simET: Tools for Simulation of Evapotranspiration of Field Crops and Soil Water Balance

Description

Supports the calculation of meteorological characteristics in evapotranspiration research and reference crop evapotranspiration, and offers three models to simulate crop evapotranspiration and soil water balance in the field, including single crop coefficient and dual crop coefficient, as well as the Shuttleworth-Wallace model. These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) <doi:10.1016/j.agwat.2006.01.018>.

Author(s)

Maintainer: Minguo Liu liumg15@lzu.edu.cn

Other contributors:

• Huimin Yang huimyang@lzu.edu.cn [data contributor, funder]

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

A example dataset of alfalfa under flood irrigation

Description

A example dataset of alfalfa under flood irrigation

Usage

FIalfalfa

Format

A data frame with 161 rows and 22 variables

Adjust the recommended Kc values at the middle and late stages

Description

Adjust the recommended Kc values at the middle and late stages

Usage

Kcb_adj_for_DualKc(Kcb_table, u2, RHmin, h)

Arguments

Value

A value for adjust Kc at middle and late stages

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Simulation of evapotranspiration using dual crop coefficient method

Description

Simulation of evapotranspiration using dual crop coefficient method

Usage

Model_DualKc(data, param)

Arguments

Value

A list for the model result including a data frame of daily model result ,a list of plots, A data frame of summary data

Note

The stages of data should include all four stages. If a crop has multiple growth cycles, each cycle should include all four stages.

Examples

 library(simET)
 data("FIalfalfa")
 names(FIalfalfa)
 #--Model parameter
 Dparam_FI<-list(Kini=0.3,#Kcb for initial stage
               Kmid=1.15,#Kcb for mid-season stage
               Kend=1.1,#Kcb for late season stage
               DI=FALSE,#Is it drip irrigation?
               fw=1,# The fraction of the surface wetted
               rootDepth=1.2,#Maximum root depth
               Dei_start=0,#Initial depletion of evaporation layer
               Dri_start=35,#Initial depletion of root layer
               FCe=0.22,#Field capacity of evaporation layer
               WPe=0.15,#Wilting point of evaporation layer
               Ze=0.15,#Depth of the surface soil layer
               REW=6,#Readily evaporable water
               TAW=297,#Total available soil water of the root zone
               p=0.55,#Evapotranspiration depletion factor
               FCrmm=430,#Field capacity of root layer
               CR_param=c(430,-0.32,310,-0.16,-1.4,6.8,1.11,-0.98)
                 #Capillary rise model parameters
               )
 #--Run model
 Model_re_FI<-Model_DualKc(data = FIalfalfa,param = Dparam_FI)
 #--The Result data
 Model_re_FI$Result
 Model_re_FI$Plot
 #--The goodness Of Fit
 estimate_goodnessOfFit(Sim = Model_re_FI$Result$Sim_SoilWater,
                         Obs = Model_re_FI$Result$SoilWater)

Simulation of evapotranspiration using Shuttleworth-Wallace model

Description

Simulation of evapotranspiration using Shuttleworth-Wallace model

Usage

Model_SW(data, param)

Arguments

Value

A list for the model result including a data frame of daily model result ,a list of plots, A data frame of summary data

Note

The stages of data should include all four stages. If a crop has multiple growth cycles, each cycle should include all four stages.

Examples

   library(simET)
   #--Data preparation
   data("FIalfalfa")
   #--Parameter preparation
   param_SW<-list(
       plant=list(
                 #the canopy extinction coefficient for net radiation
                 kRn=0.3,
                 alpha_plant=0.3,#Canopy reflectance
                 w=0.01,#Leaf width
                 Lmax=10,#Maximum leaf area index
                 a1=10,# Leaf stomatal resistance coefficients
                 a2=0.005,# Leaf stomatal resistance coefficients
                 p=0.5,#the param of reduction factor for T
                 rootDepth=1.2 #Maximum root depth
                 ),
       Soil=list(
               zs0=0.04,#The soil surface roughnesslength (m)
               tau2=2,#Soil tortuosity
               PHI_p=2,	 #Soil porosity
               #The soil pore-size distribution index
               #from the Brooks-Corey equation.
               lambda_p=0.18,
               l1=0.02,#Depth of the surface soil layer (m)
               l2=1.2,#Depth of the root layer (m)
               #Saturation water content of evaporation layer
               THETA_v_sat_1=0.36,
               THETA_v_sat_2=0.40, #Saturation water content of root layer
               THETA_start_1=0.2,#Initial water content of evaporation layer
               THETA_start_2=0.36,#Initial water content of root layer
               THETA_wp1=0.15,#Wilting point of evaporation layer
               THETA_wp2=0.15,#Wilting point of root layer
               #Empirical coefficient of evaporation layer.
               #13 for homogenous soil
               alpha1=14,
               alpha2=14,#Empirical coefficient of root layer.
               #Saturated hydraulic conductitity of evaporation layer
               Ksat_1=13.52,
               Ksat_2=0.02,#Saturated hydraulic conductitity of root layer
               #Capillary rise model parameters
               CR_param=c(430,-0.32,313,-0.16,-1.4,6.8,0.5,-0.98)
               ),
     Mete=list(
              nu=2,#The wind speed extinction coefficient
              nK=2, #The eddy diffusivity extinction coefficient(taken as 2)
              zr=2,	 #The reference height (m)
              ##The vapor diffusion coefficient in air (24.7 10-6 m2 s-1)
              Dm_v=24.7*10^(-6),
              deltaT=1 #Time step difference (day)
              )
         )
   #--Run model
   Re_SW<-Model_SW(data = FIalfalfa, param = param_SW)
   #--The Result data
   Re_SW$Result
   Re_SW$Plot
   #--The goodness Of Fit
   estimate_goodnessOfFit(Sim = Re_SW$Result$Sim_SoilWater,
                         Obs = Re_SW$Result$SoilWater)

Simulation for evapotranspiration using single crop coefficient method

Description

Simulation for evapotranspiration using single crop coefficient method

Usage

Model_single_Kc(data, param)

Arguments

Value

A list for the model result including a data frame of daily model result ,a list of plots, A data frame of summary data

Note

The stages of data should include all four stages. If a crop has multiple growth cycles, each cycle should include all four stages.

