In KWB-R/flextreat.hydrus1d: R Package for Soil Water Balance and Solute Transport Modelling Scenarios for Project Flextreat
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
is_windows <- .Platform$OS.type == "windows"
This workflow is based on using the R package kwb.hydrus1d, which is compatible with
the last official release of HYDRUS1D v4.17.0140. The
development of this new R package was necessary, due to the fact that already
available model wrappers were developed for outdated HYDRUS1D versions (e.g.
python package phydrus requires HYDRUS1D v4.08)
and/or are not well maintained (e.g. open issues/pull requests for R package
hydrusR).
The workflow below describes all the steps required for:
-
Input data preparation
-
Modifying (i.e. atmospheric input data defined in file ATMOSPHERE.in)
an already pre-prepared HYDRUS1D model (using the HYDRUS1D GUI)
-
Running it from within R by using the command line (cmd) and
-
Importing and analysing the HYDRUS1D results within R.
Install R Package
# Enable this universe
options(repos = c(
kwbr = 'https://kwb-r.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
# Install R package
install.packages('flextreat.hydrus1d')
Define Paths
paths_list <- list(
extdata = system.file("extdata", package = "flextreat.hydrus1d"),
exe_dir = "<extdata>/model",
scenario = "status-quo",
model_name = "test",
model_dir = "<exe_dir>/<model_name>",
atmosphere = "<model_dir>/ATMOSPH.IN",
a_level = "<model_dir>/A_LEVEL.out",
t_level = "<model_dir>/T_LEVEL.out",
runinf = "<model_dir>/Run_Inf.out",
solute_id = 1,
solute = "<model_dir>/solute<solute_id>.out",
soil_data = "<extdata>/input-data/soil/soil_geolog.csv"
)
paths <- kwb.utils::resolve(paths_list)
Input Data
Soil
Soil data is derived from depth-dependent grain-size analysis of soil samples taken
in Braunschweig. The following required input parameters for the van Genuchten
model used in HYDRUS1D were derived by using the pedotransfer function ROSETTA-API version 3, which was developed
by Zhang and Schaap, 2017.
library(flextreat.hydrus1d)
soil_dat <- readr::read_csv(paths$soil_data, show_col_types = FALSE) %>%
dplyr::mutate(layer_id = dplyr::if_else(.data$tiefe_cm_ende < 60,
1,
2),
thickness_cm = .data$tiefe_cm_ende - .data$tiefe_cm_start,
sand = .data$S_prozent + .data$G_prozent,
silt = .data$U_prozent,
clay = round(100 - .data$sand - .data$silt,
1)) %>%
dplyr::mutate(clay = dplyr::if_else(.data$clay < 0,
0,
.data$clay)) %>%
soilDB:::ROSETTA(vars = c("sand", "silt", "clay"),
v = "3") %>%
dplyr::mutate(alpha = 10^.data$alpha,
npar = 10^.data$npar,
ksat = 10^.data$ksat)
knitr::kable(soil_dat)
Due to similar soil characteristics, only two different layers (column layer_id)
were defined:
- layer 1: ranging from 0 cm - 55 cm depth
- layer 2: ranging from 55 cm - 210 cm depth
The spatially aggregation for each layer (geometric mean) of the input parameters
for the van Genuchten model is performed with the code below and shown in the
subsequent table.
soil_dat_per_layer <- soil_dat %>%
dplyr::group_by(.data$layer_id) %>%
dplyr::summarise(layer_thickness = max(.data$tiefe_cm_ende) - min(.data$tiefe_cm_start),
theta_r = sum(.data$theta_r*.data$thickness_cm)/.data$layer_thickness,
theta_s = sum(.data$theta_s*.data$thickness_cm)/.data$layer_thickness,
alpha = sum(.data$alpha*.data$thickness_cm)/.data$layer_thickness,
npar = sum(.data$npar*.data$thickness_cm)/.data$layer_thickness,
ksat = sum(.data$ksat*.data$thickness_cm)/.data$layer_thickness)
knitr::kable(soil_dat_per_layer)
Atmospheric Boundary Conditions
In total three different atmospheric input boundary conditions (precipitation, potential evaporation and irrigation) are used within the HYDRUS1D model and described in the following subchapters in more detail.
