Global C-Factor Estimation: Inter-Model Comparison and SSP-RCP Scenario Projections to 2070
文献类型：期刊论文
| 作者 | Xiong, Muqi |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-12-18 |
| 卷号 | 17期号:24页码:4059 |
| 关键词 | cover-management factor C-factor USLE RUSLE SSP-RCP LUH2 FVC future scenarios |
| DOI | 10.3390/rs17244059 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Highlights What are the main findings? The land use/land cover-based model (CLu) outperforms other methods in global C-factor estimation, demonstrating the highest correlation with the reference model and the lowest error. It is suitable for large-scale analysis under future scenarios. Using a 2015 baseline, global average C-factor values are projected to increase across all SSP-RCP scenarios. By 2070, 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas identified as hotspot regions for significant changes. What are the implications of the main findings? Low-income countries are highly dependent on development pathways, with C-factor values in hotspot regions decreasing by -50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5, relative to the 2015 baseline. These results highlight the urgent need for sustainable land-use policies, particularly in low-income countries, which exhibit the highest magnitude of both improvement and degradation under varying scenarios.Highlights What are the main findings? The land use/land cover-based model (CLu) outperforms other methods in global C-factor estimation, demonstrating the highest correlation with the reference model and the lowest error. It is suitable for large-scale analysis under future scenarios. Using a 2015 baseline, global average C-factor values are projected to increase across all SSP-RCP scenarios. By 2070, 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas identified as hotspot regions for significant changes. What are the implications of the main findings? Low-income countries are highly dependent on development pathways, with C-factor values in hotspot regions decreasing by -50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5, relative to the 2015 baseline. These results highlight the urgent need for sustainable land-use policies, particularly in low-income countries, which exhibit the highest magnitude of both improvement and degradation under varying scenarios.Abstract The cover-management factor (C-factor) plays a pivotal role in soil erosion control and is the most easily influenced by policymakers. Despite the availability of numerous C-factor estimation methods, systematic comparisons of their applicability and associated uncertainties remain limited, particularly for future projections under climate change scenarios. This study systematically evaluates multiple widely used C-factor estimation models and projects potential C-factor changes under future scenarios up to 2070, using 2015 as a baseline. Results reveal substantial spatial variability among models, with the land use/land cover-based model (CLu) showing the strongest correlation with the reference model (r = 0.960) and the lowest error (RMSE = 0.048). Using the CLu model, global average C-factor values are projected to increase across all Shared Socioeconomic Pathways-Representative Concentration Pathways (SSP-RCP) scenarios, rising from 0.077 to 0.079-0.082 by 2070. Statistically significant trends were observed in 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas, identified as hotspot regions (HRs). In these HRs, mean C-factor values are expected to increase by 16.1% and 33.4%, respectively, relative to the 2015 baseline. Economic development analysis revealed distinct trajectories across income categories. Low-income countries (LICs, World Bank classification) exhibited a pronounced dependency on development pathways, with C-factor values decreasing by -50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5 compared to 2015. In contrast, lower-middle-income, upper-middle-income, and high-income countries exhibited consistent C-factor increases across all scenarios. These variations were closely linked to cropland dynamics, with cropland areas in LICs decreasing by 64.6% under SSP1-RCP2.6 but expanding under other scenarios and income categories between 2015 and 2070. These findings highlight the critical importance of sustainable land-use policies, particularly in LICs, which demonstrate the highest magnitude of both improvement and degradation under varying scenarios. This research provides a scientific foundation basis for optimizing soil conservation strategies and land-use planning under future climate and socioeconomic scenarios. |
| URL标识 | 查看原文 |
| WOS关键词 | COVER-MANAGEMENT FACTOR ; SOIL-EROSION ; LAND-USE ; WATER EROSION ; CLIMATE-CHANGE ; RUSLE MODEL ; BASIN ; RIVER ; APPLICABILITY ; VALIDATION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001647391800001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219430]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Xiong, Muqi |
| 作者单位 | Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiong, Muqi. Global C-Factor Estimation: Inter-Model Comparison and SSP-RCP Scenario Projections to 2070[J]. REMOTE SENSING,2025,17(24):4059. |
| APA | Xiong, Muqi.(2025).Global C-Factor Estimation: Inter-Model Comparison and SSP-RCP Scenario Projections to 2070.REMOTE SENSING,17(24),4059. |
| MLA | Xiong, Muqi."Global C-Factor Estimation: Inter-Model Comparison and SSP-RCP Scenario Projections to 2070".REMOTE SENSING 17.24(2025):4059. |
入库方式： OAI收割
来源：地理科学与资源研究所
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