From 1e56de7d0b94d111597ac919111c341adad2a18f Mon Sep 17 00:00:00 2001
From: Delaigue Olivier <olivier.delaigue@irstea.priv>
Date: Thu, 28 Mar 2019 17:58:03 +0100
Subject: [PATCH] v1.2.12.7 CLEAN: syntax revision of cemaneige_hysteris
vignette
---
DESCRIPTION | 2 +-
NEWS.rmd | 2 +-
vignettes/V04_cemaneige_hysteresis.Rmd | 93 +++++++++++++++++---------
3 files changed, 62 insertions(+), 35 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index 74c3dd6b..44d02281 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
Package: airGR
Type: Package
Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
-Version: 1.2.12.6
+Version: 1.2.12.7
Date: 2019-03-29
Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl"), comment = c(ORCID = "0000-0002-1503-6204")),
diff --git a/NEWS.rmd b/NEWS.rmd
index 5cc4fcf9..3ad502e8 100644
--- a/NEWS.rmd
+++ b/NEWS.rmd
@@ -13,7 +13,7 @@ output:
-### 1.2.12.6 Release Notes (2019-03-28)
+### 1.2.12.7 Release Notes (2019-03-28)
diff --git a/vignettes/V04_cemaneige_hysteresis.Rmd b/vignettes/V04_cemaneige_hysteresis.Rmd
index e56eae82..85366d4b 100644
--- a/vignettes/V04_cemaneige_hysteresis.Rmd
+++ b/vignettes/V04_cemaneige_hysteresis.Rmd
@@ -46,8 +46,11 @@ The calibration period has been defined from 2000-09-01 to 2005-08-31, and the v
```{r, warning=FALSE}
## preparation of the InputsModel object
-InputsModel <- CreateInputsModel(FUN_MOD = RunModel_CemaNeigeGR4J, DatesR = BasinObs$DatesR,
- Precip = BasinObs$P, PotEvap = BasinObs$E)
+InputsModel <- CreateInputsModel(FUN_MOD = RunModel_CemaNeigeGR4J,
+ DatesR = BasinObs$DatesR, Precip = BasinObs$P,
+ PotEvap = BasinObs$E, TempMean = BasinObs$T,
+ ZInputs = median(BasinInfo$HypsoData),
+ HypsoData = BasinInfo$HypsoData, NLayers = 5)
## ---- calibration step
@@ -72,14 +75,17 @@ In order to use the hysteresis, a new argument (IsHyst) is added in the CreateRu
```{r, warning=FALSE}
## preparation of the RunOptions object for the calibration period
RunOptions_Cal <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
- InputsModel = InputsModel, IndPeriod_Run = Ind_Cal, IsHyst = TRUE)
+ InputsModel = InputsModel, IndPeriod_Run = Ind_Cal,
+ IsHyst = TRUE)
## preparation of the RunOptions object for the validation period
RunOptions_Val <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
- InputsModel = InputsModel, IndPeriod_Run = Ind_Val, IsHyst = TRUE)
+ InputsModel = InputsModel, IndPeriod_Run = Ind_Val,
+ IsHyst = TRUE)
## preparation of the CalibOptions object
-CalibOptions <- CreateCalibOptions(FUN_MOD = RunModel_CemaNeigeGR4J, FUN_CALIB = Calibration_Michel,
+CalibOptions <- CreateCalibOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
+ FUN_CALIB = Calibration_Michel,
IsHyst = TRUE)
```
@@ -88,14 +94,14 @@ In order to calibrate and assess the model performance, we will follow the recom
```{r, warning=FALSE}
## efficiency criteria: 75 % KGE'(Q) + 5 % KGE'(SCA) on each of the 5 layers
-InputsCrit_Cal <- CreateInputsCrit(FUN_CRIT = list(ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2),
+InputsCrit_Cal <- CreateInputsCrit(FUN_CRIT = rep("ErrorCrit_KGE2", 6),
InputsModel = InputsModel, RunOptions = RunOptions_Cal,
obs = list(BasinObs$Qmm[Ind_Cal], BasinObs$SCA1[Ind_Cal], BasinObs$SCA2[Ind_Cal],
BasinObs$SCA3[Ind_Cal], BasinObs$SCA4[Ind_Cal], BasinObs$SCA5[Ind_Cal]),
varObs = list("Q", "SCA", "SCA", "SCA", "SCA", "SCA"),
weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))
-InputsCrit_Val <- CreateInputsCrit(FUN_CRIT = list(ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2, ErrorCrit_KGE2),
+InputsCrit_Val <- CreateInputsCrit(FUN_CRIT = rep("ErrorCrit_KGE2", 6),
InputsModel = InputsModel, RunOptions = RunOptions_Val,
obs = list(BasinObs$Qmm[Ind_Val], BasinObs$SCA1[Ind_Val], BasinObs$SCA2[Ind_Val],
BasinObs$SCA3[Ind_Val], BasinObs$SCA4[Ind_Val], BasinObs$SCA5[Ind_Val]),
@@ -114,36 +120,41 @@ OutputsCalib <- Calibration(InputsModel = InputsModel, RunOptions = RunOptions_C
```
-Now we can run it on the calibration and validation periods and assess it.
+Now we can run it on the calibration periods and assess it.
-```{r, warning=FALSE}
+```{r, warning=FALSE, message=FALSE}
## run on the calibration period
-OutputsModel_Val <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel, RunOptions = RunOptions,
+OutputsModel_Cal <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel,
+ RunOptions = RunOptions_Cal,
Param = OutputsCalib$ParamFinalR)
## evaluation
-OutputsCrit_Cal <- ErrorCrit(InputsCrit = InputsCrit, OutputsModel = OutputsModel_Cal)
+OutputsCrit_Cal <- ErrorCrit(InputsCrit = InputsCrit_Cal, OutputsModel = OutputsModel_Cal)
```
+
Find below the performance of the model over the calibration period.
```{r, warning=FALSE}
-str(OutputsCrit_Cal)
-
+str(OutputsCrit_Cal, max.level = 2)
+```
+Now we can run it on the validation periods and assess it.
+```{r, warning=FALSE}
## run on the validation period
-OutputsModel_Val <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel, RunOptions = RunOptions,
+OutputsModel_Val <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel,
+ RunOptions = RunOptions_Val,
Param = OutputsCalib$ParamFinalR)
## evaluation
-OutputsCrit_Val <- ErrorCrit(InputsCrit = InputsCrit, OutputsModel = OutputsModel_Val)
+OutputsCrit_Val <- ErrorCrit(InputsCrit = InputsCrit_Val, OutputsModel = OutputsModel_Val)
```
Find below the performance of the model over the validation period.
```{r, warning=FALSE}
-str(OutputsCrit_Val)
+str(OutputsCrit_Val, max.level = 2)
```
@@ -154,51 +165,67 @@ Here we use the same InputsModel object and calibration and validation periods.
```{r, warning=FALSE}
## preparation of RunOptions object
RunOptions_Cal_NoHyst <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
- InputsModel = InputsModel, IndPeriod_Run = Ind_Cal, IsHyst = FALSE)
+ InputsModel = InputsModel,
+ IndPeriod_Run = Ind_Cal,
+ IsHyst = FALSE)
-RunOptions_Val_NoHyst <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR4J, InputsModel = InputsModel,
- IndPeriod_Run = Ind_Val, IsHyst = FALSE)
+RunOptions_Val_NoHyst <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
+ InputsModel = InputsModel,
+ IndPeriod_Run = Ind_Val,
+ IsHyst = FALSE)
InputsCrit_Cal_NoHyst <- CreateInputsCrit(FUN_CRIT = ErrorCrit_KGE2,
- InputsModel = InputsModel, RunOptions = RunOptions_Cal_NoHyst,
- obs = BasinData$Q[Ind_Cal], varObs = "Q")
+ InputsModel = InputsModel,
+ RunOptions = RunOptions_Cal_NoHyst,
+ obs = BasinObs$Qmm[Ind_Cal], varObs = "Q")
## preparation of CalibOptions object
CalibOptions_NoHyst <- CreateCalibOptions(FUN_MOD = RunModel_CemaNeigeGR4J,
- FUN_CALIB = Calibration_Michel, IsHyst = FALSE)
+ FUN_CALIB = Calibration_Michel,
+ IsHyst = FALSE)
```
We can now calibrate it.
