Estimating the snow water equivalent from snow depth measurements in the Italian Alps

Guyennon, Nicolas; Valt, Mauro; Salerno, Franco; Petrangeli, Anna Bruna; Romano, Emanuele

doi:10.1016/j.coldregions.2019.102859

Cited by 20 publications

(22 citation statements)

References 31 publications

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“…The overall good results for the ERMs is not particularly surprising, since they are dedicated to perform well on average. The specially calibrated versions of Pistocchi (2016) and Guyennon et al (2019) show a significantly smaller bias than their originals. The model of Jonas et al (2009) has the smallest (actually a negative) bias for their "Region 7", encompassing the dry, inneralpine Engadin as well as parts of the Southern Alps and the very East of Switzerland (Samnaun), which is partly influenced by orographic precipitation from Northwesterly flows.…”

Section: Validation and Illustrationmentioning

confidence: 91%

“…Glaciologists set the "critical density" before snow turns into firn (which is wetted snow that has survived one summer) to 400 to 800 kg m −3 (e.g., Paterson, 1998). Still, manual density measurements of seasonal snow used in previous studies hardly ever exceeded ρ b = 500 kg m −3 (e.g., Jonas et al, 2009;Guyennon et al, 2019). Armstrong and Brun (2010) limit it to approximately 400 to 500 kg m −3 too.…”

Section: Calibrationmentioning

confidence: 96%

“…The ∆SNOW.MODEL study only consists of data from selected stations with long and regular SWE readings, where also ERMs seem to work better. Guyennon et al (2019) summarize their and other studies' validation results using MAE, the mean absolute error. Table 2 provides the overview, and again it gets obvious, that ERMs perform better with this study's data, than with Guyennon et al Fig.…”

Section: Validation and Illustrationmentioning

confidence: 98%

“…RMSEs for all SWE values are constantly lower than if modeled with ERMs: a median error of 23.9 kg m −2 (∆SNOW.MODEL) faces median errors between 26.6 and 38.1 kg m −2 (ERMs). Calibrating Pistocchi (2016) and Guyennon et al (2019) results in some improvement, at least they perform much better than the ρ 278 approach after the calibration. The model of Jonas et al (2009) does a decent job also without recalibration, which is remarkable.…”

Section: Validation and Illustrationmentioning

confidence: 98%

“…From the vast number of ERMs (cf. Avanzi et al, 2015) the ones of Pistocchi (2016) and Guyennon et al (2019) were chosen to be fitted to SWE cal (standard: Pi16, Gu19; calibrated: Pi16cal, Gu19cal). These models are (quite) new and easy to calibrate.…”

Section: Validation and Illustrationmentioning

confidence: 99%

See 4 more Smart Citations

Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The ΔSNOW.MODEL

Winkler¹,

Schellander²,

Gruber³

2020

Preprint

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Abstract. Snow heights have been manually observed for many years, sometimes decades, at various places. These records are often of good quality. In addition, more and more data from automatic stations and remote sensing are available. On the other hand, records of snow water equivalent SWE – synonymous for snow load or mass – are sparse, although it might be the most important snowpack feature in fields like hydrology, climatology, agriculture, natural hazards research, etc. SWE very often has to be modeled, and those models either depend on meteorological forcing or are not intended to simulate individual SWE values, like the substantial seasonal peak SWE. The ΔSNOW.MODEL is presented as a new method to simulate local-scale SWE. It solely needs snow heights as input, though a gapless record thereof. Temporal resolution of the data series is no restriction per se. The ΔSNOW.MODEL is a semi-empirical multi-layer model and freely available as R-package. Snow compaction is modeled following the rules of Newtonian viscosity. The model considers measurement errors, treats overburden loads due to fresh snow as additional unsteady compaction, and melted mass is stepwise distributed top-down in the snowpack. Seven model parameters are subject to calibration, which was performed using 71 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps. Another 73 rather independent winters act as validation data. Results are very promising: Median bias and root mean squared error for SWE are only −4.0 kg m−2 and 23.9 kg m−2, and +2.3 kg m−2 and 23.1 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, but also much more sophisticated thermodynamic snow models not necessarily perform better. Not least, this study outlines the need for comprehensive comparison studies on SWE measurement and modeling at the point and local scale.

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Section: Validation and Illustrationmentioning

confidence: 91%

Section: Calibrationmentioning

confidence: 96%

Section: Validation and Illustrationmentioning

confidence: 98%

Section: Validation and Illustrationmentioning

confidence: 98%

Section: Validation and Illustrationmentioning

confidence: 99%

See 3 more Smart Citations

Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The ΔSNOW.MODEL

Winkler¹,

Schellander²,

Gruber³

2020

Preprint

View full text Add to dashboard Cite

show abstract

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

Szeitz

Moore

2023

Hydrological Processes

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Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra-and interyear variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A nonlinear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m À3 , a mean bias of À0.039 kg m À3 , and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within À45 to 49 mm of the observed SWE, representing prediction errors of À15% to 20%.

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Evaluating the different responses to climatic factors between snow water equivalent and snow cover area in the Central Tianshan Mountains

Zhang

et al. 2022

Theor Appl Climatol

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Estimating the snow water equivalent from snow depth measurements in the Italian Alps

Cited by 20 publications

References 31 publications

Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The ΔSNOW.MODEL

Snow Water Equivalents exclusively from Snow Heights and their temporal Changes: The ΔSNOW.MODEL

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

Evaluating the different responses to climatic factors between snow water equivalent and snow cover area in the Central Tianshan Mountains

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