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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
Water Information in Space and Time

Graph in Time

Map in Space

how is new knowledge discovered
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
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
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
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

conclusion for hydrology
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
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
Scientific progress occurs continuously, but there are great eras of synthesis – many developments happening at once that fuse into knowledge and fundamentally change the scienceGreat 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

Water Data

Water quantity and quality

Soil water

Rainfall & Snow

Modeling

Meteorology

Remote sensing

services oriented architecture
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).

html as a web language
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>

services oriented architecture for water data 2009 abstraction
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
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

waterml as a web language
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

international standardization of waterml
International Standardization of WaterML

OGC/WMO Hydrology Domain Working Group

cuahsi water data services
CUAHSI Water Data Services

43 services

15,000 variables

1.8 million sites

9 million series

4.3 billion data

number of data accessible through his central
Number of Data Accessible through HIS Central

Increase from

342 million to 4.3 billion

slide27

Services-Oriented Architecture

HydroDesktop

From Robert Vertessy, CSIRO, Australia

Pre Conference Seminar

where are we going to
Where are we going to?
  • A definition of data in “space-time”

Animation in

Space-Time

Graph in Time

Map in Space

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

A Storm Example in Space-Time
space time variables and direct sensing
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

space time variables and remote sensing
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

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