pbias: Percent Bias

Description

Percent Bias between sim and obs, with treatment of missing values.

Usage

pbias(sim, obs, ...)
# S3 method for default
pbias(sim, obs, na.rm=TRUE, ...)
# S3 method for data.frame
pbias(sim, obs, na.rm=TRUE, ...)
# S3 method for matrix
pbias(sim, obs, na.rm=TRUE, ...)
# S3 method for zoo
pbias(sim, obs, na.rm=TRUE, ...)

Arguments

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. When an 'NA' value is found at the i-th position in obs OR sim, the i-th value of obs AND sim are removed before the computation.

…

further arguments passed to or from other methods.

Value

Percent bias between sim and obs. The result is given in percentage (%)

If sim and obs are matrixes, the returned value is a vector, with the percent bias between each column of sim and obs.

Details

$P B I A S = 100 \frac{\sum_{i = 1}^{N} (S_{i} - O_{i})}{\sum_{i = 1}^{N} O_{i}}$

Percent bias (PBIAS) measures the average tendency of the simulated values to be larger or smaller than their observed ones.

The optimal value of PBIAS is 0.0, with low-magnitude values indicating accurate model simulation. Positive values indicate overestimation bias, whereas negative values indicate model underestimation bias

References

Yapo P. O., Gupta H. V., Sorooshian S., 1996. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology. v181 i1-4. 23-48

Sorooshian, S., Q. Duan, and V. K. Gupta. 1993. Calibration of rainfall-runoff models: Application of global optimization to the Sacramento Soil Moisture Accounting Model, Water Resources Research, 29 (4), 1185-1194, doi:10.1029/92WR02617.

Examples

Run this code

# NOT RUN {
obs <- 1:10
sim <- 1:10
pbias(sim, obs)

obs <- 1:10
sim <- 2:11
pbias(sim, obs)

##################
# 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 'pbias' for the "best" case
pbias(sim=sim, obs=obs)

# Randomly changing the first 2000 elements of 'sim', by using a normal distribution 
# with mean 10 and standard deviation equal to 1 (default of 'rnorm').
sim[1:2000] <- obs[1:2000] + rnorm(2000, mean=10)

# Computing the new 'pbias'
pbias(sim=sim, obs=obs)
# }

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