Seine_01: Structuration of a semi-distributed GR4J model network
David Dorchies
Source:vignettes/seinebasin/V01_First_network.Rmd
V01_First_network.Rmd## Loading required package: airGR##
## Attaching package: 'airGRiwrm'## The following objects are masked from 'package:airGR':
##
## Calibration, CreateCalibOptions, CreateInputsCrit,
## CreateInputsModel, CreateRunOptions, RunModelThe example below is inspired by the semi-distributed model developed in the ClimAware project (Theobald et al. 2014), in which we used the daily rainfall-runoff model GR4J.
The aim of this vignette is to create the semi-distributed network necessary for the hydrological model.
Semi-distributed network description
The model is distributed according to the gauging stations described in Dorchies et al. (2014).
First, we must read the list of nodes and the associated metadata:
seine_nodes <- read.table(
file = system.file("seine_data", "network_gauging_stations.txt", package = "seinebasin"),
sep = ";", header = TRUE, fileEncoding = "UTF-8", quote = "\"", stringsAsFactors = FALSE
)
seine_nodes## id_sgl id_hydro lambert2.x lambert2.y area
## 1 TRANN_01 766626.1 2369152 1557.06
## 2 GURGY_02 H2221010 689713.0 2320549 3819.77
## 3 BRIEN_03 H2482010 695313.0 2332549 2979.77
## 4 STDIZ_04 H5071010 791113.0 2407349 2347.53
## 5 PARIS_05 H5920010 602213.0 2427449 43824.66
## 6 BAR-S_06 H0400010 751913.0 2348349 2340.37
## 7 CHAUM_07 716518.4 2241747 216.50
## 8 CUSSY_08 H2172310 726013.0 2275149 247.99
## 9 STGER_09 718512.9 2266649 402.74
## 10 GUILL_10 H2322020 731013.0 2282349 488.71
## 11 AISY-_11 H2452020 742413.0 2298149 1349.51
## 12 CHABL_12 H2342010 709613.0 2314149 1116.27
## 13 NOGEN_13 685912.9 2389349 9182.39
## 14 EPISY_14 H3621010 633413.0 2371049 3916.71
## 15 MONTE_15 645112.9 2376849 21199.39
## 16 ALFOR_16 H4340020 606013.0 2420349 30784.71
## 17 NOISI_17 H5841010 620913.0 2428949 12547.72
## 18 MONTR_18 H5752020 638013.0 2431849 1184.81
## 19 LOUVE_19 H5083050 791613.0 2393949 461.74
## 20 LASSI_20 H1362010 759513.0 2385549 876.53
## 21 CHALO_21 H5201010 747713.0 2441349 6291.55
## 22 MERY-_22 H0810010 714913.0 2390949 3899.62
## 23 COURL_23 H2721010 660813.0 2370449 10687.35
## 24 ARCIS_24 H1501010 733313.0 2394749 3594.60
## 25 VITRY_25 H5172010 768513.0 2418849 2109.14
## description id_aval distance_aval
## 1 L'Aube à Trannes ARCIS_24 68100
## 2 L'Yonne à Gurgy COURL_23 83612
## 3 L'Armançon à Brienon-sur-Armançon COURL_23 84653
## 4 La Marne à Saint-Dizier CHALO_21 85570
## 5 La Seine à Paris NA
## 6 La Seine à Bar-sur-Seine MERY-_22 79766
## 7 L'Yonne à Chaumard GURGY_02 153074
## 8 Le Cousin à Cussy-les-Forges GURGY_02 91378
## 9 La Cure à St-Germain GURGY_02 94152
## 10 Le Serein à Guillon CHABL_12 66026
## 11 L'Armançon à Aisy-sur-Armançon BRIEN_03 102428
## 12 Le Serein à Chablis COURL_23 111781
## 13 La Seine à Nogent-sur-Seine MONTE_15 63215
## 14 Le Loing à Épisy ALFOR_16 89196
## 15 La Seine à Montereau ALFOR_16 94475
## 16 La Seine à Alfortville PARIS_05 9263
## 17 La Marne à Noisiel PARIS_05 39384
## 18 Le Grand Morin à Montry NOISI_17 37915
## 19 La Blaise à Louvemont CHALO_21 86165
## 20 La Voire à Lassicourt ARCIS_24 43618
## 21 La Marne à Châlons-sur-Marne NOISI_17 237937
## 22 La Seine à Méry-sur-Seine NOGEN_13 49933
## 23 L'Yonne à Courlon-sur-Yonne MONTE_15 26159
## 24 L'Aube à Arcis-sur-Aube NOGEN_13 70926
## 25 La Saulx à Vitry-en-Perthois CHALO_21 38047Using that information, we must create the GRiwrm object that lists
the nodes and describes the network diagram. It is a dataframe of class
GRiwrm with specific column names:
-
id: the identifier of the node in the network. -
down: the identifier of the next hydrological node downstream. -
length: hydraulic distance to the next hydrological downstream node. -
model: Name of the hydrological model used (E.g. “RunModel_GR4J”).NAfor other types of nodes. -
area: Area of the sub-catchment (km2). Used for hydrological models such as GR models.NAif not used.
