Comment on essd-2021-344

The article presents a comprehensive multilevel accuracy evaluation of global snow water equivalent (SWE) datasets performed for the territory of the Northern Dvina River basin, where snow plays an important role in the formation of spring runoff and the increase in winter runoff during recent decades due to ongoing climate change. Six global snow water equivalent datasets, available on the National Snow and Ice Data Center website (all currently available global SWE data), were used in this study. Snow survey data of the Roshydromet observation network were used as ground-based observations. The accuracy of dataset values of SWE was evaluated by comparison with ground observations in several successive stages. In the first stage, the global SWE values were compared with the observational data for several selected weather stations, i. e., the dataset values of SWE belonging to a particular grid cell were compared with the snow survey values of a weather station located in this cell. In the second stage, the SWE values averaged over one of the main subbasins of the Northern Dvina, the Small Northern Dvina River basin, were analyzed. The final stage was the SWE comparison for the entire Northern Dvina River basin. The methodology for ranking data depending on their accuracy based on comparison with ground-based observations of several basic parameters of quantitative changes in SWE during periods of formation and before the start of melting (maximum SWE) was specially developed to implement the accuracy evaluation and SWE comparison. A comprehensive comparative analysis performed based on the developed methodology revealed that the accuracy of global SWE data increases with the investigation area (i.e., the volume of information used) for which the comparison is being carried out. The closest to the observed SWE values for all datasets were obtained for the whole Northern Dvina River basin. The ranking methodology and the implemented analysis helped to determine the most promising global SWE data for future use.

<p>Satellite observations are the only means for timely and complete observations of the global snow cover. A range of different satellite snow products is available, the performance of which is of vital interest for the global user community. We provide an overview on goals and activities of the SnowPEx+ initiative, dedicated to the intercomparison of northern hemispheric and global satellite snow products, derived from data of long-term operational as well as recently launched satellites. SnowPEx+ is the continuation of SnowPEx (2014-2017), carried out as an international collaborative effort under the umbrella of Global Cryosphere Watch / WMO and funded by ESA.</p><p>SnowPEx+ focuses on two parameters of the seasonal snowpack, the snow extent (SE) from medium resolution optical satellite data (Sentinel-3, VIIRS, MODIS, AVHRR, etc.) and the snow water equivalent (SWE) from passive microwave satellite data. Overall, 15 hemispheric and global SE products (binary and fractional SE) and two SWE products are participating in the experiment. For intercomparison, daily SE products are transformed to a common map projection and standardized SnowPEx protocols are applied, elaborated by the international snow product community. The SE product evaluation applies statistical measures for quantifying the agreement between the various products, including the analysis of spatial patterns. Validation of SE products uses as benchmark high resolution snow maps from about 150 globally distributed Landsat scenes acquired in different climate zones, under different solar illumination conditions and over various land cover types. This snow reference data set, based on various retrieval algorithms, is generated and evaluated by the SnowPEx+ High Resolution Snow Products Focus Group. In-situ snow data from several organisations in Europe, North America and Asia are also used for validating the satellite SE and SWE products. SWE products are also inter-compared with gridded snow products from land surface models driven by atmospheric reanalysis data. In addition, the multi-year trends of the various SE and SWE products are evaluated. We provide an overview on the snow products, discuss the validation and intercomparison protocols, and report on preliminary results from the intercomparison and validation of various snow products.</p>

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.

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.

Visualizing gridded time series data with self organizing maps: An application to multi-year snow dynamics in the Northern Hemisphere

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

Assimilating remotely sensed snow observations into a macroscale hydrology model

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 twelve-member ensemble – with four LSMs and three meteorological forcings – using automated SWE, precipitation, and temperature observations from 809 Snowpack Telemetry stations over the western U.S. 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 days 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 has the largest contribution to the first principal component, which accounts for more than half of the total variance. The results provide insights into prioritizing strategies to improve SWE estimates from LSMs for hydrologic applications.

A reliable snow water equivalent (SWE) product is critical for climate and hydrology studies in Arctic regions. Passive microwave sensors aboard satellites provide a capability of observing global SWE and have produced many SWE datasets. However, these datasets have significant errors in boreal forest regions and where snowpack is deep or wet. The Gravity Recovery and Climate Experiment (GRACE) satellites are measuring changes in terrestrial water storage (TWS), of which snow mass is the primary component in winter Arctic river basins. This paper shows SWE can be derived from GRACE TWS change in regions where the ground is not covered by snow in a summer month if accurate changes in below‐ground water storage (including soil water and groundwater) can be provided by a land surface model. Based on gravity change, the GRACE‐derived SWE estimates are not affected by the boreal forest canopy and are more accurate in deep snow regions than microwave retrievals. The paper also discusses the uncertainties in the SWE retrievals.