Rainfall
Rainfall is based on historical hourly raw data downloaded from DWD open-data ftp server for station_id = 662 (i.e. Braunschweig). Data is aggregated within R to daily sums by using the code below:
install.packages("rdwd")
library(dplyr)
rdwd::updateRdwd()
rdwd::findID("Braunschweig")
rdwd::selectDWD(name = "Braunschweig", res = "daily")
url_bs_rain <- rdwd::selectDWD(name = "Braunschweig",
res = "hourly",
var = "precipitation",
per = "historical" )
bs_rain <- rdwd::dataDWD(url_bs_rain)
precipitation_hourly <- rdwd::dataDWD(url_bs_rain) %>%
dplyr::select(.data$MESS_DATUM, .data$R1) %>%
dplyr::rename("datetime" = "MESS_DATUM",
"precipitation_mm" = "R1")
usethis::use_data(precipitation_hourly)
precipitation_daily <- precipitation_hourly %>%
dplyr::mutate("date" = as.Date(datetime)) %>%
dplyr::group_by(date) %>%
dplyr::summarise(rain_mm = sum(precipitation_mm))
usethis::use_data(precipitation_daily)
DT::datatable(head(flextreat.hydrus1d::precipitation_daily))
Potential Evaporation
Is based on daily potential evaporation grids (1km x 1km) downloaded from DWD open-data server, where all grids (i.e. 46) within the Abwasserverregnungsgebiet.shp were selected and spatially aggregated (mean,
sd, min, max) by using the code below:
shape_file <- system.file("extdata/input-data/gis/Abwasserverregnungsgebiet.shp",
package = "flextreat.hydrus1d")
# Only data of full months can currently be read!
evapo_p <- kwb.dwd::read_daily_data_over_shape(
file = shape_file,
variable = "evapo_p",
from = "201701",
to = "202012"
)
usethis::use_data(evapo_p)
DT::datatable(head(flextreat.hydrus1d::evapo_p))
Irrigation
Monthly irrigation volumes were provided by Abwasserverband Braunschweig for
the time period r sprintf("%s - %s", min(flextreat.hydrus1d::irrigation$date_start), max(flextreat.hydrus1d::irrigation$date_end)) as csv file and separates between
two sources (groundwater and clearwater). Data preparation was carried
out with the code below:
irrigation_file <- system.file("extdata/input-data/Beregnungsmengen_AVB.csv",
package = "flextreat.hydrus1d")
# irrigation_area <- rgdal::readOGR(dsn = shape_file)
# irrigation_area_sqm <- irrigation_area$area # 44111068m2
## 2700ha (https://www.abwasserverband-bs.de/de/was-wir-machen/verregnung/)
irrigation_area_sqm <- 27000000
irrigation <- read.csv2(irrigation_file) %>%
dplyr::select(- .data$Monat) %>%
dplyr::rename(irrigation_m3 = .data$Menge_m3,
source = .data$Typ,
month = .data$Monat_num,
year = .data$Jahr) %>%
dplyr::mutate(date_start = as.Date(sprintf("%d-%02d-01",
.data$year,
.data$month)),
days_in_month = as.numeric(lubridate::days_in_month(.data$date_start)),
date_end = as.Date(sprintf("%d-%02d-%02d",
.data$year,
.data$month,
.data$days_in_month)),
source = kwb.utils::multiSubstitute(.data$source,
replacements = list("Grundwasser" = "groundwater.mmPerDay",
"Klarwasser" = "clearwater.mmPerDay")),
irrigation_cbmPerDay = .data$irrigation_m3/.data$days_in_month,
irrigation_area_sqm = irrigation_area_sqm,
irrigation_mmPerDay = 1000*irrigation_cbmPerDay/irrigation_area_sqm) %>%
dplyr::select(.data$year,
.data$month,
.data$days_in_month,
.data$date_start,
.data$date_end,
.data$source,
.data$irrigation_mmPerDay,
.data$irrigation_area_sqm) %>%
tidyr::pivot_wider(names_from = .data$source,
values_from = .data$irrigation_mmPerDay)
usethis::use_data(irrigation)
DT::datatable(head(flextreat.hydrus1d::irrigation))
HYDRUS-1D
Modify Input Files
All model input files were initially setup by using the HYDRUS1D-GUI but the
following below were modified manually with the
SELECTOR.in
Soil input data were entered manually in SELECTOR.in for the two layers defined
the second table in Chapter: Input Data - Soil.
