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时间调整方法用于对土壤有机碳的高分辨率大陆规模建模

Laxman Bokati¹, Anil Somenahally^2,3, Saurav Kumar⁴

¹School of Sustainable Engineering and Built Environment, Arizona State University, 777 E University Dr., 85287, AZ, Tempe, USA.

Scientific reports

|February 22, 2025

概括

这项研究引入了一种新的方法来调整土壤有机碳 (SOC) 数据以适应时间变化,提高机器学习模型的准确性. 这种方法提高了SOC股票预测,有利于碳激励计划.

科学领域:

环境科学环境科学
土壤科学土壤科学
数据科学数据科学数据科学

背景情况:

有限且分布不均的开源遗留数据阻碍了土壤有机碳 (SOC) 模型培训.
大规模数据驱动的SOC建模往往忽略了时间变化,影响了准确性.
SOC数据的时间漂移需要为有效的机器学习应用程序进行调整.

研究的目的:

开发和评估一种创新方法,用于创建基于近距离的距离矩阵.
为调整 SOC 观测生成空间解析的时间转移预测.
通过考虑时间动态来提高SOC股票预测的准确性.

主要方法:

利用来自连续美国 (CONUS) 的遗留数据来构建基于近距离的距离矩阵.
生成空间解析的时间转移预测,以调整SOC观测到目标日期.
对1980年和2020年参考年进行时间调整和没有时间调整的SOC库存估计进行了比较.

主要成果:

在有时间调整和没有时间调整的SOC股票预测之间观察到显著差异 (SOC_no-adj与SOC₂₀₂₀).
在CONUS农田的基线SOC库存估计高于SOC_no-adj (14.49 Pg C),而不是SOC₂₀₂₀ (13.29 Pg C).
牧场土地显示较高的SOC_no-adj (15.49 Pg C) 与SOC₂₀₂₀ (14.22 Pg C) 相比,而森林土地显示SOC_no-adj (39.52 Pg C) 与SOC₂₀₂₀ (40.83 Pg C) 相比.

结论:

该研究证实了提高SOC库存预测的拟议方法论的有效性和有效性.
时间调整方法显著提高了用于机器学习模型的SOC数据集的准确性.
结果对碳激励计划的利益相关者来说是有价值的,包括农民,科学家,政策制定者和工业.

关键词:

农作物种植区农作物种植区当前的碳库存目前的碳库存土地使用变化经时间调整的预测.

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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.