Snow Water Equivalent Product Research Articles

On the Sensitivity of Future Hydrology in the Colorado River to the Selection of the Precipitation Partitioning Method

AbstractThe accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall.

Fusing daily snow water equivalent from 1980 to 2020 in China using a spatiotemporal XGBoost model

Accurate snow water equivalent (SWE) data are crucial for estimating the water supply capacity of snow and are of profound importance for the communities of hydrology, climatology, and ecology. However, existing SWE products suffer from varying degrees of uncertainty, incompleteness, and inconsistency. This study proposes a novel spatiotemporal fusion model to estimate SWE by combining multisource SWE products (GlobSnow, ERA-Interim, ERA5-Land, GLDAS, and CSWE) and spatiotemporal covariates (land cover, temperature, snow-related, geospatial, and temporal features) with the extreme gradient boosting (XGBoost) model. Specifically, the proposed spatiotemporal XGBoost model can encapsulate the advantages of multisource SWE products and address the nonlinear relations between SWE and its influencing variables. Furthermore, it embeds the geospatial and temporal features to capture the heterogeneities of SWE. Evaluation results on daily SWE records from 647 meteorological sites in China show that the proposed XGBoost SWE outperforms existing SWE products and other fusion methods (RF, SVM, RR, SA, and MLR), which demonstrates the effectiveness of the model with R values of 0.77, 0.70 and MAE and RMSE values of 7.54, 8.62 mm and 12.29, 13.73 mm based on cross-validation according to year and site, respectively. Using the proposed model, a dataset of daily SWE from 1980 to 2020 with a spatial resolution of 25 km over China is provided, aiming to deliver accurate SWE data for hydrological research and water resource management.

Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry

Abstract. Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data.

From Anatolian Plateau to American Plains: A transcontinental assessment of the EUMETSAT H SAF’s new generation snow water equivalent product over Türkiye and the conterminous U.S.

The main frame of this paper is to present the first validation results of the new generation hemi-spherical daily snow water equivalent (SWE) product of the EUMETSAT H SAF, namely, H65. It utilizes data from passive microwave radiometry sensors to estimate SWE. Operating at a suitable spatial scale, it offers insights into snow accumulation and melting dynamics, advancing satellite-based snow monitoring across diverse regions. The validation period covers the 2021 snow year, from January to March 2021, during which the dry snow conditions prevail. The validation is conducted in two distinct geographic regions, Türkiye, and the conterminous U.S. For Türkiye, the in-situ snow depth measurements provided by the Turkish State Meteorological Service are employed. On the other hand, the 1-km gridded SWE dataset of NOAA National Ice and Snow Data Center is used in the validation over the U.S. The validation results over Türkiye yields an overall RMSE of 39.27 mm, whereas it reads 15.19 mm for the U.S. These results indicate that the H65 SWE product complies with the product requirement thresholds for both flat (40 mm) and mountainous (45 mm) areas.

A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

Evaluation of snow depth from multiple observation-based, reanalysis, and regional climate model datasets over a low-altitude Central European region

This study evaluates snow depth (SD) from several data sources: a combined satellite-based and in situ snow water equivalent product from the Copernicus Global Land Service (CGLS), a dataset constructed from temperature, precipitation, and relative humidity using a snow model (CARPATCLIM), two state-of-the-art reanalyses by ECMWF (ERA5 and ERA5-Land), and Weather Research and Forecasting (WRF) regional climate simulations at grid spacings of 50 km and 10 km. SD observations from weather stations are used as a reference for the pointwise comparison. The study area covers the Pannonian Basin region (part of Central and Eastern Europe). Results are presented for the 2006–2010 and 1985–2010 periods. All datasets adequately reproduce the average day-to-day variation of SD but with different error magnitudes. The ERA5 reanalysis and the CGLS product represent SD remarkably well, with correlation coefficients above 0.9 and mean errors close to zero. On the other hand, ERA5-Land and CARPATCLIM overestimate daily mean SD by 2–3 cm for some stations and display lower correlations (0.7–0.9) during the 26-year time span. The WRF simulations significantly overestimate SD in the melting period (February–March). Reduction of the grid spacing from 50 to 10 km does not improve the results. The excessive snow cover might negatively impact land–atmosphere interactions in the model and lead to biases like temperature underestimation found in previous regional climate model evaluation studies. The results indicate that even in regions where snow is not a major climatic factor, SD errors can be substantial and should be considered in model evaluation and adaptation. Over the Carpathian Mountain ranges, SD from the different data sources diverges to the extent that the sign of the monthly mean model bias changes depending on the choice of the reference dataset.