```{r, warning=FALSE}
## calibration
-OutputsCalib_NoHyst <- Calibration(InputsModel = InputsModel, RunOptions = RunOptions,
- InputsCrit = InputsCrit_Cal_NoHyst, CalibOptions = CalibOptions_NoHyst,
- FUN_MOD = RunModel_CemaNeigeGR4J,
- FUN_CALIB = Calibration_Michel)
+OutputsCalib_NoHyst <- Calibration(InputsModel = InputsModel, InputsCrit = InputsCrit_Cal_NoHyst,
+ RunOptions = RunOptions_Cal_NoHyst, CalibOptions = CalibOptions_NoHyst,
+ FUN_MOD = RunModel_CemaNeigeGR4J, FUN_CALIB = Calibration_Michel)
```
And run it over the calibration and validation periods.
```{r, warning=FALSE}
-OutputsModel_Cal_NoHyst <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel, RunOptions = RunOptions_Cal_NoHyst,
+OutputsModel_Cal_NoHyst <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel,
+ RunOptions = RunOptions_Cal_NoHyst,
Param = OutputsCalib_NoHyst$ParamFinalR)
-OutputsModel_Val_NoHyst <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel, RunOptions = RunOptions_Val_NoHyst,
- Param = OutputsCalib$ParamFinalR)
+OutputsModel_Val_NoHyst <- RunModel_CemaNeigeGR4J(InputsModel = InputsModel,
+ RunOptions = RunOptions_Val_NoHyst,
+ Param = OutputsCalib_NoHyst$ParamFinalR)
```
In order to assess the model performance over the two periods, we will use the InputsCrit objects prepared before, which allow assessing also the performance in terms of snow simulation.
-```{r, warning=FALSE}
-OutputsCrit_Cal_NoHyst <- ErrorCrit(InputsCrit = InputsCrit_Cal, OutputsModel = OutputsModel_Cal_NoHyst)
+```{r, warning=FALSE, message=FALSE}
+OutputsCrit_Cal_NoHyst <- ErrorCrit(InputsCrit = InputsCrit_Cal,
+ OutputsModel = OutputsModel_Cal_NoHyst)
+
+OutputsCrit_Val_NoHyst <- ErrorCrit(InputsCrit = InputsCrit_Val,
+ OutputsModel = OutputsModel_Val_NoHyst)
+```
+
+We can check the performance over the calibration and the validation period.
-OutputsCrit_Val_NoHyst <- ErrorCrit(InputsCrit = InputsCrit_Val, OutputsModel = OutputsModel_Val_NoHyst)
+```{r, warning=FALSE}
+str(OutputsCrit_Cal_NoHyst, max.level = 2)
+str(OutputsCrit_Val_NoHyst, max.level = 2)
```
We can see below that the performance of the initial model is similar to the new one over the calibration period in terms of discharge, but also that the new version calibrated using SCA provides better performance in terms of snow.
-However, over the validation, we see that the discharge simulated by the new version brings better performance (in addition to improved SCA also). This shows the interests of the combined use of an hysteresis and of SCA data for calibration in CemaNeige.
+However, over the validation period, we see that the discharge simulated by the new version brings better performance (in addition to improved SCA also). This shows the interests of the combined use of an hysteresis and of SCA data for calibration in CemaNeige.
Reference
--
GitLab