Title: | Modelling Actual, Potential and Reference Crop Evapotranspiration |
---|---|
Description: | Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations. |
Authors: | Danlu Guo [aut, cre] <[email protected]>, Seth Westra [aut] <[email protected]>, Tim Peterson [ctb] <[email protected]> |
Maintainer: | Danlu Guo <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.16 |
Built: | 2024-12-04 07:01:43 UTC |
Source: | CRAN |
This data set contains the raw climate data including the variables required for calculating evapotranspiration in function ET
over the observation period between 1/3/2001 and 08/31/2004 at the Kent Town station in Adelaide, Australia.
data(climatedata)
data(climatedata)
A data frame containing 10240 obserations of 9 objects:
Station.Number - weather station number,
Year - year of record,
Month - month of record,
Day - day of record,
Hour - hour of record,
Tdew - subdaily dew point temperature in degree Celcius,
RH - subdaily relative humidity in percentage,
Rs - subdaily solar radiation in Megajoule per square meter,
uz - subdaily wind speed in meter per second.
Bureau of Meteorology, Kent Town, Adelaide, Australia
This data set contains the universal constants, and examples of other variable constants required for calculating evapotranspiration in function ET
, based on the climatic condition at Kent Town station in Adelaide, Australia.
data(constants)
data(constants)
A list containing 36 constant values including:
- 20 universal constants, which should be kept unchanged for most conditions:
lambda latent heat of evaporisationin = 2.45 MJ.kg^-1 at 20 degree Celcius,
sigma Stefan-Boltzmann constant = 4.903*10^-9 MJ.K^-4.m^-2.day^-1,
Gsc solar constant = 0.0820 MJ.m^-2.min^-1
Roua mean density of air = 1.2 kg.m^-3 at 20 degree Celcius
Ca specific heat of air = 0.001013 MJ.kg^-1.K^-1
G soil heat flux negligible for daily time-step = 0 (Allen et al., 1998, page 68)
alphaA Albedo for Class-A pan = 0.14
alphaPT Priestley-Taylor coefficient:
= 1.26 for Priestley-Taylor formula (Priestley and Taylor, 1972, Sect. 6;
Eichinger et al., 1996, p.163);
= 1.31 for Szilagyi-Jozsa formula (Szilagyi and Jozsa, 2008);
= 1.28 for Brutsaert-Strickler formula (Brutsaert and Strickler, 1979),
ap constant in Penpan formula = 2.4,
b0 constant in Morton's procedure = 1 (Chiew and McMahon, 1991, Table A1),
b1 constant in Morton's procedure = 14 W.m^-2 (Chiew and McMahon, 1991, Table A1),
*Note: a re-calibrated value of 13.4 W.m^-2 was recommended to achieve achieve a Priestley-Taylor coefficient of 1.26 (Wang et al., 2009), rather the original value (14 W.m^-2) used by Morton that gave a Priestley-Taylor coefficient of 1.32;
b2 constant in Morton's procedure = 1.2 (Chiew and McMahon, 1991, Table A1),
*Note: a re-calibrated value of 1.13 was recommended to achieve achieve a Priestley-Taylor coefficient of 1.26 (Wang et al., 2009), rather the original value (1.2) used by Morton that gave a Priestley-Taylor coefficient of 1.32;
e0 constant for Blaney-Criddle formula = 0.81917 (Frevert et al., 1983, Table 1),
e1 constant for Blaney-Criddle formula = -0.0040922 (Frevert et al., 1983, Table 1),
e2 constant for Blaney-Criddle formula = 1.0705 (Frevert et al., 1983, Table 1),
e3 constant for Blaney-Criddle formula = 0.065649 (Frevert et al., 1983, Table 1),
e4 constant for Blaney-Criddle formula = -0.0059864 (Frevert et al., 1983, Table 1),
e5 constant for Blaney-Criddle formula = -0.0005967 (Frevert et al., 1983, Table 1),
epsilonMo Land surface emissivity in Morton's procedure = 0.92,
sigmaMo Stefan-Boltzmann constant in Morton's procedure = 5.67e-08 W.m^-2.K^-4.
- 16 variable constants, which are specific for the climatic condition at Kent Town station in Adelaide, Australia:
lat latitude = -34.9211 degrees for Kent Town station,
lat_rad latitude in radians = -0.6095 radians for Kent Town station,
as fraction of extraterrestrial radiation reaching earth on sunless days = 0.23 for Australia (Roderick, 1999, page 181),
bs difference between fracion of extraterrestrial radiation reaching full-sun days and that on sunless days = 0.5 for Australia (Roderick, 1999, page 181),
Elev ground elevation above mean sea level = 48m for Kent Town station,
z height of wind instrument = 10m for Kent Town station,
fz constant in Morton's procedure:
= 28.0 W.m^-2.mbar^-1 for CRAE model for T >= 0 degree Celcius;
*Note: a re-calibrated value of 29.2 W.m^-2.mbar^-1 was recommended to achieve achieve a Priestley-Taylor coefficient of 1.26 (Wang et al., 2009), rather the original value (28.0 W.m^-2.mbar^-1) used by Morton that gave a Priestley-Taylor coefficient of 1.32;
= 28.0*1.15 W.m^-2.mbar^-1 for CRAE model for T < 0 degree Celcius;
= 25.0 W.m^-2.mbar^-1 for CRWE model for T >= 0 degree Celcius;
= 28.75 W.m^-2.mbar^-1 for CRWE model for T < 0 degree Celcius (Morton, 1983a, page65).
a_0 constant for estimating sunshine hours from cloud cover data = 11.9 for Adelaide (Chiew and McMahon, 1991, Table A1),
b_0 constant for estimating sunshine hours from cloud cover data = -0.15 for Adelaide,
c_0 constant for estimating sunshine hours from cloud cover data = -0.25 for Adelaide,
d_0 constant for estimating sunshine hours from cloud cover data = -0.0107 for Adelaide,
gammaps product of Psychrometric constant and atmospheric pressure as sea level:
= 0.66 mbar. degree Celcius^-1 for CRAE model for T >= 0 degree Celcius;
= 0.66/1.15 mbar. degree Celcius^-1 for CRAE model for T < 0 degree Celcius.
