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cc_analysys_script_modified.R
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cc_analysys_script_modified.R
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library(tidyverse)
library(lubridate)
#### A Model and scenarios extraction ####
#### RCP 4.5 ####
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
#### Model 4 ####
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_4_rcp45_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp45_wb) <- colnmss
mod_4_rcp45_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp45_aqu) <- colnmsss
mod_4_rcp45_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp45_reservoir) <- colnmssss
#mod_4_rcp45_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_4_CCLM/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_4_rcp45_flocha) <- colnmsssss
#mod_4_rcp45_flocha <- mod_4_rcp45_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 4, escenario = "RCP 4.5")
# Analysed variables --> Daily scale
mod_4_rcp45_wb <- mod_4_rcp45_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_4_rcp45_basinaqu_wb <- mod_4_rcp45_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_4_rcp45_reservoir <- mod_4_rcp45_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_4_rcp45_wb_yr <- mod_4_rcp45_wb %>% left_join(., mod_4_rcp45_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_4_rcp45_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_4_rcp45_wb_yr <- mod_4_rcp45_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",
year %in% longterm_period ~ "longterm",
.default = "other")) %>%
#filter(., periodo != "other") %>%
mutate(periodo = factor(periodo, levels = (c("baseline", "midterm", "longterm", "other"))))
# Average water balance for each period
average_wb_periods_mod_4_rcp45 <- mod_4_rcp45_wb_yr %>% group_by(periodo) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq",
"gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~mean(.))
ref_values_mod_4_45 <- average_wb_periods_mod_4_rcp45[1,]
# 2. Analysis of temporal effects: Anomalies and changes, average and annual basis
# 2.1 Anomalies of each model regarding its baseline (%):
# Anomalies in average
mod_4_45_average_anomalies <- average_wb_periods_mod_4_rcp45 %>% mutate(pcp_anom = (precip - ref_values_mod_4_45$precip) / ref_values_mod_4_45$precip,
et_anom = (et - ref_values_mod_4_45$et)/ref_values_mod_4_45$et,
pet_anom = (pet - ref_values_mod_4_45$pet)/ref_values_mod_4_45$pet,
perc_anom = (perc - ref_values_mod_4_45$perc)/ref_values_mod_4_45$perc,
rchrg_anom = (rchrg - ref_values_mod_4_45$rchrg)/ref_values_mod_4_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_4_45$total_flow)/ref_values_mod_4_45$total_flow,
surq_anom = (surq - ref_values_mod_4_45$surq)/ref_values_mod_4_45$surq,
latq_anom = (latq - ref_values_mod_4_45$latq)/ref_values_mod_4_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_4_45$gwflo_cha)/ref_values_mod_4_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_4_45$gwflo_res)/ref_values_mod_4_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_4_45$total_gwflo)/ref_values_mod_4_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_4_45$reser_floin_mm)/ref_values_mod_4_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 4, escenario = "RCP 4.5")%>% .[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
# Annual anomalies
mod_4_45_year_anomalies <- mod_4_rcp45_wb_yr %>% mutate(pcp_anom = (precip - ref_values_mod_4_45$precip) / ref_values_mod_4_45$precip,
et_anom = (et - ref_values_mod_4_45$et)/ref_values_mod_4_45$et,
pet_anom = (pet - ref_values_mod_4_45$pet)/ref_values_mod_4_45$pet,
perc_anom = (perc - ref_values_mod_4_45$perc)/ref_values_mod_4_45$perc,
rchrg_anom = (rchrg - ref_values_mod_4_45$rchrg)/ref_values_mod_4_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_4_45$total_flow)/ref_values_mod_4_45$total_flow,
surq_anom = (surq - ref_values_mod_4_45$surq)/ref_values_mod_4_45$surq,
