Generally, three approaches have been presented to simulate snowpack variations, including in situ measurements, reconstruction, and space-borne measurements. In situ
SWE measurements include the direct and indirect methods
[20]. Direct methods, such as snow pillows, measure
SWE by weighing the mass of a snow column, while indirect methods of ground-based
SWE measurement are based on the attenuation of gamma radiation by water molecules. In this way, the difference in gamma radiation levels emitted from the surface (before and after snowfall) is converted to real-time
SWE via an optimized coefficient. In situ measurement is the most reliable approach for the determination of snow characteristics; however, the limited spatial extent of stations and snow spatial heterogeneities make the method inefficient on a large scale
[21][22]. As an alternative method,
SWE reconstruction is based on a backward melt calculation by the remotely sensed SCA and energy-based snowmelt to temporally and spatially calculate
SWE for the snow accumulation season
[23][24]. Although the in situ data requirement of the reconstruction method is limited, the
SWE results are only valid for the melt season, leading to its inefficiency for seasonal prediction. Furthermore, the method is sensitive to some factors that can lead to uncertainties if not accurately determined. Those factors include the SCA data, dates of snow disappearance and peak
SWE, and the snowmelt estimation methods. These factors can lead to uncertainties if they are not accurately determined.
To understand the relation between PMW brightness temperature and snowpack properties, different inversion models have been developed based on both simple and complicated concepts. Traditional inversion models are defined as the empirical relationships between T
B difference (at two low and high frequencies) and
SWE [30]. Accordingly, various static and dynamic algorithms have been developed as a result of the different choices of microwave frequency channels as well as coefficient estimation. In the algorithms based on T
B differences, the effect of the physical temperature of snowpack on the brightness temperature is thwarted because of the signal difference, and the saturation effect for thick snow can be also alleviated, to some extent, due to the use of two different low frequencies
[31][32]. However, the interlayer scattering intensifies the saturation issue by reason of different electromagnetic characteristics, which leads to temporally inconsistent
SWE results
[31]. An important approach to overcome the weakness of these methods is the use of attenuation concepts of snow radiation, i.e., physically based statistical algorithms and radiative transfer equation (RTE)-based models, and energy and water balance concepts, i.e., land surface models (LSMs) and snowpack modules. The statistical
SWE retrieval approach is based on a regression relationship between snow water equivalent and snowpack attenuation and radiation properties at different polarizations and frequencies
[33]. The RTE-based microwave emission models, as more sophisticated
SWE estimation structures, are capable of providing snow parameters such as snow depth and density by matching the simulated and observed T
B [34]. These models describing the microwave emission or scattering properties in snow medium and radiative transfer (RT) in the air–snow–soil system are different in many aspects, such as the number of layers, rough or smooth interfaces, grain size parametrization, scattering coefficient, and the solution method of the RTE
[35]. As a general classification, snow emission models have been categorized based on different theoretical foundations including semi-empirical, analytical, and numerical concepts
[15]. The semi-empirical approach, which benefits from a simple, stable structure, is based on ground measurements with a limited range of validity. The analytical approach uses Maxwell’s equations with physical input parameters; however, it assumes a simplified structure to simulate the snow. In contrast, the numerical approach accounts for certain snow structures, and scattering or emission characteristics are achieved numerically with less simplification. This approach, despite being more time-consuming, provides more accurate simulations in comparison with other types. In addition to snow emission models, the mass and energy balance of the snowpack is used to simulate snow accumulation and melting processes. The most important LSMs are the Biosphere-Atmosphere Transfer Scheme (BATS), Simple Biosphere (SiB), Variable Infiltration Capacity (VIC), Mosaic, and Noah model, which have been discussed in
[36]. It should be noted that although LSMs and snowpack simulation modules are capable of estimating
SWE with some degree of accuracy, the results have shown large uncertainties in space and time
[37].
2. SWE Products
The generation of an accurate dataset across all snowy regions, because of widely spatial changes in snow properties, requires a physical method to robustly simulate snowpack
[18] or a regional method to statistically parameterize the snowpack processes
[44]. The application of the statistical approach is mostly limited to the regions with calibrated retrieval schemes while the physical approach, unlike its challenging implementation, has widely applicable potential. Generally, available
SWE products are classified into three main groups, including satellite, reanalysis, and data assimilation datasets, explained as follows and summarized in
Table 1.
