CNS Team 3: Ecological Assessment and Services

MAINTAINING ECOLOGICAL INTEGRITY & PROVISIONING OF ECOSYSTEM SERVICES

CNS 2017 All-Hands/CPC MeetingMay 17th & 18th, 2017Harrisburg, PA

OUTLINE

• Ecological assessments, water quality, and scenarios

• Review of ecological indicators & methods

• Objective 1: Baseline condition results

• Objective 2: Linking SWAT model outputs with ecological assessments

• Objective 3 Characterizing ecological condition in scenarios

• Site comparisons

• Final thoughts

Value of Ecological Assessments

• Groundtruthing model results

• Approach to water quality

• Clean Water Act

• Safe Drinking Water Act

• Model—TMDL development

BENTHICMACROINVERTEBRATES

• Spend most or all of lives in water (cannot escape pollution)• Differ in tolerance to pollution & respond to human disturbance in

predictable ways• Biological Endpoint—Integrate the effects of stressors in combination and

over time

BIOLOGICAL INDICATORS

“Water pollution is essentially

a biological problem.” D. M. Rosenberg

“If we fail to protect the biology of our waters, we

will not protect human uses of that water.”

James. R. Karr

SWAT Ecological Model

CB Model

BMPs & Scenarios

CNS Relationship to TMDL Process

Review: Ecological Indicators, Methods,

& Baseline Condition Results

Ecological Indicators

Level 1: Landscape Index– GIS work in lab; often automated;

many sites

– Percent Land Cover, Impervious Surface

Level 2: Stream-Wetland-Riparian Index (SWR)

– rapid field assessment

– 1-2 hours in field; selected sites

Level 3: Macroinvertebrate Index of Biotic Integrity

– many hours in field/lab; few sites

Graphic by Tara Mazurczyk

Graphic Representation of SWR

Methods: SWR Index

• Brooks et al. (2009)

• Mid-Atlantic Region

• Condition of a stream and its associated wetlands and riparian areas

• 100-m x 100-m plot

• 3 transects: up, mid, down

Metric Description

Buffer Condition Adjacent land use type

Incision RatioBankfull parameters w/channel measurements

Invasives Cover Class By species

Basal Area3 Bitterlich Tree Points w/ DBH measurements

# W-R StressorsStressor checklist, location, distance from stream

# Stream StressorsStressor checklist, location distance from stream

Stream Habitat Assessment Score

Based on EPA’s Rapid Bioassessment Protocol

Methods: IBI

• PA DEP (2012) Wadeable, Freestone, Riffle-Run Streams IBI

• 6 Riffle Kicks per site, compiled

• 500 µm D-frame net

• 200-count subsample

• ID to (mostly) genus– Non-insects and

Chironomidae

Macroinvertebrate IBI Component Metrics

Taxa Richness

EPT Taxa Richness

Beck’s Index

Hilsenhoff Biotic Index

Shannon Diversity Index

Percent Sensitive Individuals

CNS - Study Watersheds

Allegheny Plateau

Piedmont

Coastal Plain

Ridge &Valley

Spring Creek Watershed Rock Springs, PA

Spring

Creek

Mahantango Creek Watershed Klingerstown, PA

Mahantango

Creek

Conewago Creek Watershed Elizabethtown, PA

Conewago

Creek

Manokin

RiverManokin River Watershed

Princess Anne, MD

Watershed Characteristics

• Spring Creek, Ridge & Valley karst, 371 km2, mixed land uses. Watershed is moving rapidly to implement stormwater controls and green infrastructure.

• Mahantango Creek, Ridge & Valley shale, 420 km2, low livestock densities, prioritizing fertilizer management. WE-38 subwatershed is a primary site for ARS research since 1967.

• Conewago Creek, Piedmont, 135 km2, Chesapeake Showcase Watershed, small farms with a diverse mixture of crops and pasture. Stakeholder engagement here is substantial.

• Manokin Creek, Coastal Plain, 303 km2, extensively studied by Maryland’s environmental agencies and University of Maryland Eastern Shore and ARS at Penn State. Poultry industry is rapidly growing here.

