Modeling snow accumulation and ablation processes in forested environments

Andreadis, Konstantinos M.; Storck, Pascal; Lettenmaier, Dennis P.

doi:10.1029/2008wr007042

Cited by 239 publications

(262 citation statements)

References 56 publications

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“…Tarboton and Luce, 1996) surements at the study site, but closer to those proposed by Jordan (1991) and Jordan et al (1999) in previous works; and (c) z 0 = 2.5 mm, which lies within the range of 0.0001-0.01 m, as proposed by various authors (i.e. Andreadis et al, 2009;Jordan, 1991;Marks and Dozier, 1992;Marks et al, 2008;Tarboton and Luce, 1996). Figures 7 and 8 show the values of h and SCF, respectively, during both the calibration and validation periods, along with the values simulated with the optimal calibration parameters.…”

Section: Calibration and Validationsupporting

confidence: 63%

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Pimentel

Herrero

Polo

2017

Hydrol. Earth Syst. Sci.

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Abstract. Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation-depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009)(2010)(2011)(2012)(2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m 2 m −2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

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Section: Calibration and Validationsupporting

confidence: 63%

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Pimentel

Herrero

Polo

2017

Hydrol. Earth Syst. Sci.

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“…VIC (Liang et al 1994) and CLM (Zeng et al 2002) represent the snowpack using multilayer snow models: VIC has a thin surface layer and a thick deeper layer (Andreadis et al 2009), and CLM has up to five snow layers. To account for subgrid variability, VIC has subgrid tiles based on elevation bands and land-cover type, whereas CLM just has subgrid tiles based on land-cover type.…”

Section: Reanalysis and Gldas Products And Validation Data A Reanalymentioning

confidence: 99%

Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?

Broxton

Zeng

Dawson

2016

Journal of Hydrometeorology

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There is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.

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“…GEOtop, and probably any physically based permafrost model, would benefit from validation with distributed time series of snow height (or SWE) in order to distinguish between snow accumulation and melting processes. Similarly, mountain permafrost models could benefit from individual calibration of parameters influencing the energy balance such as the roughness length (e.g., Andreadis et al, 2009) or ground albedo (e.g., Hoelzle, 1996;Gruber, 2005). The ground albedo, which determines the net shortwave radiation at Earth's surface in summer, was the most important parameter when modeling MAGT.…”

Section: Sensitivities and Uncertainties Of The Physically Based Modementioning

confidence: 99%

Sensitivities and uncertainties of modeled ground temperatures in mountain environments

et al. 2013

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Abstract. Model evaluation is often performed at few locations due to the lack of spatially distributed data. Since the quantification of model sensitivities and uncertainties can be performed independently from ground truth measurements, these analyses are suitable to test the influence of environmental variability on model evaluation. In this study, the sensitivities and uncertainties of a physically based mountain permafrost model are quantified within an artificial topography. The setting consists of different elevations and exposures combined with six ground types characterized by porosity and hydraulic properties. The analyses are performed for a combination of all factors, that allows for quantification of the variability of model sensitivities and uncertainties within a whole modeling domain.We found that model sensitivities and uncertainties vary strongly depending on different input factors such as topography or different soil types. The analysis shows that model evaluation performed at single locations may not be representative for the whole modeling domain. For example, the sensitivity of modeled mean annual ground temperature to ground albedo ranges between 0.5 and 4 • C depending on elevation, aspect and the ground type. South-exposed inclined locations are more sensitive to changes in ground albedo than north-exposed slopes since they receive more solar radiation. The sensitivity to ground albedo increases with decreasing elevation due to shorter duration of the snow cover. The sensitivity in the hydraulic properties changes considerably for different ground types: rock or clay, for instance, are not sensitive to uncertainties in the hydraulic properties, while for gravel or peat, accurate estimates of the hydraulic properties significantly improve modeled ground temperatures. The discretization of ground, snow and time have an impact on modeled mean annual ground temperature (MAGT) that cannot be neglected (more than 1 • C for several discretization parameters). We show that the temporal resolution should be at least 1 h to ensure errors less than 0.2 • C in modeled MAGT, and the uppermost ground layer should at most be 20 mm thick.Within the topographic setting, the total parametric output uncertainties expressed as the length of the 95 % uncertainty interval of the Monte Carlo simulations range from 0.5 to 1.5 • C for clay and silt, and ranges from 0.5 to around 2.4 • C for peat, sand, gravel and rock. These uncertainties are comparable to the variability of ground surface temperatures measured within 10 m × 10 m grids in Switzerland. The increased uncertainties for sand, peat and gravel are largely due to their sensitivity to the hydraulic conductivity.

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Modeling snow accumulation and ablation processes in forested environments

Cited by 239 publications

References 56 publications

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Why Do Global Reanalyses and Land Data Assimilation Products Underestimate Snow Water Equivalent?

Sensitivities and uncertainties of modeled ground temperatures in mountain environments

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