latq_anom = (latq - ref_values_mod_4_45$latq)/ref_values_mod_4_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_4_45$gwflo_cha)/ref_values_mod_4_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_4_45$gwflo_res)/ref_values_mod_4_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_4_45$total_gwflo)/ref_values_mod_4_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_4_45$reser_floin_mm)/ref_values_mod_4_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 4, escenario = "RCP 4.5")%>% .[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# 2.2 Changes of each model regarding its baseline (Absolute):
# Changes in average
mod_4_45_average_changes <- average_wb_periods_mod_4_rcp45 %>% mutate(pcp_chg = (precip - ref_values_mod_4_45$precip),
et_chg = (et - ref_values_mod_4_45$et),
pet_chg = (pet - ref_values_mod_4_45$pet),
perc_chg = (perc - ref_values_mod_4_45$perc),
rchrg_chg = (rchrg - ref_values_mod_4_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_4_45$total_flow),
surq_chg = (surq - ref_values_mod_4_45$surq),
latq_chg = (latq - ref_values_mod_4_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_4_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_4_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_4_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_4_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 4, escenario = "RCP 4.5")%>%
.[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
mod_4_45_average_changes %>% select(ends_with("chg"))
# Annual changes
mod_4_45_year_changes <- mod_4_rcp45_wb_yr %>% mutate(pcp_chg = (precip - ref_values_mod_4_45$precip),
et_chg = (et - ref_values_mod_4_45$et),
pet_chg = (pet - ref_values_mod_4_45$pet),
perc_chg = (perc - ref_values_mod_4_45$perc),
rchrg_chg = (rchrg - ref_values_mod_4_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_4_45$total_flow),
surq_chg = (surq - ref_values_mod_4_45$surq),
latq_chg = (latq - ref_values_mod_4_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_4_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_4_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_4_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_4_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 4, escenario = "RCP 4.5")%>%
.[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
#### Model 6 ####
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_6_rcp45_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_6_rcp45_wb) <- colnmss
mod_6_rcp45_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_6_rcp45_aqu) <- colnmsss
mod_6_rcp45_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_6_rcp45_reservoir) <- colnmssss
#mod_6_rcp45_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_6_RACMO22/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_6_rcp45_flocha) <- colnmsssss
#mod_6_rcp45_flocha <- mod_6_rcp45_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 6, escenario = "RCP 4.5")
# Analysed variables --> Daily scale
mod_6_rcp45_wb <- mod_6_rcp45_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_6_rcp45_basinaqu_wb <- mod_6_rcp45_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_6_rcp45_reservoir <- mod_6_rcp45_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_6_rcp45_wb_yr <- mod_6_rcp45_wb %>% left_join(., mod_6_rcp45_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_6_rcp45_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_6_rcp45_wb_yr <- mod_6_rcp45_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",
year %in% longterm_period ~ "longterm",
.default = "other")) %>%
#filter(., periodo != "other") %>%
mutate(periodo = factor(periodo, levels = (c("baseline", "midterm", "longterm", "other"))))
# Average water balance for each period
average_wb_periods_mod_6_rcp45 <- mod_6_rcp45_wb_yr %>% group_by(periodo) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq",
"gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~mean(.))
