Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios

Abstract

This study presents a global land projection dataset with a 1-km resolution that comprises 20 land types for 2015–2100, adopting the latest IPCC coupling socioeconomic and climate change scenarios, SSP-RCP. This dataset was produced by combining the top-down land demand constraints afforded by the CMIP6 official dataset and a bottom-up spatial simulation executed via cellular automata. Based on the climate data, we further subdivided the simulation products’ land types into 20 plant functional types (PFTs), which well meets the needs of climate models for input data. The results show that our global land simulation yields a satisfactory accuracy (Kappa = 0.864, OA = 0.929 and FoM = 0.102). Furthermore, our dataset well fits the latest climate research based on the SSP-RCP scenarios. Particularly, due to the advantages of fine resolution, latest scenarios and numerous land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.

Abstract

Measurement(s)	future land cover
Technology Type(s)	cellular automata • supervised machine learning
Factor Type(s)	spatial driving factors - socioeconomic (GDP, population, urban centre and road) • spatial driving factors - physical (temperature, precipitation, topography and soil quality)