View source: R/metab.bookkeep.R
metab.bookkeep | R Documentation |
This model is a simple model based on the assumption that movements in DO during the day are due to NEP and gas exchange. Respiration is estimated from night-time decreases. GPP is calculated from the algebraic manipulation of NEP and R. Based on Cole et al 2000.
metab.bookkeep(do.obs, do.sat, k.gas, z.mix, irr, ...)
do.obs |
Vector of dissovled oxygen concentration observations, mg L^-1 |
do.sat |
Vector of dissolved oxygen saturation values based on water temperature. Calculate using o2.at.sat |
k.gas |
Vector of kGAS values calculated from any of the gas flux models (e.g., k.cole) and converted to kGAS using k600.2.kGAS |
z.mix |
Vector of mixed-layer depths in meters. To calculate, see ts.meta.depths |
irr |
Integer vector of 1's (daytime) and 0's (nighttime), or numeric vector of irradiance that will be converted to boolean 1's and 0's if "datetime" is passed via |
... |
additional arguments to be passed, particularly |
A data.frame with columns corresponding to components of metabolism
numeric estimate of Gross Primary Production, mg O2 / L / d
numeric estimate of Respiration, mg O2 / L / d
numeric estimate of Net Ecosystem production, mg O2 / L / d
R. Iestyn Woolway, Hilary Dugan, Luke A Winslow, Ryan Batt, Jordan S Read, GLEON fellows
Cole, Jonathan J., Michael L. Pace, Stephen R. Carpenter, and James F. Kitchell. 2000. Persistence of Net Heterotrophy in Lakes during Nutrient Addition and Food Web Manipulations. Limnology and Oceanography 45 (8): 1718-1730. doi:10.4319/lo.2000.45.8.1718.
metab.bayesian, metab.mle, metab.kalman
library(rLakeAnalyzer) Sys.setenv(TZ='GMT') doobs = load.ts(system.file('extdata', 'sparkling.doobs', package="LakeMetabolizer")) wtr = load.ts(system.file('extdata', 'sparkling.wtr', package="LakeMetabolizer")) wnd = load.ts(system.file('extdata', 'sparkling.wnd', package="LakeMetabolizer")) #Subset a day mod.date = as.POSIXct('2009-07-08', 'GMT') doobs = doobs[trunc(doobs$datetime, 'day') == mod.date, ] wtr = wtr[trunc(wtr$datetime, 'day') == mod.date, ] wnd = wnd[trunc(wnd$datetime, 'day') == mod.date, ] k.gas = k600.2.kGAS.base(k.cole.base(wnd[,2]), wtr[,3], 'O2') do.sat = o2.at.sat.base(wtr[,3], altitude=300) # Must supply 1 for daytime timesteps and 0 for nighttime timesteps irr = as.integer(is.day(doobs[,1], 45)) metab.bookkeep(doobs[,2], do.sat, k.gas, z.mix=1, irr, datetime=doobs$datetime)
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