Ecological sampling points: Stream Habitat Assessment

(of SWR)

Objective 1: Baseline Ecological Condition

• Results across 3 levels

– Level 1: Landscape

– Level 2: Rapid field

– Level 3: Intensive biological

• Groundtruthing Example

Mahantango Creek Subwatershed TMDL: Northumberland and Schuylkill Counties; PA DEP, March 27, 2013

• Sediment pollution (no PS)

• 14% reduction in sediment load

• Riparian buffer zones nearly nonexistent

• Unlimited livestock access

• Streambank stabilization & fencing

• Riparian buffer strips• Conservation tillage &

strip cropping• Wetlands for stormwater

retention

Mahantango Creek (& WE38)

• 303D lists much of Mahantango watershed, including entire WE38 sub-basin as impaired by sediment

• Majority of monitoring sites failed to meet CWF attainment (impaired) based on IBI scores and ALU assessment process

Level 1: Landscape Index Results Summary of Landscape

(Level 1) Assessment

All scores have been normalized to a 0 to 1 scale in order to facilitate comparisons of condition.

Level 2: SWR Index Results

Uniform Qualitative Categories

0.00

0.25

0.50

0.75

1.00

SW

R S

core

SWR Score Distribution

Conewago Mahantango Spring Creek Manokin

Optim

al

Sub-O

ptim

al

Marg

inal

Poor

Level 2: SWR Index Results

Uniform Qualitative Categories

Ecological Metric Scoring – 3 Levels

Level 3IBI

Example Summary Figure Displaying

Ecological Condition Scores Using 3 Levels

of Assessment

Optimal

Sub-Optimal

Marginal

Poor

Ecological Condition Tiers

GROUNDTRUTHING MODELS WITH ECOLOGICAL DATA

TN (mg/L)

TP (mg/L)

NO3

(mg/L)MinP

(mg/L)Tsed

(mg/L)

Value 10.5 3.7 0.72 2.2 3239

Rank 4 3 13 6 1

GROUNDTRUTHING MODELS WITH ECOLOGICAL DATA

IBI Score

SHAScore

SWR Score

Value 88.4 214 0.64

Rank 1 2 14

Objective 2: Linking Models with Ecological Indicators

• Best indicators from each based on…

• Relationship to IBI Metrics

• Relationship to SWAT

• Identify spatial and temporal scales of interest• SWAT Spatial Comparisons

• Geographic Comparisons

• Temporal Comparisons

Finding Correlations between SWAT Output and Level 3 Ecological Metrics

*Identify Spatial and Temporal Scales of Interest

Example: Temporal: WinterSpatial: Local

Example: Total Sediment and Freestone IBI Scores

SWAT Spatial Comparison

kjkj

kj

kj

kj

3

11

5

1

7

15

2

8

6

9

10

4

18

kjkj

kj

kj

kj

All subbasin boundaries

100m buffer around streams

TotalCA = total contributing area

LocalCA = local contributing area

streams

CNS sampling sites

Geographic Comparisons

• Across Watersheds

• Multivariate, Random Forest

• Regression

• Within Watersheds

• Multivariate

• Regression

• Within Subwatershed

• Regression

FREESTONE STREAMS*

MAHANTANGO CREEK

WE38

Identifying Correlations Between Ecological Metric Assessment Levels & Between SWAT Baseline Data

and Ecological Metrics – Our Approach

Independent Variable Dependent Variable

Relationship Between Different Levels of Ecological Condition Metrics

Landscape Metrics SWR

Landscape Metrics Macroinvertebrate IBI

SWR Macroinvertebrate IBI

Relationship Between SWAT Output and Level 3 Ecological Condition Metrics

Independent Variable Dependent Variable

SWAT Output Macroinvertebrate IBI

FREESTONE STREAMS (Mahantango + Conewago + Spring)

RANDOM FOREST RESULTS

Importance values for best models

IBI Model % Nutrient Intolerant

Grazing Pressure 1.00 Bank Vegetative Protection 1.00

Embeddedness 0.63 Stream Habitat Assessment Score 0.9

Bank Vegetative Protection 0.51 Total Phosphorus (Winter) 0.71

Specific Conductivity 0.47 Total Nitrogen (Winter) 0.47

Stream Habitat Assessment Score 0.40 Sediment Deposition 0.39

Beck's Index % Nutrient Tolerant

Stream Habitat Assessment Score 1.00 Nitrate (Winter)* 1.00

Total Nitrogen (Annual) 0.58 Specific Conductivity 0.83

Mineral P (Annual) 0.56 pH 0.30

Nitrate (Annual) 0.38 Nitrate (Summer) 0.20

Embeddedness 0.36

Best Models and Predictor Variables

Freestone Streams (% Nutrient Tolerant vs SWAT Winter Nitrate)

