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1
Review of Progress on Developing Nutrient Criteria in
CaliforniaPresentation to RTAG
May 25, 2004EPA Region IX, San Francisco, California
2
Overview
• Update on previous results• Review of data and modeling for
Ecoregion 6• Recommended strategy for moving
toward numeric criteria
3
Approach Presented in EPA Guidance Documents
TN, TP, etc
Freq
uenc
y (%
)
Reference
75th Percentile 25th Percentile
General Population
Reference
General Population
75th Percentile
25th Percentile
Benefit: - Data easily accessible with adequate QA, especially for general population
Limitations: - Not validated for California- No link to impairment of beneficial use
Cum
. Fre
quen
cy (%
)
TN, TP, etc
4
The Importance of “getting it right”
• ~ 150 CA water bodies impaired (1998 303(d) list) for nutrients and nutrient related parameters (DO, pH)
• Once established, nutrient criteria will be incorporated into state standards
• Are the 304(a) criteria correctly specified? Misspecification could lead to a large number of 303(d) listings
5
California Ecoregions1 Coastal Range4 Cascades5 Sierra Nevada6 Southern and Central
California Chaparral and Oak Woodlands
8 Southern California Mountains
9 Eastern Cascades Slopes & Foothills
13 Central Basin and Range14 Southern Basin & Range22 Arizona/New Mexico
Plateau23 Arizona/New Mexico
Mountains24 Southern Deserts78 Klamath Mountains
6
The Importance of “getting it right”
EcoregionEcoregion
Total Phosphorus (approx. mg/L)Total Phosphorus (approx. mg/L)
304(a) Criterion
304(a) Criterion
Reference 75%
Reference 75%
% > 304(a)% >
304(a)STORET
25%STORET
25%% >
304(a)% >
304(a)
11 0.0100.010 0.030.03 7070 0.010.01 7070
55 0.0150.015 0.040.04 8585 0.020.02 8585
66 0.0300.030 .09.09 0.060.06 8888
88 0.0110.011 nana nana 0.0020.002 4444
99 0.0300.030 0.130.13 6767 nana nana
1414 0.0100.010 0.030.03 4747 0.030.03 8080
2222 0.0150.015 0.070.07 6262 0.020.02 9797
2323 0.0110.011 0.060.06 8585 0.0050.005 85852424 0.0180.018 0.070.07 5656 nana nana
7878 0.0320.032 0.050.05 2828 0.120.12 9898
7
The Importance of “getting it right”
Total Nitrogen (approx. mg/L)
Ecoregion 304(a)
Criterion
Reference 75%
% >
304(a)
STORET
25%
% >
304(a) 1 0.13 na na 0.17 85 5 0.29 0.36 33 0.22 62 6 0.50 0.5 0.40 69 8 0.52 na na 0.10 17 9 0.15 0.40 97 na na
14 0.67 0.25 0 0.55 66 22 0.23 0.48 60 0.18 47 23 0.28 0.48 58 0.13 47 24 0.62 0.32 12 na na 78 0.53 0.58 25 na na
8
Ecoregion 14, Southern Basin and Range
Total Phosphorus (mg/l)
0.00 0.05 0.10 0.15 0.20 0.25
Per
cent
Les
s Th
an
0
25
50
75
100
STORET DataReference
Default Criterion
Ecoregion 5, Sierra Nevada
Total Kjeldahl Nitrogen (mg/l)
0.0 0.5 1.0 1.5 2.0
Per
cent
Les
s Th
an
0
25
50
75
100
STORETReference
Default Criterion
Example Comparisons of Reference and STORET Data for Ecoregions
9
10
Modified Strategy for Developing Criteria
• Focus on a individual ecoregion, not aggregated ecoregion
• Greater emphasis on biological responses to link to protection of beneficial uses
• Use statistical and simulation models to provide better estimates of reference loads
• Models used to estimate biological responses
11
Conceptual Model of Linkage Between Nutrients and Beneficial Uses for Streams
Nutrient Load
NutrientConcentration
Excess PeriphytonGrowth
Altered BenthicHabitat
Impaired coldwaterbenthic community
Reduced juvenilefish survival
Degradedcoldwater fish
population
Clogging ofspawning gravels
Reduced eggsurvival/
hatchability
Reduced diurnalDO minimum
Reduced adult &warmwater fish
survival
Impaired COLDuse
Impaired SPAWNuse
Unaesthetic slime/odor
ExcessMacrophyte
Growth
Reduced flowvelocity
Excess planktonicalgal & bacterial
growth
IncreasedTurbidity
Reduced foragingsuccess
Cyanophyte toxinproduction,
noxious bacterialgrowth
Treatability (tasteand odor; filterclogging; Cl
demand)
Unaesthetic algalblooms, reduced
clarity
Poor quality rawwater supply
Impaired MUN use Impaired REC-2use
Altered planktonicfood chain/food
availability
Degradedwarmwater biotic
community
Impaired WARMuse
Direct N toxicity(ammonia, NOx?)
