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

Space, Time and Variables – A Look into the Space, Time and Variables – A Look into the FutureFuture

Presented by Presented by

David Maidment, University of TexasDavid Maidment, University of Texas

With the assistance of Clark Siler, Virginia With the assistance of Clark Siler, Virginia Smith, Ernest To and Tim Whiteaker, Smith, Ernest To and Tim Whiteaker,

University of TexasUniversity of Texas

Pre Conference SeminarPre Conference Seminar 22

Linking GIS and Water ResourcesLinking GIS and Water Resources

GISWater

Resources

Water EnvironmentWater Environment(Watersheds, gages, streams)(Watersheds, gages, streams)

Water ConditionsWater Conditions(Flow, head, concentration)(Flow, head, concentration)


Data CubeData Cube

Space, L

Time, T

Variables, V

D

“What”

“Where”

“When”

A simple data model


Continuous Space-Time Model – NetCDF (Unidata)Continuous Space-Time Model – NetCDF (Unidata)

Space, L

Time, T

Variables, V

D

Coordinate dimensions

{X}

Variable dimensions{Y}


Space, FeatureID

Time, TSDateTime

Variables, TSTypeID

TSValue

Discrete Space-Time Data ModelDiscrete Space-Time Data ModelArcHydroArcHydro


CUAHSI Observations Data ModelCUAHSI Observations Data Model

• A A relational databaserelational database at the single at the single observation level (atomic model)observation level (atomic model)

• Stores Stores observation dataobservation data made at made at pointspoints

• Metadata for Metadata for unambiguous unambiguous interpretationinterpretation

• Traceable heritage from Traceable heritage from rawraw measurements to measurements to usableusable information information

Streamflow

Flux towerdata

Precipitation& Climate

Groundwaterlevels

Water Quality

Soil moisture

data

Pre Conference SeminarPre Conference Seminar 77Ernest 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 Sites Variables Values Metadata

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 ModelHydrologic Observations Data Model

See http://www.cuahsi.org/his/documentation.html

http://www.cuahsi.org/his/documentation.html


ODM and HIS in an Observatory SettingODM and HIS in an Observatory Settinge.g. http://www.bearriverinfo.orge.g. http://www.bearriverinfo.org


Space, Time, Variables and Space, Time, Variables and ObservationsObservations

Variables (VariableID)Variables (VariableID)

Space (HydroID)Space (HydroID) TimeTime

Observations Data ModelObservations Data Model• Data fromData from sensors sensors (regular time series)(regular time series)

• Data from Data from field samplingfield sampling (irregular time points)(irregular time points)

An An observations data modelobservations data model archives values of variables at archives values of variables at particular spatial locations and points in timeparticular spatial locations and points in time


Space, Time, Variables and Space, Time, Variables and VisualizationVisualization



VizualizationVizualization• MapMap – Spatial distribution for a time – Spatial distribution for a time point or intervalpoint or interval• GraphGraph – Temporal distribution for a – Temporal distribution for a space point or regionspace point or region• AnimationAnimation – Time-sequenced maps – Time-sequenced maps

A A visualizationvisualization is a set of maps, graphs and animations that is a set of maps, graphs and animations that display the variation of a phenomenon in space and timedisplay the variation of a phenomenon in space and time


Space, Time, Variables and Space, Time, Variables and SimulationSimulation



Process Simulation ModelProcess Simulation Model• A A space-time pointspace-time point is unique is unique• At each point there is a At each point there is a set of set of variablesvariables

A A process simulaton modelprocess simulaton model computes values of sets of variables at computes values of sets of variables at particular spatial locations at regular intervals of timeparticular spatial locations at regular intervals of time


Space, Time, Variables and Space, Time, Variables and GeoprocessingGeoprocessing



GeoprocessingGeoprocessing• InterpolationInterpolation – Create a surface – Create a surface from point valuesfrom point values• OverlayOverlay – Values of a surface laid – Values of a surface laid over discrete featuresover discrete features• TemporalTemporal – Geoprocessing with – Geoprocessing with time stepstime steps

GeoprocessingGeoprocessing is the application of GIS tools to transform is the application of GIS tools to transform spatial data and create new data productsspatial data and create new data products


Space, Time, Variables and Space, Time, Variables and StatisticsStatistics



Statistical distributionStatistical distribution• Represented as Represented as {probability, value}{probability, value}• Summarized by Summarized by statistics statistics (mean, (mean, variance, standard deviation)variance, standard deviation)

A A statistical distributionstatistical distribution is defined for a particular variable is defined for a particular variable defined over a particular space and time domaindefined over a particular space and time domain


Space, Time, Variables and Space, Time, Variables and Statistical AnalysisStatistical Analysis



Statistical analysisStatistical analysis• Multivariate analysisMultivariate analysis – correlation – correlation of a set of variablesof a set of variables• GeostatisticsGeostatistics – correlation space – correlation space• Time Series AnalysisTime Series Analysis – correlation – correlation in timein time

