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Hydrologic Data and Modeling: Towards Hydrologic Information Science

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Hydrologic Data and Modeling: Towards Hydrologic Information Science David R. Maidment Center for Research in Water Resources University of Texas at Austin – PowerPoint PPT presentation

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Title: Hydrologic Data and Modeling: Towards Hydrologic Information Science


1
Hydrologic Data and Modeling Towards Hydrologic
Information Science
  • David R. Maidment
  • Center for Research in Water Resources
  • University of Texas at Austin
  • EPSCorR, Vermont
  • November 10, 2008

2
Hydrologic Data and Modeling
  • New knowledge in hydrology
  • Hydrologic data
  • Hydrologic modeling
  • Hydrologic information systems

3
Hydrologic Data and Modeling
  • New knowledge in hydrology
  • Hydrologic data
  • Hydrologic modeling
  • Hydrologic information systems

4
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

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 Richards equation for
    unsaturated flow in soils were derived in this
    way.

Three laws of motion and law of gravitation
(1687)
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, Darcys 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
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
10
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)
11
Hydrologic Data and Modeling
  • New knowledge in hydrology
  • Hydrologic data
  • Hydrologic modeling
  • Hydrologic information systems

12
CUAHSI Member Institutions
122 Universities as of July 2008 (and CSIRO!)
13
HIS Team and Collaborators
  • University of Texas at Austin David Maidment,
    Tim Whiteaker, Ernest To, Bryan Enslein, Kate
    Marney
  • San Diego Supercomputer Center Ilya Zaslavsky,
    David Valentine, Tom Whitenack
  • Utah State University David Tarboton, Jeff
    Horsburgh, Kim Schreuders, Justin Berger
  • Drexel University Michael Piasecki, Yoori Choi
  • University of South Carolina Jon Goodall, Tony
    Castronova
  • CUAHSI Program Office Rick Hooper, David
    Kirschtel, Conrad Matiuk
  • National Science Foundation Grant EAR-0413265

14
HIS Goals
  • Data Access providing better access to a large
    volume of high quality hydrologic data
  • Hydrologic Observatories storing and
    synthesizing hydrologic data for a region
  • Hydrologic Science providing a stronger
    hydrologic information infrastructure
  • Hydrologic Education bringing more hydrologic
    data into the classroom.

15
HIS Overview Report
  • Summarizes the conceptual framework, methodology,
    and application tools for HIS version 1.1
  • Shows how to develop and publish a CUAHSI Water
    Data Service
  • Available at

http//his.cuahsi.org/documents/HISOverview.pdf
16
Water Data
Water quantity and quality
Rainfall Snow
Soil water
Modeling
Meteorology
Remote sensing
17
Water Data Web Sites
18
HTML as a Web Language
Text and Pictures in Web Browser
19
WaterML as a Web Language
Discharge of the San Marcos River at Luling, TX
June 28 - July 18, 2002
Streamflow data in WaterML language
20
Services-Oriented Architecture for Water Data
  • Links geographically distributed information
    servers through internet
  • Web Services Description Language (WSDL from W3C)
  • We designed WaterML as a web services language
    for water data
  • Functions for computer to computer interaction

HIS Servers in the WATERS Network
HIS Central at San Diego Supercomputer Center
Web Services
21
Get Data
WaterML
National Water Metadata Catalog
Get Metadata
22
CUAHSI Point Observation Data Services
  • Data Loading
  • Put data into the CUAHSI Observations Data Model
  • Data Publishing
  • Provide web services access to the data
  • Data Indexing
  • Summarize the data in a centralized cataloging
    system

23
CUAHSI Point Observation Data Services
  • Data Loading
  • Put data into the CUAHSI Observations Data Model
  • Data Publishing
  • Provide web services access to the data
  • Data Indexing
  • Summarize the data in a centralized cataloging
    system

