predADD2 {spphpr} | R Documentation |
Prediction Function of the Accumulated Degree Days Method Using Minimum and Maximum Daily Temperatures
Description
Predicts the occurrence times using the accumulated degree days method based on observed or predicted minimum and maximum daily air temperatures (Aono, 1993; Shi et al., 2017a, b).
Usage
predADD2(S, T0, AADD, Year2, DOY, Tmin, Tmax, DOY.ul = 120)
Arguments
S |
the starting date for thermal accumulation (in day-of-year) |
T0 |
the base temperature (in |
AADD |
the expected annual accumulated degree days |
Year2 |
the vector of the years recording the climate data for predicting the occurrence times |
DOY |
the vector of the dates (in day-of-year) for which climate data exist |
Tmin |
the minimum daily air temperature data (in |
Tmax |
the maximum daily air temperature data (in |
DOY.ul |
the upper limit of |
Details
In the accumulated degree days (ADD) method (Shi et al., 2017a, b), the starting date
(S
), the base temperature
(T_{0}
), and the annual accumulated degree days (AADD, which is denoted by k
)
are assumed to be constants across different years. Let k_{i}
denote the AADD of
the i
th year, which equals
k_{i} = \sum_{j=S}^{E_{i}}\sum_{w=1}^{24}\frac{\left(T_{ijw}-T_{0}\right)}{24},
where E_{i}
represents the ending date (in day-of-year), i.e., the occurrence time of a particular
phenological event in the i
th year, and T_{ijw}
represents the estimated mean hourly temperature of
the w
th hour of the j
th day of the i
th year (in {}^{\circ}
C).
If T_{ijw} \le T_{0}
, T_{ijw} - T_{0}
is defined to be zero. This packages takes the method proposed by
Zohner et al. (2020) to estimate the mean hourly temperature (T_{w}
) for each of 24 hours:
T_{w} = \frac{T_{\mathrm{max}} - T_{\mathrm{min}}}{2}\, \mathrm{sin}\left(\frac{w\pi}{12}-
\frac{\pi}{2}\right)+\frac{T_{\mathrm{max}} + T_{\mathrm{min}}}{2},
where w
represents the w
th hour of a day, and T_{\mathrm{min}}
and T_{\mathrm{max}}
represent the minimum and maximum temperatures of the day, respectively.
\qquad
In theory, k_{i} = k
, i.e., the AADD values of different years are a constant.
However, in practice, there is a certain deviation of k_{i}
from k
that is estimated by \overline{k}
(i.e., the mean of the k_{i}
values). The following approach is used to determine the predicted occurrence time.
When \sum_{j=S}^{F}\sum_{w=1}^{24}\left(T_{ijw}-T_{0}\right)/24 = \overline{k}
(where F \geq S
),
it follows that F
is
the predicted occurrence time; when \sum_{j=S}^{F}\sum_{w=1}^{24}\left(T_{ijw}-T_{0}\right)/24 < \overline{k}
and
\sum_{j=S}^{F+1}\sum_{w=1}^{24}\left(T_{ijw}-T_{0}\right)/24 > \overline{k}
, the trapezoid method (Ring and Harris, 1983)
is used to determine the predicted occurrence time.
Value
Year |
the years with climate data |
Time.pred |
the predicted occurrence times (day-of-year) in different years |
Note
The entire minimum and maximum daily temperature data set for the spring of each year should be provided.
Author(s)
Peijian Shi pjshi@njfu.edu.cn, Zhenghong Chen chenzh64@126.com, Jing Tan jmjwyb@163.com, Brady K. Quinn Brady.Quinn@dfo-mpo.gc.ca.
References
Aono, Y. (1993) Climatological studies on blooming of cherry tree (Prunus yedoensis) by means
of DTS method. Bulletin of the University of Osaka Prefecture. Ser. B, Agriculture and life sciences
45, 155-
192 (in Japanese with English abstract).
Ring, D.R., Harris, M.K. (1983) Predicting pecan nut casebearer (Lepidoptera: Pyralidae) activity
at College Station, Texas. Environmental Entomology 12, 482-
486. doi:10.1093/ee/12.2.482
Shi, P., Chen, Z., Reddy, G.V.P., Hui, C., Huang, J., Xiao, M. (2017a) Timing of cherry tree blooming:
Contrasting effects of rising winter low temperatures and early spring temperatures.
Agricultural and Forest Meteorology 240-
241, 78-
89. doi:10.1016/j.agrformet.2017.04.001
Shi, P., Fan, M., Reddy, G.V.P. (2017b) Comparison of thermal performance equations in describing
temperature-dependent developmental rates of insects: (III) Phenological applications.
Annals of the Entomological Society of America 110, 558-
564. doi:10.1093/aesa/sax063
Zohner, C.M., Mo, L., Sebald, V., Renner, S.S. (2020) Leaf-out in northern ecotypes of wide-ranging
trees requires less spring warming, enhancing the risk of spring frost damage at cold limits.
Global Ecology and Biogeography 29, 1056-
1072. doi:10.1111/geb.13088
See Also
Examples
data(apricotFFD)
data(BJDAT)
X1 <- apricotFFD
X2 <- BJDAT
Year1.val <- X1$Year
Time.val <- X1$Time
Year2.val <- X2$Year
DOY.val <- X2$DOY
Tmin.val <- X2$MinDT
Tmax.val <- X2$MaxDT
DOY.ul.val <- 120
S.val <- 65
T0.val <- 3.8
AADD.val <- 136.5805
cand.res2 <- predADD2( S = S.val, T0 = T0.val, AADD = AADD.val, Year2 = Year2.val,
DOY = DOY.val, Tmin = Tmin.val, Tmax = Tmax.val,
DOY.ul = DOY.ul.val )
cand.res2
ind1 <- cand.res2$Year %in% intersect(cand.res2$Year, Year1.val)
ind2 <- Year1.val %in% intersect(cand.res2$Year, Year1.val)
RMSE1 <- sqrt( sum((Time.val[ind2]-cand.res2$Time.pred[ind1])^2) / length(Time.val[ind2]) )
RMSE1