APFB | R Documentation |
Annual peak flow bias between sim
and obs
, with treatment of missing values.
This function was prposed by Mizukami et al. (2019) to identify differences in high (streamflow) values. See Details.
APFB(sim, obs, ...)
## Default S3 method:
APFB(sim, obs, na.rm=TRUE, start.month=1, out.PerYear=FALSE,
fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'data.frame'
APFB(sim, obs, na.rm=TRUE, start.month=1, out.PerYear=FALSE,
fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'matrix'
APFB(sim, obs, na.rm=TRUE, start.month=1, out.PerYear=FALSE,
fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'zoo'
APFB(sim, obs, na.rm=TRUE, start.month=1, out.PerYear=FALSE,
fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
sim |
numeric, zoo, matrix or data.frame with simulated values |
obs |
numeric, zoo, matrix or data.frame with observed values |
na.rm |
a logical value indicating whether 'NA' should be stripped before the computation proceeds. |
start.month |
[OPTIONAL]. Only used when the (hydrological) year of interest is different from the calendar year. numeric in [1:12] indicating the starting month of the (hydrological) year. Numeric values in [1, 12] represent months in [January, December]. By default |
out.PerYear |
logical, indicating whether the output of this function has to include the annual peak flow bias obtained for the individual years or not. |
fun |
function to be applied to The first argument MUST BE a numeric vector with any name (e.g., |
... |
arguments passed to |
epsilon.type |
argument used to define a numeric value to be added to both It is was designed to allow the use of logarithm and other similar functions that do not work with zero values. Valid values of 1) "none": 2) "Pushpalatha2012": one hundredth (1/100) of the mean observed values is added to both 3) "otherFactor": the numeric value defined in the 4) "otherValue": the numeric value defined in the |
epsilon.value |
-) when |
The annual peak flow bias (APFB; Mizukami et al., 2019) is designed to drive the calibration of hydrological models focused in the reproduction of high-flow events.
The high flow bias (APFB) ranges from 0 to Inf, with an optimal value of 0. Higher values of APFB indicate stronger differences between the high values of sim
and obs
. Essentially, the closer to 0, the more similar the high values of sim
and obs
are.
If out.PerYear=FALSE
: numeric with the mean annual peak flow bias between sim
and obs
. If sim
and obs
are matrices, the output value is a vector, with the mean annual peak flow bias between each column of sim
and obs
.
If out.PerYear=TRUE
: a list of two elements:
APFB.value |
numeric with the mean annual peak flow bias between |
APFB.PerYear |
-) If -) If |
obs
and sim
has to have the same length/dimension
The missing values in obs
and sim
are removed before the computation proceeds, and only those positions with non-missing values in obs
and sim
are considered in the computation
Mauricio Zambrano-Bigiarini <mzb.devel@gmail.com>
Mizukami, N.; Rakovec, O.; Newman, A.J.; Clark, M.P.; Wood, A.W.; Gupta, H.V.; Kumar, R.: (2019). On the choice of calibration metrics for "high-flow" estimation using hydrologic models, Hydrology Earth System Sciences 23, 2601-2614, doi:10.5194/hess-23-2601-2019.
NSE
, wNSE
, wsNSE
, HFB
, gof
, ggof
##################
# Example 1: Looking at the difference between 'NSE', 'wNSE', and 'APFB'
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts
# Simulated daily time series, created equal to the observed values and then
# random noise is added only to high flows, i.e., those equal or higher than
# the quantile 0.9 of the observed values.
sim <- obs
hQ.thr <- quantile(obs, probs=0.9, na.rm=TRUE)
hQ.index <- which(obs >= hQ.thr)
hQ.n <- length(hQ.index)
sim[hQ.index] <- sim[hQ.index] + rnorm(hQ.n, mean=mean(sim[hQ.index], na.rm=TRUE))
# Traditional Nash-Sutcliffe eficiency
NSE(sim=sim, obs=obs)
# Weighted Nash-Sutcliffe efficiency (Hundecha and Bardossy, 2004)
wNSE(sim=sim, obs=obs)
# APFB (Garcia et al., 2017):
APFB(sim=sim, obs=obs)
##################
# Example 2:
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts
# Generating a simulated daily time series, initially equal to the observed series
sim <- obs
# Computing the 'APFB' for the "best" (unattainable) case
APFB(sim=sim, obs=obs)
##################
# Example 3: APFB for simulated values created equal to the observed values and then
# random noise is added only to high flows, i.e., those equal or higher than
# the quantile 0.9 of the observed values.
sim <- obs
hQ.thr <- quantile(obs, probs=0.9, na.rm=TRUE)
hQ.index <- which(obs >= hQ.thr)
hQ.n <- length(hQ.index)
sim[hQ.index] <- sim[hQ.index] + rnorm(hQ.n, mean=mean(sim[hQ.index], na.rm=TRUE))
ggof(sim, obs)
APFB(sim=sim, obs=obs)
##################
# Example 4: APFB for simulated values created equal to the observed values and then
# random noise is added only to high flows, i.e., those equal or higher than
# the quantile 0.9 of the observed values and applying (natural)
# logarithm to 'sim' and 'obs' during computations.
APFB(sim=sim, obs=obs, fun=log)
# Verifying the previous value:
lsim <- log(sim)
lobs <- log(obs)
APFB(sim=lsim, obs=lobs)
##################
# Example 5: APFB for simulated values created equal to the observed values and then
# random noise is added only to high flows, i.e., those equal or higher than
# the quantile 0.9 of the observed values and applying a
# user-defined function to 'sim' and 'obs' during computations
fun1 <- function(x) {sqrt(x+1)}
APFB(sim=sim, obs=obs, fun=fun1)
# Verifying the previous value, with the epsilon value following Pushpalatha2012
sim1 <- sqrt(sim+1)
obs1 <- sqrt(obs+1)
APFB(sim=sim1, obs=obs1)
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