# Multi-run simulations in LWFBrook90R

## Introduction

With ‘LWFBrook90R’, parallelized multi-run simulations can be performed conveniently, extending the basic single-run applications using the function `run_LWFB90()` described in the introductory vignette. Two different multi-run functions exist for two different problems:

1. Perform Monte-Carlo simulations with single parameters set up for variation, and
2. simulations over multiple locations, parameter sets, or climate scenarios.

For the first case, the function `run_multi_LWFB90()` is available. The second problem can be tackled using the function `run_multisite_LWFB90()`.

Both functions are wrapper functions for `run_LWFB90()` and allow for parallel processing of tasks, using a specified number of CPUs to speed up the execution of a multi-run simulation. They return lists containing the individual single run simulation results, as they are returned by `run_LWFB90()`. These result-lists can become very large if many simulations are performed, and the selected output comprises daily data sets and especially the individual soil layers’ daily soil moisture states. Huge amounts of data produced can overload the memory, this vignette therefore starts with a data management section on how to make best use of the `output_fun`-argument of `run_LWFB90()` to reduce the amount of data returned.

    library(LWFBrook90R)
    library(data.table)

## Data management (i): `output_fun`-argument

To minimize memory allocation, it is recommended to reduce the selected output to a minimum and make use of the `output_fun`-argument of `run_LWFB90()`. With this argument, it is possible to pass custom functions to `run_LWFB90()`, which directly perform on the simulation output list object. With `rtrn_output = FALSE`, the original simulation output (as selected by `output`) then can be discarded, and only the results from the `output_fun`-argument are returned. This can be very useful for model calibration or sensitivity analyses tasks comprising ten thousands of simulations in a Monte-Carlo setting. With this magnitude, memory allocation is critical, and only a relatively small output can be returned for each individual simulation (e.g., a measure of agreement between simulated and observed values). Similarly, it is possible to define functions for custom output aggregation, or to redirect the simulation output to a file or database, as we will see later.

To demonstrate the usage of the `output_fun`-argument, we perform a Monte-Carlo simulation using the function `run_multi_LWFB90()`, and define a function that returns annual mean soil water storage and transpiration during the growing season. In a first step, the function integrates depth-specific soil moisture to soil water storage down to specified soil layer (`tolayer`), and in a second step calculates mean soil water storage over the growing season, along with the sum of transpiration. The growing season thereby is defined by the input parameters `budburstdoy` and `leaffalldoy`.

    output_function <- function(x, tolayer) {
      # aggregate SWAT
      swat_tran <- x$SWATDAY.ASC[which(nl <= tolayer),
                                 list(swat = sum(swati)),
                                 by  = list(yr, doy)]
      #add transpiration from EVAPDAY.ASC
      swat_tran$tran <- x$EVAPDAY.ASC$tran

      # get beginning and end of growing season from input parameters
      vpstart <- x$model_input$param_b90$budburstdoy
      vpend <- x$model_input$param_b90$leaffalldoy
      swat_tran <- merge(swat_tran,
                         data.frame(yr = unique(swat_tran$yr),
                                    vpstart, vpend), by = "yr")
      # mean swat and tran sum
      swat_tran[doy >= vpstart & doy <= vpend,
                list(swat_vp_mean = mean(swat), tran_vp_sum = sum(tran)), by = yr]
    }

To test our custom output function we run a single-run simulation

    data("slb1_meteo")
    data("slb1_soil")
    soil <- cbind(slb1_soil, hydpar_wessolek_tab(texture = slb1_soil$texture))
    b90res <- run_LWFB90(options_b90 = set_optionsLWFB90(),
                         param_b90 = set_paramLWFB90(),
                         climate = slb1_meteo,
                         soil = soil,
                         output = set_outputLWFB90())

and apply the function to the return, to see that our custom output function works:

    output_function(b90res, tolayer = 15)

## Multi-run simulations with `run_multi_LWFB90()`

As mentioned, `run_multi_LWFB90()` is a wrapper for `run_LWFB90()`. `run_multi_LWFB90()` takes a data.frame `paramvar` containing variable parameter values in columns and their realizations in rows. For each row in `paramvar`, the respective parameter values in `param_b90` are replaced by name, and `run_LWFB90()` is called. Further arguments to `run_LWFB90()` have to be specified and are passed on.

