Multi-scenario Prediction of Land Use Change and Carbon Storage in Shaanxi Province Based on the SD-PLUS Coupled Model

Abstract

[Objective] This study was aimed to explore the impact of different land use patterns on regional ecosystem carbon storage. [Methods] In this paper, using the SD-PLUS coupled model and the coupled shared socio-economic path and representative concentration path (SSP-RCP) scenario proposed by the Coupled Model Intercomparison Project Phase 6 (CMIP6), the land use changes in Shaanxi province in 2030 were projected, and then the different future scenarios simulated by the InVEST model were used to predict the Shaanxi Province’s carbon storage and its spatial distribution under different future scenarios simulated by the InVEST model. [Results] (1) The SD model constructed was tested historically, and its error was less than 5%, and the Kappa index of land use in 2020 simulated by the PLUS model was 0.86, and the accuracy and reliability of the model generally met the requirements. (2) Under the three scenarios, the area of future construction land increased, and the scenarios with the lowest to highest growth rates were: SSP126, SSP245, SSP585. Under all scenarios, the area of forest land increased, and the area of watersheds remained stable; the area of grassland had a small increase under the SSP126 scenario, and decreased in other scenarios; and the area of arable land decreased in all three scenarios. (3) Carbon stock in Shaanxi Province decreased under all three scenarios, with the decrease in carbon stock in the Guanzhong plain being the main reason for the decrease in carbon stock in Shaanxi province. [Conclusion] The area of ecological land occupied by the expansion of construction land is the smallest under the SSP126 scenario, which takes into account both socio-economic development and the need for ecological protection, and can provide a reference model for the future protection of land resources and high-quality development in Shaanxi province.

Keywords

• land use change
• sd-plus coupled model
• future scenario prediction
• carbon storage
• invest model