The functions are used for quick computing of some common drought indexes built from wrappers of the underlying modules. For more customised needs, users may build their own indexes from the modules.
Usage
trans_thornthwaite(var, lat, na.rm = FALSE, verbose = TRUE)
idx_spi(data, .prcp, .dist = dist_gamma(), .scale = 12)
idx_spei(
data,
.tavg,
.lat,
.prcp,
.pet_method = trans_thornthwaite(),
.scale = 12,
.dist = dist_glo()
)
idx_rdi(
data,
.tavg,
.lat,
.prcp,
.pet_method = trans_thornthwaite(),
.scale = 12
)
idx_edi(data, .tavg, .lat, .prcp, .scale = 12)Arguments
- var
the variable to be transformed, see [tidyindex::variable_trans()] and [SPEI::thornthwaite()]
- lat, na.rm, verbose
see [SPEI::thornthwaite]
- data
an
id_tblobject- .dist
the distribution used for distribution fit, see [tidyindex::distribution_fit()]
- .scale
the temporal aggregation scale, see [tidyindex::temporal_aggregation()]
- .tavg, .lat, .prcp
variables to be used in the index calculation, see Details
- .pet_method
the method used for calculating potential evapotranspitation, currently only
trans_thornthwaite()
Details
Below explains the steps wrapped in each index and the intermediate variables created.
The idx_spi() function performs
a temporal aggregation on the input precipitation series,
.prcp, as.agg,a distribution fit step on the aggregated precipitation ,
.agg, as.fit, anda normalising step on the fitted values,
.fit, as.index
The idx_spei() function performs
a variable transformation step on the inut average temperature,
.tavg, to obtain the potential evapotranspiration,.pet,a dimension reduction step to calculate difference series,
.diff, between the input precipitation series,.prcp, and.pet,a temporal aggregation step on the difference series,
.diff, as.agg,a distribution fit step on the aggregated series,
.agg, as.fit, anda normalising step on the fitted value,
.fit, to obtain.index.
The idx_rdi() function performs
a variable transformation step on the input average temperature,
.tavg, to obtain potential evapotranspiration.pet,a dimension reduction step to calculate the ratio of input precipitation,
.prcp, to.petas.ratio,a temporal aggregation step on the ratio series,
.ratio, as.agga variable transformation step to take the log10 of the aggregated series,
.agg, as.y, anda rescaling step to rescale
.yby zscore to obtain.index.
The idx_edi() function performs
a dimension reduction step to aggregate the input precipitation series,
prcp, as.mult,a temporal aggregation step on the aggregated precipitation series (
.mult) as.ep, anda rescaling step to rescale
.epby zscore to obtain.index.
Examples
library(dplyr)
library(lmomco)
dt <- tenterfield |>
mutate(month = lubridate::month(ym)) |>
init(id = id, time = ym, group = month)
dt |> idx_spi()
#> # A tibble: 358 × 14
#> .dist id month ym prcp tmax tmin tavg long lat name .agg
#> <chr> <chr> <dbl> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 gamma ASN000… 12 1990 Dec 640 30.4 14.7 22.6 152. -29.0 tent… 8382
#> 2 gamma ASN000… 1 1991 Jan 1108 27.5 15.9 21.7 152. -29.0 tent… 8608
#> 3 gamma ASN000… 2 1991 Feb 628 28.0 15.5 21.8 152. -29.0 tent… 7976
#> 4 gamma ASN000… 3 1991 Mar 204 26.2 11.8 19.0 152. -29.0 tent… 7926
#> 5 gamma ASN000… 4 1991 Apr 44 24.2 6.57 15.4 152. -29.0 tent… 6376
#> 6 gamma ASN000… 5 1991 May 630 21.3 7.52 14.4 152. -29.0 tent… 5786
#> 7 gamma ASN000… 6 1991 Jun 242 19.6 3.65 11.6 152. -29.0 tent… 5634
#> 8 gamma ASN000… 7 1991 Jul 580 15.3 0.519 7.91 152. -29.0 tent… 5596
#> 9 gamma ASN000… 8 1991 Aug 14 17.8 1.67 9.76 152. -29.0 tent… 5276
#> 10 gamma ASN000… 9 1991 Sep 78 21.1 3.07 12.1 152. -29.0 tent… 5088
#> # ℹ 348 more rows
#> # ℹ 2 more variables: .fit <dbl>, .index <dbl>
dt |> idx_spi(.scale = c(12, 24))
#> # A tibble: 704 × 15
#> .dist id month ym prcp tmax tmin tavg long lat name .scale
#> <chr> <chr> <dbl> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 gamma ASN00… 12 1990 Dec 640 30.4 14.7 22.6 152. -29.0 tent… 12
#> 2 gamma ASN00… 1 1991 Jan 1108 27.5 15.9 21.7 152. -29.0 tent… 12
#> 3 gamma ASN00… 2 1991 Feb 628 28.0 15.5 21.8 152. -29.0 tent… 12
#> 4 gamma ASN00… 3 1991 Mar 204 26.2 11.8 19.0 152. -29.0 tent… 12
#> 5 gamma ASN00… 4 1991 Apr 44 24.2 6.57 15.4 152. -29.0 tent… 12
#> 6 gamma ASN00… 5 1991 May 630 21.3 7.52 14.4 152. -29.0 tent… 12
#> 7 gamma ASN00… 6 1991 Jun 242 19.6 3.65 11.6 152. -29.0 tent… 12
#> 8 gamma ASN00… 7 1991 Jul 580 15.3 0.519 7.91 152. -29.0 tent… 12
#> 9 gamma ASN00… 8 1991 Aug 14 17.8 1.67 9.76 152. -29.0 tent… 12
#> 10 gamma ASN00… 9 1991 Sep 78 21.1 3.07 12.1 152. -29.0 tent… 12
#> # ℹ 694 more rows
#> # ℹ 3 more variables: .agg <dbl>, .fit <dbl>, .index <dbl>
dt |> idx_spei(.lat = lat, .tavg = tavg)
#> [1] "Checking for missing values (`NA`): all the data must be complete. Input type is vector. Assuming the data are monthly time series starting in January, all regular (non-leap) years."
