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CUAHSI Hydrologic Information Systems David R. Maidment and Ernest To Center for Research in CUAHSI Hydrologic Information Systems David R. Maidment and Ernest To Center for Research in Water Resources, University of Texas at Austin Hydrosystems Laboratory University of Illinois at Urbana-Champaign, 18 August 2006

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo by of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

CUAHSI-Hydrologic Information Systems UCAR • CUAHSI – Consortium of Universities for the Advancement of CUAHSI-Hydrologic Information Systems UCAR • CUAHSI – Consortium of Universities for the Advancement of Hydrologic Science, Inc • Formed in 2001 as a legal entity • Program office in Washington (5 staff) • Supported by the National Science Foundation Unidata Atmospheric Sciences Earth Sciences Ocean Sciences CUAHSI HIS National Science Foundation Geosciences Directorate

CUAHSI Member Institutions 115 Universities as of August 2006 CUAHSI Member Institutions 115 Universities as of August 2006

Common Vision: WATERS Network Observatories/ Environmental Field Facilities Informatics Sensors and Measurement Facility Synthesis Common Vision: WATERS Network Observatories/ Environmental Field Facilities Informatics Sensors and Measurement Facility Synthesis A combined CLEANER-CUAHSI effort

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

Digital Watershed How can hydrologists integrate observed and modeled data from various sources into Digital Watershed How can hydrologists integrate observed and modeled data from various sources into a single description of the environment?

Digital Watershed Hydrologic Observation Data Geospatial Data (GIS) (Relational database) Digital Watershed Weather and Digital Watershed Hydrologic Observation Data Geospatial Data (GIS) (Relational database) Digital Watershed Weather and Climate Data Remote Sensing Data (Net. CDF) (EOS-HDF) A digital watershed is a synthesis of hydrologic observation data, geospatial data, remote sensing data and weather and climate data into a connected database for a hydrologic region

HIS Servers Hydrologic Observations Server GIS Data Server Digital Watershed Weather and Climate Server HIS Servers Hydrologic Observations Server GIS Data Server Digital Watershed Weather and Climate Server Remote Sensing Server HIS Servers provide hydrologic observations, weather and climate, GIS and remote sensing data. For HIS version 1. 0, the focus is a hydrologic observations server for data from gages and monitoring sites at point locations.

CUAHSI Hydrologic Information System Levels National HIS – San Diego Supercomputer Center Map interface, CUAHSI Hydrologic Information System Levels National HIS – San Diego Supercomputer Center Map interface, observations catalogs and web services for national data sources Workgroup HIS – research center or academic department 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 hydrologic scientist Application templates and Hydro. Objects for direct ingestion of data into analysis environments: Excel, Arc. GIS, Matlab, programming languages; My. DB for storage of analysis data HIS Analyst

HIS Server • Supports data discovery, delivery and publication – Data discovery – how HIS Server • Supports data discovery, delivery and publication – Data discovery – how do I find the data I want? • Map interface and observations catalogs – Data delivery – how do I acquire the data I want? • Use web services or retrieve from local database – Data Publication – how do I publish my observation data? • Use Observations Data Model

Observations Catalog Specifies what variables are measured at each site, over what time interval, Observations Catalog Specifies what variables are measured at each site, over what time interval, and how many observations of each variable are available

HIS Server Architecture • Map front end – Arc. GIS Server 9. 2 (being HIS Server Architecture • Map front end – Arc. GIS Server 9. 2 (being programmed by ESRI Water Resources for CUAHSI) • Relational database – SQL/Server 2005 or Express • Web services library – VB. Net programs accessed as a Web Service Description Language (WSDL)

National and Workgroup HIS National HIS has a polygon in it marking the region National and Workgroup HIS National HIS has a polygon in it marking the region of coverage of a workgroup HIS server For HIS 1. 0 the National and Workgroup HIS servers will not be dynamically connected. Workgroup HIS has local observations catalogs for coverage of national data sources in its region. These local catalogs are partitioned from the national observations catalogs.