ref_values_mod_6_45 <- average_wb_periods_mod_6_rcp45[1,]
# 2. Analysis of temporal effects: Anomalies and changes, average and annual basis
# 2.1 Anomalies of each model regarding its baseline (%):
# Anomalies in average
mod_6_45_average_anomalies <- average_wb_periods_mod_6_rcp45 %>% mutate(pcp_anom = (precip - ref_values_mod_6_45$precip) / ref_values_mod_6_45$precip,
et_anom = (et - ref_values_mod_6_45$et)/ref_values_mod_6_45$et,
pet_anom = (pet - ref_values_mod_6_45$pet)/ref_values_mod_6_45$pet,
perc_anom = (perc - ref_values_mod_6_45$perc)/ref_values_mod_6_45$perc,
rchrg_anom = (rchrg - ref_values_mod_6_45$rchrg)/ref_values_mod_6_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_6_45$total_flow)/ref_values_mod_6_45$total_flow,
surq_anom = (surq - ref_values_mod_6_45$surq)/ref_values_mod_6_45$surq,
latq_anom = (latq - ref_values_mod_6_45$latq)/ref_values_mod_6_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_6_45$gwflo_cha)/ref_values_mod_6_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_6_45$gwflo_res)/ref_values_mod_6_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_6_45$total_gwflo)/ref_values_mod_6_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_6_45$reser_floin_mm)/ref_values_mod_6_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 6, escenario = "RCP 4.5")%>% .[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
# Annual anomalies
mod_6_45_year_anomalies <- mod_6_rcp45_wb_yr %>% mutate(pcp_anom = (precip - ref_values_mod_6_45$precip) / ref_values_mod_6_45$precip,
et_anom = (et - ref_values_mod_6_45$et)/ref_values_mod_6_45$et,
pet_anom = (pet - ref_values_mod_6_45$pet)/ref_values_mod_6_45$pet,
perc_anom = (perc - ref_values_mod_6_45$perc)/ref_values_mod_6_45$perc,
rchrg_anom = (rchrg - ref_values_mod_6_45$rchrg)/ref_values_mod_6_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_6_45$total_flow)/ref_values_mod_6_45$total_flow,
surq_anom = (surq - ref_values_mod_6_45$surq)/ref_values_mod_6_45$surq,
latq_anom = (latq - ref_values_mod_6_45$latq)/ref_values_mod_6_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_6_45$gwflo_cha)/ref_values_mod_6_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_6_45$gwflo_res)/ref_values_mod_6_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_6_45$total_gwflo)/ref_values_mod_6_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_6_45$reser_floin_mm)/ref_values_mod_6_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 6, escenario = "RCP 4.5")%>% .[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# 2.2 Changes of each model regarding its baseline (Absolute):
# Changes in average
mod_6_45_average_changes <- average_wb_periods_mod_6_rcp45 %>% mutate(pcp_chg = (precip - ref_values_mod_6_45$precip),
et_chg = (et - ref_values_mod_6_45$et),
pet_chg = (pet - ref_values_mod_6_45$pet),
perc_chg = (perc - ref_values_mod_6_45$perc),
rchrg_chg = (rchrg - ref_values_mod_6_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_6_45$total_flow),
surq_chg = (surq - ref_values_mod_6_45$surq),
latq_chg = (latq - ref_values_mod_6_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_6_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_6_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_6_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_6_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 6, escenario = "RCP 4.5")%>%
.[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
mod_6_45_average_changes %>% select(ends_with("chg"))
# Annual changes
mod_6_45_year_changes <- mod_6_rcp45_wb_yr %>% mutate(pcp_chg = (precip - ref_values_mod_6_45$precip),
et_chg = (et - ref_values_mod_6_45$et),
pet_chg = (pet - ref_values_mod_6_45$pet),
perc_chg = (perc - ref_values_mod_6_45$perc),
rchrg_chg = (rchrg - ref_values_mod_6_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_6_45$total_flow),
surq_chg = (surq - ref_values_mod_6_45$surq),
latq_chg = (latq - ref_values_mod_6_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_6_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_6_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_6_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_6_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 6, escenario = "RCP 4.5")%>%
.[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
#### Model 7 ####
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_7_rcp45_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_7_rcp45_wb) <- colnmss
mod_7_rcp45_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_7_rcp45_aqu) <- colnmsss
mod_7_rcp45_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_7_rcp45_reservoir) <- colnmssss
#mod_7_rcp45_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_7_DMI-HIRHAM/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_7_rcp45_flocha) <- colnmsssss
#mod_7_rcp45_flocha <- mod_7_rcp45_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 7, escenario = "RCP 4.5")
# Analysed variables --> Daily scale
mod_7_rcp45_wb <- mod_7_rcp45_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_7_rcp45_basinaqu_wb <- mod_7_rcp45_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_7_rcp45_reservoir <- mod_7_rcp45_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_7_rcp45_wb_yr <- mod_7_rcp45_wb %>% left_join(., mod_7_rcp45_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_7_rcp45_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_7_rcp45_wb_yr <- mod_7_rcp45_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",
year %in% longterm_period ~ "longterm",
.default = "other")) %>%
#filter(., periodo != "other") %>%
mutate(periodo = factor(periodo, levels = (c("baseline", "midterm", "longterm", "other"))))
# Average water balance for each period
average_wb_periods_mod_7_rcp45 <- mod_7_rcp45_wb_yr %>% group_by(periodo) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq",
"gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~mean(.))
