Unified multi-task learning for hydrological processes using a shared transformer framework

Most deep learning studies in hydrology adopt single-task frameworks that address individual variables such as rainfall or streamflow independently, limiting opportunities for shared learning across related environmental processes. This study introduces a unified multi-task, multi-modal deep learning framework capable of performing both 24-hour horizon streamflow forecasting and rainfall temporal super-resolution within a shared architecture. The model employs a shared Transformer encoder with task-specific decoders to integrate temporal and spatial hydrological information within a single architecture. To assess the influence of joint optimization, the same model is also trained individually for each task, enabling direct comparison between single-task and multi-task configurations and performance. Results show that multi-task training improves streamflow forecasting accuracy while maintaining comparable rainfall reconstruction performance relative to individually trained counterparts and established baselines. The framework demonstrates stable streamflow forecasts and hydrologically consistent rainfall reconstructions, highlighting the potential of unified, process-aware architectures for representing multiple components of the hydrological cycle within one coherent learning system.