Intercomparison of snow water equivalent products in the Sierra Nevada California using airborne snow observatory data and ground observations

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

Evaluation of passive microwave dry snow detection algorithms and application to SWE retrieval during seasonal snow accumulation

The microwave emission of snow has distinctive, frequency-dependent characteristics that enable the use of passive microwave remote sensing for the detection of (dry) snow alongside the study of further snow properties. Passive microwave approaches for the retrieval of snow water equivalent (SWE) often implement dry snow detection as one of the main processing steps. Reliable dry snow detection is thus crucial; however, common algorithms tend to underestimate snow cover extent. This study conducts a long-term assessment of six dry snow detection algorithms as part of ongoing GlobSnow SWE product development. We focus on terrestrial snow on the Northern Hemisphere, using exhaustive in situ snow depth measurements and spatially complete snow maps by the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). In addition to using conventional daily snow masks, cumulative snow masks are investigated as means to counteract underestimation and the results emphasise their potential for this purpose. Best performances are achieved by the cumulative versions of the algorithm of the EUMETSAT H SAF H11 product and of the decision tree by Grody and Basist (1996), which agree with in situ data by approximately 81% and 84% whilst differing from IMS maps by only about 15% and 11%, respectively. Their validation for hemispheric SWE retrieval in the GlobSnow framework, using in situ snow course SWE observations, shows a noticeable improvement of the current statistics especially during shallow snow conditions in autumn—reducing the bias by up to 2.2 mm and the RMSE by up to 2.0 mm. This highlights the impact that the algorithm choice for dry snow detection has on the quality of SWE retrieval and therefore on long-term climate data records.

EUMETSAT H-SAF H13 uzay tabanlı kar suyu eşdeğeri ürününün 2020-2021 kar yılı için yersel kar derinliği ölçümleriyle Türkiye üzerinde doğrulanması

Timely and consistent information on the seasonal snow cover is critical for various scientific studies and operational applications, especially for hydrological purposes. Snow water equivalent (SWE) is a significant seasonal snow parameter, which serves as a key input for many hydrological and climatological models. H13 is a SWE product supplied within the frame of EUMETSAT’s H-SAF project based on the processing of passive microwave radiometer data. The basic aim of this study is to perform a validation of H13 over Turkey for the 2020-2021 snow season by using in-situ snow depth measurements. The validation covers the period between January and March 2021, and it includes 1282 ground-based observations. According to the results, annual RMSE of the H13 SWE product is obtained as 40.00 mm, which lies within the acceptable limits of the required product compliance. The minimum and maximum snow depth measurements within the validation period are 2.80 cm and 95.34 cm, respectively. The results obtained in this validation study clearly indicate the usability of the H13 SWE product in hydrological and climatic studies.

Mapping of snow water equivalent by a deep-learning model assimilating snow observations