Examples

  library(simET)
  #--Data preparation
  data("FIalfalfa")
  #--Parameter preparation
  param_SingleKc<-list(Kc_mid=1.2,#Kcb for mid-season stage
                       Kc_end=1.15,#Kcb for late season stage
                       rootDepth=1.2,#Maximum root depth
                       #The soil type used for calculating
                       #Kc for initial stage
                       soil_type="coarse soil textures",
                       Dr_start=40,#Initial depletion of root layer
                       TAW=290,#Total available soil water of the root zone
                       p=0.55,#Evapotranspiration depletion factor
                       Field_capacity=420,#Field capacity of root layer
                       fw=1,#The fraction of the surface wetted
                       #Capillary rise model parameters
                       CR_param=c(420,-0.32,303,-0.16,-1.4,6.8,1.11,-0.98)
                       )
 #--Run model
 Re_SingleKc<- Model_single_Kc(data = FIalfalfa, param = param_SingleKc)
 #--The Result data
 Re_SingleKc$Result
 Re_SingleKc$Plot
 #--The goodness Of Fit
 estimate_goodnessOfFit(Sim = Re_SingleKc$Result$Sim_SoilWater,
                         Obs = Re_SingleKc$Result$SoilWater)

A example dataset of alfalfa under subsurface drip irrigation

Description

A example dataset of alfalfa under subsurface drip irrigation

Usage

SDIalfalfa

Format

A data frame with 161 rows and 22 variables

Calculating actual vapour pressure for hourly time step

Description

Calculating actual vapour pressure for hourly time step

Usage

cal_ActualVapourPressure_for_hourly(Thr, RHhr)

Arguments

Value

A vector for average hourly actual vapour pressure [kPa].

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating actual vapour pressure derived from RHmax

Description

When using equipment where errors in estimating RHmin can be large, or when RH data integrity are in doubt, then one should use only RHmax.

Usage

cal_ActualVapourPressure_from_RHmax(Tmin, RHmax)

Arguments

Value

A vector for actual vapour pressure

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Actual vapour pressure derived from RHmax and RHmin

Description

The actual vapour pressure can also be calculated from the relative humidity. Depending on the availability of the humidity data, different equations should be used.

Usage

cal_ActualVapourPressure_from_RHmaxAndRHmin(Tmax, Tmin, RHmax, RHmin)

Arguments

Details

For periods of a week, ten days or a month, RHmax and RHmin are obtained by dividing the sum of the daily values by the number of days in that period.

Value

A vector for actual vapour pressure

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating actual vapour pressure derived from RHmean

Description

In the absence of RH max and RHmin, it can be used to estimate actual vapour pressure.

Usage

cal_ActualVapourPressure_from_RHmean(RHmean, Tmax, Tmin)

Arguments

Value

A vector for actual vapour pressure

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Actual vapour pressure derived from dewpoint temperature

Description

As the dewpint temperature is the temperature to which the air needs to be cooled to make the air saturated, the actual vapour pressure is the saturation vapour pressure at the dewpoint temperature.

Usage

cal_ActualVapourPressure_from_dewPoint(Tdew)

Arguments

Value

A vector for actual vapour pressure

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Actual vapour pressure (ea) derived from psychrometric data

Description

The actual vapour pressure can be determined from the difference between the dry and wet bulb temperatures, the so-called wet bulb depression.

Usage

cal_ActualVapourPressure_from_psychrometricData(Twet, Tdry, P, type)

Arguments

Value

A vector for Actual vapour pressure (ea)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating deep percolation

Description

Calculating deep percolation

Usage

cal_DP_for_singleKc(P, I, ETa, Dri_start)

Arguments

Value

A value for deep percolation

Deep percolation loss from the topsoil layer

Description

Deep percolation loss from the topsoil layer

Usage

cal_DPe_for_DualKc(P, I, Dei_start, fw)

Arguments

Value

A value for deep percolation loss from the topsoil layer

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Deep percolation loss from the root layer

Description

Deep percolation loss from the root layer

Usage

cal_DPr_for_DualKc(P, Irrigation, ETa, Dri_start)

Arguments

Value

A value for deep percolation loss from the root layer

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating Deep percolation

Description

Calculating Deep percolation

Usage

cal_DeepPercolation(Wa, Wfc, a, b, t)

Arguments

Value

A vector for deep percolation(mm/day).

References

Liu Y, Pereira L S, Fernando R M. Fluxes through the bottom boundary of the root zone in silty soils: Parametric approaches to estimate groundwater contribution and percolation[J]. Agricultural Water Management, 2006, 84(1):27-40.

calculating the depletion in the topsoil layer at the end of the day

Description

In fact, it performs water balance in a day

Usage

cal_Dei_for_DualKc(Dei_start, P, I, E, Dep, TEW)

Arguments

Value

A value for the depletion in the topsoil layer at the end of the day

calculating reference evapotranspiration from Penman-Monteith method

Description

The FAO Penman-Monteith method is maintained as the sole standard method for the computation of ETo from meteorological data.

Usage

cal_ET0_from_PM(delta, Rn, G, gamma, Tem, u2, es, ea)

Arguments

Value

A vector for reference evapotranspiration [mm day-1].

Note

Ten-day or monthly time step :

Notwithstanding the non-linearity in the Penman-Monteith equation and some weather parameter methods, mean ten-day or monthly weather data can be used to compute the mean ten-day or monthly values for the reference evapotranspiration. The value of the reference evapotranspiration calculated with mean monthly weather data is indeed very similar to the average of the daily ETo values calculated with daily average weather data for that month.

When the soil is warming (spring) or cooling (autumn), the soil heat flux (G) for monthly periods may become significant relative to the mean monthly Rn. In these cases G cannot be ignored and its value should be determined from the mean monthly air temperatures of the previous and next month.

