Over the past several decades, the needs of environmental flows (e-flows) programs have evolved from simplistic, prescriptive flow recommendations derived from hydrologic metrics, to dynamic, inclusive recommendations that recognize the importance of the entire flow regime to complex socio-ecological systems. Flow-ecology science is critical to this effort, and a wide range of modelling approaches have been developed to help further our understanding of flow-ecology relationships and support e-flows decision making. Despite the multitude of modeling approaches available, it is unclear which ones are being used in practice and what factors influence a models' suitability to support decision-making. Using an informal literature review, we identified and characterized the suite of potential modeling approaches. We then conducted a global online survey of e-flows managers and researchers to determine their familiarity and experience with models and the factors that influence the selection of modelling approaches in environmental flow assessments. In total, 24 managers and 42 researchers completed the survey with most respondents coming from Australia and the USA. We found that only half of the managers had experience with modeling approaches and there were significant differences between modeling approaches used within research and practice. Managers ranked resource constraints as highly important in their selection of a modeling approach and valued a models' ability to support decision making. We will present the primary modeling tools that are currently being used in practice and make recommendations regarding which models are suitable for e-flows decision making. We also present the primary concerns of managers when selecting modeling approaches so that interested researchers can work towards developing suitable models to support decision-making. This work promotes the uptake of flow-ecology modeling in e-flows assessments and decision-making, as their use contributes to building our overall knowledge of flow-ecology relationships as well as supporting localized learning within e-flows adaptive management.