A A statistical analysisstatistical analysis summarizes the variation of a set of summarizes the variation of a set of variables over a particular domain of space and timevariables over a particular domain of space and time


CUAHSI Observations Data Model

Space-Time Datasets

Sensor and laboratory databases

From Robert Vertessy, CSIRO, Australia


Example 1: Visualizing the output of the WRAP Example 1: Visualizing the output of the WRAP model (Clark Siler)model (Clark Siler)

• Water Rights Analysis Water Rights Analysis Package (WRAP)Package (WRAP) is a is a simulation model used by the simulation model used by the Texas Commission for Texas Commission for Environmental QualityEnvironmental Quality

• WRAP models have been WRAP models have been built for all built for all 23 river and 23 river and coastal basinscoastal basins in Texas in Texas

• They simulate They simulate surface water surface water withdrawalswithdrawals at about 10,000 at about 10,000 locations where water permits locations where water permits have been issued in Texashave been issued in Texas

• Uses Uses monthlymonthly time steps and time steps and ~ ~ 50 year50 year planning period planning period

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995

Year

Sto

rag

e F

ract

ion

0

1

2

3

4

5

6

Pre

cip

(in

)

Lake Athens Lake Jacksonville Lake Kurth

Lake Nacogdoches Lake Palestine Pinkston Reservoir

Sam Rayburn Reservoir Lake Tyler B A Steinhagen Lake

Lake Striker Precip

Reservoir levels Reservoir levels in the Neches basinin the Neches basin


Information Products DesiredInformation Products Desired

• A WRAP model has about 40 A WRAP model has about 40 output variables defined at each output variables defined at each water permit location and time water permit location and time pointpoint

1.1. Plot a map showing for a given time Plot a map showing for a given time pointpoint the valuethe value of a selected variable of a selected variable at each permit locationat each permit location

2.2. Plot a graph showing the time Plot a graph showing the time variationvariation of an output variable at a of an output variable at a selected permit locationselected permit location

3.3. Plot a map for a given time interval Plot a map for a given time interval of the average valueof the average value of a selected of a selected variable over that time intervalvariable over that time interval


Multivariable TableMultivariable Table

Space Time A set of variables ……

Each space-time point is unique Each space-time point is unique and is associated with a set of variablesand is associated with a set of variables

SpaceSpace

TimeTimeGraphsGraphs

MapsMaps


Example 2: Evaporation from the North American Example 2: Evaporation from the North American Regional Reanalysis of Climate (Virginia Smith)Regional Reanalysis of Climate (Virginia Smith)

• North American Regional North American Regional Reanalysis Reanalysis (NARR)(NARR) of of climate is a simulation of climate is a simulation of weather and climate over weather and climate over the US forthe US for 3 hour 3 hour time time intervals sinceintervals since 1979 by1979 by National Centers for National Centers for Environmental PredictionEnvironmental Prediction

• Data are accessible in Data are accessible in NetCDF formatNetCDF format from NCDC from NCDC

• Very good data source for Very good data source for evaporationevaporation

NARR NARR as featuresas features

NARR NARR as rasteras raster


Multidimensional Data (netCDF)Multidimensional Data (netCDF)

Time = 1

Time = 2

Time = 3

141 241 341

131 231 331

121 221 321

111 211 311

441

431

421

411

142 242 342

132 232 332

122 222 322

112 212 312

442

432

422

412

143 243 343

133 233 333

123 223 323

113 213 313

443

433

423

413

Y

X

Time

Time = 2

Time = 1


NetCDF in ArcGISNetCDF in ArcGIS

• NetCDF data is accessed asNetCDF data is accessed as• RasterRaster• FeatureFeature• TableTable

• Direct read (no scratch file)Direct read (no scratch file)

• Exports GIS data to netCDFExports GIS data to netCDF


Gridded DataGridded Data

Raster

Point Features

Regular GridsRegular Grids

Irregular GridsIrregular Grids


NetCDF ToolsNetCDF Tools

Toolbox: Multidimension ToolsToolbox: Multidimension Tools• Make NetCDF Raster LayerMake NetCDF Raster Layer• Make NetCDF Feature LayerMake NetCDF Feature Layer• Make NetCDF Table ViewMake NetCDF Table View• Raster to NetCDFRaster to NetCDF• Feature to NetCDFFeature to NetCDF• Table to NetCDFTable to NetCDF• Select by DimensionSelect by Dimension


Example 3: Voxels and 3D Geostatistics (Ernest Example 3: Voxels and 3D Geostatistics (Ernest To)To)

• WATERS WATERS is an NSF program is an NSF program to establish water to establish water “observatories” in the US“observatories” in the US

• There are 11 testbed projects, There are 11 testbed projects, one of which is inone of which is in Corpus Corpus Christi BayChristi Bay