PA annual precipitation = 285.8mm for Kent Town station,
alphaMo constant in Morton's procedure:
= 17.27 when T >= 0 degree Celcius;
= 21.88 when T < 0 degree Celcius.
betaMo constant in Morton's procedure:
= 237.3 degree Celcius when T >= 0 degree Celcius;
= 265.5 degree Celcius when T < 0 degree Celcius.
lambdaMo latent heat of vaporisation in Morton's procedure:
= 28.5W.day.kg^-1 when T >= 0 degree Celcius;
= 28.5*1.15W.day.kg^-1 when T < 0 degree Celcius.
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage. paper 56. FAO, Rome, 300, 6541.
Szilagyi, J., & Jozsa, J. 2008. New findings about the complementary relationship-based evaporation estimation methods. Journal of Hydrology, 354(1-4), 171-186.
Brutsaert, W., & Stricker, H. 1979. An advection-aridity approach to estimate actual regional evapotranspiration. Water Resources Research, 15(2), 443-450.
Chiew, F. H. S., & McMahon, T. A. 1991. The applicability of Morton's and Penman's evapotranspiration estimates in rainfall-runoff modelling. JAWRA Journal of the American Water Resources Association, 27(4), 611-620.
Frevert, D.K., Hill, R.W.Braaten, B.C. 1983, Estimation of FAO evapotranspiration coefficients, Journal of Irrigation and Drainage Engineering, vol. 109, no. 2, pp. 265-270.
Roderick, M. L. 1999. Estimating the diffuse component from daily and monthly measurements of global radiation. Agricultural and Forest Meteorology, 95(3), 169-185.
Wang, Q. J., McConachy, F. L. N., Chiew, F. H. S., James, R., de Hoedt, G. C., & Wright, W. J. 2009. Maps of Evapotranspiration. Retrieved from Melbourne, Australia: http://www.bom.gov.au/climate/averages/climatology/evapotrans/text/et-description.pdf
Morton, F. I. 1983. Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. Journal of Hydrology, 66(1-4), 1-76. doi:http://dx.doi.org/10.1016/0022-1694(83)90177-4
This data set contains the universal constants required for calculating evapotranspiration in function ET
, which should be kept unchanged for most conditions. Please note that additional constants may be ET models - check the manual for individual ET models for details.
data(defaultconstants)
data(defaultconstants)
A list containing 20 constant values including:
lambda latent heat of evaporisationin = 2.45 MJ.kg^-1 at 20 degree Celcius,
sigma Stefan-Boltzmann constant = 4.903*10^-9 MJ.K^-4.m^-2.day^-1,
Gsc solar constant = 0.0820 MJ.m^-2.min^-1
Roua mean density of air = 1.2 kg.m^-3 at 20 degree Celcius
Ca specific heat of air = 0.001013 MJ.kg^-1.K^-1
G soil heat flux negligible for daily time-step = 0 (Allen et al., 1998, page 68)
alphaA Albedo for Class-A pan = 0.14
alphaPT Priestley-Taylor coefficient:
= 1.26 for Priestley-Taylor formula (Priestley and Taylor, 1972, Sect. 6;
Eichinger et al., 1996, p.163);
= 1.31 for Szilagyi-Jozsa formula (Szilagyi and Jozsa, 2008);
= 1.28 for Brutsaert-Strickler formula (Brutsaert and Strickler, 1979),
ap constant in Penpan formula = 2.4,
b0 constant in Morton's procedure = 1 (Chiew and McMahon, 1991, Table A1),
b1 constant in Morton's procedure = 14 W.m^-2 (Chiew and McMahon, 1991, Table A1),
b2 constant in Morton's procedure = 1.2 (Chiew and McMahon, 1991, Table A1),
e0 constant for Blaney-Criddle formula = 0.81917 (Frevert et al., 1983, Table 1),
e1 constant for Blaney-Criddle formula = -0.0040922 (Frevert et al., 1983, Table 1),
e2 constant for Blaney-Criddle formula = 1.0705 (Frevert et al., 1983, Table 1),
e3 constant for Blaney-Criddle formula = 0.065649 (Frevert et al., 1983, Table 1),
e4 constant for Blaney-Criddle formula = -0.0059864 (Frevert et al., 1983, Table 1),
e5 constant for Blaney-Criddle formula = -0.0005967 (Frevert et al., 1983, Table 1),
epsilonMo Land surface emissivity in Morton's procedure = 0.92,
sigmaMo Stefan-Boltzmann constant in Morton's procedure = 5.67e-08 W.m^-2.K^-4.
various references
This data set contains the Class-A pan evaporation observed over the period between 1/3/2001 and 31/8/2004 at the Kent Town station in Adelaide, Australia.
data(E_OBS)
data(E_OBS)
A list containing 48 obserations of 5 variables
Bureau of Meteorology, Kent Town, Adelaide, Australia
A generic function including 17 different specific methods that are all named following the format of ET.methodname. Once specific function is called the corresponding calculations are performed and a calculation summary is printed to screen.
ET(data, constants, ...)
ET(data, constants, ...)
data |
A list of climate data required for estimating evapotranspiration which differs for each evapotranspiration formulations, see specific formulations for details. |
constants |
A list named |
... |
Arguments to be passed to methods which differs for each evapotranspiration formulations, see specific formulations for details. |
Individual ET methods can be called by substituting the 'methodname' by the function name (e.g. ET.Penman
to call the Penman model).
When the ET model selection is not specified by users, this function determines the default model to use based on the availability of climate data presented. Wherever data are available, the more comprehensive, physically-based models are always preferred over the empirical models, in the following hierarchy:
- If all variables of Tmax/Tmin and RHmax/RHmin and either uz or u2, and either Rs of n or Cd are available, and short crop surface is specified in argument:
Penman-Monteith FAO56 (ET.PenmanMonteith
with crop = "short");
- If all variables of Tmax/Tmin and RHmax/RHmin and either uz or u2, and either Rs of n or Cd are available, and long crop surface is specified in argument:
Penman-Monteith ASCE-EWRI (ET.PenmanMonteith
with crop = "long");
- If all variables of Tmax/Tmin and RHmax/RHmin and either uz or u2, and either Rs of n or Cd are available, and no surface is specified:
Penman (ET.Penman
);
- If all variables of Tmax/Tmin and RHmax/RHmin, and either Rs of n or Cd are available:
Priestley-Taylor (ET.PriestleyTaylor
);
- If all variables of Tmax/Tmin and either Rs of n or Cd are available:
Makkink (ET.Makkink
);
- If all variables of Tmax/Tmin are available:
Hargreaves-Samani (ET.HargreavesSamani
).
