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Fit the monthly share-of-annual-runoff regressions

Source: R/wet_share_fit.R
wet_share_fit.Rd

Chapman et al. (2018) split annual runoff into months with one regression per month of each month's share of the annual total. Here the response is the observed share (from monthly volumes, wet_station_monthly()) and the predictors, fixed in advance, are upstream means: that month's precipitation, temperature and AET plus any static columns (by default elevation and BC Albers easting and northing).

Usage

wet_share_fit(d, static = c("elev", "e_km", "n_km"))

Arguments

d

data.frame with observed shares share_01 to share_12, the monthly predictors ppt_MM, tave_MM, aet_MM, and the static columns.

static

Predictors used in every month, one value per station, each with its own coefficient in each month. The snow predictors scored in #16 enter here: static = c("elev", "e_km", "n_km", "snowmelt_doy_50", "swe_max"), the upstream means of wet_snow_normals().

Value

A wet_share_fit object: list(coef, terms, n), where coef is a 12-row matrix of coefficients (intercept first) by month.