SCIENCE for International Interagency Integrated Environmentatl Modeling

1. SCIENCE for International Interagency Integrated Environmentatl Modeling Tom Nicholson, Mary Hill Gabriel Olchin

2. Vision • Science supporting IEM and its application has evolved to inform policy decisions, communicate to society environmental problems and solutions, and better serve the process of hypothesis testing and knowledge evolution. IEM uses state of the art science-based predictive capabilities for multi-scale, multi-processes that represent environmental responses to perturbations in natural or engineered settings. Uncertainty analysis is used to critically evaluate the limitations of IEM. • Vision Details: • Databases / analysis • Data integration • Model development • Model evaluation/analysis (prediction analysis, dynamic evaluation, post-audit, etc.) • Prediction analysis • Other

3. Overview • Process science • Computer science • Information/Technology science • Social science • Behavioral science • Economics • High Profile Applications

4. High Profile Applications • Hurricane Katrina • Deep Water Horizon oil spill • Climate change impacts: global to local • Chernobyl cooling pond remediation

5. Roadmap for science: goals • Start with high profile application like Hurricane Katrina • Science categories discussed or mentioned • Process science • Data/model integration science • Computer science • Information/Technology science • Social science • Behavioral science • Economics • High Profile Applications

6. Roadmap for science: goals • Start with high profile application like Hurricane Katrina • Process science • Testing and evaluation of conceptual models that reconcile scales of processes and complexities • Develop a model that describes chemical and other environmental signaling in an ecosystem and the effects of chemical/environmental disturbance • Adaptable (multi-objective) models to address objectives and applications. These models are also adaptable to scope – the IEM model is adaptively developed to meet specific scenario/application • Develop a model that describes chemical and other environmental signaling in an ecosystem and the effects of chemical/environmental disturbance • Biogeomorphological evolution of streams during stream re-naturalization activities (simple systems to complex; succession models (e.g. biotic population models integrated with hydrologic) what are the questions that could be answered with this approach? • Physical science based ecosystem services model • Develop model to maximize species conservation in the face of climate or land-use change • Develop a physically based and distributed watershed water quality and contaminant/ecosystem/agent-based risk assessment modeling system 1-3yr

7. Roadmap for science: goals • Start with high profile application like Hurricane Katrina • Data/model integration science • Establish framework for ear-marking, and identify where major contributions of uncertainty come from and how to reduce it • Identify uncertainty across scales in linked multi-disciplinary models • Make models much more useful. • This includes being broadly searchable, having good explanations about model purpose and limitations. Could possibly include “dynamic reports: electronic reports in which Develop meta-data standards to describe models and model application area (i.e. describe model application niche so models are used appropriately, or identify a suite of suitable application scenarios). • Identify and fill critical observation gaps • Create an online directory of model software and models • Coordinate with/take advantage of iemHUB, USGS software web sites, EPA web site for COSU, etc. • GEO biodiversity observation network • Geo USER REQUIRMENTS registry – for characterizing the gaps • Sensitivity analysis • Local methods provide 70% of the understanding for 2% of the model runs

8. Which existing observations are important(or not) to predictions? OPR: Observation-PRediction statistic. Measures the change in uncertainty produced by removing existing data (shown here) or adding new data. Here, use opr(-1)to rank the 501 existing observation locations by their importance to predictions of transport on the Nevada Test site • Averaged values of opr(-1)for all the predictions are used, to obtain a measure indicating the importance of a single observation to all the predictions of interest. • Calculate opr(-100)by removing the 100 least important observations • opr(-100)= mean prediction uncertainty increases = 0.6% Hill and Tiedeman, 2007, fig. 15.9. p. 368

9. Roadmap for science: goals • Start with high profile application like Hurricane Katrina • Computer science • Large scale models that query data automatically and dynamically (from other models or data sources through web computing)  data sources that are OPEN MI compliant and feed through web services

10. Roadmap for science: goals • Start with high profile application like Hurricane Katrina • Information/Technology science • Develop standards for scientists to facilitate data exchange between modeling components. • Common modeling frameworks flexible enough to integrate varied process models from across varied scales of observation

11. Supporting science • Create incentives for modelers to create open, structured, well-documented, tested computer programs