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## Space and Time

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**Space and Time**By David R. Maidment with contributions from Steve Kopp, Steve Grise, and Tim Whiteaker**Space and Time**• Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst**Space and Time**• Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst**Linking GIS and Water Resources**Water Resources GIS Water Conditions (Flow, head, concentration) Water Environment (Watersheds, gages, streams)**Data Cube**A simple data model Time, T “When” D “Where” Space, L Variables, V “What”**Discrete Space-Time Data ModelArcHydro**Time, TSDateTime TSValue Space, FeatureID Variables, TSTypeID**Continuous Space-Time Model – NetCDF (Unidata)**Time, T Coordinate dimensions {X} D Space, L Variable dimensions {Y} Variables, V**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 CUAHSI Observations Data Model Streamflow Groundwater levels Precipitation & Climate Soil moisture data Water Quality Flux tower data**ODM and HIS in an Observatory Settinge.g.**http://www.bearriverinfo.org Pre Conference Seminar**Space, Time, Variables and Observations**An observations data model archives values of variables at particular spatial locations and points in time • Observations Data Model • Data fromsensors (regular time series) • Data from field sampling (irregular time points) Variables (VariableID) Space (HydroID) Time**Space, Time, Variables and Visualization**A visualization is a set of maps, graphs and animations that display the variation of a phenomenon in space and time • Vizualization • Map – Spatial distribution for a time point or interval • Graph – Temporal distribution for a space point or region • Animation – Time-sequenced maps Variables (VariableID) Space (HydroID) Time**Space, Time, Variables and Simulation**A process simulaton model computes values of sets of variables at particular spatial locations at regular intervals of time • Process Simulation Model • A space-time point is unique • At each point there is a set of variables Variables (VariableID) Space (HydroID) Time**Space, Time, Variables and Geoprocessing**Geoprocessingis the application of GIS tools to transform spatial data and create new data products • Geoprocessing • Interpolation – Create a surface from point values • Overlay – Values of a surface laid over discrete features • Temporal – Geoprocessing with time steps Variables (VariableID) Space (HydroID) Time**Space, Time, Variables and Statistics**A statistical distribution is defined for a particular variable defined over a particular space and time domain • Statistical distribution • Represented as {probability, value} • Summarized by statistics(mean, variance, standard deviation) Variables (VariableID) Space (HydroID) Time**Space, Time, Variables and Statistical Analysis**A statistical analysis summarizes the variation of a set of variables over a particular domain of space and time • Statistical analysis • Multivariate analysis – correlation of a set of variables • Geostatistics– correlation space • Time Series Analysis – correlation in time Variables (VariableID) Space (HydroID) Time**Space-Time Datasets**CUAHSI Observations Data Model Sensor and laboratory databases Pre Conference Seminar From Robert Vertessy, CSIRO, Australia**Space and Time**• Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst**Space-Time Cube**Time TSDateTime Data Value TSValue FeatureID Space Variable TSTypeID**Arc Hydro TSType Table**Units of measure Regular or Irregular Time interval Type Of Time Series Info Recorded or Generated Type Index Variable Name • Arc Hydro has 6 Time Series DataTypes • Instantaneous • Cumulative • Incremental • Average • Maximum • Minimum**Time Series Types**Incremental Instantaneous Average Cumulative Minimum Maximum**A Theme Layer**Synthesis over all data sources of observations of a particular variable e.g. Salinity**Texas Salinity Theme**7900 series 347,000 data 7900 series TPWD 3400 TCEQ 3350 TWDB 150**Copano and Aransas Bay Salinity**Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 3000 Copano Bay Aransas Bay**Texas Daily Streamflow Theme**USGS Data 1138 sites (400 active)**Austin – Travis Lakes Streamflow**Years of Data 0 – 10 10 – 20 20 – 40 40 – 60 60 – 110**Texas Water Temperature Theme**22,700 series 966,000 data**Austin – Travis Lakes Water Temperature**Number of Data 0 – 50 50 – 150 150 – 400 400 – 1000 1000 – 5000**Space and Time**• Introductory concepts • Discrete space-time model – Arc Hydro • Temporal Geoprocessing • Continuous space-time model – netCDF • Tracking Analyst**Time Series**{value, time} Feature Series {shape,value, time} Four Panel Diagram Raster Series {raster, time} Attribute Series {featureID, value, time}**Time series from gages in Kissimmee Flood Plain**• 21 gages measuring water surface elevation • Data telemetered to central site using SCADA system • Edited and compiled daily stage data stored in corporate time series database called dbHydro • Each time series for each gage in dbHydro has a unique dbkey (e.g. ahrty, tyghj, ecdfw, ….)**Hydraulic head**Land surface h Mean sea level (datum) Hydraulic head is the water surface elevation in a standpipe anywhere in a water system, measured in feet above mean sea level**Map of hydraulic head**Z Hydraulic head, h h(x, y) x y X Y A map of hydraulic head specifies the continuous spatial distribution of hydraulic head at an instant of time**Time sequence of hydraulic head maps**z t3 t2 t1 Hydraulic head, h x y**Inundation**d h L Depth of inundation = d IF (h - L) > 0 then d = h – L IF (h – L) < 0 then d = 0**Inundation Time Series**d(x,y,t) = h(x,y,t) – LT(x,y) h (x,y,t) LT(x,y) d(x,y,t) t Time**Ponded Water Depth**Kissimmee River June 1, 2003**Depth Classification**Depth Class 11 5 9-10 4 7-8 3 5-6 2 3-4 1 1-2 0 0 -1