Danlu Guo
# Use processed existing data set from kent Town, Adelaide data("processeddata") data("constants") # Call generic function ET() - leads to the use of Penman model results_default <- ET(processeddata, constants, save.csv="no") # Call generic function ET() - leads to the use of Penman-Monteith model results_crop <- ET(processeddata, constants, crop = "short", save.csv="no")
# Use processed existing data set from kent Town, Adelaide data("processeddata") data("constants") # Call generic function ET() - leads to the use of Penman model results_default <- ET(processeddata, constants, save.csv="no") # Call generic function ET() - leads to the use of Penman-Monteith model results_crop <- ET(processeddata, constants, crop = "short", save.csv="no")
Implementing the Abtew formulation for estimating actual evapotranspiration.
## S3 method for class 'Abtew' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Abtew' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data in class "Abtew" which contains the following items (climate variables) required by Abtew formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Abtew actual evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Abtew actual evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Abtew actual evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Abtew actual evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Abtew actual evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
Abtew, W. 1996. Evapotranspiration measurements and modeling for three wetland systems in south florida. Wiley Online Library.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Abtew under the generic function ET results <- ET.Abtew(processeddata, constants,ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Abtew under the generic function ET results <- ET.Abtew(processeddata, constants,ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Blaney-Criddle formulation for estimating reference crop evapotranspiration.
## S3 method for class 'BlaneyCriddle' ET(data, constants, ts="daily", solar="sunshine hours", height = F, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'BlaneyCriddle' ET(data, constants, ts="daily", solar="sunshine hours", height = F, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Blaney-Criddle formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
height |
Must be |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
Height adjustment for the estimations is available through argument height
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Blaney-Criddle reference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Blaney-Criddle reference crop evapotranspiration. |
ET.Annual |
Annually ggregated estimations of Blaney-Criddle reference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Blaney-Criddle reference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Blaney-Criddle reference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message3 |
A message to inform the users about if height adjustment has been applied to calculated Blaney-Criddle reference crop evapotranspiration. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Allen, R.G.Brockway, C.E. 1983, Estimating consumptive use on a statewide basis. Advances in Irrigation and Drainage@ sSurviving External Pressures, ASCE, pp. 79-89.
Allen, R. & Pruitt, W. 1986. Rational Use of The FAO Blaney-Criddle Formula. Journal of Irrigation and Drainage Engineering, 112, 139-155.
Frevert, D.K., Hill, R.W.Braaten, B.C. 1983, Estimation of FAO evapotranspiration coefficients, Journal of Irrigation and Drainage Engineering, vol. 109, no. 2, pp. 265-270.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.BlaneyCriddle under the generic function ET results <- ET.BlaneyCriddle(processeddata, constants, ts="daily", solar="sunshine hours", height= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.BlaneyCriddle under the generic function ET results <- ET.BlaneyCriddle(processeddata, constants, ts="daily", solar="sunshine hours", height= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Brutsaert-Strickler formulation for actual areal evapotranspiration
## S3 method for class 'BrutsaertStrickler' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'BrutsaertStrickler' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Brutsaert-Strickler formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of the evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
User-defined evaporative surface is allowed through argument alpha
, please see Arguments
for details.
The function also generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Brutsaert-Strickler actual areal evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Brutsaert-Strickler actual areal evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Brutsaert-Strickler actual areal evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Brutsaert-Strickler actual areal evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Brutsaert-Strickler actual areal evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.BrutsaertStrickler under the generic function ET results <- ET.BrutsaertStrickler(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.BrutsaertStrickler under the generic function ET results <- ET.BrutsaertStrickler(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Chapman formulation for estimating potential evapotranspiration.
## S3 method for class 'ChapmanAustralian' ET(data, constants, ts="daily", PenPan= T, solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'ChapmanAustralian' ET(data, constants, ts="daily", PenPan= T, solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Chapman formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
PenPan |
Must be |
solar |
Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of the evaporative surface incident radiation that is reflected back at the surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through arguments PenPan
and solar
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Chapman potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Chapman potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Chapman equivalent Penmen-Monteith evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Chapman potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Chapman potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message5 |
A message to inform the users about if the Class-A pan evaporation is from actual data or from PenPan estimation. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Chapman, T. 2001, Estimation of evaporation in rainfall-runoff models, in F. Ghassemi, D. Post, M. SivapalanR. Vertessy (eds), MODSIM2001: Integrating models for Natural Resources Management across Disciplines, Issues and Scales, MSSANZ, vol. 1, pp. 293-298.
ET
,processeddata
,defaultconstants
,constants
,ET.PenPan
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.ChapmanAustralian under the generic function ET results <- ET.ChapmanAustralian(processeddata, constants, ts="daily", PenPan= TRUE, solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.ChapmanAustralian under the generic function ET results <- ET.ChapmanAustralian(processeddata, constants, ts="daily", PenPan= TRUE, solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Granger-Gray formulation for estimating actual areal evapotranspiration.
## S3 method for class 'GrangerGray' ET(data, constants, ts="daily", solar="sunshine hours", windfunction_ver=1948, alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'GrangerGray' ET(data, constants, ts="daily", solar="sunshine hours", windfunction_ver=1948, alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Granger-Gray formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
windfunction_ver |
The version of Penman wind function that will be used within the Penman formulation. Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through arguments solar
and windfunction_ver
, please see Arguments
for details.
User-defined evaporative surface is allowed through argument alpha
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Granger-Gray actual areal evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Granger-Gray actual areal evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Granger-Gray actual areal evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Granger-Gray actual areal evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Granger-Gray actual areal evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message2 |
A message to inform the users about which version of the Penman wind function has been used. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Penman, H. L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193, 120-145.
