1. CUAHSI Hydrologic Information System Summary as of June 30, 2004 by David R. Maidment

2. CUAHSI Hydrologic Information System Participants Core Team: D. Maidment, J. Helly, P. Kumar, M. Piasecki, R. Hooper, J. Duncan Collaborators: V. Lakshmi, X. Liang, Y. Liang, U. Lall, L. Poff, K. Reckhow, D. Tarboton, I. Zaslavsky, C. Zheng

3. Hydrologic Information System Data Organization Hydrologic Digital Library (Digital files of hydrologic information in any form, indexed by a metadata catalog) Flux Assessment System Process model domains Time seriesGIS data Space-time gridsStatistics Hydrologic Data Model Application Systems

4. CUAHSI HIS is meant to facilitate: Data Acquisition –quicker, easier, usable formats Data Archiving –experimental and regional data for HO’s Data Assembly –bringing the data together, data model Data Analysis –visualization, statistics, hypothesis testing.

5. CUAHSI Hydrologic Information Systems Work of the five project partners –CUAHSI –San Diego Supercomputer Center –University of Texas –University of Illinois –Drexel University Involving the collaborators……Ken Reckhow, Yao Liang

6. Drexel University Michael Piasecki is PI, assisted by Luis Bermudez Goal is to study metadata languages and standards to find what is most suitable for hydrology Concept hierarchies stored in “ontologies” described in OWL (Ontology Web Language)

7. Content Selection of the “base” metadata specification Expression of the selection using ontologies Profiles Tools to extend metadata specifications to allow creation of profiles Sources for hydrologic vocabulary

8. Selection of base metadata specification Specification Level of Conceptualization Scope ISO:19115:2003  UML: easily transportable to an ontology  Global FGDC-STD-001-1998 Not presented in a conceptual way (text and DTD) US EML 2.0.0 XML schemaCommunity

9. Selection of base metadata specification ISO:19115:2003 FGDC-STD-001-1998 Mapping will be available EML 2.0.0 They use ideas in ISO and FGDC ( do not really use their elements ); however they present detail elements to describe format and security constraints

10. ISO Metadata formalized in Ontologies ISO 19115:2003 (Geographic Metadata) http://loki.cae.drexel.edu/~wbs/ontology/2004/04/iso-metadata ISO 19108 (Temporal Schema) http://loki.cae.drexel.edu/~wbs/ontology/2004/05/iso-19108 ISO 19107 (Spatial Schema) ISO 19110(Methodology for feature cataloguing) In progress

11. FGDC Metadata formalized in Ontology Why ? We can express the mappings in machine readable format How? Java program is coded to convert FGDC XML Schemas to OWL FGDC XML schemas available at: http://www.fgdc.gov/metadata/metaxml.html Extraction of classes and properties and cardinalities  Conversion of datatypes and codelist to be done

12. Variety of software and formats Because of CUAHSI – Profile V.1.0 1) Extend ISO 2) Set as core (Metadata elements selected to be used by CUAHSI) 3) Set some mandatory 4) Create domain list to fit needs Express in machine readable format OWL/XML fully interoperable with original ISO

13. Variety of software and formats Because of CUAHSI – Profile V.1.0 ISO 19115 ISO 19108ISO 19107 ISO 19110 CUAHSI Profile 1) Extend ISO

14. Variety of software and formats Because of CUAHSI – Profile V.1.0 2) Set as core Using: “flag” to mark the core elements core= true

15. iso:MD_Metadata + metadataConstraints[0..n] : MD_Constraints … cuahsi:MD_Metadata + metadataConstraints[1..n] : MD_Constraints … 3) Set as Mandatory - Security and legal Constraints - Data quality Lineage (including process steps) CUAHSI – Profile V.1.0

16. 4) New code lists MD Classification Code for security Constraints - World - Group - Owner CUAHSI – Profile V.1.0

17. Variety of software and formats Because of Profiles and subprofiles CUAHSI Profile V.1.0 Features-Profile TimeSeries-Profile …… … In progress

18. Variety of software and formats Because of Identifying characteristics of elements What can be created by the user? E.g. abstract. What can be created by the metadata software. E.g. metadata version, date of creation. What can automatically be created by the tools creating the data :E.g. lineage. What can be extracted from files which format is known:E.g. HDF or shape files. In progress Also will be expressed in OWL/XML using an annotation property

19. Variety of software and formats Because of User profiles To facilitate creation of metadata by reusing resources E.g. Citation Responsible Party

20. Vocabulary in metadata instances Studied the files located at: http://www.env.duke.edu/careshttp://www.env.duke.edu/cares Created an ontology with features, attributes and keywords of the ~62 files to depict the heterogeneity problems - Some have FGDC metadata, others do not - Semantic Heterogeneity is found in feature and attributes

