{
 "culture": "en-US",
 "name": "Moisture_Difference_ZScore_3yr",
 "guid": "",
 "catalogPath": "",
 "snippet": "The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ)\n                datasets developed by scientists Frank Koch, John Coulston, and William Smith of the\n                Forest Service Southern Research Station to represent drought and moisture surplus\n                across the contiguous United States. A z-score is a statistical method for assessing\n                how different a value is from the mean. Mean moisture values over 3-year windows\n                were derived from monthly historical precipitation and temperature data from PRISM,\n                between 1900 and 2022, and compared against a 1900-2017 baseline. The greater the\n                z-value, the larger the departure from average conditions, indicating larger\n                moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map\n                indicate a 3-year window with extreme drought, relative to the average conditions\n                over the past century. For further reading on the methodology used to build these\n                maps, see the publication here:\n                https://www.fs.usda.gov/treesearch/pubs/43361",
 "description": "The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ) datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station to represent drought and moisture surplus across the contiguous United States. A z-score is a statistical method for assessing how different a value is from the mean. Mean moisture values over 3-year windows were derived from monthly historical precipitation and temperature data from PRISM, between 1900 and 2022, and compared against a 1900-2017 baseline. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map indicate a 3-year window with extreme drought, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361",
 "summary": "The Moisture Deficit and Surplus map uses moisture difference z-score (MDZ)\n                datasets developed by scientists Frank Koch, John Coulston, and William Smith of the\n                Forest Service Southern Research Station to represent drought and moisture surplus\n                across the contiguous United States. A z-score is a statistical method for assessing\n                how different a value is from the mean. Mean moisture values over 3-year windows\n                were derived from monthly historical precipitation and temperature data from PRISM,\n                between 1900 and 2022, and compared against a 1900-2017 baseline. The greater the\n                z-value, the larger the departure from average conditions, indicating larger\n                moisture deficits (droughts) or surpluses. Thus, the dark orange areas on the map\n                indicate a 3-year window with extreme drought, relative to the average conditions\n                over the past century. For further reading on the methodology used to build these\n                maps, see the publication here:\n                https://www.fs.usda.gov/treesearch/pubs/43361",
 "title": "Moisture_Difference_ZScore_3yr",
 "tags": [
  "environment",
  "0.041666667 degrees",
  "USDA Forest Service",
  "USFS",
  "Drought",
  "Climate",
  "Moisture Difference Z-Scores",
  "MDZ"
 ],
 "type": "Image Service",
 "typeKeywords": [
  "Data",
  "Service",
  "Image Service",
  "ArcGIS Server"
 ],
 "thumbnail": "",
 "url": "",
 "minScale": 0,
 "maxScale": 0,
 "spatialReference": "WGS_1984_Web_Mercator_Auxiliary_Sphere",
 "accessInformation": "USDA Forest Service Office of Sustainability and Climate; Southern Research            Station",
 "licenseInfo": "The USDA Forest Service makes no warranty, expressed or implied, including the            warranties of merchantability and fitness for a particular purpose, nor assumes any            legal liability or responsibility for the accuracy, reliability, completeness or utility            of these geospatial data, or for the improper or incorrect use of these geospatial data.            These geospatial data and related maps or graphics are not legal documents and are not            intended to be used as such. The data and maps may not be used to determine title,            ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that            may be in place on either public or private land. Natural hazards may or may not be            depicted on the data and maps, and users should exercise due caution. The data are            dynamic and may change over time. The user is responsible to verify the limitations of            the geospatial data and to use the data accordingly."
}