ClimInd: Climate Indices

Description

Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).

Details

Info

See Also

Useful links:

• https://gitlab.com/indecis-eu/indecis

ClimIndNews

Description

Show the NEWS file of the ClimInd package.

Usage

ClimIndNews()

Details

(See description)

data_all

Description

See wichita

Usage

data(data_all)

Format

An object of class list of length 22.

Details

See description.

Atmospheric Clarity Index

Description

Ratio between solar radiation at surface and solar radiation at TOA (alt top of the atmosphere)

Usage

aci(data, toa, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

index value

Examples

data(data_all)
aci(data = data_all$radiation, toa = data_all$radiationtoa)

Function allow scale

Description

Function allow scale

Usage

allow_scale(name)

Arguments

Value

allow or not allow

Average snow depth

Description

Average snow depth

Usage

asd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

snow depth, m

Examples

data(data_all)
asd(data = data_all$snowdepth)

Apparent temperature

Description

Index of the percived temperature.

Usage

at(taverage, w, vapor, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

index value

Formula

AT = TG + 0.33e -0.70v -4.00

TG = air temperature in Celsius ; v = wind speed in m/s; e= water vapour pressure in hPa

Examples

data(data_all)
at(taverage = data_all$tg, w = data_all$wind, vapor = data_all$VAPOUR)

Average temperature

Description

Average temperature

Usage

average_temp(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

average temperature

Budyko Index

Description

Budyko Index is based on characteristics of the surface heat and water balance.

Usage

bi(data, pr, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

BI = 100\frac {Rn}{L*P}

Rn= annual net radiation, P = annual precipitation, L = latent heat of vaporization for water

References

Budyko M.I. The Heat Balance of the Earth's Surface U.S. Department of Commerce, Washington D.C (1958) 259 pp., translated by N.A. Stepanova

Examples

data(data_all)
bi(data = data_all$radiation, pr = data_all$rr)

TG of warmest quarter

Description

TG of the warmest quarter of the year

Usage

bio10(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio10(data = data_all$tg)

TG of coldest quarter

Description

TG of coldest quarter of the year

Usage

bio11(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio11(data = data_all$tg)

Precipitation of wettest month

Description

Total precipitation of the wettest month of the year

Usage

bio13(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio13(data = data_all$rr)

Precipitation of driest month

Description

Total precipitation of the driest month of the year

Usage

bio14(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio14(data = data_all$rr)

Precipitation coefficient of variation

Description

The coefficient of variation is a measure of the variation in monthly precipitation totals over the course of the year. This index is the ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation and is expressed as a percentage.

Usage

bio15(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Examples

data(data_all)
bio15(data = data_all$rr)

Precipitation wettest quarter

Description

Precipitation of the wettest quarter of the year

Usage

bio16(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio16(data = data_all$rr, na.rm = TRUE)

Precipitation of Driest Quarter

Description

Precipitation of the driest quarter of the year

Usage

bio17(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio17(data = data_all$rr)

Precipitation warmest quarter

Description

Precipitation of the warmest quarter of the year

Usage

bio18(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio18(pr=data_all$rr, taverage=data_all$tg)

Precipitation coldest quarter

Description

Precipitation of the coldest quarter of the year

Usage

bio19(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio19(pr=data_all$rr, taverage=data_all$tg)

Mean radiation

Description

Mean radiation (W m-2)

Usage

bio20(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

radiation, W m-2

References

Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J. and Scott, J.K. (2012) CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution, 3, 53-64. doi: 10.1111/j.2041-210X.2011.00134.x

Examples

data(data_all)
bio20(data = data_all$radiation_w)

Temperature seasonality

Description

TG standard deviation * 100

Usage

bio4(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio4(data = data_all$tg)

TX warmest month

Description

TX of the warmest month of the year

Usage

bio5(data, tmax, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio5(data = data_all$tg, tmax = data_all$tx)

TN of coldest month

Description

TN of the coldest month of the year

Usage

bio6(data, tmin, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio6(data = data_all$tg, tmin = data_all$tn)

Temperature Annual Range

Description

TX of the warmest month minus TN of coldest month

Usage

bio7(data, tmin, tmax, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio7(data = data_all$tg, tmin = data_all$tn, tmax = data_all$tx)

TG of wettest quarter

Description

TG of the wettest quarter of the year

Usage

bio8(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio8(pr = data_all$rr, taverage = data_all$tg)

TG of driest quarter

Description

TG of the driest quarter of the year

Usage

bio9(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276. https://web.archive.org/web/20190714191708/https://www.worldclim.org/bioclim

Examples

data(data_all)
bio9(pr = data_all$rr, taverage =  data_all$tg)

Data with months and years in names

Description

Data with months and years in names

Usage

byMonths_chron(data)

Arguments

Value

dates

Et0

Description

Et0

Usage

calc_eto(
  tmin,
  tmax,
  toa,
  w,
  mde,
  lat,
  tdew,
  radiation = NA,
  insolation = NA,
  rh = NA,
  na.rm = FALSE
)

Arguments

Value

et0

125. SPEI: Standardized Precipitation Evapotranspiration Index 1, 3, 6 and 12 month SPEI

Description

125. SPEI: Standardized Precipitation Evapotranspiration Index 1, 3, 6 and 12 month SPEI

Usage

calc_spei(eto, pr, data_names = NULL, scale = 3, na.rm = FALSE)

Arguments

Value

SPEI

SPI: Standardized Precipitation Index 1, 3, 6 and 12 month SPI

Description

SPI: Standardized Precipitation Index 1, 3, 6 and 12 month SPI

Usage

calc_spi(data, data_names = NULL, scale = 3, na.rm = FALSE)

Arguments

Value

SPI

Operation de data, agrupando los datos por valores de names

Description

Operation de data, agrupando los datos por valores de names

Usage

calcf_data(
  data,
  date,
  time.scale,
  extract_names = select_time_function,
  data_names,
  operation,
  ...
)

Arguments

Value

result operation

Operation de data, agrupando los datos por valores de names

Description

Operation de data, agrupando los datos por valores de names

Usage

calcf_data_(data_names, data, operation, time.scale, ...)

