[1] Environmental observations are fundamental to hydrology and water resources, and the way these data are organized and manipulated either enables or inhibits the analyses that can be performed. The Observations Data Model presented here provides a new and consistent format for the storage and retrieval of point environmental observations in a relational database designed to facilitate integrated analysis of large data sets collected by multiple investigators. Within this data model, observations are stored with sufficient ancillary information (metadata) about the observations to allow them to be unambiguously interpreted and to provide traceable heritage from raw measurements to useable information. The design is based upon a relational database model that exposes each single observation as a record, taking advantage of the capability in relational database systems for querying based upon data values and enabling cross-dimension data retrieval and analysis. This paper presents the design principles and features of the Observations Data Model and illustrates how it can be used to enhance the organization, publication, and analysis of point observations data while retaining a simple relational format. The contribution of the data model to water resources is that it represents a new, systematic way to organize and share data that overcomes many of the syntactic and semantic differences between heterogeneous data sets, thereby facilitating an integrated understanding of water resources based on more extensive and fully specified information.

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Surrogate measures like turbidity, which can be observed with high frequency in situ, have potential for generating high frequency estimates of total suspended solids (TSS) and total phosphorus (TP) concentrations. In the semiarid, snowmelt-driven, and irrigation-regulated Little Bear River watershed of northern Utah, high frequency in situ water quality measurements were recorded in conjunction with periodic chemistry sampling. Site-specific relationships were developed using turbidity as a surrogate for TP and TSS at two monitoring locations. Methods are presented for employing censored data and for investigating categorical explanatory variables (e.g., hydrologic conditions). Turbidity was a significant explanatory variable for TP and TSS at both sites, which differ in hydrologic and water quality characteristics. The relationship between turbidity and TP was stronger at the upper watershed site where TP is predominantly particulate. At both sites, the relationships between turbidity and TP varied between spring snowmelt and base flow conditions while the relationships between TSS and turbidity were consistent across hydrological conditions. This approach enables the calculation of high frequency time series of TP and TSS concentrations previously unavailable using traditional monitoring approaches. These methods have broad application for situations that require accurate characterization of fluxes of these constituents over a range of hydrologic conditions.

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The types of data and models used within the hydrologic science community are diverse. New repositories have succeeded in making data and models more accessible, but are, in most cases, limited to particular types or classes of data or models and also lack the type of collaborative and iterative functionality needed to enable shared data collection and modeling workflows. File sharing systems currently used within many scientific communities for private sharing of preliminary and intermediate data and modeling products do not support collaborative data capture, description, visualization, and annotation. In this article, we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. This article describes the generic data model and content packaging scheme for diverse hydrologic datasets and models used by a new hydrologic collaborative environment called HydroShare to enable storage, management, sharing, publication, and annotation of the diverse types of data and models used by hydrologic scientists. The flexibility of HydroShare's data model and packaging scheme is demonstrated using multiple hydrologic data and model use cases that highlight its features.

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