Applies the Hobday et al. (2016) marine heat wave definition to an input time
series of a given value (usually, but not necessarily limited to, temperature)
along with a daily date vector and pre-calculated seasonal and threshold
climatologies, which may either be created with ts2clm or some
other means.
detect_event(
data,
x = t,
y = temp,
seasClim = seas,
threshClim = thresh,
threshClim2 = NA,
minDuration = 5,
minDuration2 = minDuration,
joinAcrossGaps = TRUE,
maxGap = 2,
maxGap2 = maxGap,
coldSpells = FALSE,
protoEvents = FALSE,
categories = FALSE,
roundRes = 4,
returnDF = TRUE,
...
)A data frame with at least four columns. In the default setting
(i.e. omitting the arguments x, y, seas, and thresh;
see immediately below), the data set is expected to have the headers t,
temp, seas, and thresh. The t
column is a vector of dates of class Date, temp is the measured
variable (by default it is assumed to be temperature), seas is the seasonal
cycle daily climatology (366 days), and thresh is the seasonal cycle
daily threshold above which events may be detected. Data of the appropriate
format are created by the function ts2clm, but your own data
can be supplied if they meet the criteria specified by ts2clm.
If the column names of data match those outlined here, the following
four arguments may be ignored. Note that it is also possible to provide hourly
data in the x column as class POSIXct.
This column is expected to contain a vector of dates as per the
specification of ts2clm. If a column headed t is present in
the dataframe, this argument may be omitted; otherwise, specify the name of
the column with dates here. Note that it is also possible to provide hourly
data as class POSIXct.
This is a column containing the measurement variable. If the column
name differs from the default (i.e. temp), specify the name here.
The dafault for this argument assumes that the seasonal
climatology column is called seas as this matches the output of
ts2clm. If the column name for the seasonal climatology is
different, provide that here.
The threshold climatology column should be called
thresh. If it is not, provide the name of the threshold column here.
If one wishes to provide a second climatology threshold filter for the more rigorous detection of events, a vector or column containing logical values (i.e. TRUE FALSE) should be provided here. By default this argument is ignored. It's primary purpose is to allow for the inclusion of tMin and tMax thresholds.
The minimum duration for acceptance of detected events.
The default is 5 days.
The minimum duration for acceptance of events after
filtering by threshClim and threshClim. By default
minDuration2 = minDuration and is ignored if threshClim2 has not
been specified.
Boolean switch indicating whether to join events which
occur before/after a short gap as specified by maxGap. The default
is TRUE.
The maximum length of gap allowed for the joining of MHWs. The
default is 2 time steps.
The maximum gap length after applying both thresholds.
By default maxGap2 = maxGap and is ignored if threshClim2 has not
been specified.
Boolean specifying if the code should detect cold events
instead of warm events. The default is FALSE. Please note that the
climatological thresholds for cold-spells are considered to be the inverse
of those for MHWs. For example, the default setting for the detection of
MHWs is pctile = 90, as seen in ts2clm. Should one want
to use detect_event for MCSs, this threshold would best be generated
in ts2clm by setting pctile = 10 (see example below).
Any value may be used, but this is the setting used for the calculation of
MCSs in Schlegel et al. (2017a).
The default, protoEvents = FALSE, will return the full
output comprised of a list of two data frames, one with the climatology
and the other with the event metrics. See Value below.
If protoEvents = TRUE, the output will contain the original time series
together with columns indicating if the threshold criterion
(threshCriterion) and duration criterion (durationCriterion)
have been exceeded, a column showing if a heatwave is present
(i.e. both threshCriterion and durationCriterion
TRUE), and a sequential number uniquely identifying the detected event(s);
heatwave metrics will not be reported in the event dataframe. Note also that
if protoEvents = TRUE it will ignore whatever the user provides to the
categories argument and anything else passed to ....
Rather than using category as a separate step to determine
the categories of the detected MHWs, one may choose to set this argument to TRUE.