Arguments

Value

operation

Operation de data para los valores de oks == ok

Description

Operation de data para los valores de oks == ok

Usage

calcf_data__(ok, oks, data, operation, ...)

Arguments

Value

operation

Calculate all indexes

Description

Calculate all indexes for a point

Usage

calculate_all(
  data,
  lat = NULL,
  time.scale = YEAR,
  data_names = NULL,
  index_result = c(1:138),
  na.rm = FALSE
)

Arguments

Value

all indexes

Calculate all indexes for all time scales

Description

Calculate all indexes for a point and all time scales

Usage

calculate_all_scales(data, lat = NULL)

Arguments

Value

all indexes

Mean daily cloud cover

Description

Mean daily cloud cover (

Usage

cc(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

percentage

Examples

data(data_all)
cc(data = data_all$cloud)

Longest dry period

Description

Maximum length of consecutive dry days (RR<1)

Usage

cdd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
cdd(data = data_all$rr)

Maximum consecutive frost days

Description

Maximum number of consecutive days with TN < 0 Celsius

Usage

cfd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
cfd(data=data_all$tn)

Climatic moisture deficit

Description

ETo - evapotranspiration

Usage

cmd(
  eto,
  evapotranspiration,
  data_names = NULL,
  time.scale = YEAR,
  na.rm = FALSE
)

Arguments

Value

index value

References

Parks, S. A., Parisien, M. , Miller, C. , Holsinger, L. M. and Baggett, L. S. (2018), Fine-scale spatial climate variation and drought mediate the likelihood of reburning. Ecol Appl, 28: 573-586. doi: 10.1002/eap.1671

Examples

data(data_all)
cmd(eto = data_all$eto, evapotranspiration = data_all$evaporation)

Maximum consecutive summer days

Description

Maximum number of consecutive summer days (TX > 25 Celsius)

Usage

csd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
csd(data=data_all$tx)

Cold spell duration

Description

Count of days with at least 6 consecutive days when TN < 10th percentile. The 10th percentile is computed based on the time scale selected (month, season or year) not daily as ETCCDI does. If you want to compute daily you can use RClimDex package.

Usage

csdi(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
csdi(data=data_all$tn)

Longest wet period

Description

Maximum length of consecutive wet days (RR>=1)

Usage

cwd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
cwd(data = data_all$rr)

Days TX32

Description

Number of days whith TX >= 32 Celsius on the interval June-August.

Usage

d32(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
d32(data = data_all$tx)

Heavy precipitation days

Description

Number of days with precipitation above 50mm

Usage

d50mm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
d50mm(data = data_all$rr)

Very wet days

Description

Days with precipitation > 95th percentile. The 95th percentile is computed based on the time scale selected (month, season or year) not daily

Usage

d95p(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
d95p(data = data_all$rr)

Dry days

Description

Number of days with less than 1 mm

Usage

dd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
dd(data = data_all$rr)

Difference days above/below Tx17

Description

(days tx > 17 Celsius)-(days TX < 17 Celsius)

Usage

dd17(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
dd17(data=data_all$tx)

Days wind gusts above 21 m/s

Description

Number of days with wind gusts above 21 m/s

Usage

dfx21(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
dfx21(data = data_all$windgust)

Wet days 1mm

Description

Total number of wet days >= 1 mm

Usage

dr1mm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
dr1mm(data = data_all$rr)

Wet days 3mm

Description

Total number of Wet days >= 3mm

Usage

dr3mm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
dr3mm(data = data_all$rr)

Diurnal temperature range

Description

Mean difference between TX and TN.

Usage

dtr(tmax, tmin, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

Formula

DTR_j = \frac{ \sum_{ i = 1 } ^ { I } ( TX_{ ij } - TN_{ ij })} {I}

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5. https://www.ecad.eu/documents/WCDMP_72_TD_1500_en_1.pdf

Examples

data(data_all)
dtr(tmax=data_all$tx, tmin=data_all$tn)

Emberger aridity index

Description

Aridity index based on annual precipitation and temperature range

Usage

eai(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

EAI = \frac {100*P}{Thm^2 - Tcm^2}

P = annual precipitation; Thm = Average temperature of the hottest month in Kelvin; Tcm= Average temperature of the coldest month in Kelvin

References

Emberger L. 1930. La végétation de la région méditerranéenne: essai d'une classification des groupements végétaux Revue Générale de Botanique, 42 (641–662), pp. 705-721

Examples

data(data_all)
eai(pr = data_all$rr, taverage = data_all$tg)

Effective precipitation

Description

Precipitation minus evapotranspiration

Usage

ep(eto, pr, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

mm

Examples

data(data_all)
ep(eto = data_all$eto, pr = data_all$rr)

Reference evapotranspiration

Description

If data available using Fao-56 Penman-Monteith

Usage

eto(
  tmin,
  tmax,
  toa,
  w,
  lat,
  tdew,
  mde,
  radiation = NA,
  insolation = NA,
  rh = NA,
  data_names = NULL,
  time.scale = YEAR,
  na.rm = FALSE
)

Arguments

Value

mm

References

Chiew, F.H.S., Kamaladasa, N.N., Malano, H.M., McMahon, T.A., 1995. Penman–Monteith FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric. Water Manage. 28, 9–21

Examples

data(data_all)
eto(tmin = data_all$tn, tmax = data_all$tx, 
     toa = data_all$radiationtoa, w = data_all$wind,
     lat=data_all$lat, tdew = data_all$dewpoint, 
     mde=data_all$mde, radiation = data_all$radiation, 
     insolation=data_all$insolation, rh = data_all$humidity)

Extreme temperature range

Description

Difference between the maximum TX and the minimum TN.

Usage

etr(tmax, tmin, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
etr(tmax=data_all$tx, tmin=data_all$tn)

Frost days

Description

Number of days with TN < 0 Celsius.