ATMOSPHERE.in
Based on the atmospheric input data (see Chapter: Atmospheric Boundary Conditions) the ATMOSPHERE.in file for HYDRUS1D is prepared with the code below and starts with the hydrological summer half year
(assumption: soil is fully wetted at end of winter half year):
atm <- flextreat.hydrus1d::prepare_atmosphere_data()
atm_selected <- flextreat.hydrus1d::select_hydrologic_years(atm)
atm_selected_hydro_wide <- flextreat.hydrus1d::aggregate_atmosphere(atm_selected, "wide")
DT::datatable(atm_selected_hydro_wide)
atm_selected_hydro_long <- flextreat.hydrus1d::aggregate_atmosphere(atm_selected, "long")
atm_hydro_plot <- flextreat.hydrus1d::plot_atmosphere(atm_selected_hydro_long)
atm_hydro_plot
kwb.utils::preparePdf(pdfFile = sprintf("boundaries-temporal_%s.pdf",
paths$scenario),
width.cm = 18, height.cm = 10)
atm_hydro_plot
dev.off()
atm_prep <- flextreat.hydrus1d::prepare_atmosphere(
atm_selected,
conc_irrig_clearwater = 1, # use as "clearwater" tracer
conc_irrig_groundwater = 0,
conc_rain = 0)
atmos <- kwb.hydrus1d::write_atmosphere(
atm = atm_prep,
MaxAL = nrow(atm_prep),
round_digits = 6 # increase precision for "clearwater" tracer!
)
writeLines(atmos, paths$atmosphere)
The selected time period covers r as.integer(diff(range(atm_selected$date))) days
(r sprintf("%s - %s", min(atm_selected$date), max(atm_selected$date))), i.e. it covers r round(as.integer(diff(range(atm_selected$date)))/(365/2), 0) hydrological half years.
DT::datatable(atm_selected)
These time-series were converted with the function flextreat.hydrus1d::prepare_atmosphere() into the data format required
by HYDRUS1D. Due to the fact, that irrigation rates (i.e. sum of clearwater.mmPerDay and groundwater.mmPerDay) cannot be entered separately as input column within HYDRUS1D, these were simply added to th prec (i.e. precipitation) column. The whole time series defined in ATMOSPHERE.in is shown below:
DT::datatable(atm_prep)
Run Model
Finally the model is run automatically by using the following code:
exe_path <- kwb.hydrus1d::check_hydrus_exe(dir = paths$exe_dir,
skip_preinstalled = TRUE)
kwb.hydrus1d:::run_model(exe_path = exe_path,
model_path = paths$model_dir)
Read Results
Numerical Solution
runinf <- kwb.hydrus1d::read_runinf(paths$runinf)
summary(runinf)
Conservative Tracer
solute <- kwb.hydrus1d::read_solute(paths$solute)
solute_date <- flextreat.hydrus1d::aggregate_solute(solute,
col_aggr = "date")
solute_yearmonth <- flextreat.hydrus1d::aggregate_solute(solute,
col_aggr = "yearmonth")
solute_year_hydrologic <- flextreat.hydrus1d::aggregate_solute(solute,
col_aggr = "year_hydrologic") %>%
dplyr::filter(.data$diff_time >= 364) ### filter out as only may-october
DT::datatable(solute_year_hydrologic)
solute_date_plot <- flextreat.hydrus1d::plot_solute(solute_date)
solute_yearmonth_plot <- flextreat.hydrus1d::plot_solute(solute_yearmonth)
solute_date_plot
solute_yearmonth_plot
kwb.utils::preparePdf(pdfFile = sprintf("solute_yearmonth_%s.pdf",
paths$scenario),
width.cm = 22,
height.cm = 10)
solute_yearmonth_plot
dev.off()
Water Balance
