Evaluating Multiple Canopy‐Snow Unloading Parameterizations in SUMMA With Time‐Lapse Photography Characterized by Citizen Scientists. Issue 6 (10th June 2022) - Lumbrazo, Cassie; Bennett, Andrew; Currier, William Ryan; Nijssen, Bart; Lundquist, Jessica
Abstract: Canopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001, https://doi.org/10.1007/s003820100153 ), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009, https://doi.org/10.1029/2008wr007042 ), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4 ). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology. Plain Language Summary: Forests intercept snowfall and affect how much snow accumulates on the landscape. Canopy snow can unload by melting onto the snowpack below, sublimating back to the atmosphere, or by sluffing off and contributing to the ground's snowpack. It has been difficult to create and validate models for canopy‐snow unloading due to limited observations. However, time‐lapse photography can observe canopy‐snow unloading in remote areas. In this work, these were characterized by citizen scientists to create an observational data set of snow interception, which we used to evaluate the performance of three canopy‐snow unloading models. Models that unloaded snow as a function of wind and temperature performed better than time‐based estimates. How a model unloads snow impacts the model's estimate of water available for runoff because it changes how much intercepted snow is modeled as falling to the ground versus lost back to the atmosphere. Additionally, different models of how long snow stays in the canopy impact the models' estimates of how much radiation was reflected back to the atmosphere. This work shows that citizen scientists can substantially contribute to science and produce a novel data set that can be used to investigate processes often overlooked in hydrology. Key Points: Remote time‐lapse cameras and citizen science shed light on forest‐snow interception processes that are often overlooked Site specific interception patterns, such as wind dominated unloading and riming, impact the transferability of unloading parameterizations The choice of unloading scheme impacts the canopy albedo feedback, and the partitioning of snow on the ground versus canopy sublimation