24
Data Values indexed by What-where-when
Time, T
t
When
A data value
vi (s,t)
Where
s
Space, S
Vi
What
Variables, V
25
Data Values Table
Time, T
t
vi (s,t)
s
Space, S
Vi
Variables, V
26
Observations Data Model
Horsburgh, J. S., D. G. Tarboton, D. R. Maidment
and I. Zaslavsky, (2008), "A Relational Model for
Environmental and Water Resources Data," Water
Resour. Res., 44 W05406, doi10.1029/2007WR006392
.
27
HIS Implementation in WATERS Network Information
System
National Hydrologic Information Server San Diego
Supercomputer Center
  • 11 WATERS Network test bed projects
  • 16 ODM instances (some test beds have more than
    one ODM instance)
  • Data from 1246 sites, of these, 167 sites are
    operated by WATERS investigators

28
CUAHSI Point Observation Data Services
  • Data Loading
  • Put data into the CUAHSI Observations Data Model
  • Data Publishing
  • Provide web services access to the data
  • Data Indexing
  • Summarize the data in a centralized cataloging
    system

29
Point Observations Information Model
Utah State Univ
Data Source
Network
Little Bear River
GetSites
Sites
Little Bear River at Mendon Rd
GetSiteInfo
GetVariableInfo
Variables
Dissolved Oxygen
GetValues
Values
9.78 mg/L, 1 October 2007, 5PM
Value, Time, Metadata
  • A data source operates an observation network
  • A network is a set of observation sites
  • A site is a point location where one or more
    variables are measured
  • A variable is a property describing the flow or
    quality of water
  • A value is an observation of a variable at a
    particular time
  • A metadata quantity provides additional
    information about the value

30
Publishing an ODM Water Data Service
Texas AM Corpus Christi
Assemble Data From Different Sources
Utah State University
University of Florida
ODM Data Loader
Ingest data using ODM Data Loader
WaterML
Load Newly Formatted Data into ODM Tables in MS
SQL/Server
Observations Data Model (ODM)
USU ODM
UFL ODM
TAMUCC ODM
Wrap ODM with WaterML Web Services for Online
Publication
31
Publishing a Hybrid Water Data Service
Snotel Metadata are Transferred to the ODM
WaterML
Snotel METADATA ODM
Web Services can both Query the ODM for Metadata
and use a Web Scraper for Data Values
Snotel Water Data Service
Get Values from
Metadata From ODM Database in San Diego, CA
Snotel Web Site in Portland, OR
Calling the WSDL Returns Metadata and Data Values
as if from the same Database
32
WaterML and WaterOneFlow
Penn State
Data
GetSiteInfo GetVariableInfo GetValues
Utah State
Data
NWIS
Data
WaterML
WaterOneFlow Web Service
Data Repositories
Client
EXTRACT
TRANSFORM
LOAD
WaterML is an XML language for communicating
water data WaterOneFlow is a set of web services
based on WaterML
33
WaterOneFlow
  • Set of query functions
  • Returns data in WaterML

34
CUAHSI Point Observation Data Services
  • Data Loading
  • Put data into the CUAHSI Observations Data Model
  • Data Publishing
  • Provide web services access to the data
  • Data Indexing
  • Summarize the data in a centralized cataloging
    system

35
Data Series Metadata description
There are C measurements of Variable Vi at Site
Sj from time t1 to time t2
36
Series Catalog
Sj
Vi
t1
t2
C
37
Texas Hydrologic Information System
Sponsored by the Texas Water Development Board
and using CUAHSI technology for state and local
data sources (using state funding)
38
(No Transcript)
39
CUAHSI National Water Metadata Catalog
  • Indexes
  • 50 observation networks
  • 1.75 million sites
  • 8.38 million time series
  • 342 million data values

NWIS
STORET
TCEQ
40
Data Searching
  • Search multiple heterogeneous data sources
    simultaneously regardless of semantic or
    structural differences between them

