Reducing parameter uncertainty in hydrological modeling using MODIS-derived constraints

Hydrological models are a fundamental tool for supporting water resources management; yet, model predictions are often affected by high uncertainty. Among the other sources of uncertainty, snow-dominated catchments must also cope with modeling the snow accumulation and melting processes. Snow controls discharge by storing winter precipitation and releasing it during melt periods, thereby regulating the timing and magnitude of the streamflow. 

A widely used approach for representing snow processes is the degree-day model, which estimates snow accumulation and melt based on air temperature thresholds and a melting factor. Due to the scarcity of in situ snow observations, the degree-day parameters are commonly inferred by calibrating the discharge within a hydrological model, thereby exacerbating parameter equifinality.

In this study, we quantify the impact of constraining degree-day model parameters with MODIS, a multispectral satellite sensor that provides near-daily global observations of snow cover extent. The constrained calibration framework results in an overall improvement in discharge performance and a significant reduction in parameter uncertainty, including for the non-snow-related parameters of the other model. These results underscore the importance of integrating satellite-based snow information to mitigate equifinality and enhance the robustness of hydrological modeling in alpine environments.