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Mean flow over date windows, from monthly or daily means

Source: R/wet_flow_window.R
wet_flow_window.Rd

Turns a flow climatology into mean flow over named month-day windows, such as a species' life stage or a week, and says what it rests on. From monthly means (wet_segment_monthly(), wet_station_monthly()) each day of the window takes its month's mean and the window's flow is the mean over its days: basis = "monthly". That is an approximation where a window takes part of a month whose flow rises or falls fast. From daily means (a doy column) the window's flow is the mean of its days: basis = "daily". A caller that swaps monthly input for daily gets the same call and a basis that says so.

Usage

wet_flow_window(x, windows, by = c("linear_feature_id", "source"))

Arguments

x

A data frame with the by columns, q_m3s, and either month (1-12; month 0, the annual value, is ignored) or doy (day of a common year, 1-365).

windows

data.frame(window, start, end), with start and end as "MM-DD", both inclusive. See wet_windows_calendar().

by

Columns that separate series: c("linear_feature_id", "source") for segments, "station_number" for gauges, character() for one series.

Value

One row per series and window: the by columns, window, q_m3s and basis ("monthly" or "daily").

Details

Days are laid on a common year: February has 28 days, and an end of "02-29" means its last day. A window whose end falls before its start runs across 1 January. A window is NA when any of its days has no value. The common year differs slightly from wet_window_stats(), which counts 29 February in leap years, and from month 0, which weights months by wet_month_days() (28.25 days in February), so a window over the whole year is close to month 0 but not equal to it.

See also

wet_window_stats() for per-year statistics of a daily series.

Examples

# a segment's twelve monthly means, with a June freshet
x <- data.frame(linear_feature_id = 17094080L, month = 0:12, source = "pcic_routed",
                q_m3s = c(5.9, 1.4, 1.2, 1.5, 4.6, 15.8, 19.1, 9.6, 4.4, 3.5, 3.6, 2.6, 1.7))
w <- rbind(wet_windows_calendar()[6:7, ],
           data.frame(window = "ch_spawning", start = "08-20", end = "09-30"))
wet_flow_window(x, w)
#>   linear_feature_id      source      window     q_m3s   basis
#> 1          17094080 pcic_routed         jun 19.100000 monthly
#> 2          17094080 pcic_routed         jul  9.600000 monthly
#> 3          17094080 pcic_routed ch_spawning  3.757143 monthly