Daily time step:

Calculation of ETo with the Penman-Monteith equation on 24-hour time scales will generally provide accurate results.

As the magnitude of daily soil heat flux (G) beneath the reference grass surface is relatively small, it may be ignored for 24-hour time steps.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating reference evapotranspiration from Penman-Monteith for daily

Description

Based on lat, z, J, Tmax, Tmin, n, RHmax, RHmin, windSpeed parameters, reference evapotranspiration was calculated by Penman-Monteith.

Usage

cal_ET0_from_PM_for_daily(Latitude, Altitude, J, Tmax, Tmin, Rs, RHmean, Wind)

Arguments

Value

A vector for reference evapotranspiration (mm/day)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Examples

 library(simET)
 data("FIalfalfa")
 names(FIalfalfa)
 Result_data<- dplyr::mutate(FIalfalfa,
                      ET0=cal_ET0_from_PM_for_daily(Latitude=Latitude,
                                                   Altitude=Altitude,
                                                   J=Julian,
                                                   Tmax=Tmax,
                                                   Tmin=Tmin,
                                                   Rs=Rs,
                                                   RHmean=RHmean,
                                                   Wind=Wind))
 names(Result_data)

Calculating reference evapotranspiration from Penman-Monteith method for hourly time step

Description

Calculating reference evapotranspiration from Penman-Monteith method for hourly time step

Usage

cal_ET0_from_PM_for_hourly(
  slopVapourPressureCurve,
  netRadiation,
  soilHeatFlux,
  psychrometricConstant,
  meanHourlyTem,
  windSpeed,
  saturationVapourPressure,
  actualVapourPressure
)

Arguments

Details

In areas where substantial changes in wind speed, dewpoint or cloudiness occur during the day, calculation of the ETo equation using hourly time steps is generally better than using 24-hour calculation time steps. Such weather changes can cause 24-hour means to misrepresent evaporative power of the environment during parts of the day and may introduce error into the calculations. However, under most conditions, application of the FAO Penman-Monteith equation with 24-hour data produces accurate results.

Value

A vector for reference evapotranspiration [mm hour-1].

Note

With the advent of electronic, automated weather stations, weather data are increasingly reported for hourly or shorter periods. Therefore, in situations where calculations are computerized, the FAO Penman-Monteith equation can be applied on an hourly basis with good results. When applying the FAO Penman-Monteith equation on an hourly or shorter time scale, the equation and some of the procedures for calculating meteorological data should be adjusted for the smaller time step.

For the calculation of radiation parameters, see P74-75

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

An upper limit on the evaporation and transpiration from any cropped surface

Description

It is imposed to reflect the natural constraints placed on available energy represented by the energy balance difference Rn - G - H

Usage

cal_Kc_max_for_DualKc(u2, RHmin, h, Kcb)

Arguments

Value

A vector for the upper limit on the evaporation and transpiration from any cropped surface

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Crop coefficient for the end of the late season stage

Description

Typical values for the crop coefficient at the end of the late season growth stage, Kc end, are listed in Table 12 for various agricultural crops.

Usage

cal_Kcend_for_singleKc(RHmine, u2e, Ktable, he)

Arguments

Value

A value for Kcend value

Note

only applied when the tabulated values for Kc end exceed 0.45. The equation reduces the Kc end with increasing RHmin. This reduction in Kc end is characteristic of crops that are harvested 'green' or before becoming completely dead and dry (Kc end >= 0.45).

No adjustment is made when Kc end (Table) < 0.45 (Kc end = Kc end (Tab)). When crops are allowed to senesce and dry in the field (as evidenced by Kc end < 0.45), u2 and RHmin have less effect on Kc end and no adjustment is necessary.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating Kcini value

Description

Calculating Kcini value

Usage

cal_Kcini_for_SingleKc(Pmean, ET0, tw, type, fw)

Arguments

Value

A value for Kcini value

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Crop coefficient for the mid-season stage

Description

Typical values for the crop coefficient at the end of the late season growth stage, Kc end, are listed in Table 12 for various agricultural crops.For specific adjustment in climates where RHmin differs from 45 % or where u2 is larger or smaller than 2.0 m/s.

Usage

cal_Kcmid_for_singleKc(RHmine, u2e, Ktable, he)

Arguments

Value

A value for Kcmid value

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Dimensionless evaporation reduction coefficient

Description

It dependent on the soil water depletion (cumulative depth of evaporation) from the topsoil layer (Kr = 1 when De,i-1 is equal or lesser than REW)

Usage

cal_Kr_for_DualKc(TEW, REW, De)

Arguments

Value

A value for evaporation reduction coefficient

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating Solar radiation from actual duration of sunshine

Description

Calculating Solar radiation from actual duration of sunshine

Usage

cal_Rs_from_Na(as = 0.25, bs = 0.5, Na, Latitude, J)

Arguments

Value

A vector for solar radiation(MJ m-2 d-1)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

calculating total evaporable water

Description

maximum depth of water that can be evaporated from the soil when the topsoil has been initially completely wetted. Estimated TEW for Kr calculation

Usage

cal_TEW_for_DualKc(FC, WP, Ze)

Arguments

Value

A value for total evaporable water

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

calculating the mean daily air temperature

Description

calculating the mean daily air temperature

Usage

cal_TemMean(Tmax, Tmin)

Arguments

Details

It is only employed in the FAO Penman-Monteith equation to calculate the slope of the saturation vapor pressure curves and the impact of mean air density as the effect of temperature variations on the value of the climatic parameter is small in these cases. For standardization, Tmean for 24-hour periods is defined as the mean of mean of the daily maximum and minimum temperatures rather than as the average of hourly temperature measurements.

