Modelling the spatial variability of snow water equivalent at the catchment scale

Skaugen, Thomas

doi:10.5194/hess-11-1543-2007

Cited by 38 publications

(63 citation statements)

References 22 publications

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“…In order to specify the spatial variability of snow depth over mountainous, treeless topography for large-scale grid cells, we first need to define the pdf of snow depths in a domain size L. Commonly applied snow depth distributions at the peak of winter range from lognormal for complete snow cover (Donald et al, 1995;Pomeroy et al, 1998;DeBeer and Pomeroy, 2009) to gamma (Skaugen, 2007;Egli et al, 2012) to normal in forests (Marchand and Killingtveit, 2005). Second, we need to scale the defining parameters mean and standard deviation of the snow depth distribution, HS and σ HS , respectively, with the underlying subgrid terrain characteristics.…”

Section: Parameterizing Spatial Variability Of Snow Depthmentioning

confidence: 99%

Fractional snow-covered area parameterization over complex topography

Helbig¹,

Herwijnen²,

Magnusson³

et al. 2015

Hydrol. Earth Syst. Sci.

118

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Abstract. Fractional snow-covered area (SCA) is a key parameter in large-scale hydrological, meteorological and regional climate models. Since SCA affects albedos and surface energy balance fluxes, it is especially of interest over mountainous terrain where generally a reduced SCA is observed in large grid cells. Temporal and spatial snow distributions are, however, difficult to measure over complex topography. We therefore present a parameterization of SCA based on a new subgrid parameterization for the standard deviation of snow depth over complex topography. Highly resolved snow depth data at the peak of winter were used from two distinct climatic regions, in eastern Switzerland and in the Spanish Pyrenees. Topographic scaling parameters are derived assuming Gaussian slope characteristics. We use computationally cheap terrain parameters, namely, the correlation length of subgrid topographic features and the mean squared slope. A scale dependent analysis was performed by randomly aggregating the alpine catchments in domain sizes ranging from 50 m to 3 km. For the larger domain sizes, snow depth was predominantly normally distributed. Trends between terrain parameters and standard deviation of snow depth were similar for both climatic regions, allowing one to parameterize the standard deviation of snow depth based on terrain parameters. To make the parameterization widely applicable, we introduced the mean snow depth as a climate indicator. Assuming a normal snow distribution and spatially homogeneous melt, snow-cover depletion (SCD) curves were derived for a broad range of coefficients of variations. The most accurate closed form fit resembled an existing fractional SCA parameterization. By including the subgrid parameterization for the standard deviation of snow depth, we extended the fractional SCA parameterization for topographic influences.For all domain sizes we obtained errors lower than 10 % between measured and parameterized SCA.

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Section: Parameterizing Spatial Variability Of Snow Depthmentioning

confidence: 99%

Fractional snow-covered area parameterization over complex topography

Helbig¹,

Herwijnen²,

Magnusson³

et al. 2015

Hydrol. Earth Syst. Sci.

118

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“…A main objective of snow-hydrological modeling is to estimate the total amount of snow water equivalent (SWE) and to predict the snow melt water run-off originating from the snow. There exists a large range of snow model types, from physical deterministic spatial distributed models [e.g., Lehning et al, 2006;Liston and Elder, 2006] and lumped snowmelt models including the concept of the areal depletion curve [Luce et al, 1999] to pure statistical models using sums of correlated gamma distributed variables [Skaugen, 2007].…”

