Towards a semi-asynchronous method for hydrological modeling in climate change studies

Abstract. Hydrological impact assessments under climate change commonly rely on conventional modeling chains where climate projections are bias-corrected before being used in hydrological simulations. While this improves agreement with historical observations, it can introduce methodological uncertainties, reduce the diversity of climate ensembles, and smooth out extreme events. Asynchronous methods have been proposed as an alternative, allowing hydrological models to be calibrated directly with raw climate model outputs. However, fully asynchronous methods often fail to capture the timing of key hydrological processes, especially in snow-affected regions. This study introduces and evaluates a semi-asynchronous calibration approach that incorporates a monthly temporal structure to address these limitations. Using the physically based WaSiM model, we compare the semi-asynchronous, fully asynchronous, and conventional methods across ten snow-influenced catchments in southern Quebec, Canada, under historical and future climate conditions. The results show that while the fully asynchronous and semi-asynchronous methods perform well in preserving streamflow distributions and high-flow extremes, only the semi-asynchronous method succeeds in restoring the seasonal timing of key processes such as snowmelt and low flows. The semi-asynchronous method notably reduces intermodel variability in streamflow and snow water equivalent compared to the fully asynchronous approach. It also exhibits seasonal dynamics that closely align with observations and the conventional method, despite relying on uncorrected climate inputs. In contrast, the fully asynchronous method shows signs of desynchronization, with unrealistic snowmelt timing and elevated variability across projections. The conventional method, while more stable in the historical period, exhibits an increase in intermodel variability under future conditions, likely due to divergent magnitudes of projected change across climate models. The semi-asynchronous method presents a clear improvement over the fully asynchronous approach by restoring temporal coherence and improving the simulation of seasonal processes. It also reduces intermodel variability while maintaining the raw climate signal and preserving the distribution of streamflow. Compared to the conventional method, which benefits from stable and consistent simulations but tends to dampen extremes through bias correction, the semi-asynchronous approach offers a compelling alternative. It strikes a different balance between realism, ensemble diversity, and the ability to represent extreme events, making it particularly valuable for future-oriented climate impact assessments. This study highlights the potential of the semi-asynchronous method as an innovative and robust tool for hydrological modeling under climate change. As climate model simulations continue to improve and their biases are progressively reduced, the semi-asynchronous approach is poised to benefit significantly, enhancing its potential for future hydrological projections.