t_level <- kwb.hydrus1d::read_tlevel(paths$t_level)
t_level
## t_level aggregate
tlevel_aggr_date <- flextreat.hydrus1d::aggregate_tlevel(t_level)
tlevel_aggr_yearmonth <- flextreat.hydrus1d::aggregate_tlevel(t_level,
col_aggr = "yearmonth")
tlevel_aggr_year_hydrologic <- flextreat.hydrus1d::aggregate_tlevel(t_level,
col_aggr = "year_hydrologic") %>%
dplyr::filter(.data$diff_time >= 364) ### filter out as only may-october
DT::datatable(tlevel_aggr_year_hydrologic)
wb_date_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_date)
wb_yearmonth_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_yearmonth)
wb_yearhydrologic_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_year_hydrologic)
wb_date_plot
wb_yearmonth_plot
wb_yearhydrologic_plot
kwb.utils::preparePdf(pdfFile = sprintf("water-balance_yearmonth_%s.pdf",
paths$scenario),
width.cm = 19, height.cm = 10)
wb_yearmonth_plot
dev.off()
saveRDS(wb_yearmonth_plot,
file = sprintf("wb_yearmonth_%s.Rds", paths$scenario))
plotly::ggplotly(wb_date_plot)
plotly::ggplotly(wb_yearmonth_plot)
a_level <- kwb.hydrus1d::read_alevel(paths$a_level)
a_level
KWB-R/flextreat.hydrus1d documentation built on Jan. 13, 2025, 10:48 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) is_windows <- .Platform$OS.type == "windows"
This workflow is based on using the R package kwb.hydrus1d, which is compatible with
the last official release of HYDRUS1D v4.17.0140. The
development of this new R package was necessary, due to the fact that already
available model wrappers were developed for outdated HYDRUS1D versions (e.g.
python package phydrus requires HYDRUS1D v4.08)
and/or are not well maintained (e.g. open issues/pull requests for R package
hydrusR).
The workflow below describes all the steps required for:
-
Input data preparation
-
Modifying (i.e.
atmosphericinput data defined in fileATMOSPHERE.in) an already pre-preparedHYDRUS1Dmodel (using theHYDRUS1D GUI) -
Running it from within
Rby using the command line (cmd) and -
Importing and analysing the
HYDRUS1Dresults within R.
Install R Package
# Enable this universe options(repos = c( kwbr = 'https://kwb-r.r-universe.dev', CRAN = 'https://cloud.r-project.org')) # Install R package install.packages('flextreat.hydrus1d')
Define Paths
paths_list <- list( extdata = system.file("extdata", package = "flextreat.hydrus1d"), exe_dir = "<extdata>/model", scenario = "status-quo", model_name = "test", model_dir = "<exe_dir>/<model_name>", atmosphere = "<model_dir>/ATMOSPH.IN", a_level = "<model_dir>/A_LEVEL.out", t_level = "<model_dir>/T_LEVEL.out", runinf = "<model_dir>/Run_Inf.out", solute_id = 1, solute = "<model_dir>/solute<solute_id>.out", soil_data = "<extdata>/input-data/soil/soil_geolog.csv" ) paths <- kwb.utils::resolve(paths_list)
Input Data
Soil
Soil data is derived from depth-dependent grain-size analysis of soil samples taken
in Braunschweig. The following required input parameters for the van Genuchten
model used in HYDRUS1D were derived by using the pedotransfer function ROSETTA-API version 3, which was developed
by Zhang and Schaap, 2017.
