@article{fdi:010069394,
  title = {{A} sequential approach to calibrate ecosystem models with multiple time series data},
  author = {{O}liveros-{R}amos, {R}. and {V}erley, {P}hilippe and {E}chevin, {V}incent and {S}hin, {Y}unne-{J}ai},
  editor = {},
  language = {{ENG}},
  abstract = {{W}hen models are aimed to support decision-making, their credibility is essential to consider. {M}odel fitting to observed data is one major criterion to assess such credibility. {H}owever, 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. {T}his 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. {W}e first propose two criteria to classify the parameters of a model: the model dependency and the time variability of the parameters. {T}hen, 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. {T}he end-to-end ({E}2{E}) ecosystem model {ROMS}-{PISCES}-{OSMOSE} applied to the {N}orthern {H}umboldt {C}urrent {E}cosystem is used as an illustrative case study. {T}he 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. {T}esting 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.},
  keywords = {{S}tochastic models ; {E}cosystem model ; {M}odel calibration ; {M}odel fitting ; {I}nverse problems ; {P}arameter estimation ; {D}ata time series ; {H}umboldt {C}urrent {E}cosystem ; {P}eru ; {PEROU} ; {PACIFIQUE} {SUD} ; {HUMBOLDT} {COURANT}},
  booktitle = {},
  journal = {{P}rogress in {O}ceanography},
  volume = {151},
  numero = {},
  pages = {227--244},
  ISSN = {0079-6611},
  year = {2017},
  DOI = {10.1016/j.pocean.2017.01.002},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069394},
}