To estimate snowpack water storage in mountain basins, this study presents a framework that couples a deep-learning long short-term memory (LSTM) model and a zonal bias-correction approach for assimilating ground snow observations. We explored the framework’s ability for snow water equivalent (SWE) mapping and forecasting in a domain, including the Feather, Yuba-Bear, and American River basins in California’s Sierra Nevada. Through a series of different modeling experiments, performances of the LSTM model and bias-correction approach were independently investigated. The LSTM model trained with meteorological forcing (precipitation and temperature), landscape attributes, and antecedent lagged snow conditions, showed good agreement with SWE reanalysis data (Nash-Sutcliffe efficiency, NSE = 0.99). However, for continuous SWE projection without using SWE reanalysis as the antecedent snow condition, the LSTM model underestimated basin-scale and site-scale SWE (NSE = 0.19), reflecting large discrepancies since model bias accumulates over time. Thus, a zonal inverse-distance-weighting (IDW) bias-correction approach was proposed to assimilate ground observations and correct model trajectory. With the bias correction, SWE estimates were significantly improved (NSE = 0.79). The zonal IDW approach accounts for snow differences across different areas by zonal distance, based on historical snow patterns, showing better predictions than the original IDW, which showed larger biases in complex terrain with a rain shadow. We observed that SWE estimates from the framework were not sensitive to the difference in precipitation and temperature forcings between observation-based data and one-day-ahead weather forecasts, suggesting that the framework can use weather-forecast forcing for snowpack forecasting. Comparison between results from monthly and daily bias corrections showed that increasing bias-correction frequency and integrating more snow observations can further improve SWE estimates. Using the continuous SWE products, analyses of 170 precipitation events showed that snowmelt often occurred when daily temperature was above 3.7 °C, augmenting runoff. The proposed framework leverages ground observations and a deep-learning model to provide daily gridded snowpack estimates, which are important for runoff forecasting and water supply.

Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent

Abstract. Seasonal snow is an essential component of regional and global water and energy cycles, particularly in snow-dominant regions that rely on snowmelt for water resources. Land surface models (LSMs) are a common approach for developing spatially and temporally complete estimates of snow water equivalent (SWE) and hydrologic variables at a large scale. However, the accuracy of the LSM-based SWE outputs is limited and unclear by mixed factors such as uncertainties in the meteorological boundary conditions and the model physics. In this study, we assess the SWE, snowfall, precipitation, and air temperature products from a 12-member ensemble – with four LSMs and three meteorological forcings – using automated SWE, precipitation, and temperature observations from 809 Snowpack Telemetry stations over the western US. Results show that the mean annual maximum LSM SWE is underestimated by 268 mm. The timing of peak SWE from the LSMs is on average 36 d earlier than that of the observations. By the date of peak SWE, winter accumulated precipitation is underestimated (forcings mean: 485 mm vs. stations: 690 mm). In addition, the precipitation partitioning physics generates different snowfall estimates by an average of 113 mm with the same forcing data. Even though there are widespread cold biases (up to 3 ∘C) in the temperature forcings, larger ablations and lower ratios of SWE to total precipitation are found even in the accumulation period, indicating that melting physics in LSMs drives some SWE uncertainties. Based on the principal component analysis, we find that precipitation bias and partitioning methods have a large contribution to the first principal component, which accounts for about half of the total variance. The results provide insights into prioritizing strategies to improve SWE estimates from LSMs for hydrologic applications.

A Hydrograph-Based Approach to Improve Satellite-Derived Snow Water Equivalent at the Watershed Scale

For the past few decades, remote sensing has been a valuable tool for deriving global information on snow water equivalent (SWE), where products derived from space-borne passive microwave radiometers are favoured as they respond to snow depth, an important component of SWE. GlobSnow, a novel SWE product, has increased the accuracy of global-scale SWE estimates by combining remotely sensed radiometric data with other physiographic characteristics, such as snow depth, as quantified by climatic stations. However, research has demonstrated that passive microwaves algorithms tend to underestimate SWE for deep snowpack. Approaches were proposed to correct for such underestimation; however, they are computer intensive and complex to implement at the watershed scale. In this study, SWEmax information from the near real time 5-km GlobSnow product, provided by Copernicus and the European Space Agency (ESA) and GlobSnow product at 25 km resolution were corrected using a simple bias correction approach for watershed scale applications. This method, referred to as the Watershed Scale Correction (WSC) approach, estimates the bias based on the direct runoff that occurs during the spring melt season. Direct runoff is estimated on the one hand from SWEmax information as main input. Infiltration is also considered in computing direct runoff. An independent estimation of direct runoff from gauged stations is also performed. Discrepancy between these estimates allows for estimating the bias correction factor. This approach is advantageous as it exploits data that commonly exists i.e., flow at gauged stations and remotely sensed/reanalysis data such as snow cover and precipitation. The WSC approach was applied to watersheds located in Eastern Canada. It was found that the average bias moved from 33.5% with existing GlobSnow product to 18% with the corrected product, using the recommended recursive filter coefficient β of 0.925 for baseflow separation. Results show the usefulness of integrating direct runoff for bias correction of existing GlobSnow product at the watershed scale. In addition, potential benefits are offered using the recursive filter approach for baseflow separation of watersheds with limited in situ SWE measurements, to further reduce overall uncertainties and bias. The WSC approach should be appealing for poorly monitored watersheds where SWE measurements are critical for hydropower production and where snowmelt can pose serious flood-related damages.

