1. Space, Time and Variables – A Look into the Future Presented by David Maidment, University of Texas With the assistance of Clark Siler, Virginia Smith, Ernest To and Tim Whiteaker, University of Texas

2. Pre Conference Seminar2 Linking GIS and Water Resources GIS Water Resources Water Environment (Watersheds, gages, streams) Water Conditions (Flow, head, concentration)

3. Pre Conference Seminar3 Data Cube Space, L Time, T Variables, V D “What” “Where” “When” A simple data model

4. Pre Conference Seminar4 Continuous Space-Time Model – NetCDF (Unidata) Space, L Time, T Variables, V D Coordinate dimensions {X} Variable dimensions {Y}

5. Pre Conference Seminar5 Space, FeatureID Time, TSDateTime Variables, TSTypeID TSValue Discrete Space-Time Data Model ArcHydro

6. Pre Conference Seminar6 CUAHSI Observations Data Model A relational database at the single observation level (atomic model)A relational database at the single observation level (atomic model) Stores observation data made at pointsStores observation data made at points Metadata for unambiguous interpretationMetadata for unambiguous interpretation Traceable heritage from raw measurements to usable informationTraceable heritage from raw measurements to usable information Streamflow Flux tower data Precipitation & Climate Groundwater levels Water Quality Soil moisture data

7. Pre Conference Seminar7 Ernest To Center for Research in Water Resources University of Texas at Austin 20061011 What are the basic attributes to be associated with each single observation and how can these best be organized? A data source operates an observation network A network is a set of observation sites Data Source and Network SitesVariablesValuesMetadata Depth of snow pack Streamflow Landuse, Vegetation Windspeed, Precipitation Data Delivery Controlled Vocabulary Tables e.g. mg/kg, cfs e.g. depth e.g. Non-detect,Estimated, A site is a point location where one or more variables are measured Metadata provide information about the context of the observation. A variable is a property describing the flow or quality of water A value is an observation of a variable at a particular time Data Discovery Hydrologic Observations Data Model See http://www.cuahsi.org/his/documentation.htmlhttp://www.cuahsi.org/his/documentation.html

8. Pre Conference Seminar8 ODM and HIS in an Observatory Setting e.g. http://www.bearriverinfo.org

9. Pre Conference Seminar9 Space, Time, Variables and Observations Variables (VariableID) Space (HydroID) Time Observations Data Model Data from sensors (regular time series) Data from sensors (regular time series) Data from field sampling (irregular time points) Data from field sampling (irregular time points) An observations data model archives values of variables at particular spatial locations and points in time

10. Pre Conference Seminar10 Space, Time, Variables and Visualization Variables (VariableID) Space (HydroID) Time Vizualization Map – Spatial distribution for a time point or interval Map – Spatial distribution for a time point or interval Graph – Temporal distribution for a space point or region Graph – Temporal distribution for a space point or region Animation – Time-sequenced maps Animation – Time-sequenced maps A visualization is a set of maps, graphs and animations that display the variation of a phenomenon in space and time

11. Pre Conference Seminar11 Space, Time, Variables and Simulation Variables (VariableID) Space (HydroID) Time Process Simulation Model A space-time point is unique A space-time point is unique At each point there is a set of variables At each point there is a set of variables A process simulaton model computes values of sets of variables at particular spatial locations at regular intervals of time

12. Pre Conference Seminar12 Space, Time, Variables and Geoprocessing Variables (VariableID) Space (HydroID) Time Geoprocessing Interpolation – Create a surface from point values Interpolation – Create a surface from point values Overlay – Values of a surface laid over discrete features Overlay – Values of a surface laid over discrete features Temporal – Geoprocessing with time steps Temporal – Geoprocessing with time steps Geoprocessing is the application of GIS tools to transform spatial data and create new data products

13. Pre Conference Seminar13 Space, Time, Variables and Statistics Variables (VariableID) Space (HydroID) Time Statistical distribution Represented as {probability, value} Represented as {probability, value} Summarized by statistics (mean, variance, standard deviation) Summarized by statistics (mean, variance, standard deviation) A statistical distribution is defined for a particular variable defined over a particular space and time domain