Penman, H. L. 1956. Evaporation: An introductory survey. Netherlands Journal of Agricultural Science, 4, 9-29
ET
,processeddata
,defaultconstants
,constants
,ET.Penman
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.GrangerGray under the generic function ET results <- ET.GrangerGray(processeddata, constants, ts="daily", solar="sunshine hours", windfunction_ver=1948, alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.GrangerGray under the generic function ET results <- ET.GrangerGray(processeddata, constants, ts="daily", solar="sunshine hours", windfunction_ver=1948, alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Hamon formulation for estimating potential evapotranspiration.
## S3 method for class 'Hamon' ET(data, constants = NULL, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Hamon' ET(data, constants = NULL, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Hamon formulation: |
constants |
Dummy argument with a |
ts |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Hamon potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Hamon potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Hamon potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Hamon potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Hamon potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
Hamon, W. R. 1961. Estimating potential evapotranspiration. Journal of the Hydraulics Division, 87, 107-120.
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.Loumagne, C. 2005, Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 2-Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling. Journal of Hydrology, vol. 303, no. 1-4, pp. 290-306.
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Hamon under the generic function ET results <- ET.Hamon(processeddata, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Hamon under the generic function ET results <- ET.Hamon(processeddata, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Hargreaves-Samani formulation for estimating reference crop evapotranspiration.
## S3 method for class 'HargreavesSamani' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'HargreavesSamani' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Hargreaves-Samani formulation: Tmax, Tmin (degree Celcius) |
constants |
A list named |
ts |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Hargreaves-Samani reference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Hargreaves-Samani reference crop evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Hargreaves-Samani reference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Hargreaves-Samani reference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Hargreaves-Samani reference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Hargreaves, G.H.Samani, Z.A. 1985, Reference crop evapotranspiration from ambient air temperature. American Society of Agricultural Engineers.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.HargreavesSamani under the generic function ET results <- ET.HargreavesSamani(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.HargreavesSamani under the generic function ET results <- ET.HargreavesSamani(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Jensen-Haise formulation for estimating potential evapotranspiration.
## S3 method for class 'JensenHaise' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'JensenHaise' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Jensen-Haise formulation: Tmax, Tmin, Rs or n or Cd Tmax, Tmin (degree Celcius), Rs (Megajoules per sqm) or n (hour) or Cd (okta) |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function also generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Jensen-Haise potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Jensen-Haise potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Jensen-Haise potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Jensen-Haise potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Jensen-Haise potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
Jensen, M.E.Haise, H.R. 1963, Estimating evapotranspiration from solar radiation. Proceedings of the American Society of Civil Engineers, Journal of the Irrigation and Drainage Division, vol. 89, pp. 15-41.
Prudhomme, C.Williamson, J. 2013, Derivation of RCM-driven potential evapotranspiration for hydrological climate change impact analysis in Great Britain: a comparison of methods and associated uncertainty in future projections. Hydrol. Earth Syst. Sci., vol. 17, no. 4, pp. 1365-1377.
Xu, C.Y.Singh, V.P. 2000, Evaluation and generalization of radiation-based methods for calculating evaporation., Hydrological Processes, vol. 14, no. 2, pp. 339-349.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.JensenHaise under the generic function ET results <- ET.JensenHaise(processeddata, constants, ts="daily", solar="sunshine hours", message="yes",AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.JensenHaise under the generic function ET results <- ET.JensenHaise(processeddata, constants, ts="daily", solar="sunshine hours", message="yes",AdditionalStats="yes", save.csv="no")
Implementing the Linacre formulation for estimating actual evapotranspiration.
## S3 method for class 'Linacre' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Linacre' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Linacre formulation: |
constants |
A list named |
ts |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Linacre actual evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Linacre actual evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Linacre actual evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Linacre actual evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Linacre actual evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
Linacre, E. T. 1977. A simple formula for estimating evaporation rates in various climates, using temperature data alone. Agricultural meteorology, 18, 409-424.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Linacre under the generic function ET results <- ET.Linacre(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Linacre under the generic function ET results <- ET.Linacre(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Makkink formulation for estimating reference crop evapotranspiration.
## S3 method for class 'Makkink' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Makkink' ET(data, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Makkink formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Makkink reference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Makkink reference crop evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Makkink reference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Makkink reference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Makkink reference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
De Bruin, H. 1981, The determination of (reference crop) evapotranspiration from routine weather data. Evaporation in relation to hydrology, pp. 25-37.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Makkink under the generic function ET results <- ET.Makkink(processeddata, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Makkink under the generic function ET results <- ET.Makkink(processeddata, constants, ts="daily", solar="sunshine hours", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Matt-Shuttleworth formulation for reference crop evapotranspiration
## S3 method for class 'MattShuttleworth' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, r_s=70, CH=0.12, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'MattShuttleworth' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, r_s=70, CH=0.12, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list which contains the following items (climate variables) required by Matt-Shuttleworth formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
r_s |
Any value (seconds per metre), surface resistance depends on the type of reference crop. |
CH |
Any value (metres), crop height depends on the reference crop. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
User-defined evaporative surface is allowed through arguments alpha
, r_s
and CH
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Matt-Shuttleworth reference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Matt-Shuttleworth reference crop evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Matt-Shuttleworth reference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Matt-Shuttleworth reference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Matt-Shuttleworth reference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to Matt-Shuttleworth. |
ET_type |
Type of the estimation obtained which is Reference Crop Evapotranspiration. |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
Shuttleworth, W. & Wallace, J. 2009. Calculating the water requirements of irrigated crops in Australia using the Matt-Shuttleworth approach. Transactions of the ASABE, 52, 1895-1906.
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.MattShuttleworth under the generic function ET results <- ET.MattShuttleworth(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, r_s=70, CH=0.12, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.MattShuttleworth under the generic function ET results <- ET.MattShuttleworth(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, r_s=70, CH=0.12, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the McGuinness-Bordne formulation for estimating potential evapotranspiration.
## S3 method for class 'McGuinnessBordne' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'McGuinnessBordne' ET(data, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by McGuinness-Bordne formulation: Tmax, Tmin (degree Celcius) |
constants |
A list named |
ts |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of McGuinness-Bordne potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of McGuinness-Bordne potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of McGuinness-Bordne potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily McGuinness-Bordne potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily McGuinness-Bordne potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.Loumagne, C. 2005, Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 2-Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling. Journal of Hydrology, vol. 303, no. 1-4, pp. 290-306.
