Snowpack Evolution Research Articles

A mechanistic ecohydrological model to investigate complex interactions in cold and warm water‐controlled environments: 1. Theoretical framework and plot‐scale analysis

Numerous studies have explored the role of vegetation in controlling and mediating hydrological states and fluxes at the level of individual processes, which has led to improvements in our understanding of plot‐scale dynamics. Relatively less effort has been directed toward spatially‐explicit studies of vegetation‐hydrology interactions at larger scales of a landscape. Only few continuous, process‐oriented ecohydrological models had been proposed with structures of varying complexity. This study contributes to their further evolution and presents a novel ecohydrological model, Tethys‐Chloris. The model synthesizes the state‐of‐the‐art knowledge on individual processes and coupling mechanisms drawn from the disciplines of hydrology, plant physiology, and ecology. Specifically, the model reproduces all essential components of the hydrological cycle: it resolves the mass and energy budgets in the atmospheric surface layer at the hourly scale, while representing up to two layers of vegetation; it includes a module of snowpack evolution; it describes the saturated and unsaturated soil water dynamics, processes of runoff generation and flow routing. The component of vegetation dynamics parameterizes life cycle processes of different plant functional types, including photosynthesis, phenology, carbon allocation, and tissue turnover. This study presents a confirmation of the long‐term, plot‐scale model performance by simulating two types of ecosystems corresponding to different climate conditions. A consistent and highly satisfactory model skill in reproducing the energy and water budgets as well as physiological cycles of plants with minimum calibration overhead is demonstrated. Furthermore, these applications demonstrate that the model permits the identification of data types and observation frequencies crucial for appropriate evaluation of modeled dynamics. More importantly, through a synthesis of a wide array of process representations, the model ensures that climate, soil, vegetation, and topography collectively identify essential modes controlling ecohydrological systems, i.e., that satisfactory performance is a result of appropriate mimicking of internal processes.

Chemical Evolution of Seasonal Snowpack in the Ganga Headwater Region, Bhagirathi Valley, Garhwal Himalaya, India

Chemical Evolution of Seasonal Snowpack in the Ganga Headwater Region, Bhagirathi Valley, Garhwal Himalaya, India

Acceleration of Land Surface Model Development over a Decade of Glass

An overview of initiatives that are a part of the Global Land Atmosphere System Study (GLASS), which has ushered in an era in which land surface models (LSM) for numerical weather and climate prediction now incorporate complex vegetation responses, detailed hydrology, dynamic snowpack evolution, urban processes, and more, is presented. A critical goal of GLASS is to expand from the uncoupled point-based (PILPS) and globally based (GSWP) evaluations to include simulations that are fully coupled with the atmosphere. GLASS includes Global Soil Wetness Project Phase (GSWP) in which the earlier GSWP-2 datasets will be extended forward to the present, enabling scientific progress toward attribution of recent changes to various components of the climate system, including the terrestrial component.

Accumulation and melt dynamics of snowpack from a multiresolution regional climate model in the central Sierra Nevada, California

[1] The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point-based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

Modeling Snow–Canopy Processes on an Idealized Mountain

Abstract Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A cone-shaped idealized mountain covered with a geometrically regular pattern of coniferous forest stands and clearings was constructed. The model was applied for three winter seasons with different snowfall intensities and distributions. Results show the effects of snow–canopy processes and interactions on the pattern of ground snow cover, its duration, and the amount of meltwater release, in addition to showing under what conditions the protective effect of a forest canopy overbalances the reduced accumulation of snow on the ground. The simulations show considerable amounts of canopy-intercepted snowfall can sublimate, leading to reduced snow accumulation beneath the forest canopy. In addition, the canopy produces a shadowing effect beneath the trees that leads to reduced radiative energy reaching the ground, reduced below-canopy snowmelt rates, and increased snow-cover duration relative to nonforested areas. During snow-rich winters, the shadowing effect of the canopy dominates and snow lasts longer inside the forest than in the open, but during winters with little snow, snow sublimation losses dominate and snow lasts longer in the open areas than inside the forest. Because of the strong solar radiation influence on snowmelt rates, the details of these relationships vary for northern and southern radiation exposures and time of year. In early and high winter, the radiation protection effect of shadowing by the canopy is small. If little snow is available, an intermittent melt out of the snow cover inside the forest can occur. In late winter and spring, the shadowing effect becomes more efficient and snowmelt is delayed relative to nonforested areas.

