Toward improving streamflow prediction in the Upper Colorado River Basin via assimilating bias-adjusted satellite snow depth retrievals

Thursday, 18 December 2014
Yuqiong Liu1, Christa D Peters-Lidard2, Sujay Kumar3, Kristi R Arsenault4 and David M Mocko3, (1)University of Maryland College Park, College Park, MD, United States, (2)NASA GSFC, Greenbelt, MD, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)SAIC, Greenbelt, MD, United States
In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in the seasonal snowpack in order to support local and regional water management. Previous studies have shown that improved snow predictions can be achieved by assimilating bias corrected snow depth retrievals from satellite-based passive microwave (PMW) sensors. However, improved snow predictions do not necessarily always translate into improved predictions of streamflow on which water management heavily relies. In this presentation, we explore how the existing bias correction strategy based on the optimal interpolation algorithm can be enhanced to produce an improved satellite-gauge blended snow depth product, which, when assimilated into a distributed snowmelt-runoff model, can lead to consistently improved streamflow predictions. The methodology is applied to the bias reduction of the snow depth estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), which is then assimilated into the Noah land surface model via an ensemble Kalman Filter (EnKF) for streamflow prediction in the Upper Colorado River Basin. Our results indicate that using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) stations) and terrain aspect information in the bias correction process is critically important in achieving desirable streamflow predictions. Incorporating snow cover information (e.g., from the Moderate Resolution Imaging Spectroradiometer (MODIS)) into bias correction can further improve the streamflow results. However, increasing the spatial resolution of bias correction tends to have mixed results on streamflow prediction.