diff --git a/vignettes/V05_sd_model.Rmd b/vignettes/V05_sd_model.Rmd
index 333284e8c64c30178624ab99c070d74ad36d0a94..6c17770e9c503ee166a9ecc34c723106e65986e5 100644
--- a/vignettes/V05_sd_model.Rmd
+++ b/vignettes/V05_sd_model.Rmd
@@ -198,7 +198,7 @@ As a priori parameter set, we use the calibrated parameter set of the upstream c
 ParamDownTheo <- c(Velocity, OutputsCalibUp$ParamFinalR)
 ```
 
-The De Lavenne criterion is initialised with the a priori parameter set and the value of the KGE of the upstream basin.
+The Lavenne criterion is initialised with the a priori parameter set and the value of the KGE of the upstream basin.
 
 ```{r}
 IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2,
@@ -208,7 +208,7 @@ IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2,
                                     AprCrit = OutputsCalibUp$CritFinal)
 ```
 
-The De Lavenne criterion is used instead of the KGE for calibration with regularisation
+The Lavenne criterion is used instead of the KGE for calibration with regularisation
 
 ```{r}
 OutputsCalibDown3 <- Calibration_Michel(InputsModel = InputsModelDown2,
@@ -251,7 +251,7 @@ knitr::kable(mVelocity)
 
 ## Value of the performance criteria with theoretical calibration
 
-Theoretically, the parameters of the downstream GR4J model should be the same as the upstream one with the velocity as extra parameter :
+Theoretically, the parameters of the downstream GR4J model should be the same as the upstream one with the velocity as extra parameter:
 
 ```{r}
 OutputsModelDownTheo <- RunModel(InputsModel = InputsModelDown2,