Estimating Snow Water Equivalent (SWE) has large uncertainties from meteorological data, with no single dataset being universally superior. Our multi-forcing approach, which combines datasets, yields more accurate SWE estimates than single-forcing methods by mitigating bias. Even after data assimilation corrects for prior errors, the multi-forcing ensemble improves accuracy and uncertainty characterization, offering a more robust and reliable strategy for water resource management.