How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?*

Abstract Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature Tsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.2°C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration.

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Validation of the energy budget of an alpine snowpack simulated by several snow models (Snow MIP project)

Annals of Glaciology ◽

10.3189/172756404781814825 ◽

2004 ◽

Vol 38 ◽

pp. 150-158 ◽

Cited By ~ 159

Author(s):

Pierre Etchevers ◽

Eric Martin ◽

Ross Brown ◽

Charles Fierz ◽

Yves Lejeune ◽

...

Keyword(s):

Energy Budget ◽

Longwave Radiation ◽

Model Complexity ◽

Shortwave Radiation ◽

Avalanche Forecasting ◽

Simple Index ◽

Analysis Of Results ◽

Snow Model ◽

Global Atmospheric Circulation ◽

The Impact

AbstractMany snow models have been developed for various applications such as hydrology, global atmospheric circulation models and avalanche forecasting. The degree of complexity of these models is highly variable, ranging from simple index methods to multi-layer models that simulate snow-cover stratigraphy and texture. In the framework of the Snow Model Intercomparison Project (SnowMIP), 23 models were compared using observed meteorological parameters from two mountainous alpine sites. The analysis here focuses on validation of snow energy-budget simulations. Albedo and snow surface temperature observations allow identification of the more realistic simulations and quantification of errors for two components of the energy budget: the net short- and longwave radiation. In particular, the different albedo parameterizations are evaluated for different snowpack states (in winter and spring). Analysis of results during the melting period allows an investigation of the different ways of partitioning the energy fluxes and reveals the complex feedbacks which occur when simulating the snow energy budget. Particular attention is paid to the impact of model complexity on the energy-budget components. The model complexity has a major role for the net longwave radiation calculation, whereas the albedo parameterization is the most significant factor explaining the accuracy of the net shortwave radiation simulation.

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The Impact of Snow Model Complexity at Three CLPX Sites

Journal of Hydrometeorology ◽

10.1175/2008jhm860.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1464-1481 ◽

Cited By ~ 43

Author(s):

Xia Feng ◽

Alok Sahoo ◽

Kristi Arsenault ◽

Paul Houser ◽

Yan Luo ◽

...

Keyword(s):

Snow Cover ◽

Land Surface ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Model Complexity ◽

Snow Albedo ◽

Community Land Model ◽

Snow Model ◽

The Impact

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

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A 7-year dataset for driving and evaluating snow models at an arctic site (Sodankylä, Finland)

10.5194/gi-2015-35 ◽

2016 ◽

Cited By ~ 1

Author(s):

R. Essery ◽

A. Kontu ◽

J. Lemmetyinen ◽

M. Dumon ◽

C. B. Ménard

Keyword(s):

Wind Speed ◽

Atmospheric Pressure ◽

Snow Water Equivalent ◽

Longwave Radiation ◽

Snow Density ◽

Evaluation Data ◽

Physically Based ◽

Snow Water ◽

Snow Model ◽

Snow Temperature

Abstract. Datasets derived from measurements at Sodankylä in the Finnish Arctic that can be used for driving and evaluating snow models are presented. The driving datasets comprise all of the meteorological variables required as inputs for physically-based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically-based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees extends the duration of snow cover on the ground by several days a year.

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A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland)

Geoscientific Instrumentation Methods and Data Systems ◽

10.5194/gi-5-219-2016 ◽

2016 ◽

Vol 5 (1) ◽

pp. 219-227 ◽

Cited By ~ 19

Author(s):

Richard Essery ◽

Anna Kontu ◽

Juha Lemmetyinen ◽

Marie Dumont ◽

Cécile B. Ménard

Keyword(s):

Wind Speed ◽

Snow Water Equivalent ◽

Longwave Radiation ◽

The Arctic ◽

Evaluation Data ◽

Physically Based ◽

Snow Water ◽

Snow Model ◽

A Site ◽

First Time

Abstract. Datasets derived from measurements at Sodankylä, Finland, for driving and evaluating snow models are presented. This is the first time that such complete datasets have been made available for a site in the Arctic. The continuous October 2007–September 2014 driving data comprise all of the meteorological variables required as inputs for physically based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground. The available evaluation data include automatic and manual measurements of bulk snow depth and snow water equivalent, and profiles of snow temperature, snow density and soil temperature. A physically based snow model is driven and evaluated with the datasets to illustrate their utility. Shading by trees is found to extend the duration of both modelled and observed snow cover on the ground by several days a year.

