---
title: "delta downscaling"
#output: rmarkdown::pdf_document
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{delta downscaling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

download_data <- FALSE
create_custom_data <- FALSE
```

# Downscaling

Climate reconstructions from global circulation models are often at coarser resolutions
than desired for ecological analyses. Downscaling is the process of generating
a finer resolution raster from a coarser resolution raster. There are many methods
to downscale rasters, and several are implemented in specific `R` packages. For
example, the `terra` package can downscale reconstructions using bilinear interpolation,
a statistical approach that is simple and fast. For palaeoclimate reconstructions,
the delta method has been shown to be very effective (Beyer et al, REF). The delta
method is a simple method that computes the difference between the observed and
modelled values at a given time step (generally the present), and then applies 
this difference to the modelled
values at other time steps. This approach makes the important assumption that the fine scale structure
of the deviations between large scale model and finer scale observations is constant
over time. Whilst such an assumption is likely to hold reasonably well in the short
term, it may not hold over longer time scales.

## Delta downscaling a dataset in `pastclim`

`pastclim` includes functions to use the delta method for downscaling. In this example,
we will focus on Europe,
as it shows nicely the issues of sea level change and ice sheets, which need to 
be accounted for when applying the delta downscale method. For real applications, we would
recommend using a bigger extent in areas of large changes in land extent, as
interpolating over a small extent can lead to greater artefacts; for this example,
we keep the extent small to reduce computational time.

## An example for one variable

Whilst we are often interested in downscaling composite bioclimatic variables (such
as the warmest quarter), downscaling should be applied directly to monthly estimates
of temperature and precipitation, and high resolution bioclimatic variables should
be computed from these downscaled monthly estimates. This approach ensures that
the downscaled bioclimatic variables are consistent with each other.

For downscaling, we will use the WorldClim2 dataset as our high resolution observations.
We will use the Example dataset (a subset of the Beyer2020 dataset) as our low 
resolution model reconstructions. We start by extracting monthly temperature 
for northern Europe for both datasets:
```{r initialis_pastclim, echo=FALSE, results="hide", eval=!download_data}
library(pastclim)
set_data_path(on_CRAN = TRUE)
```
```{r}
library(pastclim)
tavg_vars <- c(paste0("temperature_0", 1:9), paste0("temperature_", 10:12))
time_steps <- get_time_bp_steps(dataset = "Example")
n_europe_ext <- c(-10, 15, 45, 60)
```
```{r eval=download_data}
download_dataset(dataset = "Beyer2020", bio_variables = tavg_vars)
tavg_series <- region_series(
  bio_variables = tavg_vars,
  time_bp = time_steps,
  dataset = "Beyer2020",
  ext = n_europe_ext
)
```

```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(tavg_series,
  file = "../inst/extdata/delta/tavg_series.RDS"
)
```

```{r echo=FALSE, results="hide", eval=!download_data}
library(pastclim)
set_data_path(on_CRAN = TRUE)
tavg_series <- terra::readRDS(system.file("extdata/delta/tavg_series.RDS",
  package = "pastclim"
))
# get back the time units that are lost when saving the rds
old_names <- names(tavg_series) # there is a bug in terra
terra::time(tavg_series, tstep = "years") <- terra::time(tavg_series)
names(tavg_series) <- old_names
rm(old_names)
```

Downscaling is performed one variable at a time. We will start with temperature in January.
So, we first need to extract the `SpatRaster` of model low resolution data from the `SpatRasterDataset`:
```{r}
tavg_model_lres_rast <- tavg_series$temperature_01
tavg_model_lres_rast
```
And we can now plot it:
```{r, fig.width=6, fig.height=5}
plot(tavg_model_lres_rast, main = time_bp(tavg_model_lres_rast))
```

We can see how that the reconstructions are rather coarse (the Beyer2020 dataset
uses 0.5x0.5 degree cells). We now need a set of
high resolutions observations for the variable of interest that we will use to 
generate the delta raster used to downscale reconstructions. We will use data from
WorldClim2 at 10 minute resolution (but other datasets such as CHELSA would be
equally suitable):

Once the variable is downloaded, we can load it at any time with:
```{r eval=download_data}
download_dataset(dataset = "WorldClim_2.1_10m", bio_variables = tavg_vars)
tavg_obs_hres_all <- region_series(
  bio_variables = tavg_vars,
  time_ce = 1985,
  dataset = "WorldClim_2.1_10m",
  ext = n_europe_ext
)
```

```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(tavg_obs_hres_all,
  file = "../inst/extdata/delta/tavg_obs_hres_all.RDS"
)
```

