A sequential approach to calibrate ecosystem models with multiple time series data

- A methodology for a sequential parameter estimation of ecosystem models is proposed.
- Model dependency and time variability of the parameters are used as decision rules.
- The calibration of an end-to-end ecosystem model is used as a case study.
- The sequential calibration under our methodology allowed to improve the model fit.

When models are aimed to support decision-making, their credibility is essential to consider. Model fitting to observed data is one major criterion to assess such credibility. However, due to the complexity of ecosystem models making their calibration more challenging, the scientific community has given more attention to the exploration of model behavior than to a rigorous comparison to observations. This work highlights some issues related to the comparison of complex ecosystem models to data and proposes a methodology for a sequential multi-phases calibration (or parameter estimation) of ecosystem models. We first propose two criteria to classify the parameters of a model: the model dependency and the time variability of the parameters. Then, these criteria and the availability of approximate initial estimates are used as decision rules to determine which parameters need to be estimated, and their precedence order in the sequential calibration process. The end-to-end (E2E) ecosystem model ROMS-PISCES-OSMOSE applied to the Northern Humboldt Current Ecosystem is used as an illustrative case study. The model is calibrated using an evolutionary algorithm and a likelihood approach to fit time series data of landings, abundance indices and catch at length distributions from 1992 to 2008. Testing different calibration schemes regarding the number of phases, the precedence of the parameters' estimation, and the consideration of time varying parameters, the results show that the multiple-phase calibration conducted under our criteria allowed to improve the model fit.