seelbach 2

Documents

multiple stressors

metabolic stressors

potential stressors

stream ecosystem responses

agricultural impacts

covarying stressors

complex causation

multistressor modelsr