1. Testing A Community Data Model for Hydrologic Observations David G Tarboton Jeff Horsburgh David R. Maidment Ilya Zaslavsky David Valentine Blair Jennings http://www.cuahsi.org/his/tk-observedb.html

2. Hydrologic Science Hydrologic conditions (Fluxes, flows, concentrations) Hydrologic Process Science (Equations, simulation models, prediction) Hydrologic Information Science (Observations, data models, visualization Hydrologic environment (Dynamic earth) Physical laws and principles (Mass, momentum, energy, chemistry) It is as important to represent hydrologic environments precisely with data as it is to represent hydrologic processes with equations

3. What is a Data Model A data model is a model that describes in an abstract way how data is represented Data models describe structured data for storage in data management systems such as relational databases. Early phases of many software development projects emphasize the design of a conceptual data model. Lets see what Wikipedia says

4. Continuous Space-Time Model – NetCDF (Unidata) Space, L Time, T Variables, V D Coordinate dimensions {X} Variable dimensions {Y}

5. Space, FeatureID Time, TSDateTime Variables, TSTypeID TSValue Discrete Space-Time Data Model ArcHydro

6. Terrain Data Models Grid Contour and flowline TIN

7. CUAHSI Point Hydrologic Observations Data Model A relational database stored in Access, PostgreSQL, SQL/Server, …. Stores observation data made at points Consistent format for storage of observations from many different sources and of many different types. Streamflow Flux tower data Precipitation & Climate Groundwater levels Water Quality Soil moisture data

8. Purposes Hydrologic Observations Data System to Enhance –Retrieval –Integrated Analysis –Multiple Investigators Standard Format for Sharing Scalable: Single study area to globe Ancillary information (metadata) to allow unambiguous interpretation and use Traceable heritage from raw measurements to usable information

9. Community Design Requirements (from comments of 22 reviewers) Incorporate sufficient metadata to identify provenance and give exact definition of data for unambiguous interpretation Spatial location of measurements Scale of measurements Depth/Offset Information Censored data Classification of data type to guide appropriate interpretation –Continuous –Indication of gaps Indicate data quality

10. Scale issues in the interpretation of data The scale triplet From: Blöschl, G., (1996), Scale and Scaling in Hydrology, Habilitationsschrift, Weiner Mitteilungen Wasser Abwasser Gewasser, Wien, 346 p. a) Extentb) Spacing c) Support

11. From: Blöschl, G., (1996), Scale and Scaling in Hydrology, Habilitationsschrift, Weiner Mitteilungen Wasser Abwasser Gewasser, Wien, 346 p.

12. Design Premise A relational database at the single observation level (atomic model) –Querying capability –Cross dimension retrieval and analysis What are the basic attributes to be associated with each single observation and how can these best be organized?

13. Schema

14. 1 1 CouplingTable WaterID (GUID) HydroID (Integer) MonitoringPoint WaterID HydroCode Name Latitude Longitude … Hydrologic Observations Data Model 1 1 OR Independent of, but coupled to Geographic Representation HODM Arc Hydro

15. Observation Type Variable, e.g. discharge Units SampleMedium, e.g. water Valuetype, e.g. field observation, laboratory sample IsRegular, e.g. Yes for regular time series or No for intermittent measurements ObsTimeSupport (averaging interval for observation) TimeUnit (for support) DataType, e.g. Continuous, Instantaneous, Categorical ObservationCategory, e.g. Climate, Water Quality m 3 /s L 3 /T

16. Data Types Continuous (Frequent sampling - fine spacing) Instantaneous (Spot sampling - coarse spacing) Cumulative Incremental Average Maximum Minimum Constant over Interval Categorical

17. Discharge, Stage, Concentration and Daily Average Example

18. Groupings and Derived From Associations

19. Stage and Discharge Example Discharge Derived from Gage Height

20. Daily Average Discharge Example Daily Average Discharge Derived from 15 Minute Discharge Data

21. Offset Distance from a datum or control point at which an observation was made OffsetType defines the type of offset, e.g. distance below water level, distance above ground surface, or distance from bank of river

22. Water Chemistry From a Lake Profile

23. Methods and Samples Method specifies the method whereby an observation is measured, e.g. Streamflow using a V notch weir, TDS using a Hydrolab, sample collected in auto-sampler SampleID is used for observations based on the laboratory analysis of a physical sample and identifies the sample from which the observation was derived. This keys to a unique LabSampleID (e.g. bottle number) and name and description of the analytical method used by a processing lab.

