├── 0Estacion_C2.csv ├── ACCESS1-0-RCP45.csv ├── Donwscale_CMIP6.r ├── DownscalePT_Blocks_V1.0.r ├── DownscalePT_Full__V1.0.r ├── DownscalePT_full_CMI6_V1.0.r ├── DownscalePT_full_V1.1.r ├── LICENSE ├── Qmapping-Daily.R ├── Qmapping.png └── README.md /Donwscale_CMIP6.r: -------------------------------------------------------------------------------- 1 | ################################################################################ 2 | # STATISTICAL DOWNSCALING OF DAILY CLIMATE DATA USING # 3 | # QUANTILE MAPPING TECHNIQUE FOR CMIP6 DATASETS # 4 | ################################################################################ 5 | 6 | #Author: Julio Montenegro Gambini, M.Sc., 7 | #PhD fellow - Technische Universiteit Delft (TU Delft), Netherlands. 8 | 9 | #Current version: 1.0 10 | 11 | #©Copyright 2013 2021 Julio Montenegro. 12 | #This script is strictly under license GPLv3 13 | #License details: https://www.gnu.org/licenses/gpl-3.0.en.html 14 | 15 | # Please, when using this script, cite as: "Montenegro, J. (2021). Statistical 16 | #downscaling of daily climate data using quantile mapping technique for CMIP6 17 | #datasets" 18 | 19 | # Installing or loading the required packages ================================== 20 | library(tidyverse) 21 | library(lubridate) 22 | library(qmap) 23 | library(zoo) 24 | library(latticeExtra) 25 | library(readxl) 26 | library(beepr) 27 | 28 | #Setting working directory ===================================================== 29 | #IMPORTANT!: The main folder has to contain sub-folders of each model 30 | #where 6 files are located. 31 | #The file names are: TASMIN_OBS.xlsx, TASMIN.csv, TASMAX_OBS.xlsx, TASMAX.csv, 32 | #PR_OBS.xlsx, PR.csv 33 | #Historical observed data: TASMAX_OBS, TASMIN_OBS, PR_OBS 34 | #Future data: TASMIN, TASMAX, PR 35 | #Each file has to contain continous daily data! 36 | #Data cannot contain missing values! 37 | 38 | setwd(paste0('C:/Users/monte/Downloads/CMIP6_DOWNSCALING/',"SSP 585")) 39 | dir <- dir() 40 | length(dir) 41 | 42 | #Pre-procesing and downscaling (EDIT HERE!) ==================================== 43 | #Creating a general loop for processing within each model (folder) 44 | for(ind in 1:1){ 45 | # Name of the folder which contains csv and xlsx excel files 46 | dir_estation <- dir[ind] 47 | 48 | # CHANGE HERE THE FILE NAMES! ================================================== 49 | #Historical observed data: TASMAX_OBS, TASMIN_OBS, PR_OBS 50 | #Future data: TASMIN, TASMAX, PR 51 | 52 | # DAILY MINIMUM TEMPERATURE PARAMETERS 53 | file_tmin_obs <- 'TASMIN_OBS.xlsx' 54 | file_tmin_mod <- 'TASMIN.csv' 55 | fecha_in_his_min <- as.Date('1981-01-01') 56 | fecha_fin_his_min <- as.Date('2019-12-31') 57 | fecha_in_min <- as.Date('1950-01-01') 58 | fecha_fin_min <- as.Date('2099-12-31') 59 | 60 | # DAILY MAXIMUM TEMPERATURE PARAMETERS 61 | file_tmax_obs <- 'TASMAX_OBS.xlsx' 62 | file_tmax_mod <- 'TASMAX.csv' 63 | fecha_in_his_max <- as.Date('1981-01-01') 64 | fecha_fin_his_max <- as.Date('2019-12-31') 65 | fecha_in_max <- as.Date('1950-01-01') 66 | fecha_fin_max <- as.Date('2099-12-31') 67 | 68 | # DAILY PRECIPITATION PARAMETERS 69 | file_pr_obs <- 'PR_OBS.xlsx' 70 | file_pr_mod <- 'PR.csv' 71 | fecha_in_his_pr <- as.Date('1981-01-01') 72 | fecha_fin_his_pr <- as.Date('2019-12-31') 73 | fecha_in_pr <- as.Date('1950-01-01') 74 | fecha_fin_pr <- as.Date('2099-12-31') 75 | 76 | # GENERATING A QUANTILE MAPPING FUNCTION ======================================= 77 | 78 | # Downscaling function for each variable 79 | qp_function <- function(time_ini_his, time_fin_his, 80 | time_ini, time_fin, 81 | file_his, file_mod, 82 | var){ 83 | 84 | time_ini <- fecha_in_min 85 | time_fin <- fecha_fin_min 86 | time_ini_his <- fecha_in_his_min 87 | time_fin_his <- fecha_fin_his_min 88 | #file_his <- file_tmin_obs 89 | #file_mod <- file_tmin_mod 90 | #var = 'MIN' 91 | #j <- 2 92 | 93 | ## Reading historical baseline and GCM/RCM future time series 94 | data_historica <- read_excel(paste0(dir_estation,'/',file_his)) 95 | data_modelada <- read.csv(paste0(dir_estation,'/',file_mod)) 96 | 97 | names_stations <- names(data_historica)[2:length(data_historica)] 98 | 99 | df_reg <- c() # empty dataframe for filling with downscaled data 100 | 101 | ## Downscaling loop for different time series (station data) 102 | for (j in 2:ncol(data_historica)) { 103 | 104 | ## Historical baseline configuration 105 | data_his <- data_historica[,c(1,j)] 106 | colnames(data_his) <- c('FECHA','STATION') 107 | data_his <- data_his %>% mutate(FECHA= as.Date(FECHA)) 108 | 109 | ## GCM/RCM data configuration 110 | if (var %in% c('MIN','MAX')) { 111 | correccion <- function(x, na.rm=FALSE) (x-273.15) 112 | } 113 | if (var %in% c('PR')) { 114 | correccion <- function(x, na.rm=FALSE) (x*86400) 115 | } 116 | 117 | data_mod <- data_modelada[,c(1,j)] %>% 118 | mutate_if(is.numeric, correccion, na.rm=FALSE) 119 | colnames(data_mod) <- c('FECHA','STATION') 120 | 121 | ####CONDICION IMPORTANTE ADICIONAL COMO CORRECCION 122 | if(data_modelada[1,1] == "1/1/1950"){ 123 | data_model <- data_mod %>% mutate(FECHA= as.Date(FECHA, format = "%m/%d/%Y")) 124 | }else{data_model <- data_mod %>% mutate(FECHA= as.Date(FECHA))} 125 | 126 | ## Date filtering 127 | 128 | ### Historical 129 | var_hist_2 <- data_his %>% 130 | filter(FECHA >= time_ini_his & FECHA <= time_fin_his) 131 | colnames(var_hist_2) <- c('isodate','hist') 132 | 133 | var_hist_2 <- var_hist_2 %>% 134 | full_join( 135 | data.frame(isodate = seq(from=time_ini_his, to=time_fin_his, by ='day')), 136 | by = 'isodate') 137 | 138 | ### Setting the variable of GCM/RCM data 139 | var_model_2 <- data_model %>% 140 | filter(FECHA >= time_ini & FECHA <= time_fin) 141 | colnames(var_model_2) <- c('isodate','mode') 142 | 143 | var_model_2 <- var_model_2 %>% 144 | full_join( 145 | data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')), 146 | by = 'isodate') 147 | 148 | ## Creating variables to be used 149 | OBS_hist <- var_hist_2 %>% 150 | rename(OBS_hist = hist) 151 | 152 | GCM_model <- var_model_2 153 | 154 | # Filling missing historical data 155 | if (var=='PR') { 156 | OBS_hist <- OBS_hist %>% 157 | mutate(OBS_hist = ifelse(is.na(OBS_hist),0.1,OBS_hist)) 158 | } 159 | if (var=='MAX' | var=='MIN') { 160 | OBS_hist <- OBS_hist %>% 161 | mutate(OBS_hist = ifelse(is.na(OBS_hist),16,OBS_hist)) 162 | } 163 | 164 | # Filling missing GCM/RCM future data 165 | if (var=='PR') { 166 | GCM_model <- GCM_model %>% 167 | mutate(mode = ifelse(is.na(mode),0.1,mode)) 168 | } 169 | if (var=='MAX' | var=='MIN') { 170 | GCM_model <- GCM_model %>% 171 | mutate(mode = ifelse(is.na(mode),15,mode)) 172 | } 173 | 174 | GCM_hist <- GCM_model 175 | 176 | ## Conversion of datasets to "ts" objects 177 | 178 | data_hist <- OBS_hist %>% 179 | read.zoo() 180 | 181 | data_mod <- GCM_model %>% 182 | read.zoo() 183 | 184 | data_wt <- GCM_model %>% 185 | read.zoo() 186 | 187 | # SETTING SEASONAL OR MONTHLY ANALYSIS ========================================= 188 | 189 | 190 | seasons_by_year <- list(c("December"),c("January"),c("February"), 191 | c("March"),c("April"),c("May"), 192 | c("June"),c("July"),c("August"), 193 | c("September"),c("October"),c("November")) 194 | #According to system region, month names should be changed (e.g. spanish): 195 | #seasons_by_year <- list(c("Diciembre"), c("Enero"), c("Febrero"), 196 | #c("Marzo"), c("Abril"), c("Mayo"), 197 | #c("Junio"), c("Julio"), c("Agosto"), 198 | #c("Septiembre"), c("Octubre"), c("Noviembre")) 199 | 200 | for(i in 1:12) { 201 | 202 | obs_sl <- data_hist[months(time(data_hist)) %in% seasons_by_year[[i]]] 203 | mod_sl <- data_mod[months(time(data_mod)) %in% seasons_by_year[[i]]] 204 | #GCM/RCM data, read!: L. Gudmundsson et al. (2012) 205 | 206 | if (sum(mod_sl, na.rm = T)==0) { 207 | mod_sl[1]<- 0.001 208 | } 209 | 210 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 211 | mod = coredata(mod_sl), 212 | qstep = 0.001, 213 | nboot = 1, 214 | wet.day = 0, # To be changed for temperatures 215 | type = "linear") 216 | 217 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 218 | 219 | data_wt[ months(time(data_wt)) %in% seasons_by_year[[i]]] <- mod_sl_qmapped 220 | 221 | } 222 | 223 | t <- as.data.frame(data_wt) 224 | 225 | if (j == 2) { 226 | df_reg <- t 227 | } else{ 228 | df_reg <- cbind(df_reg, t) 229 | } 230 | 231 | } 232 | 233 | # Adding column names 234 | colnames(df_reg) <- names_stations 235 | 236 | # Assign the date column (daily) 237 | df_empty<- data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')) 238 | 239 | df_out <- df_empty %>% 240 | cbind(df_reg) 241 | 242 | return(df_out) 243 | } 244 | 245 | # APPLYING DOWNSCALING ========================================================= 246 | 247 | # Dowscaling for minimum temperature 248 | tmin_reg <- qp_function(fecha_in_min, fecha_fin_min, 249 | fecha_in_his_min, fecha_fin_his_min, 250 | file_tmin_obs, file_tmin_mod, 'MIN') 251 | 252 | # Dowscaling for maximum temperature 253 | tmax_reg <- qp_function(fecha_in_max, fecha_fin_max, 254 | fecha_in_his_max, fecha_fin_his_max, 255 | file_tmax_obs, file_tmax_mod, 'MAX') 256 | 257 | # Dowscaling for precipitation 258 | pr_reg <- qp_function(fecha_in_pr, fecha_fin_pr, 259 | fecha_in_his_pr, fecha_fin_his_pr, 260 | file_pr_obs, file_pr_mod, 'PR') 261 | 262 | 263 | # EXPORTING DOWNSCALED DATA IN 3 FILES ========================================= 264 | tmin_reg %>% write.csv(file = paste0(dir_estation,'/tmin_reg.csv'),row.names = F) 265 | tmax_reg %>% write.csv(file = paste0(dir_estation,'/tmax_reg.csv'),row.names = F) 266 | pr_reg %>% write.csv(file = paste0(dir_estation,'/pr_reg.csv'),row.names = F) 267 | #Hear the sound when finally the three files were generated 268 | beep(15) 269 | } 270 | #In case of an error, another sound is played 271 | beep(5) 272 | -------------------------------------------------------------------------------- /DownscalePT_Blocks_V1.0.r: -------------------------------------------------------------------------------- 1 | # Packages to be used 2 | library(dplyr) 3 | library(lubridate) 4 | library(qmap) 5 | library(zoo) 6 | library(latticeExtra) 7 | library(xlsx) 8 | library(readxl) 9 | 10 | # Indicar la carpeta que contiene a las estaciones 11 | setwd('D:/downscaling/') 12 | 13 | # llamamos a los archivos historicos 14 | data_pr_his <- read_excel("Histórico_Est.xlsx") 15 | data_tmax_his <- read_excel("Tmáx_esta.xlsx") 16 | data_tmin_his <- read_excel("Tmín_esta.xlsx") 17 | 18 | #============================================================================ 19 | # Configurar segun la data 20 | 21 | ## Numero de variables 22 | n_var <- 3 23 | 24 | ## Nombre del archivo de la data modelada que sigue despues del texto 'RCP4585' 25 | vec_var_2 <- c('TASMAX','TASMIN','PR') 26 | ## Nombre resumen de la variable (recomiendo dejar como esta) 27 | vec_var_3 <- c('MAX','MIN','PR') 28 | 29 | ## Fijar las fechas historicas 30 | fecha_in_his <- as.Date('1980-01-01') 31 | fecha_fin_his <- as.Date('2009-12-31') 32 | 33 | ## Fijar las fechas de los bloques 34 | fecha_in_1 <- as.Date('2010-01-01') 35 | fecha_fin_1 <- as.Date('2039-12-31') 36 | 37 | fecha_in_2 <- as.Date('2040-01-01') 38 | fecha_fin_2 <- as.Date('2069-12-31') 39 | 40 | fecha_in_3 <- as.Date('2070-01-01') 41 | fecha_fin_3 <- as.Date('2099-12-31') 42 | 43 | ## Agrupar en vector (dejar como esta) 44 | fecha_in <- c(fecha_in_1, fecha_in_2, fecha_in_3) 45 | fecha_fin <- c(fecha_fin_1, fecha_fin_2, fecha_fin_3) 46 | #============================================================================ 47 | 48 | # Funci?n para generar la regionalizacion, para cada variable y para un solo bloque de tiempo 49 | 50 | ds <- function(time_ini_his, time_fin_his, time_ini, time_fin, data_his, data_model, var){ 51 | 52 | #time_ini <- as.Date('2010-01-01') 53 | #time_fin <- as.Date('2039-12-31') 54 | # time_ini <- as.Date('2040-01-01') 55 | # time_fin <- as.Date('2069-12-31') 56 | # time_ini <- as.Date('2070-01-01') 57 | # time_fin <- as.Date('2099-12-31') 58 | # time_ini_his <- as.Date('1980-01-01') 59 | # time_fin_his <- as.Date('2009-12-31') 60 | # data_his <- var_hist 61 | # data_model <- var_model 62 | # var = 'PR' 63 | 64 | # Creamos vectores con la informacion de los modelos 65 | #name_his <- sort(names(data_his)[which(substring(names(data_his),1,3)=='his')] ) 66 | name_mod <- sort(names(data_model)[which(substring(names(data_model),1,3)=='rcp')] ) 67 | n_model <- length(name_mod) 68 | # Creamos df vacios quienes van a contener a los resultados 69 | df_mod <- c() 70 | # Generamos la regionalizaci?n por tipo de proyeccion 71 | for (j in 1:n_model) { 72 | 73 | # configurar la variable historica 74 | var_hist_2 <- data_his %>% 75 | filter(FECHA >= time_ini_his & FECHA <= time_fin_his) 76 | colnames(var_hist_2) <- c('isodate','hist') 77 | 78 | var_hist_2 <- var_hist_2 %>% 79 | full_join( 80 | data.frame(isodate = seq(from=time_ini_his, to=time_fin_his, by ='day')), 81 | by = 'isodate') 82 | 83 | # configurar la variable del modelo 84 | var_model_2 <- data_model[, c(which(names(data_model) == 'isodate'), 85 | which(names(data_model) == name_mod[j]))] %>% 86 | filter(isodate >= time_ini & isodate <= time_fin) 87 | colnames(var_model_2) <- c('isodate','mode') 88 | 89 | var_model_2 <- var_model_2 %>% 90 | filter(isodate <= time_fin & isodate >= time_ini) %>% 91 | full_join( 92 | data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')), 93 | by = 'isodate') 94 | 95 | # aqui completo valores faltantes con 0.1 96 | if (var=='PR') { 97 | OBS_hist <- OBS_hist %>% 98 | mutate(OBS_hist = ifelse(is.na(OBS_hist),0.1,OBS_hist)) 99 | } 100 | if (var=='MAX' | var=='MIN') { 101 | OBS_hist <- OBS_hist %>% 102 | mutate(OBS_hist = ifelse(is.na(OBS_hist),15,OBS_hist)) 103 | } 104 | 105 | # forzamos a que coincida el numero de dias para ambas series 106 | if (nrow(var_hist_2) > nrow(var_model_2)) { 107 | var_model_2 <- rbind(var_model_2, data.frame(isodate=Sys.Date(),mode=var_model_2[nrow(var_model_2),2])) 108 | } 109 | if (nrow(var_model_2) > nrow(var_hist_2)) { 110 | var_model_2 <- var_model_2 %>% 111 | slice(1:nrow(var_hist_2)) 112 | } 113 | 114 | # creamos las variables a usar (solo para seguir el script de origen) 115 | OBS_hist <- var_hist_2 %>% 116 | rename(OBS_hist = hist) 117 | 118 | GCM_model <- var_model_2 119 | 120 | # aqui completo valores faltantes con 0.1 121 | if (var=='PR') { 122 | GCM_model <- GCM_model %>% 123 | mutate(mode = ifelse(is.na(mode),0.1,mode)) 124 | } 125 | if (var=='MAX' | var=='MIN') { 126 | GCM_model <- GCM_model %>% 127 | mutate(mode = ifelse(is.na(mode),15,mode)) 128 | } 129 | 130 | GCM_hist <- GCM_model 131 | 132 | data_at <- cbind(OBS_hist, GCM_model = GCM_model[,2]) %>% 133 | read.zoo() 134 | data_wt <- cbind(OBS_hist, GCM_hist = GCM_hist[,2]) %>% 135 | read.zoo() 136 | 137 | #============================================================================ 138 | # APLICACIÓN DE LA TÉCNICA DE QUANTILE MAPPING (MÉTODO EMPÍRICO) 139 | 140 | data_wt$gcm_downscaled <- data_wt$GCM_hist 141 | 142 | seasons_by_year <- list(c("Diciembre","Enero","Febrero"), 143 | c("Marzo","Abril","Mayo"), 144 | c("Junio","Julio","Agosto"), 145 | c("Setiembre","Octubre","Noviembre")) 146 | 147 | seasonal_qm_fit_model <- list() 148 | 149 | ## funcion de quantil maping 0.1 es a correcion, es linea 150 | for(i in 1:4) { 151 | obs_sl <- data_wt[months(time(data_wt)) %in% seasons_by_year[[i]]]$OBS_hist 