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ODM. A Community Data Model for Hydrologic Observations. Observations Data Model Schema. Controlled Vocabularies. Data Source and Network. Sites. Variables. Values. Metadata. e.g. mg/kg, cfs. e.g. depth. Depth of snow pack. Streamflow. e.g. Non-detect, Estimated. - PowerPoint PPT Presentation
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A Community Data Model for Hydrologic Observations
Observations Data Model Schema
ODMData Source and Network Sites Variables Values Metadata
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 networkA 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
length of time
qu
an
tity
(a) Extent
length of time
qu
an
tity
(b) Spacing
length of time
qu
an
tity
(c) Support
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 protected]
David Tarboton, Utah State University, 4110 Old Main Hill, Logan, UT 84322-8200(435) 797-3172, [email protected]
Jeffery Horsburgh, Utah State University, 8200 Old Main Hill, Logan, UT 84322-8200(435) 797-2946, [email protected]
David Maidment, University of Texas at Austin, Austin, TX 78712, [email protected]
David Valentine, San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, [email protected]
Blair Jennings, San Diego Supercomputer Center, University of California, San Diego, San Diego, CA 92093, [email protected]
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
What
Where
When
ODM Examples
This work is funded by the National Science Foundation
Daily Average Discharge ExampleDaily Average Discharge Derived from 15 Minute
Discharge Data
Water Chemistry From a Profile in a LakeStage and Streamflow Example
ODM Features
Data TypesVariable Attributes
• Continuous (Frequent sampling - fine spacing)
• Instantaneous (Spot sampling - coarse spacing)
• Cumulative• Incremental• Average• Maximum• Minimum• Constant over Interval• Categorical
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Offset
VariableName, e.g. dischargeVariableCode, e.g. 0060SampleMedium, e.g. waterValuetype, e.g. field observation, laboratory sampleIsRegular, e.g. Yes for regular or No for intermittentTimeSupport (averaging interval for observation)DataType, e.g. Continuous, Instantaneous, CategoricalGeneralCategory, e.g. Climate, Water QualityNoDataValue, 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
ObservationsDatabase
(ODM)
Base StationComputer(s)
Data ProcessingApplications
Telemetry Network
Sensors Inte
rnet
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 protected].
