{
 "culture": "en-US",
 "name": "gm",
 "guid": "BFB25DFF-21AB-494A-A27F-332D9576591D",
 "catalogPath": "",
 "snippet": "The MAP (mm) characterises the long term quantity of water available to a region for hydrological and agricultural purposes. Under non-irrigated conditions the MAP gives an upper limit to a region's sustainable agricultural potential in regard to biomass production if other factors (e.g. light, temperature, topography, soils) are not limiting. Not only is MAP important as a general statistic in its own right, but it is probably also the one climatic variable best known to hydrologists and agriculturists, and to which they can relate many other things. Mean annual precipitation was mapped using quality controlled and infilled daily rainfall values from over 9 600 stations each with ≥ 15 years of daily records, applied a Geographically Weighted Regression (GWR) approach with derivatives of altitude, latitude, longitude and slope to generate 1 arc minute (1` x 1` latitude/longitude, 1.7 km x 1.7 km) values of MAP Station residuals between simulated and observed rainfall were then superimposed over the generated surface of rainfall by interpolative techniques, akin to those used by Dent et al. (1989), to adjust the `global` surface to more local conditions.",
 "description": "",
 "summary": "The MAP (mm) characterises the long term quantity of water available to a region for hydrological and agricultural purposes. Under non-irrigated conditions the MAP gives an upper limit to a region's sustainable agricultural potential in regard to biomass production if other factors (e.g. light, temperature, topography, soils) are not limiting. Not only is MAP important as a general statistic in its own right, but it is probably also the one climatic variable best known to hydrologists and agriculturists, and to which they can relate many other things. Mean annual precipitation was mapped using quality controlled and infilled daily rainfall values from over 9 600 stations each with ≥ 15 years of daily records, applied a Geographically Weighted Regression (GWR) approach with derivatives of altitude, latitude, longitude and slope to generate 1 arc minute (1` x 1` latitude/longitude, 1.7 km x 1.7 km) values of MAP Station residuals between simulated and observed rainfall were then superimposed over the generated surface of rainfall by interpolative techniques, akin to those used by Dent et al. (1989), to adjust the `global` surface to more local conditions.",
 "title": "Map",
 "tags": [
  "mean annual precipitation",
  "daily rainfall",
  "agriculture",
  "geographically weighted regressions",
  "weather and climate"
 ],
 "type": "Map Service",
 "typeKeywords": [
  "Data",
  "Service",
  "Map Service",
  "ArcGIS Server"
 ],
 "thumbnail": "thumbnail/thumbnail.png",
 "url": "",
 "extent": [
  [
   16,
   -34
  ],
  [
   32,
   -22
  ]
 ],
 "minScale": 0,
 "maxScale": 1,
 "spatialReference": "WGS_1984_World_Mercator",
 "accessInformation": "",
 "licenseInfo": ""
}