#> # A tibble: 358 × 16
#> .dist id month ym prcp tmax tmin tavg long lat name .pet
#> <chr> <chr> <dbl> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 glo ASN000… 12 1990 Dec 640 30.4 14.7 22.6 152. -29.0 tent… 127.
#> 2 glo ASN000… 1 1991 Jan 1108 27.5 15.9 21.7 152. -29.0 tent… 118.
#> 3 glo ASN000… 2 1991 Feb 628 28.0 15.5 21.8 152. -29.0 tent… 102.
#> 4 glo ASN000… 3 1991 Mar 204 26.2 11.8 19.0 152. -29.0 tent… 86.7
#> 5 glo ASN000… 4 1991 Apr 44 24.2 6.57 15.4 152. -29.0 tent… 56.6
#> 6 glo ASN000… 5 1991 May 630 21.3 7.52 14.4 152. -29.0 tent… 49.5
#> 7 glo ASN000… 6 1991 Jun 242 19.6 3.65 11.6 152. -29.0 tent… 33.5
#> 8 glo ASN000… 7 1991 Jul 580 15.3 0.519 7.91 152. -29.0 tent… 19.8
#> 9 glo ASN000… 8 1991 Aug 14 17.8 1.67 9.76 152. -29.0 tent… 28.8
#> 10 glo ASN000… 9 1991 Sep 78 21.1 3.07 12.1 152. -29.0 tent… 41.4
#> # ℹ 348 more rows
#> # ℹ 4 more variables: .diff <dbl>, .agg <dbl>, .fit <dbl>, .index <dbl>
dt |> idx_rdi(.lat = lat, .tavg = tavg)
#> [1] "Checking for missing values (`NA`): all the data must be complete. Input type is vector. Assuming the data are monthly time series starting in January, all regular (non-leap) years."
#> # A tibble: 358 × 15
#> id ym prcp tmax tmin tavg long lat name month .pet .ratio
#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 ASN00… 1990 Dec 640 30.4 14.7 22.6 152. -29.0 tent… 12 127. 5.05
#> 2 ASN00… 1991 Jan 1108 27.5 15.9 21.7 152. -29.0 tent… 1 118. 9.36
#> 3 ASN00… 1991 Feb 628 28.0 15.5 21.8 152. -29.0 tent… 2 102. 6.15
#> 4 ASN00… 1991 Mar 204 26.2 11.8 19.0 152. -29.0 tent… 3 86.7 2.35
#> 5 ASN00… 1991 Apr 44 24.2 6.57 15.4 152. -29.0 tent… 4 56.6 0.777
#> 6 ASN00… 1991 May 630 21.3 7.52 14.4 152. -29.0 tent… 5 49.5 12.7
#> 7 ASN00… 1991 Jun 242 19.6 3.65 11.6 152. -29.0 tent… 6 33.5 7.22
#> 8 ASN00… 1991 Jul 580 15.3 0.519 7.91 152. -29.0 tent… 7 19.8 29.2
#> 9 ASN00… 1991 Aug 14 17.8 1.67 9.76 152. -29.0 tent… 8 28.8 0.486
#> 10 ASN00… 1991 Sep 78 21.1 3.07 12.1 152. -29.0 tent… 9 41.4 1.89
#> # ℹ 348 more rows
#> # ℹ 3 more variables: .agg <dbl>, .y <dbl>, .index <dbl>
dt |> idx_edi(.lat = lat, .tavg = tavg)
#> # A tibble: 358 × 13
#> id ym prcp tmax tmin tavg long lat name month .mult .ep
#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 ASN0… 1990 Dec 640 30.4 14.7 22.6 152. -29.0 tent… 12 4134. 54292.
#> 2 ASN0… 1991 Jan 1108 27.5 15.9 21.7 152. -29.0 tent… 1 7154. 55722.
#> 3 ASN0… 1991 Feb 628 28.0 15.5 21.8 152. -29.0 tent… 2 4053. 51601.
#> 4 ASN0… 1991 Mar 204 26.2 11.8 19.0 152. -29.0 tent… 3 1316. 51270.
#> 5 ASN0… 1991 Apr 44 24.2 6.57 15.4 152. -29.0 tent… 4 284. 41223.
#> 6 ASN0… 1991 May 630 21.3 7.52 14.4 152. -29.0 tent… 5 4060. 37380.
#> 7 ASN0… 1991 Jun 242 19.6 3.65 11.6 152. -29.0 tent… 6 1559. 36387.
#> 8 ASN0… 1991 Jul 580 15.3 0.519 7.91 152. -29.0 tent… 7 3735. 36122.
#> 9 ASN0… 1991 Aug 14 17.8 1.67 9.76 152. -29.0 tent… 8 90.1 34051.
#> 10 ASN0… 1991 Sep 78 21.1 3.07 12.1 152. -29.0 tent… 9 502. 32832.
#> # ℹ 348 more rows
#> # ℹ 1 more variable: .index <dbl>