One may pass the same arguments used in the category function to this function
to affect the output. Note that the default behaviour of category is to
return the event data only. To return the same list structure that detect_event
outputs by default, add the argument climatology = TRUE.
This argument allows the user to choose how many decimal places
the MHW metric outputs will be rounded to. Default is 4. To
prevent rounding set roundRes = FALSE. This argument may only be given
numeric values or FALSE.
The default (TRUE) tells the function to return the results as
type data.frame. FALSE will return the results as a data.table.
Other arguments that will be passed internally to category
when categories = TRUE. See the documentation for category for the
list of possible arguments.
The function will return a list of two data.frames,
climatology and event, which are, surprisingly, the climatology
and event results, respectively. The climatology contains the full time series of
daily temperatures, as well as the the seasonal climatology, the threshold
and various aspects of the events that were detected. The software was
designed for detecting extreme thermal events, and the units specified below
reflect that intended purpose. However, various other kinds of extreme
events may be detected according to the specifications, and if that is the
case, the appropriate units need to be determined by the user.
The climatology results will contain the same column produced by
ts2clm as well as the following:
Boolean indicating if temp exceeds
thresh.
Boolean indicating whether periods of consecutive
threshCriterion are >= min_duration.
Boolean indicating if all criteria that define an extreme event are met.
A sequential number indicating the ID and order of occurrence of the events.
The difference between temp (or whichever column is provided
for y) and seas. Only added if categories = TRUE
and climatology = TRUE.
The category classification per day. Only added
if categories = TRUE and climatology = TRUE.
The event results are summarised using a range of event metrics:
A sequential number indicating the ID and order of the events.
Start index of event.
End index of event.
Duration of event [days].
Start date of event [date].
End date of event [date].
Date of event peak [date].
Mean intensity [deg. C].
Maximum (peak) intensity [deg. C].
Intensity variability (standard deviation) [deg. C].
Cumulative intensity [deg. C x days].
Onset rate of event [deg. C / day].
Decline rate of event [deg. C / day].
The name of the event. Generated from the name
value provided and the year of the date_peak of
the event. If no name value is provided the default "Event" is used.
As proposed in Hobday et al. (2018), Moderate events are not given a name
so as to prevent multiple repeat names within the same year. If two or more events
ranked greater than Moderate are reported within the same year, they will be
differentiated with the addition of a trailing letter
(e.g. Event 2001a, Event 2001b). Only added if categories = TRUE.
The maximum category threshold reached/exceeded by the event.
Only added if categories = TRUE.
The proportion of the total duration (days) spent at or above
the first threshold, but below any further thresholds. Only added if categories = TRUE.
The proportion of the total duration (days) spent at or above
the second threshold, but below any further thresholds. Only added if categories = TRUE.
The proportion of the total duration (days) spent at or above
the third threshold, but below the fourth threshold. Only added if categories = TRUE.
The proportion of the total duration (days) spent at or above
the fourth and final threshold. Only added if categories = TRUE.
The season(s) during which the event occurred. If the event
occurred across two seasons this will be displayed as e.g. "Winter/Spring".
Across three seasons as e.g. "Winter-Summer". Events lasting across four or more
seasons are listed as "Year-round". December (June) is used here as the start of
Austral (Boreal) summer. If "start", "peak", or "end" was given to the season
argument then only the one season during that chosen period will be given.
Only added if categories = TRUE.
intensity_max_relThresh, intensity_mean_relThresh,
intensity_var_relThresh, and intensity_cumulative_relThresh
are as above except relative to the threshold (e.g., 90th percentile) rather than the seasonal climatology.
intensity_max_abs, intensity_mean_abs, intensity_var_abs, and
intensity_cumulative_abs are as above except as absolute magnitudes
rather than relative to the seasonal climatology or threshold.
Note that rate_onset and rate_decline will return NA
when the event begins/ends on the first/last day of the time series. This may be particularly evident when the function is applied to large gridded data sets. Although the other metrics do not contain any errors and provide sensible values, please take this into account in its interpretation.