Section: Introductionmentioning

confidence: 99%

Hysteretic dynamics of seasonal snow depth distribution in the Swiss Alps

Egli¹,

Jonas²

2009

Geophysical Research Letters

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The dynamics of the spatial snow distribution is a key feature when investigating a seasonal snow cover on the catchment scale. This study explores the spatial variability of snow depth during the course of snow accumulation and depletion, based on daily data from 77 weather stations in the Swiss Alps during 6 consecutive seasons. We derive a statistical description of the snow cover dynamics by analyzing temporal trajectories in plots of the standard deviation σ() over mean , where xi is a set of snow depth measurements on one particular day. The analysis reveals that the evolution of σ() closely follows a power law σ() = 0.84±0.01 during the accumulation period for all 6 winters. However, during snowmelt, the trajectory of σ() displays a clearly different path as with classical hysteresis phenomena. Surprisingly, a simplistic model of uniform melting describes this path reasonably well. We believe that the conceptual framework outlined in this study is helpful in describing important characteristics of the dynamics of seasonal snow depth spatial variability. Future studies could apply the methodology introduced here to incorporate stochastic means in modeling snow distribution, even for a broader range of length scales and different alpine terrain.

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“…With estimated values of E(z 0 ) and var(z 0 ), the fraction of wet area, p, can be determined from equation (10). In support of an assumption of a one-to-one relationship between E(z 0 ) and var(z 0 ), we have found reports in the literature on very high correlation between spatial mean and spatial standard deviation (Creutin & Obled, 1982;Barancourt et al, 1992;Skaugen, 2007), which we can use to determine the right-hand side of equation (11). In assessing this relationship, we have to consider events, over a fixed area, for which zeros are observed, but where spatial mean and variance are estimated from non-zero observations.…”

Section: Estimating the Intermittency Of A Precipitation Fieldmentioning

confidence: 81%

Simulated precipitation fields with variance-consistent interpolation

Skaugen

Andersen

2010

Hydrological Sciences Journal

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Gridded meteorological data are available for all of Norway as time series dating from 1961. A new way of interpolating precipitation in space from observed values is proposed. Based on the criteria that interpolated precipitation fields in space should be consistent with observed spatial statistics, such as spatial mean, variance and intermittency, spatial fields of precipitation are simulated from a gamma distribution with parameters determined from observed data, adjusted for intermittency. The simulated data are distributed in space, using the spatial pattern derived from kriging. The proposed method is compared to indicator kriging and to the current methodology used for producing gridded precipitation data. Cross-validation gave similar results for the three methods with respect to RMSE, temporal mean and standard deviation, whereas a comparison on estimated spatial variance showed that the new method has a near perfect agreement with observations. Indicator kriging underestimated the spatial variance by 60-80% and the current method produced a significant scatter in its estimates.Key words spatial rainfall; interpolation; spatial variance; intermittency; Norway Simulation de champs de précipitation par interpolation cohérente en termes de variance Résumé Des séries temporelles de données météorologiques maillées sont disponibles pour l'ensemble de la Norvège depuis 1961. Une nouvelle façon d'interpoler les champs de précipitations dans l'espace à partir des valeurs observées est proposée. Sur la base des critères selon lesquels les champs de précipitations interpolés dans l'espace devraient être compatibles avec les statistiques spatiales observées comme les moyennes, variances et intermittences spatiales, les champs de précipitations sont simulés selon une distribution gamma déterminée à partir de données observées, ajustées pour l'intermittence. Les données simulées sont distribuées dans l'espace à l'aide du patron spatial dérivé par krigeage. La méthode proposée est comparée à l'indicateur de krigeage et à la méthode actuellement utilisée pour produire des données de précipitations maillées. La validation croisée a donné des résultats similaires pour les trois méthodes, pour les valeurs de l'erreur quadratique moyenne, de la moyenne temporelle et de l'écart type, tandis que la comparaison sur la variance spatiale a montré que la nouvelle méthode donne un accord presque parfait avec les observations. L'indicateur de krigeage sous-estime la variance spatiale de 60-80% et la méthode actuelle produit une dispersion significative de ses estimations.

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Modelling the spatial variability of snow water equivalent at the catchment scale

Cited by 38 publications

References 22 publications

Fractional snow-covered area parameterization over complex topography

Fractional snow-covered area parameterization over complex topography

Hysteretic dynamics of seasonal snow depth distribution in the Swiss Alps

Simulated precipitation fields with variance-consistent interpolation

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