21. How to deal with this problem? In automatic data creation the feature name and attributes could be set a priori (or mapped ) from a hydrologic ontology that supplies the terms. In manual creation of metadata we could map the semantics when uploading files: Similar to Lin, K. and B. Ludäscher work at SDSC mapping geological maps Users map their semantics to a hydrologic ontology. A system should assist making good guesses from previous mappings and ontology inference : Purpose to have a usable ontology

22. Ontologies construction Creation of a top hydrologic ontology that is mapped with Wordnet ( the ontology with more usage as of today) http://loki.cae.drexel.edu/~how/upper/upper.html Creation of hydrologic units ontology  Extracting hydrologic terms to define feature and attributes : - ARC HYDRO in progress - UNESCO Thesaurus - GCMD - SWEET - GETTY 

23. What is next? Build Metadata descriptions for Neuse_files (initially 2) Core set for any data set in CUAHSI automatically set auto user profile user “by hand” want this to be very small Data set 1Data set 2 Data set specific elements and attributes => all in MIF format for HydroViewer

24. CUAHSI Hydrologic Information Systems Work of the five project partners –CUAHSI –San Diego Supercomputer Center –University of Texas –University of Illinois –Drexel University Involving the collaborators……

25. New Concept of Publication New WayOld Way

26. HydroViewer GUI Neuse River Watershed Collection HydroViewer Demo by John Helly

27. SDSC Multiview Map Viewer http://geo.sdsc.edu/website/SIO_Expl/viewer.htm

28. Wireless telecommunication of water resources data Can we use these data as a prototype for the CUAHSI observatory?

29. CUAHSI Hydrologic Information Systems Work of the five project partners –CUAHSI –San Diego Supercomputer Center –University of Texas –University of Illinois –Drexel University Involving the collaborators……

30. University of Texas David Maidment is PI, assisted by Jon Goodall, Gil Strassberg, Venkatesh Merwade Hydrologic data model development –atmospheric water, surface water, subsurface water –interoperable analysis environment

31. Information Sources Analysis and Visualization Hydrologic Information Data Model CUAHSI Hydrologic Information System GIS Experiments Simulation Monitoring Climate models 2. Integrate data into a coherent structure 3. Do science 1. Assemble data from many sources Hypothesis testing Data Assimilation Remote sensing Statistics

32. Created first for the Neuse basin and then for each of the following CUAHSI Observatory Planning basins Digital Watershed: An implementation of the CUAHSI Hydrologic Information Data Model for a particular region

33. http://neuse.crwr.utexas.edu/

34. Neuse Basin: Coastal aquifer system * From USGS, Water Resources Data Report of North Carolina for WY 2002 Section line Beaufort Aquifer

35. A 3-D Volume Model of the Beaufort Aquifer Beaufort confining layer Beaufort aquifer Built by Gil Strassberg from borehole information collected by the Neuse basin case study team

36. RUC20 – Output Samples Precipitable water in the atmosphere Cross-section of relative humidity Images created from Unidata’s Integrated Data Viewer (IDV) Wind vectors and wind speed (shading)

37. Surface Water Information ArcIMS Web Server displaying data compiled in Neuse HO Planning Study

38. Neuse basin data in Multiview http://geo.sdsc.edu/website/SIO_Expl/viewer.htm

39. Data Model

40. Ontology

41. Discrete

43. Continuous

44. Coupler

45. Coupling table FromIDToIDFeature 124 235 Where … HydroID of canal feature = 2 HydroID of upstream canal feature = 1 HydroID of downstream canal feature = 3 HydroID of upstream control structure = 4 HydroID of downstream control structure = 5

46. Mass balance

47. University of Illinois Praveen Kumar is PI, assisted by Benjamin Ruddell Developing “Modelshed” which is an generalized hydrologic modeling and data analysis environment built on top of Arc Hydro Focused on applications integrating hydrology and climate modeling

48. Flow Time Time Series HydrographyHydro Network Channel System Drainage System Arc Hydro Components

49. What is a Modelshed? A volumetric spatial model unit, registered in three dimensions by a GIS, with which time-varying data, model fluxes, spatial relationships and descriptive metadata are associated

50. CUAHSI Hydrologic Information Systems Work of the five project partners –CUAHSI –San Diego Supercomputer Center –University of Texas –University of Illinois –Drexel University Involving the collaborators……Ken Reckhow, Yao Liang

51. Involving the Collaborators….. We have a good start How do we involve the collaborators in a productive way? How should we interact with the Hydrologic Observatory proposers (meeting at Utah State on August 24-25)? Possible CUAHSI “all hands” meeting at SDSC on August 12-13