PPT 1.8 MB - Jeff Horsburgh

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A Community Data Model for
Hydrologic Observations
Observations Data Model Schema
Data Source
and Network
Sites
Variables
Values
Controlled
Vocabularies
Metadata
e.g. mg/kg, cfs
e.g. depth
Streamflow
Depth of
snow
pack
e.g. Non-detect,
Estimated
Windspeed, Precipitation
A variable is a
quantity that is
measured or observed
A value is an observation
of a variable at a
particular time
Metadata provide information about and context for the
observation
Credit for this figure goes to Ernest To, University of Texas at Austin
The CUAHSI HIS and ODM
Contact Information
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]
Ilya Zaslavsky, San Diego Supercomputer Center, University of California, San Diego, San
Diego, CA 92093, [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]
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.
When
Hydrologic observations are identified by the following
fundamental characteristics:
• The location at which the observations were made (space)
Where
• 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
What
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.
• The date and time at which the observations were made (time)
length of time
(c) Support
(b) Spacing
(a) Extent
quantity
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.
ODM Features
quantity
A site is a point
location where one or
more variables are
measured
quantity
A data source operates an
observation network
A network is a set of
observation sites
length of time
length of time
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
This work is funded by the
National Science Foundation
ODM Features
Variable Attributes
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
Data Types
• Continuous (Frequent sampling fine spacing)
• Instantaneous (Spot sampling coarse spacing)
t
• Cumulative V( t )   Q()d
t
0
V( t )   Q( )d
• Incremental
t  t
V (t )
• Average
Q (t ) 
t
• Maximum
• Minimum
• Constant over Interval
• Categorical
Groups and Derived From
Associations
Methods and Samples
Offset
Accuracy and Precision
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
Observation Series
Method specifies the method whereby an observation
is measured, e.g. Streamflow using a V notch weir,
TDS using a Hydrolab, sample collected in autosampler
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.
Data Quality
ObsAccuracyStdDev
Numeric value that expresses
measurement accuracy as the
standard deviation of the error
associated with each specific
observation
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 Examples
Stage and Streamflow Example
Daily Average Discharge Example
Water Chemistry From a Profile in a Lake
Daily Average Discharge Derived from 15 Minute
Discharge Data
ODM Utilities
Sensor to Database Capability
Data Distribution Via CUAHSI
HIS Web Services
ODM Software Tools
Data Processing
Applications
Base Station
Computer(s)
Observations
Database
(ODM)
Sensors
Internet
Telemetry
Network
Automated tools for ingesting
sensor data into the ODM
Open Comment Period
• Machine to machine communication of data over
the internet
• Users can program against the database as if it
were on their local machine
• 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.
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]
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