Abstract

Currently, socioeconomic development and climate change pose new challenges to the assessment and management of terrestrial carbon storage (CS). Accurate prediction of future changes in land use and CS under different climate scenarios is of great significance for regional land use decision-making and carbon management. Taking the Yellow River Basin (YRB) in China as the study area, this study proposed a framework integrating the land use harmonization2 (LUH2) dataset, the patch-generating land use simulation (PLUS) model, and the integrated valuation of ecosystem services and trade-offs (InVEST) model. Under this framework, we systematically analyzed the spatiotemporal evolution characteristics of land use and their impact on CS in the YRB from 1992 to 2050. The results showed that (1) CS was highest in forestland and lowest in construction land, with a spatial distribution of high in the south and low in the north. From 1992 to 2020, construction land, forestland, and grassland increased while cropland decreased, reducing the total CS by 74.04 Tg. (2) From 2020 to 2050, under SSP1–2.6 scenario, forestland increased by 158.87 %; under SSP2–4.5 scenario, unused land decreased by 65.55 %; and under SSP5–8.5 scenario, construction land increased by 13.88 %. By 2050, SSP1–2.6 scenario exhibited the highest CS (8105.25 Tg), followed by SSP2–4.5 scenario (7363.61 Tg), and SSP5–8.5 scenario was the lowest (7315.86 Tg). (3) Forestland and construction land were the most critical factors affecting the CS. Shaanxi and Shanxi had the largest CS in all scenarios, and Qinghai had a huge carbon sink potential under SSP1–2.6 scenario. Scenario modeling demonstrated that future climate and land-use changes would have significant impacts on terrestrial CS in the YRB, and green development pathways could strongly contribute to meeting the dual‑carbon target. Overall, this study provides a scientific basis for promoting low-carbon development, land-use optimization, and ecological civilization construction in YRB, China.

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