Snow Water Equivalent Product Research Articles

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.

Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.

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.

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.

Contribution of Snow-Melt Water to the Streamflow over the Three-River Headwater Region, China

Snowmelt water is essential to the water resources management over the Three-River Headwater Region (TRHR), where hydrological processes are influenced by snowmelt runoff and sensitive to climate change. The objectives of this study were to analyse the contribution of snowmelt water to the total streamflow (fQ,snow) in the TRHR by applying a snowmelt tracking algorithm and Variable Infiltration Capacity (VIC) model. The ratio of snowfall to precipitation, and the variation of the April 1 snow water equivalent (SWE) associated with fQ,snow, were identified to analyse the role of snowpack in the hydrological cycle. Prior to the simulation, the VIC model was validated based on the observed streamflow data to recognize its adequacy in the region. In order to improve the VIC model in snow hydrology simulation, Advanced Scanning Microwave Radiometer E (ASMR-E) SWE product data was used to compare with VIC output SWE to adjust the snow parameters. From 1971 to 2007, the averaged fQ,snow was 19.9% with a significant decreasing trend over entire TRHR (p < 0.05).The influence factor resulted in the rate of change in fQ,snow which were different for each sub-basin TRHR. The decreasing rate of fQ,snow was highest of 0.24%/year for S_Lantsang, which should be due to the increasing streamflow and the decreasing snowmelt water. For the S_Yangtze, the increasing streamflow contributed more than the stable change of snowmelt water to the decreasing fQ,snow with a rate of 0.1%/year. The April 1 SWE with the minimum value appearing after 2000 and the decreased ratio of snowfall to precipitation during the study period, suggested the snow solid water resource over the TRHR was shrinking. Our results imply that the role of snow in the snow-hydrological regime is weakening in the TRHR in terms of water supplement and runoff regulation due to the decreased fQ,snow and snowfall.

Improving cold-region streamflow estimation by winter precipitation adjustment using passive microwave snow remote sensing datasets

Winter precipitation estimations and spatially sparse snow observations are key challenges when predicting snowmelt-driven floods. An improvement in streamflow prediction is achieved in a snowmelt-dominant basin, i.e. the Red River Basin (RRB), by adjusting the amounts of snowfall through satellite-borne passive microwave observations of snow water equivalent (SWE). A snowfall forcing dataset is scaled to minimize the difference between simulated and observed SWE over the RRB. Advanced microwave scanning radiometer-E (AMSR-E) SWE products serve as the observed SWE to obtain the solution to the linear equation between the AMSR-E and the baseline (no snowfall-forcing adjustment) SWE to yield a multiplication factor (M factor). In the headwaters of the RRB in the United States, a Nash–Sutcliffe efficiency (NSE) of 0.74 is obtained against observed streamflow, with M factor-adjusted streamflow during the snowmelt seasons (January to April). The baseline streamflow simulation without M factor exhibits an NSE of 0.38 owing to an underestimated SWE.

Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

Using GRACE Data to Study the Impact of Snow and Rainfall on Terrestrial Water Storage in Northeast China

Water resources are important for agricultural, industrial, and urban development. In this paper, we analyzed the influence of rainfall and snowfall on variations in terrestrial water storage (TWS) in Northeast China from Gravity Recovery and Climate Experiment (GRACE) gravity satellite data, GlobSnow snow water equivalent product, and ERA5-land monthly total precipitation, snowfall, and snow depth data. This study revealed the main composition and variation characteristics of TWS in Northeast China. We found that GRACE provided an effective method for monitoring large areas of stable seasonal snow cover and variations in TWS in Northeast China at both seasonal and interannual scales. On the seasonal scale, although summer rainfall was 10 times greater than winter snowfall, the terrestrial water storage in Northeast China peaked in winter, and summer rainfall brought about only a sub-peak, 1 month later than the maximum rainfall. On the interannual scale, TWS in Northeast China was controlled by rainfall. The correlation analysis results revealed that the annual fluctuations of TWS and rainfall in Northeast China appear to be influenced by ENSO (EI Niño–Southern Oscillation) events with a lag of 2–3 years. In addition, this study proposed a reconstruction model for the interannual variation in TWS in Northeast China from 2003 to 2016 on the basis of the contemporary terrestrial water storage and rainfall data.

Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

Validation of remotely sensed estimates of snow water equivalent using multiple reference datasets from the middle and high latitudes of China

A key variable describing the mass of seasonal snow cover is snow water equivalent (SWE), which plays an important role in hydrological applications, weather forecasting and land surface process simulations. In this paper, the accuracy of an SWE product, GlobSnow-2, which combines microwave satellite data and in situ measurements, is assessed using three reference evaluation datasets north of 35°N in China. The GlobSnow-2 estimates are also compared with stand-alone satellite products (AMSR2, Chang and FY-3D SWE). The overall unbiased root mean square error (RMSE) and bias of the GlobSnow-2 SWE product validated with three reference datasets are 17.4 mm and 11.2 mm, respectively, which outperforms the AMSR2 SWE (39.3 mm and 37.3 mm, respectively) and Chang SWE (57.5 mm and 46.2 mm, respectively) products. The FY-3D SWE product performs better than the GlobSnow-2 estimate for shallow snow (SWE < 50 mm) and tends to underestimate snow cover, particularly when SWE exceeds 80 mm. A retrieval sensitivity analysis against land cover types shows that the highest SWE uncertainties for GlobSnow-2 are exhibited in grassland (unbiased RMSE, 27.8 mm), and the most serious overestimation occurs in forested areas (bias, 23.6 mm). The GlobSnow-2 performances at various elevations show an increasing bias trend, ranging from 5 to 61 mm with increasing elevation. The GlobSnow-2 estimate analyses under different snow regimes show that the GlobSnow-2 SWE product performs best in taiga snow, with high uncertainties (unbiased RMSE, 28.3 mm) in prairie snow and serious overestimations (bias, 23.2 mm) for alpine snow. The results of this study demonstrate that the GlobSnow-2 assimilation approach tends to overestimate SWE in China. One of the major reasons that overestimations occur is that the GlobSnow-2 SWE retrieval scheme utilizes a fixed density of 240 kg/m3, which is larger than the average value derived from ground measurements for China (180 kg/m3), which undoubtedly contributes to the observed SWE overestimation. Another reason is that forest effects on satellite signals remain challenging for SWE estimations in the GlobSnow-2 assimilation system. The retrieval errors in prairie and alpine are also higher than others due to the snowpack stratigraphy and complex topography. The GlobSnow-2 SWE product performance is evaluated over China in this study, and the major factors that affect the assimilation scheme accuracy are determined. These results will provide a reference to improve the GlobSnow-2 SWE product in future work.

Evaluation of long-term Northern Hemisphere snow water equivalent products

Abstract. Nine gridded Northern Hemisphere snow water equivalent (SWE) products were evaluated as part of the European Space Agency (ESA) Satellite Snow Product Intercomparison and Evaluation Exercise (SnowPEx). Three categories of datasets were assessed: (1) those utilizing some form of reanalysis (the NASA Global Land Data Assimilation System version 2 – GLDAS-2; the European Centre for Medium-Range Weather Forecasts (ECMWF) interim land surface reanalysis – ERA-Interim/Land and ERA5; the NASA Modern-Era Retrospective Analysis for Research and Applications version 1 (MERRA) and version 2 (MERRA-2); the Crocus snow model driven by ERA-Interim meteorology – Crocus); (2) passive microwave remote sensing combined with daily surface snow depth observations (ESA GlobSnow v2.0); and (3) stand-alone passive microwave retrievals (NASA AMSR-E SWE versions 1.0 and 2.0) which do not utilize surface snow observations. Evaluation included validation against independent snow course measurements from Russia, Finland, and Canada and product intercomparison through the calculation of spatial and temporal correlations in SWE anomalies. The stand-alone passive microwave SWE products (AMSR-E v1.0 and v2.0 SWE) exhibit low spatial and temporal correlations to other products and RMSE nearly double the best performing product. Constraining passive microwave retrievals with surface observations (GlobSnow) provides performance comparable to the reanalysis-based products; RMSE over Finland and Russia for all but the AMSR-E products is ∼50 mm or less, with the exception of ERA-Interim/Land over Russia. Using a seven-dataset ensemble that excluded the stand-alone passive microwave products reduced the RMSE by 10 mm (20 %) and increased the correlation from 0.67 to 0.78 compared to any individual product. The overall performance of the best multiproduct combinations is still at the margins of acceptable uncertainty for scientific and operational requirements; only through combined and integrated improvements in remote sensing, modeling, and observations will real progress in SWE product development be achieved.

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications.

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) were evaluated by seasonal snow cover and land cover classifications over the conterminous United States from 1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements). We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2 SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

AbstractIn this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite‐based SWE data. Stronger relationships were found in snow‐dominated basins (Rs &gt; = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non‐SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water‐Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE‐derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub‐basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE‐derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.

Evaluation and Intercomparison of Multiple Snow Water Equivalent Products over the Tibetan Plateau

Abstract Snow cover affects the thermal conditions of the Tibetan Plateau through snow–albedo feedback and snowmelt, which, in turn, modulates the Asian summer monsoon climate. An accurate estimation of the snow condition on the Tibetan Plateau is therefore of great importance in both seasonal forecasts and climate studies. Estimation of snow water equivalent (SWE) over the Tibetan Plateau is challenging due to the high altitude, complex terrain, and insufficient in situ observations. Multiple SWE products derived from satellite estimates, reanalyses, regional climate model simulations, and land data assimilations are intercompared in terms of daily, seasonal, and annual variations and are then evaluated against in situ SWE observations. The results show a relatively consistent seasonal to interannual variability of the SWE estimates among the products. The discrepancies in magnitude are large, however, especially in winter and spring. Evaluation against in situ SWE observations indicates that none of these products is capable of accurately characterizing both the spatial pattern and temporal variations.

