| 基于集成机器学习的祁连山草地全球变暖潜力预测 |
| 摘要点击 468 全文点击 15 投稿时间：2025-07-17 修订日期：2025-10-15 |
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| 中文关键词 祁连山草地生态系统 全球变暖潜力（GWP） 放牧强度 集成机器学习 气候情景 |
| 英文关键词 Qilian Mountain grassland ecosystem global warming potential（GWP） grazing intensity ensemble machine learning climate scenario |
| DOI 10.13227/j.hjkx.202507231 |
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| 中文摘要 |
| 祁连山草地生态系统作为重要碳储存区，其全球变暖潜力（GWP）预测对气候适应性管理至关重要. 基于1980~2024年多源数据，融合XGBoost、LightGBM、CatBoost和随机森林，构建集成的机器学习模型，预测2025~2060年在不同气候情景下的GWP动态. 结果表明：①集成机器学习模型性能最优，决定系数（R2）为0.938，均方根误差（RMSE）显著低于单一模型，能够有效刻画草地生态系统复杂的非线性关系；②土壤黏粒与粉粒含量及放牧强度是影响GWP的关键驱动因子，其中放牧强度对GWP的影响存在阈值效应；③未来情景预测显示，在SSP5-8.5情景下，GWP呈持续上升趋势，年际波动性增强，高放牧区域风险显著提升，2060年超过42 g·（m2·a）-1，而SSP1-2.6低排放情景下则保持平稳. 空间格局上，GWP呈“东南高和西北低”分布，在高排放情景下东南部高值区呈扩展态势. 研究结果可为典型高原草地全球变暖风险识别、生态系统管理分区与低碳路径制定提供理论依据与技术支持. |
| 英文摘要 |
| The Qilian Mountain grassland ecosystem serves as an important carbon reservoir， and accurate prediction of its global warming potential （GWP） is essential for climate-adaptive management. Based on multi-source data from 1980 to 2024， an ensemble machine learning model integrating XGBoost， LightGBM， CatBoost， and RF was developed to predict GWP dynamics under different climate scenarios during 2025-2060. The results indicate that： ① The ensemble machine learning model achieved the best performance， with a coefficient of determination （R2） of 0.938 and a root mean square error （RMSE） significantly lower than those of individual models， effectively capturing the complex nonlinear relationships of the grassland ecosystem. ② Soil clay and silt contents， together with grazing intensity， were identified as the key driving factors of GWP， among which grazing intensity exhibited a threshold effect. ③ Future scenario simulations suggest that under the SSP5-8.5 high-emission pathway， GWP shows a continuous upward trend with intensified interannual fluctuations， and the risk in high-grazing areas increases substantially， exceeding 42 g·（m2·a）-1 by 2060， while under the SSP1-2.6 low-emission pathway， GWP remains relatively stable. In terms of spatial patterns， GWP demonstrates a “higher in the southeast and lower in the northwest” distribution， with the southeastern high-value areas expanding further under high-emission scenarios. These findings provide theoretical support and technical guidance for global warming risk assessment， ecosystem management zoning， and low-carbon pathway development in typical alpine grasslands. |