2.1. Satellite SWE Products
The satellite
SWE datasets are based on the brightness temperature observations retrieved from the passive microwave data without any ancillary data. Most
SWE algorithms employed by space-borne observers use the combination of channels with 37 (sensitive to scattering by snow) and 19 (low-sensitive to scattering by snow) GHz. The difference of T
B at these channels, with the benefit of reducing the physical temperature effect on measured brightness temperature relative to single-frequency analysis, shows the
SWE value
[45]. Furthermore, both vertically (V) and horizontally (H) polarized channels can be used to retrieve
SWE with similar results; however, V-based channels are preferred due to less sensitivity to snow layering
[46]. The
SWE variable detected using space-borne sensors has acceptable accuracy over regions with consistent snow properties and insignificant altitudinal and vegetation variability. The most common satellite-based
SWE products are listed as follows:
The
Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E;
[47]) is a microwave radiometer onboard the Aqua satellite with microwave frequencies of 6.9, 10.7, 18.7, 23.8, 36.5, and 89 GHz in vertical and horizontal polarization. AMSR-E produces global
SWE observations based on the algorithm introduced by Chang et al. (1987)
[30] and amended by Kelly et al. (2003)
[48]. This product distinguishes the shallow and non-shallow dry snowpack by brightness temperature thresholds
[48] and estimates
SWE using the brightness temperature difference at two frequencies: 19 and 37 GHz
[49]. In addition to considering the snow grain size variations, the AMSR-E product uses the 10.7 GHz frequency to take into account the vegetation influence for deeper snow. The snow density from
[50] and
[51] and snow climate classification from
[52] are used to convert the snow depth to
SWE.
The Scanning Multichannel Microwave Radiometer (SMMR;
[53]) is a microwave instrument onboard NIMBUS-7 with frequencies of 6.6, 10.7, 18, 21, and 37 GHz in both vertical and horizontal polarization
[54]. The microwave frequencies for
SWE retrieval are 18 and 37 GHz
[45].
The
Special Sensor Microwave/Imager (SSM/I;
[55]) is an instrument onboard the DMSP F-series satellites with microwave frequencies of 19, 22, 37, and 85 GHz. All channels, except 22 GHz, which has only vertical polarization, include both vertical and horizontal polarization
[54]. In addition, the 19 and 37 GHz frequencies are employed to estimate
SWE data.
The
Advanced Microwave Scanning Radiometer 2 (AMSR2;
[47]) is a remote sensing instrument onboard the GCOM-W1 satellite launched by the JAXA, and produces global
SWE estimates by measuring weak microwave emission.
The
Advanced Microwave Sounding Unit (AMSU;
[56]) is a microwave radiometer onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES), and has two modules: AMSU-A and AMSU-B, with the nadir resolutions of 48 km and 16 km, respectively.
SWE retrieval is based on an empirical relationship that uses brightness temperature measurements at 23, 31, and 89 GHz channels. Despite the coarse spatial resolution, AMSU has larger spatial coverage and more additional channels in comparison with AMSR-E and SSM/I, which makes the
SWE estimates more robust
[56].
2.2. Reanalysis SWE Products
In reanalysis products, the best features of observations and models are used to recreate the climate variables field
[54]. The most widely used reanalysis
SWE products are presented below:
The National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR;
[57]) presents a reanalysis dataset, which is achieved by combining the globally coupled land–atmosphere–ocean–sea ice system and global precipitation analyses obtained from the Climate Prediction Center Unified (CPCU) daily gauge analysis datasets
[58] and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP;
[59]). The snowpack simulation is performed by the Noah land surface model
[60], and the CFSR snow analysis is based on the Snow Depth model (SNODEP)
[61]. The SNODEP assimilates the snow cover area obtained from the National Environmental Satellite Data and Information Service (NESDIS), Interactive Multi-sensor Snow and Ice Mapping System (IMS;
[62]), and in situ observations to produce a global snow depth analysis through the detection algorithm of the SSM/I. The SNODEP model has low accuracy in estimating
SWE due to the sparse in situ measurements
[63]. It should be noted that a 10:1 ratio is utilized to convert the snow depth data to
SWE.
