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HCI.md

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HCI

This code reads data from netcdf files (downloaded from CDS) and makes plots for selected stations showing HCI values under different RCP scenarios.

Importing libraries and data

source("common.R", local = knitr::knit_global())
## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Read and process all data for HCI:

all_hci_data <- assembledata("hci")
## [1] "historical 1986_2005 mean fair"
## [1] "historical 1986_2005 mean good"
## [1] "historical 1986_2005 mean unf"
## [1] "historical 1986_2005 10prct fair"
## [1] "historical 1986_2005 10prct good"
## [1] "historical 1986_2005 10prct unf"
## [1] "historical 1986_2005 90prct fair"
## [1] "historical 1986_2005 90prct good"
## [1] "historical 1986_2005 90prct unf"
## [1] "RCP2.6 2021_2040 mean fair"
## [1] "RCP2.6 2021_2040 mean good"
## [1] "RCP2.6 2021_2040 mean unf"
## [1] "RCP2.6 2021_2040 10prct fair"
## [1] "RCP2.6 2021_2040 10prct good"
## [1] "RCP2.6 2021_2040 10prct unf"
## [1] "RCP2.6 2021_2040 90prct fair"
## [1] "RCP2.6 2021_2040 90prct good"
## [1] "RCP2.6 2021_2040 90prct unf"
## [1] "RCP2.6 2041_2060 mean fair"
## [1] "RCP2.6 2041_2060 mean good"
## [1] "RCP2.6 2041_2060 mean unf"
## [1] "RCP2.6 2041_2060 10prct fair"
## [1] "RCP2.6 2041_2060 10prct good"
## [1] "RCP2.6 2041_2060 10prct unf"
## [1] "RCP2.6 2041_2060 90prct fair"
## [1] "RCP2.6 2041_2060 90prct good"
## [1] "RCP2.6 2041_2060 90prct unf"
## [1] "RCP2.6 2081_2100 mean fair"
## [1] "RCP2.6 2081_2100 mean good"
## [1] "RCP2.6 2081_2100 mean unf"
## [1] "RCP2.6 2081_2100 10prct fair"
## [1] "RCP2.6 2081_2100 10prct good"
## [1] "RCP2.6 2081_2100 10prct unf"
## [1] "RCP2.6 2081_2100 90prct fair"
## [1] "RCP2.6 2081_2100 90prct good"
## [1] "RCP2.6 2081_2100 90prct unf"
## [1] "RCP4.5 2021_2040 mean fair"
## [1] "RCP4.5 2021_2040 mean good"
## [1] "RCP4.5 2021_2040 mean unf"
## [1] "RCP4.5 2021_2040 10prct fair"
## [1] "RCP4.5 2021_2040 10prct good"
## [1] "RCP4.5 2021_2040 10prct unf"
## [1] "RCP4.5 2021_2040 90prct fair"
## [1] "RCP4.5 2021_2040 90prct good"
## [1] "RCP4.5 2021_2040 90prct unf"
## [1] "RCP4.5 2041_2060 mean fair"
## [1] "RCP4.5 2041_2060 mean good"
## [1] "RCP4.5 2041_2060 mean unf"
## [1] "RCP4.5 2041_2060 10prct fair"
## [1] "RCP4.5 2041_2060 10prct good"
## [1] "RCP4.5 2041_2060 10prct unf"
## [1] "RCP4.5 2041_2060 90prct fair"
## [1] "RCP4.5 2041_2060 90prct good"
## [1] "RCP4.5 2041_2060 90prct unf"
## [1] "RCP4.5 2081_2100 mean fair"
## [1] "RCP4.5 2081_2100 mean good"
## [1] "RCP4.5 2081_2100 mean unf"
## [1] "RCP4.5 2081_2100 10prct fair"
## [1] "RCP4.5 2081_2100 10prct good"
## [1] "RCP4.5 2081_2100 10prct unf"
## [1] "RCP4.5 2081_2100 90prct fair"
## [1] "RCP4.5 2081_2100 90prct good"
## [1] "RCP4.5 2081_2100 90prct unf"
## [1] "RCP8.5 2021_2040 mean fair"
## [1] "RCP8.5 2021_2040 mean good"
## [1] "RCP8.5 2021_2040 mean unf"
## [1] "RCP8.5 2021_2040 10prct fair"
## [1] "RCP8.5 2021_2040 10prct good"
## [1] "RCP8.5 2021_2040 10prct unf"
## [1] "RCP8.5 2021_2040 90prct fair"
## [1] "RCP8.5 2021_2040 90prct good"
## [1] "RCP8.5 2021_2040 90prct unf"
## [1] "RCP8.5 2041_2060 mean fair"
## [1] "RCP8.5 2041_2060 mean good"
## [1] "RCP8.5 2041_2060 mean unf"
## [1] "RCP8.5 2041_2060 10prct fair"
## [1] "RCP8.5 2041_2060 10prct good"
## [1] "RCP8.5 2041_2060 10prct unf"
## [1] "RCP8.5 2041_2060 90prct fair"
## [1] "RCP8.5 2041_2060 90prct good"
## [1] "RCP8.5 2041_2060 90prct unf"
## [1] "RCP8.5 2081_2100 mean fair"
## [1] "RCP8.5 2081_2100 mean good"
## [1] "RCP8.5 2081_2100 mean unf"
## [1] "RCP8.5 2081_2100 10prct fair"
## [1] "RCP8.5 2081_2100 10prct good"
## [1] "RCP8.5 2081_2100 10prct unf"
## [1] "RCP8.5 2081_2100 90prct fair"
## [1] "RCP8.5 2081_2100 90prct good"
## [1] "RCP8.5 2081_2100 90prct unf"