ref_values_mod_7_45 <- average_wb_periods_mod_7_rcp45[1,]
# 2. Analysis of temporal effects: Anomalies and changes, average and annual basis
# 2.1 Anomalies of each model regarding its baseline (%):
# Anomalies in average
mod_7_45_average_anomalies <- average_wb_periods_mod_7_rcp45 %>% mutate(pcp_anom = (precip - ref_values_mod_7_45$precip) / ref_values_mod_7_45$precip,
et_anom = (et - ref_values_mod_7_45$et)/ref_values_mod_7_45$et,
pet_anom = (pet - ref_values_mod_7_45$pet)/ref_values_mod_7_45$pet,
perc_anom = (perc - ref_values_mod_7_45$perc)/ref_values_mod_7_45$perc,
rchrg_anom = (rchrg - ref_values_mod_7_45$rchrg)/ref_values_mod_7_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_7_45$total_flow)/ref_values_mod_7_45$total_flow,
surq_anom = (surq - ref_values_mod_7_45$surq)/ref_values_mod_7_45$surq,
latq_anom = (latq - ref_values_mod_7_45$latq)/ref_values_mod_7_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_7_45$gwflo_cha)/ref_values_mod_7_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_7_45$gwflo_res)/ref_values_mod_7_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_7_45$total_gwflo)/ref_values_mod_7_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_7_45$reser_floin_mm)/ref_values_mod_7_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 7, escenario = "RCP 4.5")%>% .[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
# Annual anomalies
mod_7_45_year_anomalies <- mod_7_rcp45_wb_yr %>% mutate(pcp_anom = (precip - ref_values_mod_7_45$precip) / ref_values_mod_7_45$precip,
et_anom = (et - ref_values_mod_7_45$et)/ref_values_mod_7_45$et,
pet_anom = (pet - ref_values_mod_7_45$pet)/ref_values_mod_7_45$pet,
perc_anom = (perc - ref_values_mod_7_45$perc)/ref_values_mod_7_45$perc,
rchrg_anom = (rchrg - ref_values_mod_7_45$rchrg)/ref_values_mod_7_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_7_45$total_flow)/ref_values_mod_7_45$total_flow,
surq_anom = (surq - ref_values_mod_7_45$surq)/ref_values_mod_7_45$surq,
latq_anom = (latq - ref_values_mod_7_45$latq)/ref_values_mod_7_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_7_45$gwflo_cha)/ref_values_mod_7_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_7_45$gwflo_res)/ref_values_mod_7_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_7_45$total_gwflo)/ref_values_mod_7_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_7_45$reser_floin_mm)/ref_values_mod_7_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 7, escenario = "RCP 4.5")%>% .[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# 2.2 Changes of each model regarding its baseline (Absolute):
# Changes in average
mod_7_45_average_changes <- average_wb_periods_mod_7_rcp45 %>% mutate(pcp_chg = (precip - ref_values_mod_7_45$precip),
et_chg = (et - ref_values_mod_7_45$et),
pet_chg = (pet - ref_values_mod_7_45$pet),
perc_chg = (perc - ref_values_mod_7_45$perc),
rchrg_chg = (rchrg - ref_values_mod_7_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_7_45$total_flow),
surq_chg = (surq - ref_values_mod_7_45$surq),
latq_chg = (latq - ref_values_mod_7_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_7_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_7_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_7_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_7_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 7, escenario = "RCP 4.5")%>%
.[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
mod_7_45_average_changes %>% select(ends_with("chg"))
# Annual changes
mod_7_45_year_changes <- mod_7_rcp45_wb_yr %>% mutate(pcp_chg = (precip - ref_values_mod_7_45$precip),
et_chg = (et - ref_values_mod_7_45$et),
pet_chg = (pet - ref_values_mod_7_45$pet),
perc_chg = (perc - ref_values_mod_7_45$perc),
rchrg_chg = (rchrg - ref_values_mod_7_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_7_45$total_flow),
surq_chg = (surq - ref_values_mod_7_45$surq),
