Input data set: Future land-use patterns

Description:

Direct human forcings.

Time-varying historical crop-land areas for 6 land-use types: rain-fed crop land, irrigated crop-land, rain-fed bioenergy, irrigated bio-energy, pastures, natural vegetation and urban areas. For more specific information, thd data are provided in 5 different resolutions of land-use types:

landuse-totals: the land surface is broadly categorized into rainfed (cropland_rainfed) and irrigated cropland (cropland_irrigated), and into pastures (pastures)
landuse-5crops: the distribution of the five predominant crop types (c3ann, c3per, c4ann, c4per and c3nfx), delineating both occurences of rainfed/irrigated crops and occurences of nutrition/bioenergy crops
landuse-15crops: provides areas specific to either important species, e.g. wheat, or species
groups, such as pulses
landuse-pastures: pastures areas are split into managed pastures (managed_pastures) and rangeland (rangeland)
landuse-urbanareas: provides the per-cell coverage of urban areas

Data is available for the time period 2006-2099. For specifications on the different scenarios, please read the Specifications section below.

The transient LU patterns are based on patterns generated by the LU model MAgPIE (Popp et al., 2014a; Stevanović et al., 2016) where bionergy demand and greenhouse gas prices were provided by the MAGPIE-REMIND assessment, assuming population growth and economic development according to the SSP2 storyline (Popp et al 2017, Kriegler et al 2017).

LU patterns derived by MAgPIE are designed to ensure demand-fulfilling food production where demand is externally prescribed based on an extrapolation of historical relationships between population and GDP on national levels (Bodirsky et al., 2015).

In contrast to the standard SSP scenarios generated within the scenario process (Kriegler et al., 2016), LU changes assessed for ISIMIP2b additionally account for climate and atmospheric CO2 fertilization effects on the underlying patterns of potential crop yields, water availability and terrestrial carbon content. To this end the underlying crop, water, and biomes simulations by the LPJmL model are forced by atmospheric CO2 concentrations and patterns of climate change associated with RCP6.0 or RCP2.6, respectively. The LPJmL simulations account for the CO2 fertilization effect. Potential crop production under rain-fed conditions as well as full irrigation were generated by the global gridded crop component of LPJmL within the ISIMIP fast track (Rosenzweig et al., 2014) and used by MAgPIE to derive LU patterns under cost optimization (see time series of total crop land (irrigated vs. non-irrigated) in Figure 3).

Projections of climate change are taken from the four GCMs also used to force the other impacts projections within ISIMIP2b to ensure maximum consistency. As the MIROC5 climate input data were not part of the ISIMIP Fast Track, the associated crop yield projections by LPJmL were added analogously to the Fast Track simulations to calculate the associated LU patterns. Under an SSP2 storyline and based on the REMIND-MAgPIE Integrated Assessment Modelling Framework, RCP6.0 represents by a BAU greenhouse gas concentration pathway without explicit mitigation measures for the reduction of greenhouse gas emissions (Riahi et al., 2016).

Given lower emission targets, REMIND-MAgPIE is designed to derive an optimal mitigation mix under climate-policy settings, maximizing aggregate social consumption across the 21st century. To reach the low emissions RCP2.6 scenario from an RCP6.0 reference pathway, land-based mitigation measures are of great importance (Popp et al., 2014b). The REMIND_MAgPIE framework accounts for reduced emissions from LU change via avoided deforestation, reduction of non-CO2 emissions from agricultural production, and a strong expansion of bioenergy production combined with carbon capture and storage (BECCS, see total land area used for second-generation bioenergy production in Figure 3).