Snowpack Model Research Articles

Exploring the decision-making process in model development: focus on the Arctic snowpack

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Evaluating precipitation corrections to enhance High-Alpine hydrological modeling

Gridded meteorological data products often fall short in accurately capturing the amount of precipitation and its patterns in regions characterized by high elevations and complex topography. However, realistic precipitation data is crucial for high-alpine hydrological modeling. To address these discrepancies, we analyze possible corrections for solid, liquid and total precipitation of the 1 km2 gridded meteorological INCA-product in the high-alpine catchment of the Kölnbrein hydropower reservoir operated by VERBUND Energy4Business GmbH in the Malta Valley in Austria. By leveraging information from a stereo-satellite-derived snow depth map with physically-based snowpack modeling with Alpine3D, we quantitatively adjust and spatially redistribute solid precipitation, complemented by a multiplicative, stepwise correction model for liquid precipitation. We compare and evaluate five approaches using the hydrological COSERO model to our a) baseline simulation with no corrections on INCA in contrast of correcting, b) the amount and distribution of solely solid precipitation, c) the amount of liquid and solid precipitation, d) the amount of liquid and solid precipitation and the spatial distribution of the latter, e) precipitation inversely by the inflow bias, and f) calibrating the precipitation correction factor. In evaluating these strategies to improve the accuracy of reservoir inflow predictions, we found that separately correcting solid and liquid precipitation yielded the best results (c &d), with a substantial increase of up to 65 % over the study period (1.10.2015–30.9.2023), while the other correction variants ranged between 42 and 52 %. The inflow predictions by COSERO showed an increase in Nash-Sutcliffe Efficiency (NSE) by 17 % and in Kling-Gupta Efficiency by 57 % and 59 % for variants c and d, respectively, along with an almost complete elimination of model bias. The higher KGE values observed for variant d compared to c during spring, summer, and fall suggest that a more realistic snow distribution enhances the simulation of snowmelt-driven runoff dynamics. In contrast, using a global (i.e., spatially homogeneous) and uniform (i.e., not distinguishing between liquid and solid precipitation phase) correction factor, inversely derived from the inflow bias (e), or solely correcting solid precipitation (b), demonstrated less performance, with a KGE increase of 47 % and 49 %, respectively, compared to 59 % for variant d. Conversely, the calibration of the global and uniform correction factor (f) resulted in significant performance metric improvements (17 % NSE, 60 % KGE and 90 % pBias), similar to variant d, however also led to unrealistic simulations of evapotranspiration, sublimation and glacier net runoff. The simulated water balance components – evapotranspiration and sublimation, as well as glacier runoff – in variant d were deemed plausible based on our comparison with additional simulations using Alpine3D, as well as findings from other high-alpine catchments in Austria reported in the literature. Overall, our results underscore the importance of applying a dual correction strategy for both liquid and solid precipitation, particularly when significant deficiencies are present in meteorological datasets, and suggest that such corrections should be supplemented by a comprehensive analysis of the simulated high-alpine water balance components.

Operation, Expansion, and Improvement of the Snow Load Alert System “YukioroSignal”

The “YukioroSignal” system, which provides snow load alerts, was developed to inform decision-making regarding snow removal from house roofs. It was launched in Niigata Prefecture in 2018 and expanded to cover all special heavy snowfall areas in Japan, including the Hokkaido, Tohoku, and Hokuriku regions in 2024. The system uses the SNOWPACK model to estimate high-accuracy snow weight from real-time snow depth data published online at observation points. At locations where snow depth gauges are not installed, such as in mountainous areas, snow weight is estimated using inverse distance-weighted interpolation, but accuracy is reduced. To overcome this problem, this study attempted to integrate this information with the snow water equivalent distribution calculated using the simple-layer snow distribution model. To validate this improvement, manual observations of snow weight were performed at 98 sites and compared with simulation results. The accuracy of snow weight estimation at distances far away from snow depth stations was improved. The six-year operation of YukioroSignal showed the additional required information that is vulnerable to damage even with less snowfall, such as vacant houses, and caution of changes in hazard levels by an increase in snowburst in a short period.

