cal_Ts.RdEvaporative surface temperature (land surface, Tland; or leaf surface Tleaf). Land surface temperature infered by Monteith 1965 Equation.
cal_Ts(
Rn,
Tair,
D,
U2,
Pa = atm,
rH = NULL,
rs = 0,
method = c("simple", "full", "ma2021"),
...
)land surface net radiation, W m-2
2m air temperature (degC)
vapor pressure deficit (kPa)
wind speed at 2m
surface air pressure (kPa)
conductance for heat
If rs = 0, Monteith 1965 leaf evaporation Equation becomes Penman
1948 water evaporation.
ignore the influence of Ts on net cal_radiation
simple: Monteith 1965 Equation
full (not finished): Yang 2019
ma2021: Ma 2021
ignored
rH: aerodynamic resistance of heat
rs: stamotal resistance of water
Monteith, J. P. (1965). An introduction to environmental physics.
Yang, Y., & Roderick, M. L. (2019). Radiation, surface temperature and evaporation over wet surfaces. Quarterly Journal of the Royal Meteorological Society, 145(720), 1118–1129. https://doi.org/10.1002/qj.3481
Ma, N., Szilagyi, J., & Zhang, Y. (2021). Calibration-Free Complementary Relationship Estimates Terrestrial Evapotranspiration Globally. Water Resources Research, 57(9), 1–27. https://doi.org/10.1029/2021WR029691
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# The relationship of Rn, Ts
Rn = 0:200
cal_Ts(Rn, 25, D = 1, U2 = 2)
#> [1] 21.09778 21.12308 21.14839 21.17370 21.19900 21.22431 21.24961 21.27492
#> [9] 21.30023 21.32553 21.35084 21.37614 21.40145 21.42676 21.45206 21.47737
#> [17] 21.50267 21.52798 21.55329 21.57859 21.60390 21.62921 21.65451 21.67982
#> [25] 21.70512 21.73043 21.75574 21.78104 21.80635 21.83165 21.85696 21.88227
#> [33] 21.90757 21.93288 21.95818 21.98349 22.00880 22.03410 22.05941 22.08471
#> [41] 22.11002 22.13533 22.16063 22.18594 22.21125 22.23655 22.26186 22.28716
#> [49] 22.31247 22.33778 22.36308 22.38839 22.41369 22.43900 22.46431 22.48961
#> [57] 22.51492 22.54022 22.56553 22.59084 22.61614 22.64145 22.66675 22.69206
#> [65] 22.71737 22.74267 22.76798 22.79329 22.81859 22.84390 22.86920 22.89451
#> [73] 22.91982 22.94512 22.97043 22.99573 23.02104 23.04635 23.07165 23.09696
#> [81] 23.12226 23.14757 23.17288 23.19818 23.22349 23.24879 23.27410 23.29941
#> [89] 23.32471 23.35002 23.37533 23.40063 23.42594 23.45124 23.47655 23.50186
#> [97] 23.52716 23.55247 23.57777 23.60308 23.62839 23.65369 23.67900 23.70430
#> [105] 23.72961 23.75492 23.78022 23.80553 23.83084 23.85614 23.88145 23.90675
#> [113] 23.93206 23.95737 23.98267 24.00798 24.03328 24.05859 24.08390 24.10920
#> [121] 24.13451 24.15981 24.18512 24.21043 24.23573 24.26104 24.28634 24.31165
#> [129] 24.33696 24.36226 24.38757 24.41288 24.43818 24.46349 24.48879 24.51410
#> [137] 24.53941 24.56471 24.59002 24.61532 24.64063 24.66594 24.69124 24.71655
#> [145] 24.74185 24.76716 24.79247 24.81777 24.84308 24.86838 24.89369 24.91900
#> [153] 24.94430 24.96961 24.99492 25.02022 25.04553 25.07083 25.09614 25.12145
#> [161] 25.14675 25.17206 25.19736 25.22267 25.24798 25.27328 25.29859 25.32389
#> [169] 25.34920 25.37451 25.39981 25.42512 25.45043 25.47573 25.50104 25.52634
#> [177] 25.55165 25.57696 25.60226 25.62757 25.65287 25.67818 25.70349 25.72879
#> [185] 25.75410 25.77940 25.80471 25.83002 25.85532 25.88063 25.90593 25.93124
#> [193] 25.95655 25.98185 26.00716 26.03247 26.05777 26.08308 26.10838 26.13369
#> [201] 26.15900
cal_Ts(200, 25, D = 1, U2 = 2)
#> [1] 26.159
# plot(Rn, dat$Ts, type = "l", main = "(a) Ts ~ Rn")
# plot(Rn, dat$Eeq, type = "l", main = "(b) Eeq ~ Rn")
# # plot(Rn, dat$Evp, type = "l") # a constant value
# dat %<>% mutate(Rn = Rn, bowen = ET0 / (Rn * 0.086400 / lambda - ET0))
# plot(bowen ~ Rn, dat, type = "l", main = "(b) Eeq ~ Rn")