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We test how well available stellar population models can reproduce observed  u ‐,  g ‐,  r ‐,  i ‐,  z ‐band photometry of the local galaxy population (0.02 ≤  z  ≤ 0.03) as probed by the Sloan Digital Sky Survey (SDSS). Our study is conducted from the perspective of a user of the models, who has observational data in hand and seeks to convert them into physical quantities. Stellar population models for galaxies are created by synthesizing star formation histories and chemical enrichments using single stellar populations from several groups ( starburst 99,  galaxev , the Maraston models,  galev ). The role of dust is addressed through a simplistic, but observationally motivated, dust model that couples the amplitude of the extinction to the star formation history, metallicity and the viewing angle. Moreover, the influence of emission lines is considered (for the subset of models for which this component is included). The performance of the models is investigated by (1) comparing their prediction with the observed galaxy population in the SDSS using the ( u  −  g )–( r  −  i ) and ( g  −  r )–( i  −  z ) colour planes, (2) comparing predicted stellar mass and luminosity weighted ages and metallicities, specific star formation rates, mass‐to‐light ratios and total extinctions with literature values from studies based on spectroscopy. Strong differences between the various models are seen with several models occupying regions in the colour–colour diagrams where no galaxies are observed. We would therefore like to emphasize the importance of the choice of model. Using our preferred model we find that the star formation history, metallicity and also dust content can be constrained over a large part of the parameter space through the use of  u ‐,  g ‐,  r ‐,  i ‐,  z ‐band photometry. However, strong local degeneracies are present due to overlap of models with high and low extinction in certain parts of the colour space.

Observed versus modelled u ‐, g ‐, r ‐, i ‐, z ‐band photometry of local galaxies – evaluation of model performance