Upward-looking ground-penetrating radar for measuring wet-snow properties

Abstract Snow stratigraphy information is among other sources the key data for assessing avalanche danger—not only for dry snow but also for wet-snow conditions. Until now this information is obtained by traditional snow pit observations or more recently by applying more quantitative methods such as the snow micro-penetrometer or dielectric devices. All these methods are destructive and only provide a snap shot in time of snowpack evolution. We used an upward-looking ground-penetrating radar system (upGPR) to monitor snowpack evolution on a daily or, whenever necessary, hourly basis to obtain information on wet-snow properties. We focused on determining the volumetric liquid water content ( θ w ) by calculating the effective permittivity ( e eff ) of the wet snow above the radar antennas, the advance of a wetting front and the wet-snow stratigraphy. e eff was obtained using the signal velocity and snow depth recorded with nearby ultrasonic gauges; θ w was calculated with different mixing model approaches. Results were compared to in-situ measured permittivity, modelled wetting front advance and modelled and measured outflow at the bottom of the snowpack. The upGPR system clearly showed the advance of a wetting front and the arrival time was similar to the one recorded with a nearby lysimeter. Possibly weak wet layers with high liquid water content ( θ w > 6%) were detected within the radar signal by multiple reflections. However, determining the exact amount of liquid water for each layer separately is still a task for future research.

Hourly simulations of the microwave brightness temperature of seasonal snow in Quebec, Canada, using a coupled snow evolution–emission model

To interpret the snowpack evolution, and in particular to estimate snow water equivalent (SWE), passive microwave remote sensing has proved to be a useful tool given its sensitivity to snow properties. However, the main uncertainties using existing SWE algorithms arise from snow metamorphism which evolves during the winter season, and changes the snow emissivity. To consider the evolution in snow emissivity a coupled snow evolution–emission model can be used to simulate the brightness temperature (T B) of the snowpack. During a dedicated campaign in the winter season, from November to April, of 2007–2008 two surface-based radiometers operating at 19 GHz and 37 GHz continuously measured the passive microwave radiation emitted through a seasonal snowpack in southern Quebec (Canada). This paper aims at modeling and interpreting this time series of T B over the whole season, with an hourly step, using a coupled multi-layer snow evolution–emission model. The thermodynamic snow evolution model, referred as to Crocus, was driven by local meteorological measurements. Results from this model provided, in turn, the input variables to run the Microwave Emission Model of Layered Snowpacks (MEMLS) in order to predict T B at 19 GHz and 37 GHz for both vertical (V) and horizontal (H) polarizations. The accuracy of T B predicted by the Crocus–MEMLS coupled model was evaluated using continuous measurements from the surface-based radiometers. The weather conditions observed during the winter season were diverse, including several warm periods with melting snow and rain-on-snow events, producing very complex variations in the time series of T B. To aid our analysis, we identified days with melting snow versus days with dry snow. The Crocus–MEMLS coupled model was able to accurately predict melt events with a success rate of 86%. The residual error was due to an overestimation of the duration of several melt events simulated by Crocus. This problem was explained by 1) a limitation of percolation, and 2) a very long-acting melt of lower layers due to geothermal flux. When the snowpack was completely dry, the global trend of T B during the season was characterized by a decrease of T B due to growth in the snow grain size. During most of the season, Crocus–MEMLS correctly predicted the evolution of T B resulting from temperature gradient metamorphism; the root mean square errors ranged between 2.8 K for the 19 GHz vertical polarization (19V) and 6.9 K for the 37 GHz horizontal polarization (37H). However, during dry periods near the end of the season, the values of T B were strongly overestimated. This overestimation was mainly due to a limitation of the growth of large snow grains in the wet snowpack simulated by Crocus. This effect was confirmed by estimating snow grain sizes from the observed T B and the coupled model. The estimated snow grain sizes were larger and more realistic than those initially predicted by the Crocus model.