152 | mod_sl <- data_wt[months(time(data_wt)) %in% seasons_by_year[[i]]]$GCM_hist 153 | #MODEL, read!: L. Gudmundsson et al. (2012) 154 | 155 | if (sum(mod_sl, na.rm = T)==0) { 156 | mod_sl[1]<- 0.001 157 | } 158 | 159 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 160 | coredata(mod_sl), 161 | qstep = 0.01, 162 | nboot = 1, 163 | wet.day = 0, # Adaptado para temperatuas negativas 164 | type = "linear") 165 | 166 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 167 | data_wt$gcm_downscaled[ months(time(data_wt)) %in% 168 | seasons_by_year[[i]]] <- mod_sl_qmapped 169 | 170 | seasonal_qm_fit_model[[i]] <- qm_fit 171 | } 172 | 173 | #============================================================================ 174 | #INTERPOLANDO LA INFORMACION EN CASO DE DATOS MENSUALES 175 | 176 | data_at$GCM_downscaled <- data_wt$gcm_downscaled 177 | 178 | t <- as.data.frame(data_at[,3]) 179 | 180 | if (j == 1) { 181 | df_mod <- t 182 | } else{ 183 | df_mod <- cbind(df_mod, t) 184 | } 185 | 186 | } 187 | # agregamos losnombres correctas de las columnas 188 | colnames(df_mod) <- name_mod 189 | # asignamos la columna de tiempo 190 | df_empty<- data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')) 191 | if (nrow(df_mod)> nrow(df_empty)) { 192 | df_mod <- df_mod %>% 193 | slice(1:nrow(df_empty)) 194 | } 195 | 196 | df_out <- df_empty %>% 197 | cbind(df_mod) 198 | 199 | return(df_out) 200 | } 201 | 202 | #============================================================================ 203 | files_eliminar <- c("down.R","down2.R","down3.R", "Histórico_Est.xlsx", "Tmáx_esta.xlsx", "Tmín_esta.xlsx") 204 | estaciones <- setdiff(dir(),files_eliminar) 205 | 206 | # Bucle para cada estacion 207 | n_estacion <- length(estaciones) 208 | 209 | for (estacion in 1:n_estacion) { 210 | # Bucle para que ejecute la regionalizaci?n para cada bloque y los exporte en excel por cada variable 211 | for (z in 1:3) { 212 | 213 | if (vec_var_3[z]=='PR') { 214 | # Seleccionamos el archivo de una variable 215 | var_hist <- data_pr_his[,c('FECHA',estaciones[estacion])] %>% 216 | mutate(FECHA= as.Date(FECHA)) 217 | 218 | mult.86400 <- function(x, na.rm=FALSE) (x*86400) 219 | 220 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585',vec_var_2[z],'.csv')) %>% 221 | mutate_if(is.numeric, mult.86400, na.rm=FALSE) %>% 222 | mutate(isodate = as.Date(isodate)) 223 | } 224 | 225 | if (vec_var_3[z]=='MAX') { 226 | # Seleccionamos el archivo de una variable 227 | var_hist <- data_tmax_his[,c('FECHA',estaciones[estacion])] %>% 228 | mutate(FECHA= as.Date(FECHA)) 229 | 230 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 231 | 232 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585',vec_var_2[z],'.csv')) %>% 233 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 234 | mutate(isodate = as.Date(isodate)) 235 | } 236 | 237 | if (vec_var_3[z]=='MIN') { 238 | # Seleccionamos el archivo de una variable 239 | var_hist <- data_tmax_his[,c('FECHA',estaciones[estacion])] %>% 240 | mutate(FECHA= as.Date(FECHA)) 241 | 242 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 243 | 244 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585',vec_var_2[z],'.csv')) %>% 245 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 246 | mutate(isodate = as.Date(isodate)) 247 | } 248 | 249 | # Hacemos un bucle para generar la tabla en cada bloque de tiempo 250 | list_time <- list() 251 | for (i in 1:3) { 252 | list_time[[i]] <- ds(fecha_in_his, fecha_fin_his, fecha_in[i], fecha_fin[i], var_hist, var_model, var = vec_var_3[z]) 253 | } 254 | 255 | # Exportamos 256 | name_file_out <- paste0(estaciones[estacion],'/',estaciones[estacion],'_',vec_var_3[z],'.xls') 257 | 258 | write.xlsx(list_time[[1]], file = name_file_out,row.names = F, 259 | sheetName = paste0(fecha_in_1,'-',fecha_fin_1), append = FALSE) 260 | 261 | write.xlsx(list_time[[2]], file = name_file_out, row.names = F, 262 | sheetName= paste0(fecha_in_2,'-',fecha_fin_2), append=TRUE) 263 | 264 | write.xlsx(list_time[[3]], file = name_file_out,row.names = F, 265 | sheetName= paste0(fecha_in_3,'-',fecha_fin_3), append=TRUE) 266 | 267 | rm(list_time) 268 | 269 | } 270 | 271 | } 272 | -------------------------------------------------------------------------------- /DownscalePT_Full__V1.0.r: -------------------------------------------------------------------------------- 1 | # Librerias 2 | library(dplyr) 3 | library(lubridate) 4 | library(qmap) 5 | library(zoo) 6 | library(latticeExtra) 7 | library(readxl) 8 | 9 | # Indicar la carpeta que contiene a las estaciones 10 | setwd('D:/charles/regionalizacion/') 11 | 12 | # llamamos a los archivos historicos 13 | data_pr_his <- read_excel("Histórico_Est.xlsx") 14 | data_tmax_his <- read_excel("Tmáx_esta.xlsx") 15 | data_tmin_his <- read_excel("Tmín_esta.xlsx") 16 | 17 | #============================================================================ 18 | # Configurar segun la data 19 | 20 | ## Numero de variables 21 | n_var <- 3 22 | 23 | ## Nombre del archivo de la data modelada que sigue despues del texto 'RCP4585' 24 | vec_var_2 <- c('TASMAX','TASMIN','PR') 25 | ## Nombre resumen de la variable (recomiendo dejar como esta) 26 | vec_var_3 <- c('MAX','MIN','PR') 27 | 28 | ## Fijar las fechas historicas para cada variable 29 | fecha_in_his_min <- as.Date('1950-01-01') 30 | fecha_fin_his_min <- as.Date('2019-12-31') 31 | 32 | fecha_in_his_max <- as.Date('1950-01-01') 33 | fecha_fin_his_max <- as.Date('2019-12-31') 34 | 35 | fecha_in_his_pr <- as.Date('1950-01-01') 36 | fecha_fin_his_pr <- as.Date('2016-12-31') 37 | 38 | ## Fijar las fechas de inicio y fin de la data modelada 39 | fecha_in_min <- as.Date('1950-01-01') 40 | fecha_fin_min <- as.Date('2099-12-31') 41 | 42 | fecha_in_max <- as.Date('1950-01-01') 43 | fecha_fin_max <- as.Date('2099-12-31') 44 | 45 | fecha_in_pr <- as.Date('1950-01-01') 46 | fecha_fin_pr <- as.Date('2099-12-31') 47 | 48 | #============================================================================ 49 | 50 | # Funci?n para generar la regionalizacion, para cada variable y para un solo bloque de tiempo 51 | 52 | ds <- function(time_ini_his, time_fin_his, time_ini, time_fin, data_his, data_model, data_model_his, var){ 53 | 54 | # time_ini <- as.Date('1980-01-01') 55 | # time_fin <- as.Date('2099-12-31') 56 | # #time_ini <- as.Date('2040-01-01') 57 | # #time_fin <- as.Date('2069-12-31') 58 | # #time_ini <- as.Date('2070-01-01') 59 | # #time_fin <- as.Date('2099-12-31') 60 | # time_ini_his <- as.Date('1980-01-01') 61 | # time_fin_his <- as.Date('2019-12-31') 62 | # # data_his <- var_hist 63 | # # data_model <- var_model 64 | # # var = 'MIN' 65 | # #data_model_his = var_model_hist 66 | 67 | data_model <-data_model %>% 68 | dplyr::select(isodate, starts_with('rcp')) 69 | 70 | data_model_his <- data_model_his %>% 71 | left_join(data_model_his, by = 'isodate') 72 | colnames(data_model_his) <- names(data_model) 73 | 74 | data_model <- rbind(data_model_his, data_model) 75 | 76 | # Creamos vectores con la informacion de los modelos 77 | name_mod <- sort(names(data_model)[which(substring(names(data_model),1,3)=='rcp')] ) 78 | n_model <- length(name_mod) 79 | # Creamos df vacios quienes van a contener a los resultados 80 | df_mod <- c() 81 | 82 | # Generamos la regionalizaci?n por tipo de proyeccion 83 | for (j in 1:n_model) { 84 | 85 | # configurar la variable historica 86 | var_hist_2 <- data_his %>% 87 | filter(FECHA >= time_ini_his & FECHA <= time_fin_his) 88 | colnames(var_hist_2) <- c('isodate','hist') 89 | 90 | var_hist_2 <- var_hist_2 %>% 91 | full_join( 92 | data.frame(isodate = seq(from=time_ini_his, to=time_fin_his, by ='day')), 93 | by = 'isodate') 94 | 95 | # configurar la variable del modelo 96 | var_model_2 <- data_model[, c(which(names(data_model) == 'isodate'), 97 | which(names(data_model) == name_mod[j]))] %>% 98 | filter(isodate >= time_ini & isodate <= time_fin) 99 | colnames(var_model_2) <- c('isodate','mode') 100 | 101 | var_model_2 <- var_model_2 %>% 102 | full_join( 103 | data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')), 104 | by = 'isodate') 105 | 106 | # creamos las variables a usar (solo para seguir el script de origen) 107 | OBS_hist <- var_hist_2 %>% 108 | rename(OBS_hist = hist) 109 | 110 | GCM_model <- var_model_2 111 | 112 | # aqui completo valores faltantes 113 | if (var=='PR') { 114 | OBS_hist <- OBS_hist %>% 115 | mutate(OBS_hist = ifelse(is.na(OBS_hist),0.1,OBS_hist)) 116 | } 117 | if (var=='MAX' | var=='MIN') { 118 | OBS_hist <- OBS_hist %>% 119 | mutate(OBS_hist = ifelse(is.na(OBS_hist),15,OBS_hist)) 120 | } 121 | 122 | # aqui completo valores faltantes 123 | if (var=='PR') { 124 | GCM_model <- GCM_model %>% 125 | mutate(mode = ifelse(is.na(mode),0.1,mode)) 126 | } 127 | if (var=='MAX' | var=='MIN') { 128 | GCM_model <- GCM_model %>% 129 | mutate(mode = ifelse(is.na(mode),15,mode)) 130 | } 131 | 132 | GCM_hist <- GCM_model 133 | 134 | data_hist <- OBS_hist %>% 135 | read.zoo() 136 | 137 | data_mod <- GCM_model %>% 138 | read.zoo() 139 | 140 | data_wt <- GCM_model %>% 141 | read.zoo() 142 | 143 | #============================================================================ 144 | # APLICACIÓN DE LA TÉCNICA DE QUANTILE MAPPING (MÉTODO EMPÍRICO) 145 | 146 | seasons_by_year <- list(c("Diciembre"), c("Enero"), c("Febrero"), 147 | c("Marzo"), c("Abril"), c("Mayo"), 148 | c("Junio"), c("Julio"), c("Agosto"), 149 | c("Setiembre"), c("Octubre"), c("Noviembre")) 150 | 151 | for(i in 1:12) { 152 | 153 | obs_sl <- data_hist[months(time(data_hist)) %in% seasons_by_year[[i]]] 154 | mod_sl <- data_mod[months(time(data_mod)) %in% seasons_by_year[[i]]] 155 | #MODEL, read!: L. Gudmundsson et al. (2012) 156 | 157 | if (sum(mod_sl, na.rm = T)==0) { 158 | mod_sl[1]<- 0.001 159 | } 160 | 161 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 162 | mod = coredata(mod_sl), 163 | qstep = 0.001, 164 | nboot = 1, 165 | wet.day = 0, # Adaptado para temperatuas negativas 166 | type = "linear") 167 | 168 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 169 | 170 | data_wt[ months(time(data_wt)) %in% seasons_by_year[[i]]] <- mod_sl_qmapped 171 | 172 | } 173 | 174 | t <- as.data.frame(data_wt) 175 | 176 | if (j == 1) { 177 | df_mod <- t 178 | } else{ 179 | df_mod <- cbind(df_mod, t) 180 | } 181 | 182 | } 183 | 184 | # agregamos losnombres correctas de las columnas 185 | colnames(df_mod) <- name_mod 186 | 187 | # asignamos la columna de tiempo 188 | df_empty<- data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')) 189 | 190 | df_out <- df_empty %>% 191 | cbind(df_mod) 192 | 193 | return(df_out) 194 | } 195 | 196 | #============================================================================ 197 | files_eliminar <- c("down.R","down2.R","down3.R", "Histórico_Est.xlsx", "Tmáx_esta.xlsx", "Tmín_esta.xlsx") 198 | estaciones <- setdiff(dir(),files_eliminar) 199 | 200 | # Bucle para cada estacion 201 | n_estacion <- length(estaciones) 202 | 203 | for (estacion in 1:n_estacion) { 204 | # Bucle para que ejecute la regionalizaci?n para cada bloque y los exporte en excel por cada variable 205 | for (z in 1:3) { 206 | 207 | if (vec_var_3[z]=='MAX') { 208 | 209 | # Seleccionamos el archivo de una variable 210 | var_hist <- data_tmax_his[,c('FECHA',estaciones[estacion])] %>% 211 | mutate(FECHA= as.Date(FECHA)) 212 | 213 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 214 | 215 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585TASMAX.csv')) %>% 216 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 217 | mutate(isodate = as.Date(isodate)) 218 | 219 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTASMAX.csv')) %>% 220 | dplyr::select(isodate, starts_with('hist')) %>% 221 | mutate(isodate = as.Date(isodate)) %>% 222 | mutate_if(is.numeric, dif.273, na.rm=FALSE) 223 | 224 | list_time <- ds(time_ini_his = fecha_in_his_max, 225 | time_fin_his = fecha_fin_his_max, 226 | time_ini = fecha_in_max, 227 | time_fin = fecha_fin_max, 228 | data_his = var_hist, 229 | data_model = var_model, 230 | data_model_his = var_model_hist, 231 | var = 'MAX') 232 | } 233 | 234 | if (vec_var_3[z]=='MIN') { 235 | # Seleccionamos el archivo de una variable 236 | var_hist <- data_tmin_his[,c('FECHA',estaciones[estacion])] %>% 237 | mutate(FECHA= as.Date(FECHA)) 238 | 239 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 240 | 241 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585TASMIN.csv')) %>% 242 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 243 | mutate(isodate = as.Date(isodate)) 244 | 245 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTASMIN.csv')) %>% 246 | dplyr::select(isodate, starts_with('hist')) %>% 247 | mutate(isodate = as.Date(isodate)) %>% 248 | mutate_if(is.numeric, dif.273, na.rm=FALSE) 249 | 250 | list_time <- ds(time_ini_his = fecha_in_his_min, 251 | time_fin_his = fecha_fin_his_min, 252 | time_ini = fecha_in_min, 253 | time_fin = fecha_fin_min, 254 | data_his = var_hist, 255 | data_model = var_model, 256 | data_model_his = var_model_hist, 257 | var = 'MIN') 258 | 259 | } 260 | 261 | if (vec_var_3[z]=='PR') { 262 | # Seleccionamos el archivo de una variable 263 | 264 | var_hist <- data_pr_his[,c('FECHA',estaciones[estacion])] %>% 265 | mutate(FECHA= as.Date(FECHA)) 266 | 267 | mult.86400 <- function(x, na.rm=FALSE) (x*86400) 268 | 269 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585PR.csv')) %>% 270 | mutate_if(is.numeric, mult.86400, na.rm=FALSE) %>% 271 | mutate(isodate = as.Date(isodate)) 272 | 273 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTPR.csv')) %>% 274 | dplyr::select(isodate, starts_with('hist')) %>% 275 | mutate(isodate = as.Date(isodate)) %>% 276 | mutate_if(is.numeric, mult.86400, na.rm=FALSE) 277 | 278 | list_time <- ds(time_ini_his = fecha_in_his_pr, 279 | time_fin_his = fecha_fin_his_pr, 280 | time_ini = fecha_in_pr, 281 | time_fin = fecha_fin_pr, 282 | data_his = var_hist, 283 | data_model = var_model, 284 | data_model_his = var_model_hist, 285 | var = 'PR') 286 | } 287 | 288 | # Exportamos 289 | name_file_out <- paste0(estaciones[estacion],'/',estaciones[estacion],'_',vec_var_3[z],'.csv') 290 | 291 | write.csv2(list_time, file = name_file_out,row.names = F) 292 | 293 | rm(list_time) 294 | 295 | } 296 | 297 | } 298 | 299 | -------------------------------------------------------------------------------- /DownscalePT_full_CMI6_V1.0.r: -------------------------------------------------------------------------------- 1 | ################################################################################ 2 | # STATISTICAL DOWNSCALING OF DAILY CLIMATE DATA USING # 3 | # QUANTILE MAPPING TECHNIQUE FOR CMIP6 DATASETS # 4 | ################################################################################ 5 | 6 | #Author: Julio Montenegro Gambini, P.E. ASCE, M.Sc., 7 | #PhD fellow - Technische Universiteit Delft (TU Delft), Netherlands. 8 | 9 | #Current version: 1.0 10 | 11 | #©Copyright 2013 2021 Julio Montenegro. 