Future land-use pattern projections and their differences within the ISIMIP3b framework

Abstract. Land use is a key human driver affecting Earth’s biogeochemical cycles, hydrology, and biodiversity. Therefore, projecting future land use is crucial for global change impact analyses. This study compares harmonized land-use and management trends, analyzing uncertainties through a three-factor variance analysis involving socioeconomic-climate scenarios, land-use models, and climate models. The projected patterns are used as human-forcing inputs for the Intersectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b) and multiple impact modeling teams. We employ two models (IMAGE and MAgPIE) to project future land use and management under three socioeconomic-climate scenarios (SSP1-RCP2.6, SSP3-RCP7.0, and SSP5-RCP8.5), driven by impact data like yields, water demand, and carbon stocks from updated climate projections of five global models, considering CO₂ fertilization effects. On the global level, in the SSP1-RCP2.6 scenario (low adaptation and mitigation challenges), there is high agreement among land-use models on land-use trends. However, significant differences exist in management-related variables, such as the area allocated for second-generation bioenergy crops. Uncertainty in land-use variables increases with higher spatial resolution, particularly concerning the locations where cropland and grassland shrinkage could occur under this scenario. In SSP5-RCP8.5 and SSP3-RCP7.0, differences among land use models in global and regional trends are primarily associated with grassland area demand. Concerning the variance analysis, the selection of climate models minimally affects the variance in projections at different scales. However, the influence of the socioeconomic-climate scenarios, the land-use model, and interactions among the underlying factors on projected uncertainty varies for the different land-use and management variables. Our results highlight the need for more intercomparison exercises focusing on future spatially explicit projections to enhance understanding of the intricate interplay between human activities, climate, socioeconomic dynamics, land responses, and their associated uncertainties on the high-resolution level as models evolve.