Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations
Bridging the gap between GRACE and GRACE-FO missions with deep learning aided water storage simulations
复制标题
通过深度学习辅助储水模拟缩小 GRACE 和 GRACE-FO 任务之间的差距
DOI：
10.1016/j.scitotenv.2022.154701
复制
发表时间：
2022
影响因子：
9.8
通讯作者：
Mercan, Hüseyin
中科院分区：
文献类型：
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作者：
Uz, Metehan;Atman, Kazım Gökhan;Akyilmaz, Orhan;Shum, C.K.;Keleş, Merve;Ay, Tuğçe;Tandoğdu, Bihter;Zhang, Yu;Mercan, Hüseyin
在GRACE(重力恢复和气候实验)与其后续GRACE-FO(后续任务)之间长达11个月的间隔期间，每月高分辨率陆地水储存异常(TWSA)失踪。GRACE类TWSA序列的连续性和相称精度对于改进全球和区域尺度的水文模型具有重要意义。虽然以前已经进行了弥合这一差距的努力，但没有达到类似GRACE的空间分辨率和/或精度，但全球范围内的高质量TWSA模拟仍然缺乏。在这里，我们使用了一套深度学习(DL)架构、卷积神经网络(CNN)、深度卷积自动编码器(DCAE)和贝叶斯卷积神经网络(BCNN)，训练数据集包括GRACE/-FO mascon和Sarm重力测量、ECMWF再分析-5数据、归一化时间标签信息，以比GRACE/-FO高得多的分辨率(100公里全波长)重建全球陆地TWSA地图，并有效地弥合了全球11个月的数据差距。与以前的研究相反，我们没有应用事先的去趋势或去季节性，以避免由年际或更长的气候信号和极端天气事件引起的模拟的偏差/混叠。我们展示了群体和时间输入的贡献，它们显着改善了TWSA模拟，特别是在趋势分量的正确预测方面。我们的结果还表明，在填补地球物理信号时空时间序列中的大数据缺口时，必须使用独立数据进行外部验证，以保持模拟结果的稳健性。结果表明，DCAE具有较好的性能，并与前人的研究和所采用的DL方法进行了比较。我们基于DCAE的TWSA模拟使用独立的数据集进行验证，包括现场地下水位、干涉合成孔径雷达测量的地面沉降率(例如中央山谷)、在GAP范围内发生的严重山洪暴发(例如南亚洪水)和干旱(例如北美大平原)事件的发生/时间，显示出良好的一致性。
The monthly high-resolution terrestrial water storage anomalies (TWSA) during the 11-months of gap between GRACE (Gravity Recovery And Climate Experiment) and its successor GRACE-FO (-Follow On) missions are missing. The continuity of the GRACE-like TWSA series with commensurate accuracy is of great importance for the improvement of hydrologic models both at global and regional scales. While previous efforts to bridge this gap, though without achieving GRACE-like spatial resolutions and/or accuracy have been performed, high-quality TWSA simulations at global scale are still lacking. Here, we use a suite of deep learning (DL) architectures, convolutional neural networks (CNN), deep convolutional autoencoders (DCAE), and Bayesian convolutional neural networks (BCNN), with training datasets including GRACE/-FO mascon and Swarm gravimetry, ECMWF Reanalysis-5 data, normalized time tag information to reconstruct global land TWSA maps, at a much higher resolution (100 km full wavelength) than that of GRACE/-FO, and effectively bridge the 11-month data gap globally. Contrary to previous studies, we applied no prior de-trending or de-seasoning to avoid biasing/aliasing the simulations induced by interannual or longer climate signals and extreme weather episodes. We show the contribution of Swarm and time inputs which significantly improved the TWSA simulations in particular for correct prediction of the trend component. Our results also show that external validation with independent data when filling large data gaps within spatio-temporal time series of geophysical signals is mandatory to maintain the robustness of the simulation results. The results and comparisons with previous studies and the adopted DL methods demonstrate the superior performance of DCAE. Validations of our DCAE-based TWSA simulations with independent datasets, including in situ groundwater level, Interferometric Synthetic Aperture Radar measured land subsidence rate (e.g. Central Valley), occurrence/timing of severe flash flood (e.g. South Asian Floods) and drought (e.g. Northern Great Plain, North America) events occurred within the gap, reveal excellent agreements.