用机器学习将低分辨率地下水数据提升至2公里精度，助力孟加拉国水资源管理
GroundHog: Revolutionizing GLDAS Groundwater Storage Downscaling for Enhanced Recharge Estimation in Bangladesh
- 先用机器学习补全缺失数据，再通过升维模型生成高分辨率地下水位图
- 最高预测准确率达R² 0.963，整体升尺度结果达R² 0.96
- 无需官方水井数据即可独立应用，适合缺观测站点地区
长期地下水位（GWL）监测对政策制定和年极值法估算补给至关重要。但现有方法多关注短期预测，缺乏多年适用性；且实测点稀疏，导致依赖低分辨率卫星数据（如GLDAS）作为机器学习的真值，限制了精度。为此，我们首先构建机器学习模型填补数据空白，最大值与最小值预测的R²分别达0.855和0.963。随后，以预测结果和井位观测为真值，训练一个上采样模型，输入25公里分辨率的GLDAS数据，输出2公里分辨率的地下水位，整体R²达0.96。该方法成功实现了2003–2024年全球陆地水文分析系统（GLDAS）地下水储量的高分辨率重建，支持精细化补给估算，揭示关键趋势，为前瞻性资源管理提供依据。本方法可独立于官方水井数据，在任意点实现地下水储量从低分辨率到高分辨率的上采样，是决策支持的重要工具。
原文摘要 · Abstract (English)
Long-term groundwater level (GWL) measurement is vital for effective policymaking and recharge estimation using annual maxima and minima. However, current methods prioritize short-term predictions and lack multi-year applicability, limiting their utility. Moreover, sparse in-situ measurements lead to reliance on low-resolution satellite data like GLDAS as the ground truth for Machine Learning models, further constraining accuracy. To overcome these challenges, we first develop an ML model to mitigate data gaps, achieving $R^2$ scores of 0.855 and 0.963 for maximum and minimum GWL predictions, respectively. Subsequently, using these predictions and well observations as ground truth, we train an Upsampling Model that uses low-resolution (25 km) GLDAS data as input to produce high-resolution (2 km) GWLs, achieving an excellent $R^2$ score of 0.96. Our approach successfully upscales GLDAS data for 2003-2024, allowing high-resolution recharge estimations and revealing critical trends for proactive resource management. Our method allows upsampling of groundwater storage (GWS) from GLDAS to high-resolution GWLs for any points independently of officially curated piezometer data, making it a valuable tool for decision-making.
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