library(flextreat.hydrus1d) soil_dat <- readr::read_csv(paths$soil_data, show_col_types = FALSE) %>% dplyr::mutate(layer_id = dplyr::if_else(.data$tiefe_cm_ende < 60, 1, 2), thickness_cm = .data$tiefe_cm_ende - .data$tiefe_cm_start, sand = .data$S_prozent + .data$G_prozent, silt = .data$U_prozent, clay = round(100 - .data$sand - .data$silt, 1)) %>% dplyr::mutate(clay = dplyr::if_else(.data$clay < 0, 0, .data$clay)) %>% soilDB:::ROSETTA(vars = c("sand", "silt", "clay"), v = "3") %>% dplyr::mutate(alpha = 10^.data$alpha, npar = 10^.data$npar, ksat = 10^.data$ksat) knitr::kable(soil_dat)
Due to similar soil characteristics, only two different layers (column layer_id)
were defined:
- layer 1: ranging from 0 cm - 55 cm depth
- layer 2: ranging from 55 cm - 210 cm depth
The spatially aggregation for each layer (geometric mean) of the input parameters
for the van Genuchten model is performed with the code below and shown in the
subsequent table.
soil_dat_per_layer <- soil_dat %>% dplyr::group_by(.data$layer_id) %>% dplyr::summarise(layer_thickness = max(.data$tiefe_cm_ende) - min(.data$tiefe_cm_start), theta_r = sum(.data$theta_r*.data$thickness_cm)/.data$layer_thickness, theta_s = sum(.data$theta_s*.data$thickness_cm)/.data$layer_thickness, alpha = sum(.data$alpha*.data$thickness_cm)/.data$layer_thickness, npar = sum(.data$npar*.data$thickness_cm)/.data$layer_thickness, ksat = sum(.data$ksat*.data$thickness_cm)/.data$layer_thickness) knitr::kable(soil_dat_per_layer)
Atmospheric Boundary Conditions
In total three different atmospheric input boundary conditions (precipitation, potential evaporation and irrigation) are used within the HYDRUS1D model and described in the following subchapters in more detail.
Rainfall
Rainfall is based on historical hourly raw data downloaded from DWD open-data ftp server for station_id = 662 (i.e. Braunschweig). Data is aggregated within R to daily sums by using the code below:
install.packages("rdwd") library(dplyr) rdwd::updateRdwd() rdwd::findID("Braunschweig") rdwd::selectDWD(name = "Braunschweig", res = "daily") url_bs_rain <- rdwd::selectDWD(name = "Braunschweig", res = "hourly", var = "precipitation", per = "historical" ) bs_rain <- rdwd::dataDWD(url_bs_rain) precipitation_hourly <- rdwd::dataDWD(url_bs_rain) %>% dplyr::select(.data$MESS_DATUM, .data$R1) %>% dplyr::rename("datetime" = "MESS_DATUM", "precipitation_mm" = "R1") usethis::use_data(precipitation_hourly) precipitation_daily <- precipitation_hourly %>% dplyr::mutate("date" = as.Date(datetime)) %>% dplyr::group_by(date) %>% dplyr::summarise(rain_mm = sum(precipitation_mm)) usethis::use_data(precipitation_daily)
DT::datatable(head(flextreat.hydrus1d::precipitation_daily))
Potential Evaporation
Is based on daily potential evaporation grids (1km x 1km) downloaded from DWD open-data server, where all grids (i.e. 46) within the Abwasserverregnungsgebiet.shp were selected and spatially aggregated (mean,
sd, min, max) by using the code below:
shape_file <- system.file("extdata/input-data/gis/Abwasserverregnungsgebiet.shp", package = "flextreat.hydrus1d") # Only data of full months can currently be read! evapo_p <- kwb.dwd::read_daily_data_over_shape( file = shape_file, variable = "evapo_p", from = "201701", to = "202012" ) usethis::use_data(evapo_p)
DT::datatable(head(flextreat.hydrus1d::evapo_p))
Irrigation
Monthly irrigation volumes were provided by Abwasserverband Braunschweig for
