Benchmarking reservoir operation schemes for large-scale hydrological models

Abstract. There are approximately 62 000 large dams worldwide that significantly alter the hydrological regimes of most major rivers. Despite their importance, reservoirs remain poorly represented in Large-Scale Hydrological Models (LSHMs) due to the complexity of human-driven operations and a widespread lack of observational records. Consequently, reservoir routines in LSHMs must balance structural simplicity with limited data requirements. In this study, we utilize the ResOpsUS dataset to benchmark four reservoir routines of increasing complexity: LISFLOOD, CaMa-Flood, mHM, and STARFIT. We evaluate these routines across 164 reservoirs in the United States and test which target variables are most informative for parameter estimation. Our results indicate that the mHM routine consistently achieves the highest performance; however, its dependence on site-specific demand data limits its applicability at the global scale. In contrast, the CaMa-Flood routine provides a robust compromise, significantly outperforming the linear logic of LISFLOOD and matching the performance of the more complex STARFIT routine. This suggests that increased model complexity does not always yield superior results. Crucially, we find that calibrating to reservoir storage is more informative than calibrating to outflow, as it effectively captures the dynamics of both state variables. This finding paves the way for the use of satellite-derived storage products in the calibration of LSHMs. The findings of this study have been implemented in the upcoming versions of the European and Global Flood Awareness Systems (EFAS v6 and GloFAS v5).