i.evapo.time(1grass)

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

KEYWORDS
imagery, evapotranspiration

SYNOPSIS
i.evapo.time
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]

  Flags:

--overwrite
    Allow output files to overwrite existing files

--help
    Print usage summary

--verbose
    Verbose module output

--quiet
    Quiet module output

--ui
    Force launching GUI dialog

  Parameters:

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

DESCRIPTION
i.evapo.time (i.evapo.time_integration) integrates ETa in time following a reference ET (typically) from a set of meteorological stations dataset. Inputs:

    •   ETa images

    •   ETa images DOY (Day of Year)

    •   ETo images

    •   ETo DOYmin as a single value
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

NOTES
ETo images preparation: If you only have one meteorological station data set, the easiest way is:

n=0
for ETo_val in Eto[1] Eto[2] ...
do
	r.mapcalc "eto$n = $ETo_val"
	`expr n = n + 1`
done

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.

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

References

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)

SEE ALSO
i.eb.eta, i.evapo.mh, i.evapo.pt, i.evapo.pm, r.sun

AUTHOR
Yann Chemin, International Rice Research Institute, The Philippines

SOURCE CODE

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

Accessed: Monday Apr 01 03:09:08 2024

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