Terrestrial water storage anomaly (TWSA) and groundwater storage anomaly (GWSA) data are of great importance for hydrological research and water resource management. However, products derived from the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On mission (GRACE-FO) are constrained by the satellite design and variation in processing strategies among different institutions, resulting in multiple suboptimal products. These products not only have a coarse spatial resolution but also suffer from a continuous 11-month gap from July 2017 to May 2018, which represent significant limitations for related research. To address these challenges, the Bayesian-based three-cornered hat (BTCH) method was initially employed to fuse low-uncertainty GRACE-derived TWSA and GWSA products with a coarse resolution (0.5°). Subsequently, based on the fused TWSA/GWSA products and multi-source datasets, a machine learning downscaling framework based on a physically constrained sliding window (MLDF-PCSW) was developed. This framework not only enhanced the spatial resolution of the products but also filled the data gap, ultimately producing a high-resolution water storage anomaly product (HWSA v1.0), a long-term, continuous dataset on a global scale (excluding Greenland and Antarctica; variables: TWSA and GWSA; spatial resolution: 0.05°; temporal resolution: monthly; period: April 2002 to December 2022). The results of uncertainty analysis using the three-cornered hat (TCH) method indicated that the TWSA derived from the Center for Space Research (CSR) exhibited the lowest uncertainty (23.91 mm) of the three original products, and the uncertainty decreased by 35.42% (15.44 mm) after data fusion using the BTCH method. The HWSA v1.0 dataset based on MLDF-PCSW exhibited high correlation coefficients (CCs) (0.999 and 0.999 for TWSA and GWSA, respectively) and a low root mean square error (RMSE) (0.68 mm and 1.24 mm for TWSA and GWSA, respectively) with the original TWSA and GWSA products, while also providing more detailed TWSA and GWSA spatial information. Independent validation for TWSA_DYY based on the water balance method has been done across six basins and the results are great, with the CCs between 0.69 and 0.92. The GWSA from the MLDF-PCSW demonstrated acceptable correspondence with global in-situ groundwater observations, with a mean CC of 0.27. The other four previously released gap-filled TWSA products also exhibited outstanding performance, with the average CC reaching 0.98 for the 11-month gap. Collectively, these results suggest that the HWSA v1.0 dataset based on the MLDF-PCSW has significant potential as a robust data foundation for related research.

中文翻译：

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基于物理约束滑动窗口技术的机器学习降尺度框架，用于提高全球水储存异常的分辨率


陆地水储量异常（TWSA）和地下水储量异常（GWSA）数据对于水文研究和水资源管理具有重要意义。然而，重力恢复和气候实验（GRACE）任务及其后续任务（GRACE-FO）的产品受到卫星设计和不同机构之间处理策略差异的限制，导致产生多个次优产品。这些产品不仅空间分辨率较粗，而且从2017年7月到2018年5月存在连续11个月的差距，这对相关研究造成了很大的限制。为了应对这些挑战，最初采用基于贝叶斯的三角帽（BTCH）方法来融合低不确定性的 GRACE 衍生的具有粗分辨率（0.5°）的 TWSA 和 GWSA 产品。随后，基于融合的TWSA/GWSA产品和多源数据集，开发了基于物理约束滑动窗口（MLDF-PCSW）的机器学习降尺度框架。该框架不仅增强了产品的空间分辨率，而且填补了数据空白，最终产生了高分辨率的水储存异常产品（HWSA v1.0），这是一个全球范围内（不包括格陵兰岛和格陵兰岛）的长期连续数据集。南极洲；变量：TWSA 和 GWSA；空间分辨率：月度；周期：2002 年 4 月至 2022 年 12 月）。采用三角帽（TCH）方法进行不确定度分析结果表明，来自空间研究中心（CSR）的TWSA在三种原始产品中表现出最低的不确定度（23.91 mm），不确定度下降了35.42% (15.44 mm) 使用 BTCH 方法进行数据融合后。 HWSA v1。基于 MLDF-PCSW 的 0 数据集表现出高相关系数（CC）（TWSA 和 GWSA 分别为 0.999 和 0.999）和低均方根误差（RMSE）（TWSA 和 GWSA 分别为 0.68 mm 和 1.24 mm）独创TWSA和GWSA产品，同时还提供更详细的TWSA和GWSA空间信息。基于水平衡法的 TWSA_DYY 已在六个流域进行了独立验证，结果很好，CC 在 0.69 至 0.92 之间。 MLDF-PCSW 的 GWSA 证明了与全球原位地下水观测的可接受的一致性，平均 CC 为 0.27。之前发布的其他四款填补空白的TWSA产品也表现出色，11个月的空白期平均CC达到0.98。总的来说，这些结果表明基于 MLDF-PCSW 的 HWSA v1.0 数据集作为相关研究的强大数据基础具有巨大的潜力。