```{r echo=FALSE, results="hide", eval=!download_data}
tavg_obs_hres_all <- terra::readRDS(
  system.file("extdata/delta/tavg_obs_hres_all.RDS",
    package = "pastclim"
  )
)
```

For later use, we store the range of the variable, which we will use to bound
the downscaled values (arguably, it would be better to grab these limits from the
full world distribution, but for this example, we will use the European range)
```{r}
tavg_obs_range <- range(
  unlist(
    lapply(tavg_obs_hres_all, minmax, compute = TRUE)
  )
)
tavg_obs_range
```

We want to crop these reconstructions to the extent of interest
```{r, fig.width=4, fig.height=4}
tavg_obs_hres_all <- terra::crop(tavg_obs_hres_all, n_europe_ext)
# extract the January raster
tavg_obs_hres_rast <- tavg_obs_hres_all[[1]]
plot(tavg_obs_hres_rast)
```

We need to make sure that the extent of the modern observations is the same as the
extent of the model reconstructions:

```{r}
ext(tavg_obs_hres_rast) == ext(tavg_model_lres_rast)
```

If that was not the case, we would use `terra::crop` to match the extents.

We also need a high resolution global relief map (i.e. integrating both 
topographic and bathymetric values) to reconstruct past
coastlines following sea level change. We can download the ETOPO2022 relief
data, and resample to match the extent and resolution as the high resolution observations.

```{r eval=download_data}
download_etopo()
relief_rast <- load_etopo()
relief_rast <- terra::resample(relief_rast, tavg_obs_hres_rast)
```

```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(relief_rast,
  file = "../inst/extdata/delta/relief_rast.RDS"
)
```

```{r echo=FALSE, results="hide", eval=!download_data}
relief_rast <- terra::readRDS(system.file("extdata/delta/relief_rast.RDS",
  package = "pastclim"
))
```

We can now generate a high resolution land mask for the periods of interest. By
default, we use the sea level reconstructions from Spratt et al 2016, but a different
reference can be used by setting sea levels for each time step (see the man page
for `make_land_mask` for details):

```{r, fig.width=6, fig.height=5}
land_mask_high_res <- make_land_mask(
  relief_rast = relief_rast,
  time_bp = time_bp(tavg_model_lres_rast)
)
plot(land_mask_high_res, main = time_bp(land_mask_high_res))
```

Note that this land mask does take ice sheets into account, and the Black and Caspian sea are missing.
For the ice mask, we can:

```{r eval=download_data}
ice_mask_low_res <- get_ice_mask(time_bp = time_steps, dataset = "Beyer2020")
ice_mask_high_res <- downscale_ice_mask(
  ice_mask_low_res = ice_mask_low_res,
  land_mask_high_res = land_mask_high_res
)
plot(ice_mask_high_res)
```


```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(ice_mask_low_res,
  file = "../inst/extdata/delta/ice_mask_low_res.RDS"
)
```

```{r echo=FALSE, eval=!download_data}
ice_mask_low_res <- terra::readRDS(
  system.file("extdata/delta/ice_mask_low_res.RDS",
    package = "pastclim"
  )
)
ice_mask_high_res <- downscale_ice_mask(
  ice_mask_low_res = ice_mask_low_res,
  land_mask_high_res = land_mask_high_res
)
plot(ice_mask_high_res)
```

Note that there is no ice mask for the last two time steps.

We can now remove the ice mask from the land mask:
```{r}
land_mask_high_res <- mask(land_mask_high_res,
  ice_mask_high_res,
  inverse = TRUE
)
plot(land_mask_high_res)
```

If it was a region with internal seas, we could then remove them with:
```{r eval=FALSE}
internal_seas <- readRDS(system.file("extdata/internal_seas.RDS",
  package = "pastclim"
))
land_mask_high <- mask(land_mask_high_res,
  internal_seas,
  inverse = TRUE
)
```

We can now compute a delta raster and use it to downscale the model
reconstructions:

```{r}
delta_rast <- delta_compute(
  x = tavg_model_lres_rast, ref_time = 0,
  obs = tavg_obs_hres_rast
)
model_downscaled <- delta_downscale(
  x = tavg_model_lres_rast,
  delta_rast = delta_rast,
  x_landmask_high = land_mask_high_res,
  range_limits = tavg_obs_range
)
model_downscaled
```

Let's inspect the resulting data:
```{r, fig.width=6, fig.height=5}
panel(model_downscaled, main = time_bp(model_downscaled))
```

And, as a reminder, the original reconstructions:
```{r, fig.width=6, fig.height=5}
panel(tavg_model_lres_rast, main = time_bp(tavg_model_lres_rast))
```

## Computing the bioclim variables
To compute the bioclim variables, we need to repeat the procedure above for
temperature and precipitation for all months. Let us start with temperature. We
loop over each month, create a `SpatRaster` of downscaled temperature, add it to
a list, and finally convert the list into a `SpatRasterDataset`

