R/compute_annual_trends.R
compute_annual_trends.Rd
Calculates prewhitened nonlinear trends on annual streamflow data. Uses the
zyp
package to calculate trends. Review zyp
for more information
Calculates statistics from all values, unless specified. Returns a list of tibbles and plots.
All annual statistics calculated using the calc_all_annual_stats()
function which uses the following
fasstr
functions:
compute_annual_trends( data, dates = Date, values = Value, groups = STATION_NUMBER, station_number, zyp_method, basin_area, water_year_start = 1, start_year, end_year, exclude_years, annual_percentiles = c(10, 90), monthly_percentiles = c(10, 20), stats_days = 1, stats_align = "right", lowflow_days = c(1, 3, 7, 30), lowflow_align = "right", timing_percent = c(25, 33, 50, 75), normal_percentiles = c(25, 75), ignore_missing = FALSE, include_plots = TRUE, zyp_alpha )
data  Data frame of daily data that contains columns of dates, flow values, and (optional) groups (e.g. station numbers).
Leave blank if using 

dates  Name of column in 
values  Name of column in 
groups  Name of column in 
station_number  Character string vector of seven digit Water Survey of Canada station numbers (e.g. 
zyp_method  Character string identifying the prewhitened trend method to use from 
basin_area  Upstream drainage basin area, in square kilometres, to apply to observations. Three options: (1) Leave blank if (2) A single numeric value to apply to all observations. (3) List each basin area for each group/station in groups (can override HYDAT value if listed) as such 
water_year_start  Numeric value indicating the month ( 
start_year  Numeric value of the first year to consider for analysis. Leave blank to use the first year of the source data. 
end_year  Numeric value of the last year to consider for analysis. Leave blank to use the last year of the source data. 
exclude_years  Numeric vector of years to exclude from analysis. Leave blank to include all years. 
annual_percentiles  Numeric vector of percentiles to calculate annually. Set to 
monthly_percentiles  Numeric vector of percentiles to calculate monthly for each year. Set to 
stats_days  Numeric vector of the number of days to apply a rolling mean on basic stats. Default 
stats_align  Character string identifying the direction of the rolling mean on basic stats from the specified date, either by
the first ( 
lowflow_days  Numeric vector of the number of days to apply a rolling mean on low flow stats. Default 
lowflow_align  Character string identifying the direction of the rolling mean on low flow stats from the specified date,
either by the first ( 
timing_percent  Numeric vector of percents of annual total flows to determine dates. Used for 
normal_percentiles  Numeric vector of two values, lower and upper percentiles, respectively indicating the limits of the
normal range. Default 
ignore_missing  Logical value indicating whether dates with missing values should be included in the calculation. If

include_plots  Logical value indicating if annual trending plots should be included. Default 
zyp_alpha  Numeric value of the significance level (ex. 
A list of tibbles and optional plots from the trending analysis including:
a tibble of the annual statistics used for trending
a tibble of the results of the zyp trending analysis
each ggplot2 object for each annual trended statistic
References from zyp package:
Wang, X.L. and Swail, V.R., 2001. Changes in extreme wave heights in northern hemisphere oceans and related atmospheric circulation regimes. Journal of Climate, 14: 22042221.
Yue, S., P. Pilon, B. Phinney and G. Cavadias, 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16: 18071829.
Zhang, X., Vincent, L.A., Hogg, W.D. and Niitsoo, A., 2000. Temperature and Precipitation Trends in Canada during the 20th Century. AtmosphereOcean 38(3): 395429.
Sen, P.K., 1968. Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association Vol. 63, No. 324: 13791389.
if (FALSE) { # Working examples: # Compute trends statistics using a data frame and data argument with defaults flow_data < tidyhydat::hy_daily_flows(station_number = "08NM116") trends < compute_annual_trends(data = flow_data, zyp_method = "yuepilon") # Compute trends statistics using station_number with defaults trends < compute_annual_trends(station_number = "08NM116", zyp_method = "yuepilon") # Compute trends statistics and plot a trend line if the significance is less than 0.05 trends < compute_annual_trends(station_number = "08NM116", zyp_method = "yuepilon", zyp_alpha = 0.05) # Compute trends statistics and do not plot the results trends < compute_annual_trends(station_number = "08NM116", zyp_method = "yuepilon", include_plots = FALSE) }