Abstract The Gravity Recovery and Climate Experiment (GRACE) satellites provide unprecedented perspectives to hydrologists and geoscientists for observing and understanding the variation of terrestrial water storage (TWS) at continental to global scales. However, there are few reliable datasets of past TWS variations before GRACE observations were available (pre-2002). To fill this gap, we attempt to develop an approach to calibrate TWS anomalies (TWSA) data of past decades based on available GRACE solution and land surface model simulations, and a case study was conducted at the Nile River basin. Two ensemble learning algorithms, the Random Forest (RF) and the eXtreme Gradient Boost (XGB), combined with a spatially moving window structure, are used to build the reconstruction model, respectively. Reconstructed TWSA are validated against a precipitation-evapotranspiration index as well as other GRACE-based reconstructed TWSA datasets. Results show that the XGB model performs slightly better than the RF model in reconstructing GRACE TWSA data. The TWSA produced by the two ensemble learning algorithms are comparable and better than other examined reconstructed GRACE-like datasets, and are well correlation with original GRACE solution and past precipitation-evapotranspiration series. The profile soil moisture and groundwater storage show significant contributions to the RF and XGB model, but their variable importance values present different spatial patterns in the RF and XGB model. Further experiments are expected to investigate the contribution of human-induced factors to simulate terrestrial water storage dynamics, especially in intensely managed basins. Rather than modifying the structure and inputs of land surface models, this study provides an alternative way of improving the TWSA estimations of global land surface models and extending time range of GRACE datasets. The experiments are expected to promote and enrich the integration of physical and machine-learning models for optimal simulations in geoscience research.

中文翻译：

---

通过校准地表模型模拟生成过去类似 GRACE 的陆地储水解决方案的数据驱动方法

摘要 重力恢复和气候实验（GRACE）卫星为水文学家和地球科学家提供了前所未有的视角，以观察和理解大陆到全球尺度陆地储水量（TWS）的变化。然而，在 GRACE 观测可用之前（2002 年之前），过去 TWS 变化的可靠数据集很少。为了填补这一空白，我们尝试开发一种方法，基于可用的 GRACE 解决方案和地表模型模拟来校准过去几十年的 TWS 异常 (TWSA) 数据，并在尼罗河流域进行了案例研究。两种集成学习算法，随机森林 (RF) 和极限梯度增强 (XGB)，结合空间移动窗口结构，分别用于构建重建模型。重建的 TWSA 已针对降水-蒸发蒸腾指数以及其他基于 GRACE 的重建 TWSA 数据集进行验证。结果表明，XGB 模型在重建 GRACE TWSA 数据方面的性能略好于 RF 模型。由两种集成学习算法产生的 TWSA 与其他检查的重建 GRACE 类数据集相当并优于其他数据集，并且与原始 GRACE 解和过去的降水 - 蒸散序列具有良好的相关性。剖面土壤水分和地下水储量对 RF 和 XGB 模型显示出显着贡献，但它们的可变重要性值在 RF 和 XGB 模型中呈现出不同的空间模式。预计进一步的实验将研究人为因素对模拟陆地储水动态的贡献，尤其是在严格管理的盆地中。本研究没有修改地表模型的结构和输入，而是提供了一种改进全球地表模型 TWSA 估计和扩展 GRACE 数据集时间范围的替代方法。预计这些实验将促进和丰富物理和机器学习模型的集成，以实现地球科学研究中的最佳模拟。