CUAHSI Hydrologic Information System Levels

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					Web Services and the Digital
 Earth: A Perspective from
    Hydrologic Science

         Dr. Richard P. Hooper,
     Consortium of Universities for the
  Advancement of Hydrologic Science, Inc.
A Consortium of 122 Members
• Founded in 2001
• Supported by NSF
• Develop Community
   International Members include Yonsei U. (Korea); CSIRO
   (Australia); CEH (UK); U. Trento (Italy), U. Ljubljana (Slovenia)
   Water and Climate Change
• As Planetary Thermostat
  – Re-distributes heat from equator to poles
  – Possible disruption to ocean currents
• As Vital Resource
  – Agriculture
  – Extreme events
  – Habitat
Water is an environmental integrator
         A Fractured Resource
•   Important to many activities
•   Managed at multiple government levels
•   Multiple agencies monitor water
•   No one entity responsible for water
•   Hydrologic research in both earth science
    and engineering contexts
CUAHSI Hydrologic Information
• Project begun in 2005 (NSF Hydro. Sci.)
• Phase 2 began 2007 (NSF EAR/Geoinf.)
• PI’s
  – David Maidment (Texas)
  – David Tarboton (Utah State)
  – Ilya Zaslavsky (San Diego Supercomputer
  – Michael Piasecki (Drexel)
  – Jon Goodall (Duke)
  Observation Stations Map for the US

    Ameriflux Towers (NASA & DOE)           NOAA Automated Surface
                                              Observing System

USGS National Water Information System   NOAA Climate Reference Network
    Federation of Data Sources
• Multiple data providers
   – Government agencies at multiple levels
      • Monitoring Networks
      • Research Networks
   – Academic scientists
• Use of Web Services
   – Control rests with data providers
   – Modest requirements for central services
• Support of multiple users
       Abstractions in Modeling
 Real World                        “Digital Environment”
                                               DNA quantity
                                              Water Sequences
                            Hydrologist      Meteorology
                          Geomorphologist Remote sensing
                         Aquatic Ecologist Vegetation Survey
                                                 and quality
                           Frameworks Valley
                 Groundwater                  Processes?
Physical   World Contribution?                               Model
                                                  -Mathematical Formulae
                        DOC Quality?
                  Oligotrophic?                         Representations
                                          Backwater habitat Techniques
                                        Hyporheic exchange?
                  Carbon source?
                 Redox Zones?
               Q, Gradient, Roughness?
                     •Theory/Process KnowledgeValidation
                      Substrate Size, Stability?
                            Representation Thalweg?
                      Benthic Community Chemistry?
                     •Perceptions of this place
                    Well sorted?

                     •Intuition                   Measurements
         Data Representation
• Four-dimensional                 Time, T
• Continental scope                                   A data value
• Multi-scale, multi-                D
                             1:1,000,000 scale North American
  resolution                                         and Global
                                  1:500,000 scale     United States
• Points, coverages,                                Space, L
                                  1:100,000 scale
  dynamic fields                                       River Basin

                                   1:24,000 scale      Watershed
                    Variable, V
                                    1:1200 scale       River reach
                                      Point scale
                                                        A plot
                Digital Earth
• Integrating monitoring and research data yields
  a single body of information for the country
• Research Observatories contribute intensive
  information to this body
• Observatories are placed within context of
  climate, geology, soils, etc. but are not assumed
  to be representative of an area.
• Observatories composed of watersheds,
  aquifers, river reaches, ecotones placed within
  digital earth
  CUAHSI Hydrologic Information
      System Architecture
National HIS – San Diego Supercomputer Center
Map interface, observations catalogs and web services for
national data sources

Workgroup HIS – state agency, observatory group.                 HIS Server
Map interface, observations catalogs and web services for
regional data sources; observations databases and web
services for individual investigator data

Personal HIS – an individual scientist or manager
Application templates and HydroObjects for direct ingestion of   HIS Analyst
data into analysis environments: Excel, ArcGIS, Matlab,
programming languages; MyDB for storage of analysis data
     National and Workgroup HIS
  National HIS                                             Workgroup HIS

      National HIS has a polygon             Workgroup HIS has local
      in it marking the region of            observations catalogs for
      coverage of a workgroup HIS            coverage of national data
      server                                 sources in its region. These
                                             local catalogs are partitioned
For HIS 1.0 the National and Workgroup HIS
                                             from the national observations
servers will not be dynamically connected.
   WaterML and WaterOneFlow
       Locations            GetSiteInfo          Data
 Variable Codes             GetValues
     Date Ranges                              Data NAM

                           WaterOneFlow          Data
                            Web Service       Repositories
                            TRANSFORM         EXTRACT

WaterML is an XML language for communicating water data
WaterOneFlow is a set of web services based on WaterML
  Data Sources                       NASA
                       Storet                      Ameriflux

Extract      NCDC                                          Unidata

          NWIS                                                     NCAR

Transform                   CUAHSI Web Services

    Excel                                                        Visual Basic

          ArcGIS                                                C/C++

Load               Matlab                             Fortran
                                Access      Java
  Applications                        Some operational services
• Extensive data required for documenting
  and understanding climate change.
• Data is expensive and collected by many
  entities for many reasons
• Web services enable federation of data
  resources at modest cost
• Everyone gains from data integration