Point Observations Information Model USGS Data Source Streamflow gages Network Neuse River near Clayton, Point Observations Information Model USGS Data Source Streamflow gages Network Neuse River near Clayton, NC Sites Discharge, stage (Daily or instantaneous) Variables Values 206 cfs, 13 August 2006 • • • {Value, Time, Qualifier} 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 qualifier is a symbol that provides additional information about the value

Data Discovery and Delivery Data Source Observations metadata HIS Server Observations Catalog Network Data Data Discovery and Delivery Data Source Observations metadata HIS Server Observations Catalog Network Data Discovery Sites Variables Observations data Delivery Values Web services • HIS facilitates data discovery by building and maintaining observations catalogs • Data delivery occurs through web services from remote data archives or local observations databases. Water resource agencies support data delivery services.

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

Web Services with HIS Server • Publication services for local observations databases Send data Web Services with HIS Server • Publication services for local observations databases Send data out from the server • Ingestion Services for remote data archives Enable users to access data in remote archives

Water Data Water quantity and quality Soil water Meteorology Remote sensing Rainfall & Snow Water Data Water quantity and quality Soil water Meteorology Remote sensing Rainfall & Snow Modeling

Water Data Web Sites Water Data Web Sites

NWISWeb site output # agency_cd Agency Code # site_no USGS station number # dv_dt NWISWeb site output # agency_cd Agency Code # site_no USGS station number # dv_dt date of daily mean streamflow # dv_va daily mean streamflow value, in cubic-feet per-second # dv_cd daily mean streamflow value qualification code # # Sites in this file include: # USGS 02087500 NEUSE RIVER NEAR CLAYTON, NC # agency_cd site_no dv_dt dv_va dv_cd USGS 02087500 2003 -09 -01 1190 Time series of USGS 02087500 2003 -09 -02 649 USGS 02087500 2003 -09 -03 525 streamflow at a USGS 02087500 2003 -09 -04 486 gaging station USGS 02087500 2003 -09 -05 733 USGS 02087500 2003 -09 -06 585 USGS has committed USGS 02087500 2003 -09 -07 485 to supporting CUAHSI’s USGS 02087500 2003 -09 -08 463 Get. Values function USGS 02087500 2003 -09 -09 673 USGS 02087500 2003 -09 -10 517 USGS 02087500 2003 -09 -11 454

Observation Stations Map for the US Ameriflux Towers (NASA & DOE) NOAA Automated Surface Observation Stations Map for the US Ameriflux Towers (NASA & DOE) NOAA Automated Surface Observing System USGS National Water Information System NOAA Climate Reference Network

Water Quality Measurement Sites in EPA Storet Substantial variation in data availability from states Water Quality Measurement Sites in EPA Storet Substantial variation in data availability from states Data from Bora Beran, Drexel University

Water Quality Measurement Sites from Texas Commission for Environmental Quality (TCEQ) Water Quality Measurement Sites from Texas Commission for Environmental Quality (TCEQ)

Geographic Integration of Storet and TCEQ Data in HIS Geographic Integration of Storet and TCEQ Data in HIS

CUAHSI Hydrologic Data Access System http: //river. sdsc. edu/HDAS EPA NCDC NASA NWS Observatory CUAHSI Hydrologic Data Access System http: //river. sdsc. edu/HDAS EPA NCDC NASA NWS Observatory Data USGS Arc Hydro Server will be a customization of Arc. GIS Server 9. 2 for serving water observational data A common data window for accessing, viewing and downloading hydrologic information

Data Sources Storet Extract NASA Ameriflux NCDC Unidata NWIS NCAR Transform CUAHSI Web Services Data Sources Storet Extract NASA Ameriflux NCDC Unidata NWIS NCAR Transform CUAHSI Web Services Excel Visual Basic C/C++ Arc. GIS Load Matlab Applications http: //www. cuahsi. org/his/ Fortran Access Java Some operational services

