I know that resampling from coarse resolution to fine resolution is bad. Effectively you are making up data. However, if the study area is small are there any other options? Worldclim comes at around 900m resolution at it's finest. But running an SDM (species distribution model) on a small study area is relatively pointless as the the 900 x 900 cells are too big. The easiest option is to resample the worldclim data to 30m. Worldclim data is interpolated from weather stations. Would this count as a further interpolation or would it create nonsense outputs?
Raster Resampling – How to Convert WorldClim Data to 30m Resolution
rasterresampling
Related Solutions
This is easy in QGIS too, though a little less obvious. There are a couple of ways you can do it:
- Raster Calculator - simply use the raster calculator and you can set the resolution and extent there and can make them match another raster by selecting the raster band you want to match in the Raster Bands list and then clicking the "Current layer extent" button. The columns and rows fields will let you set the resolution. However, this method gives you no control over the resampling method.
Using GDAL_Warp - this tool lets you set the output resolution either by specifying the width and height of the output raster or by specifying the -tr switch (see the documentation). You can get to the GDAL_warp tool by going Raster->Projections->Warp (I did say it wasn't obvious from a resampling point of view!).
- (v2.x) If you want to use the -tr switch, fill in all the boxes for input raster and output etc (your source and target SRS values will presumably be the same in this case - though don't have to be if you're reprojecting as well). Then click the little pencil icon at the bottom and edit the auto-generated gdal-warp commandline to include your -tr switch. Gdal_wrap lets you specif the algorithm you want to use for the resampling and so is a little less of a blunt instrument than using the raster calculator.
(v3.x) The -tr switch is enabled by using the Output file resolution in target georeferenced units box. For example, to downsample a 1m DEM to a 2m DEM, you can enter 2 in that field. However, there is no option to pass two different arguments for non-square pixels. Say your target pixel size is
0.3125,0.25
, meaning thexres
is0.3125
and theyres
is0.25
. If you now pass the value0.3125
in that box, it will set-tr 0.3125 0.3125
in the command. To counter this limitation, simply copy the code, paste to the command line, edit the -tr flag and run. For example:gdalwarp -t_srs EPSG:4326 -tr 0.3125 0.25 -r near -te 71.40625 24.875 84.21875 34.375 -te_srs EPSG:4326 -of GTiff foo.tiff bar.tiff
(depending on your instalation and environment variables, you may also need to explicitly state the path to gdalwarp).
Easiest way is to convert the raster to a projected system, and get the cell size directly in meters.
That not being possible, or desirable, you can still convert lat/lon values to metric values, but you'll run into an accuracy problem. This is because, 1º of distance represents different values in metric units depending on your location and bearing (0.00045º is about 50m around the Equator, but about 30m around London). There are ways to calculate it, but you'll be generalizing somewhat your results.
If accuracy is not very important and you only want an overall idea of the cell size, you can either do it with the Haversine formula (easier but less precise) or Vincenty's inverse formula (more precise, but way more complicated).
Best Answer
Generally, resampling this kind of data using interpolation will probably lead to poor results, especially if the area concerned is mountainous (as whuber pointed out, microscale climate data is spatially highly variable and interpolates poorly). Increasing the resolution thirty times is however rather drastic and I'd think twice about the very relevance of such data. If you consider doing so, I'd search this answer for some methods.
However, a better way is to use cokriging or other covariate interpolation methods together with other temperature predictors - the relief most likely being the primary factor here, but others such as land cover type and insolation could be useful too.