The CreateGRiwrm function helps to rename the columns of
the dataframe and assign the variable classes.
seine_nodes$id_aval[seine_nodes$id_aval == ""] <- NA
seine_nodes$distance_aval <- as.double(seine_nodes$distance_aval) / 1000
seine_nodes$model <- "RunModel_GR4J"
# Generate the GRiwrm object
griwrm <- CreateGRiwrm(seine_nodes,
list(id = "id_sgl",
down = "id_aval",
length = "distance_aval"))
griwrm## id down length area model donor
## 1 TRANN_01 ARCIS_24 68.100 1557.06 RunModel_GR4J TRANN_01
## 4 STDIZ_04 CHALO_21 85.570 2347.53 RunModel_GR4J STDIZ_04
## 6 BAR-S_06 MERY-_22 79.766 2340.37 RunModel_GR4J BAR-S_06
## 7 CHAUM_07 GURGY_02 153.074 216.50 RunModel_GR4J CHAUM_07
## 8 CUSSY_08 GURGY_02 91.378 247.99 RunModel_GR4J CUSSY_08
## 9 STGER_09 GURGY_02 94.152 402.74 RunModel_GR4J STGER_09
## 10 GUILL_10 CHABL_12 66.026 488.71 RunModel_GR4J GUILL_10
## 11 AISY-_11 BRIEN_03 102.428 1349.51 RunModel_GR4J AISY-_11
## 14 EPISY_14 ALFOR_16 89.196 3916.71 RunModel_GR4J EPISY_14
## 18 MONTR_18 NOISI_17 37.915 1184.81 RunModel_GR4J MONTR_18
## 19 LOUVE_19 CHALO_21 86.165 461.74 RunModel_GR4J LOUVE_19
## 20 LASSI_20 ARCIS_24 43.618 876.53 RunModel_GR4J LASSI_20
## 25 VITRY_25 CHALO_21 38.047 2109.14 RunModel_GR4J VITRY_25
## 2 GURGY_02 COURL_23 83.612 3819.77 RunModel_GR4J GURGY_02
## 3 BRIEN_03 COURL_23 84.653 2979.77 RunModel_GR4J BRIEN_03
## 12 CHABL_12 COURL_23 111.781 1116.27 RunModel_GR4J CHABL_12
## 21 CHALO_21 NOISI_17 237.937 6291.55 RunModel_GR4J CHALO_21
## 22 MERY-_22 NOGEN_13 49.933 3899.62 RunModel_GR4J MERY-_22
## 24 ARCIS_24 NOGEN_13 70.926 3594.60 RunModel_GR4J ARCIS_24
## 13 NOGEN_13 MONTE_15 63.215 9182.39 RunModel_GR4J NOGEN_13
## 17 NOISI_17 PARIS_05 39.384 12547.72 RunModel_GR4J NOISI_17
## 23 COURL_23 MONTE_15 26.159 10687.35 RunModel_GR4J COURL_23
## 15 MONTE_15 ALFOR_16 94.475 21199.39 RunModel_GR4J MONTE_15
## 16 ALFOR_16 PARIS_05 9.263 30784.71 RunModel_GR4J ALFOR_16
## 5 PARIS_05 <NA> NA 43824.66 RunModel_GR4J PARIS_05The diagram of the network structure is represented below with in blue the upstream nodes with a GR4J model and in green the intermediate nodes with an SD (GR4J + LAG) model.
plot(griwrm)
Observation time series
The daily mean precipitation and potential evaporation at the scale of the intermediate sub-basins are extracted from the SAFRAN reanalysis (Vidal et al. 2010).