24. Water Chemistry from Laboratory Sample

25. WaterOneFlow Web Services Data accessthrough web services Data storage through web services Downloads Uploads Observatory servers SDSC HIS servers 3 rd party servers e.g. USGS, NCDC GIS Matlab IDL Splus, R D2K, I2K Programming (Fortran, C, VB) Web services interface Web portal Interface (HDAS) Information input, display, query and output services Preliminary data exploration and discovery. See what is available and perform exploratory analyses HTML -XML WSDL - SOAP Hydrologic Information System Service Oriented Architecture

26. Matlab use of CUAHSI Web Services to Query HODM % create HODM Class class createClassFromWsdl('http://water.usu.edu/HODM/hodm.asmx?WSDL'); % This creates an instance of the class. instHODM = HODM; xmlSites=GetSites(instHODM); … xmlSiteInfo=GetSiteInfo(instHODM,SiteCodes(5)); … xmlValues = GetValues(instHODM,SiteCodes(5),VariableCode,D1,D2); … plot(dnt,Qt); datetick; % Get annual maximum series years=min(yeart):max(yeart); for i=1:length(years) qa(i)=max(Qt(find(yeart==years(i)))); end qa=sort(qa); m=length(qa); p=(1:m)/(m+1); plot(qa,p GetSites GetSiteInfo GetValues Analyze Data createClass

27. Conclusions A conceptual template for the representation of hydrologic point observations in a relational database Simple - 16 tables Queries facilitate flexible data retrieval and analysis involving types, time or space Standard - a basis for effective sharing Ancillary information to support unambiguous interpretation of each observation

28. Accuracy and Precision ObsAccuracyStdDev Numeric value that expresses measurement accuracy as the standard deviation of each specific observation

29. Observation Series An Observation Series is a set of all the observations of a particular type at one place, i.e. with unique monitoring point (WaterID), observation type, offset and offsettype. The ObservationSeriesCatalog is programatically generated to provide a means by which a user can get simple descriptive information about the variables observed at a location.

30. Data Quality Data Qualifier Code indicates provides qualifying information about the observations, e.g. Estimated, Provisional, Derived, Holding time for analysis exceeded QualityControlLevel records the level of quality control that the data has been subjected to. - Level 0. Raw Data and Raw Metadata - Level 1. Quality Controlled Data and Associated Metadata - Level 2. Derived Products and Associated Metadata - Level 3. Interpreted Products and Associated Metadata - Level 4. Knowledge Products and Associated Metadata

31. Incomplete or Inexact daily total occurring. Value is not a true 24- hour amount. One or more periods are missing and/or an accumulated amount has begun but not ended during the daily period. 15 min Precipitation from NCDC

32. Irregularly sampled groundwater level

33. Soil Moisture Example

34. % create NWIS class createClassFromWsdl('http://river.sdsc.edu/NWISTS/nwis.asmx?WSDL'); % This creates an instance of the class. svsNWIS = NWIS; % Specify a SiteID to use SiteID='10109000' % Call the getDischargeValues function to get discharge data. DisVals=getDischargeValues(svsNWIS,SiteID,startDate(1),endDate(1)); % Parse the string that is returned into matrices and plot temp=sscanf(DisVals,'%4d-%2d-%2d,%f'); [n1,n2]=size(temp); ny=n1/4; ind=(1:ny)*4; year=temp((ind-3)); month=temp((ind-2)); day=temp((ind-1)); Q=temp(ind); dn=datenum(year,month,day); plot(dn,Q);datetick; Example: Matlab use of CUAHSI Web Services

35. GetSites % create HODM Class class createClassFromWsdl('http://water.usu.edu/HODM/hodm.asmx?WSDL'); % This creates an instance of the class. instHODM = HODM; xmlSites=GetSites(instHODM);

36. GetSiteInfo xmlSiteInfo=GetSiteInfo(instHODM,SiteCodes(5));

37. GetValues xmlValues = GetValues(instHODM,SiteCodes(5),VariableCode,D1,D2);

38. Matlab Analysis strValues=parse_xml(xmlValues); Nvals=str2num(strValues.child.child(2).value); for i=1:Nvals dn(i)=datenum(cellstr(strValues.child.child(3).child(i).child(1).value)); [year(i),month(i),day(i)]=datevec(dn(i)); Q(i)=str2num(strValues.child.child(3).child(i).child(2).value); End … qa=sort(qa); m=length(qa); p=(1:m)/(m+1); plot(qa,p)