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Modeling the Snowpack Energy Balance during Melt under Exposed Crop Stubble

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0039.1 ◽

2018 ◽

Vol 19 (7) ◽

pp. 1191-1214 ◽

Cited By ~ 8

Author(s):

Phillip Harder ◽

Warren D. Helgason ◽

John W. Pomeroy

Keyword(s):

Energy Balance ◽

Snow Water Equivalent ◽

Agricultural Practices ◽

Longwave Radiation ◽

Areal Density ◽

Turbulent Fluxes ◽

Shortwave Radiation ◽

Snow Surface ◽

Canadian Prairies ◽

Physically Based

Abstract On the Canadian Prairies, agricultural practices result in millions of hectares of standing crop stubble that gradually emerges during snowmelt. The importance of stubble in trapping wind-blown snow and retaining winter snowfall has been well demonstrated. However, stubble is not explicitly accounted for in hydrological or energy balance snowmelt models. This paper relates measurable stubble parameters (height, width, areal density, and albedo) to the snowpack energy balance and snowmelt with the new, physically based Stubble–Snow–Atmosphere Model (SSAM). Novel process representations of SSAM quantify the attenuation of shortwave radiation by exposed stubble, the sky and vegetation view factors needed to solve longwave radiation terms, and a resistance scheme for stubble–snow–atmosphere fluxes to solve for surface temperatures and turbulent fluxes. SSAM results were compared to observations of radiometric snow-surface temperature, stubble temperature, snow-surface solar irradiance, areal-average turbulent fluxes, and snow water equivalent from two intensive field campaigns during snowmelt in 2015 and 2016 over wheat and canola stubble in Saskatchewan, Canada. Uncalibrated SSAM simulations compared well with these observations, providing confidence in the model structure and parameterization. A sensitivity analysis conducted using SSAM revealed compensatory relationships in energy balance terms that result in a small increase in net snowpack energy as stubble exposure increases.

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Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model

The Cryosphere ◽

10.5194/tc-6-1323-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1323-1337 ◽

Cited By ~ 54

Author(s):

T. M. Saloranta

Keyword(s):

Snow Water Equivalent ◽

Statistical Evaluation ◽

Model Performance ◽

Spatiotemporal Variability ◽

Spatiotemporal Resolution ◽

Model Code ◽

Temperature And Precipitation ◽

Rugged Topography ◽

Snow Model ◽

Snow Conditions

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

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A Particle Filter scheme for multivariate data assimilation into a point-scale snowpack model in Alpine environment

10.5194/tc-2017-286 ◽

2018 ◽

Author(s):

Gaia Piazzi ◽

Guillaume Thirel ◽

Lorenzo Campo ◽

Simone Gabellani

Keyword(s):

Data Assimilation ◽

Particle Filter ◽

Multivariate Data ◽

Hydrological Regime ◽

Meteorological Forcing ◽

System Sensitivity ◽

Modelling Uncertainty ◽

Importance Resampling ◽

Snow Model ◽

The Impact

Abstract. The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, whose dynamics strongly affects the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims at investigating the performance of a multivariate Sequential Importance Resampling – Particle Filter scheme designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameters resampling on the filter updating of the snowpack state; (3) the system sensitivity to the frequency of the assimilated observations.

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Snow water scarcity induced by record-breaking warm winter in 2020 in Japan

Scientific Reports ◽

10.1038/s41598-020-75440-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Satoshi Watanabe ◽

Shunji Kotsuki ◽

Shinjiro Kanae ◽

Kenji Tanaka ◽

Atsushi Higuchi

Keyword(s):

Water Resources ◽

High Temperatures ◽

Water Scarcity ◽

Land Surface ◽

Snow Water Equivalent ◽

River Basins ◽

Reanalysis Data ◽

Temperature And Precipitation ◽

Snow Water ◽

The Impact

Abstract This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

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Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0140.1 ◽

2019 ◽

Vol 20 (4) ◽

pp. 577-594 ◽

Cited By ~ 2

Author(s):

Philippe Cantet ◽

M. A. Boucher ◽

S. Lachance-Coutier ◽

R. Turcotte ◽

V. Fortin

Keyword(s):

Spatial Distribution ◽

Particle Filter ◽

Snow Water Equivalent ◽

Spatial Coherence ◽

Temporal Correlation ◽

Optimal Interpolation ◽

Meteorological Forcing ◽

Temperature And Precipitation ◽

Spatial Estimates ◽

Snow Water

Abstract A snow model forced by temperature and precipitation is used to simulate the spatial distribution of snow water equivalent (SWE) over a 600 000 km2 portion of the province of Quebec, Canada. We propose to improve model simulations by assimilating SWE data from sporadic manual snow surveys with a particle filter. A temporally and spatially correlated perturbation of the meteorological forcing is used to generate the set of particles. The magnitude of the perturbations is fixed objectively. First, the particle filter and direct insertion were both applied on 88 sites for which measured SWE consisted of more or less five values per year over a period of 17 years. The temporal correlation of perturbations enables us to improve the accuracy and the ensemble dispersion of the particle filter, while the spatial correlation leads to a spatial coherence in the particle weights. The spatial estimates of SWE obtained with the particle filter are compared with those obtained through optimal interpolation of the snow survey data, which is the current operational practice in Quebec. Cross-validation results as well as validation against an independent dataset show that the proposed particle filter enables us to improve the spatial distribution of the snow water equivalent compared with optimal interpolation.

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