Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model

Abstract:

Accurately initializing snow model states, and in particular snow water equivalent (SWE), in hydrologic prediction models is important for predicting future snowmelt, water supplies and flooding potential. While ground-based snow observations give the most reliable information about snowpack conditions, they are spatially limited and quite sparse in regions such as the north-central USA. Satellites are the most likely source of snow observations to fill this data gap. Using the ensemble Kalman filter (EnKF) assimilation framework, we test the assimilation of AMSR-E SWE into the US National Weather Service (NWS) SNOW17 model for seven watersheds in the Upper Mississippi River basin. SNOW17 is coupled with the NWS Sacramento Soil Moisture Accounting (SACSMA) model, and both simulated SWE and discharge are evaluated. Prior to assimilation, AMSR-E data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. Updating occurs on a daily cycle for water years 2006-2011. Results show improved discharge for five of the seven study sites as compared to the SNOW17 without assimilation. The assimilation of AMSR-E SWE produced high skill for peak flows during periods of snow melt, with one study site displaying a 36% improvement in simulated peak flow. As calibrated, the SNOW17 consistently underestimates the SWE and snow melt rates in these basins. Overall results indicate that updating SNOW17 SWE with AMRS-E data is a viable and effective solution for improving simulations of the operational forecast models.