Xu, C.Y.Singh, V.P. 2000, Evaluation and generalization of radiation-based methods for calculating evaporation., Hydrological Processes, vol. 14, no. 2, pp. 339-349.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.McGuinnessBordne under the generic function ET results <- ET.McGuinnessBordne(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.McGuinnessBordne under the generic function ET results <- ET.McGuinnessBordne(processeddata, constants, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Morton CRAE formulation for estimating potential evapotranspiration, wet-environment areal evapotranspiration and actual areal evapotranspiration.
## S3 method for class 'MortonCRAE' ET(data, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= T, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'MortonCRAE' ET(data, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= T, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Morton CRAE formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
est |
Must be either |
Tdew |
Must be |
alpha |
Only needed if argument |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The type of evapotranspiration calculated can be selected through argument est
, please see Arguments
for details.
The alternative calculation options can be selected through argument solar
and Tdew
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Morton CRAE potential evapotranspiration, wet-environment areal evapotranspiration or actual areal evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Morton CRAE potential evapotranspiration, wet-environment areal evapotranspiration or actual areal evapotranspiration. |
ET.Annual |
A |
ET.MonthlyAve |
A |
ET.AnnualAve |
A |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is either |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message6 |
A message to inform the users about if actual dew point temperature has been used in the calculations or alternative calculations has been performed without dew point temperature data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Morton, F.I. 1983, Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. Journal of Hydrology, vol. 66, no. 1-4, pp. 1-76.
processeddata
,defaultconstants
,constants
,ET.MortonCRWE
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.MortonCRAE under the generic function ET results <- ET.MortonCRAE(processeddata, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= TRUE, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.MortonCRAE under the generic function ET results <- ET.MortonCRAE(processeddata, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= TRUE, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Morton CRWE formulation for estimating potential evapotranspiration or shallow lake evaporation.
## S3 method for class 'MortonCRWE' ET(data, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= T, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'MortonCRWE' ET(data, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= T, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Morton CRWE formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
est |
Must be either |
Tdew |
Must be |
alpha |
Only needed if argument |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The type of evapotranspiration calculated can be selected through argument est
, please see Arguments
for details.
The alternative calculation options can be selected through argument solar
and Tdew
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of MortonCRWE potential evapotranspiration or shallow lake evaporation. |
ET.Monthly |
Monthly aggregated estimations of MortonCRWE potential evapotranspiration or shallow lake evaporation. |
ET.Annual |
Annually aggregated estimations of MortonCRWE potential evapotranspiration or shallow lake evaporation. |
ET.MonthlyAve |
Monthly averaged estimations of daily MortonCRWE potential evapotranspiration or shallow lake evaporation. |
ET.AnnualAve |
Annually averaged estimations of daily MortonCRWE potential evapotranspiration or shallow lake evaporation. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is either |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message6 |
A message to inform the users about if actual dew point temperature has been used in the calculations or alternative calculations has been performed without dew point temperature data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Morton, F.I. 1983, Operational estimates of lake evaporation. Journal of Hydrology, vol. 66, no. 1-4, pp. 77-100.
processeddata
,defaultconstants
,constants
,ET.MortonCRWE
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.MortonCRWE under the generic function ET results <- ET.MortonCRWE(processeddata, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= TRUE, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.MortonCRWE under the generic function ET results <- ET.MortonCRWE(processeddata, constants, ts="monthly", est="potential ET", solar="sunshine hours", Tdew= TRUE, alpha = NULL, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Penman formulation for estimating open-water evaporation or potential evapotranspiration
## S3 method for class 'Penman' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.08, z0=0.001, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Penman' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.08, z0=0.001, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list which contains the following items (climate variables) required by Penman formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
wind |
Must be either |
windfunction_ver |
The version of Penman wind function that will be used within the Penman formulation. Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
z0 |
Any value (metres), roughness height of the evaporative surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through arguments solar
, wind
and windfunction_ver
, please see Arguments
for details.
User-defined evaporative surface is allowed through arguments alpha
and z0
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Penman open-water evaporation or potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Penman open-water evaporation or potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Penman open-water evaporation or potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Penman open-water evaporation or potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Penman open-water evaporation or potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is either |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message2 |
A message to inform the users about if actual wind data has been used in the calculations or alternative calculations has been performed without wind data, and which version of the Penman wind function has been used. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Penman, H. L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193, 120-145.
Valiantzas, J. D. 2006. Simplified versions for the Penman evaporation equation using routine weather data. Journal of Hydrology, 331, 690-702.
Penman, H. L. 1956. Evaporation: An introductory survey. Netherlands Journal of Agricultural Science, 4, 9-29.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Penman-Monteith formulation (including the method for FAO-56 hypothetical short grass and the method for ASCE-EWRI Standardised crop) for estimating reference crop evapotranspiration
## S3 method for class 'PenmanMonteith' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", crop="short", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'PenmanMonteith' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", crop="short", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list which contains the following items (climate variables) required by Penman-Monteith formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
wind |
Must be either |
crop |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through arguments solar
and wind
, please see Arguments
for details.
User-defined evaporative surface is allowed through arguments crop
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Penman-Monteith rerference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Penman-Monteith rerference crop evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Penman-Monteith rerference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Penman-Monteith rerference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Penman-Monteith rerference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to either |
ET_type |
A character string containing the type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message2 |
A message to inform the users about if actual wind data has been used in the calculations or alternative calculations has been performed without wind data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage. paper 56. FAO, Rome, 300, 6541.
Allen, R. G. 2005. The ASCE standardized reference evapotranspiration equation. Amer Society of Civil Engineers.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.PenmanMonteith under the generic function ET results <- ET.PenmanMonteith(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", crop = "short", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.PenmanMonteith under the generic function ET results <- ET.PenmanMonteith(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", crop = "short", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the PenPan formulation for Class-A pan evaporation.
## S3 method for class 'PenPan' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, est="potential ET", pan_coeff=0.71, overest= F, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'PenPan' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, est="potential ET", pan_coeff=0.71, overest= F, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by PenPan formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of surface surrounding the evaporation pan representing the portion of the incident radiation that is reflected back at the surface. |
overest |
Must be |
est |
Must be either |
pan_coeff |
Only required if argument |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
User-defined evaporative surface is allowed through argument alpha
, please see Arguments
for details.
