Choosing the right model (structure) for a given purpose, catchment, and data situation is a critical task in the modeling chain. However, despite model intercomparison studies, hypothesis testing approaches with flexible modelling frameworks, and continuous efforts in model development/improvement, there are still no clear guidelines for choosing an optimal model structure. We introduce a framework for Automatic Model Structure Identification (AMSI) based on the combination of the flexible hydrological model Raven and the heuristic global optimization algorithm DDS. It is the first demonstration of mixed-integer optimization algorithms to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrologic modelling. Thus, AMSI is able to sift through a vast number of combinations for a given model and parameter space in order to …