Integrating ecohydrological and socioeconomic information identified environmental flow management regimes that efficiently achieved both ecological and social objectives. Using an ecologically weighted efficient and socially weighted efficient scenario, we illustrated model outputs including a suite of cost-effective infrastructure investments and an operational plan for new and existing flow control structures including dam releases, weir height manipulation, and regulator operation on a monthly time step. Both the investments and management regimes differed substantially between the two scenarios, suggesting that the choice of weightings on ecological and social objectives is important. This demonstrates the benefit of integrating high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information for guiding the investment in and operational management of environmental flows.Investment in and operation of flow control infrastructure such as dams, weirs, and regulators can help increase both the health of regulated river ecosystems and the social values derived from them. This requires high-quality and high-resolution spatiotemporal ecohydrological and socioeconomic information. We developed such an information base for integrated environmental flow management in the River Murray in South Australia (SA). A hydrological model was used to identify spatiotemporal inundation dynamics. River ecosystems were classified and mapped as ecohydrological units. Ecological response models were developed to link three aspects of environmental flows (flood duration, timing, and inter-flood period) to the health responses of 16 ecological components at various life stages. Potential infrastructure investments (flow control regulators and irrigation pump relocation) were located by interpreting LiDAR elevation data, digital orthophotography, and wetland mapping information; and infrastructure costs were quantified using engineering cost models. Social values were quantified at a coarse scale as total economic value based on a national survey of willingness-to-pay for four key ecological assets; and at a local scale using mapped ecosystem service values. This information was integrated using a constrained, nonlinear, mixed-integer, compromise programming optimization model and solved using a stochastic Tabu search algorithm. We tested the model uncertainty and sensitivity using 390 Monte Carlo model runs at varying weights of ecological health vs. social values.