Observation-Based Constraints on Future Runoff Projections for Large Reservoirs

Abstract

Exploring the future evolution of runoff within reservoir drainage areas and its dominant drivers provides insights for future reservoir management. We combine a georeferenced reservoir data set with historical runoff simulations and future projections from an ensemble of Global Hydrological Models (GHMs) to investigate the long-term runoff evolution across 913 drainage areas of large reservoirs in China. To reduce uncertainties in the GHM ensemble, we adopt an ensemble best approach, which outperforms the ensemble mean and median. Using this approach, we incorporate observational constraints to project three aspects of runoff-related variations: trends in inter-annual magnitude, inter-annual variability, and intra-annual variability. A total of 337–438 reservoirs are projected to exhibit decreasing inter-annual magnitude, weakening inter-annual variability, and enhancing intra-annual variability during the historical period, while increasing magnitude and enhancing variability under future scenarios. These three aspects of runoff-related variations do not occur synchronously within the same reservoir groups, highlighting the need to consider each reservoir individually when developing effective management strategies. Attribution analyses indicate that anthropogenic climate change predominates over natural climate variability and human water and land management, serving as the dominant driver of runoff-related variations. Although the ensemble best approach performs well at large spatial scales, uncertainties remain at the individual reservoir level, as relative errors exceed ±50% for 90–259 reservoirs in drylands and cold regions. To promote the reliability of future projections, we advocate continued efforts to improve GHM mathematical structures and parameterization schemes, alongside the integration of additional in situ observations for model calibration.