FLORIDA STATE UNIVERSITY, THE
Grant: $105,266 - National Science Foundation - Aug. 31, 2009
33% voted satisfied - 67% voted not satisfied - 3 vote(s) cast
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Award Description: Hydrologic environments are open and complex, rendering them prone to multiple interpretations and mathematical descriptions regardless of the quantity and quality of available data. This recognition has led to a growing tendency among hydrologists to postulate several alternative hydrologic models for a site. Models here are not limited to governing equations and associated boundary/initial conditions, but refer to conceptual-mathematical representations of hydrologic systems (e.g.,their processes and interactions). Facing the alternative models, the scientific question to be answered is how to evaluate plausibility of the models so that the models can be properly used to yield optimum predictions. Evaluating model plausibility considers the entire modeling process (including model formulation, calibration, and validation), and calibration/validation data play key role in the evaluation process. Although calibrating a single model has been studied for decades, impact of calibration data on evaluating plausibility of multiple models has not been well understood. Open questions are as follows: What kinds of calibration data can be used to most effectively discriminate between models? How many data are needed to reliably evaluate model plausibility? How does data correlation (spatial and temporal) affect the evaluation? How does biased evaluation of model plausibility influence predictive performance? These fundamental questions will be addressed using an interdisciplinary approach combining Bayesian statistical and computational methods. Scientific insights gained in this project will be valuable to any environmental modeling through cost-effective data collection for refining existing models and developing new models for environmental restoration and protection. Expected Outcomes: (1) Methods of evaluating model plausibility, which is estimated, in a Bayesian framework, based on conformity of model simulations to calibration data, complexity of models, and expert judgment. Based on the model probability, one can chose a single model i.e.,Bayesian model selection) or use multiple models (i.e., Bayesian model averaging) to make predictions. Predictive performance of the Bayesian model selection or averaging will be investigated. (2) Evaluation of the method using a syntheric case (Year 2) In the synthetic case, alternative groundwater models will be developed based on different representations of site heterogeneity and boundary conditions. This work will be completed by collaborating with USGS scientist, Mary Hill. (3) Evaluation of the method using a real-world case (Year 3) The real-world modeling will be conducted at the Naturita site, Colorado, where a risk exists that uranium may reach the Colorado River. Alternative models will be developed based on different ways of formulations of uranium reactive transport models, such as surface complexation models with different numbers of functional groups. For the real-world modeling, model predictive performance will be evaluated using cross-validation methods such as leave-one-out and K-fold. This work will be conducted together with USGS scientist, Gary Curtis. Significant Deliverable and Associated Units of Measure (1) Year 1: A graduate student will be recruited. Two peer-reviewed journal articles and two conference abstract are expected. (2) Year 2: Two peer-reviewed journal articles and two conference abstracts are expected. (3) Year 3: Two peer-reviewed journal articles and two conference abstract are expected. The graduate student is expected to graduate.
Project Description: One conference abstract and one journal article have been submitted, and details of the submissions are given below: Ye, M. and D. Lu, On the relationship between model selection criteria and model complexity, Submitted to the Fall annual meeting of the American Geophysical Union, December 14-18, 2009, San Francisco, CA. Ye, M. (2009), MMA: a computer code for multi-model analysis, Submitted to Ground Water, Under Review.
Jobs Summary: Nothing to report at this time. (Total jobs reported: 0)
Project Status: Less Than 50% Completed
This award's data was last updated on Aug. 31, 2009. Help expand these official descriptions using the wiki below.
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