latq_chg = (latq - ref_values_mod_7_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_7_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_7_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_7_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_7_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 7, escenario = "RCP 4.5")%>%
.[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
#### Model 9 ####
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_9_rcp45_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_9_rcp45_wb) <- colnmss
mod_9_rcp45_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_9_rcp45_aqu) <- colnmsss
mod_9_rcp45_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_9_rcp45_reservoir) <- colnmssss
#mod_9_rcp45_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_9_MPI-M-MPI/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_9_rcp45_flocha) <- colnmsssss
#mod_9_rcp45_flocha <- mod_9_rcp45_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 9, escenario = "RCP 4.5")
# Analysed variables --> Daily scale
mod_9_rcp45_wb <- mod_9_rcp45_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_9_rcp45_basinaqu_wb <- mod_9_rcp45_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_9_rcp45_reservoir <- mod_9_rcp45_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_9_rcp45_wb_yr <- mod_9_rcp45_wb %>% left_join(., mod_9_rcp45_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_9_rcp45_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_9_rcp45_wb_yr <- mod_9_rcp45_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",
year %in% longterm_period ~ "longterm",
.default = "other")) %>%
#filter(., periodo != "other") %>%
mutate(periodo = factor(periodo, levels = (c("baseline", "midterm", "longterm", "other"))))
# Average water balance for each period
average_wb_periods_mod_9_rcp45 <- mod_9_rcp45_wb_yr %>% group_by(periodo) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq",
"gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~mean(.))
ref_values_mod_9_45 <- average_wb_periods_mod_9_rcp45[1,]
# 2. Analysis of temporal effects: Anomalies and changes, average and annual basis
# 2.1 Anomalies of each model regarding its baseline (%):
# Anomalies in average
mod_9_45_average_anomalies <- average_wb_periods_mod_9_rcp45 %>% mutate(pcp_anom = (precip - ref_values_mod_9_45$precip) / ref_values_mod_9_45$precip,
et_anom = (et - ref_values_mod_9_45$et)/ref_values_mod_9_45$et,
pet_anom = (pet - ref_values_mod_9_45$pet)/ref_values_mod_9_45$pet,
perc_anom = (perc - ref_values_mod_9_45$perc)/ref_values_mod_9_45$perc,
rchrg_anom = (rchrg - ref_values_mod_9_45$rchrg)/ref_values_mod_9_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_9_45$total_flow)/ref_values_mod_9_45$total_flow,
surq_anom = (surq - ref_values_mod_9_45$surq)/ref_values_mod_9_45$surq,
latq_anom = (latq - ref_values_mod_9_45$latq)/ref_values_mod_9_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_9_45$gwflo_cha)/ref_values_mod_9_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_9_45$gwflo_res)/ref_values_mod_9_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_9_45$total_gwflo)/ref_values_mod_9_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_9_45$reser_floin_mm)/ref_values_mod_9_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 9, escenario = "RCP 4.5")%>% .[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
# Annual anomalies
mod_9_45_year_anomalies <- mod_9_rcp45_wb_yr %>% mutate(pcp_anom = (precip - ref_values_mod_9_45$precip) / ref_values_mod_9_45$precip,
et_anom = (et - ref_values_mod_9_45$et)/ref_values_mod_9_45$et,
pet_anom = (pet - ref_values_mod_9_45$pet)/ref_values_mod_9_45$pet,
perc_anom = (perc - ref_values_mod_9_45$perc)/ref_values_mod_9_45$perc,
rchrg_anom = (rchrg - ref_values_mod_9_45$rchrg)/ref_values_mod_9_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_9_45$total_flow)/ref_values_mod_9_45$total_flow,
surq_anom = (surq - ref_values_mod_9_45$surq)/ref_values_mod_9_45$surq,
