Addressing Maryland’s Sediment Impairments

Lee Currey

TMDL Program

Non-tidal and Watershed Modeling Division

April 21, 2023

Acknowledgments

• EPA Chesapeake Bay Program • EPA Region III• ICPRB• MD Department of Natural Resources• University of Maryland• USGS• Versar, Inc• Virginia Institute of Marine Sciences

Outline

• Listing Methodologies

• 303(d) Sediment Listings

• Identifying a Sediment Stressor

• Sediment TMDL Approach

• Current Project Status

• Outstanding Issues

Listing Methodologies for Solids

• Water Clarity– Turbidity [COMAR 26.08.02.03-3(A)(5)]– May not exceed levels detrimental to aquatic life– May not exceed 150 units at any time or 50 units as a

monthly average

• Narrative Water Quality Criteria– “…State’s waters must be sufficient quality to provide for

the protection of and propagation of a balanced population of shellfish, fish and wildlife and allow for recreational activities…” [COMAR 26.08.02.01-B(2)](i.e. fishable/swimmable)

History of Maryland Sediment Impairments

• Existing water quality inventory [303(d) list] identified 97 listings for sediment

• Many watersheds assessed based on land use and likelihood of sediment impairment

• Currently no distinction between “suspended sediment” and “sedimentation”

Impound-ment Non-tidal Tidal Total

1996 3 28 65 961998 1 1Total 4 28 65 97

Waterbody Type303(d) Listing Year

Sediment Impairments

Impound-ment Non-tidal Tidal Total

1996 3 28 65 961998 1 1Total 4 28 65 97

Waterbody Type303(d) Listing Year

What is a “Clean” Sediment Impairment?

Basin erosion Channel/Bank erosion

Increased suspended sedimentsSubstrate homogeneityCurrent homogeneity

Interruption in feeding mechanismsDecreased habitat

Shift in biological community (biocriteria)

Source

Stressor

Process

Response

Identifying a Sediment Stressor

• Endpoint – Maryland Biocriteria

• Stressor – Sediment Related Physical Habitat Parameters

• Linkage – Statistical Model

MBSS and Biocriteria

• Stratified random sampling of first to fourth order stream (fourth in round 2)

• Index of biotic integrity– Biological condition indicator developed for the fish

and benthic communities– Multi-metric - aggregates multiple characteristics of

biological assemblage– Established from regional reference conditions

• Biocriteria is EPA approved – For evaluating biological data for CWA requirements

MBSS Monitoring

Round 2: Approx 10 stations per 8-digit basin

Identifying Surrogate Sediment Parameters

• Variables that best represent the presence or effects of sediment

• Combined physical habitat• Riparian and upland zone• Streambed• Channel features• Water column

• 27 variables identified from total MBSS set• Reviewed by advisory committee

Identifying Surrogate Sediment Parameters

• Further refinement of surrogate parameters:

1. Available for both rounds of MBSS sampling

2. Expected to have discriminatory power and thus not be limited in range of recorded values

3. Not confounded by stream size or other critical natural variables

4. Not completely redundant

Parameters Used in AnalysisSurrogate Variables

Definition Scoring Relationship to Sediment

Riffle/Run Quality

Depth, complexity, and functional importance of riffle/run habitat

0 to 20 High scors indicate lack of sediment deposition.

Bank stability  

Composite score. Presence or absence of riparian vegetationquantitative measures of erosion extent and erosion severity.

0 to 100 High scores indicate lack of channel erosion

Riparian buffer width 

Width of vegetated (i.e., grass, shrubs, or trees) riparian buffer

0 to 50 Indirectly related to sedimentation as buffers remove sediment in runoff and protect banks from erosion.

Instream habitat 

Perceived value of instream habitat to the fish community, including multiple habitat types, varied particle sizes, and uneven stream bottom.

0 to 20 High socres indicate lack of sediment deposition.

Epifaunal Substrate

Visual rating based on the amount and variety of hard, stable substrates usable by benthic macroinvertebrates.

0 to 20 High scores indicate lack of sediment deposition.

Embeddedness 

Percentage of gravel, cobble, and boulder particles in the streambed that are surrounded by fine sediment.

0 to 100 Direct evidence of sediment deposition.

