#' Nash-Sutcliffe Coefficient
#'
#' \tabular{ccl}{
#' NSE Range \tab Calibration Rating \tab Model Application \cr
#' 1.00 to 0.50 \tab Excellent \tab Planning, Prelim Design, Final Design \cr
#' 0.49 to 0.40 \tab Very Good \tab Planning, Prelim Design, Final Design \cr
#' 0.39 to 0.30 \tab Good \tab Planning, Prelim Design \cr
#' 0.29 to 0.20 \tab Fair \tab Planning \cr
#' 0.21 or less \tab Poor \tab Screening \cr
#' }
#'
#'
#' @param mod, vector of model data
#' @param obs, vector of observed data
#'
#' @return Nash-Sutcliffe Coefficient
#' @export
#'
#' @examples
#' NSE(1:10,1:10)
NSE <- function(mod, obs) {
# 1.00 to 0.50 Excellent - Final Design
# 0.49 to 0.40 Very Good - Final Design
# 0.39 to 0.30 Good - Prelim Design
# 0.29 to 0.20 Fair - Planning
# 0.21 or less Poor - Screening
SE <- sum((obs-mod)^2)
MSE <- SE/length(obs)
sig <- sd(obs)
sig2 <- sig^2
NSE <- 1 - MSE/sig2
return(NSE)
}
ISE <- function(mod, obs) {
# 0.0 to 3.0 Excellent - Final Design
# 3.1 to 6.0 Very Good - Final Design
# 6.1 to 10.0 Good - Prelim Design
# 10.1 to 25.0 Fair - Planning
# 25.1 or more Poor - Screening
SE <- sum((obs-mod)^2)
MSE <- SE/length(obs)
S <- sum(obs)
ISE <- sqrt(MSE)/S * 100
return(ISE)
}
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