Value

A vector for the mean daily air temperature

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating water stress coefficient

Description

Calculating water stress coefficient

Usage

cal_WaterStressCoef(Dr, TAW, p)

Arguments

Value

A value for water stress coefficient which is a dimensionless transpiration reduction factor dependent on available soil water

Calculating the volumetric water content after redistribution

Description

Calculating the volumetric water content after redistribution

Usage

cal_afterRedistribution(THETA_v_sat, alpha, Ksat, deltaT, L, THETA11_1)

Arguments

Value

A value for the volumetric water content after redistribution(m3 m-3)

Calculating air vapor pressure deficit at the mean canopy

Description

Calculating air vapor pressure deficit at the mean canopy

Usage

cal_airVaporPressureDeficit_meanCanopyflow(
  D,
  r_a_a,
  rho_cp = 1221.09,
  DELTA,
  A,
  gamma = 0.658,
  lambda_ET
)

Arguments

Value

A vector for the vapor pressure deficit at the mean canopy flow (mbar)

Note

Knowing D0 is essential because this value is used to calculate the latent and sensible heat fluxes for the soil and crop components.

Calculating anger from south

Description

A parameter used to determine the position of the sun relative to the observer (the other one is solar inclination).

Usage

cal_angerFromSouth(latitude, solar_altitude, solar_declination)

Arguments

Details

The minus and positive signs are taken before and after solar noon, receptively. The reason for having the positive-and-negative signs is merely an artificial convention so that we are able to distinguish between the sun lying westwards (posite angles and after solar noon) and eastwards (negative angles and before solar noon).

Value

A vector for anger from south (Radian)

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Examples

cal_angerFromSouth(latitude=0.52,solar_altitude=-0.715,solar_declination=-0.2974005)

Calculating atmospheric pressure

Description

The atmospheric pressure, P, is the pressure exerted by the weight of the earth's atmosphere.

Usage

cal_atmosphericPressure(elevation)

Arguments

Details

Assuming 20°C for a standard atmosphere. Evaporation at high altitudes is promoted due to low atmospheric pressure as expressed in thepsychrometric constant. The effect is, however, small and in the calculation procedures, the average value for a location is sufficient.

Value

A vector for atmospheric pressure (Kpa)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Examples

cal_atmosphericPressure(100)

Calculating bulk boundary layer resistance

Description

Calculating bulk boundary layer resistance

Usage

cal_bulkBoundaryLayerResistance(nu, u_h, w, L)

Arguments

Value

A vector for bulk boundary layer resistance (s/m)

The canopy penetration probability for net radiation

Description

The canopy penetration probability for net radiation

Usage

cal_canopyPenetrationProbabilityForNetRadiation(kRn, L)

Arguments

Value

The vector for canopy penetration probability for net radiation

Calculating canopy resistance

Description

Calculating canopy resistance

Usage

cal_canopyResistance(a1, a2, It, L, Lmax)

Arguments

Value

A vector for canopy resistance(s/m)

Calculating canopy temperature

Description

Calculating canopy temperature

Usage

cal_canopyTem(Hc, r_c_a, Hs, r_a_a, rho_cp, Tr)

Arguments

Value

A vector for the canopy (foliage) temperature (Celsius degree)

Calculating capillary rise

Description

Calculating capillary rise

Usage

cal_capillaryRise(
  a1,
  b1 = -0.17,
  a2,
  b2 = -0.27,
  a3 = -1.3,
  b3,
  a4,
  b4,
  Dw,
  Wa,
  LAI,
  ETm
)

Arguments

Value

The value for capillary Rise (mm/day).

References

Liu Y, Pereira L S, Fernando R M. Fluxes through the bottom boundary of the root zone in silty soils: Parametric approaches to estimate groundwater contribution and percolation[J]. Agricultural Water Management, 2006, 84(1):27-40.

Calculating the crop roughness length

Description

Calculating the crop roughness length

Usage

cal_cropRoughnessLength(h)

Arguments

Value

A vector for the crop roughness length(m)

Calculating Daylight hours

Description

Calculating Daylight hours

Usage

cal_daylightHours(sunsetHourAngle)

Arguments

Value

A vector for day light Hours

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating eddy diffusivity at the canopy top

Description

Calculating eddy diffusivity at the canopy top

Usage

cal_eddyDiffusivity_Canopytop(k = 0.4, u_, h)

Arguments

Value

A vector for eddy diffusivity at the canopy top(m2/s)

Calculating eddy diffusivity at height z

Description

Calculating eddy diffusivity at height z

Usage

cal_eddyDiffusivity_heightZ(Kh, nK, z, h)

Arguments

Value

A vector for eddy diffusivity at height z (m2/s)

Calculating extraterrestrial radiation for daily periods

Description

The extraterrestrial radiation, Ra, for each day of the year and for different latitudes can be estimated from the solar constant, the solar declination and the time of the year.

Usage

cal_extraterrestrialRadiation_for_daily(J, lat)

Arguments

Value

A vector for extraterrestrial radiation for daily(MJ m-2 day-1)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating extraterrestrial radiation for hourly or shorter periods

Description

Calculating extraterrestrial radiation for hourly or shorter periods

Usage

cal_extraterrestrialRadiation_for_shorter(lat, J, t, lz, lm, t1)

Arguments

Value

A vector for extraterrestrial Radiation (MJ m-2 hour-1)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating friction velocity

Description

Calculating friction velocity

Usage

cal_frictionVelocity(k = 0.4, zr = 2, u_zr, d, z0)

Arguments

Value

A vector for friction velocity(m/s)

Calculating hour angle

Description

Calculating hour angle

Usage

cal_hourAngle(th)

Arguments

Value

A vector for hour angle (Radian)

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Examples

cal_hourAngle(12)

Calculating inverse relative distance Earth-sun

Description

Calculating inverse relative distance Earth-sun

Usage

cal_inverseRelativeDistance_Earth_sun(J)

Arguments

Value

A vector for inverse relative distance Earth-sun (Radian)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating latent heat fluxes for crop

Description

Calculating latent heat fluxes for crop

Usage

cal_latentHeatFluxesForCrop(DELTA, Ac, rho_cp, D0, r_c_a, gamma, r_c_s)

Arguments

Value

A vector for latent heat fluxes for crop (W m-2 ground)

Calculating latent heat fluxes for soil

Description

Calculating latent heat fluxes for soil

Usage

cal_latentHeatFluxesForSoil(
  DELTA,
  As,
  rho_cp = 1221.09,
  D0,
  r_s_a,
  gamma = 0.658,
  r_s_s
)

Arguments

Value

A vector for soil latent heat fluxes (W m-2 ground)

Calculating local solar time

Description

Local solar time is different with local time.

