It is common for in situ hydrologic and water quality data to be collected at high frequencies and for extended durations. These data streams, which may also be collected across many monitoring sites require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a standard data model in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, an open source software application that allows users to query and export, visualize, and perform quality control post processing on time series of environmental observations data stored in an ODM database using automated Python scripting that records the corrections and adjustments made to data series in the quality control process and ensures data editing steps are traceable and reproducible

Integrated access to and analysis of data for cross-domain synthesis studies are hindered because common characteristics of observational data, including time, location, provenance, methods, and units are described differently within different information models, including physical implementations and exchange schema. We describe a new information model for spatially discrete Earth observations called the Observations Data Model Version 2 (ODM2) aimed at facilitating greater interoperability across scientific disciplines and domain cyberinfrastructures. ODM2 integrates concepts from ODM1 and other existing cyberinfrastructures to expand capacity to consistently describe, store, manage, and encode observational datasets for archival and transfer over the Internet. Compared to other systems, it accommodates a wider range of observational data derived from both sensors and specimens. We describe the identification of community information requirements for ODM2 and then present the core information model and demonstrate how it can be formally extended to accommodate a range of information requirements and use cases

Urbanization, climate, and ecosystem change represent major challenges for managing water resources. Although water systems are complex, a need exists for a generalized representation of these systems to identify important components and linkages to guide scientific inquiry and aid water management. We developed an integrated Structure‐Actor‐Water framework (iSAW) to facilitate the understanding of and transitions to sustainable water systems. Our goal was to produce an interdisciplinary framework for water resources research that could address management challenges across scales (e.g., plot to region) and domains (e.g., water supply and quality, transitioning, and urban landscapes). The framework was designed to be generalizable across all human–environment systems, yet with sufficient detail and flexibility to be customized to specific cases. iSAW includes three major components: structure (natural, built, and social), actors (individual and organizational), and water (quality and quantity). Key linkages among these components include: (1) ecological/hydrologic processes, (2) ecosystem/geomorphic feedbacks, (3) planning, design, and policy, (4) perceptions, information, and experience, (5) resource access and risk, and (6) operational water use and management. We illustrate the flexibility and utility of the iSAW framework by applying it to two research and management problems: understanding urban water supply and demand in a changing climate and expanding use of green storm water infrastructure in a semi‐arid environment. The applications demonstrate that a generalized conceptual model can identify important components and linkages in complex and diverse water systems and facilitate communication about those systems among researchers from diverse disciplines.