Modeling bulk density and snow water equivalent using daily snow depth observations

摘要

Bulk density is a fundamental property of snow relating its depth and mass.Previously, two simple models of bulk density (depending on snow depth,date, and location) have been developed to convert snow depth observationsto snow water equivalent (SWE) estimates. However, these models were notintended for application at the daily time step. We develop a new model ofbulk density for the daily time step and demonstrate its improved skill overthe existing models.Snow depth and density are negatively correlated at short (10 days)timescales while positively correlated at longer (90 days) timescales. Weseparate these scales of variability by modeling smoothed, daily snow depth(long timescales) and the observed positive and negative anomalies from thesmoothed time series (short timescales) as separate terms. A climatology offit is also included as a predictor variable.Over half a million daily observations of depth and SWE at 345 snowpack telemetry (SNOTEL) sitesare used to fit models and evaluate their performance. For each location, wetrain the three models to the neighboring stations within 70 km, transfer theparameters to the location to be modeled, and evaluate modeled time seriesagainst the observations at that site. Our model exhibits improvedstatistics and qualitatively more-realistic behavior at the daily time stepwhen sufficient local training data are available. We reduce density root mean square error (RMSE) by9.9 and 4.5% compared to previous models while increasing R2from 0.46 to 0.52 to 0.56 across models. Focusing on the 21-day window aroundpeak SWE in each water year, our model reduces density RMSE by 24 and17.4% relative to the previous models, with R2 increasing from 0.55to 0.58 to 0.71 across models. Removing the challenge of parameter transferover the full observational record increases R2 scores for both theexisting and new models, but the gain is greatest for the new model (R2 = 0.75). Our model shows general improvement over existing models when dataare more frequent than once every 5 days and at least 3 stations areavailable for training.