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Page 1  [Records 1 through 12 of 12]
Agroclimate Zones for Idaho
Idaho State Climate Services

Multivariate statistical analysis and geographic information systems were used to delineate homogeneous agroclimate zones for Idaho for the purpose of applying successful dryland agricultural research practices and management decisions throughout these areas of relative climatic uniformity. Data used to produce the classification are from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), developed at Oregon State University. PRISM has produced gridded estimates of mean monthly and annual climatic parameters from point data and a digital elevation model (DEM). Principal components analysis was performed on fifty-five variables including various temperature and precipitation parameters, the number of growing-degree days, the mean annual number of freeze-free days, the mean annual day of first freeze in the fall, and the mean annual day of last freeze in the spring. Cluster analysis was used to identify sixteen agroclimate zones each having similar climatic conditions regardless of its spatial location. As a result, successful dryland agricultural practices and management decisions that are based on new technologies and developed for one part of the state may potentially be applied to other parts of the state that fall within the same agroclimate zone.

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ESRI GRID File Download
ESRI Map Service Layer REST Endpoint (HTML)
ESRI Layer File (.lyr) [View in ArcMap]
OGC WMS GetCapabilities File
OGC WCS GetCapabilities File
OGC KML Network Link [View in Google Earth]
ESRI Map Service REST Endpoint (JSON)
ESRI Map Service Layer REST Endpoint (JSON)
ESRI Map Service SOAP Endpoint (WSDL)
Daily Climatological Data for Idaho
Idaho State Climate Services

Daily climatological data from National Weather Service (NWS) stations for Idaho from July 1892 - May 2008. For information on how these data were collected please see the NWS Cooperative Observer Program: http://www.nws.noaa.gov/om/coop/what-is-coop.html.

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Microsoft Access Database (.mdb)
Downscaled Climate Model Climate Toolbox
Idaho EPSCoR Office

Geoprocessing services that produce raster data products from downscaled climate data. The current tools operate on ArcGIS 10.0. The tools are currently being updated for ArcGIS Server 10.1.

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ESRI ArcGIS 10.0 ArcToolbox Toolbox
Downscaled Climate Model Output for the Contiguous United States from IPCC AR4 Scenarios [Bias Corrected Statistical Downscaling (BCSD) Method]
Idaho EPSCoR Office

This data series contains 2868 temporal datasets. These data are climate model outputs that have been downscaled to 4-km spatial resolution using the Bias Corrected Statistical Downscaling (BCSD) method. Moore and Walden have modified the BCSD method described by Wood et al (2002), Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres 107: 4429-4443 and Salathe (2005), Downscaling simulations of future global climate with application to hydrologic modeling. International Journal of Climatology 25: 419-436. The modifications include a different interpolation scheme between GCM grid cells and a different approach to dealing with extreme values (Z-scores versus CDF method). The spatial resolution of these data are determined by the historical dataset used to derive statisitcal relationships between the GCM and past measurements. The 4-km Parameter-elevation Relationships on Independent Slopes Model (PRISM) data are used here from Daly et al, (1994), A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33: 140-158.

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Directory Listing of All Datasets
OPeNDAP Server Directory
WebDAV Bulk Download Information
Downscaled Climate Model Output for the Salmon River Basin, Idaho from WCRP CMIP3 Scenarios used in IPCC AR4 [Delta Method]
Idaho EPSCoR Office

This data series contains 336 temporal datasets. These data are are climate model outputs, primarily precipitation and temperature. They have been bias-corrected and spatially downscaled (BCSD) to 12-km by Maurer et al. (2007) using the CMIP3 climate model data. These monthly products are available from this link (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/) which we temporally disaggregated to a daily time step using the Delta method. This temporal disaggregation involved the following steps. This included a random picking of a historical year to compute the mean of the daily precipitation and temperature of the gridded observed record for the same month as the future year. Then, by calculating the difference between the future monthly mean temperature and historical mean of monthly mean temperature, "delta t" and by calculating the ratio between the future monthly mean precipitation and historical mean of monthly mean precipitation, "r", we obtained these two ratios, namely addition (for temperature) and multiplication (for precipitation) factors. Finally, we compute the temperature and precipitation by adding "delta t" to daily temperature of the month of a randomly selected year and multiplying daily precipitation by "r" for the month of a randomly selected year for the given month.

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Directory Listing of All Datasets
OPeNDAP Server Directory
WebDAV Bulk Download Information
Downscaled Climate Model Output for the Snake River Basin, Idaho from WCRP CMIP3 Scenarios used in IPCC AR4 [Delta Method]
Idaho EPSCoR Office

This data series contains 336 temporal datasets. These data are are climate model outputs, primarily precipitation and temperature. They have been bias-corrected and spatially downscaled (BCSD) to 12-km by Maurer et al. (2007) using the CMIP3 climate model data. These monthly products are available from this link (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/) which we temporally disaggregated to a daily time step using the Delta method. This temporal disaggregation involved the following steps. This included a random picking of a historical year to compute the mean of the daily precipitation and temperature of the gridded observed record for the same month as the future year. Then, by calculating the difference between the future monthly mean temperature and historical mean of monthly mean temperature, "delta t" and by calculating the ratio between the future monthly mean precipitation and historical mean of monthly mean precipitation, "r", we obtained these two ratios, namely addition (for temperature) and multiplication (for precipitation) factors. Finally, we compute the temperature and precipitation by adding "delta t" to daily temperature of the month of a randomly selected year and multiplying daily precipitation by "r" for the month of a randomly selected year for the given month.

