Snow Monitoring Research Articles

Is snow drought a messenger for the upcoming severe drought period? A case study in the Upper Mississippi River Basin

The exacerbation of floods and the extension of droughts, attributed to climate change and human-induced factors, are posing a substantial risk to communities by causing water scarcity. The significance of safeguarding water resources and managing them is increasingly gaining prominence. Snow is an efficient source of water for recharging groundwater compared to rainfall. This is attributed to its gradual melting process and capacity to infiltrate the soil, thereby providing sustenance to the groundwater. Thus, snow drought can be considered a major contributing factor to the issue of water scarcity. The objective of this study was to investigate the evolution of snow drought over the period since 1980, as well as its impact on agricultural drought across the Upper Mississippi River Basin (UMRB). An analysis is conducted for comparison between the spatial estimations of snow drought in the UMRB and two drought indicators, namely the evaporative demand drought index (EDDI) and water deficit amounts. The effects of the El Niño and La Niña phenomena on the UMRB as well as the results of the summer agricultural drought conditions were investigated since 2000. The results point to two important findings: 1) the snow-drought-affected zones show an increasing trend since 1980 and 2) severe snow droughts in the winter (from October to April) of a water year trigger severe agricultural droughts in the summer months of the same water year since 2000. It is seen that monitoring snow droughts is as essential as following rainfall regimes in the planning of water resources, agricultural production, and irrigation methods.

Synthetic Aperture Radar Monitoring of Snow in a Reindeer-Grazing Landscape

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring.

Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

ABSTRACT Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems – Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.

Monitoring firn and wet snow on mountain glaciers: polarization and orbit effects

ABSTRACT Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance. Synthetic Aperture Radar (SAR) images are often used to monitor the characteristics of glaciers based on the backscattering coefficient. However, the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered. This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data. The investigated regions have different climate patterns. We compare backscatter coefficient distributions for wet snow and firn, derived from maximum likelihood classification under various polarizations, alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn. However, a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer. We furthermore show, by analyzing weather stations on glaciers, that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit. In contrast, firn ratios across different orbits show less variation in winter, and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences. These findings demonstrate glaciers’ sensitivity to temperature fluctuations and the radar wave’s responsiveness to surface characteristics. Consequently, when employing SAR for glacier monitoring, it is crucial to consider the influence of orbit and polarization, in combination with temperature variations, and whether the season is winter or summer.

California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term "snow deluge" and use anomalously high snowpack in California's Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California's 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges-which we define as the 20 y April 1 SWE event-are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.

An accurate snow cover product for the Moroccan Atlas Mountains: Optimization of the MODIS NDSI index threshold and development of snow fraction estimation models

In semi-arid Mediterranean areas, a significant proportion of the population living downstream depends on water resources from snowmelt and precipitation as their main source of water. Consequently, snow-covered mountain regions can be considered as a vital water tower, providing a steady supply of water, and contributing significantly to streamflow and groundwater recharge. Given the scarcity of ground-based hydroclimatic measurements, remote sensing could be an effective technique for mapping and monitoring snow cover. This study evaluates the last version of MODIS (version 6, called V6) snow cover product, optimizing the NDSI threshold for accurate snow cover mapping and developing models for local fractional snow cover estimation in the southern Mediterranean region, particularly in the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 (S2) scenes from six different regions across the Atlas Range were used to adjust the NDSI threshold and to develop FSC estimation models. In addition, a total of 8419 MOD10A1 images from March 2000 to June 2023, and 7561 MYD10A1 images from September 2002 to June 2023, were processed to improve cloud filtering and to develop a highly accurate daily snow cover product suitable for the Moroccan Atlas Mountains. The cloud correction approach significantly reduced the number of cloud-covered pixels, from 25.7% to 0.4% after filtering. Two schemes for selecting the MODIS NDSI threshold were tested: (1) the global reference of 0.4 and (2) the locally optimal threshold of 0.2. The average snow cover estimation errors using the optimal and global NDSI thresholds for Terra are an average overestimation of 0.34% and a significant underestimation of 6.13%, respectively. For Aqua, the corresponding errors are an overestimation of 1.4% and an underestimation of 6.8%. Thus, the optimal NDSI threshold of 0.2 could be more appropriate than the threshold of 0.4 for use in the southern Mediterranean region. The new FSC estimation models developed showed satisfactory performance with significant correlation coefficients (mean of 0.85 for Terra and 0.83 for Aqua), and with low RMSE and MAE values (mean of 0.17 and 0.12 for Terra and mean of 0.19 and 0.14 for Aqua) when comparing FSC derived from high-resolution S2 data with predicted FSC from MODIS NDSI. The daily snow cover product developed was compared with the high-resolution snow maps obtained from S2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of −0.17%. This research concludes that the enhanced daily snow cover product could improve the understanding of spatiotemporal dynamics of snow extent and, therefore, contribute to quantifying the snowmelt contribution to the water budget through modeling approaches in the southern Mediterranean region.