Non-Sediment Stressors

• MBSS sites with stressors not related to sediment – Acidification

• ANC < 200 μeq/l and DOC < 8 mg/l (excluding natural blackwater)

– Urbanization• Urban land use > 10%• Cl > 50 mg/l

– Low dissolved oxygen• Not removed due to instantaneous sampling

methodology

Methodology Site

Biocriteria Status

Logistic Regression

Model

Remove Sites with ANC<200

and DOC<8

Sediment Surrogate

Parameters

Statistical Model

Probability of Failing Biocriteria

Remove Sites with Cl>50 and

Urban>10%

Parameter Selection

• Select most parsimonious model

• Objective is change in Chi square value

Highland Scoring

0

10

20

30

40

0 2 4 6 8

# Variables

Ch

i-S

qu

are

Sc

ore

Region Parameter 1 Parameter 2 Parameter 3 Parameter 4

Highland Riffle run Riparian width Embeddedness -----------------

Piedmont Riparian width Instream habitat Embeddedness Epifaunal substrate

Coastal Riffle run Riparian width Instream habitat -----------------

Statewide Riffle run Riparian width Instream habitat Embeddedness

Model Summary Statistics

Table x-2. Significance of parameters and model predictive power (c)

Parameter Highland Piedmont Coastal Statewide

Intercept 0.4110 0.1157 <0.0001 <0.0001

Riffle run 0.0194 ----- <0.0001 0.0003

Riparian width 0.0413 0.1306 0.0906 0.0016

Embeddedness 0.0006 0.1350 ----- 0.0110

Instream Habitat ----- 0.4332 0.0004 <0.0001

Epifaunal substrate ----- 0.1104 ----- -----

c (area ROC) 0.7 0.6 0.8 0.8

Model ValidationModel

Validation

Rate of Correct

Classification – Fail

Rate of Correct

Classification – Pass

Average Rate of Correct Classification Inconclusive

Highland 72% 78% 74% 39%

Coastal 74% 88% 78% 27%

State 73% 71% 73% 23%

Alpha

Rate of Correct

Classification – Fail

Rate of Correct

Classification – Pass

Average Rate of Correct

Classification Inconclusive Highland 70% 63% 67% 34% Coastal 73% 74% 73% 31% State 70% 65% 67% 24%

* Inconclusive based on 90% confidence interval

Application of Methodology to Watersheds

0

10

20

30

40

50

60

70

80

90

100

Very Low Low Medium High

Watershed Classification

Probability of Sediment Impairment

(Watershed Average)

(Not Impaired)

(Impaired)

Threshold p =0.50 ^

Low Inc-Low Inc-High High

Watershed Classification

Pro

bab

ilit

y o

f S

edim

ent

Imp

airm

ent

(Wat

ersh

ed A

vera

ge)

(Not Impaired)

(Impaired)

Threshold value

Watershed Evaluation for Sediment Impairment

• Estimate likelihood of sediment impairment at appropriate management scale

• MDE currently lists sediment impairment at the MD 8-digit scale

•Average likelihood of sediment impairment per watershed

Evaluation of Model at Watershed Scale

Coastal RegionH I-H I-L L

FAIL 24% 41% 29% 6%INC 28% 20% 12% 40%PASS 0% 0% 50% 50%Total Watersheds 11 12 9 12

Peidmont Region (Using Statewide Model)H I-H I-L L

FAIL 60% 0% 0% 40%INC 0% 38% 6% 56%PASS 0% 0% 43% 57%Total Watersheds 3 6 4 15

Highland RegionH I-H I-L L

FAIL 40% 0% 20% 40%INC 18% 18% 27% 36%PASS 0% 33% 0% 67%Total Watersheds 4 3 4 8Note:

1. Min sample>=5

IBI

Surrogate Sediment Parameters

TMDL Approach – Reference Watershed

Statewide AnalysisMD 8-digit management scale

Watershedclustering(similar to

Preston, 2002)

Sedimentloads

Likelihood of Sediment Impairment

Cluster X

Pass Inconclusive Fail

Se

dim

en

t L

oa

d

Target Load

Watershed Clustering

• Reviewed previous results from USGS (Preston, 2000)

• Updated cluster analysis based on new data and focused on sediment

• Two stage clustering– hydrological and geological information

• Rainfall erosivity (R)

• soil erodibility (K)

• watershed slope

– Land use

Watershed Clustering Example

Watershed Clustering Example

CBP Phase V • Interstate coordination

• MD 8-digit watershed scale

Sediment Loads

Statewide Logistic Regression:

Loads in Tons/stream mile

Cluster avg stderr avg stderr

1 77 15 59 11 Yes2 218 13

3 261 58

4 491 122

5 280 32 171 5 No6 190 37 207 20 Yes7 539 179 281 6 No

Average 307 33 177 21 No

HI GH LOW Overlapping

90% C.I .

*Preliminary CBP Phase V Loads

Addressing 303(d) Listings

• WQA– Determined from likelihood of sediment impairment

– Inferential statistics used to address borderline cases

• TMDL– Reference watersheds

– Watershed model scenarios

– Limits of implementation

– Maximum practical reductions

Where are we now?

• Independent review of logistic regression model

• Working on two stage clusters– talking with USGS about best cluster methods

for reference watersheds

• Coordinating with CBP (USGS and ICPRB) on Phase V sediment calibration

Outstanding Issues

• Normalizing loads

• Scale of impairment

• Sediment loads for reference conditions

• Point sources

• MS4 permits

Thanks !

Questions and Comments