基于度预测模型的基础上,对石碳汇的估计方法
Yan Zhen1, Haodong Zheng1, Qiong Xiao2
1School of Geoscience and Technology, Southwest Petroleum University, Chengdu, 610500, China.
Journal of environmental management
|December 28, 2024
概括
这项研究使用先进的溶解无机碳 (DIC) 和溶解有机碳 (DOC) 模型准确估计了丽江河流域的石碳汇. 结果显示,虽然波动,但碳沉量在不断增长,空间分布稳定.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 气候变化研究 气候变化研究
背景情况:
- 化对大气和土壤的二氧化碳 (CO2) 水平产生重大影响.
- 精确量化石碳汇对于了解全球气候变化动态至关重要.
研究的目的:
- 在丽江河流域建立溶解无机碳 (DIC) 和溶解有机碳 (DOC) 度的预测模型.
- 估计丽江河流域的石灰岩碳汇的历史和未来趋势.
- 评估DIC和DOC度的空间和时间变化.
主要方法:
- 收集了14个月的DIC和DOC度数据.
- 碳度与环境因素 (海拔,坡度,面积,降雨量,温度) 之间的确定的关系.
- 使用堆叠组合模型 (随机森林,增强回归树,BP神经网络) 进行DIC和DOC预测.
- 利用溶解物负载方法估计了23年间的岩碳汇.
- 应用时间序列分析用于未来的碳汇预测.
主要成果:
- 开发了DIC (91%) 和DOC (83%) 的高精度预测模型.
- 预测DIC和DOC的稳定空间分布,与盆地石质学相关联.
- 据估计,丽江河流域的23年石碳汇呈现出具有显著波动的整体增长趋势.
- 未来的预测表明持续的波动性和趋势与历史数据一致.
结论:
- 开发的模型提供了精确和适用的估计,在流域规模的石碳汇.
- 岩过程在区域碳循环中发挥着重要作用,影响了气候变化减缓.
- 长期监测和先进的建模对于理解和预测岩地区碳汇动态至关重要.
关键词:
DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC DIC这是DOC DOC.石灰岩的碳汇,就是一个碳汇.机器学习是机器学习.时间序列预测时间序列预测更多相关视频
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