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Services-Oriented Architecture for Water Data
David R. Maidment Fall 2009
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Linking Geographic Information Systems and Water Resources
GIS
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Water Information in Space and Time
Graph in Time Map in Space
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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: By deduction from existing knowledge By experiment in a laboratory By observation of the natural environment
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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. Three laws of motion and law of gravitation (1687)
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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
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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
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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.
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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)
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Great Eras of Synthesis
2020 Hydrology (synthesis of water observations leads to knowledge 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 2000 1980 Geology (observations of seafloor magnetism lead to plate tectonics) 1960 1940 1920 Physics (relativity, structure of the atom, quantum mechanics) 1900
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Water quantity and quality
Water Data Water quantity and quality Soil water Rainfall & Snow Modeling Meteorology Remote sensing
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Data are Published in Many Formats
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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).
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HTML as a Web Language HyperText Text and Pictures 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> HyperText Markup Language Text and Pictures in Web Browser
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Internet operation for text-based information
(http “Get” request)
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Services-Oriented Architecture for Water Data (2009) : Abstraction
Data Discovery and Integration platform Metadata Search Metadata Services Data Publication platform Data Services Data Synthesis and Research platform
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Services-Oriented Architecture for Water Data (2009)
HIS Central Service registration Service and time series metadata Catalog harvesting Data carts HIS Server Hydro Desktop Water Data Services Spatial Data Services
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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
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International Standardization of WaterML
OGC/WMO Hydrology Domain Working Group
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CUAHSI Water Data Services
15,000 variables 1.8 million sites 9 million series 4.3 billion data
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Services-Oriented Architecture for Water Data (2009)
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HIS Central – Catalog and Search
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GetValues Requests Per Day from HIS Central
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Number of Data Accessible through HIS Central
Increase from 342 million to 4.3 billion
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HIS Server – Store and Publish
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HydroDesktop – Access and Analyze Data
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From Robert Vertessy, CSIRO, Australia
HydroDesktop Services-Oriented Architecture Pre Conference Seminar From Robert Vertessy, CSIRO, Australia
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Where are we going to? A definition of data in “space-time”
Map in Space Animation in Space-Time Graph in Time
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A Storm Example in Space-Time
Projected on x-y plane Projected on to the x-time plane Projected on to the y-time plane
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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 from sensors (regular time series) Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time
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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 from sensors (regular time series) Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time
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HydroDesktop – Access and Analyze Data
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