AntochaCaecidoteaPhysellaTipulaTurbellaria

Smith et al. 2007

IBI Condition CategoriesIBI scores for all Freestone streams (across all watersheds)

by condition category

SWAT, IBIs, & Water Quality Standards

Blue line=0.06 (recommended for sensitive salmonid embryos). Green line = 1.0 mg/L (natural levels of nitrate and recommended limit for protection of sensitive species); 1.5 mg/L = Ohio TMDL target; 2.0 mg/L (absolute limit for protection of salmonid embryos)

Mahantango Creek

IBI Score Beck's Index SDI % Nutr Intolerant % Nutrient Tolerant

SWAT TCA SWAT LCA SWAT TCA SWAT LCA SWAT TCA SWAT LCA SWAT TCA SWAT LCA SWAT TCA SWAT LCA

TP-SU 0.4 NO3-YR 0.3 TP-SU 0.3 Min P-WI 0.4 NO3-SP 0.4* * * *

NO3-YR 0.3 NO3-YR 0.3 TP-SU 0.3 MinP-SU 0.3

WE38

TN-SU 0.6 NO3-WI 0.6 TN-SU 0.5 NO3-WI 0.5 TN-YR 0.5 TSed-WI 0.7

* *

MinP-YR 0.5 MinP-WI 0.4

TN-WI 0.6 NO3-YR 0.6 TN-WI 0.4 NO3-YR 0.5 TN-FA 0.5 TSed-YR 0.7 TP-YR 0.5 TP-WI 0.4

TN-YR 0.6 TSed-WI 0.6 TN-YR 0.4 NO3-SP 0.4 MinP-SP 0.5 Tsed-FA 0.7 TP-FA 0.5 MinP-YR 0.3

MinP-SP 0.5 TN-WI 0.6 MinP-SP 0.4 TN-SU 0.4 TN-WI 0.6 TP-WI 0.5 TP-YR 0.3

TP-SP 0.4 TN-YR 0.5 NO3-FA 0.6 MinP-FA 0.5

TN-SP 0.4 NO3-SP 0.5 TN-FA 0.6 MinP-SU 0.5

NO3-YR 0.4 Tsed-FA 0.5 TSed-SP 0.6 TN-FA 0.4

TN-FA 0.4 TN-FA 0.5 NO3-WI 0.6 MinP-WI 0.4

TSed-YR 0.5 TN-YR 0.5

NO3-FA 0.5 NO3-YR 0.5

TN-SP 0.4

Non-WE38 Mahantango

NO3-SU 0.6

*

NO3-SU 0.6

* * *

TN-SU 0.5 Q-WI 0.4

*

NO3-SU 0.5

NO3-YR 0.5 NO3-YR 0.5 NO3-WI 0.4

MinP-SU 0.5 NO3-WI 0.5 NO3-SU 0.4

NO3-WI 0.5 MinP-SU 0.5 NO3-FA 0.4

NO3-FA 0.5 MinP-SU 0.4

NO3-YR 0.4

TN-SP 0.3

WATERSHED & SUBWATERSHED (SWAT VARIABLES VS BIOLOGICAL METRICS)

Values are R2 from regression analysis.TCA = Total Contributing Area; LCA = Local Contributing Area

Sediment

• Sub-Watershed (WE38)

• Local Contributing Area

R² = 0.5273

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00

IBI S

core

SWR Score

Mahantango Creek (WE38)

R² = 0.5812

0

5

10

15

20

25

30

0.00 0.25 0.50 0.75 1.00

Bec

ks V

3

Landscape Index (200m)

Mahantango Creek (WE38)Level 1 Level 3

Level 2 Level 3

Comparison between assessment levels

• Stream Habitat

– Bank Condition

– Bank Vegetative Protection

– Grazing Pressure

– Riparian Zone Width

• SWR

– Buffer Score

– Floodplain-Wetland Condition

– SWR Score

Level 1 & Level 2 Best IndicatorsBiology & SWAT

• Landscape

– % Forest (200-m)

– Landscape Development Index (LDI)

• SPATIAL EXTENT– Total Contributing Area best for

larger areas (e.g., across watersheds)

– Local Contributing Area better for sub-watershed scale

• SEASONAL– Winter best predictor

across all geographic scales but depends on metric

– Spring (Total CA) or Summer (Local CA) most likely to exceed WQ thresholds that impact communities (also greatest reductions)

SWAT Comparisons

Objective 2: SummaryLINKING SWAT WITH ECOLOGICAL CONDITION

• Overall SWAT variables produce weak relationships at best with IBI scores/metrics; better relationships with specific metrics –IF going to make the leap, here are some guidelines

• Small watershed or subwatershed studies appear to produce better results compared to regional studies focusing on multiple watersheds

• limits the range and sources of natural variability • produces more realistic model estimates of nutrient yields when coupled with detailed

knowledge of field-level characteristics, management, and rotations.

