# 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(),
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_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,
cores = 2)
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.