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topocast

coarse climate grids at the resolution of your terrain

Downscale a coarse raster onto fine terrain by moving-window regression.

A coarse blocky precipitation grid beside the same field downscaled onto fine terrain, sharing one color scale.

plot of chunk downscale

Give topocast a coarse variable and a fine predictor it tracks. In a window around every cell it learns how the variable depends on the predictor, then evaluates that local relationship on the fine predictor. A 1 km precipitation grid and a 100 m elevation model become a 100 m precipitation grid.

Installation 

# install.packages("pak")
pak::pak("gcol33/topocast")

Quick start 

library(topocast)
library(terra)

names(prec_1km) <- "prec"      # coarse variable, what you want at high resolution
names(dem_100m) <- "elev"      # fine predictor it tracks

prec_100m <- topocast(prec ~ elev, data = prec_1km, onto = dem_100m, radius = 15)

prec_100m is precipitation on the elevation model’s grid. Coarse to fine is one call: name the response and the predictor in a formula, pass the coarse grid as data and the fine grid as onto.

What goes in, what comes out

  • data is the coarse variable you want at higher resolution, as a SpatRaster, Raster*, or stars grid. The layer on the left of the formula is the response.
  • onto is the target: a fine grid holding the predictor(s) the variable tracks, often a digital elevation model. It can also be a set of station or plot points carrying those predictors as attributes.
  • the result is the response on the geometry of onto, in the class of onto: a raster at the fine resolution for a grid target, a prediction column for a point target.

When the only fine layer you have is the predictor itself, as in the example above, topocast derives the coarse predictor from onto for you, so a single coarse climate layer and a DEM are enough to start.

How it works

The relationship is fit locally, in a square window around every coarse cell, with summed-area tables: each window fit reduces to four lookups per sufficient statistic, so a radius of 30 costs the same as a radius of 3. The fitted intercept and slope grids are resampled to the fine grid and combined with the fine predictors as fitted = intercept + sum(slope * predictor), so the output carries the fine-scale structure of the terrain with locally varying coefficients. This is the regression step behind high-resolution climate surfaces such as CHELSA (Karger et al. 2017); topocast runs it locally and takes any number of named predictors.

What’s in the box

  • One front door. topocast() takes a formula, the coarse data, and the fine onto, and returns the downscaled response.
  • Any number of predictors. prec ~ elev + slope + twi fits elevation, slope, topographic wetness, or any aligned covariate jointly. The formula names match layers between data and onto, so a missing layer is reported by name.
  • Several responses in one call. cbind(prec, tmin) ~ elev downscales variables that share the terrain together. The window design is built once and solved against each response, so a second response costs little more than the first.
  • Constant cost per window. Summed-area tables keep each per-cell fit at four lookups for any radius.
  • Common spatial classes. SpatRaster, Raster* (raster), and stars grids are all accepted; the result comes back in the class of onto, or the class named by output.
  • Prediction at points. Pass an sf or SpatVector of stations or plots as onto and receive a prediction column, with the points carrying the fine predictor values.
  • Time series in one call. Pass a stack of periods as anomaly to fit the baseline once and carry each period onto it, "ratio" for precipitation or "additive" for temperature.
  • Fit quality on the map. diagnostics = TRUE returns an r.squared grid showing where the terrain relationship is strong, and clamp = TRUE bounds the output to the observed range of the coarse response.
  • A matrix engine. window_regression() exposes the kernel for callers who hold their data as matrices.

Usage

Several predictors, matched by name between the two grids:

coarse <- c(prec_1km, elev_1km, twi_1km, slope_1km)
names(coarse) <- c("prec", "elev", "twi", "slope")
terrain <- c(elev_100m, twi_100m, slope_100m)
names(terrain) <- c("elev", "twi", "slope")

prec_100m <- topocast(prec ~ elev + twi + slope, data = coarse, onto = terrain,
                      radius = 15)

Several responses that share the terrain, downscaled in one pass and returned as one layer each:

climate_100m <- topocast(cbind(prec, tmin, tmax) ~ elev, data = coarse,
                         onto = terrain, radius = 15)

A monthly series sharing one terrain relationship across periods:

series <- topocast(prec ~ elev, data = coarse, onto = terrain, radius = 15,
                   anomaly = prec_monthly_1km, type = "ratio")

Downscaling straight to plot locations, returned as a column on the points:

at_plots <- topocast(prec ~ elev, data = coarse, onto = plots_sf, radius = 15)

The local coefficient grids, to read the fitted lapse rate:

coefs <- topocast(prec ~ elev, data = coarse, onto = terrain, radius = 15,
                  coefficients = TRUE)

Documentation

Support

“Software is like sex: it’s better when it’s free.” — Linus Torvalds

I’m a PhD student who builds R packages in my free time because I believe good tools should be free and open. I started these projects for my own work and figured others might find them useful too.

If this package saved you some time, buying me a coffee is a nice way to say thanks. It helps with my coffee addiction.

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License and citation

MIT. If you use topocast in published work, please cite it:

@software{topocast,
  author = {Colling, Gilles},
  title  = {topocast: Moving-Window Regression Downscaling of Raster Data},
  year   = {2026},
  url    = {https://github.com/gcol33/topocast}
}