A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China
A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China
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DOI：
10.1109/tgrs.2024.3349548
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发表时间：
2024
影响因子：
8.2
通讯作者：
Songwei Gu;Yun Zhou;Long Zhao;Mingguo Ma;X. She;Lifu Zhang;Yao Li
中科院分区：
文献类型：
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作者：
Songwei Gu;Yun Zhou;Long Zhao;Mingguo Ma;X. She;Lifu Zhang;Yao Li
重力恢复和气候实验（GRACE）卫星为监测大规模陆地储水（TWS）变化提供了前所未有的工具。然而，在西南等水文地质环境复杂的地区，其分辨率较低，限制了其有效性。为了解决这一限制，我们提出了一种新的方法来提高GRACE观测的空间分辨率。我们的方法利用深度学习降尺度模型，该模型集成了生成对抗网络（gan）和变形注意力机制，以导出TWS变化的空间模式。为了提高GRACE数据的分辨率和精度，该模型结合了GRACE数据估计的总储水量变化和一些水文变量，包括数字高程模型（DEM）、土壤湿度、蒸散发、温度和降水。通过实现该方法，我们成功地将GRACE观测的空间分辨率从0.25°提高到0.05°。该神经网络降尺度模型能准确表征局部储水量变化，NSE值在0.58 ~ 0.92之间。此外，该模型不仅显著提高了空间分辨率，而且保持了空间分布，为区域水资源管理和促进小尺度水文研究提供了有价值的见解。研究结果对可持续水资源管理和气候变化评估具有重要意义。
The Gravity Recovery and Climate Experiment (GRACE) satellite provides an unprecedented tool for monitoring large-scale terrestrial water storage (TWS) changes. Yet, its coarse resolution restricts its effectiveness in areas with complex hydrogeological environments, such as southwestern China. To address this limitation, we propose a novel method to improve the spatial resolution of GRACE observations. Our approach leverages a deep learning downscaling model that integrates generative adversarial networks (GANs) and transformer attention mechanisms to derive the spatial patterns of TWS variations. The model incorporates the estimated total water storage changes from GRACE and some hydrological variables—including the digital elevation model (DEM), soil moisture, evapotranspiration, temperature, and precipitation—to enhance the resolution and accuracy of GRACE data. By implementing this method, we successfully increased the spatial resolution of GRACE observations from 0.25° to 0.05°. The advanced neural network downscaling model can accurately characterize local water storage variations, with Nash–Sutcliffe efficiency (NSE) values ranging from 0.58 to 0.92. Moreover, this model not only significantly increases the spatial resolution but also maintains the spatial distribution, offering valuable insights for regional water resources management and fostering small-scale hydrological research. The results have profound implications for sustainable water resources management and climate change assessment.