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Directory Listing of All Datasets
OPeNDAP Server Directory
WebDAV Bulk Download Information
Downscaled Climate Model Output for the Western United States from IPCC AR4 Scenarios [Multivariate Adaptive Constructed Analog (MACA) Method]
Idaho EPSCoR Office

This data series contains 540 temporal datasets. Wildfire adheres to meteorological enablers and drivers across a spectrum of timescales. However, a majority of downscaling methods are ill suited for wildfire application due the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias-Corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA), that directly incorporate daily data were validated over the Western United States with reanalysis data. While both methods outperformed the null interpolation only method, MACA exhibited additional skill in temperature, humidity, wind and precipitation due to its ability to jointly downscale temperature and dew point temperature and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, due to its ability to more accurately capture relative humidity and winds, MACA outperformed the daily BCSD.

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Directory Listing of All Datasets
OPeNDAP Server Directory
WebDAV Bulk Download Information
Koppen Climate Classification for the Conterminous United States
Idaho State Climate Services

The Koppen Climate Classification grid coverages were produced using gridded estimates of precipitation, temperature, and elevation from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The model was developed at Oregon State University and information about the gridded ASCII data sets can be obtained from: http://www.ocs.orst.edu/prism/.

Access these Data:
ESRI GRID File Download
ESRI Map Service Layer REST Endpoint (HTML)
ESRI Layer File (.lyr) [View in ArcMap]
OGC WMS GetCapabilities File
OGC WCS GetCapabilities File
OGC KML Network Link [View in Google Earth]
ESRI Map Service REST Endpoint (JSON)
ESRI Map Service Layer REST Endpoint (JSON)
ESRI Map Service SOAP Endpoint (WSDL)
Precipitation for Idaho; Mean Annual (1961-90)
Idaho State Climate Services

These data represent mean annual precipitation (in inches) for Idaho for the climatological period 1961-90. Average annual precipitation is the average of the annual amount of precipitation for a location over a year. Data used to delineate these boundaries are from Idaho weather stations (1961-90).

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ESRI Shapefile (.shp) File Download
ESRI Map Service Layer REST Endpoint (HTML)
ESRI Layer File (.lyr) [View in ArcMap]
OGC WMS GetCapabilities File
OGC WFS GetCapabilities File
OGC KML Network Link [View in Google Earth]
ESRI Map Service REST Endpoint (JSON)
ESRI Map Service Layer REST Endpoint (JSON)
ESRI Map Service SOAP Endpoint (WSDL)
USDA Plant Hardiness Zones of Idaho
Idaho Geospatial Data Clearinghouse

This data set portrays U.S. Department of Agriculture (USDA) plant hardiness zones of Idaho. Plant hardiness zones indicate the approximate planting times for various plants by temperature zones. The exact way in which this data set was created is unknown. The file is thought to have originated at The Central National Technology Support Center (CNTSC) for the USDA Natural Resources Conservation Service (NRCS) is located in Fort Worth, TX. Steve Nechero, 817-509-3366 or Larry Davis, 817-509-3378 may have more information. Mr. Nechero was pretty confident that it had been copied from the print version. The original map maker, Mr. M. Kramer (kramer@mesamity.com) stated that the map was prepared in 22 or 26 plates and then pieced together. He assumes that a "heads-up" digitizing was done from a JPG version of the plate.

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ESRI Shapefile (.shp)
ESRI Map Service Layer REST Endpoint
ESRI Layer File (.lyr)
OGC WMS
OGC WFS
KML (Google Earth)
Weather Potential in August
U.S. Department of the Interior (DOI), Bureau of Land Management (BLM), Idaho State Office

As used in this assessment, "fire weather" is analogous to "extreme fire weather potential" used by Schmidt et al. (2002). The 3 main components of fire weather include temperature, humidity, and wind. We used average maximum temperature and average humidity (vapor pressure) for the month of August from DAYMET (http://www.ntsg.umt.edu/); and average surface wind speed for August from the ICBEMP (www.icbemp.gov). These data were 1000-m and 9317-m resolution, respectively. We "smoothed" the wind data by resampling it to 1000-m resolution so that it would have a similar resolution as the temperature and humidity data. A proxy for fire weather was then generated spatially by averaging the standardized values for the 3 weather variables: Wx_potential = (max_temp + (1- rel_humidity) + rel_wind_ros) / 3

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ESRI GRID
Weather Stations for Idaho
Idaho State Climate Services

This data set reflects National Weather Service (NWS) and National Resources Conservation Service (NRCS) stations for the state of Idaho. There are 213 stations in this data set and these are the stations used to compile the mean annual precipitation map for Idaho which was created by Myron Molnau.

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ESRI Shapefile (.shp)
ESRI Map Service Layer REST Endpoint
ESRI Layer File (.lyr)
OGC WMS
OGC WFS
KML (Google Earth)
Page 1  [Records 1 through 12 of 12]