Example working with flux data

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1 . Example working with flux data All eddy covariance flux datasets that will be used in this course will be made available on the SwissFluxnet server. From there the data can be directly read into R without the need of copying the data to your own computer. The only requirement is that your computer is online. Here is an example from the year measured at Oensingen (time period May to end of July). The steps to read in the data and produce a first graphical overview over all data is presented here as an example. All other datasets can be used by adapting the R code to the various datasets. Working with gapfilled and partitioned flux data The measured flux data are not always of high quality (e.g. during rainfall events etc.), and sometimes there are no data at all due to instrument failures or power outages. To obtain daily or monthly (or annual) totals of fluxes, there is thus the issue how to deal with gaps in the data. The standard procedure is to use a model that partitions the measured NEE fluxes into the two main components assimilation (GPP, gross primary production), and ecosystem respiration (Reco). This model is then also used to fill the data gaps. Such a dataset is available from Oensingen. We re reading the file with the code below and plot NEE, GPP and Reco on one single plot. The meaning of each variable is described in Table GAPFILLED <- " gf <- read.csv(gapfilled) gf$timestamp <- as.posixlt(gf$timestamp, tz= UTC ) where the third line is optional and simply converts the text variable TIMESTAMP to a continuous time variable. Before executing the third line of R code, TIMESTAMP is a text variable (string, character string). Since it is formatted in the ISO format for date and time, we can directly convert it to a time variable in R. In this conversion we use the timezone information that we can pass to read.eddypro() via tz=.... Internally, the variable TIMESTAMP is now a numeric variable in seconds since -- :: UTC. By using as.posixlt() for the conversion we overlay this numeric variable with a list that contains the following members: sec ( : seconds), min ( : minutes), hour ( : hours), mday ( : day of the month), mon ( : months after the first of the year), year (years since ), wday ( day of the week, starting on Sunday), and yday ( : day of the year). A note of caution: the day of year (yday) starts at, whereas we normally start counting with, hence has to be added to get day of year (DOY). Also the month (mon) starts with for January, hence we have to add to be compatible with how most people count months. The reason why seconds (sec) can be up to instead of is the following: leap seconds can be inserted by the authorities up to a maximum of seconds in a minute, hence it is theoretically Table.: Contents of gapfilled and partitioned flux data Variable name TIMESTAMP NEEdespiked_WithUstar_f GPP_WithUstar_f Reco_WithUstar Description Timestamp Gap filled despiked net ecosystem exchange using ustar filtering Estimated gross primary production from Reichstein Method using ustar filtering Estimated ecosystem respiration from Reichstein Method using ustar filtering

2 possible to deal with leap seconds (although in reality this is an issue, which does not bother us in this course, however). An example for a first graphical display of NEE together with its partitioned components GPP and Reco for the second half of June : trange <- as.posixct(c(" ::", " ::"), tz= UTC ) yrange <- c(-25, 15) plot(gf$timestamp, gf$needespiked_withustar_f, type="l", col="steelblue", lwd=2, xlim=trange, ylim=yrange, ann=false, axes=false, frame=true) lines(gf$timestamp, -gf$gpp_withustar_f, col="green3") lines(gf$timestamp, gf$reco_withustar, col="orangered") abline(h=, lty=3) axis.posixct(1, gf$timestamp, at=seq(trange[1], trange[2], by="day"), format="%d %b") axis(2) title(ylab=expression("flux Component ("*mu*"mol m"^{-2}*" s"^{-1}*")")) legend("top", c("-gpp","nee","reco"), lwd=c(1,2,1), col=c("green3","steelblue","orangered"), bty="n") Flux Component (µmol m 2 s 1 ) GPP NEE Reco 15 Jun 17 Jun 19 Jun 21 Jun 23 Jun 25 Jun 27 Jun 29 Jun 1 Jul Note that the definition of GPP is to be a positive number, hence we changed the sign for this graph in such a way that GPP + Reco = NEE. In the gapfilled and partitioned dataset there are two sets of variables for NEE, GPP and Reco available. We used the version that passed a u threshold filter which removed measurements under low-turbulence conditions and filled these conditions with modeled data. The variables that passed a u threshold filter have the names NEEdespiked_WithUstar_f, GPP_WithUstar_f, and Reco_WithUstar in the R code above.

