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.

We were initially a little disappointed that the error in the UpSampling model was higher than the baseline ResNet-50. We hypothesized that the source of this problem was a subtle issue: units in early layers in the DownSampling and UpSampling models have receptive fields that are too large. The receptive field of a unit in a CNN is the number of input pixels that it can see, or the number of input pixels that can possibly influence its activation. Indeed, after examining the strides and receptive fields of each layer in the network, we found that halfway through a vanilla ResNet-50, units have receptive fields of about 70. Just as far through our naively assembled UpSampling model the receptive fields are already 110px, larger because our DCT input layer has [stride, receptive field] of [8, 8] instead of the typical typical [1, 1] pixel input layer.