All relevant data is in alldata, where column stationid is the index of the grid point in the raw imported data, scenario is either “historical”, “RCP2.6”, “RCP4.5” or “RCP8.5”, time_period is either “1986-2005”, “2021-2040”, “2041-2060” or “2081-2100”, metric is the mean, 10th percentile or 90th percentile, day_cat is either “fair”, “good” or “unf” (category determined based on HCI value), datapoint is the count of days in day_cat category for a specific month and metric, month is the month for which the datapoint was calculated.

head(all_hci_data)
##   stationid   scenario time_period metric day_cat month datapoint
## 1       661 historical   1986-2005   mean    fair   jan 16.941667
## 2       661 historical   1986-2005   mean    fair   feb 13.875000
## 3       661 historical   1986-2005   mean    fair   mar 11.141667
## 4       661 historical   1986-2005   mean    fair   apr  7.383333
## 5       661 historical   1986-2005   mean    fair   may  4.583333
## 6       661 historical   1986-2005   mean    fair   jun  2.091667

Plots

Plot data for all stations and scenarios:

for (stat_id in gridpoint_indexes) {
    for (scen in scenarios) {
        p <- plothcidata(stat_id, scen, all_hci_data)
        print(p)
    }
}

Save all the plots:

for (stat_id in gridpoint_indexes) {
    for (scen in scenarios) {
        stat_name <- names[match(stat_id, gridpoint_indexes)]
        print(paste("HCI", stat_name, scen))

        p <- plothcidata(stat_id, scen, all_hci_data)
        
        ggsave(paste("copernicus-HCI_", gsub(" ", "_", stat_name), "_", scen, ".pdf", sep=""), p, width=9, height=4, units="in", path="../output/pdf/copernicus-HCI", device=cairo_pdf)
        ggsave(paste("copernicus-HCI_", gsub(" ", "_", stat_name), "_", scen, ".eps", sep=""), p, width=9, height=4, units="in", path="../output/eps/copernicus-HCI", device=cairo_ps)
        ggsave(paste("copernicus-HCI_", gsub(" ", "_", stat_name), "_", scen, ".svg", sep=""), p, width=9, height=4, units="in", path="../output/svg/copernicus-HCI")
        ggsave(paste("copernicus-HCI_", gsub(" ", "_", stat_name), "_", scen, ".png", sep=""), p, width=9, height=4, units="in", path="../output/png/copernicus-HCI", dpi=500)
    }
}