For the multi-run simulation, we set up two parameters for variation, the maximum leaf area index (`maxlai`) and the maximum leaf conductance (`glmax`). We define a data.frame with two columns, containing 50 random uniform realizations of the two parameters:

    set.seed(2021)
    N=50
    paramvar <- data.frame(maxlai = runif(N, 4,7),
                           glmax = runif(N,0.003, 0.01))

Now we can run the simulation. We suppress the selected simulation result objects and model input from being returned, and only return the values from our `output_fun` defined above. We pass `tolayer = 15` so that soil water storage is integrated down to the 15th soil layer, corresponding to 0-100 cm soil depth. Note that the `param_b90` object (and thus parameters `budburstdoy` and `leaffalldoy`) is available to our `output_fun`, although it is not included in the return (`rtrn_input = FALSE`).

    mrun_res <- run_multi_LWFB90(paramvar = paramvar,
                                 param_b90 = set_paramLWFB90(),
                                 cores = 2, # arguments below are passed to run_LWFB90()
                                 options_b90 = set_optionsLWFB90(),
                                 climate = slb1_meteo,
                                 soil = soil,
                                 output = set_outputLWFB90(),
                                 rtrn_input = FALSE, rtrn_output = FALSE,
                                 output_fun = output_function,
                                 tolayer = 15) # argument to output_fun

The result is a list of the individual single-run results, from which we can easily extract the results of our output function and `rbindlist` them together in a data.table:

    mrun_dt <- rbindlist(lapply(mrun_res, function(x) x$output_fun[[1]]),
                         idcol = "singlerun")

Now we can display the results of the 50 simulations using boxplots:

We ran 50 simulations, all with the same climate, soil, and parameters except for `maxlai` and `glmax`, that where varied randomly. In the next section we will learn how to make use of multiple climate, soil, and parameter sets using the function `run_multisite_LWFB90()`.

## Multi-Site simulations using `run_multisite_LWFB90()`

A basic multi-site simulation with the function `run_multisite_LWFB90()` runs through lists of `param_b90`, `climate`, and `soil`-objects, and evaluates the specified parameter sets for each of the soil/climate combinations. To demonstrate its usage, we define two parameter sets, that we want to run on three ‘sites’. We include the two parameter sets in a list `parms_l`:

    parms_beech <- set_paramLWFB90(maxlai = 6)
    parms_spruce <- set_paramLWFB90(maxlai = 4.5, winlaifrac = 0.8)
    parms_l <- list(beech = parms_beech, spruce = parms_spruce)

We pretend that the three sites all have individual climates and soils, and set up lists for soil and climate input:

    soils_l <- list(soil1 = soil, soil2 = soil, soil3 = soil)
    climates_l <- list(clim1 = slb1_meteo, clim2 = slb1_meteo, clim3 = slb1_meteo)

Now we can run a small example:

    msite_run1 <- run_multisite_LWFB90(
      options_b90 = set_optionsLWFB90(startdate = as.Date("2002-06-01"),
                                      enddate = as.Date("2002-06-30")),
      param_b90 = parms_l,
      climate = climates_l,
      soil = soils_l,
      cores = 2)
    str(msite_run1, max.level = 1)

The results are returned as a list of single run objects, with their names being concatenated from the names of the input list entries holding the individual `param_b90`, `climate`, and `soil` input objects.

## Data management (ii): A function as `climate`-argument

The function `run_multisite_LWFB90()` can easily be set up to run a few dozens of sites with individual climate data. However, simulating thousands of sites potentially is impossible, because such a large list of `climate` data.frames might overload the memory of a usual desktop computer. Fortunately, it is possible to pass a function instead of a data.frame as `climate`-argument to `run_LWFB90()`. Such a function can be used to create the `climate`-data.frame from a file or database-connection within `run_LWFB90()` or `run_multisite_LWFB90()` on the fly. For `run_LWFB90()`, we can simply provide arguments to the function via the `...`-placeholder. For `run_multisite_LWFB90()`, we need to pass arguments to a `climate`-function (possibly with individual values for individual site, e.g. a file name) via the `climate_args`-argument.

To demonstrate this mechanism, we write three files with climatic data to a temporary location, from where we will read them back in later:

    tdir <- tempdir()
    fnames <- paste0(tdir, "/clim", 1:3, ".csv")
    write.csv(slb1_meteo[year(slb1_meteo$dates) %in% c(2002,2003),],
              file = fnames[1], row.names = FALSE)
    write.csv(slb1_meteo[year(slb1_meteo$dates) %in% c(2002,2003),],
              file = fnames[2], row.names = FALSE)
    write.csv(slb1_meteo[year(slb1_meteo$dates) %in% c(2002,2003),],
              file = fnames[3], row.names = FALSE)