Glacier subsurface heat-flux characterizations for energy-balance modelling in the Donjek Range, southwest Yukon, Canada

AbstractWe apply a point-scale energy-balance model to a small polythermal glacier in the St Elias Mountains of Canada in order to investigate the applicability and limitations of different treatments of the glacier surface temperature and subsurface heat flux. These treatments range in complexity from a multilayer subsurface model that simulates snowpack evolution, to the assumption of a constant glacier surface temperature equal to 0°C. The most sophisticated model includes dry densification of the snowpack, penetration of shortwave radiation into the subsurface, internal melting, refreezing of percolating meltwater and generation of slush layers. Measurements of subsurface temperature and surface lowering are used for model validation, and highlight the importance of including subsurface penetration of shortwave radiation in the model. Using an iterative scheme to solve for the subsurface heat flux as the residual of the energy-balance equation results in an overestimation of total ablation by 18%, while the multilayer subsurface model underestimates ablation by 6%. By comparison, the 0°C surface assumption leads to an overestimation of ablation of 29% in this study where the mean annual air temperature is about −8°C.

Review of Snow and climate: physical processes, surface energy exchange and modeling, edited by Richard L Armstrong & Eric Brun (2008).

At its peak extent, snow can cover more than 40% of the Northern Hemisphere land surface, greatly affecting surface exchange processes, and hence global climate. Snow is one of the most dynamic surface types and its distinct physical properties imply that it requires special attention when modelling short-term weather conditions and long-term climate. Natural hazards such as avalanches, reduced visibility from blowing snow, and snowmelt-induced flooding all necessitate the precise monitoring and modelling of snowpack evolution and attendant atmospheric conditions. It is therefore critical that we develop a better understanding of snow accumulation/ablation processes and their impacts on climate.

Snow Cover Effects on Glacier Ice Surface Temperature

Snowpack evolution and glacier ice surface temperatures were studied on the Indren glacier (Northwestern Alps, Italy) under different meteorological conditions: in winter 2002–2003, rich in snow from the beginning of the season, and in winter 2005–2006, poor in snow until February. Periodical snow profiles were made to measure the physical properties of snow, while data loggers measured the snow/ice interface temperature. Furthermore, in winter 2002–2003, the influence on the snowpack evolution of an artificial increase in the snow density was evaluated.During the season rich in snow there was a prevalence of rounded crystals originated by melt-freeze metamorphism, while in the season poor in snow depth hoar and faceted crystals prevailed, due to the higher temperature gradient.From these two winter seasons, it appeared that a deep snow cover of at least 100 cm was able to maintain the snow/ice temperature at around −5°C until the snow cover reached isothermal conditions, whereas, during the winter of 2005–2006, the shallow depth of snow did not allow basal temperature to reach an equilibrium value and the snow/ice interface temperature oscillated between −2 and −8°C. The altered snow density had no effect on the snow/ice interface temperature, whereas it caused a delay in the time of reaching isothermal conditions, thus allowing snow cover on the glacier to persist longer.

Improving simulated soil temperatures and soil freeze/thaw at high‐latitude regions in the Simple Biosphere/Carnegie‐Ames‐Stanford Approach model

Proper simulation of soil temperature and permafrost at high latitudes in land surface models requires proper simulation of the processes that control snowpack development. The Simple Biosphere/Carnegie‐Ames‐Stanford Approach (SiBCASA) did not account for depth hoar development and wind compaction, which dominate snow processes at high latitudes. Consequently, SiBCASA had difficulty properly simulating seasonal soil freeze/thaw and permafrost. We improved simulated soil temperatures at high latitudes by (1) incorporating a snow classification scheme that includes depth hoar development and wind compaction, (2) including the effects of organic matter on soil physical properties, and (3) increasing the soil column depth. We ran test simulations at eddy covariance flux tower sites using the North American Regional Reanalysis (NARR) as input meteorology. The NARR captured the observed variability in air temperature, but tended to overestimate precipitation. These changes produced modest improvements in simulated soil temperature at the midlatitude sites because the original snow model already included the weight compaction, thermal aging, and melting processes that dominate snowpack evolution at these locations. We saw significant improvement in simulated soil temperatures and active layer depth at the high‐latitude tundra and boreal forest sites. Adding snow classifications had the biggest effect on simulated soil temperatures at the tundra site while the organic soil properties had the biggest effect at the boreal forest site. Implementing snow classes, a deeper soil column, or organic soil properties separately was not sufficient to produce realistic soil temperatures and freeze/thaw processes at high latitudes. Only the combined effects of simultaneously implementing all three changes significantly improved the simulated soil temperatures and active layer depth at the tundra and boreal sites.