• CUAHSI HIS Server and CUAHSI HIS Server and observations data model observations data model have have been used to integrate been used to integrate observational data for the bayobservational data for the bay

• Science goalScience goal is to is to understand hypoxia (low understand hypoxia (low dissolved oxygen), which is dissolved oxygen), which is related to related to salinity patternssalinity patterns in in the baythe bay

08/02/2005

Ingleside

Port Aransas

PackeryChannel

Laguna Madre

OsoBay

1

2

12

11

10/D

14

18

3934

309

24

8

21


Corpus Christi Bay Environmental Information SystemCorpus 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)

USGS NCDC TCOON Dr. Paul Montagna TCEQ SERF

ET 20061116


Salinity varies with latitude, longitude, depth and timeSalinity varies with latitude, longitude, depth and time


VoxelsVoxels

• 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 arrayRed 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


Kriging Results for Aug 2, 2005.Kriging Results for Aug 2, 2005.

08/02/2005

Ingleside

Port Aransas

PackeryChannel

Laguna Madre

OsoBay

1

2

12

11

10/D

14

18

3934

309

24

8

21


Space-Time IntegrationSpace-Time Integration

35

40

45

50

8/1/2005 8/6/2005 8/11/2005 8/16/2005 8/21/2005 8/26/2005 8/31/2005

Salinity (psu) Salinity vs t

timeline

What happened in between the observations?

? ? ?


Example 4: OpenMI – integrating models with Example 4: OpenMI – integrating models with data (Tim Whiteaker)data (Tim Whiteaker)

• OpenMIOpenMI is a software framework developed in Europe by DHI, Delft is a software framework developed in Europe by DHI, Delft Hydraulics and Hydraulic Research Wallingford (Hydraulics and Hydraulic Research Wallingford (http://www.openmi.orghttp://www.openmi.org))

• It It integrates simulation modelsintegrates simulation models for hydrology, hydraulics and water for hydrology, hydraulics and water quality quality

• Simulation codesSimulation codes are reduced to “engines” and made into OpenMI are reduced to “engines” and made into OpenMI componentscomponents

• Data sourcesData sources can similarly be made into OpenMI components can similarly be made into OpenMI components

http://www.openmi.org/


OpenMI Conceptual FrameworkOpenMI Conceptual Framework

Interconnection of dynamic simulation models

Space, L

Time, T

Variables, V

D


OpenMI – Links Data and Simulation ModelsOpenMI – Links Data and Simulation Models

CUAHSI Observations Data Model as an OpenMI component

Simple River Model

Trigger (identifies what value should be calculated)


Typical model architectureTypical model architecture

ApplicationApplicationUser interface + engineUser interface + engine

EngineEngineSimulates a process – flow in a channelSimulates a process – flow in a channelAccepts inputAccepts inputProvides outputProvides output

ModelModelAn engine set up to represent a An engine set up to represent a particular location e.g. a reach of the particular location e.g. a reach of the ThamesThames

Engine

Output data

Input data

Model application

Run

Write

Write

Read

User interface


AcceptsAccepts ProvidesProvides

RainfallRainfall

(mm)(mm)

RunoffRunoff

(m(m33/s)/s)

TemperatureTemperature

(Deg C)(Deg C)

EvaporationEvaporation

(mm)(mm)

AcceptsAccepts ProvidesProvides

Upstream InflowUpstream Inflow

(m(m33/s)/s)

OutflowOutflow

(m(m33/s)/s)

Lateral inflowLateral inflow

(m(m33/s)/s)

AbstractionsAbstractions

(m(m33/s)/s)

DischargesDischarges

(m(m33/s)/s)

River Model

Linking modelled quantitiesLinking modelled quantities

Rainfall Runoff Model


Data transfer at run timeData transfer at run time

Rainfall runoff

Output data

Input data

User interface

River

Output data

Input data

User interface

GetValues(..)


Models for the processesModels for the processes

River(InfoWorks RS)

Rainfall(database)

Sewer(Mouse)

RR(Sobek-Rainfall

-Runoff)


Data exchangeData 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


Interface for Hydro Data ExchangeInterface for Hydro Data Exchange

Rainfall runoffGet values

HydraulicGet values

EcologyGet values

EconomicGet values

OpenMI defines an Interface with a GetValues method, among others

Interface


ConclusionsConclusions

• GISGIS focuses on focuses on spatial dataspatial data structures and their structures and their attributesattributes

• Water observationsWater observations data focus on variables and data focus on variables and timetime

• Water simulation modelsWater simulation models focus on variables and focus on variables and time in a spatial contexttime in a spatial context

• StatisticsStatistics of variables are derived for of variables are derived for a domain of a domain of space and timespace and time

• We need a clearly thought out We need a clearly thought out space-time-space-time-variable frameworkvariable framework that combines GIS, that combines GIS, observations, statistics and modelingobservations, statistics and modeling

Documents

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