The role of the cryosphere for runoff in a highly glacierised alpine catchment, an approach with a coupled model and in situ data

Abstract Runoff from heavily glacierised catchments, its seasonality and the contribution from different storage units are highly relevant for assessing the seasonal and long-term water supply and flood prediction. Modelling studies on runoff from such basins often use simulated meteorological input (e.g. downscaling products) based on remote observations. We investigate the contribution of snow, firn and ice to runoff in the Vernagtferner basin (Ötztal Alps) from 2020 to 2022, using a physical modelling chain driven by a local observation network. We use the SNOWPACK model to simulate snow/ice development at observation sites and the Alpine3D model to calculate accumulation and melt at the catchment scale. Basin discharge is estimated using a gridded version of the HBV-ETH model. This approach largely reproduces observed glacier mass balances, while modelled and measured basin discharge are in good agreement. Snowmelt dominates discharge in the early melt season, while ice melt becomes increasingly important during summer. This is in strong contrast to the near-equilibrium mass balances in the 1980s, when ice melt played a minor role for annual discharge. The strong reduction of the accumulation area leads to a fundamental change from a snowmelt regime to an ice melt regime, which is especially pronounced in 2022.

How does a dynamic surface roughness affect snowpack modeling?

The SNOWPACK model is a cryosphere model which incorporates several environmental model parameters, one of which being the aerodynamic roughness length (z0). The z0 is considered a static parameter, however, research has shown that the z0 of the surface is variable due to the changing nature of the snowpack surface throughout the winter season. This study highlights the sensitivity of the z0 within the SNOWPACK model based on the outputs of sublimation, SWE, and sensible heat. The z0 values were calculated in two ways, anemometrically (z0-A), using a wind profile, and geometrically (z0-G), measuring surface geometry. Calculated z0-A values were between 1.03 × 10−6 to 0.12 m. The z0-G values were calculated from a terrestrial lidar scan using various resolution values of post-process resolutions. These resolutions of 0.01, 0.1, and 1 m resulted in z0-G values of 0.26, 0.08, and 0.01 m, respectively. Therefore, as the resolution coarsened, the z0-G values decreased. Lastly, these calculated z0-G values, a variable run, using weekly measured z0-G values, and 0.002 (SNOWPACK default), 0.02, and 0.2 m values were incorporated into the SNOWPACK model. When applied, cumulative sublimation, SWE, and sensible heat outputs varied by 131%, −71%, and −49%, when compared to the default z0 value used within the model.

Assessing the key concerns in snow storage: a case study for China

Abstract. Snow security plays a crucial role in ensuring the success of winter sports events and supporting the ski industry. One effective approach to enhancing snow reliability is through snow storage. Despite its potential benefits, investigations of snow storage in China have been limited. To address this gap, we implemented snow storage covered with geotextiles at two venues, namely the Big Air Shougang (BAS) in Beijing for the Beijing 2022 Winter Olympic Games and the National Biathlon Center (NBC) in Chongli for the Beijing 2022 Winter Paralympic Games, in response to possible extreme meteorological conditions. To assess the key concerns associated with snow storage, ablation (the process of snow loss) and snow properties, we introduced a fine-snow-pile monitoring system and the SNOWPACK model. Our observations revealed that, by 18 February, the snow pile at BAS had lost 158.6 m3 of snow (equivalent to 6.7 % of the initial volume). Subsequently, the accelerated ablation was influenced by meteorological conditions and a thin geotextile layer. Between 16 January and 15 April, the snow pile at BAS experienced a total loss of 1242.9 m3 of snow (corresponding to 52.7 % of the original volume). Regarding snow properties, no significant variations were modeled at the study sites, except for the upper part of the snow piles. Notably, the evaporation of the geotextiles contributed to slowing the ablation process. Consequently, we discourage the use of impermeable coverage schemes for snow storage. Instead, the thickness of the cover played a vital role in preserving the snow pile. At Beijing and Chongli, the 0.7 and 0.4 m thick cover layers, respectively, were found to protect approximately half of the snow pile height over the summer season. Importantly, the evolution of snow properties was consistent across the different cover thicknesses. The findings of our study have implications for the ski industry in China, as they provide valuable insights into snow storage techniques and their impact on snow reliability.

