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GIS for Atmospheric Sciences and Hydrology By David R. Maidment University of Texas at GIS for Atmospheric Sciences and Hydrology By David R. Maidment University of Texas at Austin National Center for Atmospheric Research, 6 July 2005

GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data Weather and climate data Common data model

Atmospheric science – hydrology • Weather and climate fields are the drivers – continuous Atmospheric science – hydrology • Weather and climate fields are the drivers – continuous in space and time across the nation • Hydrologic flows in watersheds are the reactors – behaving according to watershed location and characteristics

Issues • Atmospheric science describes a fluid domain continuous in space and time, globally Issues • Atmospheric science describes a fluid domain continuous in space and time, globally connected • The earth’s surface is a static, highly spatially varied domain whose water properties vary continuously in time, where water is concentrated in flow paths (streams and rivers)

Issues • Atmospheric science data are spatially extensive (e. g. North America), involve many Issues • Atmospheric science data are spatially extensive (e. g. North America), involve many variables, are “thin” in time (one day, one forecast period, one month), and use UTC time coordinates • Hydrologic data are spatially localized (e. g. my watershed), involve few variables (precipitation, evaporation, runoff), are “deep” in time (many decades), and use local time coordinates This space-time recompositing problem Is not trivial!

Issues • Atmospheric science data are stored in vary large binary files with specialized Issues • Atmospheric science data are stored in vary large binary files with specialized formats (Grib, net. CDF, XMRG, …. ) whose georeferencing may not be strong • Hydrologic data are stored in tables in GIS and relational databases, and accessed using GIS spatial and SQL queries How do we connect these very different data worlds?

Data Cube Time, T D Space, L Variables, V Data Cube Time, T D Space, L Variables, V

Continuous Space-Time Data Model -- Net. CDF Time, T Coordinate dimensions {X} D Space, Continuous Space-Time Data Model -- Net. CDF Time, T Coordinate dimensions {X} D Space, L Variables, V Variable dimensions {Y}

Discrete Space-Time Data Model -- Arc Hydro Time, TSDate. Time TSValue Space, Feature. ID Discrete Space-Time Data Model -- Arc Hydro Time, TSDate. Time TSValue Space, Feature. ID Variables, TSType. ID

Geospatial Time Series Properties (Type) Value A Value-Time array Time Shape A time series Geospatial Time Series Properties (Type) Value A Value-Time array Time Shape A time series that knows what geographic feature it describes and what type of time series it is

GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data Weather and climate data Common data model

Data Model for Hydrologic Observations Relationships Data Model for Hydrologic Observations Relationships

USGS National Water Information System Access is rapid enough that it is as if USGS National Water Information System Access is rapid enough that it is as if NWIS is a local disk on your computer

CUAHSI Data Portal CUAHSI Data Portal

CUAHSI Data Portal CUAHSI Data Portal

Plot from the Hydrology Data Portal Produced using a CUAHSI Hydrology Web Service: get. Plot from the Hydrology Data Portal Produced using a CUAHSI Hydrology Web Service: get. Daily. Streamflow. Chart

Applications and Services Web application: Data Portal Your application • Excel, Arc. GIS, Matlab Applications and Services Web application: Data Portal Your application • Excel, Arc. GIS, Matlab • Fortran, C/C++, Visual Basic • Hydrologic model • ……………. Your operating system • Windows, Unix, Linux, Mac Internet Web Services Library

GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data Weather and climate data Common data model

A retrospective study of weather and climate made by the National Centers for Environmental A retrospective study of weather and climate made by the National Centers for Environmental Prediction’s (NCEP) numerical weather prediction model and observations from 1979 to 2003 to make 3 hour forecasts. 3 hour, daily and monthly data are available on a 32 km grid over North America. http: //wwwt. emc. ncep. noaa. gov/mmb/rreanl/

Using IDV and THREDDS to access NARR at Asheville, NC IDV in Austin, TX Using IDV and THREDDS to access NARR at Asheville, NC IDV in Austin, TX NARR. xml Get NARR. xml from NARR home page

Altitude Precipitable Water and Specific Humidity over Gulf 0. 005 0. 020 Specific humidity Altitude Precipitable Water and Specific Humidity over Gulf 0. 005 0. 020 Specific humidity (kg/kg)

Altitude Precipitable Water and Specific Humidity over Texas 0. 005 0. 020 Specific humidity Altitude Precipitable Water and Specific Humidity over Texas 0. 005 0. 020 Specific humidity (kg/kg)

Precipitation July 2003, 1800 Z Precipitation July 2003, 1800 Z

Surface evaporation July 2003, 1800 Z Surface evaporation July 2003, 1800 Z

GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data GIS for Atmospheric Science and Hydrology • • Space-time data models Hydrologic observations data Weather and climate data Common data model

Net. CDF-Java version 2. 2 Common Data Model John Caron Unidata/UCAR Dec 10, 2004 Net. CDF-Java version 2. 2 Common Data Model John Caron Unidata/UCAR Dec 10, 2004

Application Scientific Datatypes Grid Station Net. CDF-Java version 2. 2 architecture Image Netcdf. Dataset Application Scientific Datatypes Grid Station Net. CDF-Java version 2. 2 architecture Image Netcdf. Dataset Netcdf. File THREDDS Open. DAP ADDE HDF 5 Catalog. xml Net. CDF-3 I/O service provider Net. CDF-4 NIDS GRIB GINI Nexrad … DMSP

Goal: N + M instead of N * M things on your TODO List Goal: N + M instead of N * M things on your TODO List File Format #1 CDM Visualization &Analysis Net. CDF file Format #2 Data Server File Format #N Web Service

Arc. GIS Model. Builder Application for Automated Water Balancing Arc. GIS Model. Builder Application for Automated Water Balancing

Conclusions • Data access through web services can mask the variations in data structure Conclusions • Data access through web services can mask the variations in data structure between relational databases and data file systems • We need a “Common, common” data model to better integrate GIS and weather and climate information • We need tools for space-time recompositing of weather and climate information to make it suitable for hydrology