Coupled hydrological and atmospheric modeling is an efficient method for snowmelt runoff forecast in large basins. We use short-range precipitation forecasts of mesoscale atmospheric Weather Research and Forecasting (WRF) model combining them with ground-based and satellite observations for modeling snow accumulation and snowmelt processes in the Votkinsk reservoir basin (184,319 km2). The method is tested during three winter seasons (2012–2015). The MODIS-based vegetation map and leaf area index data are used to calculate the snowmelt intensity and snow evaporation in the studied basin. The GIS-based snow accumulation and snowmelt modeling provides a reliable and highly detailed spatial distribution for snow water equivalent (SWE) and snow-covered areas (SCA). The modelling results are validated by comparing actual and estimated SWE and SCA data. The actual SCA results are derived from MODIS satellite data. The algorithm for assessing the SCA by MODIS data (ATBD-MOD 10) has been adapted to a forest zone. In general, the proposed method provides satisfactory results for maximum SWE calculations. The calculation accuracy is slightly degraded during snowmelt periods. The SCA data is simulated with a higher reliability than the SWE data. The differences between the simulated and actual SWE may be explained by the overestimation of the WRF-simulated total precipitation and the unrepresentativeness of the SWE measurements (snow survey).

High elevation streamflow integrates the hydrological response to snow accumulation and melt. Accordingly, streamflow observations hold valuable but often underutilized information about snow water equivalent (SWE), offering a means to reconstruct historical SWE dynamics. We develop an inverse hydrological modelling framework to derive SWE estimates from streamflow: the framework generates a range of prior SWE scenarios, feeds them into a hydrological model and computes their likelihood based on how well corresponding streamflow simulations match observed streamflow. A critical step hereby is the choice of model performance metrics to be used as likelihood functions. To test our framework, we perform a range of tests in a synthetical setting, where we use known SWE data that we feed into the hydrological model and then apply the inversion framework to retrieve the SWE time series from the streamflow alone. The goal of this synthetic setting is to determine which streamflow metrics select realistic SWE scenarios (measured in terms of errors between the known SWE time series and generated scenarios).Our results reveal that classical streamflow metrics, such as the Kling-Gupta and Nash-Sutcliffe efficiencies, show no correlation with any SWE timing or magnitude error metrics. Accordingly, minimizing these streamflow metrics does not result in an efficient selection of  SWE scenarios. In contrast, minimizing the mismatch of selected streamflow signatures, such as the baseflow index and the mean melt-season discharge, does lead to a selection of SWE scenarios with smaller errors. Overall, however, our results show that correlations between streamflow performance metrics and SWE performance metrics are generally weak and show significant year-to-year variability, indicating that streamflow metrics are often not informative for reconstructing SWE. Our synthetic modeling experiments are conducted in the Dischma catchment in Switzerland, using the OSHD Swiss SWE reanalysis product as the synthetic SWE observations (Mott et al., 2023). Synthetic streamflow observations are generated by feeding OSHD snow melt and MeteoSwiss rainfall into the hydrological model. Our findings are relevant for future studies aiming to evaluate or calibrate SWE simulations against streamflow observations, and will help us in the application of the inverse hydrological framework to real-world SWE reconstructions. Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Front. Earth Sci., 11, 1228158, https://doi.org/10.3389/feart.2023.1228158, 2023.

Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry are suitable for the precise measurement of terrestrial water storage (TWS) and lake water level variations from space. In this study, two GRACE solutions, namely, spherical harmonics (SH) and mascon (MSC), are utilized with the Global Land Data Assimilation System (GLDAS) model to estimate the spatial and temporal variations of TWS in the Upper Indus Basin (UIB) for the study period of January 2003 to December 2016. The TWS estimated by SH, MSC, and the GLDAS model are consistent and generally show negative trends of -4.47 ± 0.38 mm/year, -4.81 ± 0.49 mm/year, and -3.77 ± 0.46 mm/year, respectively. Moreover, we use the GLDAS model data to understand the roles of variations in land surface state variables (snow water equivalent (SWE), soil moisture, and canopy water storage) in enhancing or dissipating the TWS in the region. Results indicate that SWE, which has a significant contribution to GRACE TWS variability, is an important parameter. Spearman's rank correlations are calculated to demonstrate the relationship of the GLDAS land surface state variables and the GRACE signals. A highly positive correlation between SWE with TWS is estimated by SH and MSC as 0.691 and 0.649, respectively, indicating that the TWS signal is mainly reliant on snow water in the study region. The ground water storages estimated by SH and MSC solutions are nearly stable with slight increasing trends of 0.63 ± 0.48 mm/year and 0.29 ± 0.51 mm/year, respectively. We also take advantage of the potential of satellite altimetry in measuring lake water level variations, and our result indicates that Cryosat-2 SARin mode altimetry data can be used in estimating small water bodies accurately in the high mountainous region of the UIB. Moreover, the climate indices data of El-Niño Southern Oscillation and Pacific Decadal Oscillation are analyzed to determine the influence of pacific climatic variability on TWS.

