Commit 9cbefd78 authored by Delaigue Olivier's avatar Delaigue Olivier
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docs(vignette): format text in the 'sd_model' vignette

parent 156a20c1
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...@@ -198,7 +198,7 @@ As a priori parameter set, we use the calibrated parameter set of the upstream c ...@@ -198,7 +198,7 @@ As a priori parameter set, we use the calibrated parameter set of the upstream c
ParamDownTheo <- c(Velocity, OutputsCalibUp$ParamFinalR) ParamDownTheo <- c(Velocity, OutputsCalibUp$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} ```{r}
IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2, IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2,
...@@ -208,7 +208,7 @@ IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2, ...@@ -208,7 +208,7 @@ IC_Lavenne <- CreateInputsCrit_Lavenne(InputsModel = InputsModelDown2,
AprCrit = OutputsCalibUp$CritFinal) AprCrit = OutputsCalibUp$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} ```{r}
OutputsCalibDown3 <- Calibration_Michel(InputsModel = InputsModelDown2, OutputsCalibDown3 <- Calibration_Michel(InputsModel = InputsModelDown2,
...@@ -251,7 +251,7 @@ knitr::kable(mVelocity) ...@@ -251,7 +251,7 @@ knitr::kable(mVelocity)
## Value of the performance criteria with theoretical calibration ## 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} ```{r}
OutputsModelDownTheo <- RunModel(InputsModel = InputsModelDown2, OutputsModelDownTheo <- RunModel(InputsModel = InputsModelDown2,
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