Usage

fd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
fd(data=data_all$tn)

Mac Arthur Forest Fire Danger Index

Description

Implementation of the Mac Arthur's Forest Fire Danger Index (FFDI)

Usage

ffdiIndex(madf, t, h, w)

Arguments

Value

A vector of (daily) FFDI data

Author(s)

Joaquin Bedia-Jiménez

References

McArthur, A.G. (1973) Forest Fire Danger Meter Mk.5. Commonwealth of Australian Forestry and Timber Bureau.

See Also

kbdindex

Finnish Forest Fire Danger Index

Description

Implementation of the FFFDI for vector data

Usage

fffdi(pr, pet, Wvol.init = 0.5, z = 60)

Arguments

Details

Volumetric moisture The default is 0.5, indicating that the soil is very wet and near field capacity. This is so, assuming that the index is started in early spring. This value is applied to all locations as a spatially constant initialization value. However, Vajda et al. (2014, Table 1) provide reference values for different soil moisture conditions. This value ranges from 0.1 (very dry) to 0.5 (very wet).

Value

A numeric vector containing FFFDI time series

Author(s)

Joaquin Bedia-Jiménez

References

Vajda, A., Venalainen, A., Suomi, I., Junila, P. and Makela, H., 2014. Assessment of forest fire danger in a boreal forest environment: description and evaluation of the operational system applied in Finland. Meteorol. Appl., 21: 879-887, DOI: 10.1002/met.1425

Mean of daily mean wind strength

Description

Mean of daily FG

Usage

fg(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

wind, m/s

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
fg(data = data_all$wind)

Number of days with averaged wind above 10.8m/s

Description

Number of days with FG >=6 Bft (10.8 m/s)

Usage

fg6bft(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

ECA&D website: European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
fg6bft(data = data_all$wind)

Calm days

Description

Number of calm days (FG <=2 m/s)

Usage

fgcalm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

ECA&D website: European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
fgcalm(data = data_all$wind)

Foggy days

Description

Number of days with fog.

Usage

fod(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Rastogi, B., A.P. Williams, D.T. Fischer, S.F. Iacobellis, K. McEachern, L. Carvalho, C. Jones, S.A. Baguskas, and C.J. Still, 2016: Spatial and Temporal Patterns of Cloud Cover and Fog Inundation in Coastal California: Ecological Implications. Earth Interact., 20, 1–19, doi: 10.1175/EI-D-15-0033.1

Examples

data(data_all)
fod(data = data_all$cloud100)

Date of first permanent snow cover

Description

First day of the longest period with consecutive snow cover day (day of the hydrological year).

Usage

fpsc(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

date

Examples

data(data_all)
fpsc(data = data_all$snowdepth)

Date of first snow cover

Description

First day when there is measurable snow cover (day of the hydrological year)

Usage

fsc(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

date

Examples

data(data_all)
fsc(data = data_all$snowdepth)

Number of snow days

Description

Number of snow days

Usage

fsd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
fsd(data = data_all$snowfall)

Fire Weather Index applied to 1D data

Description

Implementation of the Canadian Fire Weather Index System for vector data

Usage

fwi1D(
  dates,
  Tm,
  H,
  r,
  W,
  lat = 46,
  what = "FWI",
  init.pars = c(85, 6, 15),
  spin.up = 0
)

Arguments

Value

A matrix with the time (days) arranged in rows and the components selected in what in columns. The attribute colnames gives the component ordering. Default to a single-colum matrix returning FWI.

Daylength adjustment factors

By default, the function applies the original FWI daylength adjustment factors for DC and DMC (van Wagner 1987), although it is possible to adjust them by as a function of latitude through the argument lat. The reference values used for each latitudinal range are those indicated in p.71 and Tables A3.1 and A3.2 (p69) in Lawson and Armitage (2008).

FWI spin-up

FWI is initialized with some values for FFMC, DMC and DC components. This means that the first values of the series are not reliable, until the index is iterated over several time steps and stabilizes (typically a few days suffice). Thus, the first index values can be optionally set to NA. The number of days at the beginning of the series to be set to NA is controlled via the spin.up argument.

Note

The FWI system should be computed by definition upon instantaneous values of temperature, relative humidity and wind speed measured a "noon local standar time", plus 24-h accumulated precipitation. The use of inadequate temporal resolution has important implicationas, as highlighted by Herrera et al. (2013). However, prior studies rely on adequate proxies, in order to exploit model databases containing just daily data (see e.g. Bediat et al. (2014)).

Author(s)

Joaquin Bedia-Jiménez

References

• Lawson, B.D. & Armitage, O.B., 2008. Weather guide for the Canadian Forest Fire Danger Rating System. Northern Forestry Centre, Edmonton (Canada).

• van Wagner, C.E., 1987. Development and structure of the Canadian Forest Fire Weather Index (Forestry Tech. Rep. No. 35). Canadian Forestry Service, Ottawa, Canada.

• van Wagner, C.E., Pickett, T.L., 1985. Equations and FORTRAN program for the Canadian forest fire weather index system (Forestry Tech. Rep. No. 33). Canadian Forestry Service, Ottawa, Canada.

• Herrera, S., Bedia, J., Gutierrez, J.M., Fernandez, J., Moreno, J.M., 2013. On the projection of future fire danger conditions with various instantaneous/mean-daily data sources. Climatic Change 118, 827-840.

• Bedia, J., Herrera, S., Camia, A., Moreno, J.M., Gutierrez, J.M., 2014. Forest Fire Danger Projections in the Mediterranean using ENSEMBLES Regional Climate Change Scenarios. Climatic Change 122, 185-199.

Daily maximum wind gust

Description

Maximum value of daily maximum wind gust (m/s)

Usage

fxx(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

wind, m/s

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
fxx(data = data_all$windgust)

Growing degree days

Description

Sum of degree days of TG over 4 Celsius (the daily mean temperature is less than 4 celsius, it is set equal to 4 celsius)

Usage

gd4(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

McMaster, G. S., & Wilhelm, W. W. (1997). Growing degree-days: One equation, two interpretations. Agricultural and Forest Meteorology, 87(4), 291-300

Examples

data(data_all)
gd4(data=data_all$tg)

Growing season length

Description

Annual count of days between the first span of at least 6 days with TG > 5 Celsius and first span after 1 July of 6 days with TG < 5 Celsius.

