Development of a Community Hydrologic Information System

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Development of a
Community Hydrologic
Information System
Jeffery S. Horsburgh
Utah State University
David G. Tarboton
Utah State University
Hydrologic Science
It is as important to represent hydrologic environments precisely with
data as it is to represent hydrologic processes with equations
Physical laws and principles
(Mass, momentum, energy, chemistry)
Hydrologic Process Science
(Equations, simulation models, prediction)
Hydrologic conditions
(Fluxes, flows, concentrations)
Hydrologic Information Science
(Observations, data models, visualization
Hydrologic environment
(Dynamic earth)
Water quantity
and quality
Soil water
Water Data
Remote sensing
Meteorology
Rainfall & Snow
Modeling
•
Objective
Provide access to multiple heterogeneous data sources
simultaneously, regardless of semantic or structural differences
between them
What we are doing now …
NWIS
request
return
request
return
request
return
NAWQA
request
return
NAM-12
request
return
request
return
request
return
request
return
NARR
Slide from Michael Piasecki, Drexel University
What we would like to do …..
GetValues
GetValues
NWIS
generic
request
CUAHSI HIS
GetValues
GetValues
GetValues
GetValues
NAWQA
GetValues
GetValues
NARR
ODM
Slide from Michael Piasecki, Drexel University
CUAHSI Hydrologic Data Access System
USGS
EPA
NCDC
NASA
NWS
Observatory Data
A common data window for accessing, viewing
and downloading hydrologic information
Hydrologic Data Access System Website Portal
and Map Viewer
Information input, display, query and output services
Uploads
Downloads
Preliminary data exploration and discovery. See
what is available and perform exploratory analyses
Web services
interface
HTML -XML
WaterOneFlow
Web Services
e.g. USGS,
NCDC
Observatory
data servers
ODM
WSDL - SOAP
3rd party data
servers
Data access
through web
services
Data storage
through web
services
GIS
Matlab
IDL
CUAHSI HIS
data servers
ODM
Splus, R
Excel
Programming
(Fortran, C, VB)
Web Services
• A set of protocols that together provide a
mechanism for machine-to-machine
communication over the Internet
• Advantages
– Interoperability across operating systems and
programming languages (XML based)
– Application developers interact with web
services similar to the way they interact with
any other software library within a
programming environment
Data Sources
NASA
Storet
Extract
Ameriflux
NCDC
Unidata
NWIS
NCAR
Transform
CUAHSI Web Services
Excel
Visual Basic
C/C++
ArcGIS
Load
Matlab
Applications
Fortran
Access
http://www.cuahsi.org/his.html
Java
Some operational services
Data Consumption and
Analysis
Local Data Sources
With Multiple Formats
Excel
Files
Text
Files
Acces
s
Files
Sensor
Data
Local Data Sources
With Multiple Formats
Excel
Files
Acces
s
Files
Local Data
Data Consumption and
Analysis
•Text
No efficient online data delivery system
•Files
Disparate file formats
ODM with
• Different types, frequencies,
etc.
Web Services
Sensor
Data
Data Mediation
XML
CUAHSI Observations Data Model
• A relational database at the
single observation level
Streamflow
(atomic model)
• Stores observation data made
at points
• Metadata for unambiguous Precipitation
& Climate
interpretation
ODM
• Traceable heritage from raw
measurements to usable
information
• Standard format for data
Water Quality
sharing
• Cross dimension retrieval and
analysis
Groundwater
Levels
Soil
Moisture
Data
Flux Tower
Data
Data Processing
Applications
Internet
ODM and HIS in The Little Bear River Test Bed
Integration of Sensor Data With HIS
Base Station
Computer(s)
Observations
Database
(ODM)
Internet
Telemetry
Network
Data discovery, visualization,
analysis, and modeling
through Internet enabled
applications
Environmental Sensors
Workgroup HIS
Server
Workgroup HIS Tools
Programmer interaction
through web services
Managing Data Within ODM - ODM Tools
• 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.
Little Bear River Integrated
Monitoring System
Sensors, data
collection, and
telemetry network
Sensors
(Streamflow
Water Quality
Climate)
Wet Chemistry
Measurements
Bayesian Networks to
control monitoring
system, triggering
sampling for storm
events and base flow
CUAHSI HIS ODM
– central storage
and management
of observations
data
Telemetry Network
A
B
Sensor
Bayes
Network
Central
Observations
Database
C
Site specific correlations
between sensor signals
and other water quality
variables
Constituent
Bayes A
Net
Little Bear River at Mendon Road (4905000)
Nutrient
Estimates
300
B
Bayesian Networks to
construct water quality
measures from
surrogate sensor
signals to provide high
frequency estimates of
water quality and
loading
C
y = 2.3761x
R2 = 0.6993
175
200
150
125
Residue Total Nonfiltrable; mg/L
TOtal Suspended Solids (mg/L)
250
150
100
75
50
25
100
0
1980
50
0
0
15
30
45
Turbidity (NTU)
60
75
1990
Date
2000
Exogenous
Variables
(GIS, Land Use,
Management)
End result: high frequency
estimates of nutrient
concentrations and
loadings
Conclusion
Advancement of water science is critically
dependent on integration of water information
Models
Data Models: Structured data sets to
facilitate data integrity and effective
sharing and analysis.
- Standards
ODM
- Metadata
- Unambiguous interpretation
Web Services
Databases
Analysis
Analysis: Tools to provide windows
into the database to support
visualization, queries, analysis, and
data driven discovery.
Models: Numerical implementations of
hydrologic theory to integrate process
understanding, test hypotheses and
provide hydrologic forecasts.
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