A seasonal snowpack model forced with dynamically downscaled forcing data resolves hydrologically relevant accumulation patterns

The Mountain snowpack stores months of winter precipitation at high elevations, supplying snowmelt to lowland areas in drier seasons for agriculture and human consumption worldwide. Accurate seasonal predictions of the snowpack are thus of great importance, but such forecasts suffer from major challenges such as resolving interactions between forcing variables at high spatial resolutions. To test novel approaches to resolve these processes, seasonal snowpack simulations are run at different grid resolutions (50 m, 100 m, 250 m) and with variable forcing data for the water year 2016/2017. COSMO-1E data is either dynamically downscaled with the High-resolution Intermediate Complexity Atmospheric Research (HICAR) model or statistically downscaled to provide forcing data for snowpack simulations with the Flexible Snowpack Model (FSM2oshd). Simulations covering complex terrain in the Swiss Alps are carried out with the operational settings of the FSM2oshd model or with a model extension including wind- and gravitational-induced snow transport (FSM2trans). The simulated snow height is evaluated against observed snow height collected during LiDAR flights in spring 2017. Observed spatial snow accumulation patterns and snow height distribution are best matched with simulations using dynamically downscaled data and the FSM2trans model extension, indicating the importance of both accurate meteorological forcing data and snow transport schemes. This study demonstrates for the first time the effects of applying dynamical downscaling schemes to snowpack simulations at the seasonal and catchment scale.

From snow accumulation to snow depth distributions by quantifying meteoric ice fractions in the Weddell Sea

Abstract. Year-round snow cover is a characteristic of the entire Antarctic sea ice cover, which has significant implications for the energy and mass budgets of sea ice, e.g., by preventing surface melt in summer and enhancing sea ice growth through extensive snow ice formation. However, substantial observational gaps in the seasonal cycle of Antarctic sea ice and its snow cover limit the understanding of important processes in the ice-covered Southern Ocean. They also introduce large uncertainties in satellite remote sensing applications and climate studies. Here we present results from 10 years of autonomous snow observations from Snow Buoys in the Weddell Sea. To distinguish between actual snow depth and potential snow ice thickness within the accumulated snowpack, a one-dimensional thermodynamic sea ice model is applied along the drift trajectories of the buoys. The results show that potential snow ice formation, with an average maximum thickness of 35 cm, was detected along 41 % of the total track length of the analyzed Snow Buoy tracks, which corresponds to about one-quarter of the snow accumulation. In addition, we simulate the evolution of internal snow properties along the drift trajectories with the more complex SNOWPACK model, which results in superimposed ice thicknesses between 0 and 14 cm on top of the snow ice layer. These estimates will provide an important reference dataset for both snow depth and meteoric ice rates in the Southern Ocean.

