Ambiguities in the representation of environmental processes have manifested themselves in a plethora of hydrological models, differing in almost every aspect of their conceptualization and implementation. The current overabundance of models is symptomatic of an insufficient scientific understanding of environmental dynamics at the catchment scale, which can be attributed to difficulties in measuring and representing the heterogeneity encountered in natural systems. This commentary advocates using the method of multiple working hypotheses for systematic and stringent testing of model alternatives in hydrology. We discuss how the multiple-hypothesis approach provides the flexibility to formulate alternative representations (hypotheses) describing both individual processes and the overall system. When combined with incisive diagnostics to scrutinize multiple model representations against observed data, this provides hydrologists with a powerful and systematic approach for model development and improvement. Multiple-hypothesis frameworks also support a broader coverage of the model hypothesis space and hence improve the quantification of predictive uncertainty arising from system and component nonidentifiabilities. As part of discussing the advantages and limitations of multiple-hypothesis frameworks, we critically review major contemporary challenges in hydrological hypothesis-testing, including exploiting different types of data to investigate the fidelity of alternative process representations, accounting for model structure ambiguities arising from major uncertainties in environmental data, quantifying regional differences in dominant hydrological processes, and the grander challenge of understanding the self-organization and optimality principles that may functionally explain and describe the heterogeneities evident in most environmental systems. We assess recent progress in these research directions, and how new advances are possible using multiple-hypothesis methodologies.