integrated modeling at epa: vision and planned activities ecoinformatics meeting rtp, nc april 11,...

Integrated Modeling at EPA: Integrated Modeling at EPA: Vision and Planned ActivitiesVision and Planned Activities

EcoInformatics Meeting

RTP, NC

April 11, 2008

Gary Foley, Office of the Science AdvisorJohn M Johnston, Ecological Research Program

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

Council for Regulatory Council for Regulatory Environmental ModelingEnvironmental Modeling

Environment is complex and its components are not separable

Courtesy: NASA

The Need and The Challenge

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

Expanding Accountability

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

Chesapeake Bay Decision Support System

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Area of Criteria Exceedence

Area of AllowableCriteria

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Airshed Model,Land Change Model,Data

Watershed Model

Bay Model

CriteriaAssessmentProcedures

Effects

Allocations

Gary Shenk

A mechanistically consistent infrastructurefor 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

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

EPA Coordinating Workgroup

Integrated ModelingScience

Integrated Modeling

Technology

IntegratedModelingProjects

Community Of Practice

MYPs

Legacy Science &

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Experience/Guidance

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OEI

AgencyStrategicPlans

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Ecological Research ProgramEcological Research Program

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.

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.

Monitoring, Modeling and Mapping Relationships (LTG2 )

Monitoring Modeling MappingInput Output

Information FlowEcosystem Services

Indicators Indicators

Landscape characterization

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?

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

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

Requirements of Modeling

Coherence Transparency Reproducibility Characterization of uncertainty Quality assured

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