Resumen
The groundwater (GW) and surface water (SW) interaction (SW-GW) through the hyporheic zone is a significant component in sustainable water resource management. The complexities in SW-GW interactions increase from a local to a regional scale and are affected by variation in hydraulic, hydrologic, and hydrogeologic (3H) processes. Controlling factors and their upscaling of these processes to assess SW-GW interaction have not been addressed sufficiently in previous studies. Additionally, it is unclear what the effective factors are at different scales during the upscaling. Therefore, the present review focused on controlling factors of 3H processes in SW-GW interaction and their upscaling techniques. Relevancy of controlling factors was identified at different scales. Applications of different approaches and their uncertainties were also discussed for the characterization of SW-GW interactions. The study revealed that the improved data from different approaches is crucial for machine learning training and its application in the SW and GW assessment at local, sub-catchment, and catchment scales. Based on the outcomes, a framework has been proposed to execute modalities of controlling factors using remote sensing, geophysics, and artificial intelligence. The proposed framework could help in handling big data and accurate upscaling for water resource management.