Abstract. This study analyzes the added value of a regional climate model hindcast with respect to snow water equivalent (SWE) for Siberia when compared to SWE estimates from forcing NCEP-R. In addition, we examine the discrepancies of simulated SWE to several recent reanalysis products (NCEP-R2 NCEP-CFSR, ERA-Interim). We apply the regional climate model COSMO-CLM (CCLM) to a 50 km grid spacing using NCEP-R1 as driving force to obtain a 63 yr (1948 to 2010) gridded dataset of historical SWE. Simulated regional climate data is necessary because of the absence of station data in that region. To perform large-scale assessments we use the satellite-derived daily SWE product of ESA DUE GlobSnow from 1987 to 2010. Russian station SWE data is used for cross-checking the findings. In January (mid-winter), the SWE hindcast is in good agreement with GlobSnow, whereas it overestimates SWE during the melting season. CCLM shows a clear added value in providing realistic SWE information compared to the driving reanalysis. The temporal consistency of CCLM is higher than that presented by ERA-Interim and NCEP-R2.

Seasonal snow occurs in high latitude and altitude regions of the globe, and throughout the winter and post-winter period can rapidly alter the makeup of these regions. Commonly studied snow features include snow depth, snow water equivalent (SWE), and snow density. These features can be measured on the ground while also capable of being remotely sensed with airborne and spaceborne instruments. Individually both approaches can be utilized to assess these snow features. However, field-based techniques tend to be limited to smaller spatial scales while remotely sensed methods tend to excel at larger spatial scales. At local scales (10 km2) a hybrid technique can be employed to better estimate such snow features. This can be realized by utilizing machine learning modeling to upscale field measurements with high-resolution remote sensing imagery. We performed this over a 10 km2 area in Sodankylä, Finland by combining repeat field snow depth and SWE data with 2-meter resolution WorldView-2 (WV-2) and Light Detection and Ranging (LiDAR) data over a winter period between the middle of December 2022 to the end of April 2023. Snow depth field measurements were upscaled to a local spatial scale with an object-based machine learning approach before harnessing the estimated snow depth products to permit an enhanced estimation of SWE to the same local scale from more limited field measurements. Snow density was then determined from the predicted snow depth and SWE. A weighted ensemble approach of multiple machine learning models proved to be most effective compared to the chosen base models of Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR). The fluctuating outputs from these features over the winter period were found to strongly connect to dry and wet peatbogs and with forests containing carbon and mineral surface soils.

In the present study, Himalayan cedar samples from eight climate‐stressed sites were collected and used to examine the snowfall variability over the Lahaul‐Spiti region. The tree‐growth and climate relationship was explored using response function analysis, which revealed a positive tree‐growth relationship with precipitation and a negative relationship with temperature. The response function analysis also suggested that cool and moist climatic conditions favour tree growth, while warm and dry conditions reduce tree radial growth over the Lahaul‐Spiti region. Taking this relationship as a guide, we further tested the relationship of tree‐ring chronologies with observational Snow Water Equivalent (SWE) data. An observed statistically significant positive correlation encouraged us to develop the linear regression model for reconstruction. The linear regression models were used to develop a new November to April SWE record for the cold‐arid Lahaul‐Spiti region, Himachal Pradesh. The SWE reconstruction extends back to AD 1290 and revealed low SWE values in the year 1626 (223.6 mm), 1970 (254.4 mm), 1329 (270.1 mm), 1705 (271.9 mm), 2008 (278.5 mm), and high SWE values in the year 1306 (791.7 mm), 1348 (771.0 mm), 1412 (769.7 mm), 1569 (769.1 mm) and 1661 (765.9 mm). The reconstructed SWE is consistent with available precipitation, drought, and river flow records developed from different regions of the western Himalaya, underpinning its importance in understanding the regional hydro‐climatic variability. The cross‐field correlation analysis of the present SWE record with gridded precipitation, PDSI, and MADA also revealed a significant correlation for the westerly dominated Himalayan regions. The positive spatial correlation of SWE with snow cover area of the Northern Hemisphere further endorses the strength of our reconstruction. We also noted strong linkages of SWE with the El‐Niño Southern Oscillation in the present study, while the dominance of the Indo‐Pacific climate system is observed in the spectral analysis. Therefore, we believe that our attempt to develop a seven‐century‐long SWE record from a data‐scarce Himalayan region will strengthen our understanding of the hydrological variations in the long‐term perspective.