Usage

gsl(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
gsl(data=data_all$tg)

Growing season precipitation

Description

Growing season (april to october) total precipitation

Usage

gsr(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

Examples

data(data_all)
gsr(data = data_all$rr)

Mean TG

Description

Mean of daily mean air temperature

Usage

gtg(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
gtg(data=data_all$tg)

Mean TN

Description

Mean of daily minimum air temperature

Usage

gtn(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
gtn(data=data_all$tn)

Mean TX

Description

Mean of daily maximum air temperature

Usage

gtx(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
gtx(data=data_all$tg)

Heating degree days

Description

accumulated degree when TG is below 17 Celsius

Usage

hd17(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

Formula

HD17_j = \sum_{j-1}^{I} (17 ^ oC - TG_ij)

References

Quayle, R. G., & Diaz, H. F. (1980). Heating degree day data applied to residential heating energy consumption. Journal of Applied Meteorology, 19(3), 241-246. doi: 10.1175/1520-0450(1980)019<0241:HDDDAT>2.0.CO;2

Examples

data(data_all)
hd17(data=data_all$tg)

Heat Index

Description

Combines air temperature and relative humidity to determine the human-perceived equivalent temperature

Usage

hi(taverage, rh, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

index value

Formula

HI= -42,379+2,04901523*TG+10,14333127*rh-0,22475541*TG*rh-0.00683783*TG^2-0.05481717*rh^2+0.0122874*TG^2*rh+0.00085282*TG*rh^2-0.00000199*TG^2*rh^2

. Where TG is air temperature in ºF and rh is relative humidity in

References

The Heat Index Equation https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml

Examples

data(data_all)
hi(taverage = data_all$tg, rh = data_all$humidity)

Heavy snowy days

Description

Number of days with snow depth more than 50 cm.

Usage

hsd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
hsd(data = data_all$snowdepth)

Hydrological years

Description

Hydrological years

Usage

hydrological_years(time)

Arguments

Value

seasonals by years

Ice days

Description

Number of days with TX < 0 Celsius.

Usage

id(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
id(data=data_all$tx)

Johansson Continentality Index

Description

The Johansson Continentality Index is usually used for the climatic differentiation between continental and oceanic climates.

Usage

jci(data, data_names = NULL, value, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

JCI = \frac {1.7(Thm-Tcm)}{sinf}-20.4

Thm = Average temperature of the hottest month (Celsius); Tcm = Average temperature of the coldest month (Celsius); f = geographical latitude

References

Chronopoulou-Sereli A. 1996. Courses of Agricultural Meteorology.Publications Agricultural University of Athens: Athens, OH

Examples

data(data_all)
jci(data = data_all$tg, value = data_all$lat)

Keetch-Byram drought index and Mac Arthur Drought Factor

Description

Implementation of the Keetch Byram Drought Index and Mac Arthur's Drought Factor for vector data

Usage

kbdindex(dates, t, p, wrs = 5, start.date = NULL, what = "kbdi")

Arguments

Details

The physical theory for the Keetch-Byram Drought Index (Keetch and Byram, 1968) is based on a number of assumptions: The first assumption is that soil moisture is at field capacity with a water depth equivalent to about 200 mm. The second assumption is that the rate of moisture loss in an area depends on the vegetation cover in the area, and vegetation density is a function of the mean annual rainfall. Hence, daily transpiration is approximated by an inverse exponential function of the mean annual rainfall. Finally, the evaporation rate of soil moisture with time is assumed to be an estimation of relative evapotranspiration from exponential function of the daily maximum air temperature. Sensitivity analyses from earlier researchers have revealed that KBDI decays exponentially with an assumed maximum soil water deficit and is sensitive to the daily maximum air temperature (Dennison et al., 2013). Its values range from 0 to 800 (inches), with 800 in (203.2 mm after conversion) indicating extreme drought and zero indicating saturated soil.

The McArthur's Drought Factor was developed to predict the amount of fine fuel which would be available to be consumed in the flaming front of a fire. The predictive model used by McArthur was based on a combination of the Keetch Byram Drought Index, and the amount, and time since fall, of recent rain. That is the reason both indices are calculated by the same function.

Value

A numeric vector containing the (daily) KBDI (or MADF) time series

Note

The original equations of the code presented by Keetch and Byram (1968) were later corrected for two significant typographical errors affecting the index output Alexander (1990).

Author(s)

Joaquin Bedia-Jiménez

References

• Keetch, J.J. and Byram, G.M. (1968) A drought index for forest fire control. USDA Forest Service.

• Alexander, M.E., 1990. Computer calculation of the Keetch-Byram Drought Index - programmers beware. Fire Management Notes 51, 23–25.

• Dennison, P.E., Roberts, D.A., Thorgusen, S.R., Regelbrugge, J.C., Weise, D., Christopher, L., 2003. Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index. Remote Sensing of the Environment 88, 442–452.

Kerner Oceanity Index

Description

KOI analysed the oceanity assuming that marine climates have colder spring months in comparison with the autum months.

Usage

koi(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

KOI = \frac {100(TGo-TGa)}{Thm-Tcm}

TGo = Average temperature of October TGa = Average temperature of April Thm = Average temperature of the hottest month (Celsius); Tcm = Average temperature of the coldest month (Celsius)

References

Zambakas J. 1992.General Climatology. Department of Geology,National & Kapodistrian University of Athens, Athens. Gavilan RG. 2005. The use of climatic parameters and indices in vege-tation distribution. A case study in the Spanish System Central.Int. J.Biometeorol.50: 111–120.

Examples

data(data_all)
koi(data = data_all$tg)

Date of last permanent snow cover

Description

Last day of the longest period with consecutive snow cover day (day of the hydrological year).