Usage

cal_localDolarTime(td, t, gamma, gamma_sm)

Arguments

Value

A vector for local solar time(Hour)

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Examples

cal_localDolarTime(td=1,t=12,gamma=0.52,gamma_sm=2.09)

Calculating mean canopy flow to reference level

Description

Calculating mean canopy flow to reference level

Usage

cal_meanCanopyFlowToReferenceLevel(k = 0.4, u_, zr, d, h, nK, z0)

Arguments

Value

A vector for mean canopy flow to reference level

Calculating mean saturation vapour pressure

Description

Due to the non-linearity of the above equation, the mean saturation vapour pressure for a day, week, decade or month should be computed as the mean between the saturation vapour pressure at the mean daily maximum and minimum air temperatures for that period.

Usage

cal_meanSaturationVapourPressure(Tmax, Tmin)

Arguments

Details

Using mean air temperature instead of daily minimum and maximum temperatures results in lower estimates for the mean saturation vapour pressure. The corresponding vapour pressure deficit (a parameter expressing the evaporating power of the atmosphere) will also be smaller and the result will be some underestimation of the reference crop evapotranspiration. Therefore,the mean sauration vapour pressure should be calculated as the mean between the saturation vapour pressure at both the daily maximum and minimum air temperature.

Value

A vector for mean saturation vapour pressure (es)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating net longwave radiation Rnl

Description

Calculating net longwave radiation Rnl

Usage

cal_netLongwaveRadiation(TKmax, TKmin, ea, Rs, Rso)

Arguments

Value

A vector for net outgoing longwave radiation [MJ m-2 day-1]

Note

The rate of longwave energy emission is proportional to the absolute temperature of the surface raised to the fourth power. This relation is expressed quantitatively by the Stefan-Boltzmann law. The net energy flux leaving the earth's surface is, however, less than that emitted and given by the Stefan-Boltzmann law due to the absorption and downward radiation from the sky. Water vapour, clouds, carbon dioxide and dust are absorbers and emitters of longwave radiation. Their concentrations should be known when assessing the net outgoing flux. As humidity and cloudiness play an important role, the Stefan-Boltzmann law is corrected by these two factors when estimating the net outgoing flux of longwave radiation. It is thereby assumed that the concentrations of the other absorbers are constant. An average of the maximum air temperature to the fourth power and the minimum air temperature to the fourth power is commonly used in the Stefan-Boltzmann equation for 24-hour time steps. The term (0.34-0.14*sqrt(ea)) expresses the correction for air humidity, and will be smaller if the humidity increases. The effect of cloudiness is expressed by (1.35 Rs/Rso - 0.35). The term becomes smaller if the cloudiness increases and hence Rs decreases. The smaller the correction terms, the smaller the net outgoing flux of longwave radiation. Note that the Rs/Rso term in Equation 39 must be limited so that Rs/Rso <= 1.0. Where measurements of incoming and outgoing short and longwave radiation during bright sunny and overcast hours are available, calibration of the coefficients in Equation 39 can be carried out.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating net radiation Rn

Description

The net radiation (Rn) is the difference between the incoming net shortwave radiation (Rns) and the outgoing net longwave radiation (Rnl).

Usage

cal_netRadiation(Rns, Rnl)

Arguments

Value

A vector for net radiation

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating net radiation available to the crop

Description

Calculating net radiation available to the crop

Usage

cal_netRadiationForCrop(pRn, Rn)

Arguments

Value

A vector for net radiation available to the crop (W/m2 ground)

Calculating net radiation available to the soil

Description

Calculating net radiation available to the soil

Usage

cal_netRadiationForSoil(pRn, Rn, G)

Arguments

Value

A vector for net radiation available to the soil (W/m2 ground)

Calculating net radiation available to the system(soil and crop)

Description

Calculating net radiation available to the system(soil and crop)

Usage

cal_netRadiationForSystem(Ac, As)

Arguments

Value

A vector for net radiation available to the system(soil and crop) (W/m2 ground)

Calculating net solar (shortware radiation) Rns

Description

The net shortwave radiation resulting from the balance between incoming and reflected solar radiation.

Usage

cal_netSolarRadiation(alpha, Rs)

Arguments

Value

A vector for net solar or shortwave radiation [MJ m-2 day-1].

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating percolation for excess water

Description

Calculating percolation for excess water

Usage

cal_percolationForExcessWater(THETA_i_t0, Pe_i_t1, THETA_sat_i)

Arguments

Value

A value for percolation for excess water (mm)

Calculating psychrometric constant

Description

Calculating psychrometric constant

Usage

cal_psychrometriCconstant(atmospheric_pressure)

Arguments

Value

A vector for Psychrometric constant (kPa/degree Celsius)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Examples

cal_psychrometriCconstant(100.1235)

Calculating reduction factor for evaporation

Description

Calculating reduction factor for evaporation

Usage

cal_reductionFactorForE(THETA, THETA_sat)

Arguments

Value

A value for reduction factor for evaporation

calculating reduction factor for transpiration

Description

calculating reduction factor for transpiration

Usage

cal_reductionFactorForT(THETA_v_wp, p, THETA_v_sat, THETA_v)

Arguments

Value

A value for reduction factor for transpiration

Calculating relative humidity

Description

The relative humidity (RH) expresses the degree of saturation of the air as a ratio of the actual (ea) to the saturation (eo(T)) vapour pressure at the same temperature (T).

Usage

cal_relativeHumidity(ea, e0)

Arguments

Details

Relative humidity is the ratio between the amount of water the ambient air actually holds and the amount it could hold at the same temperature. It is dimensionless and is commonly given as a percentage. Although the actual vapour pressure might be relatively constant throughout the day, the relative humidity fluctuates between a maximum near sunrise and a minimum around early afternoon (Figure 12). The variation of the relative humidity is the result of the fact that the saturation vapour pressure is determined by the air temperature. As the temperature changes during the day, the relative humidity also changes substantially.