12 | #This script is strictly under license GPLv3 13 | #License details: https://www.gnu.org/licenses/gpl-3.0.en.html 14 | 15 | # Please, when using this script, cite as: "Montenegro, J. (2021). Statistical 16 | #downscaling of daily climate data using quantile mapping technique for CMIP6 17 | #datasets" 18 | 19 | # Installing or loading the required packages ================================== 20 | library(tidyverse) 21 | library(lubridate) 22 | library(qmap) 23 | library(zoo) 24 | library(latticeExtra) 25 | library(readxl) 26 | 27 | #Directories and folders ======================================================= 28 | 29 | # Setting working directory 30 | setwd('C:/Users/Julio/Downloads/QMAP_CMIP6/qmap2/') 31 | 32 | # Name of the folder which contains csv and xlsx excel files 33 | dir_estation <- '585 M3' 34 | 35 | # Parameter configuration ====================================================== 36 | 37 | # DAILY MINIMUM TEMPERATURE PARAMETERS 38 | file_tmin_obs <- 'TASMIN_OBS.xlsx' 39 | file_tmin_mod <- 'TASMIN.csv' 40 | fecha_in_his_min <- as.Date('1981-01-01') 41 | fecha_fin_his_min <- as.Date('2019-12-31') 42 | fecha_in_min <- as.Date('2015-01-01') 43 | fecha_fin_min <- as.Date('2099-12-31') 44 | 45 | # DAILY MAXIMUM TEMPERATURE PARAMETERS 46 | file_tmax_obs <- 'TASMAX_OBS.xlsx' 47 | file_tmax_mod <- 'TASMAX.csv' 48 | fecha_in_his_max <- as.Date('1981-01-01') 49 | fecha_fin_his_max <- as.Date('2019-12-31') 50 | fecha_in_max <- as.Date('2015-01-01') 51 | fecha_fin_max <- as.Date('2099-12-31') 52 | 53 | # DAILY PRECIPITATION PARAMETERS 54 | file_pr_obs <- 'PR_OBS.xlsx' 55 | file_pr_mod <- 'PR.csv' 56 | fecha_in_his_pr <- as.Date('1981-01-01') 57 | fecha_fin_his_pr <- as.Date('2016-12-31') 58 | fecha_in_pr <- as.Date('2015-01-01') 59 | fecha_fin_pr <- as.Date('2099-12-31') 60 | 61 | #Quantile mapping function ===================================================== 62 | 63 | # Downscaling function for each variable 64 | qp_function <- function(time_ini_his, time_fin_his, 65 | time_ini, time_fin, 66 | file_his, file_mod, 67 | var){ 68 | 69 | time_ini <- fecha_in_min 70 | time_fin <- fecha_fin_min 71 | time_ini_his <- fecha_in_his_min 72 | time_fin_his <- fecha_fin_his_min 73 | file_his <- file_tmin_obs 74 | file_mod <- file_tmin_mod 75 | var = 'MIN' 76 | j <- 2 77 | 78 | ## Reading historical baseline and GCM/RCM future time series 79 | data_historica <- read_excel(paste0(dir_estation,'/',file_his)) 80 | data_modelada <- read.csv(paste0(dir_estation,'/',file_mod)) 81 | 82 | names_stations <- names(data_historica)[2:length(data_historica)] 83 | 84 | df_reg <- c() # empty dataframe for filling with downscaled data 85 | 86 | ## Downscaling loop for different time series (station data) 87 | for (j in 2:ncol(data_historica)) { 88 | 89 | ## Historical baseline configuration 90 | data_his <- data_historica[,c(1,j)] 91 | colnames(data_his) <- c('FECHA','STATION') 92 | data_his <- data_his %>% mutate(FECHA= as.Date(FECHA)) 93 | 94 | ## GCM/RCM data configuration 95 | if (var %in% c('MIN','MAX')) { 96 | correccion <- function(x, na.rm=FALSE) (x-273.15) 97 | } 98 | if (var %in% c('PR')) { 99 | correccion <- function(x, na.rm=FALSE) (x*86400) 100 | } 101 | 102 | data_mod <- data_modelada[,c(1,j)] %>% 103 | mutate_if(is.numeric, correccion, na.rm=FALSE) 104 | colnames(data_mod) <- c('FECHA','STATION') 105 | data_model <- data_mod %>% mutate(FECHA= as.Date(FECHA)) 106 | 107 | ## Date filtering 108 | 109 | ### Historical 110 | var_hist_2 <- data_his %>% 111 | filter(FECHA >= time_ini_his & FECHA <= time_fin_his) 112 | colnames(var_hist_2) <- c('isodate','hist') 113 | 114 | var_hist_2 <- var_hist_2 %>% 115 | full_join( 116 | data.frame(isodate = seq(from=time_ini_his, to=time_fin_his, by ='day')), 117 | by = 'isodate') 118 | 119 | ### Setting the variable of GCM/RCM data 120 | var_model_2 <- data_model %>% 121 | filter(FECHA >= time_ini & FECHA <= time_fin) 122 | colnames(var_model_2) <- c('isodate','mode') 123 | 124 | var_model_2 <- var_model_2 %>% 125 | full_join( 126 | data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')), 127 | by = 'isodate') 128 | 129 | ## Creating variables to be used 130 | OBS_hist <- var_hist_2 %>% 131 | rename(OBS_hist = hist) 132 | 133 | GCM_model <- var_model_2 134 | 135 | # Filling missing historical data 136 | if (var=='PR') { 137 | OBS_hist <- OBS_hist %>% 138 | mutate(OBS_hist = ifelse(is.na(OBS_hist),0.1,OBS_hist)) 139 | } 140 | if (var=='MAX' | var=='MIN') { 141 | OBS_hist <- OBS_hist %>% 142 | mutate(OBS_hist = ifelse(is.na(OBS_hist),16,OBS_hist)) 143 | } 144 | 145 | # Filling missing GCM/RCM future data 146 | if (var=='PR') { 147 | GCM_model <- GCM_model %>% 148 | mutate(mode = ifelse(is.na(mode),0.1,mode)) 149 | } 150 | if (var=='MAX' | var=='MIN') { 151 | GCM_model <- GCM_model %>% 152 | mutate(mode = ifelse(is.na(mode),15,mode)) 153 | } 154 | 155 | GCM_hist <- GCM_model 156 | 157 | ## Conversion of datasets to "ts" objects 158 | 159 | data_hist <- OBS_hist %>% 160 | read.zoo() 161 | 162 | data_mod <- GCM_model %>% 163 | read.zoo() 164 | 165 | data_wt <- GCM_model %>% 166 | read.zoo() 167 | 168 | #============================================================================ 169 | # QUANTILE MAPPING APPLICATION (EMPIRICAL AS DEFAULT) 170 | 171 | seasons_by_year <- list(c("December"), c("January"), c("February"), 172 | c("March"), c("April"), c("May"), 173 | c("June"), c("July"), c("August"), 174 | c("September"), c("October"), c("November")) 175 | #According to system region, month names should be changed (e.g. spanish): 176 | #seasons_by_year <- list(c("Diciembre"), c("Enero"), c("Febrero"), 177 | #c("Marzo"), c("Abril"), c("Mayo"), 178 | #c("Junio"), c("Julio"), c("Agosto"), 179 | #c("Septiembre"), c("Octubre"), c("Noviembre")) 180 | 181 | for(i in 1:12) { 182 | 183 | obs_sl <- data_hist[months(time(data_hist)) %in% seasons_by_year[[i]]] 184 | mod_sl <- data_mod[months(time(data_mod)) %in% seasons_by_year[[i]]] 185 | #GCM/RCM data, read!: L. Gudmundsson et al. (2012) 186 | 187 | if (sum(mod_sl, na.rm = T)==0) { 188 | mod_sl[1]<- 0.001 189 | } 190 | 191 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 192 | mod = coredata(mod_sl), 193 | qstep = 0.001, 194 | nboot = 1, 195 | wet.day = 0, # To be changed for temperatures 196 | type = "linear") 197 | 198 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 199 | 200 | data_wt[ months(time(data_wt)) %in% seasons_by_year[[i]]] <- mod_sl_qmapped 201 | 202 | } 203 | 204 | t <- as.data.frame(data_wt) 205 | 206 | if (j == 2) { 207 | df_reg <- t 208 | } else{ 209 | df_reg <- cbind(df_reg, t) 210 | } 211 | 212 | } 213 | 214 | # Adding column names 215 | colnames(df_reg) <- names_stations 216 | 217 | # Assign the date column (daily) 218 | df_empty<- data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')) 219 | 220 | df_out <- df_empty %>% 221 | cbind(df_reg) 222 | 223 | return(df_out) 224 | } 225 | 226 | #Applying downscaling function and exporting results =========================== 227 | 228 | # Dowscaling for minimum temperature 229 | tmin_reg <- qp_function(fecha_in_min, fecha_fin_min, 230 | fecha_in_his_min, fecha_fin_his_min, 231 | file_tmin_obs, file_tmin_mod, 'MIN') 232 | 233 | # Dowscaling for maximum temperature 234 | tmax_reg <- qp_function(fecha_in_max, fecha_fin_max, 235 | fecha_in_his_max, fecha_fin_his_max, 236 | file_tmax_obs, file_tmax_mod, 'MAX') 237 | 238 | # Dowscaling for precipitation 239 | pr_reg <- qp_function(fecha_in_pr, fecha_fin_pr, 240 | fecha_in_his_pr, fecha_fin_his_pr, 241 | file_pr_obs, file_pr_mod, 'PR') 242 | 243 | 244 | # Exporting downscaled data in 3 csv files ===================================== 245 | tmin_reg %>% write.csv(file = paste0(dir_estation,'/tmin_reg.csv'),row.names = F) 246 | tmax_reg %>% write.csv(file = paste0(dir_estation,'/tmax_reg.csv'),row.names = F) 247 | tmin_reg %>% write.csv(file = paste0(dir_estation,'/pr_reg.csv'),row.names = F) 248 | -------------------------------------------------------------------------------- /DownscalePT_full_V1.1.r: -------------------------------------------------------------------------------- 1 | ################################################################################ 2 | # STATISTICAL DOWNSCALING OF DAILY CLIMATE DATA USING # 3 | # QUANTILE MAPPING (QPM)TECHNIQUE # 4 | ################################################################################ 5 | 6 | #Author: Julio Montenegro Gambini, P.E. ASCE, M.Sc., 7 | #PhD fellow - Technische Universiteit Delft (TU Delft), Netherlands. 