14. Pre Conference Seminar14 Space, Time, Variables and Statistical Analysis Variables (VariableID) Space (HydroID) Time Statistical analysis Multivariate analysis – correlation of a set of variables Multivariate analysis – correlation of a set of variables Geostatistics – correlation space Geostatistics – correlation space Time Series Analysis – correlation in time Time Series Analysis – correlation in time A statistical analysis summarizes the variation of a set of variables over a particular domain of space and time

15. Pre Conference Seminar15 CUAHSI Observations Data Model Space-Time Datasets Sensor and laboratory databases From Robert Vertessy, CSIRO, Australia

16. Pre Conference Seminar16 Example 1: Visualizing the output of the WRAP model (Clark Siler) Water Rights Analysis Package (WRAP) is a simulation model used by the Texas Commission for Environmental Quality Water Rights Analysis Package (WRAP) is a simulation model used by the Texas Commission for Environmental Quality WRAP models have been built for all 23 river and coastal basins in Texas WRAP models have been built for all 23 river and coastal basins in Texas They simulate surface water withdrawals at about 10,000 locations where water permits have been issued in Texas They simulate surface water withdrawals at about 10,000 locations where water permits have been issued in Texas Uses monthly time steps and ~ 50 year planning period Uses monthly time steps and ~ 50 year planning period Reservoir levels in the Neches basin

17. Pre Conference Seminar17 Information Products Desired A WRAP model has about 40 output variables defined at each water permit location and time point A WRAP model has about 40 output variables defined at each water permit location and time point 1.Plot a map showing for a given time point the value of a selected variable at each permit location 2.Plot a graph showing the time variation of an output variable at a selected permit location 3.Plot a map for a given time interval of the average value of a selected variable over that time interval

18. Pre Conference Seminar18 Multivariable Table Space Time A set of variables …… Each space-time point is unique and is associated with a set of variables SpaceTimeGraphs Maps

19. Pre Conference Seminar19 Example 2: Evaporation from the North American Regional Reanalysis of Climate (Virginia Smith) North American Regional Reanalysis (NARR) of climate is a simulation of weather and climate over the US for 3 hour time intervals since 1979 by National Centers for Environmental Prediction North American Regional Reanalysis (NARR) of climate is a simulation of weather and climate over the US for 3 hour time intervals since 1979 by National Centers for Environmental Prediction Data are accessible in NetCDF format from NCDC Data are accessible in NetCDF format from NCDC Very good data source for evaporation Very good data source for evaporation NARR as features NARR as raster

20. Pre Conference Seminar20 Multidimensional Data (netCDF) Time = 1 Time = 2 Time = 3 141241341 131231331 121221321 111211311 441 431 421 411 142242342 132232332 122222322 112212312 442 432 422 412 143243343 133233333 123223323 113213313 443 433 423 413 Y X Time Time = 2 Time = 1

21. Pre Conference Seminar21 NetCDF in ArcGIS NetCDF data is accessed asNetCDF data is accessed as Raster Raster Feature Feature Table Table Direct read (no scratch file)Direct read (no scratch file) Exports GIS data to netCDFExports GIS data to netCDF

22. Pre Conference Seminar22 Gridded Data Raster Point Features Regular Grids Irregular Grids

23. Pre Conference Seminar23 NetCDF Tools Toolbox: Multidimension Tools Make NetCDF Raster Layer Make NetCDF Raster Layer Make NetCDF Feature Layer Make NetCDF Feature Layer Make NetCDF Table View Make NetCDF Table View Raster to NetCDF Raster to NetCDF Feature to NetCDF Feature to NetCDF Table to NetCDF Table to NetCDF Select by Dimension Select by Dimension

24. Pre Conference Seminar24 Example 3: Voxels and 3D Geostatistics (Ernest To) WATERS is an NSF program to establish water “observatories” in the US WATERS is an NSF program to establish water “observatories” in the US There are 11 testbed projects, one of which is in Corpus Christi BayThere are 11 testbed projects, one of which is in Corpus Christi Bay CUAHSI HIS Server and observations data model have been used to integrate observational data for the bayCUAHSI HIS Server and observations data model have been used to integrate observational data for the bay Science goal is to understand hypoxia (low dissolved oxygen), which is related to salinity patterns in the bay Science goal is to understand hypoxia (low dissolved oxygen), which is related to salinity patterns in the bay 08/02/2005 Ingleside Port Aransas Packery Channel Laguna Madre Oso Bay 1 2 12 11 10/D 14 18 39 34 30 9 2424 8 21