Value

A vector for relative humidity %

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating saturation vapour pressure

Description

saturation vapour pressure at the air temperature T.

Usage

cal_saturationVapourPressure(Tem)

Arguments

Value

A vector for saturation vapour pressure at the air temperature T (kPa).

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating sensible heat fluxes for crop

Description

Calculating sensible heat fluxes for crop

Usage

cal_sensibleHeatFluxesForCrop(gamma, Ac, r_c_s, r_c_a, rho_cp, D0, DELTA)

Arguments

Value

A vector for sensible heat fluxes for crop

Calaulating sensible heat fluxes for soil

Description

Calaulating sensible heat fluxes for soil

Usage

cal_sensibleHeatFluxesForSoil(
  gamma = 0.659,
  As,
  r_s_s,
  r_s_a,
  rho_cp,
  D0,
  DELTA
)

Arguments

Value

A vector for soil sensible heat fluxes(W m-2 ground)

Calculating clear sky solar radiation with as and bs

Description

The calculation of the clear-sky radiation, Rso, when n = N, is required for computing net longwave radiation.

Usage

cal_skySolarRadiation_withas_bs(as, bs, Ra)

Arguments

Value

A vector for clear-sky solar radiation [MJ m-2 day-1].

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating clear sky solar radiation with elevation

Description

The calculation of the clear-sky radiation, Rso, when n = N, is required for computing net longwave radiation.

Usage

cal_skySolarRadiation_withas_elevation(z, Ra)

Arguments

Value

A vector for clear-sky solar radiation [MJ m-2 day-1].

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating slope of saturation vapour pressure curve

Description

Calculating slope of saturation vapour pressure curve

Usage

cal_slopeOfSaturationVapourPressureCurve(Tem)

Arguments

Details

In the FAO Penman-Monteith equation, where it occurs in the numerator and denominator, the slope of the vapour pressure curve is calculated using mean air temperature.

Value

A vector for slope of saturation vapour pressure curve at air temperature T

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating Soil/ground heat flux

Description

Calculating Soil/ground heat flux

Usage

cal_soilHeatFlux(pRn, Rn)

Arguments

Value

A vector for the soil heat flux (W/m2 ground)

Calculating soil heat flux(G) for day/ten-day periods

Description

As the magnitude of the day or ten-day soil heat flux beneath the grass reference surface is relatively small, it may be ignored .

Usage

cal_soilHeatFlux_day()

Value

A value for 0

References

FAO Irrigation and drainage paper 56 (P54)

Calculating soil heat flux (G) for general

Description

Complex models are available to describe soil heat flux. Because soil heat flux is small compared to Rn, particularly when the surface is covered by vegetation and calculation time steps are 24 hours or longer, a simple calculation procedure is presented here for long time steps, based on the idea that the soil temperature follows air temperature.

Usage

cal_soilHeatFlux_general(cs, T1, T0, delta_t, delta_z)

Arguments

Value

A vector for soil heat flux [MJ m-2 day-1]

Note

Complex models are available to describe soil heat flux. Because soil heat flux is small compared to Rn, particularly when the surface is covered by vegetation and calculation time steps are 24 hours or longer, a simple calculation procedure is presented here for long time steps, based on the idea that the soil temperature follows air temperature.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating soil heat flux(G) for hourly/shorter periods

Description

For hourly (or shorter) calculations, G beneath a dense cover of grass does not correlate well with air temperature.

Usage

cal_soilHeatFlux_hourly(Rn, periods)

Arguments

Value

A vector for soil heat flux [MJ m-2 day-1]

Note

Where the soil is warming, the soil heat flux G is positive. The amount of energy required for this process is subtracted from Rn when estimating evapotranspiration.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating soil heat flux(G) for monthly periods

Description

When assuming a constant soil heat capacity of 2.1 MJ m-3 °C-1 and an appropriate soil depth, cal_soilHeatFlux_general can be used to derive G for monthly periods.

Usage

cal_soilHeatFlux_monthly(T1, T0, Tmonth2 = TRUE)

Arguments

Value

A vector for soil heat flux [MJ m-2 day-1]

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating soil surface resistance

Description

Calculating soil surface resistance

Usage

cal_soilSurfaceResistance(
  tau,
  l,
  PHI_p,
  Dm_v,
  lambda_p,
  THETA_v_l,
  THETA_v_sat_l
)

Arguments

Value

A vector for the soil surface resistance (s m-1)

Calculating soil surface to mean canopy flow

Description

the resistance between the soil surface and the mean canopy flow (s m-1)

Usage

cal_soilSurfaceToMeanCanopyFlow(h, nK, Kh, zs0, z0, d)

Arguments

Value

A vector for aerodynamic resistancessoil surface to mean canopy flow (s m-1)

Calculating solar declination

Description

Calculating solar declination

Usage

cal_solarDeclination(td)

Arguments

Value

A vector for solar declination (Radian)

Note

The solar declination actually varies throughout the day too but its variation is very small; thus, it is often ignored. Negative angles occur when the angle is below the equator plane, positive for above the equator.

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Examples

cal_solarDeclination(34)

Calculating solar declination with FAO56 method

Description

Calculating solar declination with FAO56 method

Usage

cal_solarDeclination_in_FAO(J)

Arguments

Value

A vector for solar declination(Radian)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating solar inclination

Description

A parameter used to determine the position of the sun relative to the observer (the other one is the angle from south).Conversion relationship with solar altitude angle: solar inclination=pi/2-solar altitude.

Usage

cal_solarInclination(solar_declination, latitude, hour_anger)

Arguments

Value

A vector for solar inclination (Radian)

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Examples

cal_solarInclination(solar_declination=-0.297,latitude=30,hour_anger=0)

Calculating Solar radiation

Description

If the solar radiation, Rs, is not measured, it can be calculated with the Angstrom formula, which relates solar radiation to extraterrestrial radiation and relative sunshine duration. This is a shortwave radiation.

