Presentation on theme: "A Community Data Model for Hydrologic Observations Observations Data Model Schema ODM Data Source and Network SitesVariables ValuesMetadata Depth of snow."— Presentation transcript:
A Community Data Model for Hydrologic Observations Observations Data Model Schema ODM Data Source and Network SitesVariables ValuesMetadata Depth of snow pack Streamflow Windspeed, Precipitation Controlled Vocabularies e.g. mg/kg, cfs e.g. depth e.g. Non-detect, Estimated A site is a point location where one or more variables are measured A data source operates an observation network A network is a set of observation sites Metadata provide information about and context for the observation A variable is a quantity that is measured or observed A value is an observation of a variable at a particular time Credit for this figure goes to Ernest To, University of Texas at Austin ODM Features ODM is designed to store hydrologic observations and sufficient metadata about the values to provide traceable heritage from raw measurements to usable information, allowing the data to be unambiguously interpreted and used. 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) The scale and uncertainty or inaccuracy of each measurement The TimeSupport attribute of each variable quantifies the temporal averaging support associated with each measurement. Spatial support is quantified by the method and site position. Contact Information Ilya Zaslavsky, San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, email@example.com David Tarboton, Utah State University, 4110 Old Main Hill, Logan, UT 84322-8200 (435) 797-3172, firstname.lastname@example.org Jeffery Horsburgh, Utah State University, 8200 Old Main Hill, Logan, UT 84322-8200 (435) 797-2946, email@example.com David Maidment, University of Texas at Austin, Austin, TX 78712, firstname.lastname@example.org David Valentine, San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, email@example.com Blair Jennings, San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, firstname.lastname@example.org The CUAHSI HIS and ODM The CUAHSI Hydrologic Information System (HIS) project is developing information technology infrastructure to support hydrologic science. Hydrologic information science involves the description of hydrologic environments in a consistent way, using data models for information integration. This includes a hydrologic observations data model for the storage and retrieval of hydrologic observations in a relational database designed to facilitate data retrieval for integrated analysis of information collected by multiple investigators. It is intended to provide a standard format to facilitate the effective sharing of information between investigators and to facilitate analysis of information within a single study area or hydrologic observatory, or across hydrologic observatories and regions. The Scale Triplet of Measurements comprising extent, spacing and support (Grayson and Blöschl, 2000, chapter 2). Grayson, R. and G. Blöschl, ed. (2000), Spatial Patterns in Catchment Hydrology: Observations and Modelling, Cambridge University Press, Cambridge, 432p. http://www.catchment.crc.org.au/special_publications1.html http://www.catchment.crc.org.au/special_publications1.html What Where When
ODM Examples This work is funded by the National Science Foundation Daily Average Discharge Example Daily Average Discharge Derived from 15 Minute Discharge Data Water Chemistry From a Profile in a LakeStage and Streamflow Example ODM Features Data Types Variable Attributes Continuous (Frequent sampling - fine spacing) Instantaneous (Spot sampling - coarse spacing) Cumulative Incremental Average Maximum Minimum Constant over Interval Categorical Offset VariableName, e.g. discharge VariableCode, e.g. 0060 SampleMedium, e.g. water Valuetype, e.g. field observation, laboratory sample IsRegular, e.g. Yes for regular or No for intermittent TimeSupport (averaging interval for observation) DataType, e.g. Continuous, Instantaneous, Categorical GeneralCategory, e.g. Climate, Water Quality NoDataValue, e.g. -9999 OffsetValue Distance from a datum or control point at which an observation was made OffsetType defines the type of offset, e.g. distance below water level, distance above ground surface, or distance from bank of river Methods and Samples Method specifies the method whereby an observation is measured, e.g. Streamflow using a V notch weir, TDS using a Hydrolab, sample collected in auto- sampler SampleID is used for observations based on the laboratory analysis of a physical sample and identifies the sample from which the observation was derived. This keys to a unique LabSampleID (e.g. bottle number) and name and description of the analytical method used by a processing lab. Groups and Derived From Associations Accuracy and Precision ObsAccuracyStdDev Numeric value that expresses measurement accuracy as the standard deviation of the error associated with each specific observation Observation Series Data Quality Qualifier Code and Description provides qualifying information about the observations, e.g. Estimated, Provisional, Derived, Holding time for analysis exceeded QualityControlLevel records the level of quality control that the data has been subjected to. - Level 0. Raw Data - Level 1. Quality Controlled Data - Level 2. Derived Products - Level 3. Interpreted Products - Level 4. Knowledge Products ODM Utilities Load – import existing data directly to ODM Query and export – export data series and metadata Visualize – plot and summarize data series Edit – delete, modify, adjust, interpolate, average, etc. ODM Software Tools Observations Database (ODM) Base Station Computer(s) Data Processing Applications Telemetry Network Sensors Internet Sensor to Database Capability Machine to machine communication of data over the internet Users can program against the database as if it were on their local machine Data Distribution Via CUAHSI HIS Web Services Automated tools for ingesting sensor data into the ODM Open Comment Period See http://www.cuahsi.org/his/documentation.html for the detailed design specification document. Comments on this design are welcome. The open comment period ends January 31, 2007. Send comments to email@example.com://www.cuahsi.org/his/documentation.html
Your consent to our cookies if you continue to use this website.