3 An example of a boxplot of the mean diel cycle of GPP (using positive values) is: select <- gf$timestamp >= trange[1] & gf$timestamp <= trange[2] boxplot(gpp_withustar_f ~ TIMESTAMP$hour, data=gf, subset=select, col="green3") title(ylab=expression("gpp ("*mu*"mol m"^{-2}*" s"^{-1}*")")) 3 GPP (µmol m 2 s 1 ) Importing meteorological data Our meteorological data are simple CSV files with only one specialty: the missing values are coded with NAN, whereas R uses NA. Importing is thus easy: METEOFILE <- " meteo <- read.csv(meteofile, as.is=true, na.string=c("na","nan")) meteo$timestamp <- as.posixlt(meteo$timestamp, tz= UTC ) where the third line is optional and simply converts the text variable TIMESTAMP to a continuous time variable. Since there are two variants of missing values present in our meteorological data files, we have to specify this in the second line of code with the argument na.string=c("na","nan"). An example for a first graphical display of air temperature as a time series can be done with: plot(meteo$timestamp, meteo$ta_avg_t1_2_1, type="l") Merging flux data with meteorological data If an analysis uses flux data as a function of meteorological conditions, then the two datasets need to be merged. This is done via a key variable, in our case TIMESTAMP. This however must be a sorted variable, and hence we have to convert TIMESTAMP back to the numeric values. In order not to destroy our nice datasets gf and meteo we first make a copy and then do the merging using the copies of the original datasets:

4 Table.: Variable naming convention for Grassland Sciences Group meteorological data (excerpt) GROUP LABEL DESCRIPTION UNIT (preferred) or FORMAT MET_SOIL G Soil Heat Flux W m-2 GWL Ground Water Level m SWC Soil Water Content % vol SDP Soil Dielectric Permitivity adimensional TS Soil Temperature C PH PH Probe numerical WP Water Potential kpa TIR Surface temperature measured by IR thermometer C MET_ATM PA Atmospheric Pressure kpa PA_PRF Atmospheric Pressure in Profile kpa PBLH Planetary Boundary Layer Height m RH Relative Humidity % RH_PRF Relative Humidity in Profile % T_SONIC SonicTemperature K TA Air Temperature C TA_PRF Air Temperature in Profile C VPD Vapor Pressure Deficit kpa FOG Fog presence VIS Visibility as recorded by PWD-11 at Chamau MET_WIND WD_VANE Wind Direction measured by Windvane from North WS_CUP Wind Speed measured by Cup Anemometer m s-1 WS_PROPELLER Scalar aggregated Wind Speed measured by propeller anemometer (e.g. Young) m s 1 WS_XXX_PRF Wind Speed PRF Sonic Anemometer, where XXX is the Sonic Model eg. HS5, HS1, CSAT3, METEK m s-1 WS_XXX_EC Wind Speed EC Sonic Anemometer, where XXX is the Sonic Model eg. HS5, HS1, CSAT3, METEK m s-1 MET_RAD ALB Albedo adimensional AW_IN All-wave incoming radiation without correction (Pyrradiometer) W m 2 AW_OUT All-wave outgoing radiation without correction (Pyrradiometer) W m 2 LW_IN Longwave Incoming Radiation with blackbody correction W m-2 LW_OUT Longwave Outgoing Radiation with blackbody correction W m-2 LW _IN_RAW Longwave Incoming Radiation without blackbody correction W m-2 LW _OUT_RAW Longwave Incoming Radiation without blackbody correction W m-2 NDVI Normalized Difference Vegetation Index adimensional NETRAD Net Radiation W m-2 PPFD_BC_IN Below Canopy PPFD, incoming µmol m -2 s -1 PPFD_DIF Diffuse PPFD µmol m -2 s -1 PPFD_IN Photosynthetic Photon Flux Density, incoming µmol m -2 s -1 PPFD_OUT Reflected PPFD µmol m -2 s -1 SW_DIF Shortwave Diffuse Radiation W m-2 SW_IN Shortwave Incoming Radiation W m-2 SW_OUT Shortwave Outgoing Radiation W m-2 MET_PRECIP D_SNOW Snow Depth/Height m PREC Total Precipiation mm PREC_RAIN rainfall; Liquid Precipitation mm PREC_SNOW Solid (Snow) Precipitation mm INSTRUMENTS AGC Automatic Gain Control LI-75 % SV_EC Supply Voltage EC system V SV_iDL Supply Voltage Logger V BV_EC Battery Voltage EC system V BV_iDL Internal Battery Voltage Logger V (location and replicate number is essential) SIGNAL_STRENGTH Signal Strength LI-72 % T_RAD Case Sensor Temperature of CNR1 / CNR4 etc. C T_iDL Logger Temperature C T_COOLER Temperature of gas cooler/equilibrator C UNISPEC Unispec - WEBCAM Webcam pictures Jpeg, Raw T_CELL Laser spectrometer cell temperature K P_CELL Laser spectrometer cell pressure Torr Q Mass flow slpm OSO On Site Operation adimensional TIMEKEEPING DATE date yyyy-mm-dd DAY 2 digit day of month dd DOY 3 digit day of year ddd HOUR 2 digit hour of the day HH MINUTE 2 digit minute of the day MM MONTH 2 digit month of year mm TIME time of day HH:MM TIMESTAMP general timestamp format yyyy-mm-dd HH:MM YEAR 4 digit year yyyy AGGREGATION AVG Average - CNT Suffix for counts - MAX Maximum - MIN Minimum - MIN Minimum - SD Standard deviation - SMP Sample - TOT Total of values over a given time interval (e.g. precipitation) - SCALAR_XXX Scalar averaging for windspeeds (in combination with one of the above) - VECTOR_XXX Vectorial averaging for windspeeds (in combination with one of the above)

