Modern and historic flood risk management involves accommodating multiple sources of sources of uncertainty and potential impacts across a broad range of interrelated sectors. Sources of uncertainty that affect planning include internal climate variability, anthropogenic changes such as land use and system performance expectations, and more recently changes in climatology that affect the resources supporting the system. Flood management systems potentially impact human settlements within and beyond the systems’ scope of planning, local weather patterns, and associated ecological systems. Federal guidelines across nations have called for greater consideration of uncertainty and impacts of water resources planning projects, but methods for meeting these needs remain poorly established. At the same time, there is increased attention to the ecological impacts of water resources systems and growing expectations that negative impacts be mitigated. The confluence of climate change and increasing demand for environmental quality presents a challenging flood management decision context. This work presents several alternative methods for incorporating ecological impacts into flood risk management and evaluation procedures alongside climate uncertainty, which are illustrated through application to a flood management system on the Iowa River. First, to integrate climate change and uncertainty information into these decision models, the dissertation presents a decision-centric trend detection test in which the threshold for accepting or rejecting a trend in observed data is determined by the expected cost of drawing a false conclusion. Next, the dissertation presents a decision model to choose a portfolio of adaptation options based on portfolios’ expected economic and monetized ecological performance under uncertain future flood hazard. The dissertation also develops a robust optimization model with an alternate treatment of ecological performance to maximize the range of future conditions over which performance is acceptable in both economic and ecological impact sectors. Lastly, the dissertation presents a method for deriving a posterior distribution of changes in climate parameters based on a combination of a prior constructed based on climate model projections and likelihood based on the historic record. The goals of this work are to develop enhanced decision support tools that accommodate the unique context of flood risk management decisions and to improve the set of methods available to characterize future flood hazard and its associated uncertainty.