Comparison and Error Analysis of Reconstructed SWE to Airborne Snow Observatory Measurements in the Upper Tuolumne Basin, CA

We present scientific and computing improvements to our new reconstruction model compared to a previous model. Snow water equivalent (SWE) reconstruction involves building a snowpack up in reverse, from melt out to peak SWE, given estimates of melt energy and fractional snow covered area (fSCA). The model was initially tested at an energy balance site on Mammoth Mountain, near the Upper Tuolumne basin, where nearly all the output could be verified and the disappearance of snow was known precisely. A full energy balance version of the model, that computes melt hourly, accurately estimated SWE and most energy balance terms. We were not able to verify a key parameter, the snow albedo, possibly because of low snow depths that affected our ability to measure solar radiation reflected by the snow. A net radiation/degree-day version of the model, that computes melt daily, underestimated SWE, probably because of its coarse daily time step. We then used SWE from the Airborne Snow Observatory (ASO) in 2014 as ground truth to verify the energy balance reconstruction model. Given the high spatial and spectral resolution of ASO measurements, we assume they are an accurate ground truth. Compared to the Snow Data Assimilation System (SNODAS) and the Advanced Microwave Radiometer 2 (AMSR2), reconstruction (with two different fSCA inputs) was by far the most accurate, with a bias of 36-40 mm at the basin wide maximum SWE, and an RMSE of 32-43 mm for all ASO measurement dates. The AMSR2 SWE estimates were generally too low, probably because of deep snow and interference from the canopy. SNODAS tended to overestimate SWE, perhaps because of an overreliance on measurements from snow pillows which are purposely located at heavy snow sites. Finally, we tested the reconstruction model over snow pillows located across the entire Sierra in 2014. The bias was 3 mm and the RMSE 140 mm at the maximum SWE accumulation, showing significant lower than a previous model.

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