Towards learning human influences in a highly regulated basin using a hybrid DL-process based framework

Hybrid models have shown impressive performance for streamflow simulation, offering better accuracy than process-based hydrological models (PBMs) and superior interpretability than deep learning models (DLMs). A recent paradigm for streamflow modeling, integrating DLMs and PBMs within a differentiable framework, presents considerable potential to match the performance of DLMs while simultaneously generating untrained variables that describe the entire water cycle. However, the potential of this framework has mostly been verified in small and unregulated headwater basins and has not been explored in large and highly regulated basins. Human activities, such as reservoir operations and water transfer projects, have greatly changed natural hydrological regimes. Given the limited access to operational water management records, PBMs generally fail to achieve satisfactory performance and DLMs are challenging to train directly. This study proposes a coupled hybrid framework to address these problems. This framework is based on a distributed PBM, the Xin'anjiang (XAJ) model, and adopts embedded deep learning neural networks to learn the physical parameters and replace the modules of the XAJ model reflecting human influences through a differentiable structure. Streamflow observations alone are used as training targets, eliminating the need for operational records to supervise the training process. The Hanjiang River basin (HRB), one of the largest subbasins of the Yangtze River basin, disturbed by large reservoirs and national water transfer projects, is selected to test the effectiveness of the framework. The results show that the hybrid framework can learn the best parameter sets of the XAJ model depicting natural and human influences to improve streamflow simulation. It performs better than a standalone XAJ model and achieves similar performance to a standalone LSTM model. This framework sheds new light on assimilating human influences to improve simulation performance in disturbed river basins with limited operational records.