The daily naturalized flow is provided by Hydratec (2011).
These data are embedded in the R package ‘seinebasin’, which is not publicly available.
Generate the GRiwrm InputsModel object
The GRiwrm InputsModel object is a list of airGR InputsModel objects. The identifier of the sub-basin is used as a key in the list, which is ordered from upstream to downstream.
The airGR CreateInputsModel function is extended in order to handle the GRiwrm object that describes the basin diagram:
InputsModel <- CreateInputsModel(griwrm, DatesR, Precip, PotEvap)## CreateInputsModel.GRiwrm: Processing sub-basin TRANN_01...## CreateInputsModel.GRiwrm: Processing sub-basin STDIZ_04...## CreateInputsModel.GRiwrm: Processing sub-basin BAR-S_06...## CreateInputsModel.GRiwrm: Processing sub-basin CHAUM_07...## CreateInputsModel.GRiwrm: Processing sub-basin CUSSY_08...## CreateInputsModel.GRiwrm: Processing sub-basin STGER_09...## CreateInputsModel.GRiwrm: Processing sub-basin GUILL_10...## CreateInputsModel.GRiwrm: Processing sub-basin AISY-_11...## CreateInputsModel.GRiwrm: Processing sub-basin EPISY_14...## CreateInputsModel.GRiwrm: Processing sub-basin MONTR_18...## CreateInputsModel.GRiwrm: Processing sub-basin LOUVE_19...## CreateInputsModel.GRiwrm: Processing sub-basin LASSI_20...## CreateInputsModel.GRiwrm: Processing sub-basin VITRY_25...## CreateInputsModel.GRiwrm: Processing sub-basin GURGY_02...## CreateInputsModel.GRiwrm: Processing sub-basin BRIEN_03...## CreateInputsModel.GRiwrm: Processing sub-basin CHABL_12...## CreateInputsModel.GRiwrm: Processing sub-basin CHALO_21...## CreateInputsModel.GRiwrm: Processing sub-basin MERY-_22...## CreateInputsModel.GRiwrm: Processing sub-basin ARCIS_24...## CreateInputsModel.GRiwrm: Processing sub-basin NOGEN_13...## CreateInputsModel.GRiwrm: Processing sub-basin NOISI_17...## CreateInputsModel.GRiwrm: Processing sub-basin COURL_23...## CreateInputsModel.GRiwrm: Processing sub-basin MONTE_15...## CreateInputsModel.GRiwrm: Processing sub-basin ALFOR_16...## CreateInputsModel.GRiwrm: Processing sub-basin PARIS_05...Save data for next vignettes
dir.create("_cache", showWarnings = FALSE)
save(seine_nodes, griwrm, InputsModel, file = "_cache/V01.RData")References
Dorchies, David, Guillaume Thirel, Maxime Jay-Allemand, Mathilde
Chauveau, Florine Dehay, Pierre-Yves Bourgin, Charles Perrin, et al.
2014. “Climate Change Impacts on Multi-Objective Reservoir
Management: Case Study on the Seine River Basin,
France.” International Journal of River Basin
Management 12 (3): 265–83. https://doi.org/10.1080/15715124.2013.865636.
Hydratec. 2011. “Actualisation de La Base de Données Des Débits
Journaliers ‘Naturalisés’ - Phase 2.”
26895 - LME/TL.
Theobald, S., K. Träbing, K. Kehr, V. Aufenanger, M. Flörke, C.
Schneider, C. Perrin, et al. 2014. “ClimAware:
Impacts of Climate Change on Water Resources Management.
Regional Strategies and European View. Final
Report.” http://irsteadoc.irstea.fr/cemoa/PUB00040932.
Vidal, Jean-Philippe, Eric Martin, Laurent Franchistéguy, Martine
Baillon, and Jean-Michel Soubeyroux. 2010. “A 50-Year
High-Resolution Atmospheric Reanalysis over France with the
Safran System.” International Journal of
Climatology 30 (11): 1627–44. https://doi.org/10.1002/joc.2003.