1. Introduction to CUAHSI Water Web Services and Texas HIS David R. Maidment The University of Texas at Austin

2. HIS Team and Collaborators University of Texas at Austin – David Maidment, Tim WhiteakerUniversity of Texas at Austin – David Maidment, Tim Whiteaker San Diego Supercomputer Center – Ilya Zaslavsky, David Valentine, Tom WhitenackSan Diego Supercomputer Center – Ilya Zaslavsky, David Valentine, Tom Whitenack Utah State University – David Tarboton, Jeff Horsburgh, Kim Schreuders, Justin BergerUtah State University – David Tarboton, Jeff Horsburgh, Kim Schreuders, Justin Berger Drexel University – Michael Piasecki, Yoori ChoiDrexel University – Michael Piasecki, Yoori Choi University of South Carolina – Jon Goodall, Tony CastronovaUniversity of South Carolina – Jon Goodall, Tony Castronova Idaho State University – Dan Ames, Ted Dunsford, Teva VeluppillaiIdaho State University – Dan Ames, Ted Dunsford, Teva Veluppillai CUAHSI Program Office – Rick Hooper, David Kirschtel, Conrad MatiukCUAHSI Program Office – Rick Hooper, David Kirschtel, Conrad Matiuk WATERS Network – Testbed Data ManagersWATERS Network – Testbed Data Managers HIS Standing CommitteeHIS Standing Committee USGS – Bob Hirsch, David Briar, Scott McFarlaneUSGS – Bob Hirsch, David Briar, Scott McFarlane NCDC – Rich BaldwinNCDC – Rich Baldwin ESRI – Dean Djokic, Christine Eggers, and many othersESRI – Dean Djokic, Christine Eggers, and many others

3. CUAHSI Water Web Services and Texas HIS CUAHSI Hydrologic Information SystemCUAHSI Hydrologic Information System CUAHSI Water Data ServicesCUAHSI Water Data Services Texas Water Data ServicesTexas Water Data Services

4. CUAHSI Water Web Services and Texas HIS CUAHSI Hydrologic Information SystemCUAHSI Hydrologic Information System CUAHSI Water Data ServicesCUAHSI Water Data Services Texas Water Data ServicesTexas Water Data Services

5. Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) 118 Universities in North America (and 3 in Europe) NSF supports building a Hydrologic Information System (HIS)

6. Synthesis and communication of the nation’s water data http://his.cuahsi.org http://his.cuahsi.org Federal Water DataAcademic Water Data State and Local Water Data To make a complete picture of water data, we need to be able to add state and local data to national and academic data sources. Regional Water Data Systems

7. Rainfall Water quantity Groundwater Water Observations Data Meteorology Soil water Water quality

8. Data are Published in Many Formats

9. 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). Services-Oriented Architecture

10. We are at a tipping point… Web pagesWeb pages Web servicesWeb services Internet People interact with a remote information server People interact with a network of Information services Internet WaterML

11. HTML as a Web Language Text and Pictures in Web Browser Vermont EPSCoR --> HyperText Markup Language

12. Point Water Observations Time Series A point location in spaceA series of values in time

13. WaterML as a Web Language Discharge of the San Marcos River at Luling, TX June 28 - July 18, 2002 Streamflow data in WaterML language

14. CUAHSI Water Web Services and Texas HIS CUAHSI Hydrologic Information SystemCUAHSI Hydrologic Information System CUAHSI Water Data ServicesCUAHSI Water Data Services Texas Water Data ServicesTexas Water Data Services

15. Base Station Computer(s) Telemetry Network Sensors Query, Visualize, and Edit data using ODM Tools Excel, text ODM Database ODM Data Loader Streaming Data Loader GetSites GetSiteInfo GetVariableInfo GetValues WaterOneFlow Web Service WaterML Discovery HydroSeek Access Analysis GIS Matlab Splus R IDL Java C++ VB HIS Desktop Water Metadata Catalog Harvester Service RegistryHydroTagger HIS Central CUAHSI Water Data Services System USGS NWIS EPA STORET NCDCOthers

16. Point Observations Information Model Data Source Network Sites Variables Values {Value, Time, Metadata} Utah State Univ Little Bear River Little Bear River at Mendon Rd Dissolved Oxygen 9.78 mg/L, 1 October 2007, 5PM 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 GetSites GetSiteInfo GetVariableInfo GetValues Information is transmitted through the internet in WaterML as web services

17. Data Values – indexed by “What-where- when” Variables, V Space, S Time, T s t ViVi v i (s,t) “Where” “What” “When” Data Values Table

18. Data Series – Metadata description Space Variable, V i Site, S j End Date Time, t 2 Begin Date Time, t 1 Time Variables Count, C There are C measurements of Variable V i at Site S j from time t 1 to time t 2

19. Series Catalog Space Variable, V i Site, S j End Date Time, t 2 Begin Date Time, t 1 Time Variables Count, C ViVi SjSj t2t2 t1t1 C

20. http://his.cuahsi.org/odmdatabases.html CUAHSI Observations Data Model Values Series

21. Spatial References Table Spatial ReferenceIDSRSIDSRSName 0Unknown 14267NAD27 24269NAD83 Sites Table SiteIDSiteCodeSiteNameLatitudeLongitudeLatLongID 1AcmeP1Backyard Pond34.565-93.2321 2AcmePR2Mill River gage Station34.2-93.41 Simplified ODM Structure

22. HIS Servers at Universities in the WATERS Network HIS Central at San Diego Supercomputer Center

23. CUAHSI Water Data Services 35 services 15,000 variables 1.75 million sites 8.33 million series 342 million data

24. CUAHSI Water Web Services and Texas HIS CUAHSI Hydrologic Information SystemCUAHSI Hydrologic Information System CUAHSI Water Data ServicesCUAHSI Water Data Services Texas Water Data ServicesTexas Water Data Services

25. Texas Water Data Services 10 services 7,010 variables 15,870 sites 645,566 series 23,272,357records

26. Ingest Data From Different Sources Transform Data into Uniform Format Load Newly Formatted Data into ODM Tables in MS SQL/Server Wrap ODM with WaterML Web Services for Online Publication TPWD Coastal Fisheries Raw Data TWDB Coastline Raw Data TIFP Lower Sabine Publishing an ODM Water Data Service Observations Data Model (ODM) WaterML TPWD ODM TWDB ODM TIFP ODM

27. TCOON METADATA ODM TCOON DataValues WaterML Metadata From: ODM Database in Austin TCOON Web Site in Corpus Christi TCOON Water Data Service Publishing a Hybrid Water Data Service TCOON Metadata are Transferred from XML to the ODM Web Services can both Query the ODM for Metadata and use a Web Scraper for Data Values Calling the WSDL Returns Metadata and Data Values as if from the same Database GetSites GetSiteInfo GetVariableInfo Get Values from: http://his.crwr.utexas.edu/tcoonts/tcoon.asmx?WSDL

28. Web Services in Space and Time Water Markup Language (WaterML) is a schema for encoding water observations time series data and metadata;Water Markup Language (WaterML) is a schema for encoding water observations time series data and metadata; Geographic Markup Language (GML) encodes spatial data about sets of geographic features;Geographic Markup Language (GML) encodes spatial data about sets of geographic features; so that you can transmit water data in space and timeso that you can transmit water data in space and time

29. WFS and WaterML Observations Data in Time in WaterML Observations Metadata in Space in GML as a Web Feature Service

30. A Theme Layer Synthesis over all data sources of observations of a particular variable e.g. Salinity

31. Texas Salinity Theme 7900 series 347,000 data 7900 series TPWD 3400 TCEQ 3350 TWDB 150

32. Copano and Aransas Bay Salinity Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 3000 Copano Bay Aransas Bay

33. Texas Daily Streamflow Theme USGS Data 1138 sites (400 active)

34. Austin – Travis Lakes Streamflow Years of Data 0 – 10 10 – 20 20 – 40 40 – 60 60 – 110

35. Texas Water Temperature Theme 22,700 series 966,000 data

36. Austin – Travis Lakes Water Temperature Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 5000

37. Texas Natural Resources Information System Data Viewer

38. Texas Natural Resources Information System Data Viewer – Observation Sites

39. Time Series

40. Conclusions We have built a successful and functioning services-oriented architecture for water observations data in the United StatesWe have built a successful and functioning services-oriented architecture for water observations data in the United States WaterML is critical as the common water data languageWaterML is critical as the common water data language A lot of water information is best accessed and indexed at the state and regional levelA lot of water information is best accessed and indexed at the state and regional level CUAHSI HIS would like to work with academic partners to build state and regional HISCUAHSI HIS would like to work with academic partners to build state and regional HIS

41. HIS Website – his.cuahsi.org Download tools, research publications, contribute to the effort – it’s all here