Searching each data source separately
Michael Piasecki Drexel University
41
Semantic Mediation
Searching all data sources collectively
GetValues
GetValues
GetValues
GetValues
generic request
GetValues
GetValues
Michael Piasecki Drexel University
GetValues
GetValues
42
Hydroseekhttp//www.hydroseek.org
Bora Beran, Drexel
Supports search by location and type of data
across multiple observation networks including
NWIS and Storet
43
HydroTagger
Ontology A hierarchy of concepts
Each Variable in your data is connected to a
corresponding Concept
44
Data Sources
NASA
Storet
Snotel
Unidata
NCDC
Extract
NWIS
Academic
Transform
CUAHSI Web Services
Excel
Visual Basic
ArcGIS
Java
Load
Matlab
Applications
Operational services
http//www.cuahsi.org/his/
45
HydroExcel
46
HydroGET An ArcGIS Web Service Client
http//his.cuahsi.org/hydroget.html
47
Direct analysis from your favorite analysis
environment. e.g. Matlab
create NWIS Class and an instance of the
class createClassFromWsdl('http//water.sdsc.edu/w
ateroneflow/NWIS/DailyValues.asmx?WSDL') WS
NWISDailyValues GetValues to get the
data siteid'NWIS02087500' bdate'2002-09-30T00
0000' edate'2006-10-16T000000' variable'NWI
S00060' valuesxmlGetValues(WS,siteid,variable,b
date,edate,'')
48
Synthesis and communication of the nations water
data http//his.cuahsi.org
Government Water Data
Academic Water Data
National Water Metadata Catalog
Hydroseek
WaterML
49
Hydrologic Data and Modeling
  • New knowledge in hydrology
  • Hydrologic data
  • Hydrologic modeling
  • Hydrologic information systems

50
  • Project sponsored by the European Commission to
    promote integration of water models within the
    Water Framework Directive
  • Software standards for model linking
  • Uses model core as an engine
  • http//www.openMI.org

51
OpenMI Links Data and Simulation Models
Simple River Model
Trigger (identifies what value should be
calculated)
CUAHSI Observations Data Model as an OpenMI
component
52
Typical model architecture
Model application
  • Application
  • User interface engine
  • Engine
  • Simulates a process flow in a channel
  • Accepts input
  • Provides output
  • Model
  • An engine set up to represent a particular
    location e.g. a reach of the Thames

Write
Input data
Run
Read
Engine
Write
Output data
53
Linking modelled quantities
Accepts Provides
Upstream Inflow (m3/s) Outflow (m3/s)
Lateral inflow (m3/s)
Abstractions (m3/s)
Discharges (m3/s)
Accepts Provides
Rainfall (mm) Runoff (m3/s)
Temperature (Deg C)
Evaporation (mm)
54
Data transfer at run time
55
Models for the processes
Rainfall(database)
RR (Sobek-Rainfall -Runoff)
River (InfoWorks RS)
Sewer (Mouse)
56
Data exchange
Rainfall(database)
4
RR (Sobek-Rainfall -Runoff)
2 RR.GetValues
1 Trigger.GetValues
5
8
River (InfoWorks-RS)
call
Sewer (Mouse)
9
data
57
Hydrologic Data and Modeling
  • New knowledge in hydrology
  • Hydrologic data
  • Hydrologic modeling
  • Hydrologic information systems

58
Data Cube What, Where, When
When
A data value
Where
What
59
Continuous Space-Time Data Model -- NetCDF
Time, T
Coordinate dimensions X
D
Space, L
Variable dimensions Y
Variables, V
60
Discrete Space-Time Data Model
Time, TSDateTime
TSValue
Space, FeatureID
Variables, TSTypeID
61
Hydrologic Statistics
Time Series Analysis
Geostatistics
Multivariate analysis
How do we understand space-time correlation
fields of many variables?
62
CUAHSI Hydrologic Information Systems
  • A system for integrating water data and models
  • CUAHSI HIS team invites EPSCoR scientists to
    publish their data using CUAHSI Water Data
    Services and to help us build HIS Desktop during
    2009
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