Half the world's population resides within 310 transboundary lake and river basins shared among 151 riparian nations. Approximately 60% of these basins lack cooperative frameworks to share water. The complexities of sharing water necessitate identifying approaches for managing transboundary international freshwater resources. While much has been written about the histories, theory, and mechanisms of transboundary water management, conflict, and cooperation among riparian nations, we draw attention to scholarship written about what we believe is the central tool for cooperation: data and data sharing. The 1997 United Nations' Convention on the Law of the Non-Navigational Uses of International Watercourses (UN Watercourse Convention) recognizes sharing water resources data is vital to river basin cooperation. Data sharing builds trust between riparian states, aids in mitigating conflict, and improves environmental, economic, and social outcomes. Despite calls to increase data sharing in transboundary basins to support cooperative management, few papers review the role of data sharing in transboundary water management, including how often and what types of water resources data and information are shared. We synthesize the role of data in conflict and collaboration from peer-reviewed papers on transboundary water management from the year the UN Watercourse Convention went into force, 2014 to May 2022. We outline what scholars argue are the types of water-related data to be shared, the frequency of data sharing, and the mechanisms for sharing data for facilitating cooperation in transboundary waters.

Perfect foresight hydroeconomic optimization models are tools to evaluate impacts of water infrastructure investments and policies considering complex system interlinkages. However, when assuming perfect foresight, optimal management decisions are found assuming perfect knowledge of climate and runoff, which might bias the economic evaluation of investments and policies. We investigate the impacts of assuming perfect foresight by using Model Predictive Control (MPC) as an alternative. We apply MPC in WHAT-IF, a hydroeconomic optimization model, for two study cases: a synthetic setup inspired by the Nile River, and a large-scale investment problem on the Zambezi River Basin considering the water–energy–food nexus. We validate the MPC framework against Stochastic Dynamic Programming and observe more realistic modeled reservoir operation compared to perfect foresight, especially regarding anticipation of spills and droughts. We find that the impact of perfect foresight on total system benefits remains small (<2%). However, when evaluating investments and policies using with-without analysis, perfect foresight is found to overestimate or underestimate values of investments by more than 20% in some scenarios. As the importance of different effects varies between scenarios, it is difficult to find general, case-independent guidelines predicting whether perfect foresight is a reasonable assumption. However, we find that the uncertainty linked to climate change in our study cases has more significant impacts than the assumption of perfect foresight. Hence, we recommend MPC to perform the economic evaluation of investments and policies, however, under high uncertainty of future climate, increased computational costs of MPC must be traded off against computational costs of exhaustive scenario exploration.