Usage

cal_solarRadiation(as = 0.25, bs = 0.5, n, N, Ra)

Arguments

Value

A vector for solar or shortwave radiation [MJ m-2 day-1]

Note

Rs is expressed in the above equation in MJ m-2 day-1. The corresponding equivalent evaporation in mm day-1 is obtained by multiplying Rs by 0.408 (Equation 20). Depending on atmospheric conditions (humidity, dust) and solar declination (latitude and month), the Angstrom values as and bs will vary. Where no actual solar radiation data are available and no calibration has been carried out for improved as and bs parameters, the values as = 0.25 and bs = 0.50 are recommended.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating sunset hour angle

Description

Calculating sunset hour angle

Usage

cal_sunsetHourAngle(lat, solar_declination)

Arguments

Value

A vector for sunset Hour Angle(Radian)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating the local solar time for sunset/sunrise

Description

Calculating the local solar time for sunset/sunrise.

Usage

cal_sunsetTime(solar_declination, latitude)

Arguments

Details

Knowing the time of sunrise can calculate the time of sunset. sunrise_time=24-sunset_time. Day_length=2*(sunset_time-12).

Value

A vector for the local solar time for sunset/sunrise

References

Teh CBS.Introduction to mathematical modeling of crop growth: How the equations are derived and assembled into a computer model. Brown Walker Press, 2006.

Calculating total latent heat flux

Description

Calculating total latent heat flux

Usage

cal_totalLatentHeatFlux(
  DELTA,
  gamma,
  r_a_a,
  r_c_a,
  r_s_a,
  r_c_s,
  r_s_s,
  A,
  rho_cp = 1221.09,
  D,
  As,
  Ac
)

Arguments

Value

A vector for the total latent heat flux (W m-2 ground)

Calculating wind speed above and within the canopies.

Description

Calculating wind speed above and within the canopies.

Usage

cal_windSpeed_Canopy(z, h, u_, k = 0.4, d, z0, nu = 2)

Arguments

Value

A vector for the wind speed (m/s) at height z(m)

Calculating zero plane displacement height

Description

Calculating zero plane displacement height

Usage

cal_zeroPlaneHeight(h)

Arguments

Value

A vector for zero plane displacement height (m)

Show the results of different models

Description

Show the results of different models

Usage

compare_model_plot(model_list, names)

Arguments

Value

A list for ggplot2 plot

Convert date to day of year

Description

Convert date to day of year

Usage

convert_Date_to_dayofyear(Date)

Arguments

Value

A vector for the day of year.

Convert Fahrenheit to degrees Celsius

Description

Convert Fahrenheit to degrees Celsius

Usage

convert_Fahrenheit_to_degreesCelsius(Fahrenheit)

Arguments

Value

A vector for temperature in degrees Celsius(°C)

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Convert radiation unit

Description

Type has the following types: MJ_m2_day_to_J_cm2_day; MJ_m2_day_to_cal_cm2_day; MJ_m2_day_to_W_m2; cal_cm2_day_to_MJ_m2_day cal_cm2_day_to_J_cm2_day cal_cm2_day_to_W_m2 cal_cm2_day_to_mm_day W_m2_to_MJ_m2_day W_m2_to_J_cm2_day W_m2_to_cal_cm2_day W_m2_to_mm_day mm_day_to_MJ_m2_day mm_day_to_J_cm2_day mm_day_to_cal_cm2_day mm_day_to_W_m2

Usage

convert_Rad_unit(rad, type)

Arguments

Value

A vector for radiation converted unit

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Converting angert to radian

Description

Converting the unit of angle in longitude and latitude into the unit of radian.

Usage

convert_angert_to_radian(anger)

Arguments

Value

A vector for longitude or dimension in radian.

Examples

convert_angert_to_radian(98.8)

Convert degrees Celsius to Fahrenheit

Description

Convert degrees Celsius to Fahrenheit

Usage

convert_degreesCelsius_to_Fahrenheit(degrees_Celsius)

Arguments

Value

A vector for atemperature in Fahrenheit(°F).

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Convert wind speed to the standard of 2m

Description

For the calculation of evapotranspiration, wind. Speed measured at 2 m above the surface is required. To adjust wind speed data obtained from instruments placed at elevations other than the standard height of 2 m, a logarithmic wind speed profile may be used for measurements above a short grassed surface.

Usage

convert_windSpeed_to_2m(uz, z)

Arguments

Value

A vector for wind speed at 2 m above ground surface [m s-1].

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Create a csv file or a dataframe in R to store the model data

Description

#'@title Converting the every sheets in XLSX file to csv files #'@param xlsx_file the xlsx file path #'@export #'@importFrom utils write.csv #'@return No return value

Usage

create_modelDF(TreNum = 1, rowNum = 1)

Arguments

Details

convert_xlsx_to_csv<-function(xlsx_file) # library(readxl) # library(stringr) #sheetsname Sheetnames<-readxl::excel_sheets(xlsx_file)

#xlsx2csv

for (i in Sheetnames) data<-readxl::read_xlsx(xlsx_file,sheet=i) write.csv(data,file = stringr::str_c(i,".csv"),row.names = FALSE)

Latitude and Longitude use radians as units; Altitude use 'm' as units; Na use 'hour' as units;Tmax and Tmin use Celsius as units; Wind use m/s as units;RHmean and RHmin use percent sign as uits;Rs use MJ M-2 day-1 as units Height use cm as units;SoilWater and Irrigation use mm as units; GroundwaterDepth use cm as unit.

Value

A dataframe and a csv file (if to_CSV_file==TRUE)

Note

The column of soilwater refers to the measured soil water (mm) in the maximum root layer, which is used to compare the difference between the simulated value and the measured value, which is an optional variable. The columns of Stage includes four stages: Ini, Development, Mid, End, which are mainly determined by LAI and growth status and can refer to Allen et al., (1998).

Estimating ET0 with Tmax and Tmin

Description

When solar radiation data, relative humidity data and/or wind speed data are missing, they should be estimated using the procedures presented in this section. As an alternative, ETo can be estimated using the Hargreaves ETo equation.

