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 Weather and climate data Common data model 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 connected The earths 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 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 formats (Grib, netCDF, 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? Space, L Time, T Variables, V D Data Cube Continuous Space-Time Data Model -- NetCDF Space, L Time, T Variables, V D Coordinate dimensions {X} Variable dimensions {Y} Space, FeatureID Time, TSDateTime Variables, TSTypeID TSValue Discrete Space-Time Data Model -- Arc Hydro Geospatial Time Series Value Time Shape Time Series Properties (Type) A Value-Time array 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 Weather and climate data Common data model Data Model for Hydrologic Observations Relationships 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 Plot from the Hydrology Data Portal Produced using a CUAHSI Hydrology Web Service: getDailyStreamflowChartgetDailyStreamflowChart Applications and Services Web Services Library Web application: Data Portal Your application Excel, ArcGIS, Matlab Fortran, C/C++, Visual Basic Hydrologic model . Your operating system Windows, Unix, Linux, Mac Internet 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 Predictions (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. Using IDV and THREDDS to access NARR NARR.xml Get NARR.xml from NARR home page NARR at Asheville, NC IDV in Austin, TX Precipitable Water and Specific Humidity over Gulf Specific humidity (kg/kg) Altitude Precipitable Water and Specific Humidity over Texas Specific humidity (kg/kg) Altitude Precipitation July 2003, 1800Z Surface evaporation July 2003, 1800Z GIS for Atmospheric Science and Hydrology Space-time data models Hydrologic observations data Weather and climate data Common data model NetCDF-Java version 2.2 Common Data Model John Caron Unidata/UCAR Dec 10, 2004 NetcdfDataset Application Scientific Datatypes GridStationImage NetCDF-Java version 2.2 architecture ADDEOpenDAP THREDDS Catalog.xml NetCDF-3 HDF5 I/O service provider GRIB GINI NIDS NetcdfFile NetCDF-4 Nexrad DMSP File Format #N File Format #2 File Format #1 CDM Visualization &Analysis Goal: N + M instead of N * M things on your TODO List NetCDF file Data Server Web Service ArcGIS ModelBuilder Application for Automated Water Balancing 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