A Call for More Snow Sampling

Fassnacht, S. R.

doi:10.3390/geosciences11110435

Cited by 7 publications

(5 citation statements)

References 83 publications

(170 reference statements)

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“…Reducing overall uncertainty will require improvements of instrumentation and design of observation networks and improvements in processes understanding and algorithms. Coordination between monitoring networks such that measurement data for multiple water budget components are co‐located within the same gaged basin and are placed such that they represent the range of conditions within the basin could facilitate these improvements (Fasschnat, 2021; Kampf et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Uncertainties in measuring and estimating water‐budget components: Current state of the science

et al. 2023

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Reducing uncertainty in quantifying basin‐scale water‐budget components is one of the most vexing problems for water‐resource managers. Although a vast literature addresses methods and scales of component estimates, uncertainty associated with these estimates is often lacking. Here we review sources of uncertainty and compile reported uncertainty estimates for measurement and estimation methods for six water‐budget components: precipitation, streamflow, evapotranspiration, subsurface discharge, infiltration, and recharge. Quantifying the uncertainty in each water‐budget component data is required before total water‐budget uncertainty can be determined. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Methods

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Section: Discussionmentioning

confidence: 99%

Uncertainties in measuring and estimating water‐budget components: Current state of the science

et al. 2023

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“…This is likely due to the substantial March snowfall that was observed at FA and FF (Figure 3). The snow depths used herein are from manual depth probe measurements [18], which have inherent biases due to over-probing [34] and location uncertainty [34][35][36]. More efficient data collection methods could enable a better assessment of intra-and inter-annual variability, such as automated depth probes [34], various forms of lidar (e.g., Deems et al [37]), and photogrammetry techniques (e.g., Nolan et al [38]).…”

Section: Discussionmentioning

confidence: 99%

Snow Surface Roughness across Spatio-Temporal Scales

Fassnacht

Suzuki

Sanow

et al. 2023

Water

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The snow surface is at the interface between the atmosphere and Earth. The surface of the snowpack changes due to its interaction with precipitation, wind, humidity, short- and long-wave radiation, underlying terrain characteristics, and land cover. These connections create a dynamic snow surface that impacts the energy and mass balance of the snowpack, blowing snow potential, and other snowpack processes. Despite this, the snow surface is generally considered a constant parameter in many Earth system models. Data from the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment (CLPX) collected in 2002 and 2003 across northern Colorado were used to investigate the spatial and temporal variability of snow surface roughness. The random roughness (RR) and fractal dimension (D) metrics used in this investigation are well correlated. However, roughness is not correlated across scales, computed here from snow roughness boards at a millimeter resolution and airborne lidar at a meter resolution. Process scale differences were found based on land cover at each of the two measurement scales, as appraised through measurements in the forest and alpine.

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“…The ranges reported by López‐Moreno et al (2013) fully encompass the maximum range observed in our derived density datasets, whereas Fassnacht et al (2010) observed ranges more comparable to those we derived at Grand Mesa. It can be noted that, depending on the dominant processes, snowpack density can vary spatially at the 0.10 m scale (Fassnacht, 2021).…”

Section: Discussionmentioning

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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A Call for More Snow Sampling

Cited by 7 publications

References 83 publications

Uncertainties in measuring and estimating water‐budget components: Current state of the science

Uncertainties in measuring and estimating water‐budget components: Current state of the science

Snow Surface Roughness across Spatio-Temporal Scales

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

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