A Physics-Constrained Neural Differential Equation Framework for Data-Driven Snowpack Simulation
This addresses the challenge of accurate and generalizable snowpack simulation for climate modeling and hydrology, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of modeling seasonal snow depth evolution using a physics-constrained neural differential equation framework, achieving under 9% median error and Nash Sutcliffe Efficiencies over 0.94 across diverse snow climates, with generalization to unseen sites.
This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% median error and Nash Sutcliffe Efficiencies over 0.94 across a wide variety of snow climates. The parameterization also generalizes to new sites not seen during training, which is not often true for calibrated snow models. Requiring the parameterization to predict snow water equivalent in addition to snow depth only increases error to ~12%. The structure of the approach guarantees the satisfaction of physical constraints, enables these constraints during model training, and allows modeling at different temporal resolutions without additional retraining of the parameterization. These benefits hold potential in climate modeling, and could extend to other dynamical systems with physical constraints.