Multi-scale Integrated Models of Ecosystem Services

From L. Shawn Matott, Ph.D.
U.S. Environmental Protection Agency
Ecosystems Research Division

Modeling frameworks such as MIMES and FRAMES can be leveraged to conduct integrated environmental modeling in support of the ERP core focus on ecosystems services. For such tools to be useful in a policy-relevant context, elucidating model uncertainty is a challenging but vital task.

To start off the discussion, let’s consider a few definitions and related concepts:

1. Uncertainty may be classified into two ‘types’: aleatory, i.e. irreducible and stemming from natural variability, and epistemic, i.e. reducible and stemming from lack of knowledge. Aleatory and epistemic uncertainties have different ramifications for policy decisions.

2. Uncertainty may also be classified in terms of its ’sources’, which include uncertain model input (e.g. amount of rainfall), uncertain model parameters (e.g. various rate constants), and uncertain model structure (e.g. the degree to which the model equations represent the real world).

3. Formal uncertainty analysis (UA) methods propagate sources of uncertainty through the model to generate statistical moments or complete probability distributions for various model outputs. Traditional UA schemes wrap a stochastic Monte Carlo shell around one or more linked (and possibly calibrated) models. More recently, there has been increased emphasis on Hierarchical Bayesian approaches, which utilize sampling procedures that explicitly condition model output on available data.

4. Less formal methods, such as scenario analysis and alternative futures, can also be important tools for uncertainty analysis; albeit in a more qualitative sense. Along these same lines, the NUSAP (Numeral, Unit, Spread, Assessment, Pedigree) system of Funtowicz and Ravetz can serve as an important tool for assessing and assuring quality throughout the modeling process.

5. Sensitivity analysis (SA) studies the degree to which model output is influenced by changes in model input. By measuring model input importance, SA methods enable identification of critical areas where knowledge or data are lacking. In this way, SA can assist decision-makers in prioritizing future research and data collection activities.

Given these definitions and concepts, we can discuss what (if any) infrastructure is available within the MIMES and FRAMES systems to support the UA/SA needs of the ERP.

This entry was posted on Thursday, November 8th, 2007 at 10:44 am and is filed under EPA 2007. You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.