15 A Multi-Faceted Evaluation of National Water Model Snow Processes in Complex Terrain.

Thursday, 16 July 2020
Virtual Meeting Room
Francesca Viterbo, CIRES, Boulder, CO; and M. Hughes, K. Mahoney, R. Cifelli, M. Barlage, D. J. Gochis, J. Lundquist, and C. S. Draper

Handout (50.6 MB)

Snow accumulation and melting processes strongly influence both water resources management at the seasonal scale and streamflow at the event scale. Despite this fundamental importance, the representation and evaluation of snow from atmospheric forcings to hydrological processes remain a challenge. The challenges arise due to both sparse observations and from models struggling to parameterize complex snow processes at the land-atmosphere interface. The observational challenges are exacerbated in the complex terrain of the Western U.S., which contains a large portion of the Continental US (CONUS)’s seasonally persistent snowpack.

The goal of this work is to understand how snow processes are represented by NOAA’s National Water Model and its underlying land surface model, Noah MP, through a process-based study focused in California. Through a comparison with a network of snow pillows, we demonstrate that, when forced by NLDAS, the NWM underestimates snow water equivalent (SWE) systematically across the Sierra Nevada. This result is reinforced through a comparison in the Tuolumne River basin (TRB) using Airborne Snow Observatory SWE estimates, which finds large underestimates in NWM SWE at the highest elevations of the basin. The reasons for this underestimation are then explored with an idealized single-column Noah-MP simulation at a location in the TRB with very high-quality, long-term observations. Some differences in SWE accumulation and melt are understandable through comparison of differences between observed and NLDAS forcing, while other differences are harder to explain and suggest more systematic model deficiencies.

The outcome of this study will guide future improvements in NWM forcings and NWM physical processes inside the land surface model, and to develop snow data-assimilation techniques to potentially improve NWM prediction.

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