The Performance Gains of Assimilating Limited Information Into a Fully Distributed Snowpack Model Depend on Model Complexity and Input Data Quality

Abstract Estimating snowpack conditions in mountainous regions is critical for water resource management, flood forecasting, and avalanche hazard mitigation. However, most snowpack observations lack either spatial or temporal resolution, while estimates from numerical snow models are inherently uncertain. Data assimilation (DA) can mitigate these shortcomings by combining both sources of information. Yet, designing DA frameworks under specific scientific or operational constraints remains challenging. A particular issue is the effective propagation of limited observational information, such as sparse in situ measurements or infrequent remote sensing data, to unobserved locations and times. This study assesses the potential of assimilating point‐scale observations into a distributed snow model across different model complexities and input data qualities. To evaluate DA design choices, we test varying ensemble generation strategies, prior constraints, definitions of observation error, and further assess the effect of thinning observational data. Our analysis suggests that the benefits of DA primarily depend on the information content of the observations relative to the model prior. Standard models forced with lower‐quality inputs required more substantial corrections and benefited most from advanced assimilation schemes. Meanwhile, more complex and tuned models, driven by high‐quality data, achieved similar or even better performance with precipitation corrections alone. However, in overly simplistic models, DA provided limited benefits compared to improving the model in the first place. We demonstrate specific tipping points beyond which the given observations no longer provide sufficient information to effectively improve predictions for unobserved locations.