Integrated remote sensing and observation methods to prioritise water well rehabilitation

Water shortage in arid and semi-arid regions has made groundwater the primary source of water supply. However, the performance of water wells gradually declines due to physical, chemical or biological clogging. Identifying wells with the highest rehabilitation potential is of significant practical and economic importance. This study proposes advanced environmental impact assessment methodologies to prioritise water well rehabilitation. Specifically, remote sensing-based (RSB) and observation-based (OB) assessment approaches are developed to determine high-priority well revival cases. The proposed RSB methodology selects wells based on land-subsidence values derived from the differential interferometric synthetic-aperture radar technique. These values are used as indicators to classify well rehabilitation potential, considering aquifer stratification and sand pumping characteristics. In the OB assessment stage, dynamic water level, well depth, submersible pump type and well videometry data are incorporated to evaluate well conditions. A theoretical model is introduced to predict dynamic water levels and identify wells exhibiting the most significant variations in the well performance index. The results show that the proposed model effectively captures the relationships among dimensionless parameters, explaining 91.2% of the total variance, while cross-validation confirms its robustness and applicability to new data sets. Furthermore, different well clogging types were identified, and appropriate rehabilitation strategies were recommended.