The Iterative Research Cycle: Process-Based Model-Data Fusion in Vadose Zone Research.

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 vadose zone models that simulate a myriad of processes at different spatial and temporal scales. Reconciling these numerical system models with increasingly larger volumes of 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 present different elements of a process-based model calibration approach. This approach uses signature behaviors and patterns observed in the input-output data and likelihood-free (ABC) inference to illuminate to what degree a representation of the real world has been adequately achieved and how the model should be refined for the purpose of learning and scientific discovery. A few preliminary case studies involving a soil-plant-atmosphere continuum and geophysical model will be used to demonstrate the proposed methodology.