ErrorCrit_KGE2.Rd 3.08 KB
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\encoding{UTF-8}
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\name{ErrorCrit_KGE2}
\alias{ErrorCrit_KGE2}
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\title{Error criterion based on the KGE' formula}
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\usage{
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ErrorCrit_KGE2(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
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}
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\arguments{
\item{InputsCrit}{[object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details}

\item{OutputsModel}{[object of class \emph{OutputsModel}] see \code{\link{RunModel_GR4J}} or \code{\link{RunModel_CemaNeigeGR4J}} for details}

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\item{warnings}{(optional) [boolean] boolean indicating if the warning messages are shown, default = \code{TRUE}}

\item{verbose}{(optional) [boolean] boolean indicating if the function is run in verbose mode or not, default = \code{TRUE}}
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}
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\value{
[list] list containing the function outputs organised as follows:
         \tabular{ll}{
         \emph{$CritValue      }   \tab   [numeric] value of the criterion \cr
         \emph{$CritName       }   \tab   [character] name of the criterion \cr
         \emph{$SubCritValues  }   \tab   [numeric] values of the sub-criteria \cr
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         \emph{$SubCritNames   }   \tab   [character] names of the components of the criterion \cr
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         \emph{$CritBestValue  }   \tab   [numeric] theoretical best criterion value \cr
         \emph{$Multiplier     }   \tab   [numeric] integer indicating whether the criterion is indeed an error (+1) or an efficiency (-1) \cr
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         \emph{$Ind_notcomputed}   \tab   [numeric] indices of the time steps where \emph{InputsCrit$BoolCrit} = \code{FALSE} or no data is available \cr
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         }
}
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\description{
Function which computes an error criterion based on the KGE' formula proposed by Kling et al. (2012).
}
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\details{
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In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows 
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the use of the function for model calibration: the product CritValue * Multiplier is the criterion to be minimised (Multiplier = -1 for KGE2).\cr\cr
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The KGE' formula is
\deqn{KGE' = 1 - \sqrt{(r - 1)^2 + (\gamma - 1)^2 + (\beta - 1)^2}}{KGE' = 1 - sqrt((r - 1)² + (\gamma - 1)² + (\beta - 1)²)}
with the following sub-criteria:
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\deqn{r = \mathrm{the\: linear\ correlation\: coefficient\: between\:} sim\: \mathrm{and\:} obs}{r = is the linear correlation coefficient between Q[sim] and Q[obs]}
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\deqn{\gamma = \frac{CV_{sim}}{CV_{obs}}}{\gamma = CV[sim] / CV[obs]}
\deqn{\beta = \frac{\mu_{sim}}{\mu_{obs}}}{\beta = \mu[sim] / \mu[obs]}
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}
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\examples{
## see example of the ErrorCrit function
}
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\author{
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Laurent Coron
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}
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\references{
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Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009), 
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      Decomposition of the mean squared error and NSE performance criteria: Implications
      for improving hydrological modelling, Journal of Hydrology, 377(1-2), 80-91, doi:10.1016/j.jhydrol.2009.08.003. \cr
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  Kling, H., Fuchs, M. and Paulin, M. (2012), 
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      Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios,
      Journal of Hydrology, 424-425, 264-277, doi:10.1016/j.jhydrol.2012.01.011.
}
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\seealso{
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\code{\link{ErrorCrit}}, \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}
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}