diff --git a/R/.scenarios_add-trace-organics_vs.R b/R/.scenarios_add-trace-organics_vs.R new file mode 100644 index 0000000..433f238 --- /dev/null +++ b/R/.scenarios_add-trace-organics_vs.R @@ -0,0 +1,443 @@ +remotes::install_github("kwb-r/kwb.hydrus1d@dev") +remotes::install_github("kwb-r/flextreat.hydrus1d@dev") +library(magrittr) + +prepare_solute_input <- function(dat, selector, diff_w = 0, diff_g = 0) { + + stopifnot(nrow(dat) <= 10) + + if(selector$solute$No.Solutes != nrow(dat)) { + selector$solute$No.Solutes <- nrow(dat) + } + + solute_names <- sprintf("solute_%d", seq_len(nrow(dat))) + + solutes_new <- setNames(lapply(seq_len(nrow(dat)), function(i) { + dat_sel <- dat[i,] + + ks <- kd(porosity = selector$waterflow$soil$ths - selector$waterflow$soil$thr, + retardation = dat_sel$retard, + bulk_density = selector$solute$transport$Bulk.d.) + + cols <- c("Ks", "Nu", "Beta", "Henry", "SnkL1", "SnkS1", "SnkG1", "SnkL1'", + "SnkS1'", "SnkG1'", "SnkL0", "SnkS0", "SnkG0", "Alfa") + + reaction <- matrix(data = 0, ncol = length(cols), nrow = length(ks)) %>% + as.data.frame() %>% tibble::as_tibble() + names(reaction) <- cols + reaction$Beta <- 1 + reaction$Ks <- round(ks, 2) + reaction$SnkL1 <- round(halftime_to_firstorderrate(dat_sel$half_life_days), + 2) + + list(diffusion = tibble::tibble(DifW = diff_w, DifG = diff_g), + reaction = reaction) + }), nm = solute_names) + + sel_tmp <- selector$solute[!names(selector$solute) %in% solute_names] + + solutes_new_list <- c(sel_tmp[1:which(names(sel_tmp) == "transport")], + solutes_new, + sel_tmp[which(names(sel_tmp) == "kTopSolute"):length(sel_tmp)]) + + c(selector[names(selector) != "solute"], + list(solute = solutes_new_list)) +} + +get_mean <- function(col) { + x <- col %>% stringr::str_split_fixed("-", n = 2) + mode(x) <- "numeric" + round(rowMeans(x), digits = 2) +} + + +kd <- function(porosity, retardation, bulk_density) { + + #https://www3.epa.gov/ceampubl/learn2model/part-two/onsite/retard.html + (retardation - 1) * porosity / bulk_density + +} + +halftime_to_firstorderrate <- function(half_time) { + + #https://chem.libretexts.org/Courses/Bellarmine_University/BU%3A_Chem_104_(Christianson)/Phase_2%3A_Understanding_Chemical_Reactions/4%3A_Kinetics%3A_How_Fast_Reactions_Go/4.5%3A_First_Order_Reaction_Half-Life#mjx-eqn-21.4.2 + half_time / 0.693 +} + + +path <- "Y:/WWT_Department/Projects/FlexTreat/Work-packages/AP3/3_1_2_Boden-Grundwasser/daten_karten/Sickerwasserprognose/column-studies/Stoffeigenschaften_Säulen.xlsx" + +soil_columns <- kwb.db::hsGetTable(path, "my_results", stringsAsFactors = FALSE) %>% + janitor::clean_names() %>% + dplyr::mutate(half_life_days = dplyr::case_when( + grepl(">", hwz_tage) ~ hwz_tage %>% stringr::str_remove(">") %>% as.numeric() %>% round(digits = 2), + grepl("<", hwz_tage) ~ hwz_tage %>% stringr::str_remove("<") %>% as.numeric() %>% round(digits = 2), + grepl("-", hwz_tage) ~ get_mean(hwz_tage), + .default = as.numeric(hwz_tage) %>% round(digits = 2)), + retard = dplyr::case_when( + grepl("-", retardation) ~ get_mean(retardation), + is.na(retardation) ~ 1L, + .default = as.numeric(retardation) %>% round(digits = 2))) + + +scenarios <- "soil-2m_irrig-01days" + +seq_start <- seq(1,nrow(soil_columns),10) +seq_end <- seq(10,nrow(soil_columns),10) +if(length(seq_end) < length(seq_start)) seq_end <- c(seq_end, nrow(soil_columns)) + +solute_ids <- tibble::tibble(start = seq_start, + end = seq_end) + + +sapply(scenarios, function(scenario) { + +sapply(seq_len(nrow(solute_ids)), function(i) { + + paths_list <- list( + #extdata = system.file("extdata", package = "flextreat.hydrus1d"), + #root_server = "Y:/WWT_Department/Projects/FlexTreat/Work-packages/AP3/3_1_4_Prognosemodell/Vivian/Rohdaten/H1D", + root_local = "C:/kwb/projects/flextreat/3_1_4_Prognosemodell/Vivian/Rohdaten/H1D", + #root_local = "C:/kwb/projects/flextreat/hydrus/Szenarien_10day", + #root_local = system.file("extdata/model", package = "flextreat.hydrus1d"), + exe_dir = "", + solute_id_start = sprintf("%02d", solute_ids$start[i]), + solute_id_end = sprintf("%02d", solute_ids$end[i]), + scenario = scenario, + model_name_org = "1a2a__tracer_0110", + model_name = "1a2a__soil-column_", #"1a2a_BTA_korr_test_40d", + model_gui_path_org = "/.h1d", + model_gui_path = "/.h1d", + modelvs_gui_path = "/_vs.h1d", + model_dir_org = "/", + model_dir = "/", + model_dir_vs = "/_vs", + scenario = "xxx", + atmosphere = "/ATMOSPH.IN", + atmosphere_vs = "/ATMOSPH.IN", + a_level = "/A_LEVEL.out", + hydrus1d = "/HYDRUS1D.DAT", + selector = "/SELECTOR.IN", + profile = "/PROFILE.DAT", + obs_node = "/Obs_Node.out", + balance = "/BALANCE.out", + t_level = "/T_LEVEL.out", + t_level_vs = "/T_LEVEL.out", + runinf = "/Run_Inf.out", + solute_id = "1", + solute = "/solute.out", + solute_vs = "/solute.out", + soil_data = "/input-data/soil/soil_geolog.csv" + ) + + paths <- kwb.utils::resolve(paths_list) + + +no_irrig <- stringr::str_detect(paths$model_dir, "no-irrig") +irrig_pattern <- "irrig-[0-9][0-9]?days" +irrig_int <- stringr::str_detect(paths$model_dir, irrig_pattern) + +if(irrig_int) { + +string_irrig <- stringr::str_extract(paths$model_dir, sprintf("_%s", irrig_pattern)) + +if(!fs::dir_exists(paths$model_dir)) { + +fs::dir_copy(paths$model_dir_org, paths$model_dir) +} + +if(!fs::file_exists(paths$model_gui_path)) { + fs::file_copy(paths$model_gui_path_org, paths$model_gui_path) + +} + +string_irrig_int <- stringr::str_extract(paths$model_dir, "[0-9][0-9]?days") + +irrig_interval <- sprintf("%s %s", + string_irrig_int %>% + stringr::str_extract("\\d+") %>% + as.integer(), + string_irrig_int %>% + stringr::str_extract("[a-z]+")) +} + + +library(flextreat.hydrus1d) + +solute_start_id <- as.numeric(paths$solute_id_start) +solute_end_id <- as.numeric(paths$solute_id_end) +n_solutes <- solute_end_id - (solute_start_id - 1) + +selector <- kwb.hydrus1d::read_selector(path = paths$selector) + +solutes_new <- prepare_solute_input(dat = soil_columns[solute_start_id:solute_end_id,], + selector = selector, + diff_w = 0, + diff_g = 0) + +kwb.hydrus1d::write_selector(solutes_new, paths$selector) + + +atm <- flextreat.hydrus1d::prepare_atmosphere_data() + +#no-irrigation +if(no_irrig) atm[,c("groundwater.mmPerDay", "clearwater.mmPerDay")] <- 0 + + +sum_per_interval <- function(data, interval) { + + data_org <- data + + data <- data %>% + dplyr::select(tidyselect::all_of(c("date", + "groundwater.mmPerDay", + "clearwater.mmPerDay"))) + + cols_sum <- names(data)[names(data) != "date"] + + data_summed <- data %>% + dplyr::mutate(group = lubridate::floor_date(date, unit = interval)) %>% # Konvertiere