Páramos, a neotropical alpine grassland‐peatland biome of the northern Andes and Central America, play an essential role in regional and global cycles of water, carbon, and nutrients. They act as water towers, delivering water and ecosystem services from the high mountains down to the Pacific, Caribbean, and Amazon regions. Páramos are also widely recognized as a biodiversity and climate change hot spots, yet they are threatened by anthropogenic activities and environmental changes. Despite their importance for water security and carbon storage, and their vulnerability to human activities, only three decades ago, páramos were severely understudied. Increasing awareness of the need for hydrological evidence to guide sustainable management of páramos prompted action for generating data and for filling long‐standing knowledge gaps. This has led to a remarkably successful increase in scientific knowledge, induced by a strong interaction between the scientific, policy, and (local) management communities. A combination of well‐established and innovative approaches has been applied to data collection, processing, and analysis. In this review, we provide a short overview of the historical development of research and state of knowledge of the hydrometeorology, flux dynamics, anthropogenic impacts, and the influence of extreme events in páramos. We then present emerging technologies for hydrology and water resources research and management applied to páramos. We discuss how converging science and policy efforts have leveraged traditional and new observational techniques to generate an evidence base that can support the sustainable management of páramos. We conclude that this co‐evolution of science and policy was able to successfully cover different spatial and temporal scales. Lastly, we outline future research directions to showcase how sustainable long‐term data collection can foster the responsible conservation of páramos water towers.

The hydrological evidence on which water resource management and broader governance decisions are based is often very limited. This issue is especially pronounced in lower-and middleincome countries, where not only data are scarce but where pressure on water resources is often already very high and increasing. Historically, several governance theories have been put forward to examine water resource management. One of the more influential is Elinor Ostrom's theory of common-pool resources. However while used very widely, the underlying principles of Ostrom's approach make pronounced implicit assumptions about the role of data and evidence in common-2 pool resource systems. We argue here this overlooks how power relations, user characteristics, system arrangements, and technological advances modulate fundamental associations between data, evidence, and governance, which we contend need to be considered explicitly. Examining the case of water allocations in Quito, Ecuador, we develop a set of concrete criteria to inform the ways in which Ostrom's principles can be applied in a data-scarce, institutionally complex, polycentric context. By highlighting the variable impact of data availability on subsequent evidence generation, these criteria have the potential to test the applicability of common assumptions about how to achieve water security in a developmental context, and hence offer the possibility of developing a more encompassing theory about the interactions between water data, evidence, and governance.

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Mountainous regions are a hotspot for water scarcity and anthropogenic pressure on water resources. Substantial uncertainty surrounds projections of future climate and water availability. Furthermore, quantitative and distributed data on water demand are generally scarce, dispersed, and highly heterogeneous. This forms a major bottleneck to studying water resources issues and developing strategies to improve water resource management. Here we present a methodology to produce and evaluate highresolution gridded maps of anthropogenic surface water demand with application to the Andean region. These data are disaggregated according to the major types of water demand: domestic users, irrigated area, and hydropower. This dataset was built by homogenizing, integrating, and interpolating data obtained from various national institutions in charge of water resource management as well as relevant global datasets. The maps can be used to research anthropogenic impacts on water resources, and to guide regional decision-making in regions such as the Andes.