Isolating forest process effects on modelled snowpack density and snow water equivalent

Bonner, Hannah M.; Raleigh, M. S.; Small, E. E.

doi:10.1002/hyp.14475

Cited by 15 publications

(9 citation statements)

References 80 publications

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“…The sway‐to‐mass data for both trees highlight a collection of storms where the cohesion effect apparently prevails despite the potential for less rigid branches (Figures 9b and 9e). Related to snow cohesion, the density of canopy snow is important for interception amount and duration, and is a source of model uncertainty (Bonner et al., 2022). Combining estimates of intercepted snow mass from sway with intercepted snow volume from remote sensing (e.g., from TLS, Russell et al., 2020) has potential to quantify snow density of canopy snow in future work.…”

Section: Discussionmentioning

confidence: 99%

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Raleigh

Gutmann

Stan

et al. 2022

Water Resources Research

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Snowpack accumulation in forested watersheds depends on the amount of snow intercepted in the canopy and its partitioning into sublimation, unloading, and melt. A lack of canopy snow measurements limits our ability to evaluate models that simulate canopy processes and predict snowpack. We tested whether monitoring changes in wind‐induced tree sway is a viable technique for detecting snow interception and quantifying canopy snow water equivalent (SWE). Over a 6 year period in Colorado, we monitored hourly sway of two conifers, each instrumented with an accelerometer sampling at 12 Hz. We developed an approach to distinguish changes in sway frequency due to thermal effects on tree rigidity versus intercepted snow mass. Over 60% of days with canopy snow had a sway signal that could not be distinguished from thermal effects. However, larger changes in tree sway could not generally be attributed to thermal effects, and canopy snow was present 93%–95% of the time, as confirmed with classified PhenoCam imagery. Using sway tests, we converted changes in sway to canopy SWE, which were correlated with total snowstorm amounts from a nearby SNOTEL site (Spearman r = 0.72 to 0.80, p < 0.001). Greater canopy SWE was associated with storm temperatures between −7°C and 0°C and wind speeds less than 4 m s−1. Lower canopy SWE prevailed in storms with lower temperatures and higher wind speeds. Monitoring tree sway is a viable approach for quantifying canopy SWE, but challenges remain in converting changes in sway to mass and distinguishing thermal and snow mass effects on tree sway.

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Section: Discussionmentioning

confidence: 99%

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Raleigh

Gutmann

Stan

et al. 2022

Water Resources Research

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“…Empirical models can be calibrated to specific study domains using nearby weather stations which measure snow water equivalent (SWE) and snow depth (McCreight & Small, 2014;Pistocchi, 2016). We chose to forego calibration for two reasons: (1) many of the regions that would benefit from SWE remote sensing are poorly instrumented and, therefore, not capable of model calibration, and (2) the main source of our calibration would be SNOTEL stations near the study areas, which are located in small forest openings, where snow density tends to be underestimated when compared to the unforested areas where most of our transects were located (Bonner et al, 2022). Empirical models were run for our surveys using inputs from the lidar snow depth rasters, but only using grid cells where relative permittivity and density values were derived.…”

Section: Conflict Of Interest Statementmentioning

confidence: 99%

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

Bonnell,

McGrath,

Hedrick

et al. 2023

Hydrological Processes

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Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

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“…Slopes with high radiation inputs will be more likely to have snowmelt, introducing liquid water into the snow, which also increases snow density by filling the pore space with liquid water (Wetlaufer et al, 2016). The average snow density in forest areas was 8 %-13 % less than that in open areas (Zhong et al, 2014), and these ob-served density differences are attributed to either mass, delivery, wind, or radiation effects (Bonner et al, 2022). Mass effect is a reduction in the snow mass due to canopy interception loss, with lower compaction rates and snow density.…”

Section: Introductionmentioning

confidence: 99%

Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data

et al. 2023

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Abstract. Snow density plays a critical role in estimating water resources and predicting natural disasters such as floods, avalanches, and snowstorms. However, gridded products for snow density are lacking for understanding its spatiotemporal patterns. In this study, considering the strong spatiotemporal heterogeneity of snow density, as well as the weak and nonlinear relationship between snow density and the meteorological, topographic, vegetation, and snow variables, the geographically and temporally weighted neural network (GTWNN) model is constructed for estimating daily snow density in China from 2013 to 2020, with the support of satellite, ground, and reanalysis data. The leaf area index of high vegetation, total precipitation, snow depth, and topographic variables are found to be closely related to snow density among the 20 potentially influencing variables. The 10-fold cross-validation results show that the GTWNN model achieves an R2 of 0.531 and RMSE of 0.043 g cm−3, outperforming the geographically and temporally weighted regression model (R2=0.271), geographically weighted neural network model (R2=0.124), and reanalysis snow density product (R2=0.095), which demonstrates the superiority of the GTWNN model in capturing the spatiotemporal heterogeneity of snow density and the nonlinear relationship to the influencing variables. The performance of the GTWNN model is closely related to the state and amount of snow, in which more stable and plentiful snow would result in higher snow density estimation accuracy. With the benefit of the daily snow density map, we are able to obtain knowledge of the spatiotemporal pattern and heterogeneity of snow density in China. The proposed GTWNN model holds the potential for large-scale daily snow density mapping, which will be beneficial for snow parameter estimation and water resource management.

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Isolating forest process effects on modelled snowpack density and snow water equivalent

Cited by 15 publications

References 80 publications

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Challenges and Capabilities in Estimating Snow Mass Intercepted in Conifer Canopies With Tree Sway Monitoring

Snowpack relative permittivity and density derived from near‐coincident lidar and ground‐penetrating radar

Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data

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