The Value of Accurate High-Resolution and Spatially Continuous Snow Information to Streamflow Forecasts

Abstract Previous studies have shown limited success in improving streamflow forecasting for snow-dominated watersheds using physically based models, primarily due to the lack of reliable snow water equivalent (SWE) information. Here we use a hindcasting approach to evaluate the potential benefit that a high-resolution, spatiotemporally continuous, and accurate SWE reanalysis product would have on the seasonal streamflow forecast in the snow-dominated Sierra Nevada mountains of California if such an SWE product were available in real time. We tested the efficacy of a physically based ensemble streamflow prediction (ESP) framework when initialized with the reanalysis SWE. We reinitialized the SWE over the Sierra Nevada at the time when the Sierra Nevada had domain-wide annual maximum SWE for each year in 1985–2015, and on 1 February of the driest years within the same period. The early season forecasts on 1 February provide valuable lead time for mitigating the impact of drought. In both experiments, initializing the ESP with the reanalysis SWE reduced the seasonal streamflow forecast errors; compared with existing operational statistical forecasts, the peak-annual SWE insertion and the 1 February SWE insertion reduced the overall root-mean-square error of the seasonal streamflow forecasts by 13% and 23%, respectively, over the 13 major rivers draining the Sierra Nevada. The benefits of the reanalysis SWE insertion are more pronounced in areas with greater snow accumulation, while the complex snow and runoff-generation processes in low-elevation areas impede the forecasting skill improvement through SWE reinitialization alone.

Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data

Accurate predictions of snow characteristics have an essential function in water resources management, especially in the high mountainous areas. Remote sensing presents a possibility for snow characteristics observation, such as snow water equivalent (SWE), in the large basins. Many studies are focused on the assessment of remote sensing product, especially global SWE data. However, regional effects such as topography, land cover, and meteorological conditions may lead to uncertainty in the estimation of the snow characteristics. In this research, the Advanced Microwave Scanning Radiometer-Eos (AMSR-E) data and the GLDAS model data (2006–2011) were used to estimate SWE in the northwest basins of Iran. The evaluation was performed by the root mean square error (RMSE) and percent bias (PBIAS) criteria. The results indicated a significant correlation (at 1% level) between the observed and estimated SWE data. According to the results, the estimation accuracy decreased with increasing altitude, land slope, and the normalized difference vegetation index (NDVI). The best estimation was detected at altitudes between 1350 and 1600 m. Generally, the SWE products of the AMSR-E and GLDAS data on the north-facing slope shows good accuracy in the SWE estimation compared to the other aspects.

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia.

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed

Abstract. Hydrological modelling in the Canadian sub-Arctic is hindered by sparse meteorological and snowpack data. The snow water equivalent (SWE) of the winter snowpack is a key predictor and driver of spring flow, but the use of SWE data in hydrological applications is limited due to high uncertainty. Global re-analysis datasets that provide gridded meteorological and SWE data may be well suited to improve hydrological assessment and snowpack simulation. To investigate representation of hydrological processes and SWE for application in hydropower operations, global re-analysis datasets covering 1979–2014 from the European Union FP7 eartH2Observe project are applied to global and local conceptual hydrological models. The recently developed Multi-Source Weighted-Ensemble Precipitation (MSWEP) and the WATCH Forcing Data applied to ERA-Interim data (WFDEI) are used to simulate snowpack accumulation, spring snowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded by the European Space Agency with daily coverage from 1979 to 2014 is evaluated against in situ SWE measurement over the local watershed. Results demonstrate the successful application of global datasets for streamflow prediction, snowpack accumulation and snowmelt timing in a snowmelt-driven sub-Arctic watershed. The study was unable to demonstrate statistically significant correlations (p &lt; 0.05) among the measured snowpack, global hydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peak discharge. The GlobSnow-2 product is found to under-predict late-season snowpacks over the study area and shows a premature decline of SWE prior to the true onset of the snowmelt. Of the datasets tested, the MSWEP precipitation results in annual SWE estimates that are better predictors of snowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This study demonstrates the operational and scientific utility of the global re-analysis datasets in the sub-Arctic, although knowledge gaps remain in global satellite-based datasets for snowpack representation, for example the relationship between passive-microwave-measured SWE to snowmelt runoff volume.

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

AbstractA 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.