title: "Simulating a reservoir with semi-distributed GR4J model"
author: "David Dorchies"
bibliography: V00_airgr_ref.bib
output: rmarkdown::html_vignette
vignette: >
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@@ -20,7 +21,7 @@ library(imputeTS)
The **airGR** package implements semi-distributed model capabilities using a lag model between subcatchments. It allows to chain together several lumped models as well as integrating anthropogenic influence such as reservoirs or withdrawals.
`RunModel_LAG` documentation gives an example of simulating the influence of a reservoir in a lumped model. Try `example(RunModel_LAG)` to get it.
`RunModel_Lag` documentation gives an example of simulating the influence of a reservoir in a lumped model. Try `example(RunModel_Lag)` to get it.
In this vignette, we show how to calibrate 2 sub-catchments in series with a semi-distributed model consisting of 2 GR4J models. For doing this we compare two strategies for calibrating the downstream subcatchment:
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@@ -64,18 +65,18 @@ The operations are exactly the same as the ones for a GR4J lumped model. So we d
# Calibration of the downstream subcatchment with upstream flow observations
Observed flow data contain `NA` values and a complete time series is mandatory for running the LAG model. We propose to complete the observed upstream flow with linear interpolation:
Observed flow data contain `NA` values and a complete time series is mandatory for running the Lag model. We propose to complete the observed upstream flow with linear interpolation:
rownames(mLag) = c("theoretical", "calibrated with observed upstream flow",
"calibrated with simulated upstream flow")
colnames(mLag) = c("LAG parameter")
colnames(mLag) = c("Lag parameter")
knitr::kable(mLag)
```
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@@ -183,7 +184,7 @@ knitr::kable(mLag)
Theoretically, the parameters of the downstream GR4J model should be the same as the upstream one and we know the lag time. So this set of parameter should give a better performance criteria: