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i.evapo.time - Computes temporal integration of satellite ET actual (ETa) following the daily ET reference (ETo) from meteorological station(s).


imagery, evapotranspiration


i.evapo.time --help
i.evapo.time eta=name[,name,...] eta_doy=name[,name,...] eto=name[,name,...] eto_doy_min=float start_period=float end_period=float output=name [--overwrite] [--help] [--verbose] [--quiet] [--ui]


Allow output files to overwrite existing files
Print usage summary
Verbose module output
Quiet module output
Force launching GUI dialog


eta=name[,name,...] [required]
Names of satellite ETa raster maps [mm/d or cm/d]
eta_doy=name[,name,...] [required]
Names of satellite ETa Day of Year (DOY) raster maps [0-400] [-]
eto=name[,name,...] [required]
Names of meteorological station ETo raster maps [0-400] [mm/d or cm/d]
eto_doy_min=float [required]
Value of DOY for ETo first day
start_period=float [required]
Value of DOY for the first day of the period studied
end_period=float [required]
Value of DOY for the last day of the period studied
output=name [required]
Name for output raster map

Table of contents


i.evapo.time (i.evapo.time_integration) integrates ETa in time following a reference ET (typically) from a set of meteorological stations dataset. Inputs: Method:
  1. each ETa pixel is divided by the same day ETo and become ETrF
  2. each ETrF pixel is multiplied by the ETo sum for the representative days
  3. Sum all n temporal [ETrF*ETo_sum] pixels to make a summed(ET) in [DOYmin;DOYmax]
representative days calculation: let assume i belongs to range [DOYmin;DOYmax]
DOYbeforeETa[i] = ( DOYofETa[i] - DOYofETa[i-1] ) / 2
DOYafterETa[i] = ( DOYofETa[i+1] - DOYofETa[i] ) / 2


ETo images preparation: If you only have one meteorological station data set, the easiest way is:
for ETo_val in Eto[1] Eto[2] ...
	r.mapcalc "eto$n = $ETo_val"
	`expr n = n + 1`
with Eto[1], Eto[2], etc being a simple copy and paste from your data file of all ETo values separated by an empty space from each other.

If you have several meteorological stations data, then you need to grid them by generating Thiessen polygons or using different interpolation methods for each day.

For multi-year calculations, just continue incrementing DOY values above 366, it will continue working, up to maximum input of 400 satellite images.

Temporal integration from a weather station
This is an example of a temporal integration from a weather station as done by Chemin and Alexandridis (2004)


Chemin and Alexandridis, 2004. Spatial Resolution Improvement of Seasonal Evapotranspiration for Irrigated Rice, Zhanghe Irrigation District, Hubei Province, China. Asian Journal of Geoinformatics, Vol. 5, No. 1, September 2004 (PDF)


i.eb.eta,,,, r.sun


Yann Chemin, International Rice Research Institute, The Philippines


Available at: i.evapo.time source code (history)

Latest change: Wednesday Dec 27 16:18:17 2023 in commit: 2e4507f76058083509a784e64f9325fdd732aa87

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