SWECast: Enhancing Snow Water Equivalent Forecasting through Prior Modulation and Periodicity Modeling

Snow water equivalent (SWE) directly measures the amount of water stored in snowpack. Accurate SWE forecasting is essential for water resource management and flood risk assessment in regions relying on seasonal snowpack. However, SWE evolution varies across locations and years and is sensitive to abrupt weather changes during the forecast horizon, which makes accurate SWE forecasting particularly challenging. We therefore propose SWECast, a novel model that improves SWE forecasting by combining periodicity modeling with prior modulation. We select informative meteorological inputs and construct an input configuration comprising complete historical snow seasons, current snow season history, and future meteorological inputs. We design a Periodicity Modeling Module (PMM) that analyzes SWE and meteorological inputs in the frequency domain, inferring the current SWE state and characterizing its response to weather. Based on this information, a gating mechanism dynamically weights each feature at each time step to incorporate meteorological information over the forecast horizon. In parallel, a Prior Conditioner (PC) derives priors from complete historical snow seasons in the time domain, capturing how SWE evolves at each location and thereby addressing the variability in SWE evolution across locations. Finally, the priors from the PC modulate the representations generated by the PMM. The evaluation results at stations excluded from training demonstrate that SWECast consistently outperforms all baselines. Over a forecast horizon of two weeks, SWECast reduces MAE by 13.1% and RMSE by 14.5% compared with the strongest baseline. By providing accurate SWE forecasting, SWECast can inform reservoir operations and flood preparedness in snow-dependent regions.