SWECO25: a cross-thematic raster database for ecological research in Switzerland

Abstract

Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.

Fig. 1

SWECO25 development workflow. 1) Identification and panel discussion about existing datasets. 2) Selection of ecologically relevant datasets meeting spatial requirements. 3) Standardization of selected datasets to SWECO25 standards. 4) Public upload on Zenodo (https://zenodo.org/communities/sweco25/).

Fig. 2

Overview of SWECO25 layer diversity and example illustrations. (a) Example layers extracted from three environmental categories, out of the ten available. (b) Example distance statistics layers made available for linear features (i.e., transportation and hydrological networks). (c) Example focal statistics layers computed using 13 measurement radii for 12 datasets. (d) Example scenarios layers for the chclim25 dataset for three radiative concentration pathways (RCPs).

Fig. 3

SWECO25 folder and file naming structure. In this example, the “tave” (temperature average) variable, from the “bioclim” (bioclimatic) category, in the “chclim25” dataset, for the “future” period, “2020_2049” sub period, and the “rcp45” scenario is stored in the folder “bioclim/chclim25/future/2020_2049/rcp45/tave”. The filename for this variable is “bioclim_chclim25_future_2020_2049_rcp45_tave.tif”.