Usage

lpsc(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

date

Examples

data(data_all)
lpsc(data = data_all$snowdepth)

De Martonne aridity index

Description

De Martonne aridity index is the ratio between the annual amount of precipitation and anual mean of temperature plus 10 Celsius.

Usage

mai(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

MAI = \frac{P} {TG+10}

P = annual precipitation (mm); TG = mean annual air temperature (Celsius)

References

De Martonne E., 1926. Une nouvelle fonction climatologique: L’indice d’aridité. La Meteorologie, 449-458.

Examples

data(data_all)
mai(pr = data_all$rr, taverage = data_all$tg)

max with na.rm TRUE

Description

max with na.rm TRUE

Usage

maxf(..., na.rm = TRUE)

Arguments

Value

max

Maximum temperature

Description

Maximum temperature

Usage

maximum_temp(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

maximum temperature

mean with na.rm TRUE

Description

mean with na.rm TRUE

Usage

meanf(..., na.rm = TRUE)

Arguments

Value

mean

Modified Fournier Index

Description

The precipitation concentration index is frequently associated to erosion risk. Values: 0-60 very low; 60-90 Low; 90-120 moderate; 120-160 high; > 160 very high.

Usage

mfi(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

MFI = \sum_{i=1}^{12} \frac{P_i ^ 2} {P_t}

References

Fournier F. 1960. Climat et Erosion. PUF: Paris. Arnoldus HM. 1980. An approximation of the rainfall factor in the Uni-versal Soil Loss Equation. In Assessments of Erosion, de Boodts M,Gabriels D (eds). John Wiley and Sons Ltd, Chichester 127–132. De Luis M., González-Hidalgo J.C., Longares L.A. Is rainfal erosivity increasing in the Mediterranean Iberian Peninsula?. Land Degradation & Development, 21: 139-144.

Examples

data(data_all)
mfi(data = data_all$rr)

Mould index

Description

Number of days with a relative humidity over 90

Usage

mi(taverage, rh, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
mi(taverage = data_all$tg, rh = data_all$humidity)

min with na.rm TRUE

Description

min with na.rm TRUE

Usage

minf(..., na.rm = TRUE)

Arguments

Value

min

Minimum temperature

Description

Minimum temperature

Usage

minimum_temp(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

minimum temperature

Marsz Oceanity Index

Description

The annual range of monthly mean air temperatures grados

Usage

moi(data, lat, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

MOI=\frac {0.731 \phi +1.767}{Thm-Tcm}

Phi = geographical latitude; Thm = Average temperature of the hottest month (Celsius); Tcm = Average temperature of the coldest month (Celsius)

References

Marsz A, Rakusa-Suszczewskis S. 1987. Charakterystyka ekologiczna rejonu Zatoki Admiralicji (King George Island, SouthShetland Islands). 1. Klimat i obszary wolne od lodu.Kosmos36:103–127.

Examples

data(data_all)
moi(data = data_all$tg, lat = data_all$lat)

Select quarter days

Description

Select quarter days

Usage

months_quarter(functionValues, selectFunction, selectValues, na.rm = FALSE)

Arguments

Value

quarter days

Months by years

Description

Months by years

Usage

months_years(time)

Arguments

Value

months by years

Maximum snow depth

Description

Maximum snow depth (m)

Usage

ms(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

snow depth, m

Examples

data(data_all)
ms(data = data_all$snowdepth)

Mild snowy days

Description

Number of days with snow depth > 5 cm.

Usage

msd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
msd(data = data_all$snowdepth)

Nesterov Index

Description

Implementation of the Nesterov Index for fire danger estimation

Usage

nesterovIndex(t, rh, p, modified = FALSE)

Arguments

Details

The Nesterov Index (NI) was developed in former Soviet Union as an empirical function reflecting the relationship between observed weather conditions and fire occurrence, defined as follows:

NI=\sum_{\forall p_i\leq 3mm}^i T_i(T_i-Td_i)

where T is midday temperature and Td is the dewpoint temperature at that moment, calculated from relative humidity and T. NI is a cumulative index, but summation is performed for those days when the daily precipitation (p) does not exceed 3 mm. At p >3 mm, the NI value is reset to zero. Usually, the values from NI are divided into five ranges to provide an estimate of fire danger potential. Conditions with NI<300 (regime I) are not considered hazardous. Conditions in the ranges 300-1000, 1000-4000, 4000-10000, and above 10000 are considered regimes with low (II), moderate (III), high (IV), and extreme (V) level of fire hazard.

Previous studies reveal that NI may be unstable in some cases, and a modification on this index has been proposed by introducing to its values a K scale coefficient, in the range 0-1, accounting for the amount of precipitation and previous dryness in a more detailed way than the original NI equation. Details on the values of K are provided by Groisman et al. 2007. @references

• Groisman, P.Y., Sherstyukov, B.G., Razuvaev, V.N., Knight, R.W., Enloe, J.G., Stroumentova, N.S., Whitfield, P.H., Forland, E., Hannsen-Bauer, I., Tuomenvirta, H., Aleksandersson, H., Mescherskaya, A.V., Karl, T.R., 2007. Potential forest fire danger over Northern Eurasia: Changes during the 20th century. Global and Planetary Change 56, 371–386.

• Holsten, A., Dominic, A.R., Costa, L., Kropp, J.P., 2013. Evaluation of the performance of meteorological forest fire indices for German federal states. Forest Ecology and Management 287, 123–131.

