1. Integrated Modeling at EPA: Vision and Planned Activities EcoInformatics Meeting RTP, NC April 11, 2008 Gary Foley, Office of the Science Advisor John M Johnston, Ecological Research Program

2. Presentation Outline  Council for Regulatory Environmental Modeling  Workshop, White Paper and Action Plan  Ecological Research Program  Modeling science Modeling = Data + Model + Application  Modeling technology Modern Integrated Modeling and Technology  Next steps and direction A path forward  Identifying opportunities for EPA-EU collaboration in integrated modeling

3. Council for Regulatory Environmental Modeling

4. Environment is complex and its components are not separable Courtesy: NASA The Need and The Challenge

5. Integrated Modeling for Integrated Environmental Decision Making Workshop  Why: An opportunity for EPA modelers and policy-makers to discuss a long- term vision for integrated models and strengthening intra- and inter-Agency modeling collaboration  Who:  150 participants  Staff from across EPA involved in the development and application of models and interpretation of model outcomes in EPA’s Core, Program and Regional Offices.  Key international and US experts  Representatives from Environment Canada, EU

6. Expanding Accountability Using models that integrate multiple data sources to better assess effectiveness of past policies John Bachmann

7. Chesapeake Bay Decision Support System Airshed Model, Land Change Model, Data Watershed Model Bay Model Criteria Assessment Procedures Effects Allocations Gary Shenk

9. A mechanistically consistent infrastructure for both exposure assessment and health impact analysis: CERM/MENTOR and ebCTC/DORIAN address the source-to-outcome continuum This schematic has evolved from various graphical representations of the source-to-outcome sequence, that were developed in recent years by USEPA. CERM: Center for Exposure and Risk Modeling MENTOR: Modeling ENvironment for TOtal Risk studies ebCTC: environmental bioinformatics and Computational Toxicology Center DORIAN: DOse-Response Information Analysis system Satsry Isakupalli

10. For further information go to: www.OpenMI.org 10 Inside the wrapper River Model Rainfall Model Data. GetValue s Y N Already calculated ? Run Model RetrieveValues Apply conversions Buffer Data Gather data Data RainfallRunoffModel.GetValues (LinkRunoffToInflow, t1) Rainfall.GetValues (LinkRainToCatchm, t1) David Fortune

11. Integrated Modeling for Integrated Environmental Decision Making  Need for Integrated Modeling/ Analysis:  Integrating models and scientific components across media, disciplines and scales offers valuable insights  Need for Integrated Modeling to Inform Decision Making:  Enhancing stakeholder collaboration and decision making transparency.  Need for Organization-Level Solution/ Enabling Environment:  Promoting consistency and repeatability of analyses  Establish and maintain a connected stakeholder community  Promote better understanding of integrated modeling  Understanding new needs  Sharing experience, knowledge, technologies  Develop standards to facilitate reuse and interoperability of existing and new integrated modeling technologies

12. EPA Coordinating Workgroup Integrated Modeling Science Integrated Modeling Technology Integrated Modeling Projects Community Of Practice MYPs Legacy Science & Technology Experience/ Guidance INTEGRATED MODELING STAKEHOLDER COMMUNITY Air Water Waste Pesticides Regions...... OEI Agency Strategic Plans...

13. Ecological Research Program

14. Research Direction ERP Vision  A comprehensive theory and practice for characterizing, quantifying, and valuing ecosystem services, and their relationship to human well-being is consistently incorporated into environmental decision making. ERP Mission:  Provide the information and methods needed by decision makers to assess the benefits of ecosystem goods and services to human well- being for inclusion in management alternatives.

15. LTG 1: Decision Support Platform  By 2013 ORD will provide an innovative online decision support platform that offers EPA, Regions, States, local communities and resource managers the ability to integrate, visualize, and maximize use of diverse data, models and tools at multiple scales to generate alternative decision options and understand the consequences of management decisions on the sustainability of ecosystem services, their value and human well-being.

16. Monitoring, Modeling and Mapping Relationships (LTG2 ) Monitoring Modeling Mapping Input Output Information Flow Ecosystem Services Indicators Landscape characterization

17.  Monitoring  Frame for national and place-based model applications  Frame for mapping  Modeling  Source for non-monitored data (atm. dep. via CMAQ)  Source for time varying data  Mapping  Methods for spatial aggregation, disaggregation of data  Summary of modeled and monitored output for decisionmaking What can each provide the other?

18. Modeling Infrastructures (Frameworks)  Purpose and Benefits  Facilitate the development and application of integrated systems  Standards based  Facilitates collaboration and additional levels of research  Minimizes production of non-science software (more resources focused on science components)  Elements and Functionality  Execution management  Data flow management  User interfaces (hierarchical – system levels down to components)  Modeling support software (data access/retrieval/processing, visualization, quality assurance)  Limitations and Issues  Standards (like opinions, everyone framework has one -- need community wide standards)  Ongoing maintenance of large software systems is challenging  Misperception that infrastructures solve science integration problems

19. Principles of the ERP The treatment of uncertainty in the scientific methodology and our appreciation of uncertainty in decisionmaking must evolve Our success depends on an emergent property, a community understanding, appreciation and expertise for ecosystem services science and valuation

20. Requirements of Modeling  Coherence  Transparency  Reproducibility  Characterization of uncertainty  Quality assured

21. EPA-EU Collaboration in Integrated Modeling  Identify some scenarios that would benefit from interoperability  Gain better understanding of interoperability challenges and needs  Identify lessons learned from the Open MI project and EPA projects (e.g. FRAMES)?  Develop projects to facilitate reuse and interoperability of integrated modeling frameworks  Develop and share methods for uncertainty analysis and characterization