pairEvents {hydroEvents}

Pair Events

Description

Pairing of events performed either forwards or backwards within specified lag times.

Usage

pairEvents(events.1, events.2, lag = 5, type = 1)

Arguments

events.1

Events of first data set

events.2

Events of second data set

lag

Maximum lag time (search radius) for pairing

type

Method used to pair events (see details)

Details

Pairing can be performed forwards and backwards and centrally.events.1 and events.2 need to be a dataframe with column names appropriate to the method type. That is, if pairing needs a time of maximum then "which.max" is expected (see examples). Column names are taken from the function event matching functions. The method types are:

• Type = 1: Search for the peak in events.2 within the start of event.1 to the end of event.1 + lag

• Type = 2: Search for an end in events.2 within the start of event.1 to the end of event.1 + lag

• Type = 3: Search for the peak in events.1 within the start of event.2 - lag to the peak in event.2

• Type = 4: Search for a start in events.1 within the start of event.2 - lag to the start of event.2

• Type = 5: Search for the peak in events.2 within the peak of event.1 - lag to the peak of event.1 + lag

It is appropriate to pick a lag time that is equivalent to the catchment time of concentration if matching rainfall to streamflow.

Value

Returns indices of start and end of events as well as the matched events as a four column dataframe.

See Also

calcStats eventBaseflow eventMaxima eventMinima eventPOT

Examples

# Load package
library(hydroEvents)
# Identify events
srt = as.Date("2015-02-05")
end = as.Date("2015-04-01")
idx = which(dataCatchment$`105105A`$Date >= srt & dataCatchment$`105105A`$Date <= end)
dat = dataCatchment$`105105A`[idx,]
events.P = eventPOT(dat$Precip_mm, threshold = 1, min.diff = 2)
events.Q = eventMaxima(dat$Flow_ML, delta.y = 2, delta.x = 1, thresh = 70)
# Plot events
oldpar <- par(mfrow = c(2, 1), mar = c(3, 2.7, 2, 1))
plotEvents(dat$Precip_mm, events = events.P, type = "hyet", colpnt = "#E41A1C",
   colline = "#E41A1C", ylab = "Precipitation (mm)", xlab = "Index", main = "2015")
plotEvents(dat$Flow_ML, events = events.Q, type = "lineover", colpnt = "#E41A1C",
   colline = "#377EB8", ylab = "Flow (ML/day)", xlab = "Index", main = "")
par(oldpar)
# Pair events
matched.1 = pairEvents(events.P, events.Q, lag = 5,  type = 1)
matched.2 = pairEvents(events.P, events.Q, lag = 5,  type = 2)
matched.3 = pairEvents(events.P, events.Q, lag = 3,  type = 3)
matched.4 = pairEvents(events.P, events.Q, lag = 7, type = 4)
matched.5 = pairEvents(events.P, events.Q, lag = 5, type = 5)
# Plot Pairs
oldpar <- par(mfrow = c(5, 1), mar = c(2, 3, 2, 3))
plotPairs(data.1 = dat$Precip_mm, data.2 = dat$Flow_ML, events = matched.1,
   col = rainbow(nrow(events.P)), ylab.1 = "P (mm)", ylab.2 = "Q (ML/day)", cex.2 = 0.66)
plotPairs(data.1 = dat$Precip_mm, data.2 = dat$Flow_ML, events = matched.2,
   col = rainbow(nrow(events.P)), ylab.1 = "P (mm)", ylab.2 = "Q (ML/day)", cex.2 = 0.66)
plotPairs(data.1 = dat$Precip_mm, data.2 = dat$Flow_ML, events = matched.3,
   col = rainbow(nrow(events.P)), ylab.1 = "Q (ML/day)", ylab.2 = "P (mm)", cex.2 = 0.66)
plotPairs(data.1 = dat$Precip_mm, data.2 = dat$Flow_ML, events = matched.4,
   col = rainbow(nrow(events.P)), ylab.1 = "Q (ML/day)", ylab.2 = "P (mm)", cex.2 = 0.66)
plotPairs(data.1 = dat$Precip_mm, data.2 = dat$Flow_ML, events = matched.5,
   col = rainbow(nrow(events.P)), ylab.1 = "P (mm)", ylab.2 = "Q ML/day)", cex.2 = 0.66)
par(oldpar)