latq_anom = (latq - ref_values_mod_9_45$latq)/ref_values_mod_9_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_9_45$gwflo_cha)/ref_values_mod_9_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_9_45$gwflo_res)/ref_values_mod_9_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_9_45$total_gwflo)/ref_values_mod_9_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_9_45$reser_floin_mm)/ref_values_mod_9_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 9, escenario = "RCP 4.5")%>% .[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# 2.2 Changes of each model regarding its baseline (Absolute):
# Changes in average
mod_9_45_average_changes <- average_wb_periods_mod_9_rcp45 %>% mutate(pcp_chg = (precip - ref_values_mod_9_45$precip),
et_chg = (et - ref_values_mod_9_45$et),
pet_chg = (pet - ref_values_mod_9_45$pet),
perc_chg = (perc - ref_values_mod_9_45$perc),
rchrg_chg = (rchrg - ref_values_mod_9_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_9_45$total_flow),
surq_chg = (surq - ref_values_mod_9_45$surq),
latq_chg = (latq - ref_values_mod_9_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_9_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_9_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_9_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_9_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 9, escenario = "RCP 4.5")%>%
.[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
mod_9_45_average_changes %>% select(ends_with("chg"))
# Annual changes
mod_9_45_year_changes <- mod_9_rcp45_wb_yr %>% mutate(pcp_chg = (precip - ref_values_mod_9_45$precip),
et_chg = (et - ref_values_mod_9_45$et),
pet_chg = (pet - ref_values_mod_9_45$pet),
perc_chg = (perc - ref_values_mod_9_45$perc),
rchrg_chg = (rchrg - ref_values_mod_9_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_9_45$total_flow),
surq_chg = (surq - ref_values_mod_9_45$surq),
latq_chg = (latq - ref_values_mod_9_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_9_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_9_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_9_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_9_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 9, escenario = "RCP 4.5")%>%
.[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
#### Model 14 ####
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_14_rcp45_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_14_rcp45_wb) <- colnmss
mod_14_rcp45_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_14_rcp45_aqu) <- colnmsss
mod_14_rcp45_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_14_rcp45_reservoir) <- colnmssss
#mod_14_rcp45_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_45/TxtInOut_14_MOHC-HadGEM2/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_14_rcp45_flocha) <- colnmsssss
#mod_14_rcp45_flocha <- mod_14_rcp45_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 14, escenario = "RCP 4.5")
# Analysed variables --> Daily scale
mod_14_rcp45_wb <- mod_14_rcp45_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_14_rcp45_basinaqu_wb <- mod_14_rcp45_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_14_rcp45_reservoir <- mod_14_rcp45_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_14_rcp45_wb_yr <- mod_14_rcp45_wb %>% left_join(., mod_14_rcp45_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_14_rcp45_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_14_rcp45_wb_yr <- mod_14_rcp45_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",
year %in% longterm_period ~ "longterm",
.default = "other")) %>%
#filter(., periodo != "other") %>%
mutate(periodo = factor(periodo, levels = (c("baseline", "midterm", "longterm", "other"))))
# Average water balance for each period
average_wb_periods_mod_14_rcp45 <- mod_14_rcp45_wb_yr %>% group_by(periodo) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq",
"gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~mean(.))