Adjustment for overestimation on the estimations are available through argument height
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of PenPan Class-A pan evaporation/potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of PenPan Class-A pan evaporation/potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of PenPan Class-A pan evaporation/potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily PenPan Class-A pan evaporation/potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily PenPan Class-A pan evaporation/potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Rotstayn, L. D., Roderick, M. L. & Farquhar, G. D. 2006. A simple pan-evaporation model for analysis of climate simulations: Evaluation over Australia. Geophysical Research Letters, 33.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.PenPan under the generic function ET results <- ET.PenPan(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, est="potential ET", pan_coeff=0.71, overest= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from # kent Town, Adelaide data("processeddata") data("constants") # Call ET.PenPan under the generic function ET results <- ET.PenPan(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, est="potential ET", pan_coeff=0.71, overest= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Priestley-Taylor formulation for potential evaporation
## S3 method for class 'PriestleyTaylor' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'PriestleyTaylor' ET(data, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list which contains the following items (climate variables) required by Priestley-Taylor formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
User-defined evaporative surface is allowed through argument alpha
, please see Arguments
for details.
The function generates a list containing the following components, which is saved into a csv
file named as ET_PriestleyTaylor.csv in the working directory:
ET.Daily |
Daily aggregated estimations of Priestley-Taylor potential evaporation. |
ET.Monthly |
Monthly aggregated estimations of Priestley-Taylor potential evaporation. |
ET.Annual |
Annually aggregated estimations of Priestley-Taylor potential evaporation. |
ET.MonthlyAve |
Monthly averaged estimations of daily Priestley-Taylor potential evaporation. |
ET.AnnualAve |
Annually averaged estimations of daily Priestley-Taylor potential evaporation. |
ET_formulation |
A character string containing the name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Priestley, C. & Taylor, R. 1972, On the assessment of surface heat flux and evaporation using large-scale parameters'. Monthly Weather Review, vol. 100, no. 2, pp. 81-92.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.PriestleyTaylor under the generic function ET results <- ET.PriestleyTaylor(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.PriestleyTaylor under the generic function ET results <- ET.PriestleyTaylor(processeddata, constants, ts="daily", solar="sunshine hours", alpha=0.23, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Romanenko formulation for estimating potential evapotranspiration.
## S3 method for class 'Romanenko' ET(data, constants = NULL, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Romanenko' ET(data, constants = NULL, ts="daily", message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Romanenko formulation: |
constants |
Dummy argument with a |
ts |
Must be either |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
This formulation provides a single calculation method with no alternatives available.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Romanenko potential evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Romanenko potential evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Romanenko potential evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Romanenko potential evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Romanenko potential evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
Danlu Guo
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andreassian, V., Anctil, F.Loumagne, C. 2005, Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 2-Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling. Journal of Hydrology, vol. 303, no. 1-4, pp. 290-306.
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Romanenko under the generic function ET results <- ET.Romanenko(processeddata, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Romanenko under the generic function ET results <- ET.Romanenko(processeddata, ts="daily", message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Szilagyi-Jozsa formulation for estimating actual evapotranspiration
## S3 method for class 'SzilagyiJozsa' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.23, z0=0.2, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'SzilagyiJozsa' ET(data, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.23, z0=0.2, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Szilagyi-Jozsa formulation: |
constants |
A list named |
ts |
Must be either |
solar |
Must be either |
wind |
Must be either |
windfunction_ver |
The version of Penman wind function that will be used within the Penman formulation. Must be either |
alpha |
Any numeric value between 0 and 1 (dimensionless), albedo of evaporative surface representing the portion of the incident radiation that is reflected back at the surface. |
z0 |
Any value (metres), roughness height of the evaporative surface. |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through arguments solar
, wind
and windfunction_ver
, please see Arguments
for details.
User-defined evaporative surface is allowed through arguments alpha
and z0
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Szilagyi-Jozsa actual evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Szilagyi-Jozsa actual evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Szilagyi-Jozsa actual evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Szilagyi-Jozsa actual evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Szilagyi-Jozsa actual evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
A character string containing the type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message2 |
A message to inform the users about if actual wind data has been used in the calculations or alternative calculations has been performed without wind data, and which version of the Penman wind function has been used. |
Danlu Guo
Szilagyi, J. 2007. On the inherent asymmetric nature of the complementary relationship of evaporation. Geophysical Research Letters, 34, L02405.
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Penman, H. L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193, 120-145.
Valiantzas, J. D. 2006. Simplified versions for the Penman evaporation equation using routine weather data. Journal of Hydrology, 331, 690-702.
Penman, H. L. 1956. Evaporation: An introductory survey. Netherlands Journal of Agricultural Science, 4, 9-29.
ET
,processeddata
,defaultconstants
,constants
,ET.Penman
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.SzilagyiJozsa under the generic function ET results <- ET.SzilagyiJozsa(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.23, z0=0.2, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.SzilagyiJozsa under the generic function ET results <- ET.SzilagyiJozsa(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver=1948, alpha=0.23, z0=0.2, message="yes", AdditionalStats="yes", save.csv="no")
Implementing the Turc formulation for estimating reference crop evapotranspiration.
## S3 method for class 'Turc' ET(data, constants, ts="daily", solar="sunshine hours", humid= F, message="yes", AdditionalStats="yes", save.csv="no", ...)
## S3 method for class 'Turc' ET(data, constants, ts="daily", solar="sunshine hours", humid= F, message="yes", AdditionalStats="yes", save.csv="no", ...)
data |
A list of data which contains the following items (climate variables) required by Turc formulation: |
constants |
A list named The following constants are also required when argument |
ts |
Must be either |
solar |
Must be either |
humid |
Must be |
message |
Must be either |
AdditionalStats |
"yes" or "no" indicating whether monthly averaged and annual averaged ET should be calculated. |
save.csv |
Must be either |
... |
Dummy for generic function, no need to define. |
The alternative calculation options can be selected through argument solar
, please see Arguments
for details.
Humidity adjustment for the estimations is available through argument humid
, please see Arguments
for details.
