Advancing Streamflow Prediction in Data-Scarce Regions Through Vegetation-Constrained Distributed Hybrid Ecohydrological Models

Hybrid models that combine deep learning with physical principles have recently shown significant promise in improving streamflow prediction in data-scarce regions, achieving generally greater accuracy and reliability than either method alone. However, existing hybrid hydrological models underutilize the extensive and readily available remote sensing data on vegetation, which plays a critical role in hydrological processes. Incorporating vegetation information can better constrain model processes by accounting for the interactions and feedbacks between vegetation and hydrology, thereby enabling more robust predictions. In this context, we present a novel distributed hybrid ecohydrological model that integrates dynamic vegetation processes into a distributed hybrid hydrological model, capable of simultaneously simulating leaf area index (LAI) and streamflow through multitask learning. Our results indicate that in scenarios with limited available streamflow data, the combined constraint of LAI spatiotemporal patterns and streamflow significantly enhances the model's ability to generalize streamflow simulation across space and time compared to models trained solely with either LAI or streamflow. Even with the increased availability of streamflow data, models utilizing both LAI and streamflow continue to exhibit superior robustness in streamflow prediction. The distributed ecohydrological model, which employs multi-process coupling and multi-source data constraints, accurately simulates the target variables (i.e., streamflow and LAI) while reliably capturing dynamic changes in other ecohydrological processes, such as evapotranspiration. This capability highlights its potential for multi-process diagnostics. Overall, our approach offers a new perspective for streamflow prediction in data-scarce regions and represents an important step towards integrating observational constraints from multiple subsystems into hybrid Earth system modeling.