ref_values_mod_14_45 <- average_wb_periods_mod_14_rcp45[1,]
# 2. Analysis of temporal effects: Anomalies and changes, average and annual basis
# 2.1 Anomalies of each model regarding its baseline (%):
# Anomalies in average
mod_14_45_average_anomalies <- average_wb_periods_mod_14_rcp45 %>% mutate(pcp_anom = (precip - ref_values_mod_14_45$precip) / ref_values_mod_14_45$precip,
et_anom = (et - ref_values_mod_14_45$et)/ref_values_mod_14_45$et,
pet_anom = (pet - ref_values_mod_14_45$pet)/ref_values_mod_14_45$pet,
perc_anom = (perc - ref_values_mod_14_45$perc)/ref_values_mod_14_45$perc,
rchrg_anom = (rchrg - ref_values_mod_14_45$rchrg)/ref_values_mod_14_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_14_45$total_flow)/ref_values_mod_14_45$total_flow,
surq_anom = (surq - ref_values_mod_14_45$surq)/ref_values_mod_14_45$surq,
latq_anom = (latq - ref_values_mod_14_45$latq)/ref_values_mod_14_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_14_45$gwflo_cha)/ref_values_mod_14_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_14_45$gwflo_res)/ref_values_mod_14_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_14_45$total_gwflo)/ref_values_mod_14_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_14_45$reser_floin_mm)/ref_values_mod_14_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 14, escenario = "RCP 4.5")%>% .[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
# Annual anomalies
mod_14_45_year_anomalies <- mod_14_rcp45_wb_yr %>% mutate(pcp_anom = (precip - ref_values_mod_14_45$precip) / ref_values_mod_14_45$precip,
et_anom = (et - ref_values_mod_14_45$et)/ref_values_mod_14_45$et,
pet_anom = (pet - ref_values_mod_14_45$pet)/ref_values_mod_14_45$pet,
perc_anom = (perc - ref_values_mod_14_45$perc)/ref_values_mod_14_45$perc,
rchrg_anom = (rchrg - ref_values_mod_14_45$rchrg)/ref_values_mod_14_45$rchrg,
total_flow_anom = (total_flow - ref_values_mod_14_45$total_flow)/ref_values_mod_14_45$total_flow,
surq_anom = (surq - ref_values_mod_14_45$surq)/ref_values_mod_14_45$surq,
latq_anom = (latq - ref_values_mod_14_45$latq)/ref_values_mod_14_45$latq,
gwflocha_anom = (gwflo_cha - ref_values_mod_14_45$gwflo_cha)/ref_values_mod_14_45$gwflo_cha,
gwflores_anom = (gwflo_res - ref_values_mod_14_45$gwflo_res)/ref_values_mod_14_45$gwflo_res,
totalgwflo_anom = (total_gwflo - ref_values_mod_14_45$total_gwflo)/ref_values_mod_14_45$total_gwflo,
res_floin_anom = (reser_floin_mm - ref_values_mod_14_45$reser_floin_mm)/ref_values_mod_14_45$reser_floin_mm) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 14, escenario = "RCP 4.5")%>% .[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
# 2.2 Changes of each model regarding its baseline (Absolute):
# Changes in average
mod_14_45_average_changes <- average_wb_periods_mod_14_rcp45 %>% mutate(pcp_chg = (precip - ref_values_mod_14_45$precip),
et_chg = (et - ref_values_mod_14_45$et),
pet_chg = (pet - ref_values_mod_14_45$pet),
perc_chg = (perc - ref_values_mod_14_45$perc),
rchrg_chg = (rchrg - ref_values_mod_14_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_14_45$total_flow),
surq_chg = (surq - ref_values_mod_14_45$surq),
latq_chg = (latq - ref_values_mod_14_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_14_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_14_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_14_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_14_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 14, escenario = "RCP 4.5")%>%
.[,c(27,26,1,2,14, 3, 15, 4 ,16, 5 ,17, 6, 18, 7, 19, 8, 20 ,9, 21, 10, 22, 11, 23, 12, 24, 13, 25)]
mod_14_45_average_changes %>% select(ends_with("chg"))
# Annual changes
mod_14_45_year_changes <- mod_14_rcp45_wb_yr %>% mutate(pcp_chg = (precip - ref_values_mod_14_45$precip),
et_chg = (et - ref_values_mod_14_45$et),
