Note that WaterML is broader in scope than ODM. ODM is defined over observations made at, or aggregated for, point locations referenced as sites, while WaterML is extended to incorporate other spatial feature types. Individual observations are organized into an observation series (a regular sequences of observations of a specific variable made at a specific site), which are in turn referenced in a series catalog. The SeriesCatalog table or view in ODM lists each unique site, variable, source, method and quality control level combination found in ODM’s Values table, and identifies each by a unique series identifier, SeriesID. A series catalog is an element of an observation network, which represents a collection of sites where a particular set of variables is measured. A responsible organization can maintain one or more observation networks. In addition to point measurements described in the ODM specification, hydrologic information may be available as observations or model outcomes aggregated over user-defined regions or grid cells. While USGS NWIS and EPA STORET exemplify the former case, sources such as MODIS and Daymet are examples of the latter. In this latter case, as in the case of other remote sensing products or model-generated grids, the observation or model-generated data are treated as coverages, and sources of such data are referenced in WaterML as datasets, as opposed to sites. In other words, WaterML’s dataset element refers to a type of observations data source that is queried by specifying a rectangular region of interest, and the returned time series typically represent some aggregation over the region of interest. Hydrologic observations are identified by the following fundamental characteristics: The location at which the observations were made (space) The date and time at which the observations were made (time) The type of variable that was observed, such as streamflow, water surface elevation, water quality concentration, etc. (variable) Elements in WaterML covers three characteristics in a similar fashion: sites and datasets cover the spatial characteristic values, which are lists of dateTime-value pairs, cover the time characteristic variables WaterML is about conveying the message between clients and servers. The messages are simplified and generic. The message may not include the full details that a data source has about the instruments used, or even specific details that might be included, such as a Hydrologic Unit Code (HUC), or a responsible party.
Note that WaterML is broader in scope than ODM. ODM is defined over observations made at, or aggregated for, point locations referenced as sites, while WaterML is extended to incorporate other spatial feature types. Individual observations are organized into an observation series (a regular sequences of observations of a specific variable made at a specific site), which are in turn referenced in a series catalog. The SeriesCatalog table or view in ODM lists each unique site, variable, source, method and quality control level combination found in ODM’s Values table, and identifies each by a unique series identifier, SeriesID. A series catalog is an element of an observation network, which represents a collection of sites where a particular set of variables is measured. A responsible organization can maintain one or more observation networks. In addition to point measurements described in the ODM specification, hydrologic information may be available as observations or model outcomes aggregated over user-defined regions or grid cells. While USGS NWIS and EPA STORET exemplify the former case, sources such as MODIS and Daymet are examples of the latter. In this latter case, as in the case of other remote sensing products or model-generated grids, the observation or model-generated data are treated as coverages, and sources of such data are referenced in WaterML as datasets, as opposed to sites. In other words, WaterML’s dataset element refers to a type of observations data source that is queried by specifying a rectangular region of interest, and the returned time series typically represent some aggregation over the region of interest. Hydrologic observations are identified by the following fundamental characteristics: The location at which the observations were made (space) The date and time at which the observations were made (time) The type of variable that was observed, such as streamflow, water surface elevation, water quality concentration, etc. (variable) Elements in WaterML covers three characteristics in a similar fashion: sites and datasets cover the spatial characteristic values, which are lists of dateTime-value pairs, cover the time characteristic variables WaterML is about conveying the message between clients and servers. The messages are simplified and generic. The message may not include the full details that a data source has about the instruments used, or even specific details that might be included, such as a Hydrologic Unit Code (HUC), or a responsible party.
WaterML_1_0_part1.docx