Evaluation of High Mountain Asia‐Land Data Assimilation System (Version 1) From 2003 to 2016: 2. The Impact of Assimilating Satellite‐Based Snow Cover and Freeze/Thaw Observations Into a Land Surface Model

AbstractThis second paper of the two‐part series focuses on demonstrating the impact of assimilating satellite‐based snow cover and freeze/thaw observations into the hyper‐resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region from 2003 to 2016. To this end, this study systematically evaluates a total of six sets of 0.01° (∼1 km) model simulations forced by different precipitation forcings, with and without the dual assimilation scheme enabled, at point‐scale, basin‐scale, and domain‐scale. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near‐surface soil temperature, snow depth, snow water equivalent (SWE), and total runoff. First, the point‐scale assessment is mainly conducted via evaluating against ground‐based measurements. In general, the assimilation enabled estimates are better than no‐assimilation counterparts. Second, the basin‐scale runoff assessment demonstrates that across three snow‐dominated basins, the assimilation enabled experiment yields systematic improvements in all goodness‐of‐fit statistics through mitigating the negative effects brought by the fixed long‐term precipitation correction factors. For example, when forced by the bias‐corrected precipitation, the assimilation‐enabled experiment improves the bias by 69%, the root‐mean‐squared error by 30%, and the unbiased root‐mean‐squared error by 18% (relative to the no‐assimilation counterpart). Finally, the domain‐scale assessment is conducted via evaluating against satellite‐based SWE and skin temperature products. Both sets of domain‐scale analysis further corroborate the findings in the point‐scale evaluations. Overall, this study suggests the benefits of the proposed multi‐variate assimilation system in improving the cryospheric‐hydrological process within a land surface model for use in HMA.

Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product

The European Space Agency (ESA) Snow Climate Change Initiative (CCI+) provides long-term, global time series of daily snow cover fraction and snow water equivalent (SWE). The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) to the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is also removed.

Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach

Abstract. The snow water equivalent (SWE) is an important parameter of surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing SWE products. In the land region above 45∘ N, the existing SWE products are associated with a limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of SWE data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing SWE products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare SWE products on a global scale. We evaluated the accuracy of the RRM SWE product using hemispheric-scale snow course (HSSC) observational data and Russian snow survey data. The mean absolute error (MAE), RMSE, R, and R2 between the RRM SWE products and observed SWEs are 0.21, 25.37 mm, 0.89, and 0.79, respectively. The accuracy of the RRM SWE dataset is improved by 28 %, 22 %, 37 %, 11 %, and 11 % compared with the original AMSR-E/AMSR2 (SWE), ERA-Interim SWE, Global Land Data Assimilation System (GLDAS) SWE, GlobSnow SWE, and ERA5-Land SWE datasets, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely heavily on an independent SWE product; it takes full advantage of each SWE dataset, and it takes into consideration the altitude factor. The MAE ranges from 0.16 for areas within <100 m elevation to 0.29 within the 800–900 m elevation range. The MAE is best in the Russian region and worst in the Canadian region. The RMSE ranges from 4.71 mm for areas within <100 m elevation to 31.14 mm within the >1000 m elevation range. The RMSE is best in the Finland region and worst in the Canadian region. This method has good stability, is extremely suitable for the production of snow datasets with large spatial scales, and can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate SWE data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from “A Big Earth Data Platform for Three Poles” (https://doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

