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

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

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

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

5. Deduction – Isaac Newton 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. (1687) Three laws of motion and law of gravitation http://en.wikipedia.org/wiki/Isaac_Newton

6. Experiment – Louis Pasteur 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. Pasteur showed that microorganisms cause disease & discovered vaccination Foundations of scientific medicine http://en.wikipedia.org/wiki/Louis_Pasteur

7. Observation – Charles Darwin 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 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 Hydrologic conditions (Fluxes, flows, concentrations) Hydrologic Process Science (Equations, simulation models, prediction) Hydrologic Information Science (Observations, data models, visualization Hydrologic environment (Physical earth) Physical laws and principles (Mass, momentum, energy, chemistry) It is as important to represent hydrologic environments precisely with data as it is to represent hydrologic processes with equations

10. Great Eras of Synthesis 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 1900 1960 1940 1920 1980 2000 Physics (relativity, structure of the atom, quantum mechanics) Geology (observations of seafloor magnetism lead to plate tectonics) Hydrology (synthesis of water observations leads to knowledge synthesis) 2020

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

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 Vermont EPSCoR --> HyperText Markup Language

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

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

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

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 19 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) 21

22. HIS Central – Catalog and Search 22

23. GetValues Requests Per Day from HIS Central

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

25. HIS Server – Store and Publish 25

26. HydroDesktop – Access and Analyze Data 26

27. Pre Conference Seminar27 From Robert Vertessy, CSIRO, Australia Services-Oriented ArchitectureHydroDesktop

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

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

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

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

32. HydroDesktop – Access and Analyze Data 32