Description Usage Arguments Value Author(s) References See Also Examples
Load raw date and climate data, perform pre-processing, check for missing and error entries and then compile data list of daily time step.
1 2 3 4 5 6 7 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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)
|
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
The maximum acceptable percentage of date indices is 10 %
The maximum acceptable percentage of missing data is 10 %
The maximum acceptable percentage of continuous missing data is 3 %
Warning: missing values in 'uz' (sub-daily wind speed)
Number of missing values in uz: 3
% missing data: 0.03 %
Maximum duration of missing data as percentage of total duration: 0.02 %
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