This function assumes that the input time series consists of continuous
daily values with few missing values. Time ranges which start and end
part-way through the calendar year are supported. The accompanying function
ts2clm aids in the preparation of a time series that is
suitable for use with detect_event, although this may also be accomplished
'by hand' as long as the criteria are met as discussed in the documentation
to ts2clm.
The calculation of onset and decline rates assumes that the events
started a half-day before the start day and ended a half-day after the
end-day. This is consistent with the duration definition as implemented,
which assumes duration = end day - start day + 1. An event that is already
present at the beginning of a time series, or an event that is still present
at the end of a time series, will report the rate of onset or the rate of
decline as NA, as it is impossible to know what the temperature half a
day before or after the start or end of the event is.
For the purposes of event detection, any missing temperature values not
interpolated over (through optional maxPadLength in ts2clm)
will be set equal to the seasonal climatology. This means they will trigger
the end/start of any adjacent temperature values which satisfy the event
definition criteria.
If the code is used to detect cold events (coldSpells = TRUE),
then it works just as for heat waves except that events are detected as
deviations below the (100 - pctile)th percentile (e.g., the 10th instead of
90th) for at least 5 days. Intensities are reported as negative values and
represent the temperature anomaly below climatology.
The original Python algorithm was written by Eric Oliver, Institute for Marine and Antarctic Studies, University of Tasmania, Feb 2015, and is documented by Hobday et al. (2016). The marine cold spell option was implemented in version 0.13 (21 Nov 2015) of the Python module as a result of our preparation of Schlegel et al. (2017), wherein the cold events receive a brief overview.
Hobday, A.J. et al. (2016). A hierarchical approach to defining marine heatwaves, Progress in Oceanography, 141, pp. 227-238, doi:10.1016/j.pocean.2015.12.014
Schlegel, R. W., Oliver, C. J., Wernberg, T. W., Smit, A. J. (2017). Nearshore and offshore co-occurrences of marine heatwaves and cold-spells. Progress in Oceanography, 151, pp. 189-205, doi:10.1016/j.pocean.2017.01.004
data.table::setDTthreads(threads = 1)
res_clim <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"))
out <- detect_event(res_clim)
# show a portion of the climatology:
out$climatology[1:10, ]
#> doy t temp seas thresh threshCriterion durationCriterion event
#> 1 1 1982-01-01 20.94 21.6080 22.9605 FALSE FALSE FALSE
#> 2 2 1982-01-02 21.25 21.6348 22.9987 FALSE FALSE FALSE
#> 3 3 1982-01-03 21.38 21.6621 23.0376 FALSE FALSE FALSE
#> 4 4 1982-01-04 21.16 21.6895 23.0771 FALSE FALSE FALSE
#> 5 5 1982-01-05 21.26 21.7169 23.1130 FALSE FALSE FALSE
#> 6 6 1982-01-06 21.61 21.7436 23.1460 FALSE FALSE FALSE
#> 7 7 1982-01-07 21.74 21.7699 23.1775 FALSE FALSE FALSE
#> 8 8 1982-01-08 21.50 21.7958 23.2080 FALSE FALSE FALSE
#> 9 9 1982-01-09 21.40 21.8217 23.2366 FALSE FALSE FALSE
#> 10 10 1982-01-10 21.36 21.8478 23.2649 FALSE FALSE FALSE
#> event_no
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 NA
