用图神经网络重建1940年以来的全球水储量变化，揭示气候事件影响。
Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
- 基于气象数据与卫星观测构建时空图神经网络，捕捉水文耦合关系。
- 网格相关性达0.69，流域均值相关性0.94，重现2015/16厄尔尼诺等事件。
- 仅需少量输入变量，模型简洁高效，适合长期水循环研究者使用。
陆地水储量（TWS）整合了积雪、土壤湿度、地表水和地下水，是气候变化与人类活动重塑全球水循环的关键指标。GRACE与GRACE-FO卫星任务提供了自2002年起唯一直接、全球一致的TWS变化观测记录，但时长不足以支持多数气候尺度分析。本文提出一种深度学习方法，通过学习每日ERA5气象强迫（降水、蒸散发、径流）与月度GRACE观测之间的关系，将月度GRACE类水储量异常（TWSA）重建至1940年。不同于以往基于网格单元回归、CNN或LSTM的重建方法，我们采用多变量时间序列图神经网络（MTGNN）架构，该模型原用于城市传感器网络的交通预测，现首次应用于卫星重力测量任务。空间依赖关系由一个静态、可解释的混合邻接矩阵编码，结合地理距离与气候时间序列的滞后相关性，同时捕捉局部水文耦合与大尺度遥相关。重建结果在网格单元层面达到0.69的皮尔逊相关系数，流域均值达0.94，偏差接近零，并成功再现2015/16厄尔尼诺与2020/21拉尼娜事件的空间指纹。与现有方法（GTWS-MLrec、RM-REC、GRAiCE）系统的对比显示，该图模型在流域尺度上统计表现相当，相关性仅比最优基线低0.025，且仅需其一半至十分之一的预测变量；所有模型在干旱区域均有明显弱点。完整代码已开源：github.com/hcu-cml/MTGNN-TWS-Reconstruction-GRACE。
原文摘要 · Abstract (English)
Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle. The GRACE and GRACE-FO satellite missions provide the only direct, globally consistent observations of TWS change, but their record only begins in 2002 which is too short for many climate-scale analyses. We present a deep learning application that reconstructs monthly GRACE-like TWS anomalies (TWSA) back to 1940 by learning the relationship between daily ERA5 meteorological forcing (precipitation, evapotranspiration, runoff) and monthly GRACE observations. In contrast to prior reconstruction approaches based on grid-cell-wise regression, CNNs, or LSTMs, we adapt a multi-variate time series graph neural network (MTGNN) architecture, which was originally developed for mobility and traffic forecasting on urban sensor networks to this satellite-geodesy task. Spatial dependencies are encoded in a static, interpretable hybrid adjacency matrix that combines geodesic proximity with lagged correlations of climatic time series, capturing both local hydrological coupling and large-scale teleconnections. The reconstruction achieves a grid-cell Pearson correlation of 0.69, a basin-mean correlation of 0.94, and a near-zero bias, and it reproduces the spatial fingerprints of the 2015/16 El Niño and 2020/21 La Niña events. A systematic comparison with established reconstruction approaches (GTWS-MLrec, RM-REC, GRAiCE) shows that the graph-based model is statistically competitive at basin scale, reaching a correlation within 0.025 of the best baseline while using only roughly half to a tenth of the predictors the other models require and revealing characteristic weaknesses in arid regions in all models. The complete implementation is publicly available at github.com/hcu-cml/MTGNN-TWS-Reconstruction-GRACE
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