– Season matters• Winter may be best to link overall patterns with biology BUT• Other seasons (spring, summer, or fall) most important for exceeding water quality

thresholds (acute responses and fish kills); thus, season depends on pollutant and work objective

• Strong links between habitat and biotic integrity (IBIs) suggest that instream physical habitat may be the most important factor limiting biotic integrity in agricultural streams. These results support this.

Objective 3: Characterize ecological condition in scenarios

• Using baseline comparisons (space for time substitution), results from Objective 2, and literature/case studies to make case for predicting if condition may change

• Illustrate with site examples

• What sites showed marked improvement in each of SWAT parameters between baseline and WIP?

• What were the BMPs implemented at these sites?

EVALUATING EFFECTS OF BMPS:

EVALUATING EFFECTS OF WIP:

Site Comparisons

#*

#*

#*

#*MH12

MH11

MH10

MH09

TILLAGE PRACTICES IN WE38MH11 (Conventional) & MH12 (Conventional w/ Cover Crop)

MH11

MH09MH10

MH12

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Cover crops

No till

Conservation tillage

Perennialization

Conservation Cropping

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_currentBaseline landuse

Manure injection

Converted

Forested

Grass

None

CGAA

CPST

GRFL

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Tree planting

Farm Infrastructure

Manure/Nutrient Management

Urban Stormwater Management

None

Grass

Forested

Converted

LU_WE38_manureinj

LU_WE38_CoverCrops

LU_WE38_GravelRoads

LU_WE38_FarmStrc

LU_WE38_NoTill

LU_WE38_MinTill

GRFL

LU_WE38_scenarios

None

Grass

Forested

Converted

LU_WE38_manureinj

LU_WE38_CoverCrops

LU_WE38_GravelRoads

LU_WE38_FarmStrc

LU_WE38_NoTill

LU_WE38_MinTill

GRFL

LU_WE38_scenarios

Gravel road improvement

Barnyard runoff

Converted

Forested

Grass

None

CGAA

CPST

DGAB

EGAB

EGAC

FGAC

LU_WE38_current

Converted

Grass

Forested

None

LU_WE38_manureinj

LU_WE38_CoverCrops

LU_WE38_GravelRoads

LU_WE38_FarmStrc

LU_WE38_NoTill

LU_WE38_MinTill

GRFL

LU_WE38_scenarios

Grass or forested buffer

Forested buffer

Grass buffer

None

49

MH11

MH12

MH11

MH12

Local Contributing Area Av WIP %

Pollutant MH11 MH12 WQ Standard Reduction

NO3 (mg/L) (Winter) 1.02 0.81 0.06 - 1.0 mg/L 11

TP (mg/L) (Winter) 3.15 0.50 0.025 to 0.037 mg/L 9

Tsed (mg/L) Winter 537 88 ?? 5

MH11 MH12

% Slope 4.6 % Slope 13.9

Level 1: % Forest 58 % Forest 46.0

LDI 2.9 LDI 3.5

Level 3: IBI Score 34.0 IBI Score 54.4

Beck's Index 8 Beck's Index 11

% Nut. Int. 1.7 % Nut. Int. 12.1

% Nut. Tol. 1.7 % Nut. Tol. 0

MH11 MH12

Level 2: Buffer Score 0.17 Buffer Score 0.02

FP-W Cond. 0.30 FP-W Cond. 0.51

SWR Index 0.36 SWR Index 0.59

Flow Status 8 Flow Status 17

Bank Cond. 9 Bank Cond. 9

Graz. Press. 12 Graz. Press. 6

Rip. Zone 12 Rip. Zone 6

SHA Score 116 SHA Score 125

Forested Sites Downstream: MH09 & MH10MH09 (Forest) MH10 (Forest)

% Slope 5.3 % Slope 8.5

Level 1: % Forest 72.8 % Forest 70.7

LDI 1.7 LDI 2.2

Level 3: IBI Score 59.1 IBI Score 79.3

Beck's Index 14 Beck's Index 23

% Nut. Int. 3.4 % Nut. Int. 24.7

% Nut. Tol. 0.8 % Nut. Tol. 0

MH09 (Forest) MH10 (Forest)

