---
title: "Multi-run simulations in LWFBrook90R"
output:
rmarkdown::html_vignette:
toc: true
bibliography: refs.bib
vignette: >
%\VignetteIndexEntry{Multi-run simulations in LWFBrook90R}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## 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()`, which is described in detail in the vignette ['Multi-Site simulations'](LWFBrook90R-4-Multisite-Runs.html).
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.
```{r, warning = FALSE, message = FALSE, eval = TRUE}
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 (`output`, `layer_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`, passed via `...` to
`output_fun`), 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`.
```{r}
output_function <- function(x, tolayer) {
# aggregate SWAT
swat_tran <- x$layer_output[which(nl <= tolayer),
list(swat = sum(swati)),
by = list(yr, doy)]
#add transpiration from EVAPDAY.ASC
swat_tran$tran <- x$output$tran
# get beginning and end of growing season from input parameters
vpstart <- x$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]
}
```
```{r, eval = TRUE, echo = FALSE}
data("slb1_meteo")
data("slb1_soil")
soil <- cbind(slb1_soil, hydpar_wessolek_tab(texture = slb1_soil$texture))
b90res <- LWFBrook90R:::b90res
```
To test our custom output function we run a single-run simulation
```{r, eval = FALSE}
data("slb1_meteo")
data("slb1_soil")
soil <- cbind(slb1_soil, hydpar_wessolek_tab(texture = slb1_soil$texture))
b90res <- run_LWFB90(options_b90 = set_optionsLWFB90(),
param_b90 = set_paramLWFB90(),
climate = slb1_meteo,
soil = soil)
```
and apply the function to the return, to see that our custom output function works:
```{r}
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:
```{r}
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`).
```{r,eval = 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,
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:
```{r, eval = FALSE}
mrun_dt <- rbindlist(lapply(mrun_res, function(x) x$output_fun[[1]]),
idcol = "singlerun")
```
```{r, eval = TRUE, echo = FALSE}
mrun_dt <- LWFBrook90R:::mrun_dt
```
Now we can display the results of the 50 simulations using boxplots:
```{r boxplots, fig.width=7, fig.height = 5, echo =FALSE, fig.cap = "Growing season soil water storage and transpiration of 50 simulations, with random variation of parameters 'maxlai' and 'glmax'" }
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1,2))
boxplot(swat_vp_mean~yr, data = mrun_dt, col = "blue")
boxplot(tran_vp_sum~yr, data = mrun_dt, col = "green")
par(oldpar)
```
We ran 50 simulations, all with the same climate, soil, and parameters except
for `maxlai` and `glmax`, that where varied randomly. In the next vignette ['Multi-Site simulations'](LWFBrook90R-4-Multisite-Runs.html), we
will learn how to make use of multiple climate, soil, and parameter sets using
the function `run_multisite_LWFB90()`, to simulate a set of different sites.