developing relations among - us epa
TRANSCRIPT
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