ERA40 [64] is a common reanalysis product from the European Centre for Medium-Range Weather Forecasting (ECMWF). The data assimilated within ERA40 include observational snow depths and SSM/I brightness temperatures. Reichler and Kim (2008)
[65] introduced ERA40 as one of the best available reanalysis products.
ERA-Interim (ERA-I;
[66]) is a global reanalysis dataset obtained from the ECMWF. The land component of ERA-I called the Tiled ECMWF Scheme of Surface Exchanges over Land (TESSEL) is driven using the ECMWF with the snow module proposed by Douville et al. (1995)
[67]. ERA-I updates the snow analysis based on the assimilated IMS snow cover and the Cressman analysis of snow depth observations
[68][69]. According to
[70], ERA-I detects the melt season for forested areas too early and also underpredicts snowfall values. Moreover, Kapnick and Delworth (2013)
[71] reported a negative bias for ERA-Interim
SWE in coastal areas and a snow underestimation for locations with midlatitude.
ERA-Interim/Land (ERA-I/L;
[72]) is another reanalysis dataset achieved from the ECMWF. The land component of the ERA-I/L is based on the offline Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) model, which is a revised version of TESSEL with a simple, single layer snowpack module developed by Dutra et al. (2010)
[70]. The forcing data include the ERA-Interim atmospheric dataset and precipitation modified using the Global Precipitation Climatology Project (GPCP;
[73]). The ERA-I/L, unlike the ERA-I, does not assimilate any station-based snow data into the model.
ERA5, the ECMWF Reanalysis version 5, is similar to ERA-I/L in terms of its land component and snowpack module. The dataset updates the snow reanalysis using a two-dimensional optimal interpolation of in situ snow depth and IMS snow cover.
ERA5-Land [74] is the improved version of ERA5, which provides global snow data, including snow cover, albedo, density, temperature, depth, snowfall, snowmelt, and
SWE from 1981 onwards.
Crocus [75] has been obtained from the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model forced by ERA-Interim meteorological data. The snowpack module of the dataset is the Crocus snow scheme, which is a complex physically based snowpack model with multiple snow layers representing distinct snowfall events. Each snow layer is identified by the liquid water content, density, temperature, thickness, and grain properties.
Japanese 55-year Reanalysis (JRA-55;
[76]) simulates the land component based on the offline SiB model
[77] driven by precipitation that is revised using precipitable water (PW), which is obtained from the SSM/I brightness temperature
[78]. The snow analysis is carried out based on the two-dimensional optimal interpolation of observed snow depth and snow cover data retrieved from the Special Sensor Microwave Imager/Sounder (SSMIS) and SSM/I. It should be noted that the SiB uses the maximum depth between 2 cm and the climatological depth to simulate ice sheets due to the lack of the physical processes for ice sheets within the SiB model.
Modern-Era Retrospective Analysis for Research and Applications (MERRA;
[79][80]) is a global reanalysis product that assimilates NASA’s EOS satellite observations into a climate model
[79]. The land component of the dataset is simulated using the Catchment model
[81] driven by precipitation that is modified based on the CPCU and CMAP data, similar to the CFSR. The snow scheme of the model has an intermediate complexity with up to three snow layers, in which the processes of snow accumulation, melting, compaction, and refreezing are simulated without any snow data assimilation
[82]. The snow depth data derived from MERRA showed a bias of 21.0 cm and a correlation of 0.56 in comparison with in situ observations of the World Meteorological Organization (WMO). By rerunning the land surface component with atmospheric data of the MERRA product, except for precipitation that is obtained from NOAA’s CPCU dataset, the MERRA-Land model has been produced.
2.3. Data Assimilation-Based SWE Products
Examples of DA-based products, which estimate SWE data by combining the land surface models and snow modules with different types of observations, are listed below:
The
Global Land Data Assimilation System (GLDAS;
[83]) provides a DA-based dataset using the different forcing data and land surface models. The first version of GLDAS uses the NOAA Global Data Assimilation System (GDAS) with precipitation obtained from the CPC CMAP, while the GLDAS version 2 is forced by the Global Meteorological Forcing Dataset from Princeton University. On the other hand, the land surface models used to estimate
SWE include the VIC land surface model
[84]; the Mosaic land surface model
[85], the Community Land Model (CLM)
[86], and the Noah land surface model. The structure of snowpack modules implemented in land surface models varies from simple single-layer structures (in the Mosaic and Noah models) to intermediate complex structures (in VIC and CLM). It is worth noting that no snow data assimilation is carried out in the GLDAS dataset.
