Precip_model | R Documentation |
Extrapolate precipitation gauge measurements to another heights. In this package version you can use the classical linear gradient model or a modified version which sets a threshold altitude for precipitation increment (avoiding unreliable estimations).
Precip_model( model, inputData, zmeteo, ztopo, param )
model |
numeric value with model option:
|
inputData |
numeric vector with precipitation gauge series [mm/Δ t]. |
zmeteo |
numeric value indicating the altitude of the precipitation gauge [masl]. |
ztopo |
numeric value with the target height [masl]. |
param |
numeric vector with the following parameters: LP
LPM
|
Numeric vector with the extrapolated precipitation series.
For some interesting work on precipitation gradients at catchment and synoptic scale see:
Immerzeel, W.W., Petersen, L., Ragettli, S., Pellicciotti, F., 2014. The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas. Water Resour. Res. 50, 2212–2226. https://doi.org/10.1002/2013WR014506
Viale, M., Nuñez, M.N., 2010. Climatology of Winter Orographic Precipitation over the Subtropical Central Andes and Associated Synoptic and Regional Characteristics. J. Hydrometeorol. 12, 481–507. https://doi.org/10.1175/2010JHM1284.1
# The following is a toy example. I strongly recommend to see # the package vignettes in order to improve your skills on HBV.IANIGLA ## LP case set.seed(369) precLP <- Precip_model(model = 1, inputData = runif(n = 365, max = 30, min = 0), zmeteo = 3000, ztopo = 4700, param = c(5)) ## LPM case set.seed(369) precLPM <- Precip_model(model = 2, inputData = runif(n = 365, max = 30, min = 0), zmeteo = 3000, ztopo = 4700, param = c(5, 4500))
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