the time period r sprintf("%s - %s", min(flextreat.hydrus1d::irrigation$date_start), max(flextreat.hydrus1d::irrigation$date_end)) as csv file and separates between
two sources (groundwater and clearwater). Data preparation was carried
out with the code below:
irrigation_file <- system.file("extdata/input-data/Beregnungsmengen_AVB.csv", package = "flextreat.hydrus1d") # irrigation_area <- rgdal::readOGR(dsn = shape_file) # irrigation_area_sqm <- irrigation_area$area # 44111068m2 ## 2700ha (https://www.abwasserverband-bs.de/de/was-wir-machen/verregnung/) irrigation_area_sqm <- 27000000 irrigation <- read.csv2(irrigation_file) %>% dplyr::select(- .data$Monat) %>% dplyr::rename(irrigation_m3 = .data$Menge_m3, source = .data$Typ, month = .data$Monat_num, year = .data$Jahr) %>% dplyr::mutate(date_start = as.Date(sprintf("%d-%02d-01", .data$year, .data$month)), days_in_month = as.numeric(lubridate::days_in_month(.data$date_start)), date_end = as.Date(sprintf("%d-%02d-%02d", .data$year, .data$month, .data$days_in_month)), source = kwb.utils::multiSubstitute(.data$source, replacements = list("Grundwasser" = "groundwater.mmPerDay", "Klarwasser" = "clearwater.mmPerDay")), irrigation_cbmPerDay = .data$irrigation_m3/.data$days_in_month, irrigation_area_sqm = irrigation_area_sqm, irrigation_mmPerDay = 1000*irrigation_cbmPerDay/irrigation_area_sqm) %>% dplyr::select(.data$year, .data$month, .data$days_in_month, .data$date_start, .data$date_end, .data$source, .data$irrigation_mmPerDay, .data$irrigation_area_sqm) %>% tidyr::pivot_wider(names_from = .data$source, values_from = .data$irrigation_mmPerDay) usethis::use_data(irrigation)
DT::datatable(head(flextreat.hydrus1d::irrigation))
HYDRUS-1D
Modify Input Files
All model input files were initially setup by using the HYDRUS1D-GUI but the
following below were modified manually with the
SELECTOR.in
Soil input data were entered manually in SELECTOR.in for the two layers defined
the second table in Chapter: Input Data - Soil.
ATMOSPHERE.in
Based on the atmospheric input data (see Chapter: Atmospheric Boundary Conditions) the ATMOSPHERE.in file for HYDRUS1D is prepared with the code below and starts with the hydrological summer half year
(assumption: soil is fully wetted at end of winter half year):
atm <- flextreat.hydrus1d::prepare_atmosphere_data() atm_selected <- flextreat.hydrus1d::select_hydrologic_years(atm) atm_selected_hydro_wide <- flextreat.hydrus1d::aggregate_atmosphere(atm_selected, "wide") DT::datatable(atm_selected_hydro_wide) atm_selected_hydro_long <- flextreat.hydrus1d::aggregate_atmosphere(atm_selected, "long") atm_hydro_plot <- flextreat.hydrus1d::plot_atmosphere(atm_selected_hydro_long) atm_hydro_plot kwb.utils::preparePdf(pdfFile = sprintf("boundaries-temporal_%s.pdf", paths$scenario), width.cm = 18, height.cm = 10) atm_hydro_plot dev.off() atm_prep <- flextreat.hydrus1d::prepare_atmosphere( atm_selected, conc_irrig_clearwater = 1, # use as "clearwater" tracer conc_irrig_groundwater = 0, conc_rain = 0) atmos <- kwb.hydrus1d::write_atmosphere( atm = atm_prep, MaxAL = nrow(atm_prep), round_digits = 6 # increase precision for "clearwater" tracer! ) writeLines(atmos, paths$atmosphere)
The selected time period covers r as.integer(diff(range(atm_selected$date))) days
(r sprintf("%s - %s", min(atm_selected$date), max(atm_selected$date))), i.e. it covers r round(as.integer(diff(range(atm_selected$date)))/(365/2), 0) hydrological half years.