Usage

estimate_ET0_with_TmaxAndTmin(Tmean, Tmax, Tmin, Ra)

Arguments

Value

A vector for reference evapotranspiration (mm day-1).

Note

Units for both ETo and Ra in Equation 52 are mm day-1. Equation 52 should be verified in each new region by comparing with estimates by the FAO Penman-Monteith equation (Equation 6) at weather stations where solar radiation, air temperature, humidity, and wind speed are measured. If necessary, Equation 52 can be calibrated on a monthly or annual basis by determining empirical coefficients where ETo = a + b ETo Eq.52, where the Eq. 52 subscript refers to ETo predicted using Equation 52. The coefficients a and b can be determined by regression analyses or by visual fitting. In general, estimating solar radiation, vapor pressure and wind speed as described in Equations 48 to 51 and Table 4 and then utilizing these estimates in Equation 6 (the FAO Penman-Monteith equation) will provide somewhat more accurate estimates as compared to estimating ETo directly using Equation 52. This is due to the ability of the estimation equations to incorporate general climatic characteristics such as high or low wind speed or high or low relative humidity into the ETo estimate made using Equation 6. Equation 52 has a tendency to underpredict under high wind conditions (u2 > 3 m/s) and to overpredict under conditions of high relative humidity.

Estimate LAI for alfalfa

Description

Estimate LAI for alfalfa

Usage

estimate_LAI_for_alfalfa(hc)

Arguments

Value

A vector for leaf are index of alfalfa

References

Zhao C , Feng Z , Chen G . Soil water balance simulation of alfalfa (Medicago sativa L.) in the semiarid Chinese Loess Plateau[J]. Agricultural Water Management, 2004, 69(2):0-114.

Estimating solar radiation for island locations

Description

For island locations, where the land mass has a width perpendicular to the coastline of 20 km or less, the air masses influencing the atmospheric conditions are dominated by the adjacent water body in all directions. The temperature method is not appropriate for this situation. Where radiation data from another location on the island are not available, a first estimate of the monthly solar average can be obtained from the empirical relation.

Usage

estimate_Rs_for_islandLocations(Ra, b = 4)

Arguments

Value

A vector for solar radiation

Note

This relationship is only applicable for low altitudes (from 0 to 100 m). The empirical constant represents the fact that in island locations some clouds are usually present, thus making the mean solar radiation 4 MJ m-2 day-1 below the nearly clear sky envelope (0.7 Ra). Local adjustment of the empirical constant may improve the estimation. The method is only appropriate for monthly calculations. The constant relation between Rs and Ra does not yield accurate daily estimates.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Estimating solar radiation data derived from air temperature differences

Description

The difference between the maximum and minimum air temperature is related to the degree of cloud cover in a location. Clear-sky conditions result in high temperatures during the day (Tmax) because the atmosphere is transparent to the incoming solar radiation and in low temperatures during the night (Tmin) because less outgoing longwave radiation is absorbed by the atmosphere. On the other hand, in overcast conditions, Tmax is relatively smaller because a significant part of the incoming solar radiation never reaches the earth's surface and is absorbed and reflected by the clouds. Similarly, Tmin will be relatively higher as the cloud cover acts as a blanket and decreases the net outgoing longwave radiation. Therefore, the difference between the maximum and minimum air temperature (Tmax - Tmin) can be used as an indicator of the fraction of extraterrestrial radiation that reaches the earth's surface. This principle has been utilized by Hargreaves and Samani to develop estimates of ETo using only air temperature data.

Usage

estimate_Rs_from_airTemDiff(Ra, Tmax, Tmin, locations)

Arguments

Value

A vector for solar radiation

Note

The temperature difference method is recommended for locations where it is not appropriate to import radiation data from a regional station, either because homogeneous climate conditions do not occur, or because data for the region are lacking. For island conditions, the methodology of Equation 50 is not appropriate due to moderating effects of the surrounding water body. Caution is required when daily computations of ETo are needed. The advice given for Equation 49 fully applies. It is recommended that daily estimates of ETo that are based on estimated Rs be summed or averaged over a several-day period, such as a week, decade or month to reduce prediction error.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Estimating missing humidity data

Description

Where humidity data are lacking or are of questionable quality, an estimate of actual vapour pressure, ea, can be obtained by assuming that dewpoint temperature (Tdew) is near the daily minimum temperature (Tmin). This statement implicitly assumes that at sunrise, when the air temperature is close to Tmin, that the air is nearly saturated with water vapour and the relative humidity is nearly 100 %.

Usage

estimate_ea(Tmin)

Arguments

Value

A vector for humidity

Note

The relationship Tdew near Tmin holds for locations where the cover crop of the station is well watered. However, particularly for arid regions, the air might not be saturated when its temperature is at its minimum. Hence, Tmin might be greater than Tdew and a further calibration may be required to estimate dewpoint temperatures. In these situations, "Tmin" in the above equation may be better approximated by subtracting 2-3 degrees Celsius from Tmin. Appropriate correction procedures are given in Annex 6. In humid and subhumid climates, Tmin and Tdew measured in early morning may be less than Tdew measured during the daytime because of condensation of dew during the night. After sunrise, evaporation of the dew will once again humidify the air and will increase the value measured for Tdew during the daytime. This phenomenon is demonstrated in Figure 5.4 of Annex 5. However, it is standard practice in 24-hour calculations of ETo to use Tdew measured or calculated durin early morning. The estimate for ea from Tmin should be checked. When the prediction by Equation 48 is validated for a region, it can be used for daily estimates of ea.

References

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations, 1998.

Calculating the goodness-of-fit indicators between measured and simulated values

Description

Calculating the goodness-of-fit indicators between measured and simulated values

Usage

estimate_goodnessOfFit(Sim, Obs)

Arguments

Value

A vector for the goodness-of-fit indicators

linear interpolation for vector

Description

Linear interpolation is performed by using the values on both sides of the missing values.

Usage

linear_interpolation(DataVector)

Arguments

Value

A interpolated vector