@author Joaquin Bedia-Jiménez

Value

A vector of (daily) NI data

Non-growing season precipitation

Description

Total precipitation from October to April

Usage

ngsr(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

precipitation, mm

Examples

data(data_all)
ngsr(data = data_all$rr)

Select no empty parameters

Description

Select no empty parameters

Usage

no_null(data)

Arguments

Value

no empty parameter

Minimum TG

Description

Minimum value of daily mean air temperature

Usage

ntg(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

Average temperature

Examples

data(data_all)
ntg(data=data_all$tg)

Onset of growing season 10 days

Description

Date of the start of the first span with at least 10 days with TG > 5 Celsius

Usage

ogs10(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

date

Examples

data(data_all)
ogs10(data=data_all$tg)

Onset of growing season 6 days

Description

Date of the start of the first span with at least 6 days with TG >5 Celsius

Usage

ogs6(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

date

Examples

data(data_all)
ogs6(data=data_all$tg)

Precipitation Concentration Index

Description

Index to evaluate precipitation heterogeneity at a monthly scale. Values <10 (uniform monthly rainfall distribution); values 11-15 (moderate concentration of precipitation); values 16-20 (irregular distribution); and >20 ((high precipitation concentration)

Usage

pci(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

PCI = \frac{\sum_{i=1}^{12} P_i ^ 2} {(P_t) ^ 2} * 100

References

Oliver, J.E. (1980) Monthly precipitation distribution: a comparative index. Professional Geographer, 32, 300–309

Examples

data(data_all)
pci(data = data_all$rr)

FAO-56 Penman-Monteith reference evapotranspiration (ET_0)

Description

FAO-56 Penman-Monteith reference evapotranspiration (ET_0)

Usage

penman_fao_diario(
  Tmin,
  Tmax,
  U2,
  J,
  Ra = NA,
  lat = NA,
  Rs = NA,
  tsun = NA,
  CC = NA,
  ed = NA,
  Tdew = NA,
  RH = NA,
  P = NA,
  P0 = NA,
  z = NA,
  crop = "short",
  na.rm = FALSE
)

Arguments

Value

et0, mm/day

Transforma datos de in en r o al revés

Description

Transforma datos de in en r o al revés

Usage

penman_rs(J, lat = NA, tsun = NA, z = NA, ret = RADIATION)

Arguments

Value

insolación en horas de sol o radiación en ¿MJ/m2?

Pinna Combinative Index

Description

Pinna combinative index is an aridity–humidity index

Usage

pici(pr, taverage, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

Formula

PICI = \frac {1}{2} \left(\frac{P}{TG+10}+\frac{12Pdm}{TGdm+10}\right)

P = annual precipitation (mm); TG = annual mean temperature (Celsius); Pdm= precipitation of the driest month; TGdm= temperature of the driest month

References

Zambakas J. 1992. General Climatology. Department of Geology, National & Kapodistrian University of Athens: Athens, Greece.

Examples

data(data_all)
pici(pr = data_all$rr, taverage = data_all$tg)

Total precipitation wet days

Description

Precipitation amount on days with RR >= 1 mm

Usage

prcptot(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
prcptot(data = data_all$rr)

Sums positive

Description

Sums of positive TG calculated for the 1st of February to the 10th April interval

Usage

ptg(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
ptg(data = data_all$tg)

quantile null

Description

quantile null

Usage

quantile_null(x, ...)

Arguments

Value

operation

Days precipitation >= R10mm

Description

Days with daily precipitation amount >= 10mm

Usage

r10mm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
r10mm(data = data_all$rr)

Days precipitation >= R20mm

Description

Days with daily precipitation amount >= 20mm

Usage

r20mm(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
r20mm(data = data_all$rr)

Percentage precipitation of very wet days

Description

Precipitation at days exceeding the 95th percentile divided by total precipitation expressed in percentage. The 95th percentile is computed based on the time scale selected (month, season or year) not daily.

Usage

r95tot(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

Examples

data(data_all)
r95tot(data = data_all$rr, time.scale="month")

Precipitation fraction extremely wet days

Description

Precipitation at days exceeding the 99th percentile divided by total precipitation expressed in percentage, The 99th percentile is computed based on the time scale selected (month, season or year) not daily

Usage

r99tot(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

Examples

data(data_all)
r99tot(data = data_all$rr)

Transforma radiancia en insolación

Description

Transforma radiancia en insolación

Usage

r_to_in(radiation, lat, mde)

Arguments

Value

insolación en horas

Total precipitation

Description

Total amounts of precipitation

Usage

rti(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

Examples

data(data_all)
rti(data = data_all$rr)

Maximum precipitation

Description

The highest amount of daily precipitation

Usage

rx1day(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
rx1day(data = data_all$rr)

Maximum 5 days R

Description

Maximum consecutive 5-day precipitation

Usage

rx5d(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
rx5d(data = data_all$rr)

Scales allow

Description

Scales allow

Usage

scale_name(name)

Arguments

Value

scales allow

Number of snow covered days

Description

Number of snow covered days (snow depth > 0)

Usage

scd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
scd(data = data_all$snowdepth)

Snow depth 1-10

Description

Number of days with snow depth in the range 1-10 cm

Usage

sd0_10(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
sd0_10(data = data_all$snowdepth)

Snow depth 10-20

Description

The number of days with snow depth of 10-20 cm

Usage

sd10_20(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
sd10_20(data = data_all$snowdepth)

Simple precipitation intensity index

Description

Sum of precipitation in wet days (days with >1mm of precipitation), and dividing that by the number of wet days in the period.

Usage

sdii(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

precipitation, mm

References

Michele Brunetti, Maurizio Maugerib, Teresa Nanni, (2001) Changes in total precipitation, rainy days and extreme events in northeastern Italy, International Journal of Climatology

Examples

data(data_all)
sdii(data = data_all$rr)

Seasonals

Description

Seasonals

Usage

seasonals(time)

Arguments

Value

seasonals

Seasonals by years

Description

Seasonals by years

Usage

seasonals_years(time)

Arguments

Value

seasonals by years

Function to select all "time" data

Description

Function to select all "time" data

Usage

select_all_time_function(time.scale)

Arguments

Value

function

Function to select data

Description

Function to select data

Usage

select_time_function(time.scale)

Arguments

Value

function

Name data station or month

Description

Name data station or month

Usage

select_value_for_data(data, value, time.scale)

Arguments

Value

function

Sunny days

Description

Days with mean cloud cover less than 10

Usage

snd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
snd(data = data_all$cloud)

Standardised Precipitation-Evapotranspiration Index 1

Description

Standardized precipitation-evapotranspiration index calculated at 1-month time scale

Usage

spei1(eto, pr, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Clim., 23(7), doi: 10.1175/2009JCLI2909.1, 2010.