For testing, we perform a single run with `run_LWFB90()` and use the `fread` function from the ‘data.table’-package as `climate`-argument. The function reads ‘csv’-files, and takes a `file` name as argument that we include in the call. It points to the first of our three climate files:

    srun <- run_LWFB90(
      options_b90 = set_optionsLWFB90(),
      param_b90 = set_paramLWFB90(),
      soil = soil,
      output = set_outputLWFB90(),
      climate = fread,
      file = fnames[1],
      rtrn.input = FALSE)

The same construct basically works with the function `run_multisite_LWFB90()`. The only difference to single-run simulations is that the arguments for the function have to be specified in a named list of lists, one sub-list for each site. We set it up as follows:

    clim_args <- list(climfromfile1 = list(file = fnames[1]),
                      climfromfile2 = list(file = fnames[2]),
                      climfromfile3 = list(file = fnames[3]))

Now we call `run_multisite_LWFB90()`, and set up the function `fread` as `climate`-parameter. Our list of lists with individual arguments for `fread` is passed to the function via `climate_args`:

    msite_run2 <- run_multisite_LWFB90(
      options_b90 = set_optionsLWFB90(),
      param_b90 = parms_l,
      soil = soils_l,
      climate = fread,
      climate_args = clim_args,
      cores = 2)

    str(msite_run2, max.level = 1)

We simulated two parameter sets using three different climate/soil combinations. The names of the climate used in the result names are now coming from the top-level names of our list `clim_args`, because we used a function as `climate`-argument. The function `fread` is evaluated directly within `run_multisite_LWFB90()`, and is not passed to `run_LWFB90()`, because otherwise it would have been evaluated for each single-run simulation. In this way, `fread` is evaluated only three times for in total six simulations which saves us some execution time, if we want to simulate multiple parameter sets using the same climatic data.

## Multi-site simulation: Input from file, output to file

Now that we learned how to use a function as climate input, we can combine this input facility with an `output_fun` that writes the simulation results to a file. To do so, we extend our output function from the first example so that it writes the results to a file in a specified directory. The file name is constructed from the names of the current soil, climate, and parameter object, which are passed automatically from `run_multisite_LWFB90()` to `run_LWFB90()` as character variables `soil_nm`, `clim_nm`, and `param_nm`. In this way, the names of currently processed input objects are accessible to `output_fun`-functions within `run_LWFB90()`.

    output_function2 <- function(x, tolayer, basedir = getwd(),
                                soil_nm, clim_nm, param_nm ) {
      # file-name
      filenm = file.path(basedir, paste(soil_nm, clim_nm, param_nm, sep = "_"))

      # aggregate SWAT
      swat_tran <- x$SWATDAY.ASC[which(nl <= tolayer),
                                 list(swat = sum(swati)),
                                 by  = list(yr, doy)]
      #add transpiration from EVAPDAY.ASC
      swat_tran$tran <- x$EVAPDAY.ASC$tran

      # get beginning and end of growing season from input parameters
      vpstart <- x$model_input$param_b90$budburstdoy
      vpend <- x$model_input$param_b90$leaffalldoy
      swat_tran <- merge(swat_tran,
                         data.frame(yr = unique(swat_tran$yr),
                                    vpstart, vpend), by = "yr")
      # mean swat and tran sum
      swattran_vp <- swat_tran[doy >= vpstart & doy <= vpend,
                list(swat_vp_mean = mean(swat), tran_vp_sum = sum(tran)), by = yr]

      write.csv(swattran_vp, file = paste0(filenm, ".csv"))
    }

Now we can run the simulations, with climate data coming from files, and the results being written to file our temporary directory `tdir`:

    msite_run3 <- run_multisite_LWFB90(
      options_b90 = set_optionsLWFB90(),
      param_b90 = parms_l,
      soil = soils_l,
      climate = fread,
      climate_args = clim_args,
      output = set_outputLWFB90(),
      rtrn_input = FALSE, rtrn_output = FALSE,
      output_fun = output_function2,
      tolayer = 15,
      basedir = tdir,
      cores = 2)

After the simulation has finished, we can list the files and see that our attempt was successful:

    list.files(tdir, pattern = "csv")

Note that we can also use database connection objects instead of files to read climate data and save simulation results. For the input of climate data, connection objects can be defined in advance, and passed directly to the `climate`-function. However, this does not work for `output_fun` in a parallel setting like in `run_multisite_LWFB90()` or `run_multi_LWFB90()`, because file or database connections in R are not exported to parallel workers. Connections therefore have to be set up (and closed again) within an `output_fun`-function.