Summary
Abstract Hydrological process knowledge has advanced significantly during the past six decades. During the same period catchment models have undergone major developments including simple black box models, lumped conceptual models, hydrological response unit models, spatially distributed process‐based models and, recently, the emergence of machine learning hybrid models. This development has been enabled by improved understanding of hydrological processes together with ever increasing computer power and improved availability and accessibility of data. During the first couple of decades, a key assumption motivating the development towards increasing complexity of model codes was that more detailed process description would lead to more accurate model simulations and enable prediction of impa
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