Snow Water Equivalent Estimates Research Articles

The Challenges of Simulating SWE Beneath Forest Canopies are Reduced by Data Assimilation of Snow Depth

AbstractIntermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling assimilation to the basin scale is its performance at forested locations—where canopy‐snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) technique to assimilate intermittent snow depth observations into the Flexible Snow Model, and validate model outputs against snow density and SWE measurements across adjacent forest and open sites, at two locations with different climates and forest structures. Assimilation reduces snow depth error by 70%–90%, density error by 5%–30%, and SWE error by 50%–70% at forest locations (relative to controls). The PF simulates the seasonal evolution of the snowpack under forest canopy, including where interception losses decrease SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). Model outputs are sensitive to canopy‐related parameters, but DA reduces the range in snow depth and SWE estimates resulting from variations or uncertainties in these parameters by over 50%. These results demonstrate that the importance of accurately measuring, estimating, or calibrating canopy‐related parameters is reduced when snow depth observations are assimilated. Finally, we assimilate remotely sensed snow depth observations at 50 m resolution across the East River Basin, Colorado (1,000 km2). Across elevations, the PF increases estimated precipitation at forested sites by ∼15% relative to open sites, likely compensating for excessive sublimation of intercepted snow.

Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System

AbstractThe NASA LIS/WRF‐Hydro system is a coupled modeling framework that combines the modeling and data assimilation (DA) capabilities of the NASA Land Information System (LIS) with the multi‐scale surface hydrological modeling capabilities of the WRF‐Hydro model, both of which are widely used in both operations and research. This coupled modeling framework builds on the linkage between land surface models (LSMs), which simulate surface boundary conditions in atmospheric models, and distributed hydrologic models, which simulate horizontal surface and sub‐surface flow, adding new land DA capabilities. In the present study, we employ this modeling framework in the Tuolumne River basin in central California. We demonstrate the added value of the assimilation of NASA Airborne Snow Observatory (ASO) snow water equivalent (SWE) estimates in the Tuolumne basin. This analysis is performed in both LIS as an LSM column model and LIS/WRF‐Hydro, with hydrologic routing. Results demonstrate that ASO DA in the basin reduced snow bias by as much as 30% from an open‐loop (OL) simulation compared to three independent datasets. It also reduces downstream streamflow runoff biases by as much as 40%, and improves streamflow skill scores in both wet and dry years. Analysis of soil moisture and evapotranspiration (ET) also reveals the impacts of hydrologic routing from WRF‐Hydro in the simulations, which would otherwise not be resolved in an LSM column model. By demonstrating the beneficial impact of SWE DA on the improving streamflow forecasts, the article outlines the importance of such observational inputs for reservoir operations and related water management applications.

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and additionally derives liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events, and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient, and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS sensors under a broad range of climatic conditions, and LWC and HS are valuable add-ons.

Testing remote sensing estimates of snow water equivalent in the framework of the European Drought Observatory

We evaluated the feasibility for operational snow drought monitoring over Europe based on the near-real-time snow water equivalent (SWE) satellite product from the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). To do so, the consistency of this dataset with the consolidated dataset of the Canadian Meteorological Centre (CMC), as well as with the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts, was tested in terms of both spatial snow coverage and detection of anomalies from the long-term climatology. The analysis confirms a general good agreement among the three products as well as substantial differences over mountainous terrains, with the H-SAF product capturing only about 30% of the areas identified by CMC as snow-covered in those areas, while a better match between the ERA5 and the CMC spatial coverage is observed. However, significant inconsistencies in the correlation between all three SWE anomalies are observed over mountain areas. Due to the lack of a reliable reference dataset, the observed inconsistencies and the coarse spatial resolution (0.25 deg) of all three products limit the possibility for snow drought monitoring over key European regions such as the Alps.

Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

Abstract Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model

This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> ] and without [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> ] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v2}}$</tex-math></inline-formula> . Comparison of SWE estimates from Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. River discharge using Sentinel-1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\rm {DA}}_{\rm {v1}}$</tex-math></inline-formula> improved the Nash–Sutcliffe efficiency at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.

The Effects of Dry Snow on the SAR Impulse Response and Feasibility for Single Channel Snow Water Equivalent Estimation

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation.

Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada

Abstract. Cold content (CC) is an internal energy state within a snowpack and is defined by the energy deficit required to attain isothermal snowmelt temperature (0 ∘C). Cold content for a given snowpack thus plays a critical role because it affects both the timing and the rate of snowmelt. Measuring cold content is a labour-intensive task as it requires extracting in situ snow temperature and density. Hence, few studies have focused on characterizing this snowpack variable. This study describes the multilayer cold content of a snowpack and its variability across four sites with contrasting canopy structures within a coniferous boreal forest in southern Québec, Canada, throughout winter 2017–2018. The analysis was divided into two steps. In the first step, the observed CC data from weekly snowpits for 60 % of the snow cover period were examined. During the second step, a reconstructed time series of modelled CC was produced and analyzed to highlight the high-resolution temporal variability of CC for the full snow cover period. To accomplish this, the Canadian Land Surface Scheme (CLASS; featuring a single-layer snow model) was first implemented to obtain simulations of the average snow density at each of the four sites. Next, an empirical procedure was used to produce realistic density profiles, which, when combined with in situ continuous snow temperature measurements from an automatic profiling station, provides a time series of CC estimates at half-hour intervals for the entire winter. At the four sites, snow persisted on the ground for 218 d, with melt events occurring on 42 of those days. Based on snowpit observations, the largest mean CC (−2.62 MJ m−2) was observed at the site with the thickest snow cover. The maximum difference in mean CC between the four study sites was −0.47 MJ m−2, representing a site-to-site variability of 20 %. Before analyzing the reconstructed CC time series, a comparison with snowpit data confirmed that CLASS yielded reasonable bulk estimates of snow water equivalent (SWE) (R2=0.64 and percent bias (Pbias) =-17.1 %), snow density (R2=0.71 and Pbias =1.6 %), and cold content (R2=0.93 and Pbias =-3.3 %). A snow density profile derived by utilizing an empirical formulation also provided reasonable estimates of layered cold content (R2=0.42 and Pbias =5.17 %). Thanks to these encouraging results, the reconstructed and continuous CC series could be analyzed at the four sites, revealing the impact of rain-on-snow and cold air pooling episodes on the variation of CC. The continuous multilayer cold content time series also provided us with information about the effect of stand structure, local topography, and meteorological conditions on cold content variability. Additionally, a weak relationship between canopy structure and CC was identified.

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Combining ground-based and remotely sensed snow data in a linear regression model for real-time estimation of snow water equivalent

Effective water resources management in California relies substantially on real-time information of snow water equivalent (SWE) at basin scale and mountain ranges given that mountain snowpacks provide the primary water supply for the State. However, SWE estimation based solely on remote sensing, modeling, or ground observations does not meet contemporary operational requirements. In this context, this study develops a data-fusion framework that combines multi-source datasets including satellite-observed daily mean fractional snow-covered area (DMFSCA), snow pillow SWE measurements, physiographic data, and historical SWE patterns into a linear regression model (LRM) to improve SWE estimates in real-time. We test two LRMs: a baseline regression model (LRM-baseline) that uses physiographic data and historical SWE patterns as independent variables, and an FSCA-informed regression model (LRM-FSCA) that includes the DMFSCA from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery as an additional independent variable. By incorporating the satellite-observed DMFSCA, LRM-FSCA outperforms LRM-baseline with increased median R2 from 0.54 to 0.60, and reduced median PBIAS of basin average SWE from 2.6% to 2.2% in the snow pillow SWE cross-validation. LRM-FSCA explains 87% of the variance in the snow course SWE measurements with 0.1% PBIAS, while LRM-baseline explains a lower 81% variance with 1.4% PBIAS, both of which show higher accuracy than SWE estimates from the two operational SWE datasets: the Snow Data Assimilation System (SNODAS, 73% and -2.4%, respectively) and Nationtional Water Model (NWM, 75% and -15.9%, respectively). Additionally, LRM-FSCA explains 85% of the median variance in the Airborne Snow Observatory SWE with -9.2% PBIAS, which is comparable to the LRM-baseline (86% and -11.3%, respectively) and considerably better than SNODAS (64% and 28.2%, respectively) and NWM (33% and -30.1%, respectively). This study shows a substantial model improvement by constraining the geographical and seasonal variation on snow-cover via satellite observation and highlights the values of using multi-source observations in real-time SWE estimation. The developed SWE estimation framework has crucial implications for effective water supply forecasting and management in California, where climate extremes (e.g., droughts and floods) require particularly skillful monitoring practices.

Solving Challenges of Assimilating Microwave Remote Sensing Signatures With a Physical Model to Estimate Snow Water Equivalent.

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships.

