evaluateDroughtNemaTemepratureMain: Evaluates Drought Conditions (Main Criteria) Relating to...
In henningte/herdersWDA: Functions for Raster Based Weather Time Series Analysis

Description Usage Arguments Details Examples

evaluateDroughtNemaTemepratureMain evaluates based on temperature values for a target time period and representing long term mean values as raster based time series (RasterBrick or RasterStack object, if for a given spatial and temporal point drought or near drought conditions are prevalent or not (according to the main criteria classification of the NEMA). This function considers only main criteria related to temerature. See evaluateDroughtNemaTemepratureAdditional for temperature related threshold values that represent additional criteria.

1 2	evaluateDroughtNemaTemepratureMain(temperature, ltmtemperature, ltsdtemperature, temperature_t, ltmtemperature_t)

`temperature`	A `RasterBrick` or `RasterStack` object with (mean) temperature values for each time interval within the target time period. See the details section. Time intervals are supposed to represent fixed ten-day intervals.
`ltmtemperature`	A `RasterBrick` or `RasterStack` object with long-term mean temperature values for fixed ten-day intervals within a year. See the details section.
`ltsdtemperature`	A `RasterBrick` or `RasterStack` object with standard deviation values of mean air temperature values within the time interval considered as long-term time interval. `ltsdairtemperature` must have the same number of layers as `ltmtemperature`.
`ltmtemperature_t`	A `POSIXct` vector containing the time information for all layers in `ltmtemperature` as returned by `intervalMean` (each element denoting the first day of the respective ten-day interval).
`temeprature_t`	A `POSIXct` vector containing the time information for all layers in `temperature` as returned by `weatherMean` (each element denoting the first day of the respective ten-day interval).

The start and end time point of the target time interval are derived from timedate_daily. All time intervals of timedate_aggregated are supposed to fit perfectly within timedate_daily (i.e. there are no more or less days in timedate_daily). All Raster* time series data are supposed to have the same spatial extent and resolution.