feat(CreateInputsCrit): add default a priori celerity parameter

Refs #58

R/Calibration.GRiwrmInputsModel.R

@@ -85,11 +85,12 @@ getInputsCrit_Lavenne <- function(id, OutputsModel, InputsCrit) {
     stop("'InputsCrit[[id]]' must be of class InputsCritLavenneFunction")
   }
   AprioriId <- attr(InputsCrit[[id]], "AprioriId")
+  AprCelerity <- attr(InputsCrit[[id]], "AprCelerity")
   Lavenne_FUN <- attr(InputsCrit[[id]], "Lavenne_FUN")
   AprParamR <- OutputsModel[[AprioriId]]$Param
   if(!inherits(OutputsModel[[AprioriId]], "SD")) {
-    # Add neutral velocity parameter for upstream catchment
-    AprParamR <- c(NA, AprParamR)
+    # Add default velocity parameter for a priori upstream catchment
+    AprParamR <- c(AprCelerity, AprParamR)
   }
   AprCrit <- ErrorCrit(InputsCrit[[AprioriId]], OutputsModel[[AprioriId]])$CritValue
   return(Lavenne_FUN(AprParamR, AprCrit))

R/CreateInputsCrit.GRiwrmInputsModel.R

@@ -6,6 +6,7 @@ CreateInputsCrit.GRiwrmInputsModel <- function(InputsModel,
                                                Obs,
                                                AprioriIds = NULL,
                                                k = 0.15,
+                                               AprCelerity = 1,
                                                ...) {
   # Parameter checks
 
@@ -80,6 +81,7 @@ CreateInputsCrit.GRiwrmInputsModel <- function(InputsModel,
           ...
         )
       attr(InputsCrit[[IM$id]], "AprioriId") <- AprioriIds[IM$id]
+      attr(InputsCrit[[IM$id]], "AprCelerity") <- AprCelerity
       class(InputsCrit[[IM$id]]) <- c("InputsCritLavenneFunction", class(InputsCrit[[IM$id]]))
     }
   }

R/CreateInputsCrit.R

@@ -8,6 +8,7 @@
 #' @param Obs [numeric], [matrix] or [data.frame] series of observed flows, see details
 #' @param AprioriIds (optional) named [list] or named [vector] of [character] used for the parameter regularisation (see details)
 #' @param k (optional) [numeric] weight coefficient used in the parameter regularisation (See [airGR::CreateInputsCrit_Lavenne])
+#' @param AprCelerity (optional) [numeric] Default celerity used as a priori parameter for upstream catchments
 #' @param ... arguments passed to [airGR::CreateInputsCrit], see details
 #'
 #' @details See [airGR::CreateInputsCrit] documentation for a complete list of arguments.
@@ -17,8 +18,8 @@
 #'
 #' With a \emph{GRiwrmInputsModel} object, all arguments are applied on each sub-catchments of the network.
 #'
-#' Parameter regularisation consists of defining a priori parameters which are used in a composed criterion based on the formula proposed by de Lavenne et al. (2019) (See [airGR::CreateInputsCrit_Lavenne]).
-#' The parameter `AprioriIds` allows to define which upstream sub-catchment is used for providing a priori parameters. Its format is as follows: `AprioriIds <- c("Downstream sub-catchment 1" = "A priori upstream sub-catchment 1", ...)` where the quoted strings are the ids of the sub-catchments. See vignettes for more details.
+#' Parameter regularisation consists of defining a priori parameters which are used in a composed criterion based on the formula proposed by Lavenne et al. (2019) (See [airGR::CreateInputsCrit_Lavenne]).
+#' The parameter `AprioriIds` allows to define which upstream sub-catchment is used for providing a priori parameters. Its format is as follows: `AprioriIds <- c("Downstream sub-catchment 1" = "A priori upstream sub-catchment 1", ...)` where the quoted strings are the ids of the sub-catchments. See vignettes for more details. The parameter `AprCelerity` is a default value used as a priori for the parameter 'Celerity' in case of an upstream catchment (without celerity parameter) is used as a priori catchment.
 #'
 #' @return Depending on the class of `InputsModel` argument (respectively `InputsModel` and `GRiwrmInputsModel` object), the returned value is respectively:
 #' - a `InputsCrit` object (See [airGR::CreateInputsCrit])

man/CreateInputsCrit.Rd

@@ -14,6 +14,7 @@
   Obs,
   AprioriIds = NULL,
   k = 0.15,
+  AprCelerity = 1,
   ...
 )
 
@@ -34,6 +35,8 @@ CreateInputsCrit(InputsModel, ...)
 
 \item{k}{(optional) \link{numeric} weight coefficient used in the parameter regularisation (See \link[airGR:CreateInputsCrit_Lavenne]{airGR::CreateInputsCrit_Lavenne})}
 
+\item{AprCelerity}{(optional) \link{numeric} Default celerity used as a priori parameter for upstream catchments}
+
 \item{...}{arguments passed to \link[airGR:CreateInputsCrit]{airGR::CreateInputsCrit}, see details}
 }
 \value{
@@ -54,8 +57,8 @@ Then, each column of the \link{matrix} or \link{data.frame} represents the obser
 
 With a \emph{GRiwrmInputsModel} object, all arguments are applied on each sub-catchments of the network.
 
-Parameter regularisation consists of defining a priori parameters which are used in a composed criterion based on the formula proposed by de Lavenne et al. (2019) (See \link[airGR:CreateInputsCrit_Lavenne]{airGR::CreateInputsCrit_Lavenne}).
-The parameter \code{AprioriIds} allows to define which upstream sub-catchment is used for providing a priori parameters. Its format is as follows: \code{AprioriIds <- c("Downstream sub-catchment 1" = "A priori upstream sub-catchment 1", ...)} where the quoted strings are the ids of the sub-catchments. See vignettes for more details.
+Parameter regularisation consists of defining a priori parameters which are used in a composed criterion based on the formula proposed by Lavenne et al. (2019) (See \link[airGR:CreateInputsCrit_Lavenne]{airGR::CreateInputsCrit_Lavenne}).
+The parameter \code{AprioriIds} allows to define which upstream sub-catchment is used for providing a priori parameters. Its format is as follows: \code{AprioriIds <- c("Downstream sub-catchment 1" = "A priori upstream sub-catchment 1", ...)} where the quoted strings are the ids of the sub-catchments. See vignettes for more details. The parameter \code{AprCelerity} is a default value used as a priori for the parameter 'Celerity' in case of an upstream catchment (without celerity parameter) is used as a priori catchment.
 }
 \references{
 De Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H., Perrin, C., 2019. A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model. Water Resources Research 55, 8821–8839. \doi{10.1029/2018WR024266}