Spatial resolution

Land and its characteristics are the key elements of our modeling approach. In order to enable global bio-physical process modeling of agricultural and forest production, a comprehensive database has been built (Skalsky et al., 2008)¹, which contains geo-spatial data on soil, climate/weather, topography, land cover/use, and crop management (e.g. fertilization, irrigation). The data are available from various research institutes (NASA, JRC, FAO, USDA, IFRPI, etc.) and significantly vary with respect to spatial, temporal, and attribute resolutions, thematic relevance, accuracy, and reliability.

Therefore, data were harmonized into several common spatial resolution layers. Homogeneous Response Units (HRU) have been delineated by geographically clustering of 5 arcmin pixels according to only those parameters of the landscape, which are generally not changing over time and are thus invariant with respect to land use and management or climate change. At the global scale, we have included five altitude classes, seven slope classes, and six soil classes, resulting into 157 HRUs.

Source: Skalsky et al., 2008

In a second step, the HRU layer is intersected with a 0.5° × 0.5° grid and with country boundaries to delineate more than 200 000 Simulation Units (SimU) which contain other relevant information such as global climate data, land category/use data, irrigation data, etc. The SimUs are the basis for estimation of land use/management parameters in all GLOBIOM-supporting models like EPIC. In GLOBIOM, we can choose at which level of resolution it is run, and aggregate the inputs consistently.

¹ Skalský, R., Tarasovičová, Z., Balkovič, J., Schmid, E., Fuchs, M., Moltchanova, E., Kindermann, G. and Scholtz, P. (2008). GEO-BENE global database for bio-physical modeling v. 1.0. Concepts, methodologies and data.Technical Report, IIASA, Laxenburg. [download]

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