Enhancing Snow Ablation Modeling in the Generation of Gridded Snow Water Equivalent Data

Snow Water Equivalent (SWE) is a critical parameter for understanding water availability in regions with seasonal snow cover. Ensuring an accurate representation of SWE across regular spatial and temporal intervals is essential and plays a pivotal role in hydrological and climatological studies. This work critically examines the ablation modeling strategy employed by the University of Arizona daily 4km SWE dataset (UA SWE), a widely adopted SWE gridded product in the United States, highlighting limitations inherent in methodologies that rely solely on temperature data. Recognizing the utility of a more nuanced perspective to capture the complexities of snowmelt dynamics, we propose a novel method that incorporates a diverse set of meteorological and terrain characteristics as input variables in the predictive modeling of snow ablation. Our approach is further extended to directly model SWE, eliminating the need for intermediate ablation estimation and providing a more intuitive solution for empirical SWE prediction. Our versatile methodology can be easily applied to produce high-resolution gridded SWE data. By addressing deficiencies in a leading empirical approach, our technique aims to enhance the accuracy of SWE representation at both the point and grid levels.