Evaluation of miniature temperature‐loggers to monitor snowpack evolution at mountain permafrost sites, northwestern Canada

AbstractA technique to monitor snowpack development using miniature temperature‐loggers mounted in a vertical array is evaluated from nearly 100 site‐years of measurements in northwestern Canada. The method shows good agreement between interpreted values and actual snow depths checked during site visits. Inferred snowpack build‐up and ablation follow the patterns recorded at nearby climatological stations while absolute amounts and the duration of snow cover vary with elevation and vegetation type. Interpretation of snowpack evolution was possible at 93 per cent of the monitoring sites, a success rate that is judged to be acceptable given the low costs of the technique and the difficulty of obtaining the same information through other methods. Copyright © 2008 John Wiley & Sons, Ltd.

Development of the Pan-Arctic Snowfall Reconstruction: New Land-Based Solid Precipitation Estimates for 1940–99

Abstract A new product, the Pan-Arctic Snowfall Reconstruction (PASR), is developed to address the problem of cold season precipitation gauge biases for the 1940–99 period. The method used to create the PASR is different from methods used in other large-scale precipitation data products and has not previously been employed for estimating pan-arctic snowfall. The NASA Interannual-to-Seasonal Prediction Project Catchment Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. The method is tested at four stations in the United States and Canada where results are examined in depth. Reconstructed snowfall at Dease Lake, British Columbia, and Barrow, Alaska, is higher than gauge observations. Reconstructed snowfall at Regina, Saskatchewan, and Minot, North Dakota, is lower than gauge observations, probably because snow is transported by wind out of the Prairie region and enters the hydrometeorological cycle elsewhere. These results are similar to gauge biases estimated by a water budget approach. Reconstructed snowfall is consistently higher than snowfall from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) but does not have a consistent relationship with snowfall derived from the WMO Solid Precipitation Intercomparison Project correction algorithms. Advantages of the PASR approach include that 1) the assimilation of snow depth observations captures blowing snow where it is deposited and 2) the modeling approach takes into account physical snowpack evolution. These advantages suggest that the PASR product could be a valuable alternative to statistical gauge corrections and that arctic ground-based solid precipitation observing networks might emphasize snow depth measurements over gauges.

Inversion of a passive microwave snow emission model for water equivalent estimation using airborne and satellite data

Snow Water Equivalent (SWE) is an important variable in climate and hydrological studies in northern latitudes. To date, remote sensing tools, especially in the microwave domain, have shown their potential for monitoring this parameter. However, operational use at the regional and global scales needs further improvements. Strong uncertainties remain, mainly due to snow metamorphism as well to the presence of vegetation cover. The objective of this paper is to use the semi-empirical Helsinski University of Technology (HUT) snow emission model to estimate SWE with a minimum number of ancillary ground information. Non linear inversion techniques were used to compute SWE considering snow grain size as a free parameter, and initializing the inversion process with rough values of snow depth and density. Results were validated by comparing SWE estimations derived from airborne and spaceborne 19.35 and 37.0 GHz brightness temperatures to ground based SWE measurements over Central Canada. We showed that remotely sensed SWE estimations, retrieved with an average root mean square error of 24 mm, permit the monitoring of the seasonal snow pack evolution, as well as interannual variation (tested over 11 years), useful for hydrological applications.

