1. CUAHSI, WATERS and HIS by Richard P. Hooper, David G. Tarboton and David R. Maidment

2. The Need: Hydrologic Information 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. -Mathematical Formulae -Solution Techniques Abstractions in Modeling Physical World Conceptual Frameworks Data Representation Model Representations “Digital Environment”Real World Measurements Theory/Process Knowledge Perceptions of this place Intuition Water quantity and quality Meteorology Remote sensing Geographically Referenced Mapping Hypothesis Testing DNA Sequences Vegetation Survey Hydrologist Q, Gradient, Roughness? Groundwater Contribution? Snowmelt Processes? Biogeochemist Hyporheic exchange? Mineralogy? Chemistry? Redox Zones? DOC Quality? Geomorphologist Glaciated Valley Perifluvial Well sorted? Thalweg? Aquatic Ecologist Backwater habitat Substrate Size, Stability? Benthic Community Oligotrophic? Carbon source?

4. Abstractions in Modeling How do different disciplines view the same place?

18. “Digital Environment”

19. Digital Environment Use GIS to explicitly map conceptual model to real digital representation –What do data represent to scientist? Assess utility of data to support multiple conceptual models Pilot Projects: –WATERS Test beds: Digital watersheds –Critical Zone Observatories

20. Databases: Structured data sets to facilitate data integrity and effective sharing and analysis. - Standards - Metadata - Unambiguous interpretation Analysis: Tools to provide windows into the database to support visualization, queries, analysis, and data driven discovery. Models: Numerical implementations of hydrologic theory to integrate process understanding, test hypotheses and provide hydrologic forecasts. Advancement of water science is critically dependent on integration of water information Databases Analysis Models ODM Web Services

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

22. CUAHSI Observations Data Model A relational database at the single observation level (atomic model) Stores observation data made at points Metadata for unambiguous interpretation Traceable heritage from raw measurements to usable information Standard format for data sharing Cross dimension retrieval and analysis Streamflow Flux tower data Precipitation & Climate Groundwater levels Water Quality Soil moisture data

23. CUAHSI Observations Data Model http://www.cuahsi.org/his/odm.html

24. Stage and Streamflow Example

25. ODM to Datacube A data cube is a database specifically for data mining (OLAP) –Organizes data along dimensions such as time, site, or variable type –Easy to group, filter, and aggregate data in a variety of ways –Simple aggregations such as sum, min, or max can be pre-computed for speed –Additional calculations such as median can be computed dynamically SQL Server Analysis Services (SSAS) provides the OLAP engine SQL Server Business Intelligence Development Studio is used to define and tune Excel and other client tools enable simple browsing Slide from Catharine van Ingen, Microsoft Research

26. ODM to Datacube A data cube is a database specifically for data mining (OLAP) –Organizes data along dimensions such as time, site, or variable type –Easy to group, filter, and aggregate data in a variety of ways –Simple aggregations such as sum, min, or max can be pre-computed for speed –Additional calculations such as median can be computed dynamically SQL Server Analysis Services (SSAS) provides the OLAP engine SQL Server Business Intelligence Development Studio is used to define and tune Excel and other client tools enable simple browsing Slide from Catharine van Ingen, Microsoft Research

27. ODM and HIS in an Observatory Setting Integration of Sensor Data With HIS Observations Database (ODM) Base Station Computer(s) Data Processing Applications Internet Telemetry Network Sensors Data discovery, visualization, analysis, and modeling through Internet enabled applications Programmer interaction through web services Internet Workgroup HIS Tools Workgroup HIS Server

28. Bayesian Networks to construct water quality measures from surrogate sensor signals to provide high frequency estimates of water quality and loading Site specific correlations between sensor signals and other water quality variables Sensors, data collection, and telemetry network Bayesian Networks to control monitoring system, triggering sampling for storm events and base flow CUAHSI HIS ODM – central storage and management of observations data End result: high frequency estimates of nutrient concentrations and loadings Integrated Monitoring System

29. Managing Data Within ODM - ODM Tools Load – import existing data directly to ODM Query and export – export data series and metadata Visualize – plot and summarize data series Edit – delete, modify, adjust, interpolate, average, etc.

30. Linking GIS and Water Resources GIS Water Resources

31. Hydrologic Information System GIS – the water environment Water Resources – the water itself

32. Point Observations Information Model Data Source Network Sites Variables Values {Value, Time, Qualifier, Offset} USGS Streamflow gages Neuse River near Clayton, NC Discharge, stage (Daily or instantaneous) 206 cfs, 13 August 2006 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 An offset allows specification of measurements at various depths in water http://www.cuahsi.org/his/webservices.html GetSites GetSiteInfo GetVariables GetVariableInfo GetValues

33. Locations Variable Codes Date Ranges WaterML and WaterOneFlow GetSiteInfo GetVariableInfo GetValues WaterOneFlow Web Service Client STORET NAM NWIS Data Repositories Data EXTRACT TRANSFORM LOAD WaterML WaterML is an XML language for communicating water data WaterOneFlow is a set of web services based on WaterML

34. WaterOneFlow Set of query functions Returns data in WaterML

35. HIS Servers WATERS Network Information System NSF has funded work at 11 testbed sites, each with its own science agenda. A CUAHSI Hydrologic Information Server is installed at each site. Utah State University Texas A&M Corpus Christi National HIS Server at San Diego SuperComputer Center

36. Multiscale Information System Global data National data State data Project in region …. Principal investigator data

37. Corpus Christi Bay WATERS Testbed site Montagna stations SERF stations TCOON stations USGS gages TCEQ stations Hypoxic Regions NCDC station National Datasets (National HIS)Regional Datasets (Testbed HIS) USGSNCDCTCOONDr. Paul MontagnaTCEQSERF

38. Hydrologic Information Server Supports data discovery, delivery and publication –Data discovery – how do I find the data I want? Map interface and observations catalogs Metadata based Search –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

39. Observation Stations Ameriflux Towers (NASA & DOE)NOAA Automated Surface Observing System USGS National Water Information SystemNOAA Climate Reference Network Map for the US http://river.sdsc.edu/DASH

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

41. Hydrologic Information Server Microsoft SQLServer Relational Database Observations Data Geospatial Data GetSites GetSiteInfo GetVariables GetVariableInfo GetValues DASH – data access system for hydrologyWaterOneFlow services ArcGIS Server

42. Data Heterogeneity Syntactic mediation –Heterogeneity of format –Use WaterML to get data into the same format Semantic mediation –Heterogeneity of meaning –Each water data source uses its own vocabulary –Match these up with a common controlled vocabulary –Make standard scientific data queries and have these automatically translated into specific queries on each data source

43. Search multiple heterogeneous data sources simultaneously regardless of semantic or structural differences between them Objective NWIS NARR NAWQA NAM-12 request request return return What we are doing now ….. Michael Piasecki Drexel University

44. Semantic Mediator What we would like to do ….. NWIS NAWQA NARR generic request GetValues GetValues HODM Michael Piasecki Drexel University

45. HydroSeek: http://www.hydroseek.org http://www.hydroseek.org