The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier

Abstract. The McMurdo Dry Valleys (MDV) are home to a unique microbial ecosystem that is dependent on the availability of freshwater. This is a polar desert and freshwater originates almost entirely from surface and near-surface melt of the cold-based glaciers. Understanding the future evolution of these environments requires the simulation of the full chain of physical processes from net radiative forcing, surface energy balance, melt, runoff and transport of meltwater in stream channels from the glaciers to the terminal lakes where the microbial community resides. To establish a new framework to do this, we present the first application of WRF-Hydro/Glacier in the MDV, which as a fully distributed hydrological model has the capability to resolve the streams from the glaciers to the bare land that surround them. Given that meltwater generation in the MDV is almost entirely dependent on small changes in the energy balance of the glaciers, the aim of this study is to optimize the multi-layer snowpack scheme that is embedded in WRF-Hydro/Glacier to ensure that the feedbacks between albedo, snowfall and melt are fully resolved. To achieve this, WRF-Hydro/Glacier is implemented at a point scale using automatic weather station data on Commonwealth Glacier to physically model the onset, duration and end of melt over a 7-month period (1 August 2021 to 28 February 2022). To resolve the limited energetics controlling melt, it was necessary to (1) limit the percolation of meltwater through the ice layers in the multi-layer snowpack scheme and (2) optimize the parameters controlling the albedo of both snow and ice over the melt season based on observed spectral signatures of albedo. These modifications enabled the variability of broadband albedo over the melt season to be accurately simulated and ensured that modelled surface and near-surface temperatures, surface height change and runoff were fully resolved. By establishing a new framework that couples a detailed snowpack model to a fully distributed hydrological model, this work provides a stepping stone to model the spatial and temporal variability of melt and streamflow in the future, which will enable some of the unknown questions about the hydrological connectivity of the MDV to be answered.

Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests

Abstract. The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface model against surface energy flux, snow depth and soil temperature observations from four eddy-covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

Wet Snow Detection From Satellite SAR Images by Machine Learning With Physical Snowpack Model Labeling

A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion, and settlement in dry snow (IvoriFEM v0.1.0)

Abstract. The poor treatment (or complete omission) of water vapor transport has been identified as a major limitation suffered by currently available snowpack models. As vapor and heat fluxes are closely intertwined, their mathematical representation amounts to a system of nonlinear and tightly coupled partial differential equations that are particularly challenging to solve numerically. The choice of the numerical scheme and the representation of couplings between processes are crucial to ensure an accurate and robust solution that guarantees mass and energy conservation while also allowing time steps in the order of 15 min. To explore the numerical treatments fulfilling these requirements, we have developed a highly modular finite-element program. The code is written in Python. Every step of the numerical formulation and solution is coded internally, except for the inversion of the linearized system of equations. We illustrate the capabilities of our approach to tackle the coupled problem of heat conduction, vapor diffusion, and settlement within a dry snowpack by running our model on several test cases proposed in recently published literature. We underline specific improvements regarding energy and mass conservation as well as time step requirements. In particular, we show that a fully coupled and fully implicit time-stepping approach enables accurate and stable solutions with little restriction on the time step.

Geotextile protection of glacier: Observed and simulated impacts on energy and mass balance

The detailed physical processes involved in slowing glacier ablation by material cover remain poorly understood so far. In the present study, using the snow cover model SNOWPACK, the effect of geotextile cover on the energy and mass balance at the tongue of the Urumqi Glacier No. 1 (Chinese Tien Shan) was simulated between July 12, 2022 and August 31, 2022. The mass changes and the energy fluxes with and without material cover were compared. The results indicated that the geotextile covering reduced glacier ablation by approximately 68% compared to the ablation in the uncovered regions. The high solar reflectivity of the geotextile reduced the net short-wave radiation energy available for the melt by 45%. Thermal insulation of the geotextile reduced the sensible heat flux by 15%. In addition, the wet geotextile exerted a cooling effect through long-wave radiation and negative latent heat flux. This cooling effect reduced the energy available for ablation by 20%. Consequently, only 37% of the energy was used for melting compared to that used in the uncovered regions (67%). Sensitivity experiments revealed that the geotextile cover used at a thickness range of 0.045–0.090 m reduced the ice loss by approximately 68%–72%, and a further increase in the thickness of the geotextile cover led to little improvements. A higher temperature and greater wind speed increased glacier ablation, although their effects were small. When the precipitation was set to zero, it led to a significantly increased melt. Overall, the geotextile effectively protected the glacier tongue from rapid melting, and the observed results have provided inspiration for developing an effective and sustainable approach to protect the glaciers using geotextile cover.