The
North American Land Data Assimilation System (NLDAS;
[87][88][89]) is a DA-based reanalysis product, with forcing data obtained from the North American Regional Reanalysis (NARR) model
[90] and in situ precipitation of the CPC adjusted by the Parameter-Elevation Regressions on Independent Slopes Model (PRISM)
[91]. Land surface models used within the NLDAS include VIC, Mosaic, and Noah.
Sierra Nevada Snow Reanalysis (SNSR;
[92]) is another assimilation-based reanalysis dataset, with input data obtained from the NLDAS dataset. The
SWE estimates are updated and constrained by the Landsat snow cover fraction data, leading to optimal results. Though the SNSR considers the canopy effect on the snow accumulation and melting processes, the forest cover has no significant effect on the reanalysis algorithm on a watershed scale. According to an extensive validation of the dataset against in situ data, a bias of less than 3 cm, R greater than 0.95, and RMSE less than 13 cm were achieved
[92].
The
Canadian Meteorological Centre (CMC;
[93]) is an assimilation-based dataset providing daily snow depth across the Northern Hemisphere through integrating in situ snow depth and model simulations. Using the snow density derived from snow course measurements, the
SWE data with a monthly temporal scale are produced. The results mostly depend on the model simulation in regions with few observations, although
SWE is well-constrained by observational data in areas with a dense network. According to
[94], the CMC, in comparison with snow course data, tends to estimate a little snow cover during the summer and begins the snowmelt in the spring too early.
North American Regional Reanalysis (NARR;
[90]) is an assimilation-based dataset over North America generated by NCEP. The NARR dataset reaches peak
SWE nearly three months prior to the Snowpack Telemetry (SNOTEL) data
[95]. In addition, it is found that NARR is incapable of indicating an obvious annual cycle for
SWE so that the melting and accumulation processes occur multiple times throughout the season. In this regard, Salzmann and Mearns (2012)
[95] concluded that NARR is more appropriate for snow cover diagnosis than
SWE, possibly owing to its poor correlation with SNOTEL
SWE.
The
National Weather Service Snow Data Assimilation System (SNODAS;
[96]) is another assimilation-based dataset, developed by the National Weather Service (NWS), and produces the
SWE data over the continental United States by merging station data, snow model simulations, and airborne observations. The assimilated data include satellite snow cover, radar precipitation data, airborne gamma radiation, and in situ measurements of snow courses and snow pillows. It has been shown that SNODAS performs well in forest regions, and conversely has a poor performance in alpine areas
[97][98]. Furthermore, SNODAS underestimates snow depth for deep snowpacks and dense canopies
[98].
Global Snow Monitoring for Climate Research (GlobSnow;
[42]) is a global dataset released by the European Space Agency (ESA), which combines satellite-based PMW with ground-based snow depth measurements to quantify
SWE using Bayesian non-linear iterative assimilation
[42][99]. The retrieval scheme is based on the HUT model and vertically polarized observations of brightness temperature at 19 and 37 GHz obtained from the SSM/I, SMMR, and SSMIS instrument on the DMSP
[100]. According to
[42], the kriged snow depth field is used as the input for the forward microwave emission model to estimate the snow grain size and snow depth in two iterations. The algorithm accounts for the effect of forest transmissivity and snow density variation on the snow depth by the empirical model presented by Kruopis et al. (1999)
[101] to refine the brightness temperature. It should be noted that the alpine regions are excluded from the simulation process as the approach is incapable of estimating
SWE in complex terrains
[42]. The comparisons of
SWE retrievals with airborne or ground-based observations showed that the model is capable of simulating snow water equivalent under various land cover types and snow conditions
[102][103]. In addition, the model demonstrated comparable performance with other models driven by ground-based data
[104][105], and better performance than other
SWE products
[42]. However, it performs poorly in wet snow as well as deep dry snowpack, due to greater absorption of microwave signals rather than scattering
[42][99][104].
Table 1. A summary of available SWE datasets.