Download
services oriented architecture for water data n.
Skip this Video
Loading SlideShow in 5 Seconds..
Services-Oriented Architecture for Water Data PowerPoint Presentation
Download Presentation
Services-Oriented Architecture for Water Data

Services-Oriented Architecture for Water Data

366 Vues Download Presentation
Télécharger la présentation

Services-Oriented Architecture for Water Data

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Services-Oriented Architecture for Water Data David R. Maidment Fall 2009

  2. Linking Geographic Information Systems and Water Resources Water Resources GIS

  3. Water Information in Space and Time Graph in Time Map in Space

  4. By deduction from existing knowledge By experiment in a laboratory By observation of the natural environment How is new knowledge discovered? After completing the Handbook of Hydrology in 1993, I asked myself the question: how is new knowledge discovered in hydrology? I concluded:

  5. Deduction is the classical path of mathematical physics Given a set of axioms Then by a logical process Derive a new principle or equation In hydrology, the St Venant equations for open channel flow and Richard’s equation for unsaturated flow in soils were derived in this way. Deduction – Isaac Newton Three laws of motion and law of gravitation http://en.wikipedia.org/wiki/Isaac_Newton (1687)

  6. Experiment is the classical path of laboratory science – a simplified view of the natural world is replicated under controlled conditions In hydrology, Darcy’s law for flow in a porous medium was found this way. Experiment – Louis Pasteur Pasteur showed that microorganisms cause disease & discovered vaccination Foundations of scientific medicine http://en.wikipedia.org/wiki/Louis_Pasteur

  7. Observation – direct viewing and characterization of patterns and phenomena in the natural environment In hydrology, Horton discovered stream scaling laws by interpretation of stream maps Observation – Charles Darwin Published Nov 24, 1859 Most accessible book of great scientific imagination ever written

  8. Conclusion for Hydrology • Deduction and experiment are important, but hydrology is primarily an observational science • discharge, water quality, groundwater, measurement data collected to support this.

  9. 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 (Physical earth)

  10. Scientific progress occurs continuously, but there are great eras of synthesis – many developments happening at once that fuse into knowledge and fundamentally change the science Great Eras of Synthesis 2020 Hydrology (synthesis of water observations leads to knowledge synthesis) 2000 1980 Geology (observations of seafloor magnetism lead to plate tectonics) 1960 1940 1920 Physics (relativity, structure of the atom, quantum mechanics) 1900

  11. Water Data Water quantity and quality Soil water Rainfall & Snow Modeling Meteorology Remote sensing

  12. Data are Published in Many Formats

  13. Services-Oriented Architecture A services‐oriented architecture is a concept that applies to large, distributed information systems that have many owners, are complex and heterogeneous, and have considerable legacies from the way their various components have developed in the past (Josuttis, 2007).

  14. HTML as a Web Language Text and Pictures in Web Browser HyperText Markup Language <head> <meta http-equiv="content-type" content="text/html; charset=utf-8" /> <title>Vermont EPSCoR</title> <link rel="stylesheet" href="epscor.css" type="text/css" media="all" /> <!-- <script type='text/javascript' language='javascript‘ src='Presets.inc.php'>--> </head>

  15. Internet operation for text-based information (http “Get” request)

  16. Services-Oriented Architecture for Water Data (2009) : Abstraction Data Discovery and Integration platform Metadata Search Metadata Services Data Services Data Synthesis and Research platform Data Publication platform

  17. Services-Oriented Architecture for Water Data (2009) HIS Central Service and time series metadata Service registration Data carts Catalog harvesting Hydro Desktop HIS Server Water Data Services Spatial Data Services

  18. WaterML as a Web Language Discharge of the San Marcos River at Luling, TX June 28 - July 18, 2002 USGS Streamflow data in WaterML WaterML is constructed as a Web Services Definition Language using WWW standards

  19. International Standardization of WaterML OGC/WMO Hydrology Domain Working Group

  20. CUAHSI Water Data Services 43 services 15,000 variables 1.8 million sites 9 million series 4.3 billion data

  21. Services-Oriented Architecture for Water Data (2009)

  22. HIS Central – Catalog and Search

  23. GetValues Requests Per Day from HIS Central

  24. Number of Data Accessible through HIS Central Increase from 342 million to 4.3 billion

  25. HIS Server – Store and Publish

  26. HydroDesktop – Access and Analyze Data

  27. Services-Oriented Architecture HydroDesktop From Robert Vertessy, CSIRO, Australia Pre Conference Seminar

  28. Where are we going to? • A definition of data in “space-time” Animation in Space-Time Graph in Time Map in Space

  29. Projected on x-y plane Projected on to the x-time plane Projected on to the y-time plane A Storm Example in Space-Time

  30. Space, Time, Variables and Direct Sensing An observations data model archives values of variables measured at particular spatial locations and points in time at gages and sampling sites • Observations Data Model • Data fromsensors (regular time series) • Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time

  31. Space, Time, Variables and Remote Sensing An remote sensing image depicts values of variables over a domain in space at repeated points in time • Observations Data Model • Data fromsensors (regular time series) • Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time

  32. HydroDesktop – Access and Analyze Data