8 | 9 | #Current version: 1.1 10 | 11 | # © Copyright 2021 Julio Montenegro. 12 | # This script is strictly under license GPLv3 13 | # License details: https://www.gnu.org/licenses/gpl-3.0.en.html 14 | 15 | # Please, when using this script, cite as: "Montenegro, J. (2021). Statistical 16 | # downscaling of daily climate data using quantile mapping technique with 17 | # NASA-NEX GDDP source " 18 | 19 | #For this script you need: 20 | #' - 3 files of the observed data in the working folder. 21 | #1 folder with name of the station and inside it, 3 files with historical modelled data (matching the observed data) 22 | #3 files with the historical modelled data (matching with the observed data) 23 | #and 3 files with the future modelled data 24 | 25 | # Required packages: 26 | library(tidyverse) 27 | library(lubridate) 28 | library(qmap) 29 | library(zoo) 30 | library(latticeExtra) 31 | library(readxl) 32 | 33 | # Setting working directory: 34 | setwd('C:/Users/Julio/Downloads/qmap/qmap') 35 | 36 | # llamamos a los archivos historicos 37 | data_pr_his <- read_excel("Histórico_Est.xlsx") 38 | data_tmax_his <- read_excel("Tmáx_esta.xlsx") 39 | data_tmin_his <- read_excel("Tmín_esta.xlsx") 40 | 41 | #============================================================================ 42 | # Settings according to data structure 43 | 44 | ## Numero de variables 45 | n_var <- 3 46 | 47 | ## File name of the modelled data file following the text 'RCP4585'. 48 | vec_var_2 <- c('TASMAX','TASMIN','PR') 49 | ## Short name of variables 50 | vec_var_3 <- c('MAX','MIN','PR') 51 | 52 | ## Setting historical time intervals 53 | fecha_in_his_min <- as.Date('1980-01-01') 54 | fecha_fin_his_min <- as.Date('2019-12-31') 55 | 56 | fecha_in_his_max <- as.Date('1980-01-01') 57 | fecha_fin_his_max <- as.Date('2019-12-31') 58 | 59 | fecha_in_his_pr <- as.Date('1980-01-01') 60 | fecha_fin_his_pr <- as.Date('2016-12-31') 61 | 62 | ## Setting model time intervals 63 | fecha_in_min <- as.Date('1950-01-01') 64 | fecha_fin_min <- as.Date('2099-12-31') 65 | 66 | fecha_in_max <- as.Date('1950-01-01') 67 | fecha_fin_max <- as.Date('2099-12-31') 68 | 69 | fecha_in_pr <- as.Date('1950-01-01') 70 | fecha_fin_pr <- as.Date('2099-12-31') 71 | 72 | #============================================================================ 73 | 74 | # Downscaling function for each variable and for a single time block. 75 | 76 | ds <- function(time_ini_his, time_fin_his, time_ini, time_fin, data_his, data_model, data_model_his, var){ 77 | 78 | # time_ini <- as.Date('1980-01-01') 79 | # time_fin <- as.Date('2099-12-31') 80 | # # time_ini <- as.Date('2040-01-01') 81 | # # time_fin <- as.Date('2069-12-31') 82 | # # time_ini <- as.Date('2070-01-01') 83 | # # time_fin <- as.Date('2099-12-31') 84 | # time_ini_his <- as.Date('1980-01-01') 85 | # time_fin_his <- as.Date('2019-12-31') 86 | # data_his <- var_hist 87 | # data_model <- var_model 88 | # var = 'MAX' 89 | # data_model_his = var_model_hist 90 | 91 | 92 | data_model <-data_model %>% 93 | dplyr::select(isodate, starts_with('rcp')) 94 | 95 | data_model_his <- data_model_his %>% 96 | left_join(data_model_his, by = 'isodate') 97 | colnames(data_model_his) <- names(data_model) 98 | 99 | data_model <- rbind(data_model_his, data_model) 100 | 101 | # Creating vectors with model datasets 102 | name_mod <- sort(names(data_model)[which(substring(names(data_model),1,3)=='rcp')] ) 103 | n_model <- length(name_mod) 104 | # Generating empty dataframes 105 | df_mod <- c() 106 | 107 | # Downscaling for each pathway (4.5 and 8.5) 108 | for (j in 1:n_model) { 109 | 110 | # configurar la variable historica 111 | var_hist_2 <- data_his %>% 112 | filter(FECHA >= time_ini_his & FECHA <= time_fin_his) 113 | colnames(var_hist_2) <- c('isodate','hist') 114 | 115 | var_hist_2 <- var_hist_2 %>% 116 | full_join( 117 | data.frame(isodate = seq(from=time_ini_his, to=time_fin_his, by ='day')), 118 | by = 'isodate') 119 | 120 | # Setting the model variables 121 | var_model_2 <- data_model[, c(which(names(data_model) == 'isodate'), 122 | which(names(data_model) == name_mod[j]))] %>% 123 | filter(isodate >= time_ini & isodate <= time_fin) 124 | colnames(var_model_2) <- c('isodate','mode') 125 | 126 | var_model_2 <- var_model_2 %>% 127 | full_join( 128 | data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')), 129 | by = 'isodate') 130 | 131 | # Creating the variables to be used 132 | OBS_hist <- var_hist_2 %>% 133 | rename(OBS_hist = hist) 134 | 135 | GCM_model <- var_model_2 136 | 137 | # Filling missing data 138 | if (var=='PR') { 139 | OBS_hist <- OBS_hist %>% 140 | mutate(OBS_hist = ifelse(is.na(OBS_hist),0.1,OBS_hist)) 141 | } 142 | if (var=='MAX' | var=='MIN') { 143 | OBS_hist <- OBS_hist %>% 144 | mutate(OBS_hist = ifelse(is.na(OBS_hist),16,OBS_hist)) 145 | } 146 | 147 | # Again, filling missing data 148 | if (var=='PR') { 149 | GCM_model <- GCM_model %>% 150 | mutate(mode = ifelse(is.na(mode),0.1,mode)) 151 | } 152 | if (var=='MAX' | var=='MIN') { 153 | GCM_model <- GCM_model %>% 154 | mutate(mode = ifelse(is.na(mode),15,mode)) 155 | } 156 | 157 | GCM_hist <- GCM_model 158 | 159 | data_hist <- OBS_hist %>% 160 | read.zoo() 161 | 162 | data_mod <- GCM_model %>% 163 | read.zoo() 164 | 165 | data_wt <- GCM_model %>% 166 | read.zoo() 167 | 168 | #============================================================================ 169 | # APPLICATION OF QUANTILE MAPPING (EMPIRICAL METHOD) 170 | 171 | seasons_by_year <- list(c("December"), c("January"), c("February"), 172 | c("March"), c("April"), c("May"), 173 | c("June"), c("July"), c("August"), 174 | c("September"), c("October"), c("November")) 175 | 176 | for(i in 1:12) { 177 | 178 | obs_sl <- data_hist[months(time(data_hist)) %in% seasons_by_year[[i]]] 179 | mod_sl <- data_mod[months(time(data_mod)) %in% seasons_by_year[[i]]] 180 | #MODEL, read!: L. Gudmundsson et al. (2012) 181 | 182 | if (sum(mod_sl, na.rm = T)==0) { 183 | mod_sl[1]<- 0.001 184 | } 185 | 186 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 187 | mod = coredata(mod_sl), 188 | qstep = 0.001, 189 | nboot = 1, 190 | wet.day = 0, # May be changed in case of temperatures 191 | type = "linear") 192 | 193 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 194 | 195 | data_wt[ months(time(data_wt)) %in% seasons_by_year[[i]]] <- mod_sl_qmapped 196 | 197 | } 198 | 199 | t <- as.data.frame(data_wt) 200 | 201 | if (j == 1) { 202 | df_mod <- t 203 | } else{ 204 | df_mod <- cbind(df_mod, t) 205 | } 206 | 207 | } 208 | 209 | # Add column names 210 | colnames(df_mod) <- name_mod 211 | 212 | # Add time column 213 | df_empty<- data.frame(isodate = seq(from=time_ini, to=time_fin, by ='day')) 214 | 215 | df_out <- df_empty %>% 216 | cbind(df_mod) 217 | 218 | return(df_out) 219 | } 220 | 221 | #============================================================================ 222 | files_eliminar <- c("down.R","down2.R","down3.R", "Histórico_Est.xlsx", "Tmáx_esta.xlsx", "Tmín_esta.xlsx") 223 | estaciones <- setdiff(dir(),files_eliminar) 224 | 225 | # Bucle para cada estacion 226 | n_estacion <- length(estaciones) 227 | 228 | for (estacion in 1:n_estacion) { 229 | # Downscaling loop for each block and export to excel for each variable. 