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

Like

lihoo

d of

Impa

irmen

t

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