Altered pH
Flow (dilutioncapacity,velocity)
Light/Shade,Scour/Flushing
Temperature
Habitat Quality
Major ExogenousFactors
Conceptual Model for Impairment of Streams by NutrientsSTRESSOR
IMPACT
Impaired REC-1(contact) use
Note: AGR use is assumed not to be impaired by nutrient loadsNAV is insensitive, but could be impacted by macrophytes
Sediment Load
12
Region Selected for Study: California Oak and Chaparral (Ecoregion 6)
• 680 stations with data• Stations evenly
distributed over ecoregion
• Data obtained from federal, state, and municipal agencies, and research institutions
13
Empirical Data Analysis:Station
Classification
14
Empirical Data Analysis for Ecoregion 6:NO3 Levels in Streams by Impairment Classification
of Water Body
NO3-N, Summer Months
Minim Impact Unimpaired Imp (unknown) Imp (Nutr)
Con
cent
ratio
n (m
g/l)
0.001
0.01
0.1
1
10
100
1000
Default TNCriterion
15
TKN, Summer
Minim Impact Unimpaired Imp (unknown) Imp (Nutr)
Con
cent
ratio
n (m
g/l)
0.01
0.1
1
10
100
Default TNCriterion
Empirical Data Analysis for Ecoregion 6:TKN Levels in Streams by Impairment Classification
of Water Body
16
TP, Summer Months
Minim Impact Unimpaired Imp (unknown) Imp (Nutr)
Con
cent
ratio
n (m
g/l)
0.001
0.01
0.1
1
10
100
Default TPCriterion
Empirical Data Analysis for Ecoregion 6:TP Levels in Streams by Impairment Classification
of Water Body
17
Constituent Impaired (mg/l) Unimpaired (mg/l) Minimally Impacted (mg/l)NH3 0.082 0.082 0.010NO2 0.010 0.021 0.002NO3 0.700 0.100 0.050PO4 0.010 0.142 0.017TKN 0.400 0.500 0.200TP 0.020 0.033 0.050
Constituent Impaired (mg/l) Unimpaired (mg/l) Minimally Impacted (mg/l)NH3 0.050 0.020 0.016NO2 0.030 0.017 0.002NO3 2.918 0.361 0.050PO4 0.080 0.080 0.043TKN 0.630 0.400 0.250TP 0.080 0.068 0.080
Lakes-Median Concentrations
Streams-Median Concentrations
Summary of Nutrient Data for Ecoregion 6
Nitrate appears to the most only significant discriminator between the different types of stations.
18
a) Ecoregion 6 Streams
TKN + NO3 + NO2 (mg/l)
0.001 0.01 0.1 1 10 100
TP (m
g/l)
0.001
0.01
0.1
1
10
100
N-Limited
P-Limited
RedfieldRatio
b) Ecoregion 6 Lakes
TKN (mg/l)
0.001 0.01 0.1 1 10 100
TP (m
g/l)
0.001
0.01
0.1
1
10
100
N-Limited
P-Limited
RedfieldRatio
Empirical Data Analysis:
TN:TP Ratios
Finding: Nitrogen limitations are very common in Ecoregion 6 streams
19
Watersheds Associated with Monitoring Stations
• Watershed for 82 stream stations with enough data were mapped
• Stations evenly distributed over ecoregion
• Data obtained for:– Land cover (urban, agriculture,
conifer, shrub, etc.)– Precipitation– Slope– Elevation– Soil erodibility– Soil organic carbon content– Soil water conductivity
20
NH3
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s C
once
ntra
tion
(mg/
L)
0.001
0.01
0.1
1
10
NO3
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s C
once
ntra
tion
(mg/
L)
0.01
0.1
1
10
100
TKN
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s C
once
ntra
tion
(mg/
L)
0.01
0.1
1
10
TP
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s C
once
ntra
tion
(mg/
L)
0.001
0.01
0.1
1
10
Nutrient Concentrations and Developed Land Data for Entire Year
21
NH3
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s (m
g/L)
0.001
0.01
0.1
1
NO3
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s (m
g/L)
0.001
0.01
0.1
1
10
100
TKN
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s (m
g/L)
0.01
0.1
1
10
TP
Developed Land
0.0001 0.001 0.01 0.1 1
Nut
rient
s (m
g/L)
0.001
0.01
0.1
1
10
Nutrient Concentrations and Developed Land Data for May Through September
22
Nutrient Concentrations and Mean PrecipitationData for Entire Year
NH3
Mean Precipitation (inches)
0 10 20 30 40 50 60
Nut
rient
Con
cent
ratio
n (m
g/l)
0.001
0.01
0.1
1
10
NO3
Mean Precipitation (inches)
0 10 20 30 40 50 60
Nut
rient
Con
cent
ratio
n (m
g/l)
0.01
0.1
1
10
100
TKN
Mean Precipitation (inches)
0 10 20 30 40 50 60
Nut
rient
Con
cent
ratio
n (m