25. Pre Conference Seminar25 Corpus Christi Bay Environmental Information System Montagna stations SERF stations TCOON stations USGS gages TCEQ stations Hypoxic Regions NCDC station National Datasets (National HIS)Regional Datasets (Workgroup HIS) USGSNCDCTCOONDr. Paul MontagnaTCEQSERF ET 20061116

26. Pre Conference Seminar26 Salinity varies with latitude, longitude, depth and time

27. Pre Conference Seminar27 Voxels Voxels = volume pixels or 3D pixelsVoxels = volume pixels or 3D pixels A voxel volume is formed by superpositioning four 3D arrays:A voxel volume is formed by superpositioning four 3D arrays: –Red array + Green array + Blue array +Opacity array Manipulation of the opacity array can make inner voxels visibleManipulation of the opacity array can make inner voxels visible Plotted with data from head.dat from IDL 6.3 examples

28. Pre Conference Seminar28 Kriging Results for Aug 2, 2005. 08/02/2005 Ingleside Port Aransas Packery Channel Laguna Madre Oso Bay 1 2 12 11 10/D 14 18 39 34 30 9 2424 8 21

29. Pre Conference Seminar29 Space-Time Integration timeline What happened in between the observations? ???

30. Pre Conference Seminar30 Example 4: OpenMI – integrating models with data (Tim Whiteaker) OpenMI is a software framework developed in Europe by DHI, Delft Hydraulics and Hydraulic Research Wallingford (http://www.openmi.org) OpenMI is a software framework developed in Europe by DHI, Delft Hydraulics and Hydraulic Research Wallingford (http://www.openmi.org)http://www.openmi.org It integrates simulation models for hydrology, hydraulics and water quality It integrates simulation models for hydrology, hydraulics and water quality Simulation codes are reduced to “engines” and made into OpenMI componentsSimulation codes are reduced to “engines” and made into OpenMI components Data sources can similarly be made into OpenMI components Data sources can similarly be made into OpenMI components

31. Pre Conference Seminar31 OpenMI Conceptual Framework Interconnection of dynamic simulation models Space, L Time, T Variables, V D

32. Pre Conference Seminar32 OpenMI – Links Data and Simulation Models CUAHSI Observations Data Model as an OpenMI component Simple River Model Trigger (identifies what value should be calculated)

33. Pre Conference Seminar33 Typical model architecture Application User interface + engine Engine Simulates a process – flow in a channel Accepts input Provides output Model An engine set up to represent a particular location e.g. a reach of the Thames Engine Output data Input data Model application Run Write Read User interface

34. Pre Conference Seminar34 AcceptsProvides Rainfall(mm)Runoff (m 3 /s) Temperature (Deg C) Evaporation(mm) AcceptsProvides Upstream Inflow (m 3 /s) Outflow Lateral inflow (m 3 /s) Abstractions Discharges River Model Linking modelled quantities

35. Pre Conference Seminar35 Data transfer at run time Rainfall runoff Output data Input data User interface River Output data Input data User interface GetValues(..)

36. Pre Conference Seminar36 Models for the processes River (InfoWorks RS) Rainfall (database) Sewer (Mouse) RR (Sobek-Rainfall -Runoff)

37. Pre Conference Seminar37 Data exchange 3 Rainfall.GetValues River (InfoWorks-RS) Rainfall (database) Sewer (Mouse) 2 RR.GetValues 7 RR.GetValues RR (Sobek-Rainfall -Runoff) 1 Trigger.GetValues 6 Sewer.GetValues call data 4 5 8 9

38. Pre Conference Seminar38 Interface for Hydro Data Exchange Rainfall runoff Get values Hydraulic Get values Ecology Get values Economic Get values OpenMI defines an Interface with a GetValues method, among others Interface

39. Pre Conference Seminar39 Conclusions GIS focuses on spatial data structures and their attributesGIS focuses on spatial data structures and their attributes Water observations data focus on variables and timeWater observations data focus on variables and time Water simulation models focus on variables and time in a spatial contextWater simulation models focus on variables and time in a spatial context Statistics of variables are derived for a domain of space and timeStatistics of variables are derived for a domain of space and time We need a clearly thought out space-time- variable framework that combines GIS, observations, statistics and modeling We need a clearly thought out space-time- variable framework that combines GIS, observations, statistics and modeling