ORD’s Environmental Monitoring and Assessment Program (EMAP)

1. ORD’s Environmental Monitoring and Assessment Program (EMAP) Sound Science for Measuring Ecological Condition www.epa.gov/emap

2. Key EPA Monitoring Questions • What are the current conditions of our ecosystems? • Where are the conditions improving or declining? • What stresses are associated with declines? • Are our management programs and policies working?

3. What’s at Stake? • >$1B/y spent on monitoring • Condition of estuaries, coastlines, streams, rivers, wetlands and lakes are still unknown. • Effectiveness of protection and restoration programs and policies are often unknown

4. GOALS of EMAP • Develop the scientific basis for consistent, unbiased, cost-effective measurement of the condition of the Nation’s aquatic ecosystems • Status • Trends • Build state and tribal capacity for monitoring condition and transfer our technology • Make data generally available to all stakeholders (STORET)

5. States conduct Probability survey With suite of indicators Integrated Monitoring Condition Associated Stressors 305(b) Reports Comparison of # of Expected 303(d) Sites to known sites Non-point Source Point Source < or > = State of the Environment Reports Dose - Response Likelihood Criteria Probability of Impairment Assessment Models Accept State 303(d) list Standards 303(d) List Waterbody has Moderate Probability of impairment Waterbody has low Probability of Impairment Waterbody has high Probability of Impairment Diagnosis Intensive sampling to confirm impairment De-list No additional Sampling (continue to Monitor as part of 5-year cycle) TMDL Development Waterbody Impairment Confirmed Waterbody Not impaired Remediation

6. Science Behind the Scenes Indicators Assessments = Status (Trends) Ecoregion Framework Reference Condition Variance Estimation Panel Rotation Index Regionalization Index Calibration Spatial Balance Index Construction Frame Development Designs + Analysis Data Field Sampling Variable Density Approaches Training Partnerships Population Identification

7. CWA: Resource Monitoring Needs Great Lakes Lakes Wetlands Resource Areas Large Rivers Streams Coastal R-EMAP/Small Scale Tests Regional Demonstration National Demonstration Implementation National Implementation Nationally Consistent Design Design Reference Conditions Indicators Classification Strata Land cover/use Science Barriers

8. EMAP Extramural Research Areas GRI • Coastal Initiative – 60% to State Co-ops • Western Pilot – 60% to State Co-ops • GRI – 60% to State and other Co-ops • R-EMAP – 100% to EPA Regions • STAR – 100% to Academic Research Institutions STAR Grants Western Pilot R-EMAP Coastal Initiative

9. EMAP Design Approach • Probabilistic Design Framework– Randomized statistical designs that allow interpretation of monitoring data with known uncertainty, extrapolation to the entire population of interest with a small sample size, and the ability to statistically aggregate similar data to larger geographic areas • Classification - meaningful groupings within resource types and/or ecosystem types to allow better statistical design and analysis • Biological Indicators - Direct measures of aquatic ecosystem condition, integrates stressors, and the public can relate to them • Streams, rivers, estuaries, lakes, reservoirs, wetlands

10. Fully Supporting 13% Not Supporting 13% Delaware Not Supporting 87% Fully Supporting 87% Probability Survey Traditional Targeted Monitoring Not Supporting 5% Not Supporting 25% Nebraska Fully Supporting 95% Fully Supporting 75% Probability Survey Traditional Targeted Monitoring Probabilistic Survey Design Advantages • Representative and allows inference to system of interest • Adaptable to resource characteristics • Adjusts sample sizes to meet precision requirements • Adaptable to temporal and spatial scales of interest • Unbiased • Cost-effective Condition of streams

11. EMAP Uses Biological Indicators • Historic Aquatic Indicators – Measured physical/chemical characteristics and related them to the biological condition of an aquatic system • Aquatic Biological Indicators – Direct measure of condition of aquatic ecosystem, integrates stressors, and the public can relate

12. % Impaired Lakes Sampling costs Effectiveness of Design • Eutrophication of NE US lakes • 4219 mostly problem lakes sampled by states for 305(b) • 2756 non-random lakes censused (Rohm et al. 1995) • 344 lakes with EMAP probability design (11,076 lakes total) • Alabama reduced the cost of estuarine monitoring by ~33%, and can now report on all estuarine waters