The function generates a list containing the following components:
ET.Daily |
Daily aggregated estimations of Turc reference crop evapotranspiration. |
ET.Monthly |
Monthly aggregated estimations of Turc reference crop evapotranspiration. |
ET.Annual |
Annually aggregated estimations of Turc reference crop evapotranspiration. |
ET.MonthlyAve |
Monthly averaged estimations of daily Turc reference crop evapotranspiration. |
ET.AnnualAve |
Annually averaged estimations of daily Turc reference crop evapotranspiration. |
ET_formulation |
Name of the formulation used which equals to |
ET_type |
Type of the estimation obtained which is |
message1 |
A message to inform the users about how solar radiation has been calculated by using which data. |
message4 |
A message to inform the users about if adjustment for non-humid conditions has been applied to calculated Turc reference crop evapotranspiration. |
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Turc, L. 1961, Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann. Agron, vol. 12, no. 1, pp. 13-49.
Alexandris, S., Stricevic, R.Petkovic, S. 2008, Comparative analysis of reference evapotranspiration from the surface of rainfed grass in central Serbia, calculated by six empirical methods against the Penman-Monteith formula. European Water, vol. 21, no. 22, pp. 17-28.
ET
,processeddata
,defaultconstants
,constants
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Turc under the generic function ET results <- ET.Turc(processeddata, constants, ts="daily", solar="sunshine hours", humid= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Turc under the generic function ET results <- ET.Turc(processeddata, constants, ts="daily", solar="sunshine hours", humid= FALSE, message="yes", AdditionalStats="yes", save.csv="no")
Produce comparison plots for results and statistics from different estimations produced by using different formulations and/or different input data. The number of different sets of results can be between 2 and 7. Plotting type can be selected among daily aggregation, monthly aggregation, annual aggregation, monthly average and annual average. For each type three comparison plots will be produced including time series, non-exceedance probability and box plot.
ETComparison(results1, results2, results3 = NULL, results4 = NULL, results5 = NULL, results6 = NULL, results7 = NULL, labs, Sdate = NULL, Edate = NULL, type = "Monthly", ylim = rep(NA,2))
ETComparison(results1, results2, results3 = NULL, results4 = NULL, results5 = NULL, results6 = NULL, results7 = NULL, labs, Sdate = NULL, Edate = NULL, type = "Monthly", ylim = rep(NA,2))
results1 |
A list named |
results2 |
A list named |
results3 |
A list named |
results4 |
A list named |
results5 |
A list named |
results6 |
A list named |
results7 |
A list named |
labs |
A character vector with the length equal to the number of sets of results to compare, defining the labels for the comparison plots |
Sdate |
Only used when argument |
Edate |
Only used when argument |
ylim |
A numeric vector of length 2 defining the lower and upper limit of the y-axis for plotting, if missing the default is from 0 to 1.5 times of maximum value from the first set of result that is used to compare with others. |
type |
A character string indicating the type of plot produced, can be one of the following: |
Three plots are generated for each type of comparison plot selected, including:
1) time series plot of the estimated/aggregated/averaged values from each set of result;
2) non-exceedance plot of the distribution of estimated/aggregated/averaged values from each set of result;
3) box plot of the distribution of estimated/aggregated/averaged values from each set of result.
Danlu Guo
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results_Penman <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Call ET.PenmanMonteith under the generic function ET results_PenmanMonteith <- ET.PenmanMonteith(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", crop = "short", save.csv="no") # Plot the estimated Penman open-water evaporation against average temperature ETComparison(results_Penman, results_PenmanMonteith, type = "Monthly", ylim=c(0,400), labs=c("Penman","PenmanMonteith"))
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results_Penman <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Call ET.PenmanMonteith under the generic function ET results_PenmanMonteith <- ET.PenmanMonteith(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", crop = "short", save.csv="no") # Plot the estimated Penman open-water evaporation against average temperature ETComparison(results_Penman, results_PenmanMonteith, type = "Monthly", ylim=c(0,400), labs=c("Penman","PenmanMonteith"))
Produce plot of daily, monthly and annual averaged estimated evapotranspiration with selected climate variables of the same time step.
ETForcings(data, results, forcing)
ETForcings(data, results, forcing)
data |
A list of data named |
results |
A list named |
forcing |
A character string as the name of a climate variable that the estimated evapotranspiration should be plotted against, can be any of: |
Three plots are generated for the response of calculated evapotranspiration to each climate variable, including:
1) daily evapotranspiration estimate vs. daily average temperature;
2) monthly mean daily evaporationion estimate vs. monthly average temperature;
3) annual mean daily evaporationion estimate vs. annual average temperature.
Danlu Guo
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Plot the estimated Penman open-water evaporation against average temperature ETForcings(processeddata, results, forcing = "Tmax")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Plot the estimated Penman open-water evaporation against average temperature ETForcings(processeddata, results, forcing = "Tmax")
Produce plot of aggregated estimations of evapotranspiration in daily, monthly and annual steps, or averaged daily estimations in monthly or annual steps.
ETPlot(results, type = "Aggregation", OBS, OBSplot, Sdate = time(results$ET.Daily)[1], Edate = time(results$ET.Daily)[length(results$ET.Daily)])
ETPlot(results, type = "Aggregation", OBS, OBSplot, Sdate = time(results$ET.Daily)[1], Edate = time(results$ET.Daily)[length(results$ET.Daily)])
results |
A list named |
type |
A character string of either |
OBS |
A list named |
OBSplot |
Must be eith TRUE or FALSE. TRUE indicates that the observed evaporation will be plotted together with the estimations and FALSE indicates that the observations will not be shown on the plots. |
Sdate |
Only used when type = |
Edate |
Only used when type = |
If argument type
is Aggregation
, three plots are displayed in the following order (the next one appears after pressing enter):
1) Daily evapotranspiration estimates;
2) Monthly evapotranspiration estimates aggregated from daily estimates;
3) Annual evapotranspiration estimates aggregated from daily estimates.
If argument type
is Average
, two plots are displayed in the following order
1) Monthly averaged daily estimations of evapotranspiration;
2) Annually averaged daily estiamtions of evapotranspriation.