pet_chg = (pet - ref_values_mod_14_45$pet),
perc_chg = (perc - ref_values_mod_14_45$perc),
rchrg_chg = (rchrg - ref_values_mod_14_45$rchrg),
totalflo_chg = (total_flow - ref_values_mod_14_45$total_flow),
surq_chg = (surq - ref_values_mod_14_45$surq),
latq_chg = (latq - ref_values_mod_14_45$latq),
gwflocha_chg = (gwflo_cha - ref_values_mod_14_45$gwflo_cha),
gwflores_chg = (gwflo_res - ref_values_mod_14_45$gwflo_res),
totalgwflo_chg = (total_gwflo - ref_values_mod_14_45$total_gwflo),
res_floin_chg = (reser_floin_mm - ref_values_mod_14_45$reser_floin_mm)) %>%
mutate_if(is.numeric, ~ round(., 3)) %>%
mutate(model = 14, escenario = "RCP 4.5")%>%
.[,c(28, 27,1, 2, 15, 3, 16, 4, 17, 5, 18, 6, 19, 7, 20, 8, 21, 9, 22, 10, 23, 11, 24, 12, 25, 13, 26)]
#### RCP 8.5 ####
# Periods definition
#rm(list = ls())
baseline_period <- 2006:2019
midterm_period <- 2046:2065
longterm_period <- 2080:2099
area_dren <- 367943566
#### Model 4 ####
# 1. Data extraction and manipulation
# Analysed files --> basin_wb and aquifer_basin, reservoir and channel
mod_4_rcp85_wb <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/basin_wb_day.txt", skip = 3)
colnmss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/basin_wb_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp85_wb) <- colnmss
mod_4_rcp85_aqu <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/basin_aqu_day.txt", skip = 3)
colnmsss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/basin_aqu_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp85_aqu) <- colnmsss
mod_4_rcp85_reservoir <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/reservoir_day.txt", skip = 3)
colnmssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/reservoir_day.txt", skip = 1, col_names = F) %>% .[1,]
colnames(mod_4_rcp85_reservoir) <- colnmssss
#mod_4_rcp85_flocha <- read.table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/channel_sd_day.txt", skip = 3)
#colnmsssss <- read_table("C:/ASG/Guajaraz_modelo/Txt_In_Out_CClimatico/RCP_85/TxtInOut_4_CCLM/channel_sd_day.txt", skip = 1, col_names = F) %>% .[1,]
#colnames(mod_4_rcp85_flocha) <- colnmsssss
#mod_4_rcp85_flocha <- mod_4_rcp85_flocha %>% select(day, unit, mon, yr, flo_out) %>% mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
# filter(unit == 5) %>% mutate(model = 4, escenario = "RCP 8.5")
# Analysed variables --> Daily scale
mod_4_rcp85_wb <- mod_4_rcp85_wb %>% select(., mon, day, yr, precip, et, pet, perc,
latq_cha, latq_res,
surq_gen, surq_ls) %>%
mutate(surq = surq_gen - surq_ls,
latq = latq_cha + latq_res,
date = dmy(paste(day,mon, yr, sep = "/"))) %>%
select(date, precip, et, pet, perc, surq, latq)
mod_4_rcp85_basinaqu_wb <- mod_4_rcp85_aqu %>% select(., mon, day, yr, dep_wt, stor, rchrg,
seep, revap, flo_cha, flo_res) %>%
mutate(date = dmy(paste(day,mon, yr, sep = "/")),
total_gwflo = flo_cha + flo_res) %>%
select(., date, rchrg , stor , revap, dep_wt, total_gwflo, flo_cha, flo_res) %>%
rename(gwflo_cha = flo_cha, gwflo_res = flo_res)
mod_4_rcp85_reservoir <- mod_4_rcp85_reservoir %>% select(day, mon, yr, flo_in, flo_out, flo_stor) %>%
mutate(date = dmy(paste(day, mon, yr))) %>% select(., -c(day, mon, yr)) %>%
mutate(reser_floin_mm = (flo_in * 1000) / area_dren )
# Analysed variables --> Aggregation to monthly/yearly values
mod_4_rcp85_wb_yr <- mod_4_rcp85_wb %>% left_join(., mod_4_rcp85_basinaqu_wb, "date") %>%
mutate(total_flow = surq + latq + total_gwflo) %>%
left_join(., mod_4_rcp85_reservoir, "date") %>%
group_by(year = year(date)) %>%
summarise_at(., c("precip", "et", "pet", "perc",
"rchrg", "total_flow", "surq", "latq", "gwflo_cha", "gwflo_res", "total_gwflo", "reser_floin_mm"), ~ sum(.))
# Introduction of periods
mod_4_rcp85_wb_yr <- mod_4_rcp85_wb_yr %>% mutate(periodo = case_when(year %in% baseline_period ~ "baseline",
year %in% midterm_period ~ "midterm",