A parameterization of the specific surface area of seasonal snow for field use and for models of snowpack evolution

The specific surface area (SSA) of snow is needed to model air‐snow exchange of chemical species. SSA is related to many snow physical properties, such as albedo and permeability. However, it is not described in models of snowpack evolution, in part because it is difficult to measure. Snowpack models often predict snow grain shape and snow density, and the goal of this paper is to propose parameterizations of snow SSA, based on snow density and grain shape. SSA values of 345 snow samples from snowpacks of the Alpine, maritime, tundra and taiga types are presented. Samples are regrouped into three main types: fresh (F), recent (R), and aged (A) snows, with several subtypes referring to grain shapes. Overall, there is a clear inverse correlation between SSA and density, d. Empirical equations of the form SSA = A ln(d) + B are proposed for the F and R types. For aged snows, separate correlations are proposed for subtypes A1 (rounded grains), A2 (faceted crystals), A3 (depth hoar), and A4 (lightly melted snow). Within subtypes A1, A2, and A3, more elaborate classifications are made by considering the snowpack type (Alpine, taiga, or tundra). For A1, A2, and A3 types, different trends are related to different intensities of wind action, which increases in the order taiga, Alpine, and tundra. We finally propose three parameterizations of snow SSA with increasing sophistication, by correlating SSA to snow type, then to snow type and density, and finally to snow type, density, and snowpack type.

A Case Study of the Energy Budget of a Snowpack in the Arid, Subtropical Climate of the Southwestern United States

ABSTRACT An extensive micrometeorological and numerical modeling case study was conducted at Happy Jack, Arizona, during hydrologic year 2003. The goal of the study was to provide an initial assessment of the potential difference in the energy budget of a snow covered surface between an arid subtropical climate and more popularly studied regions. Modeling the snowpack evolution using the SNTHERM snowmelt model produced snow water equivalent values that were in good agreement with measured values for three different modeled ablation periods. The importance of the net radiation flux in ablation of the snow pack was decreased compared to that in more traditionally studied regions due to the earlier timing of ablation and subsequent lower solar altitude angles at the Arizona site. Consequently, the relative importance of the sensible heat flux increased and the relative importance of the latent heat flux decreased. The ground heat flux, ignored in most studies, accounted for up to 18% of the melt energy in th...

Distributed energy-balance modeling of snow-cover evolution and melt in rugged terrain: Tobacco Root Mountains, Montana, USA

Summary A distributed energy-balance model was developed for simulating snowpack evolution and melt in rugged terrain. The model, which was applied to a 43-km 2 watershed in the Tobacco Root Mountains, Montana, USA, used measured ambient data from nearby weather stations to drive energy-balance calculations and to constrain the model of Liston and Sturm [Liston, G.E., Sturm, M., 1998. A snow-transport model for complex terrain. Journal of Glaciology 44 (148), 498–516] for calculating the initial snowpack thickness. Simulated initial snow-water equivalent ranged between 1 cm and 385 cm w.e. (water equivalent) with high values concentrated on east-facing slopes below tall summits. An interpreted satellite image of the snowcover distribution on May 6, 1998, closely matched the simulated distribution with the greatest discrepancy occurring in the floor of the main trunk valley. Model simulations indicated that snowmelt commenced early in the melt season, but rapid meltout of snow cover did not occur until after the average energy balance of the entire watershed became positive about 45 days into the melt season. Meltout was fastest in the lower part of the watershed where warmer temperatures and tree cover enhanced the energy income of the underlying snow. An interpreted satellite image of the snowcover distribution on July 9, 1998 compared favorably with the simulated distribution, and melt curves for modeled canopy-covered cells mimicked the trends measured at nearby snow pillow stations. By the end of the simulation period (August 3), 28% of the watershed remained snow covered, most of which was concentrated in the highest parts of the watershed where initially thick accumulations had been shaded by surrounding summits. The results of this study provide further demonstration of the critical role that topography plays in the timing and magnitude of snowmelt from high mountain watersheds.

