By integrating with land use demands generated from IAMs, CA models are capable of simulating scenario-based gridded LULC datasets with a finer resolution (normally 1 km). Moreover, the deviations in LULC projections may cause a spread of uncertainty in earth system models (ESMs), thus jeopardizing the accuracy of ESM output data as well as the applicability of land use products 11, 17.Ĭellular automata (CA) have proved to be efficient in allocating land use to more spatially-explicit details, which are capable of defining the rules of cell-to-cell transformation as well as their adaptive behaviors and thus simulating the complex geospatial patterns. 15, some widely-adopted land cover products even at a grid scale of 10 km may yield substantial deviations when depicting the geospatial changes of land cover patterns, which may in particular cause severe distortions to global urban land patterns 16. Yet, the spatial resolution in these studies, ranging in a wide spectrum from five arc minutes to 0.5° 8, 9, 10, 11, 12, 13, 14, is not sufficient to capture the geographic heterogeneity of land use dynamics. 10 further refined the spatial information of global LULC from 2015 to 2100 under an enriched package of SSP-RCP scenarios by developing the Demeter tool, and downscaled future land use demands obtained from GCAM to a 0.05° resolution. To improve the spatial resolution, the Land Use Harmonization project as part of the CMIP6 produced future-scenario-based global LULC on a 0.5° grid for Land Use Harmonization Version 1 (LUH1) 8 and later boosted the spatial resolution to 0.25° for LUH2 9. Therefore, IAM-based LULC projections suffer from a lack of geospatial details or a coarse resolution 5, 6, 7. For instance, AIM simulates future regional land use dynamics by categorizing the world into 17 geopolitical regions 4, as compared to a classification of 26 world regions in the MAGNET model 5. Furthermore, global LULC at a suitable geospatial resolution serves as a key input of the earth system model, which represents a crucial component for simulating the geographic heterogeneity of earth system dynamics as well as anthropogenic environmental impacts.Įarly LULC projections mostly forecast global land use demands at subregional levels, which are generally obtained from integrated assessment models (IAMs) such as AIM (Asia‐Pacific integrated model), modular applied general equilibrium tool (MAGNET), Integrated Model to Assess the Global Environment (IMAGE), and Global Change Assessment Model (GCAM). Predicting global-scale land use dynamics under future socio-economic and climate scenarios is essential for implementing effective land utilization decisions towards sustainable development goals. This trend may continue to intensify in the foreseeable future with the ongoing population growth and economic development. From 1982 to 2016, 60% of land transformation was due to anthropogenic activities such as the invasion of cropland and built-up area 3. Land use and land cover (LULC) change reflects the intricate interaction between climate change and intensive human activities 1 and is closely correlated to various terrestrial processes such as biodiversity, earth surface energy balance, atmospheric circulation, and carbon cycle 2. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction.
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