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Services-Oriented Architecture for Water Data David R. Maidment Fall 2009 Linking Geographic Information Systems and Water Resources Water Resources GIS Water Information in Space and Time Graph in Time Map in Space By deduction from existing knowledge By experiment in a laboratory

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Water information in space and time l.jpg
Water Information in Space and Time

Graph in Time

Map in Space


How is new knowledge discovered l.jpg

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:


Deduction isaac newton l.jpg

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)


Experiment louis pasteur l.jpg

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


Observation charles darwin l.jpg

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


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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.


Hydrologic science l.jpg
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)


Great eras of synthesis l.jpg

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


Slide11 l.jpg

Water Data eras of synthesis –

Water quantity and quality

Soil water

Rainfall & Snow

Modeling

Meteorology

Remote sensing


Data are published in many formats l.jpg
Data are Published in Many Formats eras of synthesis –


Services oriented architecture l.jpg
Services-Oriented Architecture eras of synthesis –

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).


Html as a web language l.jpg
HTML as a Web Language eras of synthesis –

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>


Internet operation for text based information l.jpg
Internet operation for text-based information eras of synthesis –

(http “Get” request)


Services oriented architecture for water data 2009 abstraction l.jpg
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


Services oriented architecture for water data 2009 l.jpg
Services-Oriented Architecture for Water Data (2009) Abstraction

HIS Central

Service and time series metadata

Service registration

Data carts

Catalog harvesting

Hydro Desktop

HIS Server

Water Data Services

Spatial Data Services


Waterml as a web language l.jpg
WaterML as a Web Language Abstraction

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


International standardization of waterml l.jpg
International Standardization of WaterML Abstraction

OGC/WMO Hydrology Domain Working Group


Cuahsi water data services l.jpg
CUAHSI Water Data Services Abstraction

43 services

15,000 variables

1.8 million sites

9 million series

4.3 billion data





Number of data accessible through his central l.jpg
Number of Data Accessible through HIS Central Abstraction

Increase from

342 million to 4.3 billion




Slide27 l.jpg

Services-Oriented Architecture Abstraction

HydroDesktop

From Robert Vertessy, CSIRO, Australia

Pre Conference Seminar


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Where are we going to? Abstraction

  • A definition of data in “space-time”

Animation in

Space-Time

Graph in Time

Map in Space


A storm example in space time l.jpg

Projected on x-y plane Abstraction

Projected on to the x-time plane

Projected on to the y-time plane

A Storm Example in Space-Time


Space time variables and direct sensing l.jpg
Space, Time, Variables and AbstractionDirect 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


Space time variables and remote sensing l.jpg
Space, Time, Variables and AbstractionRemote 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



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