g/l)
0.001
0.01
0.1
1
10
TP
Mean Precipitation (inches)
0 10 20 30 40 50 60
Nut
rient
Con
cent
ratio
n (m
g/l)
0.001
0.01
0.1
1
10
23
Classification and Regression Tree (CART) Analysis: I
TKN in May through September
24
Classification and Regression Tree (CART) Analysis: II
TP in May through September
25
Classification and Regression Tree (CART) Analysis: III
PO4 in May through September
26
Findings from Data Analysis
• Chemistry data alone is difficult to directly link to impairment, although some land characteristics are good estimators of concentrations (such as the proportion of developed land and precipitation)
• Insufficient, consistently-measured biological data collected over Ecoregion 6
• Limited reference station data on chemistry
27
Additional Sources of Information
• Estimates of reference loads of nutrients– Simulation modeling using SWAT– Empirical approaches such USGS-SPARROW
• Estimates of biological responses– Lake responses using the BATHUB model– Stream responses using
• Empirical responses (Dodds, Biggs, etc.)• Modified version of QUAL2K model
• Goal: Develop a more robust basis for reference concentrations, and biological responses to excess nutrients
28
Watershed Modeling Using SWAT
• SWAT (Surface Water Assessment Tool) was used to estimate nutrient loads and concentrations in streams.
• Designed for use without calibration.
• A set of eight, relatively unimpaired watersheds was used for validation testing.
• Goal: To identify landscape stratification features as directed by RTAG
29
SWAT Validation
• Data sparse for unimpaired watersheds; potentially unrepresentative
• SWAT expected to perform best on loads, not instantaneous concentration
• Problems encountered in default SWAT parameters for biomass simulation when applied to Ecoregion 6
30
NH3 - Annual
0.0001
0.001
0.01
0.1
1
10
3132 3133 3210 3134 455 478 200Station
Con
cent
ratio
n (m
g/l)
Median ModeledFlow-weighted ModeledMed ian Measured
NO3 - Annual
0.001
0.01
0.1
1
10
3132 3133 3210 3134 455 478 200Station
Con
cent
ratio
n (m
g/l)
Med ian Mo deledFlow-weig hted Mo deledMed ian Measured
Mineral P - Annual
0.001
0.01
0.1
1
3132 3133 3210 3134 455 478 200Station
Con
cent
ratio
n (m
g/l)
Med ian Mo deledFlo w-weig hted Mo deledMed ian Measured
TKN - Annual
0.001
0.01
0.1
1
10
100
3132 3133 3210 3134 455 478 200Station
Con
cent
ratio
n (m
g/l)
Med ian Mo deledFlow-weig hted Mo deledMed ian Measured
Comparison of Data and Modeled Values from SWAT
31
Role of SWAT in California Nutrient Criteria Development
• Validation efforts have been inconclusive• Without calibration, the model is most appropriate for a
qualitative or relative evaluation of nutrient load response to soils, cover, and other factors
• Proper calibration requires monitoring data sets of sufficient size (along with flow) to estimate seasonal and annual loads for comparison to the model
• SWAT continues to be tested further for this study• Evidence from SWAT that indicates the importance of
soils and vegetative cover as stratifying variables
32
USGS SPARROW Approach• SPARROW = Spatially Referenced Regression on
Watersheds• A statistical approach to estimate loading rates and
concentrations of nutrients in streams based on regressions using NAWQA water quality data
• SPARROW has been used in two modes: for calculating loads and concentrations for current, human-impaired conditions and for estimating natural background loads and concentrations
• By using a subset of water quality stations in the most pristine watersheds, concentrations representing pre-disturbance conditions have been estimated for all RF1 level streams
33
Predicted Total Nitrogen Concentrations
Source: Smith et al., 2003
34
Predicted Total Phosphorus Concentrations
Source: Smith et al., 2003
35
Role of SPARROW in California Nutrient Criteria Development
• We have used SPARROW in conjunction with SWAT-calculated terrestrial loads to estimate downstream concentrations in streams