DT::datatable(atm_selected)
These time-series were converted with the function flextreat.hydrus1d::prepare_atmosphere() into the data format required
by HYDRUS1D. Due to the fact, that irrigation rates (i.e. sum of clearwater.mmPerDay and groundwater.mmPerDay) cannot be entered separately as input column within HYDRUS1D, these were simply added to th prec (i.e. precipitation) column. The whole time series defined in ATMOSPHERE.in is shown below:
DT::datatable(atm_prep)
Run Model
Finally the model is run automatically by using the following code:
exe_path <- kwb.hydrus1d::check_hydrus_exe(dir = paths$exe_dir, skip_preinstalled = TRUE) kwb.hydrus1d:::run_model(exe_path = exe_path, model_path = paths$model_dir)
Read Results
Numerical Solution
runinf <- kwb.hydrus1d::read_runinf(paths$runinf) summary(runinf)
Conservative Tracer
solute <- kwb.hydrus1d::read_solute(paths$solute) solute_date <- flextreat.hydrus1d::aggregate_solute(solute, col_aggr = "date") solute_yearmonth <- flextreat.hydrus1d::aggregate_solute(solute, col_aggr = "yearmonth") solute_year_hydrologic <- flextreat.hydrus1d::aggregate_solute(solute, col_aggr = "year_hydrologic") %>% dplyr::filter(.data$diff_time >= 364) ### filter out as only may-october DT::datatable(solute_year_hydrologic)
solute_date_plot <- flextreat.hydrus1d::plot_solute(solute_date) solute_yearmonth_plot <- flextreat.hydrus1d::plot_solute(solute_yearmonth) solute_date_plot solute_yearmonth_plot kwb.utils::preparePdf(pdfFile = sprintf("solute_yearmonth_%s.pdf", paths$scenario), width.cm = 22, height.cm = 10) solute_yearmonth_plot dev.off()
Water Balance
t_level <- kwb.hydrus1d::read_tlevel(paths$t_level) t_level ## t_level aggregate tlevel_aggr_date <- flextreat.hydrus1d::aggregate_tlevel(t_level) tlevel_aggr_yearmonth <- flextreat.hydrus1d::aggregate_tlevel(t_level, col_aggr = "yearmonth") tlevel_aggr_year_hydrologic <- flextreat.hydrus1d::aggregate_tlevel(t_level, col_aggr = "year_hydrologic") %>% dplyr::filter(.data$diff_time >= 364) ### filter out as only may-october DT::datatable(tlevel_aggr_year_hydrologic)
wb_date_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_date) wb_yearmonth_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_yearmonth) wb_yearhydrologic_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_year_hydrologic) wb_date_plot wb_yearmonth_plot wb_yearhydrologic_plot kwb.utils::preparePdf(pdfFile = sprintf("water-balance_yearmonth_%s.pdf", paths$scenario), width.cm = 19, height.cm = 10) wb_yearmonth_plot dev.off() saveRDS(wb_yearmonth_plot, file = sprintf("wb_yearmonth_%s.Rds", paths$scenario))
plotly::ggplotly(wb_date_plot) plotly::ggplotly(wb_yearmonth_plot)
a_level <- kwb.hydrus1d::read_alevel(paths$a_level) a_level
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