date in datetime-Format + dplyr::group_by(group) %>% # Gruppiere nach Zeitintervallen + dplyr::summarise_at(.vars = tidyselect::all_of(cols_sum), + .funs = sum) %>% # Berechne die Summe für jedes Intervall + dplyr::rename(date = group) + + data_org[, cols_sum] <- 0 + data_org[data_org$date %in% data_summed$date, cols_sum] <- data_summed[,cols_sum] + data_org +} + +atm_selected <- flextreat.hydrus1d::select_hydrologic_years(atm) + +if(irrig_int) { +atm_selected <- sum_per_interval(data = atm_selected, + interval = irrig_interval) + +} + +atm_prep <- flextreat.hydrus1d::prepare_atmosphere(atm = atm_selected, + conc_irrig_clearwater = rep(100, + n_solutes), + conc_irrig_groundwater = 0, + conc_rain = 0 + ) +#atm <- atm_selected +# days_monthy <- lubridate::days_in_month(seq.Date(from = min(atm$date), +# to = max(atm$date), +# by = "month")) +# +# days_total <- cumsum(days_monthy) +# +# indeces <- as.integer(paths$months_start):as.integer(paths$months_end) +# +# c_tops <- lapply(indeces, function(i) { +# +# x <- rep(0, nrow(atm)) +# if(i == 1) { +# x_min = 1 +# } else { +# x_min = days_total[i - 1] + 1 +# } +# x[x_min:days_total[i]] <- rep(100, days_monthy[i]) +# +# +# tib <- data.frame(x) +# colnames(tib) <- if(i == indeces[1]) { +# "cTop"} else { +# sprintf("cTop%d", which(indeces %in% i)) +# } +# +# tib +# }) %>% dplyr::bind_cols() + + +# +# +# if(no_irrig) { +# atm_prep <- flextreat.hydrus1d::prepare_atmosphere(atm = atm_selected, +# conc_irrig_clearwater = 0, +# conc_irrig_groundwater = 0, +# conc_rain = 0 +# ) +# } else { +# atm_prep <- flextreat.hydrus1d::prepare_atmosphere(atm = atm_selected, +# conc_irrig_clearwater = c_tops, +# conc_irrig_groundwater = c_tops, +# conc_rain = c_tops +# ) +# } + +writeLines(kwb.hydrus1d::write_atmosphere(atm = atm_prep), + paths$atmosphere) + + +hydrus1d <- kwb.hydrus1d::read_hydrus1d(paths$hydrus1d) +hydrus1d$Main$NumberOfSolutes <- n_solutes +kwb.hydrus1d::write_hydrus1d(hydrus1d, paths$hydrus1d) + + +kwb.hydrus1d::run_model(model_path = paths$model_dir) + +atmos <- kwb.hydrus1d::read_atmosph(paths$atmosphere) + + +#atmos$data[names(c_tops)] <- c_tops + +atm_default <- atmos + +tlevel <- kwb.hydrus1d::read_tlevel(paths$t_level) + +vs_atm <- flextreat.hydrus1d::recalculate_ctop_with_virtualstorage( + atm = atm_default$data, + tlevel = tlevel, + crit_v_top = - 0.05 +) + +atmos$data[names(vs_atm$df)] <- vs_atm$df + +fs::dir_copy(paths$model_dir, paths$model_dir_vs, overwrite = TRUE) +fs::file_copy(paths$model_gui_path, paths$modelvs_gui_path, overwrite = TRUE) + +writeLines(kwb.hydrus1d::write_atmosphere(atm = atmos$data), + paths$atmosphere_vs) + + +kwb.hydrus1d::run_model(model_path = paths$model_dir_vs) + +}) +}) + +solute <- kwb.hydrus1d::read_solute(paths$solute_vs) %>% + dplyr::mutate(difftime = c(0,diff(time))) + +plot(solute$time, solute$c_top) +points(solute$c_bot, col = "red") +(1 - max(solute$sum_cv_top)/sum(atmos$data$Prec*atmos$data$cTop)) * 100 + + +paths$solute_vs2 <- "C:/kwb/projects/flextreat/3_1_4_Prognosemodell/Vivian/Rohdaten/H1D/1a2a_tracer_vs/solute2.out" + +solute <- kwb.hydrus1d::read_solute(paths$solute) %>% + dplyr::mutate(difftime = c(0,diff(time))) + +(1 - max(solute$sum_cv_top)/sum(atmos$data$Prec*atmos$data$cTop2)) * 100 + + +sum(atmos$data$Prec) +max(tlevel$sum_infil) +max(tlevel$sum_evap) + +balance <- kwb.hydrus1d::read_balance(paths$balance) + +balance %>% + dplyr::filter(subdomain_id == 0, + time < 400, + parameter == "CncBalT") %>% + ggplot2::ggplot(mapping = ggplot2::aes(x = time, y = value, col = as.factor(solute_id))) + ggplot2::geom_point() + + + +balance %>% + dplyr::filter(subdomain_id == 0, + time < 400, + parameter == "CncBalR") %>% + ggplot2::ggplot(mapping = ggplot2::aes(x = time, y = value, col = as.factor(solute_id))) + + ggplot2::geom_point() + +sum(solute$difftime * as.numeric(solute$c_top) * as.numeric(tlevel$r_top)) + +solute_day <- flextreat.hydrus1d::aggregate_solute(solute) + + +plot(solute_day$date, solute_day$c_top) +abline(h = 7) + +plot(atmos$data$tAtm, atmos$data$cTop) + +sum(solute_day$cv_top[solute_day$cv_top > 0]) +sum(atmos$data$Prec*atmos$data$cTop) +sum(atmos$data$Prec*atmos$data$cTop)/sum(solute_day$cv_top[solute_day$cv_top > 0]) +sum(solute$cv_top) + +sum((as.numeric(solute$cv_top) * solute$difftime)) +sum((solute$cv_ch1 * c(0,diff(solute$time)))) + +max(solute$sum_cv_top) + +condition <- solute$cv_top > 0 +sum(solute$cv_top[condition]) +condition <- solute$cv_top < 0 +sum(solute$cv_top[condition]) + +solute_aggr_date <- flextreat.hydrus1d::aggregate_solute(solute) + +obsnode <- kwb.hydrus1d::read_obsnode(paths$obs_node) + +obsnode %>% + dplyr::filter(variable == "flux") %>% + dplyr::group_by(node_id) %>% + dplyr::summarise(sum = sum(value)) + +profile <- kwb.hydrus1d::read_profile(paths$profile) + +p <- obsnode %>% + dplyr::left_join(profile[,c("node_id", "x")]) %>% + dplyr::filter(variable == "conc1") %>% + ggplot2::ggplot(mapping = ggplot2::aes(x = time, + y = value, + col = as.factor(x))) + + ggplot2::geom_point() + + ggplot2::theme_bw() + +plotly::ggplotly(p) + +solute_aggr_date +View(tlevel_aggr_date) +View(solute_aggr_date) + + +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 + + +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 + + +solute$time[min(which(solute$sum_cv_top == max(solute$sum_cv_top)))] + +paths$model_dir_vs + +solute_files <- fs::dir_ls(paths$exe_dir, + regexp = "soil-column.*_vs/solute\\d\\d?.out", + recurse = TRUE) + +solutes <- setNames(lapply(solute_files, function(file) { + +kwb.hydrus1d::read_solute(file) +}), nm = solute_files) %>% dplyr::bind_rows(.id = "path") + + +solute_files_df <- tibble::tibble(path = solute_files, + model_solute_id = path %>% basename() %>% stringr::str_extract(pattern = "[0-9][0-9]?") %>% as.integer(), + soilcolumn_id_start = path %>% dirname() %>% stringr::str_extract(pattern = "[0-9]{4}") %>% stringr::str_sub(1,2) %>% as.integer(), + soilcolumn_id_end = path %>% dirname() %>% stringr::str_extract(pattern = "[0-9]{4}") %>% stringr::str_sub(3,4) %>% as.integer(), + soil_column_id = soilcolumn_id_start + model_solute_id - 1) %>% + dplyr::left_join(soil_columns, by = c(soil_column_id = "nr")) + + +v<- dplyr::left_join(solutes, solute_files_df) + diff --git a/R/.virtual_storage.R b/R/.virtual_storage.R index dab017f..d4cabf4 100644 --- a/R/.virtual_storage.R +++ b/R/.virtual_storage.R @@ -66,6 +66,7 @@ paths_list <- list( atmosphere_vs = "/ATMOSPH.IN", a_level = "/A_LEVEL.out", hydrus1d = "/HYDRUS1D.DAT", + selector = "/SELECTOR.IN", profile = "/PROFILE.DAT", obs_node = "/Obs_Node.out", balance = "/BALANCE.out",