Snow‐Atmosphere Humidity Exchange at the Ice Sheet Surface Alters Annual Mean Climate Signals in Ice Core Records

AbstractSurface processes alter the water stable isotope signal of the surface snow after deposition. However, it remains an open question to which extent surface post‐depositional processes should be considered when inferring past climate information from ice core records. Here, we present simulations for the Greenland Ice Sheet, combining outputs from two climate models with an isotope‐enabled snowpack model. We show that surface vapor exchange and associated fractionation imprint a climate signal into the firn, resulting in an increase in the annual mean value of δ18O by +2.3‰ and a reduction in d‐excess by −6.3‰. Further, implementing isotopic fractionation during surface vapor exchange improves the representation of the observed seasonal amplitude in δ18O from 65.0% to 100.2%. Our results stress that surface vapor exchange is important in the climate proxy signal formation and needs consideration when interpreting ice core climate records.

Sensitivity of the MAR regional climate model snowpack to the parameterization of the assimilation of satellite-derived wet-snow masks on the Antarctic Peninsula

Abstract. Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections.

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years. To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother). The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

Estimation of the Snow Water Equivalent Using Muon Scattering Radiography

AbstractDespite the important hydrological and ecological implications of the snowpack, its real time monitoring remains challenging. This is particularly relevant in relation to the Snow Water Equivalent (SWE), as the available technologies which measure it, exhibit a number of limitations that hinder their operational implementation. In this work, we explore the potential of a new technology, Muon Scattering Radiography, to infer the SWE. We coupled snowpack simulations generated by the SNOWPACK model, with a muon scattering simulation program based on GEANT4. The SWE is modeled as a function of the muon scattering distributions. Predictions of the SWE along the year are provided showing a root‐mean‐square error of 12 mm for 5 hr continuous measurements. We also performed laboratory measurements using ice samples, confirming the SWE estimation capabilities and the potential of the technique to operate as a SWE monitoring tool.

Scale Dependence of Errors in Snow Water Equivalent Simulations Using ERA5 Reanalysis over Alpine Basins

This study aims to evaluate the potential of ERA5 precipitation and temperature reanalysis for snow water equivalent (SWE) simulation by considering the role of catchment spatial scale in controlling the errors obtained by comparison with corresponding SWE simulations from ground stations. This is obtained by exploiting a semi-distributed snowpack model (TOPMELT) implemented over the upper Adige River basin in the Eastern Italian Alps, where 16 sub-catchments of varying sizes are considered. The comparison is carried out from 1992 to 2019. The findings show that ERA5 precipitation overestimated low-intensity rainfall (drizzle problem) and underestimated high-intensity rainfall, while ERA5 temperature underestimated observations. The overestimation of low-intensity rainfall created fictitious low-intensity snowfall events, which, when combined with colder ERA5 temperature, resulted in delayed snowmelt and increased fictitious snow-cover days over the study area. The quantile mapping (QM) technique was used to remove errors in ERA5 variables. It was shown that ERA5 could struggle to resolve the orographic enhancement in precipitation, which may be particularly important during high-SWE years. This reduces the positive precipitation bias during those years, thus reducing comparatively the ability of the quantile mapping technique to correct for bias homogeneously during all years. This study highlighted the importance of temperature correction over precipitation correction in SWE simulation, particularly for smaller basins.

CRYOWRF—Model Evaluation and the Effect of Blowing Snow on the Antarctic Surface Mass Balance

AbstractThe surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very simplified way in current meteorological and climatological models. To address this problem, the CRYOWRF model has been developed, a coupled model between the meteorological Weather Research and Forecasting model (WRF) and the snow model SNOWPACK, augmented by a detailed treatment of drifting and blowing snow. Applying CRYOWRF to the SMB of Antarctica, we find that the model reproduces measurements of SMB with similar errors than current models. Drifting and blowing snow and its sublimation play a particularly important role, especially in regions of strong katabatic winds. The CRYOWRF simulations are also in line with satellite estimates of blowing snow frequency. There is a need to further consolidate results by simulations with a higher grid resolution and by including more measurements of SMB contributions from snow fall to transport and sublimation.