developing relations among - us epa

Developing relations among human activities, stressors, and stream ecosystem responsesfor 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, INProject Period: 5/1/2003-4/30/2006

Project Cost: $748,527evenson et al

Goals• Relate patterns of human 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.

Natural Ecosystems Are ComplexSeptic

Systems Silviculture LivestockGrazing IrrigationCrop & Lawn

Fertilizers Construction

Organic/Part PNCPO4NOxNH3 Heat Sediments Hydrologic

Variability

NitrifyingBacteria

PeriphyticMicroalgae

BenthicMacroalgae

OtherBacteria

BenthicInvertebrates Fish

DissolvedOxygen

Sewers &Treatment

Herb BufferStrips

TreeCanopy

LivestockFences

Ret. Basins& Wetlands Other BMPs

Light

Stevenson et al.

Natural Ecosystems Are Complexbut can be Organized for Management

SepticSystems Silviculture Livestock

Grazing IrrigationCrop & LawnFertilizers Construction

Organic/Part PNCPO4NOxNH3 Heat Sediments Hydrologic

Variability

NitrifyingBacteria

PeriphyticMicroalgae

BenthicMacroalgae

OtherBacteria

BenthicInvertebrates Fish

DissolvedOxygen

Sewers &Treatment

Herb BufferStrips

TreeCanopy

LivestockFences

Ret. Basins& Wetlands Other BMPs

Light

Hum

an A

ctiv

ities

Stre

ssor

sEn

dpoi

nts

Ecosystem ServicesValued Ecological Attributes – Management Targets

TroutBassALU

Complicating Issues>Opportunities

• 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)

• 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.

Approaches1. Building on other

assessment & modeling by team (MI, IN, KY, OH, IL, WI)

2. Multi-scale approach:1. reach scale vs watershed2. regional vs intensive site

3. Modeling1. empirical (statistical) models2. process-based (mechanistic)

models using existing platforms and an integrated modeling system

Where We Are Working

(New Data)1. Early morning DO surveys2. Reach metabolism models

3. Watershed LULC (MRW & all MI)4. Watershed modeling

Regional, Reach Scale Statistical Models

• E.g. DO = f (TP), DO = f (TP, stream gradient)• Early morning, baseflow sampling

– 2004, 74 sites– 2005, 98 sites

• Endpoint: dissolved oxygen minima• Stressors

– Direct: water column algae, benthic algae– Indirect: nutrients, temperature, land use, hydrologic

features• Classification variables: e.g. watershed gradient• Used in MDEQ Nutrient Criteria Development

Comparison of DO = f(TP) for surveys without and with early morning sampling

constraint

10 1000

5

10

15

20

25

10 1000

5

10

15

20

25

DO

(ppm

)

TP(ppb)

Early Morning7-22:00

R2 = 0.056p = 0.007β = -1.014

R2 = 0.102p < 0.001β = -0.865

Thresholds, Nutrient Criteria & % Use Support

10 100TP (ppb)

0

5

10

15

DO

(pp m

)

0 5 10 15 20 25TP (ppb)

0.00.10.20.30.40.50.60.70.80.91.0

Frac

tion

of D

ata

0 5 10 15 20 25TP (ppb)

0.00.10.20.30.40.50.60.70.80.91.0

Frac

tion

of D

ata

2004+2005 Early Morning DO Survey2005 7-22:00 Survey

Potential covarying factors: gradient, flow, GW input

Indirect indicators of nutrient availability often better than direct measures

(2004 survey data only)

10 100TP (ppb)

0

5

10

15

0 20 40 60 80 100% Ag Land Use

0

5

10

15

DO

(ppm

)

R2 = 0.028p = 0.115

R2 = 0.260p < 0.001

Interpretation of Indirect RelationshipsSeptic

Systems Silviculture LivestockGrazing IrrigationCrop & Lawn

Fertilizers Construction

Organic/Part PNCPO4NOxNH3 Heat Sediments Hydrologic

Variability

NitrifyingBacteria

PeriphyticMicroalgae

BenthicMacroalgae

OtherBacteria

BenthicInvertebrates Fish

DissolvedOxygen

Sewers &Treatment

Herb BufferStrips

TreeCanopy

LivestockFences

Ret. Basins& Wetlands Other BMPs

Light

Why indirect relations more

precise?1. Other factors

regulate DO, too1. flow, 2. GW flow,3. org matter,4. temp…

2. P does not regulate BOD in low gradient streams

3. TP ≠ PO44. ……..

Chl a/Nutrient Model Improves with Diatom Inferred TSI

10 100Total P (µg/L)

0.10

1.00

10.00

100.00

Ben

thi c

Chl

a (µ

g/c m

2 )