Examples

data(data_all)
spei1(eto = data_all$eto, pr = data_all$rr, na.rm = TRUE)

Standardised Precipitation-Evapotranspiration Index 12

Description

Standardized precipitation-evapotranspiration index calculated at 12-month time scale

Usage

spei12(eto, pr, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Clim., 23(7), doi: 10.1175/2009JCLI2909.1, 2010.

Examples

data(data_all)
spei12(eto = data_all$eto, pr = data_all$rr)

Standardised Precipitation-Evapotranspiration Index 3

Description

Standardized precipitation-evapotranspiration index calculated at 3-month time scale

Usage

spei3(eto, pr, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Clim., 23(7), doi: 10.1175/2009JCLI2909.1, 2010.

Examples

data(data_all)
spei3(eto = data_all$eto, pr = data_all$rr)

Standardised Precipitation-Evapotranspiration Index 6

Description

Standardized precipitation-evapotranspiration index calculated at 6-month time scale

Usage

spei6(eto, pr, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

Vicente-Serrano, S. M., Beguería, S. and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Clim., 23(7), doi: 10.1175/2009JCLI2909.1, 2010.

Examples

data(data_all)
spei6(eto = data_all$eto, pr = data_all$rr)

Standardized precipitation index 1

Description

Standardized precipitation index calculated at 1-month time scale

Usage

spi1(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

McKee, T. B., Doesken, N. J. and Kleist, J.: The relationship of drought frequency and duration to time scales, Eighth Conf. Appl. Climatol., 179–184, 1993.

Examples

data(data_all)
spi1(data = data_all$rr)

Standardized precipitation index 12

Description

Standardized precipitation index calculated at 12-month time scale

Usage

spi12(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

McKee, T. B., Doesken, N. J. and Kleist, J.: The relationship of drought frequency and duration to time scales, Eighth Conf. Appl. Climatol., 179–184, 1993.

Examples

data(data_all)
spi12(data = data_all$rr)

Standardized precipitation index 3

Description

Standardized precipitation index calculated at 3-month time scale

Usage

spi3(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

McKee, T. B., Doesken, N. J. and Kleist, J.: The relationship of drought frequency and duration to time scales, Eighth Conf. Appl. Climatol., 179–184, 1993.

Examples

data(data_all)
spi3(data = data_all$rr)

Standardized precipitation index 6

Description

Standardized precipitation index calculated at 6-month time scale

Usage

spi6(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

index value

References

McKee, T. B., Doesken, N. J. and Kleist, J.: The relationship of drought frequency and duration to time scales, Eighth Conf. Appl. Climatol., 179–184, 1993.

Examples

data(data_all)
spi6(data = data_all$rr)

Snowfall sum

Description

Sum of snowfall

Usage

ss(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

snow, mm

Examples

data(data_all)
ss(data = data_all$snowfallmm)

Sum of sunshine duration

Description

Sum of sunshine duration (hours)

Usage

ssd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

hours of sun

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
ssd(data = data_all$insolation)

Sunshine duration percentage

Description

Sunshine duration fraction with respect to day length (

Usage

ssp(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

percentage

Formula

SSP = \frac{SS} {SSmax} * 100

SS: sum of sunshine duration (h); SSmax: maximun daylight (h)

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
ssp(data = data_all$insolation)

Sums TN-10

Description

Sum of degree days when TN <=-10 Celsius recorded in December-February interval

Usage

stn10(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
stn10(data = data_all$tn)

Sums TN-15

Description

Sum of degree days when TN <= -15 Celsius recorded in December-February interval

Usage

stn15(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
stn15(data = data_all$tn)

Sums TX32

Description

Sum of degree days when TX >= 32 Celsius on the interval June-August. The 32 celsius limit is the critical biological threshold for the maximum air temperature from which the physiological optimal growth and development of wheat and maize plants.

Usage

stx32(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
stx32(data = data_all$tx)

Summer days

Description

Number of days with daily maximum temperature > 25 Celsius.

Usage

su(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
su(data=data_all$tx)

sum with na.rm TRUE

Description

sum with na.rm TRUE

Usage

sumf(..., na.rm = TRUE)

Arguments

Value

sum

Growing season (Apr-Oct)

Description

Growing season (april to october) mean TG

Usage

ta_o(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
ta_o(data=data_all$tg)

Dew point to relative humidity

Description

Dew point to relative humidity

Usage

td_to_rh(tmax, tmin, td)

Arguments

Value

rh

Dew point to water vapour pressure

Description

Dew point to water vapour pressure

Usage

td_to_vapor(td)

Arguments

Value

vapor

Growing season(May-Sep)

Description

Growing season (may to september) mean TG

Usage

tm_s(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
tm_s(data=data_all$tg)

Percentage of cold nights

Description

Percentages of days with TN lower than the 10th percentile. The 10th percentile is computed based on the time scale selected (month, season or year) not daily as ETCCDI does. If you want to compute daily you can use RClimDex package.

Usage

tn10p(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

percentage

Formula

cn = \frac{No. days TN < 10p} {No. days} * 100

Examples

data(data_all)
tn10p(data=data_all$tn)

Warm nights

Description

Percentages of days with TN higher than the 90th percentile. The 90th percentile is computed based on the time scale selected (month, season or year) not daily.

Usage

tn90p(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
tn90p(data=data_all$tn)

Minimum TN

Description

Minimum of daily minimum air temperature

Usage

tnn(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5. https://www.ecad.eu/documents/WCDMP_72_TD_1500_en_1.pdf

Examples

data(data_all)
tnn(data=data_all$tn)

Maximum TN

Description

Maximum of daily minimum air temperature

Usage

tnx(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5. https://www.ecad.eu/documents/WCDMP_72_TD_1500_en_1.pdf

Examples

data(data_all)
tnx(data=data_all$tn)

Tropical nights

Description

Number of days with TN > 20 Celsius.

