detect_event3: Detect heatwaves and cold-spells.

In robwschlegel/heatwaveR: Detect Heatwaves and Cold-Spells

Detect heatwaves and cold-spells.

Description

detect_event3 is a data.table version of the earlier detect_event. It 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 ts2clm3 or some other means.

Usage

detect_event3(
  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,
  ...
)

Arguments

data	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 ts2clm3, but your own data can be supplied if they meet the criteria specified by ts2clm3. If the column names of data match those outlined here, the following four arguments may be ignored.
x	This column is expected to contain a vector of dates as per the specification of ts2clm3. If a column headed t is present in the data.table, this argument may be omitted; otherwise, specify the name of the column with dates here.
y	This is a column containing the measurement variable. If the column name differs from the default (i.e. temp), specify the name here.
seasClim	The default for this argument assumes that the seasonal climatology column is called seas as this matches the output of ts2clm3. If the column name for the seasonal climatology is different, provide that here.
threshClim	The threshold climatology column should be called thresh. If it is not, provide the name of the threshold column here.
threshClim2	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.
minDuration	The minimum duration for acceptance of detected events. The default is 5 days.
minDuration2	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.
joinAcrossGaps	Boolean switch indicating whether to join events which occur before/after a short gap as specified by maxGap. The default is TRUE.
maxGap	The maximum length of gap allowed for the joining of MHWs. The default is 2 days.
maxGap2	The maximum gap length after applying both thresholds. By default maxGap2 = maxGap and is ignored if threshClim2 has not been specified.
coldSpells	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 ts2clm3. Should one want to use detect_event3 for MCSs, this threshold would best be generated in ts2clm3 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).
protoEvents	Boolean. With the default setting of protoEvents = FALSE a list with two components will be reported. The first component (climatology) will have the original time series returned by ts2clm3 augmented 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 are TRUE), and a sequential number uniquely identifying the detected event(s). The second list component (event) will contain the heatwave event metrics. If protoEvents = TRUE then only the climatology will be reported. Note also that if protoEvents = TRUE it will ignore whatever the user provides to the categories argument and anything else passed to ....
categories	Boolean. 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_event3 outputs by default, add the argument climatology = TRUE. By default categories = FALSE.
roundRes	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.
...	Allows unused arguments to pass through the functions.

Details

1. 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 ts2clm3 aids in the preparation of a time series that is suitable for use with detect_event3, although this may also be accomplished 'by hand' as long as the criteria are met as discussed in the documentation to ts2clm3.

2. 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.

3. For the purposes of event detection, any missing temperature values not interpolated over (through optional maxPadLength in ts2clm3) 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.

4. 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.

Value

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.

Note that the exact content of the output depends on specific combinations of the arguments protoEvents, categories, and climatology:

threshCriterion	Boolean indicating if temp exceeds thresh.
durationCriterion	Boolean indicating whether periods of consecutive threshCriterion are >= min_duration.
event	Boolean indicating if all criteria that define an extreme event are met.
event_no	A sequential number indicating the ID and order of occurrence of the events.
intensity	The difference between temp (or the column provided for y) and seas. Only added if categories = TRUE and climatology = TRUE.
category	The category classification per day. Only added if categories = TRUE and climatology = TRUE.

The event results are summarised using a range of event metrics:

event_no	A sequential number indicating the ID and order of the events.
index_start	Start index of event.
index_end	End index of event.
duration	Duration of event [days].
date_start	Start date of event [date].
date_end	End date of event [date].
date_peak	Date of event peak [date].
intensity_mean	Mean intensity [deg. C].
intensity_max	Maximum (peak) intensity [deg. C].
intensity_var	Intensity variability (standard deviation) [deg. C].
intensity_cumulative	Cumulative intensity [deg. C x days].
rate_onset	Onset rate of event [deg. C / day].
rate_decline	Decline rate of event [deg. C / day].

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.

Author(s)

Albertus J. Smit, Robert W. Schlegel, Eric C. J. Oliver

References

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

Examples

data.table::setDTthreads(threads = 1) # optimise for your code and local computer
res_clim <- ts2clm3(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"))
out <- detect_event3(res_clim)
# show a portion of the climatology:
out$climatology[1:10, ]
# show some of the heat waves:
out$event[1:5, 1:10]

# Or if one wants to calculate MCSs
res_clim <- ts2clm3(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"),
                    pctile = 10)
out <- detect_event3(res_clim, coldSpells = TRUE)
# show a portion of the climatology:
out$climatology[1:10, ]
# show some of the cold-spells:
out$event[1:5, 1:10]

# 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)$threshold
# Then we use that output when detecting our events
events_19 <- detect_event3(ts2clm3(sst_Med, climatologyPeriod = c("1982-01-01", "2011-12-31")),
                           threshClim2 = thresh_19$exceedance, minDuration2 = 10, maxGap2 = 0)

# If we want to use two different percentile thresholds
thresh_95 <- detect_event3(ts2clm3(sst_Med, pctile = 95,
                                   climatologyPeriod = c("1982-01-01", "2011-12-31")),
                           minDuration = 2, maxGap = 0)$climatology
# Then we use that output when detecting our events
events_95 <- detect_event3(ts2clm3(sst_Med, climatologyPeriod = c("1982-01-01", "2011-12-31")),
                           threshClim2 = thresh_95$event, minDuration2 = 2, maxGap2 = 0)

robwschlegel/heatwaveR documentation built on April 23, 2024, 10:24 p.m.