Accurate representation of the hydrological cycle under environmental change and anthropogenic stressors remains a critical challenge for water availability assessments and extreme event predictions, yet current physical models inadequately represent human management impacts and are computationally prohibitive at global scales. We demonstrate an approach that integrates deep learning-based digital twin (DT) models with land reanalysis products from the NASA Land Information System, remote sensing observations, and seasonal forecasts to enable robust scenario development for future water risks. By leveraging data assimilation methods that have proven effective for characterizing historical human interventions, we extend this capability forward through machine learning architectures trained on comprehensive land surface reanalysis datasets assimilating multi-source satellite observations. This DT technology-driven platform capitalizes on advancements in physics-based modeling, data assimilation, deep learning, parallel computing, and transfer learning to overcome the fundamental limitation of unobserved anthropogenic impacts in traditional hydrologic simulations, delivering enhanced quantitative assessments of water availability, floods, and droughts across diverse global hotspot regions where environmental and human processes intersect.