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Abstract Models based on ecological processes (process‐explicit models) are often used to predict ecosystem responses to environmental changes or management scenarios. However, models are imperfect and need to be validated, ideally by testing their assumptions and outputs against independent empirical data sets. Examples of validation of process‐explicit models are rare. Recently, stochastic population models have been developed to predict the likely responses (over 10–120 years) of a riverine fish (golden perch, Macquaria ambigua) to flow management in the Murray–Darling Basin (MDB) in eastern Australia, one of the world's most regulated river basins. Declines in golden perch (and other species) are a direct consequence of altered hydrology, and managers require information to predict how fish will respond to possible future hydrological conditions to guide substantial investments in flow management. Here, we use two independent field data sets to validate our population model. We compared model predictions to observed trends to ask: (1) How do predicted population sizes and growth rates compare with observed data? (2) Does the correlation between predicted and observed population sizes and growth rates vary among populations? (3) Does the correlation between predicted and observed population sizes and growth rates vary across observed hydrological conditions? (4) How do modeled and observed fish movement rates compare? We found reasonable correlations between fish population sizes and growth rates as predicted by the model and observed in independent data sets for several populations (Aim 1), but the strength of these correlations varied among populations (Aim 2) and hydrological conditions (Aim 3). Predicted and observed fish movement rates were strongly correlated (Aim 4). Population models are frequently used in conservation decision‐making but are rarely validated. We demonstrate that: (1) validation can identify model strengths and weaknesses; (2) observed data sets often have inherent limitations that can preclude robust validations; (3) validation is likely to be more common if appropriate observed data sets are available; and (4) validation should consider the purpose of modeling. Wider consideration of these messages would contribute to more critical examinations of models, so they can be most appropriately used in conservation decision‐making.