Download - Developing relations among - US EPA
![Page 1: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/1.jpg)
![Page 2: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/2.jpg)
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
![Page 3: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/3.jpg)
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.
![Page 4: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/4.jpg)
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.
![Page 5: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/5.jpg)
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
![Page 6: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/6.jpg)
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.
![Page 7: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/7.jpg)
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
![Page 8: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/8.jpg)
Where We Are Working
(New Data)1. Early morning DO surveys2. Reach metabolism models
3. Watershed LULC (MRW & all MI)4. Watershed modeling
![Page 9: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/9.jpg)
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
![Page 10: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/10.jpg)
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
![Page 11: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/11.jpg)
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
![Page 12: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/12.jpg)
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
![Page 13: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/13.jpg)
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. ……..
![Page 14: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/14.jpg)
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
![Page 15: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/15.jpg)
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
![Page 16: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/16.jpg)
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
![Page 17: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/17.jpg)
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/
![Page 18: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/18.jpg)
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
![Page 19: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/19.jpg)
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”
![Page 20: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/20.jpg)
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
![Page 21: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/21.jpg)
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
![Page 22: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/22.jpg)
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
![Page 23: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/23.jpg)
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
![Page 24: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/24.jpg)
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)
![Page 25: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/25.jpg)
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
![Page 26: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/26.jpg)
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
![Page 27: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/27.jpg)
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
![Page 28: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/28.jpg)
Next Steps• Model refinements & Synthesis
– Watershed & reach scale– Empirical & processed-based (including P)
• Test models with biological endpoints– Small-scale and regional approach
![Page 29: Developing relations among - US EPA](https://reader031.vdocuments.net/reader031/viewer/2022012411/616b826a4fd1b956d9734859/html5/thumbnails/29.jpg)
Integrated Assessment/Management FrameworkEcological
Assessment
RiskModeling
Criteria Development
Land Transformation
TMDL OptionsVulnerability
Analysis
StressorIdentification
Supporting USEPA, regions, and states