• Smith et al. (2003) natural background concentrations estimated are also proposed to be used in our framework
• Estimated concentrations have no link to beneficial uses• Accuracy at the level of ecoregions is unknown –
published error statistics lead to large uncertainty bounds• Underlying data to be made available in June, 2004
36
Lake Modeling Using BATHUB
• BATHTUB is a steady-state model that calculates nutrient and chlorophyll-a concentrations, turbidity, andhypolimnetic oxygen depletion based on nutrient loadings, hydrology, lake morphometry, and internal nutrient cycling processes
• BATHTUB was used to establish allowable receiving water nutrient loading as a function of hydraulic residence time and other key variables
• 3-D loading response surfaces were established with acceptable/unacceptable conditions plotted as a function of residence time, nitrogen load, and phosphorus load
37
2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.63.0
3.2
3.4
3.6
3.8
4.0
4.2
Lake Chlorophyll-a Concentration versus Phosphorous and Nitrogen Loading (lake volume normalized) for aResidence Time of 0.25 Years and Non-algal turbidity of 1.25 1/m
Chlorophyll-a Concentration (ug/L)
P lim
ited
N limited
Tota
l Nitr
ogen
Loa
ding
nor
mal
ized
to la
ke v
olum
e (u
g/Y
ear-
L, L
og S
cale
)
Total Phosphorous Loading normalized to lake volume (ug/Year-L, Log Scale)
BATHUB Lake Model Results
38
Res
iden
ce T
ime
(yea
rs, L
og S
cale
)
Chlorophyll-a Target Concentrations (10 ug/l) versus Phosphorous and Nitrogen Loading (lake volume normalized)for Residence Times from 0.05 to 17 Years and non-algal turbidity of 1.25 1/m
Total Nitrogen normalized to lake volume
(ug/Year-L, Log Scale)To
tal P
hosp
horo
us no
rmali
zed t
o lak
e vol
ume
(ug/
Year
-L, L
og S
cale)
Res
iden
ce T
ime
(yea
rs, L
og S
cale
)
-1.30-1.25-1.20-1.15-1.10-1.05-1.00-0.95-0.90-0.85-0.80-0.75-0.70-0.65-0.60-0.55-0.50-0.45-0.40-0.35
BATHUB Lake Model Results
39
BATHUB Lake Model Summary of Results
• Phosphorous limited algal growth when nitrogen loadings exceed 5,000 ug/L-year and phosphorous loadings are less than 200 ug/L-year
• Nitrogen limited algal growth when phosphorous loadings exceed 500 ug/L-year and nitrogen loadings are less than 2,000 ug/L-year
• Approximately log-linear inverse relationship between allowable normalized nutrient concentrations and residence times, with allowable normalized nutrient concentrations increasing with decreasing residence time and increasing turbidity values
• Much larger range of nutrient and residence time parameter values exceeds the 10 ug/L target than the 25 or 40 ug/L target values
• Results very sensitive to residence time and moderately sensitive to turbidity over range of Ecoregion 6 values
40
Empirical Analysis of Stream Response to Nutrients
• Goal is to develop statistical relationships using available data, either from Ecoregion 6 or from larger scale national studies; no mechanistic modeling
• Examples:– Global relationship between benthic Chl a and
nutrient concentrations (e.g., Dodds, Smith, and Zander, 1997)
– Periphyton chl-a data for RB-6 and EMAP
41
Empirical Data Analysis:Planktonic Chl a in Streams
TP--May through September
TP (mg/l)
0.001 0.01 0.1 1 10 100
Chl
orop
hyll
a (m
g/l)
0.001
0.01
0.1
1
10
100
1000
10000
r ² = 0.13
TKN--May through September
TKN (mg/l)
0.01 0.1 1 10 100
Chl
orop
hyll
a (m
g/l)
0.001
0.01
0.1
1
10
100
1000
10000
r ² = 0.22
log10(CHL-A) = 0.687 + ( 0.629 * log10(TKN)) + ( 0.182 * log10(TP)); r2 = 0.23log10(CHL-A) = 0.650 + ( 0.809 * log10(TKN)); r2 = 0.22
42
Dodds et al. (1997) Relationship Between BenthicChl-a and Total Nitrogen – US streams
Nutrients define upper bound, not mean of observations
Biggs (2000): Similar results for New Zealand
43
Boundaries for Stream Trophic Classifications Proposed by Dodds et al. (1998)
Variable Oligotrophic-Mesotrophic
Boundary