Danlu Guo
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Read evaporation data data("E_OBS") OBS <- ReadOBSEvaporation(E_OBS, processeddata) # Plot the aggregation of estimated Penman open-water evaporation with observed evaporation ETPlot(results, type = "Aggregation", OBS, OBSplot = TRUE, Sdate = "2001-05-01", Edate = "2004-05-01")
# Use processed existing data set and constants from kent Town, Adelaide data("processeddata") data("constants") # Call ET.Penman under the generic function ET results <- ET.Penman(processeddata, constants, ts="daily", solar="sunshine hours", wind="yes", windfunction_ver = "1948", alpha = 0.08, z0 = 0.001, save.csv="no") # Read evaporation data data("E_OBS") OBS <- ReadOBSEvaporation(E_OBS, processeddata) # Plot the aggregation of estimated Penman open-water evaporation with observed evaporation ETPlot(results, type = "Aggregation", OBS, OBSplot = TRUE, Sdate = "2001-05-01", Edate = "2004-05-01")
This data set contains the processed climate data including the variables required for calculating evapotranspiration in function ET
over the observation period between 1/3/2001 and 31/8/2004 at the Kent Town station in Adelaide, Australia.
data(processeddata)
data(processeddata)
A list containing 11 non-empty variables:
Date.daily - date in daily time step,
Date.monthly - date in monthly time step,
J - julian days,
i - month,
ndays - days in month,
Tmax - daily maximum temperature in degree Celcius,
Tmin - daily minimum temperature in degree Celcius,
RHmax - daily maximum relative humidity in percentage,
RHmin - daily minimum relative humidity in percentage,
uz - daily wind speed in meters per second,
Rs - daily solar radiation in Megajoule per square meter.
Bureau of Meteorology, Kent Town, Adelaide, Australia
Load raw date and climate data, perform pre-processing, check for missing and error entries and then compile data list of daily time step.
ReadInputs(varnames, climatedata, constants, stopmissing, timestep = "daily", interp_missing_days = FALSE, interp_missing_entries = FALSE, interp_abnormal = FALSE, missing_method = NULL, abnormal_method = NULL, message = "yes")
ReadInputs(varnames, climatedata, constants, stopmissing, timestep = "daily", interp_missing_days = FALSE, interp_missing_entries = FALSE, interp_abnormal = FALSE, missing_method = NULL, abnormal_method = NULL, message = "yes")
varnames |
A character vector with length equals to the number of climate variables to be processed. Can include any element from:
Tmax, Tmin, Temp, Tdew,
RHmax, RHmin, RH,
Rs, n, Cd, Precip,
uz, u2, Epan, va, vs. Each variable is detailed as below: Tmax - daily maximum temperature in degree Celcius, |
climatedata |
A data frame named "climatedata" containing the raw data of date and climate variables. |
timestep |
Should be either |
constants |
A list named "constants" consists of constants required for data pre-processing which may contain the following items: |
stopmissing |
A numeric vector of length 3: |
interp_missing_days |
|
interp_missing_entries |
|
interp_abnormal |
|
missing_method |
A character string for the name of the interpolated methods chosen for filling in missing days and missing data entries. Can be either: |
abnormal_method |
A character string for the name of the interpolated methods chosen for replacing data entries with abnormal values. Can be either: |
message |
"yes" or "no" indicating whether checking messages should be printed on screen. |
This function returns a list with all components of class zoo
which have been processed from the raw data, including:
Date.daily |
A |
Date.monthly |
A |
J |
A |
i |
A |
ndays |
A |
Tmax |
A |
Tmin |
A |
u2 |
A |
uz |
A |
Rs |
A |
n |
A |
Cd |
A |
Precip |
A |
Epan |
A |
RHmax |
A |
RHmin |
A |
Tdew |
A |
Note that the components might have value of NULL
when the corresponding input variable cannot be found in the raw data (i.e. "climatedata").
Danlu Guo
McMahon, T., Peel, M., Lowe, L., Srikanthan, R. & McVicar, T. 2012. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrology and Earth System Sciences Discussions, 9, 11829-11910.
Chiew, F. H. & McMahon, T. A. 1991. The applicability of Morton's and Penman's evapotranspiration estimates in rainfall-runoff modeling1. JAWRA Journal of the American Water Resources Association, 27, 611-620.
Narapusetty, B., DelSole, T.Tippett, M.K. 2009, Optimal Estimation of the Climatological Mean. Journal of Climate, vol. 22, no. 18, pp. 4845-4859.
# ReadInputs climate data data("climatedata") data("constants") data <- ReadInputs(varnames = c("Temp","Tdew","n","RH","uz"), climatedata, constants, stopmissing=c(10,10,3), timestep = "subdaily", interp_missing_days = FALSE, interp_missing_entries = FALSE, interp_abnormal = FALSE, missing_method = NULL, abnormal_method = NULL)
# ReadInputs climate data data("climatedata") data("constants") data <- ReadInputs(varnames = c("Temp","Tdew","n","RH","uz"), climatedata, constants, stopmissing=c(10,10,3), timestep = "subdaily", interp_missing_days = FALSE, interp_missing_entries = FALSE, interp_abnormal = FALSE, missing_method = NULL, abnormal_method = NULL)
Load raw date and evaporation data and then compile data list of daily time step.
ReadOBSEvaporation(E_OBS, data)
ReadOBSEvaporation(E_OBS, data)
E_OBS |
A list of evaporation data named |
data |
A list of data named |
This function returns a list with all components of class zoo
which have been processed from the raw data, including:
Date.OBS |
A |
E_obs.Daily |
A |
E_obs.Monthly |
A |
E_obs.Annual |
A |
E_obs.MonthlyAve |
A |
E_obs.AnnualAve |
A |
Note that the components might have value of NULL
when the corresponding raw data cannot be found in E_OBS
.
Danlu Guo
# Get the time period from "data" # Use processed existing data set from kent Town, Adelaide data("processeddata") data("constants") # Reading obsevations of evaporation within specified time period data("E_OBS") OBS <- ReadOBSEvaporation(E_OBS, processeddata)
# Get the time period from "data" # Use processed existing data set from kent Town, Adelaide data("processeddata") data("constants") # Reading obsevations of evaporation within specified time period data("E_OBS") OBS <- ReadOBSEvaporation(E_OBS, processeddata)