Water theft claims between 30% and 50% of the global water supply, and despite policy efforts to tackle it, is still on the rise. The policy failure to tackle water theft has been attributed to the nonlinear adaptive responses by economic agents such as irrigators, which can affect and be affected by other socioeconomic (e.g., growing crop prices) and ecological processes (e.g., water scarcity) via feedback loops with cascading impacts that are difficult to foresee. This has led to adaptation surprises with unexpected policy consequences, which have increased rather than reduced water theft, thus depleting water bodies and hampering sustainable development. Here I aim to break new ground by developing a novel approach to forecast adaptation surprises in complex human-water systems. To this end, I will 1) combine microeconomic mathematical programming models with behavioral economic methods to forecast the nonlinear adaptive responses of individual agents over time; 2) integrate the behavior of individual agents into agent-based models and macroeconomic models to forecast nonlinear spatial trends emerging from human interactions at the local to global level; 3) endogenize these socioeconomic processes into human-water system models to forecast nonlinear socio-hydrological phenomena; and 4) use ensemble experiments to quantify scenario and modeling uncertainties, and forecast nonlinearities that may emerge or be amplified due to issues of model parameterization/structure or scenario design. These innovations will allow me to predict the emergence of nonlinearities and track their impact across coupled human-water systems, thus discovering adaptation surprises and their drivers. Methods will be empirically applied and tested in 3 living labs in Spain, Australia, and the US experiencing water theft.