The Iterative Research Cycle: Process-Based Model Evaluation

Abstract:

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex physics based models that simulate a myriad of processes at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. In this talk I will give an overview of our latest research on process-based model calibration and evaluation. This approach, rooted in Bayesian theory, uses summary metrics of the calibration data rather than the data itself to help detect which component(s) of the model is (are) malfunctioning and in need of improvement. A few case studies involving hydrologic and geophysical models will be used to demonstrate the proposed methodology.