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Periurban farmers in India are operating in fast-paced transformative environments of uncertain, quickly changing hydrosocial landscapes while simultaneously responding to different urban, rural and periurban demands. The urge is growing toward a more sustainable, integrative agricultural transformation, in which local stakeholders have more agency to control their own development. Yet, farmers are mostly passive in the process of shaping periurban futures as they face challenges in dealing with growing uncertainties in their hydrosocial environments. From a political ecology perspective, the concept of the hydrosocial cycle helps in understanding these uncertainties and their impacts on farmers' livelihoods and in classifying water-society dynamics. We adopted this concept to critically assess different perceptions of uncertainties based on the effects of uneven hydrosocial development. Extending beyond this analysis, we then followed a multi-perspective, scenario-based planning approach to show a methodology to support farmers in adapting and planning accordingly. We applied a modified Delphi method that combines local knowledge of actors from the village Paud in periurban Pune (India) with the expertise of Indian and international experts. We used the method to determine actions and institutions for different future scenarios and to understand which drivers and signals interfere and affirm each scenario's feasibility. From both bodies of knowledge, we could identify one realistic preferred/business-as-usual scenario and two alternatives with eight different drivers that cause complex, hydrosocial uncertainties. Both bodies of knowledge suggest that farming will continue to be an important water-based livelihood in Paud in the future. Yet, we were able to contrast different mechanisms involved in the future thinking of actors and experts. This research contributes to understanding possible processes of adaptation through co-creation of knowledge. The applied methodology can enable farmers to reflect on possible futures, activate their available capabilities, and may facilitate more sustainable and adaptive decision-making. After further refinements, the method employed could in future be useful for policy making and planning.