Mesotrophic-Eutrophic Boundary
Mean benthic chlorophyll a (mg/m2)
20 70
Maximum benthic chlorophyll a (mg/m2)
60 200
TN (µg/L) 700 1500
TP (µg/L) 25 75
44
Provisional Data from Regional Board 6
RB 6 Provisional Data
0.1
1
10
100
0.01 0.1 1 10 100
Total N (mg/L)
Peri
phyt
on (m
g/m
2 chl
-a)
RB 6 Provisional Data
0.1
1
10
100
0.001 0.01 0.1 1
Total P (mg/L)
Perip
hyto
n (m
g/m
2 chl
-a)
log(mean Chl a) = -3.20 + 2.94 log(TN) – 0.512 (log(TN))2 + 0.0914 log(TP), r2 = 0.2
45
EMAP Data For California
0.1
1
10
100
1000
10 100 1000 10000 100000
Total N (µg/L)
Ben
thic
Chl
orop
hyll
a (m
g/m
2 )
0.1
1
10
100
1000
1 10 100 1000 10000
Total P (µg/L)
Bent
hic
Chlo
roph
yll a
(mg/
m2 )
46
Periphyton Response
• Benthic chlorophyll a limited by light availability, scour, and grazing pressure
• Nutrients predict maximum potential, rather than average observed chlorophyll a
• Biggs (2000) approach to incorporating scour via days of accrual gave large increase in R2 (from 32% to 74%).
47
Fraction of Potential Maximum Periphyton Biomass as a Function of
Days of Accrual (Biggs, 2000)
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200
da
B(d 0
)/Bm
ax
48
Summary of Empirical Analysis of Stream Biological Reponse
• Both RB-6 and EMAP data showed greater sensitivity to TN than to TP
• Both data sets indicate that the chl-a values lie near the mean values predicted by regression equations in Dodds et al. (2002)
• Nutrients generally explain <50% of chl-a levels, largely as a result of the influence of exogenous factors such as scouring by high flows and/or light limitation
49
Modeling Stream Response Using QUAL2K
• QUAL2K is a revised version of the QUAL2E water quality model and incorporates benthic algal growth
• In the absence of adequate site-specific data, QUAL2K can be run using reasonable default parameters and can be “tuned” to a Dodds-type model
• Estimated Chl-a values are those that would result in the absence of the constraining factors such as grazing pressure and flood scour
50
Predicted Maximum Periphyton Biomass (in the absence of light limitation)
0
50
100
150
200
250
300
350
400
0 0.001 0.005 0.01 0.03 0.1 1 5.05 61.25
Inorganic Nitrogen (mg/L)
Per
iph
yto
n (
g/m
2 )
0
50
100
150
200
250
300
350
400
0.000
00.0
001
0.000
30.0
005
0.001
00.0
015
0.002
00.0
030
0.005
00.0
100
0.020
00.0
500
0.100
00.4
200
12.55
00
Inorganic Phosphorus (mg/L)P
erip
hyt
on
(g
/m2 )
Nitrogen Limited Phosphorus Limited
51
Response Surface for Maximum Periphyton Biomass
Inorganic P (mg/L)
Inor
gani
cN
(mg/
L)
3503253002752502252001751501251007550250
Periphyton (g/m )2
10
1
.1
.01
.001
.0001.0001 .001 .01 .1 1 10
52
Relating Dodds et al. Data to Model Output
0.5
1
1.5
2
2.5
3
0.5 1 1.5 2 2.5 3
Log QUAL2K Predicted Chl a (mg/m2)
Log
Dod
ds M
ax P
redi
cted
Chl
a (m
g/m
2 )
Based on QUAL2K parameters that were adjusted to Dodds et al.’s results
53
Application of QUAL2K Framework to RB-3 Data
54
Max
imum
Ben
thic
Bio
mas
s (g
/m2
AFD
W)
Eutrophic
Mesotrophic
Oligotrophic0
50
100
150
200
25030
8LS
U
308B
SR
304S
OQ
308M
IL
314M
IG
309P
SO
312C
UY
305F
RA
306C
AR
315S
MC
310A
RG
306M
C
305T
HU
309D
AV
310S
LB
312B
CF
312O
FC
Cumulative Distribution of Maximum Potential Benthic Algal Biomass Predicted by QUAL2K Approach for RB 3 Sites
without Considering Light Limitation
55
Eutrophic
Mesotrophic
Oligotrophic0
50
100
150
200
250
308L
SU
310S
CP
309A
TS
315J
AL
309N
AC
305C
HE
304A
PT
309U
SA
317C
HO
306C
AR
317E
ST
309G
RN
310C
AN
310B
ER
309S
BR
313S
AIMax
imum
Ben
thic
Bio
mas
s (g
/m2
AFD
W)
Cumulative Distribution of Maximum Potential Benthic Algal Biomass Predicted by QUAL2K Approach for RB 3 Sites
with Light Limitation by Shade and Turbidity
56
0
50
100
150
200
250
308L
SU
310S
CP
309A
TS
315J
AL
309N
AC
305C
HE
304A
PT
309U
SA
317C
HO
306C
AR
317E
ST
309G
RN
310C
AN
310B
ER
309S
BR
313S
AI
Predictions without light limitation
Predictions with light limitation
Max
imum
Ben
thic
Bio
