Wang Jielong, Shen Yunzhong, Awange Joseph, Tabatabaeiasl Maryam, Song Yongze, Liu Chang
College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China; School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University, Perth, WA, Australia.
College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, PR China.
Sci Total Environ. 2025 Mar 15;969:178874. doi: 10.1016/j.scitotenv.2025.178874. Epub 2025 Feb 24.
The coarse spatial resolution of about 300 km in Total Water Storage Anomalies (TWSA) data from the Gravity Recovery And Climate Experiment (GRACE) and its follow-on (GRACE-FO, hereafter GRACE) missions presents significant challenges for local water resource management. Previous approaches to addressing this issue through statistical downscaling have been limited by the reliance on the scale-invariance assumption, residual correction, hydrological models, and a lack of consideration for spatial correlations among the TWSA grids. This study introduces the DownGAN generative adversarial network, which downscales GRACE TWSA to 25 km, as exemplified in the Yangtze River Basin (YRB) and the Nile River Basin (NRB). Additionally, we propose a novel downscaling scheme to address the above limitations. DownGAN receives static and dynamic variables as inputs while considering their potential time-delay effects. The downscaled TWSA is validated using a synthetic example, in-situ runoff, groundwater levels, and two hydrological models. The potential benefits of the downscaled TWSA in closing the water balance budget and monitoring hydrological droughts in the YRB and NRB are explored. The synthetic example indicates that DownGAN trained using the proposed downscaling scheme can downscale the YRB and NRB's TWSA from 1° to 0.5° and 0.25°, respectively. DownGAN outperforms RecNet, a fully convolutional neural network, producing continuous, consistent, and realistic downscaled TWSA. The downscaled TWSA exhibits high correlations with the runoff and groundwater levels in the YRB and NRB, respectively. In addition, DownGAN demonstrates better performance in closing the water balance budget and monitoring drought events in both the YRB and NRB than HR GRACE mascon products, as evidenced by its higher correlations with the total water storage changes derived from the water balance equation and two drought indices, respectively. DownGAN is adaptable to other downscaling tasks and regions, offering a flexible downscaling factor, minimal assumptions, cost-effectiveness, and realistic predictions.
重力恢复与气候实验（GRACE）及其后续任务（GRACE后续任务，以下简称GRACE）的总储水量异常（TWSA）数据约300公里的粗略空间分辨率给当地水资源管理带来了重大挑战。以往通过统计降尺度来解决这一问题的方法受到尺度不变性假设、残差校正、水文模型的限制，并且没有考虑TWSA网格之间的空间相关性。本研究引入了DownGAN生成对抗网络，将GRACE TWSA降尺度至25公里，以长江流域（YRB）和尼罗河流域（NRB）为例进行了说明。此外，我们提出了一种新颖的降尺度方案来解决上述局限性。DownGAN在考虑静态和动态变量潜在时间延迟效应的同时，将其作为输入。使用一个综合示例、原位径流、地下水位和两个水文模型对降尺度后的TWSA进行了验证。探讨了降尺度后的TWSA在长江流域和尼罗河流域闭合水平衡预算和监测水文干旱方面的潜在益处。综合示例表明，使用所提出的降尺度方案训练的DownGAN可以分别将长江流域和尼罗河流域的TWSA从1°降尺度至0.5°和0.25°。DownGAN优于全卷积神经网络RecNet，生成连续、一致且逼真的降尺度TWSA。降尺度后的TWSA分别与长江流域和尼罗河流域的径流和地下水位具有高度相关性。此外，DownGAN在长江流域和尼罗河流域闭合水平衡预算和监测干旱事件方面的表现优于高分辨率GRACE质量块产品，这分别体现在其与水平衡方程得出的总储水量变化以及两个干旱指数的更高相关性上。DownGAN适用于其他降尺度任务和区域，提供灵活的降尺度因子、最少的假设、成本效益以及逼真的预测。