A Coupled Model to Simulate Snow Behavior on Roads

Abstract To develop a decision-making tool for road management in winter, a numerical model resulting from the coupling of a soil model and a snow model was developed and validated using experimental results from a comprehensive experimental field campaign during three winters (1997/98, 1998/99, and 1999/2000). The coupling of the models has been done through an implicit calculation of the conduction flux between snow and road. An equivalent thermal resistance has been used to take into account the different road–snow interface configurations. For this purpose, a parameterization of water-saturated snow was introduced. This model permits the simulation of the snow behavior on a road, and it takes into account different interfacial configurations according to snow and road types and the snowpack evolution (freezing, melting, grain type). Comparisons of experimental and simulated results for typical snowfall events or over the entire winter showed that the model was able to simulate road surface temperature, snow occurrence on the road, and snow-layer evolution with good accuracy.

Simulation of snow shielding corrections for cosmogenic nuclide surface exposure studies

Snow cover reduces cosmogenic nuclide production rates in bedrock. Corrections for snow cover can be more than 10% in mountainous, mid-latitude regions where many glacial chronologies have been constructed using cosmogenic nuclide surface dating of landforms. Most published snow corrections use historic climate data of limited duration that are not likely to reflect adequately the full range of snow conditions over the time of exposure. We present a model for describing the impact of snow burial on long-term exposure histories of landforms. The model applies an energy balance approach to snowpack evolution and incorporates both historic and long-term climate proxy data. Attenuation of cosmogenic fast neutrons is modeled alternatively as a simple exponential decrease with increased shielding or as a thin surface layer with constant production followed by an exponential decrease with increasing depth. The choice of attenuation model has little effect on the modeled results for the Cairngorms but will have a more significant effect in regions characterized by thinner, less dense snowpacks. Spatial variability in snow cover is modeled as a function of elevation only, ignoring local variability in snow accumulation as a result of slope aspect, wind redistribution and local topography. Thus, model results reveal general spatial and temporal trends in snow shielding effects, rather than site-specific corrections. Applications to data from the Cairngorm Mountains of Scotland show that the constant-plus-exponential (CPE) production rate-depth profile reduces but does not eliminate snow-shielding effects. Under present-day conditions, snow at 900 m in the Cairngorm Mountains reduces average production rates by 6% using the CPE profile and 9% with the exponential profile (EP). Long-term climate simulations from 15.5 ka through today produce larger snow shielding effects, mainly because they predict an increased proportion of precipitation as snowfall during the Younger Dryas. At 900 m, this long-term simulation reduces average cosmogenic isotope production rates by 12% (CPE) and 14% (EP). These results indicate that snow-shielding corrections based on historic climate records may be a potential source of systematic error in midlatitude mountainous regions.

Numerical Simulations of MODIS Sensitivity Potential for Assessing Near Surface Mountain Snow Melt

Variations in amount of near surface snow melt produce changes in snow grain size. We evaluated Moderate Resolution Imaging Spectroradiometer (MODIS) visible and near‐infrared bands for monitoring snow pack ripeness using a Mie scattering model, coupled with a simple approach for computing directional hemispherical reflectance (albedo or total flux leaving surface divided by total irradiance). Results suggest MODIS visible and near‐infrared bands may have the potential to detect changes in grain size as an indicator of mountain snow pack evolution during the melt season. Modeled at‐sensor radiance was produced for grain size changes ranging from 10μm to 1000μm. Differences in simulated at‐sensor radiance as a function of grain size, solar zenith angle, surface slope, and amount of diffuse irradiance were compared to band specific noise equivalent radiance (NEΔL) thresholds. MODIS wavelengths greater than 667nm may be sensitive to large changes in grain sizes, particularly those bands with coarse spatial resolution (1000m). Longer wavelengths showed greater sensitivity to small changes in smaller grains than to small changes in larger grains. Shorter wavelengths with small fine spatial resolution (500m) appear less effective overall for monitoring changes in grain size. Variation in solar zenith angle has a greater impact on changes in larger grains (100‐1000μm) than smaller (10‐100μm). The overall magnitude of difference in modeled at‐sensor radiance demonstrates that the affect of diffuse irradiance may not be substantial. Surface slope does not appreciably affect changes in model at‐sensor radiances for various grain sizes. This may not be true given the model does not account for snow BRDF.