#> 10 NA
# show some of the heat waves:
out$event[1:5, 1:10]
#> event_no index_start index_peak index_end duration date_start date_peak
#> 1 1 885 887 889 5 1984-06-03 1984-06-05
#> 2 2 899 901 904 6 1984-06-17 1984-06-19
#> 3 3 908 922 926 19 1984-06-26 1984-07-10
#> 4 4 1023 1027 1029 7 1984-10-19 1984-10-23
#> 5 5 1033 1034 1037 5 1984-10-29 1984-10-30
#> date_end intensity_mean intensity_max
#> 1 1984-06-07 1.7042 1.9218
#> 2 1984-06-22 1.9851 2.1063
#> 3 1984-07-14 1.9094 2.2165
#> 4 1984-10-25 1.4626 1.8304
#> 5 1984-11-02 1.3034 1.4887
# Or if one wants to calculate MCSs
res_clim <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"),
pctile = 10)
out <- detect_event(res_clim, coldSpells = TRUE)
# show a portion of the climatology:
out$climatology[1:10, ]
#> doy t temp seas thresh threshCriterion durationCriterion event
#> 1 1 1982-01-01 20.94 21.6080 20.5471 FALSE FALSE FALSE
#> 2 2 1982-01-02 21.25 21.6348 20.5713 FALSE FALSE FALSE
#> 3 3 1982-01-03 21.38 21.6621 20.5961 FALSE FALSE FALSE
#> 4 4 1982-01-04 21.16 21.6895 20.6218 FALSE FALSE FALSE
#> 5 5 1982-01-05 21.26 21.7169 20.6499 FALSE FALSE FALSE
#> 6 6 1982-01-06 21.61 21.7436 20.6776 FALSE FALSE FALSE
#> 7 7 1982-01-07 21.74 21.7699 20.7053 FALSE FALSE FALSE
#> 8 8 1982-01-08 21.50 21.7958 20.7362 FALSE FALSE FALSE
#> 9 9 1982-01-09 21.40 21.8217 20.7655 FALSE FALSE FALSE
#> 10 10 1982-01-10 21.36 21.8478 20.7927 FALSE FALSE FALSE
#> event_no
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 NA
#> 6 NA
#> 7 NA
#> 8 NA
#> 9 NA
#> 10 NA
# show some of the cold-spells:
out$event[1:5, 1:10]
#> event_no index_start index_peak index_end duration date_start date_peak
#> 1 1 83 83 87 5 1982-03-24 1982-03-24
#> 2 2 194 199 207 14 1982-07-13 1982-07-18
#> 3 3 270 272 277 8 1982-09-27 1982-09-29
#> 4 4 296 303 306 11 1982-10-23 1982-10-30
#> 5 5 672 681 685 14 1983-11-03 1983-11-12
#> date_end intensity_mean intensity_max
#> 1 1982-03-28 -1.4543 -1.5877
#> 2 1982-07-26 -1.8033 -2.7304
#> 3 1982-10-04 -1.2378 -1.3891
#> 4 1982-11-02 -1.0167 -1.2813
#> 5 1983-11-16 -1.1806 -1.4954
# It is also possible to calculate the categories of events directly
# See the \code{\link{category}} documentation for more functionality
res_clim <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"))
out_event <- detect_event(res_clim, categories = TRUE)
out_list <- detect_event(res_clim, categories = TRUE, climatology = TRUE)
# It is also possible to give two separate sets of threshold criteria
# To use a second static threshold we first use the exceedance function
thresh_19 <- exceedance(sst_Med, threshold = 19, minDuration = 10, maxGap = 0)$threshold
# Then we use that output when detecting our events
events_19 <- detect_event(ts2clm(sst_Med, climatologyPeriod = c("1982-01-01", "2011-12-31")),
threshClim2 = thresh_19$exceedance, minDuration2 = 10, maxGap2 = 0)
# If we want to use two different percentile thresholds we use detect_event
thresh_95 <- detect_event(ts2clm(sst_Med, pctile = 95,
climatologyPeriod = c("1982-01-01", "2011-12-31")),
minDuration = 2, maxGap = 0)$climatology
# Then we use that output when detecting our events
events_95 <- detect_event(ts2clm(sst_Med, climatologyPeriod = c("1982-01-01", "2011-12-31")),
threshClim2 = thresh_95$event, minDuration2 = 2, maxGap2 = 0)