mas
s (g
/m2
AFD
W)
Effects of Light Limitation on Benthic Biomass Predictions
57
0
0.2
0.4
0.6
0.8
1
1.2
0 0.2 0.4 0.6 0.8 1
Light Limitation
Nut
rient
Lim
itatio
n
High Nutrients, High Light
Low Nutrients, High Light
High Nutrients, Low Light
Nutrient and Light Limitation in RB 3 Data Set
58
Role of QUAL2K Modeling in California Nutrient Criteria
• Riparian shading/turbidity limitations on light can be incorporated directly
• As structured the model does not directly account for factors such as flood scour, but these can be incorporated using alternative correlations (Biggs)
• QUAL2K be used to provide one line of evidence of likely biological responses in conjunction with data
59
Phased Approach to Implement Criteria: Phase One Recommendation
• Use combination of data and estimated values to classify water bodies into three categories (a “triad” approach)– Nutrient impairment unlikely and/or corresponds
to natural background range (Tier I)– Nutrient impairment possible and exceeds natural
background range (Tier II)– Nutrient impairment very likely in the absence of
other anthropogenic stressors (Tier III)
60
Like
lihoo
d of
Impa
irmen
t
Concentration Species 1
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lihoo
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Primary Biological Response 1
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Concentration Species 2
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Primary Biological Response 2
Tier I
Tier II
Tier III
Tier I
Tier I
Tier ITier II
Tier II
Tier II
Tier IIITier III
Tier III
Form of the Standard
• Includes chemical and biological parameters
• All individual criteria must be exceeded before water body is placed in higher tier
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Consequences of Classification
• Tier I: No action needed• Tier II: Further study to determine whether
beneficial uses are threatened– Site specific factors influencing response– Potential anti-degradation analysis
• Tier III: Nutrient load reduction may be needed; possible permit load caps andTMDLs
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Tier 2: Low-Medium Concentrations / High Biological Response
• Are these conditions consistent with the system’s Designated Uses?
• Are the nutrients tied up in attached biomass?
Measure nutrients in periphyton.• Are degraded physical habitat conditions
contributing to high biological response?Evaluate shading, scour, and other
habitat qualities.
Tier 1: Low Concentrations / Low Biological Response
• No further follow up action is indicated.
Tier 3: High Concentrations / High Biological Response
• Is this system naturally eutrophic?Are these conditions consistent with
the system’s designated uses?Evaluate natural background loading
(e.g., SPARROW)Is high biological response caused by
degraded physical habitat conditions (e.g., reduced canopy cover)
Tier 2: Mid-Range Concentrations / Low Biological Response
• Are physical / chemical factors affecting biological activity?
Evaluate shading, scour, habitat quality, other toxic chemicals.
How the Tiered Criteria May Be Used
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Summary of Approach
• Use available data, make estimates where no data are available
• Use best available information to develop a framework for criteria that recognizes uncertainties
• Leave open the door for improvement in the criteria framework if new chemical or biological data become available
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Next Steps• Continue to refine modeling data analysis
tools• Provide a “Monitoring Needs Report” to
SWAMP and collaborators• Assist with data collection • Conduct case study using enhanced QUAL2K
and BATHTUB tools• Provide technical support to workgroup to
develop language for triad categories --including background information for NPDES considerations
• Refine ranges for triad categories