Level 2: Buffer Score 0.80 Buffer Score 0.75

FP-W Cond. 0.57 FP-W Cond. 0.63

SWR Index 0.67 SWR Index 0.79

Flow Status 10 Flow Status 15

Bank Cond. 10 Bank Cond. 16

Graz. Press. 17 Graz. Press. 19

Rip. Zone 17 Rip. Zone 19

SHA Score 164 SHA Score 187

PROBABLE SCENARIO: • Sites along treated reach better

condition but…• MH11 & MH09 probably impacted

more by low flows; • Effects from WIP implementation

may be affected by wet/dry years

#*MH18

Site Comparisons: Mahantango Mainstem

#*MH15

MH18

MH15

Minimum Till→Min. Till w Cover Crop

Minimum Till→Min. Till w Cover Crop

Minimum Till→Grass Buffer

#*MH18

#*MH15

Total Contributing Area Av WIP %

Pollutant MH18 MH15 WQ Standard Reduction

NO3 (mg/L) (Winter) 0.70 0.63 0.06 - 1.0 mg/L 25

TP (mg/L) (Winter) 1.91 1.57 0.025 to 0.037 mg/L 26

MH18

MH15

Site Comparisons: Mahantango Mainstem

MH18--downstream MH15--upstream

MH18 MH15

% Slope 12.3 4.4

Level 1: % Forest 75.5 10.8

LDI 1.8 5.1

Level 3: IBI Score 56.5 43.1

Beck's Index 10.0 2.0

% Nut. Int. 2.9 0.4

% Nut. Tol. 0.8 3.7

MH18 MH15

Level 2: Buffer Score 0.64 0.10

FP-W Cond. 0.54 0.38

SWR Index 0.74 0.69

Flow Status 19 18

Bank Cond. 12 12

Graz. Press. 18 12

Rip. Zone 18 12

SHA Score 186 139PROBABLE SCENARIO: • Forested site probably best expected; high quality wetland areas in riparian zone• Key focus on stream restoration in the mainstem and nutrient/sediment reductions in

headwaters.

A Final Note on Nitrate

Riparian corridors generate a diversity of

ecosystem services

www.beslter.org

PROCESSING OF NITRATE IN INCISED STREAMS

Denitrification Potential in Various Stream Features

0

500

1000

1500

2000

2500

OrganicDebris Dam

Gravel Bar Pool Riffle MuckyGravel Bar

VegetatedGravel Bar

Baltimore Ecosystem Study LTER (www.beslter.org)

Den

itri

fica

tio

n P

ote

nti

al (

µgN

/kg

/h

**Highest in areas with highest organic matter content

#*

#*#*

#*

#*

#*

#*#*MH21

MH07

MH05

MH04

MH03MH02

MH01

MH21M

MH07 MH03

MH04

MH01

Changes Along Stream Reach in WE38MH07 (Forest), MH03, MH04, MH01 (Minimal Tillage & Permanent Pasture)

MH07 MH01 MH03 MH04

MH07 MH04 MH03 MH01

% Slope 13.5 5.4 10.6 1.3

Level 1: % Forest 94.2 0.0 0.0 13.7

LDI 1.2 4.2 3.9 3.8

Level 3: IBI Score 72.5 46.1 54.4 31.1

Beck's Index 28.0 7.0 15.0 4.0

% Nut. Int. 31.4 0.9 2.9 0.0

% Nut. Tol. 0.0 0.9 0.4 0.0

MH07 MH04 MH03 MH01

Level 2: Buffer Score 0.75 0.21 0.00 0.21

FP-W Cond. 0.66 0.33 0.39 0.45

SWR Index 0.70 0.56 0.67 0.59

Flow Status 10 18 16 18

Bank Cond. 19 4 13 6

Graz. Press. 18 9 5 3

Rip. Zone 18 9 5 3

SHA Score 169 122 129 103

PROBABLE SCENARIO: • Most likely physical habitat changes brought

about by agriculture and excessive grazing• MH04 very wet and may be critical source

area; prioritize over MH01 & MH03?

SUMMARY

• Relationships between model outputs of nutrients and sediment with ecological condition easily confounded by other variables

• Using models to target hotspots for improving water quality should include additional information on biological condition coupled with local knowledge or current issues and practices.

2. Multiple modeling and ecosystem approaches indicate that space, place, and time of stressor and management-related activities matter greatly.

4. The 3 main factors driving watershed simulation success include: 1) expertise on the science, the application and limitations of model; 2) the quality and availability of data; and 3) local watershed stakeholder feedback