230 | for (z in 1:3) { 231 | 232 | if (vec_var_3[z]=='MAX') { 233 | 234 | # Select the file of a variable 235 | var_hist <- data_tmax_his[,c('FECHA',estaciones[estacion])] %>% 236 | mutate(FECHA= as.Date(FECHA)) 237 | 238 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 239 | 240 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585TASMAX.csv')) %>% 241 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 242 | mutate(isodate = as.Date(isodate)) 243 | 244 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTASMAX.csv')) %>% 245 | dplyr::select(isodate, starts_with('hist')) %>% 246 | mutate(isodate = as.Date(isodate)) %>% 247 | mutate_if(is.numeric, dif.273, na.rm=FALSE) 248 | 249 | list_time <- ds(time_ini_his = fecha_in_his_max, 250 | time_fin_his = fecha_fin_his_max, 251 | time_ini = fecha_in_max, 252 | time_fin = fecha_fin_max, 253 | data_his = var_hist, 254 | data_model = var_model, 255 | data_model_his = var_model_hist, 256 | var = 'MAX') 257 | } 258 | 259 | if (vec_var_3[z]=='MIN') { 260 | # Select the file of a variable 261 | var_hist <- data_tmin_his[,c('FECHA',estaciones[estacion])] %>% 262 | mutate(FECHA= as.Date(FECHA)) 263 | 264 | dif.273 <- function(x, na.rm=FALSE) (x-273.15) 265 | 266 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585TASMIN.csv')) %>% 267 | mutate_if(is.numeric, dif.273, na.rm=FALSE) %>% 268 | mutate(isodate = as.Date(isodate)) 269 | 270 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTASMIN.csv')) %>% 271 | dplyr::select(isodate, starts_with('hist')) %>% 272 | mutate(isodate = as.Date(isodate)) %>% 273 | mutate_if(is.numeric, dif.273, na.rm=FALSE) 274 | 275 | list_time <- ds(time_ini_his = fecha_in_his_min, 276 | time_fin_his = fecha_fin_his_min, 277 | time_ini = fecha_in_min, 278 | time_fin = fecha_fin_min, 279 | data_his = var_hist, 280 | data_model = var_model, 281 | data_model_his = var_model_hist, 282 | var = 'MIN') 283 | 284 | } 285 | 286 | if (vec_var_3[z]=='PR') { 287 | # Select the file of a variable 288 | 289 | var_hist <- data_pr_his[,c('FECHA',estaciones[estacion])] %>% 290 | mutate(FECHA= as.Date(FECHA)) 291 | 292 | mult.86400 <- function(x, na.rm=FALSE) (x*86400) 293 | 294 | var_model <- read.csv(paste0(estaciones[estacion],'/RCP4585PR.csv')) %>% 295 | mutate_if(is.numeric, mult.86400, na.rm=FALSE) %>% 296 | mutate(isodate = as.Date(isodate)) 297 | 298 | var_model_hist <- read.csv(paste0(estaciones[estacion],'/HISTPR.csv')) %>% 299 | dplyr::select(isodate, starts_with('hist')) %>% 300 | mutate(isodate = as.Date(isodate)) %>% 301 | mutate_if(is.numeric, mult.86400, na.rm=FALSE) 302 | 303 | list_time <- ds(time_ini_his = fecha_in_his_pr, 304 | time_fin_his = fecha_fin_his_pr, 305 | time_ini = fecha_in_pr, 306 | time_fin = fecha_fin_pr, 307 | data_his = var_hist, 308 | data_model = var_model, 309 | data_model_his = var_model_hist, 310 | var = 'PR') 311 | } 312 | 313 | # Exporting data 314 | name_file_out <- paste0(estaciones[estacion],'/',estaciones[estacion],'_',vec_var_3[z],'.csv') 315 | 316 | write.csv(list_time, file = name_file_out,row.names = F) 317 | 318 | rm(list_time) 319 | 320 | } 321 | 322 | } 323 | 324 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Qmapping-Daily.R: -------------------------------------------------------------------------------- 1 | ############################################################################### 2 | #DOWNSCALLING ESTADÍSTICO DE DATOS CLIMÁTICOS USANDO MAPEO DE QUANTILES (QMAP)# 3 | ############################################################################### 4 | #Referencias: 5 | #Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods 6 | #https://www.hydrol-earth-syst-sci.net/16/3383/2012/ 7 | #Qmap package information - CRAN: 8 | #https://cran.r-project.org/web/packages/qmap/qmap.pdf 9 | #============================================================================== 10 | # INSTALAR Y CARGAR LOS PAQUETES NECESARIOS 11 | setwd("C:/Users/Julio/Documents/INVESTIGACION/R SCRIPTS/Downscaling/Diario") 12 | getwd() 13 | ls() 14 | rm(list=ls()) 15 | #por ejemplo: 16 | #install.packages(c("qmap", "zoo","latticeExtra")) 17 | 18 | # CARGAR LAS LIBRERIAS 19 | library(qmap) 20 | library(zoo) 21 | library(latticeExtra) 22 | 23 | #============================================================================= 24 | #CARGANDO LOS ARCHIVOS NECESARIOS 25 | 26 | # Observacion historica PISCO 27 | OBS_hist <- read.zoo("0Estacion_C2.csv", header = TRUE, sep = ",", 28 | format = "%Y-%m-%d") 29 | # Observacion del modelo GCM 30 | GCM_model <- read.zoo("ACCESS1-0-RCP45.csv", header = TRUE, sep = ",", 31 | format = "%Y-%m-%d") 32 | 33 | # Ajustando la serie hasta el final de la data hist?rica 34 | GCM_hist <- window(GCM_model, end = "2039-12-31") 35 | 36 | data_at <- cbind(OBS_hist, GCM_model) 37 | data_wt <- cbind(OBS_hist, GCM_hist) 38 | 39 | #============================================================================ 40 | #GRAFICANDO LAS SERIES DE TIEMPO 41 | plot(data_at, plot.type = "single", col = c(1, 2), lwd = 0.01 42 | , ylab = "pp (mm/day)", xlab = "years") 43 | title(expression(bold("OBSERVED DAILY HISTORICAL DATA vs"*phantom("GCM DAILY HISTORICAL DATA"))), col.main = "black") 44 | title(expression(bold(phantom("OBSERVED DAILY HISTORICAL DATA vs ")*"GCM DAILY HISTORICAL DATA")), col.main = "red") 45 | 46 | plot(data_wt, plot.type = "single", col = c(1, 2), lwd = 0.01 47 | , ylab = "pp (mm/day)", xlab = "years") 48 | title(expression(bold("OBSERVED DAILY DATA vs"*phantom("GCM DAILY DATA"))), col.main = "black") 49 | title(expression(bold(phantom("OBSERVED DAILY DATA vs ")*"GCM DAILY DATA")), col.main = "red") 50 | 51 | 52 | # months(time(data_wt)) correr y luego cambiar de acuerdo al nombre de los meses 53 | plot(data_wt[months(time(data_wt)) %in% c("December","January","February","March")], 54 | plot.type = "single", col = c(1, 2), lwd = 1, type='p' 55 | , ylab = "pp (mm/day)", xlab = "years") 56 | title(expression(bold("OBSERVED DAILY DATA VS "*phantom("GCM DAILY DATA")*"DURING RAINY SEASON (DEC-JAN-FEB-MAR)")), col.main = "black", cex.main=1) 57 | title(expression(bold(phantom("OBSERVED DAILY DATA vs ")*"GCM DAILY DATA"*phantom("DURING RAINY SEASON (DEC-JAN-FEB-MAR)"))), col.main = "red", cex.main=1) 58 | 59 | 60 | # GRAFICANDO SCATTERPLOT 61 | plot(GCM_hist~OBS_hist, coredata(data_wt), col = c(1,2), ylab = "PP GCM (mm)", xlab = "PP OBSERVED (mm)") 62 | title(expression(bold("SCATTER PLOT OF OBSERVED DAILY DATA VS "*phantom("GCM DAILY DATA"))), col.main = "black", cex.main=1) 63 | title(expression(bold(phantom("SCATTER PLOT OF OBSERVED DAILY DATA vs ")*"GCM DAILY DATA")), col.main = "red", cex.main=1) 64 | 65 | plot(GCM_hist~OBS_hist, 66 | coredata(data_wt[months(time(data_wt)) %in% 67 | c("December","January","February","March")]), col = c(1,2),ylab = "PP GCM (mm)", xlab = "PP OBSERVED (mm)") 68 | title(expression(bold("SCATTER PLOT OF OBSERVED DAILY DATA VS"*phantom(" GCM DAILY DATA ")* "DURING RAINY SEASON (DEC-JAN-FEB-MAR)")), col.main = "black", cex.main=0.8) 69 | title(expression(bold(phantom("SCATTER PLOT OF OBSERVED DAILY DATA vs ")*" GCM DAILY DATA "* phantom("DURING RAINY SEASON (DEC-JAN-FEB-MAR)"))), col.main = "red", cex.main=0.8) 70 | 71 | # GRAFICANDO ECDF 72 | ecdfplot(~ OBS_hist + GCM_hist, data = data.frame(data_wt), lwd = 2, col = c(1, 2),main="CDF PLOT OF OBSERVED DAILY DATA VS GCM DAILY DATA",ylab = "Empirical CDF", xlab = "Daily GCM (red) and Observed (black) data") 73 | 74 | ecdfplot(~ OBS_hist + GCM_hist, 75 | data = data.frame(data_wt[months(time(data_wt)) %in% 76 | c("December","January","February")]), 77 | lwd = 2, col = c(1, 2),main="CDF PLOT OF DAILY OBS. VS GCM DATA DURING RAINFALL SEASON",ylab = "Empirical CDF", xlab = "Daily GCM (red) and Observed (black) data") 78 | 79 | #============================================================================ 80 | # APLICACIÓN DE LA TÉCNICA DE QUANTILE MAPPING (MÉTODO EMPÍRICO) 81 | 82 | data_wt$gcm_downscaled <- data_wt$GCM_hist 83 | 84 | seasons_by_year <- list(c("December","January","February"), 85 | c("March","April","May"), 86 | c("June","July","August"), 87 | c("September","October","November")) 88 | 89 | seasonal_qm_fit_model <- list() 90 | 91 | ## funcion de quantil maping 0.1 es a correcion, es linea 92 | for(i in 1:4) { 93 | obs_sl <- data_wt[months(time(data_wt)) %in% seasons_by_year[[i]]]$OBS_hist 94 | mod_sl <- data_wt[months(time(data_wt)) %in% seasons_by_year[[i]]]$GCM_hist 95 | #MODEL, read!: L. Gudmundsson et al. (2012) 96 | qm_fit <- fitQmapQUANT(obs = coredata(obs_sl), 97 | coredata(mod_sl), 98 | qstep = 0.01, 99 | nboot = 1, 100 | wet.day = 0, # Adaptado para temperatuas negativas 101 | type = "linear") 102 | 103 | mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), qm_fit, type = "linear") 104 | data_wt$gcm_downscaled[ months(time(data_wt)) %in% 105 | seasons_by_year[[i]]] <- mod_sl_qmapped 106 | 107 | seasonal_qm_fit_model[[i]] <- qm_fit 108 | } 109 | 110 | #============================================================================ 111 | # GR?FICOS DE SERIES TEMPORALES 112 | plot(data_wt, plot.type = "single", col = c(1, 2, 4), lwd = 1,xlab = "Years", ylab = "pp (mm/day)") 113 | title(expression(bold("OBSERVED DAILY HISTORICAL DATA vs" * phantom(" GCM DAILY DATA ") * phantom("vs DOWNSCALED GCM DAILY DATA"))), col.main = "black",cex.main=0.9) 114 | title(expression(bold(phantom("OBSERVED DAILY HISTORICAL DATA vs ") * " GCM DAILY DATA " * phantom("vs DOWNSCALED GCM DAILY DATA"))), col.main = "red",cex.main=0.9) 115 | title(expression(bold(phantom("OBSERVED DAILY HISTORICAL DATA vs ") * phantom(" GCM DAILY DATA ") *"vs DOWNSCALED GCM DAILY DATA")), col.main = "dodgerblue3",cex.main=0.9) 116 | 117 | plot(data_wt[months(time(data_wt)) %in% c("December","January","February")], plot.type = "single", col = c(1, 2, 4), 118 | lwd = 2, type = "p",xlab = "Years", ylab = "pp (mm/mes)") 119 | title(expression(bold("OBSERVED DAILY HISTORICAL DATA vs" * phantom(" GCM DAILY DATA ") * phantom(" vs DOWNSCALED GCM DAILY DATA ")*"DURING RAINY SEASON")), col.main = "black",cex.main=0.73) 120 | title(expression(bold(phantom("OBSERVED DAILY HISTORICAL DATA vs ") * " GCM DAILY DATA " * phantom(" vs DOWNSCALED GCM DAILY DATA ")*phantom("DURING RAINY SEASON"))), col.main = "red",cex.main=0.73) 121 | title(expression(bold(phantom("OBSERVED DAILY HISTORICAL DATA vs ") * phantom(" GCM DAILY DATA ")*" vs DOWNSCALED GCM DAILY DATA "*phantom("DURING RAINY SEASON"))), col.main = "dodgerblue3",cex.main=0.73) 122 | 123 | plot(gcm_downscaled~OBS_hist, coredata(data_wt), col = c(4,1)) 124 | title(expression(bold("SCATTER PLOT OF OBSERVED DAILY DATA VS "*phantom("GCM DAILY DATA"))), col.main = "black", cex.main=1) 125 | title(expression(bold(phantom("SCATTER PLOT OF OBSERVED DAILY DATA vs ")*"GCM DAILY DATA")), col.main = "red", cex.main=1) 126 | 127 | plot(gcm_dowscaled~OBS_hist, coredata(data_wt[months(time(data_wt))%in%c("December","January","February")]), col = c(4,1),ylab = "PP GCM DOWNSCALED (mm)", xlab = "PP OBSERVED (mm)") 128 | title(expression(bold("SCATTER PLOT OF OBSERVED DAILY DATA VS "*phantom("GCM DOWNSCALED DAILY DATA")*"DURING RAINY SEASON")), col.main = "black", cex.main=0.8) 129 | title(expression(bold(phantom("SCATTER PLOT OF OBSERVED DAILY DATA vs ")*"GCM DOWNSCALED DAILY DATA"*phantom("DURING RAINY SEASON"))), col.main = "dodgerblue3", cex.main=0.8) 130 | 131 | 132 | ecdfplot(~ OBS_hist + GCM_hist + gcm_downscaled, data = data.frame(data_wt), lwd = 3, col = c(1, 2, 4), 133 | main="CDF PLOT OF OBS. DAILY DATA VS GCM AND GCM DOWNSCALED DAILY DATA", 134 | ylab = "Empirical CDF", xlab = "Daily GCM (red), GCM downscaled (blue) and Observed (black) data") 135 | 136 | ecdfplot(~ OBS_hist + GCM_hist + gcm_downscaled,data = data.frame(data_wt[months(time(data_wt)) 137 | %in%c("December","January","February")]), 138 | lwd = 3, col = c(1, 2, 4), main="CDF PLOT OF OBS.vsGCMvsGCM DOWNSC. DAILY DATA - RAINY SEASON", 139 | ylab = "Empirical CDF", xlab = "Daily GCM (red), GCM downscaled (blue) and Observed (black) data") 140 | 141 | ecdfplot(~ OBS_hist + gcm_downscaled, data = data.frame(data_wt), lwd = 3, col = c(1, 2, 4), 142 | main="CDF PLOT OF OBS. DAILY DATA VS GCM DOWNSCALED DAILY DATA", ylab = "Empirical CDF", 143 | xlab = "Daily GCM downscaled (red) and Observed (black) data") 144 | 145 | #============================================================================ 146 | #INTERPOLANDO LA INFORMACION EN CASO DE DATOS MENSUALES 147 | 148 | data_at$GCM_downscaled <- data_wt$gcm_downscaled 149 | 150 | #for(i in 1:4) { 151 | #mod_sl <- data_at[months(time(data_at)) %in% seasons_by_year[[i]]]$GCM_model 152 | #mod_sl_qmapped <- doQmapQUANT(coredata(mod_sl), seasonal_qm_fit_model[[i]], type = "linear") 153 | #data_at$GCM_downscaled[ months(time(data_at)) %in% seasons_by_year[[i]]] <- mod_sl_qmapped 154 | #} 155 | # verificar la nueva tabla y el grafico 156 | View(data_at) 157 | View(data_wt) 158 | 159 | plot(data_at, plot.type = "single", col = c(1, 2, 4), lwd = 1, 160 | main = c("OBS vs GCM VS GCM DOWNSCALED"), 161 | ylab = "pp (mm/mes)", xlab = "years") 162 | 163 | # .............................................................................. 164 | # GUARDAR DATOS ESCALADOS EN UN ARCHIVO .CSV 165 | 166 | write.zoo(data_at[,3],file = "OUT.csv", sep = ",") 167 | print("El proceso se ha completado satisfactoriamente!") 168 | plot(data_at, plot.type = "single", col = c(1, 2, 4), lwd = 1, 169 | main = c("OBS vs GCM VS GCM DOWNSCALED"), 170 | ylab = "pp (mm/mes)", xlab = "years") 171 | 172 | # .............................................................................. 173 | # GUARDAR DATOS ESCALADOS EN UN ARCHIVO .CSV 174 | 175 | write.zoo(data_at[,3],file = "Out2.csv", sep = ",") 176 | print("El proceso se ha completado satisfactoriamente!") 177 | -------------------------------------------------------------------------------- /Qmapping.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Hydroenvironment/Statdownscaling/9920be63011760940cf19a7f5e7d5c6016ee17c7/Qmapping.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Statistical Downscaling of climate data 2 | [![R build status](https://github.com/cosimameyer/overviewR/workflows/R-CMD-check/badge.svg)](https://github.com/Hydroenvironment/CMIP6-WORLDCLIM-HANDLING/actions) 3 | [![Project Status: Active – The project has reached a stable, usable 4 | state and is being actively 5 | developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) 6 | [![License](https://img.shields.io/badge/license-GPL--3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0.en.html) 7 | [![CMIP6-WORLDCLIM-HANDLING badge](https://img.shields.io/badge/overviewR-ready%20to%20use-brightgreen)](https://github.com/Hydroenvironment/CMIP6-WORLDCLIM-HANDLING/) 8 | [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/dwyl/esta/issues) 9 | ![Optional Text](https://github.com/Hydroenvironment/Statdownscaling/blob/master/Qmapping.png) 10 | 11 | 🌏 Statistical downscaling is a two-step process consisting of i) the development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors (e.g., pressure fields), and ii) the application of such relationships to the output of global climate model experiments to simulate local climate characteristics in the future. 12 | 13 | ✅Scripts for point-based time series and gridded datasets. 14 | 15 | 16 | Author: Julio Montenegro Gambini, MSc. 17 | 18 | 19 | --------------------------------------------------------------------------------