Usage

tr(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5.

Examples

data(data_all)
tr(data=data_all$tn)

Percentage of cold days

Description

Percentages of days with TX lower than the 10th percentile. The 10th percentile is computed based on the time scale selected (month, season or year) not daily as ETCCDI does. If you want to compute daily you can use RClimDex package.

Usage

tx10p(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

percentage

Formula

cd = \frac{No. days TX < 10p} {No. days} * 100

Examples

data(data_all)
tx10p(data=data_all$tx)

Warm days

Description

Total numbers of days with TX higher than the 90th percentile. The 90th percentile is computed based on the time scale selected (month, season or year) not daily as ETCCDI does. If you want to compute daily you can use RClimDex package.

Usage

tx90p(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
tx90p(data=data_all$tx)

Minimum TX

Description

Minimum of daily maximum air temperature

Usage

txn(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5. https://www.ecad.eu/documents/WCDMP_72_TD_1500_en_1.pdf

Examples

data(data_all)
txn(data=data_all$tx)

Maximum TX

Description

Maximum of daily maximum air temperature

Usage

txx(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

References

Klein Tank AMG, Zwiers FW, Zhang X. 2009. Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, climate data and monitoring WCDMP-No 72, WMO-TD No 1500, p 5. https://www.ecad.eu/documents/WCDMP_72_TD_1500_en_1.pdf

Examples

data(data_all)
txx(data=data_all$tx)

UNEP Aridity Index

Description

P/Eto

Usage

uai(eto, pr, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

index value

References

Huiping Huang, Yuping Han, Mingming Cao, Jinxi Song, and Heng Xiao Spatial-Temporal Variation of Aridity Index of China during 1960–2013. Advances in Meteorology, vol. 2016, Article ID 1536135, 10 pages, 2016. doi: 10.1155/2016/1536135

Examples

data(data_all)
uai(eto = data_all$eto, pr = data_all$rr)

Universal Thermal Climate Index

Description

The Universal Thermal Climate is defined as the air temperature of the reference condition causing the same model response as actual conditions. The deviation of UTCI from air temperature, depends on the values of air and mean radiant temperature), wind speed and humidity.

Usage

utci(
  taverage,
  rh,
  w,
  tmrt,
  data_names = NULL,
  time.scale = YEAR,
  na.rm = FALSE
)

Arguments

Value

index value

References

Blazejczyk, K.; Jendritzky, G.; Bröde, P.; Fiala, D.; Havenith, G.; Epstein, Y., Psikuta, A.; Kampmann, B. 2013. An introduction to the Universal Thermal Climate Index (UTCI). Geographia Polonica, 86 (1), pp.5-10. http://www.utci.org/

Examples

data(data_all)
utci(ta = data_all$tg, rh = data_all$humidity, w = data_all$wind, 
     tmrt = data_all$radiationtemperature)

Very cold days

Description

Days with TN <1st percentile. The 1th percentile is computed based on the time scale selected (month, season or year).

Usage

vcd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
vcd(data=data_all$tn)

Mean daily difference DTR

Description

Mean absolute day-to-day difference in DTR

Usage

vdtr(tmax, tmin, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

temperature, Celsius

Formula

vDTR_j = \frac{ \sum_{ i = 1 } ^ { I } \mid ( TX_{ ij } - TN_{ ij } ) - ( TX_{i-1,j} -TN_{ i - 1,j }) \mid } {I}

References

European Climate Assessment & Dataset. Indices dictionary. https://www.ecad.eu//indicesextremes/indicesdictionary.php

Examples

data(data_all)
vdtr(tmax=data_all$tx, tmin=data_all$tn)

Very warm days

Description

Days with TX >99th percentile per year. The 99th percentile is computed based on the time scale selected (month, season or year).

Usage

vwd(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
vwd(data=data_all$tx)

Wind chill index

Description

Wind chill index is the lowering of body temperature due to the passing-flow of lower-temperature air. It combines air temperature and wind speed.

Usage

wci(taverage, w, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

index value

Formula

WCI = 13.12 + 0.6215 * TG - 11.37 * v ^ {+ 0.16} + 0.3965 * TG * v ^ {+ 0.16}

Where TG in celsius and v is wind speed in Km/h

References

Osczevski, Randall; Bluestein, Maurice (2005). The new wind chill equivalent temperature chart. Bulletin of the American Meteorological Society. 86 (10): 1453–1458

Examples

data(data_all)
wci(taverage = data_all$tg, w = data_all$wind)

which with arr.ind TRUE

Description

which with arr.ind TRUE

Usage

whichf(x, arr.ind = TRUE, useNames = TRUE)

Arguments

Value

which

Winkler index

Description

Sum of degree days over 10 celsius of TG from April 1 until October 31

Usage

wki(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

References

Winkler, A.J., J.A. Cook, W.M. Kliewer, and L.A. Lider. 1974. General Viticulture. 4th ed. University of California Press, Berkeley.

Examples

data(data_all)
wki(data = data_all$tg)

Winter Severity

Description

Mean TG of the coldest month of the year

Usage

ws(data, data_names = NULL, na.rm = FALSE, ...)

Arguments

Value

temperature, Celsius

Examples

data(data_all)
ws(data = data_all$tg)

Warm spell duration

Description

Number of days which are part of groups of at least 6 consecutive days when TX > 90th percentile. The 90th percentile is computed based on the time scale selected (month, season or year) not daily as ETCCDI does. If you want to compute daily you can use RClimDex package.

Usage

wsdi(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
wsdi(data=data_all$tx)

Maximum TG

Description

Maximum of daily mean air temperature

Usage

xtg(data, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

Average temperature

Examples

data(data_all)
xtg(data=data_all$tg)

Zero crossing days

Description

Number of days with TX > 0 Celsius and TN < 0 Celsius.

Usage

zcd(tmax, tmin, data_names = NULL, time.scale = YEAR, na.rm = FALSE)

Arguments

Value

days

Examples

data(data_all)
zcd(tmax=data_all$tx, tmin=data_all$tn)