一个机器学习方法总水储存异常扩展到1980年 (ML-TWiX)
Peyman Saemian1, Mohammad J Tourian2, Karim Douch3
1Institute of Geodesy, University of Stuttgart, Stuttgart, Germany. peyman.saemian@gis.uni-stuttgart.de.
Scientific data
|January 29, 2026
概括
ML-TWiX提供了一个新的全球数据集,记录了从1980年到2012年的每月总储水异常 (TWSA). 这扩展了卫星记录,有助于长期的气候和水文研究.
科学领域:
- 地球科学 地球科学 地球科学
- 水文学的水文学
- 气候科学 气候科学
背景情况:
- 像GRACE这样的卫星任务提供了关于总储水异常 (TWSA) 的有价值数据,但它们的时间有限限制了长期的气候和水文研究.
- 将TWSA记录延伸到GRACE前的时代对于对全球水资源和气候变化的全面分析至关重要.
研究的目的:
- 开发ML-TWiX,这是从1980年到2012年每月TWSA的全球数据集,扩展了GRACE任务之前的观测记录.
- 利用机器学习模型重建TWSA,整合水文和土地表面模型模拟.
- 为各种科学应用提供统一的TWSA产品与不确定性估计.
主要方法:
- 采用了三种机器学习模型:随机森林,XGBoost和高斯过程回归.
- 在全球水文和陆地表面模拟上训练模型,在0.5°×0.5°网格上重建月度TWSA.
- 来自单个模型的组合输出通过集合平均生成一个统一的数据集与不确定性量化.
主要成果:
- 成功重建了一个连续的全球数据集每月TWSA从1980年到2012年.
- 根据独立数据源验证了ML-TWiX数据集,包括卫星激光测距和海平面预算估计.
- 由此产生的数据集提供了空间显式不确定性估计.
结论:
- ML-TWiX为全球TWSA提供了有价值的,扩展的记录,对于长期的气候和水文研究至关重要.
- 该数据集支持广泛的应用,包括水资源评估和理解气候驱动的水文变化.
- 机器学习为重建历史气候和水文变量提供了一种强大的方法.
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