5 flux <- gf met <- meteo flux$timestamp <- as.numeric(flux$timestamp) met$timestamp <- as.numeric(flux$timestamp) oensingen <- merge(flux, met, by="timestamp", all=true) oensingen$timestamp <- as.posixlt(oensingen$timestamp, tz= UTC, origin=" ::") To convert back the numeric TIMESTAMP into the date and time information that we d like to have, we have to explicitly tell R what a time of zero second means. This is done with the optional argument origin=" ::" in as.posixlt(). As an example we produce two boxplots with all data, one showing how NEE depends on air temperature during the day and one how NEE depends on air temperature during the night (more precisely: when it is not day ): day <- oensingen$ppfd_in_avg_m1_1_1_gapfill > 2 boxplot(oensingen$needespiked_withustar_f ~ round(oensingen$ta_avg_t1_2_1, ), subset=day, col="yellow2") abline(h=, lwd=.5) boxplot(oensingen$needespiked_withustar_f ~ round(oensingen$ta_avg_t1_2_1, ), subset=!day, col="gray5") abline(h=, lwd=.5) CO 2 Flux (µmol m 2 s 1 ) CO 2 Flux (µmol m 2 s 1 ) Air Temperature ( C) Air Temperature ( C) Plotting diel cycles Looking at median diel cycles and the inter-quartile range of fluxes and other environmental variables is often used to reduce the noise seen in flux data. Diel is the correct term for something related to a -hour period. Often this is also called diurnal cycle, but when using the term diurnal it is often unclear how this distinguishes from nocturnal since one meaning of diurnal is: when there was daylight (which is not hours on most places on the world). Similar to the boxplot function we have prepared a function plot.iqr() which only uses the inter-quartile ranges of the boxplot and ignores the outer % of the data when producing a diel cycle plot. Typically one aggregates over a month, two weeks, or a week for such a display. Using our oensingen data frame we can easily produce such a graph after having imported the new plot function with source("

6 As an example we plot the median NEE during the first week of July in our oensingen data frame with the commands select <- oensingen$timestamp >= as.posixct(" ::", tz= UTC )& oensingen$timestamp <= as.posixct(" ::", tz= UTC ) plot.iqr(needespiked_withustar_f ~ TIMESTAMP$hour, data=oensingen, subset=select, col="springgreen", ylim=c(-12,12)) title(ylab=expression("net CO"[2]*" Flux ("*mu*"mol m"^{-2}*" s"^{-1}*")")) title(main=expression("oensingen Net CO"[2]*" Flux 1-7 July 26"), col.main="steelblue", line=-1, adj=.92, cex.main=1.75) 1 Oensingen Net CO 2 Flux 1 7 July 26 Net CO 2 Flux (µmol m 2 s 1 ) The IQR (green band) is the same as the % confidence interval of the median. As you can see IRQ at midnight is much larger than during the day: in general, turbulence is much lower at night and sometimes becomes intermittent, which strongly affects the flux measured by an eddy covariance system. During the day, but also shortly before sunrise the IQR is much closer to the median than around midnight. The first half of the night is normally more turbulent and more variable than the second half of the night when turbulence has decayed to a good degree under fair-weather conditions, and the atmosphere is very calm. With sunrise, turbulence increases quickly again as the sun starts to warm the Earth s surface.

Gapfilling of EC fluxes

Gapfilling of EC fluxes Pasi Kolari Department of Forest Sciences / Department of Physics University of Helsinki EddyUH training course Helsinki 23.1.2013 Contents Basic concepts of gapfilling Example