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In the United States, greater attention has been given to developing water supplies and quantifying available waters than determining who uses water, how much they withdraw and consume, and how and where water use occurs. As water supplies are stressed due to an increasingly variable climate, changing land‐use, and growing water needs, greater consideration of the demand side of the water balance equation is essential. Data about the spatial and temporal aspects of water use for different purposes are now critical to long‐term water supply planning and resource management. We detail the current state of water‐use data, the major stakeholders involved in their collection and applications, and the challenges in obtaining high‐quality nationally consistent data applicable to a range of scales and purposes. Opportunities to improve access, use, and sharing of water‐use data are outlined. We cast a vision for a world‐class national water‐use data product that is accessible, timely, and spatially detailed. Our vision will leverage the strengths of existing local, state, and federal agencies to facilitate rapid and informed decision‐making, modeling, and science for water resources. To inform future decision‐making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water‐use data. Research Impact Statement: To inform future decision‐making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water‐use data. [ABSTRACT FROM AUTHOR]

Abstract Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata. Graphical abstract Image 1 Highlights • We present a data model to organize water resources systems data and models. • Controlled vocabularies link native terms across different datasets and models. • Software tools manage controlled vocabularies and help load datasets. • Modelers can identify and compare available data then serve data to models. [ABSTRACT FROM AUTHOR]

Water systems modelers have developed multiple, independent, model- and study-area specific tools to store, query, visualize, and share their data. This fragmentation makes difficult comparisons, and synthesis within or across study areas. This paper identifies the common components of four existing tools for data storage, web visualization, and repository, and relates them correctly in syntax and semantics. Connecting the tools in a state-of-the-art, open-source software ecosystem allowed comparing the effects of population growth and water conservation in simulation and optimization models for the Bear River Watershed, U.S. and Monterrey, Mexico that have different spatial coverage and urban and agricultural representations. The software ecosystem also publishes the models and their data for discovery. The software ecosystem can help modelers tap the best features of individual software tools to answer a next generation of water management questions that seek to discover, use, reproduce, extend, compare, or synthesize within or across study areas. • We built a software ecosystem to visualize, compare, edit, run, and publish data, models, and scenarios for multiple water systems models. • The software ecosystem encourages reuse of tools and reproducibility of analysis. • The software is demonstrated using three models in the Bear River Watershed, U.S, and in Monterrey, Mexico. [ABSTRACT FROM AUTHOR]

In the original version of this Article the author of reference #38 was incorrectly stated as Quinn, J. et al. This has been corrected to Xu, W. et al. in both the HTML and PDF versions. [ABSTRACT FROM AUTHOR]