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Deployment and Evaluation of an Observations Data Model

Deployment and Evaluation of an Observations Data Model. Jeffery S Horsburgh David G Tarboton Ilya Zaslavsky David R. Maidment David Valentine. Support EAR 0622374. http://www.cuahsi.org/his.html. Data Access System for Hydrology (DASH) Website Portal and Map Viewer

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Deployment and Evaluation of an Observations Data Model

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  1. Deployment and Evaluation of an Observations Data Model Jeffery S Horsburgh David G Tarboton Ilya Zaslavsky David R. Maidment David Valentine Support EAR 0622374 http://www.cuahsi.org/his.html

  2. Data Access System for Hydrology (DASH) Website Portal and Map Viewer Information input, display, query and output services Preliminary data exploration and discovery. See what is available and perform exploratory analyses Web services interface 3rd party data servers GIS e.g. USGS, NCDC Matlab IDL Splus, R Excel Programming (Fortran, C, VB) Downloads Uploads HTML -XML Data access through web services WaterOneFlow Web Services WSDL - SOAP Data storage through web services Observatory data servers CUAHSI HIS data servers ODM ODM

  3. A relational database at the single observation level (atomic model) Stores observation data made at points Metadata for unambiguous interpretation Traceable heritage from raw measurements to usable information Standard format for data sharing Cross dimension retrieval and analysis Streamflow Groundwater levels Precipitation & Climate Soil moisture data Water Quality Flux tower data CUAHSI Observations Data Model

  4. CUAHSI Observations Data Model http://www.cuahsi.org/his/odm.html

  5. Discharge, Stage, Concentration and Daily Average Example

  6. Stage and Streamflow Example

  7. ODM Implementation in WATERS Network Information System National Hydrologic Information Server San Diego Supercomputer Center • 11 WATERS Network test bed projects • 16 ODM networks (some test beds have more than one network) • Data from 1246 sites, of these, 167 sites are operated by WATERS investigators

  8. Florida – Santa Fe Watershed Nitrate Nitrogen (mg/L) Millpond Spring PI: Wendy Graham, ….; DM: Kathleen McKee, Mark Newman

  9. Utah – Little Bear River and Mud Lake Turbidity Continuous turbidity observations at the Little Bear River at Mendon Road from two different turbidity sensors.

  10. 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.

  11. Methods for Data Loading Interactive Data Loader Scheduled Data Loader SQL Server Integration Services

  12. Direct analysis from your favorite analysis environment. e.g. Matlab % create NWIS Class and an instance of the class createClassFromWsdl('http://water.sdsc.edu/wateroneflow/NWIS/DailyValues.asmx?WSDL'); WS = NWISDailyValues; % GetValues to get the data siteid='NWIS:02087500'; bdate='2002-09-30T00:00:00'; edate='2006-10-16T00:00:00'; variable='NWIS:00060'; valuesxml=GetValues(WS,siteid,variable,bdate,edate,'');

  13. Summary • Syntactic heterogeneity (File types and formats) • Semantic heterogeneity • Language for observation attributes • Language to encode observation attribute values • A national network of consistent data • Enhanced data availability • Metadata to facilitate unambiguous interpretation • Enhanced analysis capability

  14. Future Considerations • Additional data types (grid, image etc.) • Additional catalog sets to enhance discovery • Unit standardization and conversion • Ownership, security, authentication, provenance • Improve controlled vocabulary constraints to enhance integrity

  15. Advancement of water science is critically dependent on integration of waterinformation Databases: Structured data sets to facilitate data integrity and effective sharing and analysis. - Standards - Metadata - Unambiguous interpretation 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. Models ODM Web Services Databases Analysis

  16. HIS Websitehttp://www.cuahsi.org/his.html • Project Team – Introduces members of the HIS Team • Data Access System for Hydrology – Web map interface supporting data discovery and retrieval • Prototype Web Services – WaterOneFlow web services facilitating downlad of time series data from numerous national repositories of hydrologic data • Observations Data Model – Relational database schema for hydrologic observations • HIS Tools – Links to end-user applications developed to support HIS • Documentation and Reports – Status reports, specifications, workbooks and links related to HIS • Feedback – Let us know what you think • Austin Workshop – Material from WATERS workshop in Austin

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