CUAHSI Web Services Web Application: Data Portal Your application • Excel, Arc. GIS, Matlab CUAHSI Web Services Web Application: Data Portal Your application • Excel, Arc. GIS, Matlab • Fortran, C/C++, Visual Basic • Hydrologic model • ……………. Your operating system • Windows, Unix, Linux, Mac Internet Web Services Library Simple Object Access Protocol

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

Example: Corpus Christi Bay Environmental Info System • Workgroup HIS implementation • Uses ODM Example: Corpus Christi Bay Environmental Info System • Workgroup HIS implementation • Uses ODM to store hydrology and environmental data from state agencies and academic investigators. • Contains web-services to regional data repositories (e. g. TCOON). Water quality data sites in Corpus Christi Bay (maps by Tyler Jantzen) Demo: TXHIS ODM webservice

ODM (Observations Data Model) = Observations Catalog + Values Table +Metadata Tables ODM (Observations Data Model) = Observations Catalog + Values Table +Metadata Tables

How Excel connects to ODM Excel • • Obtains inputs for CUAHSI web methods How Excel connects to ODM Excel • • Obtains inputs for CUAHSI web methods from relevant cells. Available Web methods are Get. Site. Info, Get. Variable. Info Get. Values methods. Hydro. Objects parses user inputs into a standardized CUAHSI web method request. CUAHSI Web service converts standardized request to SQLquery. SQL query Observations Data Model Response imports VB object into Excel and graphs it converts XML to VB object converts response to a standardized XML.

CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • CUAHSI Hydrologic Information Systems • Introduction • HIS Server • CUAHSI web services • Demo of Corpus Christi Bay by Ernest To • Data models and some longer range thinking

Series and Fields Features Series – ordered sequence of numbers Point, line, area, volume Series and Fields Features Series – ordered sequence of numbers Point, line, area, volume Discrete space representation Surfaces Time series – indexed by time Frequency series – indexed by frequency Fields – multidimensional arrays Continuous space representation Scalar fields – single value at each location Vector fields – magnitude and direction Random fields – probability distribution

North American Regional Reanalysis of Climate Precipitation Evaporation Variation during the day, July 2003 North American Regional Reanalysis of Climate Precipitation Evaporation Variation during the day, July 2003 Net. CDF format mm / 3 hours

Data Cube – What, Where, When Time, T “When” A data value D Space, Data Cube – What, Where, When Time, T “When” A data value D Space, L “Where” Variable, V “What”

Continuous Space-Time Data Model -- Net. CDF Time, T Coordinate dimensions {X} D Space, Continuous Space-Time Data Model -- Net. CDF Time, T Coordinate dimensions {X} D Space, L Variables, V Variable dimensions {Y}

Discrete Space-Time Data Model Time, TSDate. Time TSValue Space, Feature. ID Variables, TSType. ID Discrete Space-Time Data Model Time, TSDate. Time TSValue Space, Feature. ID Variables, TSType. ID

Hydrologic Statistics Time Series Analysis Geostatistics Multivariate analysis How do we understand space-time correlation Hydrologic Statistics Time Series Analysis Geostatistics Multivariate analysis How do we understand space-time correlation fields of many variables?

Water One. Flow • Like Geospatial One. Stop, we need a “Water One. Flow” Water One. Flow • Like Geospatial One. Stop, we need a “Water One. Flow” – a common window for water data and models Federal State Local Academic • Advancement of water science is critically dependent on integration of water information

Conclusions • This is a complex and important problem that will not be solved Conclusions • This is a complex and important problem that will not be solved soon • Web services architecture will work and is valuable • Major water agencies are buying into our web services design, in particular the USGS • We need to think more deeply and abstractly about the way data is used to represent water and the water environment