Perez-Priego_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### introduction\n",
    "\n",
    "Here we will go through application of the Pérez-Priego partitioning algorithm to eddy covariance data. The script is designed to run on [FLUXNET2015](https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/) .csv files directly, which ensures consistent variable names, processing and units. The tutorial will use data from the [Hyytiälä forest](http://sites.fluxdata.org/FI-Hyy/) in Finland, but can be applied to any FLUXNET2015 dataset.\n",
    "\n",
    "Some experience in R will make things easy, but I will try to explain the process step by step so as to be accessible to all backgrounds.\n",
    "\n",
    "This example has been adapted from the original example:\n",
    "\n",
    "https://github.com/oscarperezpriego/ETpartitioning/blob/master/inst/main_ETpartitioning.r\n",
    "\n",
    "### first things first\n",
    "\n",
    "The firs step is to import all needed packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: deSolve\n",
      "Loading required package: rootSolve\n",
      "Loading required package: coda\n"
     ]
    }
   ],
   "source": [
    "library(ETpartitioning) # The package containing the partitioning code\n",
    "library(FME)            # Package for parameter optimization\n",
    "library(bigleaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loading the dataset\n",
    "\n",
    "Here we open the .csv file. Only the years from 2008 to 2013 are used from the full timeseries. This will load all variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = read.csv(\"FLX_FI-Hyy_FLUXNET2015_FULLSET_HH_2008-2010_1-3.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### estimating long-term effective $\\chi_{o}$ and $WUE_{o}$ parameters\n",
    "\n",
    "The first step is to calculate $\\chi_{o}$ via:\n",
    "\n",
    "$$ln\\left ( \\frac{\\chi_{o}}{1-\\chi_{o}} \\right ) = 0.0545 \\left ( \\overline{Temp}-25 \\right ) - 0.58 \\cdot ln(\\overline{D}) - 0.0815 \\cdot Z + C$$\n",
    "\n",
    "where $\\overline{Temp}$ is the mean seasonal temperature and $\\overline{D}$ in the mean seasonal VPD. This is accomplished using the `calculate_chi_o` function below. Here `Z` is the site altitude in kilometers and `C` is an empirical coefficient for  C3 species (Wang et al., 2017). The function takes the dataset `ds` as well as designations for the GPP (`ColPhotos`), VPD (`ColVPD`), and air temperature (`ColTair`) columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chi_o <- calculate_chi_o(data= ds\n",
    "                         ,ColPhotos = \"GPP_NT_VUT_USTAR50\"\n",
    "                         ,ColVPD = \"VPD_F\"\n",
    "                         ,ColTair = \"TA_F\"\n",
    "                         ,C = 1.189 ##<< Empirical coeficient for C3 species (see Wang et al., 2017; Plant Nature).\n",
    "                         ,Z=0.181) ##<< altitude (km)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second we calculate $WUE_{o}$ via:\n",
    "\n",
    "$$WUE_{o} = C_{a} \\cdot P_{a} \\frac{  \\left ( 1- \\chi_{o} \\right ) }{1.6 \\cdot \\overline{D}}$$\n",
    "\n",
    "where $C_{a} and P_{a}$ are set to 390 ppm and 96 kPa, respectively. This is accomplished using the `calculate_WUE_o` function below with the same inputs as `calculate_chi_o`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "WUE_o <- calculate_WUE_o(data= ds\n",
    "                         ,ColPhotos = \"GPP_NT_VUT_USTAR50\"\n",
    "                         ,ColVPD = \"VPD_F\"\n",
    "                         ,ColTair = \"TA_F\"\n",
    "                         ,C = 1.189 ##<< Empirical coeficient for C3 species (see Wang et al., 2017; Plant Nature).\n",
    "                         ,Z=0.181) ##<< altitude (km)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### prepare for the partitioning\n",
    "\n",
    "The partitioning step optimizes parameters for each day using a 5 day moving window. Here we convert the timestamp string in the original file into both the individual date and time components, giving each day an identifyer to use within the loop (`ds$loop`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds$year <- (substr(ds$TIMESTAMP_END, 1, 4))\n",
    "ds$Month <- as.numeric(substr(ds$TIMESTAMP_END, 5, 6))\n",
    "ds$DD <- as.numeric(substr(ds$TIMESTAMP_END, 7, 8))\n",
    "ds$Hour <- (substr(ds$TIMESTAMP_END, 9, 10))\n",
    "ds$Min <-(substr(ds$TIMESTAMP_END, 11, 12))\n",
    "ds$rDate <- strptime(paste0(ds$year,\"/\",ds$Month,\"/\",ds$DD,\" \", ds$Hour,\":\",ds$Min) , format=\"%Y/%m/%d %H:%M\", tz='GMT')\n",
    "ds$date <- strptime(paste0(paste0(ds$year,\"/\",ds$Month,\"/\",ds$DD)) , format=\"%Y/%m/%d\", tz='GMT')\n",
    "ds$loop <- as.numeric(as.Date(ds$date))-min(as.numeric(as.Date(ds$date)), na.rm=T)+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### running a subset\n",
    "\n",
    "The 4 model parameters (a1, Do, Topt and beta, see Perez-Priego et al., 2018) are estimated using a multi-constraint Markov Chain Monte Carlo (MCMC) over a given time period (e.g. 5 days). In the present form, the number of iterations was set to 20000, and the first half of the chains was discarded. We updated the proposed distribution every 500 iterations. Please note that this setup is computationally expensive, i.e. on my laptop with a single processor, one day takes about 2.5 to 3 minutes, or $\\approx16$ hours for a year. As such, we will only run one day to demonstrate the process. If you have the time, feel free to run more days. Note that the MCMC can be customized as required in the `optimal_parameters` function to possibly reduce runtimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"2008-07-18 GMT\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "days_to_run <- unique(ds$loop)[200:200]\n",
    "\n",
    "unique(ds$date)[days_to_run]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### partitioning\n",
    "\n",
    "For each day in `days_to_run` we make a temperary dataset (`tmp`) containing the 5 day window. The optimized model parameters are then estimated (`optimal_parameters`), which are then passed on the the transpiration model(`transpiration_mod`). The resulting `ET`, `transpiration`, and `evaporation` are then saved in the `estimates` dataset. Again, this is the step that takes the longest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of accepted runs: 16008 out of 20000 (80.04%) \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Time difference of 2.88858 mins"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimation_out <-  list() ##<< Emptly list to save the estimates\n",
    "\n",
    "start_time <- Sys.time() ##<< Save the start time to time the partitioning step\n",
    "\n",
    "for (i in days_to_run){\n",
    "  tmp <- subset(ds,loop %in% c(i-2,i-1, i,i+1, i+2)) ##<< Defining 5 days window in a loop\n",
    "\n",
    "  tmp_day <- subset(tmp, NIGHT == 0)\n",
    "\n",
    "  #-- optimize model parameters\n",
    "\n",
    "  ans <-  optimal_parameters(par_lower= c(0,0, 10, 0)\n",
    "                              ,par_upper = c(400,0.4, 30, 1)\n",
    "                              ,data=tmp_day\n",
    "                              ,ColPhotos=\"GPP_NT_VUT_USTAR50\"\n",
    "                              ,ColPhotos_unc=\"NEE_VUT_USTAR50_JOINTUNC\"\n",
    "                              ,ColH=\"H_F_MDS\"\n",
    "                              ,ColVPD=\"VPD_F\"\n",
    "                              ,ColTair=\"TA_F\"\n",
    "                              ,ColPair=\"PA_F\"\n",
    "                              ,ColQ=\"PPFD_IN\"\n",
    "                              ,ColCa=\"CO2_F_MDS\"\n",
    "                              ,ColUstar=\"USTAR\"\n",
    "                              ,ColWS=\"WS_F\"\n",
    "                              ,ColSW_in=\"SW_IN_F\"\n",
    "                              ,Chi_o = Chi_o\n",
    "                              ,WUE_o= WUE_o)\n",
    "\n",
    "  par <- as.numeric(ans)\n",
    "\n",
    "  #-- estimating transpiration rates\n",
    "    \n",
    "  transpiration_mod <- transpiration_model(\n",
    "      par=par\n",
    "      ,data=tmp\n",
    "      ,ColPhotos=\"GPP_NT_VUT_USTAR50\"\n",
    "      ,ColH=\"H_F_MDS\"\n",
    "      ,ColVPD=\"VPD_F\"\n",
    "      ,ColTair=\"TA_F\"\n",
    "      ,ColPair=\"PA_F\"\n",
    "      ,ColQ=\"PPFD_IN\"\n",
    "      ,ColCa=\"CO2_F_MDS\"\n",
    "      ,ColUstar=\"USTAR\"\n",
    "      ,ColWS=\"WS_F\"\n",
    "      ,ColSW_in=\"SW_IN_F\"\n",
    "      ,Chi_o = Chi_o\n",
    "      ,WUE_o= WUE_o)\n",
    "\n",
    "    \n",
    "    \n",
    " \n",
    "  #-- Converting units and estimating evaporation rates\n",
    "\n",
    "  transpiration_mod <- transpiration_mod * (18.01528/1e6) *1800  # from mmol m-2 s-2 to mm per half hour\n",
    "  ET <- LE.to.ET(tmp$LE_F_MDS,tmp$TA_F) * 1800 # from Wm-2 to mm per half hour\n",
    "  evaporation_mod <- ET-transpiration_mod\n",
    "    \n",
    "  #-- output the estimates\n",
    "\n",
    "  tmp$ET <- ET\n",
    "  tmp$transpiration_mod <- transpiration_mod\n",
    "  tmp$evaporation_mod <- evaporation_mod\n",
    "\n",
    "  tmp <- tmp[tmp$loop == i,] ## selecting the central day\n",
    "  estimation_out[[i]] <- tmp\n",
    "\n",
    "}\n",
    "end_time <- Sys.time()\n",
    "end_time - start_time\n",
    "\n",
    "estimates <- do.call(rbind, estimation_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can make a simple plot. If you have also gone through the TEA_tutorial.ipynb, you can compare to two plots which both show ET, E, and T for July 18th 2008."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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8mToQ6Uuxx+HOAATp4A4BiCnf0+/3mp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\nwY6IiIhIJP4fPskoKrI/77MAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours = seq(0,23.5,0.5)\n",
    "plot(hours, estimates$ET, type=\"l\", ylab=expression(water~flux~(mm~half-hour^-1)), xlab=\"hour\")\n",
    "lines(hours, estimates$transpiration_mod, col=\"green\")\n",
    "lines(hours, estimates$evaporation_mod, col=\"red\")\n",
    "legend(\"topleft\", legend = c(\"ET\", \"plant transpiration\",  \"evaporation\"), bty = \"n\",\n",
    "lwd=c(1,1,1), cex = 1, col = c( \"black\",\"green\",\"red\"), pch = c(1, 1,1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}