Using CloudSat‐Derived Snow Accumulation Estimates to Constrain Gridded Snow Water Equivalent Products

AbstractChanges in the quantity of terrestrial Arctic snow have far‐reaching implications for the global water‐energy budget, ecosystem development, and cold region flooding. Snow water equivalent (SWE) is a useful metric for monitoring these changes, however only a sparse observing network is available in the Arctic. Space‐based remote sensing offers the potential to fill these measurement gaps, and the CloudSat cloud profiling radar (CPR) has been shown to provide high‐quality estimates of surface snowfall rates across the Arctic. We propose a novel method to leverage monthly information from CloudSat to identify data quality issues in the Blended‐4 (B4) gridded SWE product. A regression of estimated monthly mean snow accumulation between CloudSat and B4 provides a confidence interval to objectively flag individual months in B4 with data quality issues. Applying this method to a case study in the Canadian high Arctic identifies an unphysical January melt event in B4 which is traced back to a measurement error in the assimilated station observations. We generalize this technique to a one‐degree grid and find a total of 4,885 cases in B4 with low data quality. We find that the low‐quality SWE product values are not random errors, and that they introduce a systematic bias of −3.1 mm on B4's estimates of mean Arctic SWE. CloudSat is uniquely positioned as one of the only observational data sets for observing snowfall at high latitudes, and when combined with the methodology developed here, its estimates can be used to further enhance the accuracy of current gridded SWE products.

Estimating Terrestrial Snow Mass via Multi‐Sensor Assimilation of Synthetic AMSR‐E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals

AbstractThis study explores multi‐sensor data assimilation (DA) using synthetic Advanced Microwave Scanning Radiometer (AMSR‐E) passive microwave brightness temperature spectral differences () and synthetic Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) retrievals to improve estimates of snow water equivalent (SWE), subsurface water storage, and TWS across snow‐covered terrain. Results show that multi‐sensor DA improves SWE estimates by reducing the RMSE by 14.1% relative to a model‐only simulation. Multi‐sensor assimilation also yields the smallest TWS RMSE (reduced by 13.0% relative to a model‐only simulation). However, multi‐sensor DA does not always yield complementary updates, and can sometimes lead to conflicting changes to SWE, where the assimilation of synthetic generates positive SWE increments while the assimilation of synthetic TWS removes SWE, which can ultimately degrade the posterior SWE estimates. This synthetic experiment provides useful insight for future DA experiments using real‐world AMSR‐E/AMSR‐2 observations and GRACE/GRACE‐FO TWS retrievals to better characterize terrestrial freshwater storage across regional scales.

Estimating wind slab thickness in a Tundra snowpack using Ku-band scatterometer observations

ABSTRACT Estimating snow water equivalent (SWE) in the northern high latitudesis important from climate, ecological and human perspectives since it enables us to track changes in spatiotemporal distribution of snow. The snow in much of this region is described as tundra, comprised of wind slab and depth hoar. Recent work in tundra environments has identified the potential of wind slab to limit radar sensitivity to SWE at 17.2 GHz, which has negative implications for SWE retrievals and demonstrates a need to constrain retrieval parameters. Radar measurements at 17.2 GHz were made in Trail Valley Creek using the University of Waterloo Scatterometer (UWScat), and combined with the Freeman-Durden polarimetric decomposition to address this need by introducing a novel relationship between wind slab thickness and double-bounce scattering, which can be used to constrain wind slab thickness. The relationship strengthens with path length through wind slab and was strong at incidence angles ≥ 46° and wind slab with thickness ≥ 19 cm. Wind slab thickness and SWE were estimated with an RMSE of 6.0 cm and 5.5 mm, respectively. This relationship is valid for use in tundra snow with depth hoar. More testing is recommended to determine the maximum detectable wind slab thickness.

Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach

Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the Överuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.

Quantifying the observational requirements of a space-borne LiDAR snow mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R &gt; 0.92) for depth measurements up to 5.5 m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of 0.108 g/cm3. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique.

GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

We describe the Northern Hemisphere terrestrial snow water equivalent (SWE) time series covering 1979–2018, containing daily, monthly and monthly bias-corrected SWE estimates. The GlobSnow v3.0 SWE dataset combines satellite-based passive microwave radiometer data (Nimbus-7 SMMR, DMSP SSM/I and DMSP SSMIS) with ground based synoptic snow depth observations using bayesian data assimilation, incorporating the HUT Snow Emission model. The original GlobSnow SWE retrieval methodology has been further developed and is presented in its current form in this publication. The described GlobSnow v3.0 monthly bias-corrected dataset was applied to provide continental scale estimates on the annual maximum snow mass and its trend during the period 1980 to 2018.

Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy

Abstract. Snow water equivalent (SWE) is an important variable in describing global seasonal snow cover. Traditionally, SWE has been measured manually at snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant snow density used in modelling emission of snow. In this paper, three versions of spatially and temporally varying snow density fields were implemented using snow transect data from Eurasia and Canada and automated snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. Decadal snow density information, i.e. fields where snow density for each day of the year was taken as the mean calculated for the corresponding day over 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.

Investigating ANN architectures and training to estimate snow water equivalent from snow depth

Abstract. Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.