Daily snow water equivalent product with SMMR, SSM/I and SSMIS from 1980 to 2020 over China

ABSTRACT The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system. Thus, a long-time snow water equivalent (SWE) dataset is necessary. This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction. The unbiased root-mean-square error of snow depth is about 5–7 cm, corresponding to 10–15 mm for SWE, when compared with stations measurements and field snow course data. The spatial patterns and trends of SWE over China present significant regional differences. The overall slope trend presented an insignificant decreasing pattern during 1980–2020 over China; however, there is an obvious fluctuation, i.e. a significant decrease trend during the period 1980–1990, an upward trend from 2005 to 2009, a significant downward trend from 2009 to 2018. The increase of SWE occurred in the Northeast Plain, with an increase trend of 0.2 mm per year. Whereas in the Hengduan Mountains, it presented a downward trend of SWE, up to −0.3 mm per year. In the North Xinjiang, SWE has an increasing trend in the Junggar Basin, while it shows a decreasing trend in the Tianshan and Altai Mountains.

Recent Changes in Groundwater and Surface Water in Large Pan-Arctic River Basins

Surface and groundwater in large pan-Arctic river basins are changing rapidly. High-quality estimates of these changes are challenging because of the limits on the data quality and time span of satellite observations. Here, the term pan-Arctic river refers to the rivers flowing to the Arctic Ocean basin. In this study, we provide a new evaluation of groundwater storage (GWS) changes in the Lena, Ob, Yenisei, Mackenzie and Yukon River basins from the GRACE total water storage anomaly product, in situ runoff, soil moisture form models and a snow water equivalent product that has been significantly improved. Seasonal Trend decomposition using Loess was utilized to obtain trends in GWS. Changes in surface water (SW) between 1984 and 2019 in these basins were also examined based on the Joint Research Centre Global Surface Water Transition data. Results suggested that there were great GWS losses in the North American river basins, totaling approximately −219 km3, and GWS gains in the Siberian river basins, totaling ~340 km3, during 2002–2017. New seasonal and permanent SWs are the primary contributors to the SW transition, accounting for more than 50% of the area of the changed SW in each basin. Changes in the Arctic hydrological system will be more significant and various in the case of rapid and continuous changes in permafrost.

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.

Global Snow Depth Retrieval From Passive Microwave Brightness Temperature With Machine Learning Approach

Current global snow retrieval algorithms based on spaceborne microwave measurements inherit noticeable biases and uncertainties regarding spatial distribution and temporal variations. In this article, we present an improved spatiotemporally dynamic global snow depth retrieval algorithm to account for the heterogeneity of snowpacks in different seasons worldwide. The proposed model adopts nonlinear machine learning to retrieve snow depths from passive microwave measurements and other auxiliary information. We indirectly characterized the variation in snow grain size using the daily profiles of the temperature gradient within the snowpack. In addition, a zoning and multitemporal modeling strategy was employed to reduce the bias and uncertainty caused by snow heterogeneity across different ecoregions and seasons. The proposed model was implemented to retrieve the global daily snow depth from 2001 to 2010. The results were validated by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> observations and compared with the NASA Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow water equivalent product (AE_DySno). Satisfactory accuracy was achieved for different ecoregions with regard to daily, monthly, and yearly validations (the root-mean-square error (RMSE) varied from ~7.5 to ~12 cm; the Pearson correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> ranged from 0.75 to 0.85). The results of ten trials indicated the promising stability of the proposed model in different ecoregions with small variations in RMSE and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> values. Compared with the AE_DySno products, the estimation results did not exhibit the overestimation problem and provided snow depth patterns with greater spatial heterogeneity, showing RMSEs ~5 cm lower and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> values ~0.3 higher than those of the AE_DySno products.

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.