2.5 3.5 4.5 5.5MAIA TSI

0.10

1.00

10.00

100.00

R2=0.270P<0.001

R2=0.053P=0.007

Site-Intensive, Reach ScaleProcess Based Modeling

1. Refine processed based models

2. Test hypothesis that cause-effect relations in regional, statistical models are plausible

• Crane Creek– > Severe DO

problems

Anthropogenic stressorsNatural drivers

Climatechange

UrbanizationAgricultureChannel

modifications

NutrientsBOD

NBOD

Climate

LandscapeStructure

Biologicalmetabolism

HydrologyHEC-HMS or Gauge records

Channel hydraulicsHEC-RAS or acoustic doppler

MRI_DOHSAMCumulative DO and Hydraulic Stress

Assessment Model

Coupling Reach-specific modeling to explore Multi-stressor dynamics

High resolution oxygen and flow monitoring

at Crane Creek

In collaboration with USGS & USFWS, high resolution data arebeing generated in Crane Creek (a watershed of the Ottawa National Wildlife Refuge) using a combination of (2) fixed station, telemetered YSI 6000 sondes; short-term mobile platforms with recording doppler sonar units (Sontek PC-ADP, ADP, and shallow-water Argonaut units) and YSI 600 series sondes; and an array of digital water level recorders.

http://www.wqdata.com/

20 40 60 80 100 120 140 160 1800123456789

101112

12

0

O2j

SATj

daz 24⋅10 hourj

0.01 0.1 10.01

0.1

1

10

100100

.01

SortO2i

1.01 exceedFreqi0.01 0.1 1

1 .10 3

0.01

0.1

1

10

100max shear( )

.001

SortSheari

1.01 exceedFreqi

20 40 60 80 100 120 140 160 1800

0.5

1max depth( ) 1.5⋅

0

diffcoefj

1

ddepth floor hourj( )speed floor hourj( )

daz 24⋅10 hourj

Exceedence frequencies forDissolved oxygen and bed mobilizationStress summary: as % of period

Scour_stress = 56.8O2 stress = 2.5Combined = 59.1Simultaneous = <.1

MRI_DOHSAMcumulative DO & Hydraulic Stress

AssessmentModel

{under development}

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

Specified stress thresholds:O2 : 4 ppmIncipient Bed mobilization : ratio of ave. shear to D84critical shear/5

Open/bare

Forest

Urban

Temperature Wetlands Water

Agriculture

-0.8

0

0.8

R

DO = f (% LULC)

Regional, Watershed Scale

Statistical Models

0.00

0.25

0.50

SRP TP

R

Total Sourceshed Riparian Buffer• Endpoints & Stressors

= f (land use/cover, natural landscape features)

• Refine inference models for watershed contamination based on flow-path weighted “routes of exposure/transport”

Flow-path Weighted LULC Watershed Characterizations

Value of cell represents distance of center point of DEM cell (at 26m) from Sample point if water flows through the DEM

The amount of uses aggregated by flow length distances in km for total sourceshed in Cedar Creek

Flow Path-dependent Distances

0

1000

2000

3000

4000

5000

6000

7000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

distance (in km)

coun

t

urbanagshrubfor

Watershed Scale, & Intensive

Processed-Based Models

• Endpoints & Stressors • = f (land use/cover, natural

landscape features)• Refine inference models for

watershed contamination based on flow-path weighted “routes of exposure/transport”

Cedar Creek example

Cedar Creek (GW influenced watershed)

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

Groundwater Modeling:Simulate Transient Fluxes to SW

• MODFLOW• Inputs:

– Land Use– Regional Geology– NEXRAD Precipitation– NOAA Snow Depth– MODIS LAI– DEM– Solar radiation– Streamflow (transducer)

Upper Cedar Creek

0

20000

40000

60000

80000

1/1/2003 1/1/2004

Q, m

3/d

Actual StreamflowExtracted BaseflowSimulated Baseflow

MODFLOW simulates the groundwater component of streamflow well

Lower Cedar Creek

0

50000

100000

150000

200000

1/1/2003 1/1/2004

Q, m

3/d

Actual StreamflowExtracted BaseflowSimulated Baseflow

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 & GW 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 TemperatureObserved 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

(ugN

/L)

Observed Nitrate

Simulated Nitrate

Next Steps• Model refinements & Synthesis

– Watershed & reach scale– Empirical & processed-based (including P)

• Test models with biological endpoints– Small-scale and regional approach

Integrated Assessment/Management FrameworkEcological

Assessment

RiskModeling

Criteria Development

Land Transformation

TMDL OptionsVulnerability

Analysis

StressorIdentification

Supporting USEPA, regions, and states