```{r}
tavg_downscaled_list <- list()
for (i in 1:12) {
  delta_rast <- delta_compute(
    x = tavg_series[[i]], ref_time = 0,
    obs = tavg_obs_hres_all[[i]]
  )
  tavg_downscaled_list[[i]] <- delta_downscale(
    x = tavg_series[[i]],
    delta_rast = delta_rast,
    x_landmask_high = land_mask_high_res,
    range_limits = tavg_obs_range
  )
}
tavg_downscaled <- terra::sds(tavg_downscaled_list)
```

Quickly inspect the resulting dataset:
```{r}
tavg_downscaled
```
As expected, we have 12 months (subdatasets), each with 5 time steps.

We now want to repeat the same procedure for precipitation. In this example
we will downscale precipitation in its natural scale, but often we use logs.
We now need to create a series for precipitation:
```{r eval=download_data}
prec_vars <- c(paste0("precipitation_0", 1:9), paste0("precipitation_", 10:12))
prec_series <- region_series(
  bio_variables = prec_vars,
  time_bp = time_steps,
  dataset = "Beyer2020",
  ext = n_europe_ext
)
```


```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(prec_series,
  file = "../inst/extdata/delta/prec_series.RDS"
)
```
```{r echo=FALSE, results="hide", eval=!download_data}
prec_vars <- c(paste0("precipitation_0", 1:9), paste0("precipitation_", 10:12))
prec_series <- terra::readRDS(system.file("extdata/delta/prec_series.RDS",
  package = "pastclim"
))
# get back the time units that are lost when saving the rds
old_names <- names(prec_series) # there is a bug in terra
terra::time(prec_series, tstep = "years") <- terra::time(prec_series)
names(prec_series) <- old_names
rm(old_names)
```
Get some high resolution observations:
```{r eval=download_data}
download_dataset(dataset = "WorldClim_2.1_10m", bio_variables = prec_vars)
prec_obs_hres_all <- region_series(
  bio_variables = prec_vars,
  time_ce = 1985,
  dataset = "WorldClim_2.1_10m",
  ext = n_europe_ext
)
```

```{r echo=FALSE, results="hide", eval=create_custom_data}
terra::saveRDS(prec_obs_hres_all,
  file = "../inst/extdata/delta/prec_obs_hres_all.RDS"
)
```

```{r echo=FALSE, results="hide", eval=!download_data}
prec_obs_hres_all <- terra::readRDS(
  system.file("extdata/delta/prec_obs_hres_all.RDS",
    package = "pastclim"
  )
)
```

Estimate the range of observed precipitation:
```{r}
prec_obs_range <- range(
  unlist(
    lapply(prec_obs_hres_all, minmax,
      compute = TRUE
    )
  )
)
prec_obs_range
```


And finally downscale precipitation: 
```{r}
prec_downscaled_list <- list()
for (i in 1:12) {
  delta_rast <- delta_compute(
    x = prec_series[[i]], ref_time = 0,
    obs = prec_obs_hres_all[[i]]
  )
  prec_downscaled_list[[i]] <- delta_downscale(
    x = prec_series[[i]],
    delta_rast = delta_rast,
    x_landmask_high = land_mask_high_res,
    range_limits = prec_obs_range
  )
}
prec_downscaled <- terra::sds(prec_downscaled_list)
```

We are now ready to compute the bioclim variables:
```{r}
bioclim_downscaled <- bioclim_vars(
  tavg = tavg_downscaled,
  prec = prec_downscaled
)
```

Let's inspect the object:
```{r}
bioclim_downscaled
```

And plot the first variable (bio01):
```{r}
panel(bioclim_downscaled[[1]], main = time_bp(bioclim_downscaled[[1]]))
```

We can now save the downscaled `sds` to a netcdf file:
```{r}
terra::writeCDF(bioclim_downscaled,
  paste0(tempdir(), "/EA_bioclim_downscaled.nc"),
  overwrite = TRUE
)
```

And then use it as a custom dataset for any function in `pastclim`. Let's extract
a region series for three variables:
```{r}
custom_data <- region_series(
  bio_variables = c("bio01", "bio04", "bio19"),
  dataset = "custom",
  path_to_nc = paste0(tempdir(), "/EA_bioclim_downscaled.nc")
)
```

We can quickly inspect the resulting `sds` object:
```{r}
custom_data
```

And plot it (it should be identical to the earlier plot obtained when we created
the dataset):
```{r}
panel(custom_data$bio01, main = time_bp(custom_data$bio01))
```