13. Potential Stressors Fish IBI Good Sedimentation 25% (Insufficient Data) Riparian Habitat 24% Fair Mine Drainage 14% Acidic Deposition 11% 10% Tissue Contamination Poor Phosphorus 5% 5% Nitrogen Acid Mine Drainage 1% Proportion of Stream Length 40% 0% 10% 20% 30% % of Stream Length 34% Introduced Fish 0% 10% 20% 30% 40% Stream Conditions in MAHA

14. Louisianian Province Virginian Province Degraded 18 ± 8% Degraded 30 ± 6% Undegraded 82 ± 8% Undegraded 70 ± 6% Condition Unknown 10% Unknown 39% Low Dissolved Oxygen 49% Habitat 14% Metals 42% Low D.O. Contaminants 10% Contaminants 28% Both 2% Toxicity 4% Stressors Associated with Degraded Condition Estuarine Conditions

15. Statistical Change Detection 50 * 40 Change in Percent Area of Chesapeake Bay with Impaired Benthic Community 30 % area with impaired benthos 20 10 0 1991-93 1997-98

16. EMAP National Demonstrations • Estuaries – All 24 marine coastal states monitoring with core EMAP design and indicators • Streams – Mid-Atlantic States and 12 Western States • Great Rivers – Mississippi River Basin

17. States conduct Probability survey With suite of indicators Integrated Monitoring and Assessment 1 Condition 2 3 5 8 Associated Stressors 305(b) Reports Comparison of # of Expected 303(d) Sites to known sites Non-point Source Point Source 4 < or > = State of the Environment Reports 6,7 9,10 Dose - Response Thresholds of Impairment Probability of Impairment Assessment Models (2 levels) Accept State 303(d) list Standards 10 10 10 303(d) List Waterbody has Moderate Probability of impairment Waterbody has low Probability of Impairment Waterbody has high Probability of Impairment 10 Diagnosis Intensive sampling to confirm impairment 10 De-list No additional Sampling (continue to Monitor as part of 5-year cycle) TMDL Development Waterbody Impairment Confirmed Waterbody Not impaired 10 Remediation

18. 1 Example of Integrated Monitoring and Assessment with Maryland Biological Stream Survey Data MBSS probability survey for benthic IBI and fish IBI measures of stream condition (impairment for BIBI < 3, FIBI < 3), chemical and physical measurements taken, land cover data available Analysis: cumulative distribution functions (cdfs) conditional probabilities conditional cdfs

19. 2 Condition of Streams in Maryland 54% of 1st order stream miles are impaired (BIBI < 3) 40% of 2nd order stream miles are impaired (BIBI < 3) 47% of 1st order stream miles are impaired (FIBI < 3) 24% of 2nd order stream miles are impaired (FIBI < 3)

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22. Associated Stressors 5

23. 6 Thresholds of Impairment MBSS-derived thresholds of impairment: • pH < 5 • ANC < 200 μeq/l • Nitrate-nitrogen > 2 mg/l • DO < 5 ppm • Sulfate > 24 mg/l • DOC > 8.0 ppm Conditional probability thresholds of impairment: 1st order steams: DO < 5, DO > 12 pH < 6, pH > 8 NO3 < 5, <15 SO4 < 40-50 Temp <5, temp > 25 Hilsenoff <1, Hilsenhoff > 6 2nd order steams: DO < 3, DO > 11 pH < 5, pH > 8.5 NO3 < ? SO4 < 75 Temp <10, temp > 28 Hilsenoff <2, 7

24. British Columbia Conditional value 7 Percent Fines in Substrate

25. 8 Impaired Streams in Maryland 8800 stream miles stream miles in MD 66% 1st order - 5808 17% 2nd order 7304 miles in 1st and 2nd order streams 3725 miles of 1st and 2nd order streams should be on 303(d) List based on benthic impairment

26. Probability of Impairment Models Data to Drive Modeling 9 Combine condition information with land cover data to predict probability of impairment Agriculture on >3% Slopes Spatial Models for Probability of Impairment 10

27. Probability of Stream Benthic Impairment for Exceeding Levels of Catchment Urbanization 10