Monitoring Snow Cover in Typical Forested Areas Using a Multi-Spectral Feature Fusion Approach

Accurate snow cover monitoring is greatly significant for research on the hydrology model and regional climate variation, especially in Northeast China where forests cover almost forty percent of the total area. However, effectively monitoring snow cover under the forest canopy is still challenging with either in situ or remote sensing observations. The global SNOWMAP algorithm pertinent to the fixed normalized difference snow index (NDSI) threshold is, therefore, no longer applicable in a typical forested region of Northeast China. In order to achieve the goal of improving the accuracy of monitoring snow cover in areas with forest, utilizing MOD09GA and MOD13A1 products, a new approach of snow mapping was developed in this study, and it exploits the fusion and coupling of spectral features by integrating and analyzing the normalized difference forest snow index (NDFSI), the normalized difference vegetation index (NDVI), and the NDSI index. Then, Landsat 8 OLI images of high resolution were used to evaluate snow cover mapping precision. The experimental results indicated that the NDFSI index combined with the NDVI index showed great potential for extracting the snow cover distribution in forested regions. Compared with the snow distribution obtained from the Landsat 8 images, the average bias and FAR (false alarm ratio) values of snow cover mapping obtained by this algorithm were 1.23 and 13.54%, which were reduced by 1.98 and 29.36%, respectively. The overall accuracy of 81.31% was reached, which is improved by 20.19%. Thus, the snow classification scheme combining multiple spectral features from MODIS data works effectively in improving the precision of automatic snow cover mapping in typical forested areas of Northeast China, which provides essential support and significant perspectives for the next step of establishing a runoff model and rationally regulating forest water resources.

Perennial snow and ice cover change from 2001 to 2021 in the Hindu-Kush Himalayan region derived from the Landsat analysis-ready data

The changing climate directly affects spatial and temporal patterns of snow and ice cover globally and in the Hindu-Kush Himalayan (HKH) region. In the HKH, around 3.3 billion people across 11 countries depend on water originating from mountain glaciers and snowfields, and melting snow cover has a direct impact on their livelihood and well-being. Various studies have shown that the snow and ice cover in the HKH is declining at an alarming rate but have been limited in geographic, spatial and temporal scales. Here, we employed the Global Land Analysis and Discovery analysis ready Landsat time-series data (GLAD ARD) to map changes in perennial snow and ice between 2001 and 2021 at five-year epochs using decision tree ensemble models. These maps were used to create a stratified sampling design for reference data collection to estimate area and map accuracy. All five-year epoch maps have user's accuracies above 90% and producer's accuracies above 91%. Our sample data analysis showed that one-eighth of the extent of perennial snow and ice in the HKH, totalling 15,770 km2 (CI ± 3195 km2), disappeared over the last two decades with 105,935 km2 (CI ± 3396 km2) remaining in 2021. From map-based estimates, the largest decline of perennial snow and ice cover among the mountain ranges was found in the Himalayas with an estimated reduction of 5741 km2. Among HKH countries, China had the largest area of perennial snow and ice reduction of 10,654 km2, Nepal had the highest net perennial snow and ice reduction rate (31%). Among the river basins, the maximum net loss of perennial snow and ice cover between 2001 and 2021 was in the Indus (24.8%), followed by Brahmaputra (18.3%), and Tarim (15.7%). Our results confirm wide-spread loss of perennial snow and ice across the HKH region and provide both regionally consistent maps with derived area estimates at landform, national and basin-levels and methodology to continue monitoring snow and ice melt.

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Snow plays a crucial role in the global water cycle, providing water to over 20% of the world’s population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications.

The utility of monitoring snow for microplastics in the Arctic: a pilot study from Iqaluktuuttiaq, Nunavut

Plastic pollution, including microplastics (<5 mm) has been identified as an emerging contaminant of Arctic concern and has been observed in wildlife, water, sediment, air, and snow. Because snow is relatively easy to sample and process for microplastics, it may be a useful compartment to monitor to assess patterns of microplastic contamination in polar regions. Microplastics can enter the Arctic through both long-range transport pathways and from local sources. By sampling snow across spatial scales, and multiple distances from local communities, researchers can explore local and distant sources of microplastics, thereby informing management strategies. With this in mind, we aimed to quantify mass concentrations of microplastics in snow samples collected north-east of Iqaluktuuttiaq, Nunavut. We sampled five sites in a transect moving away from town and quantified microplastics using Pyrolysis/gas chromatography with mass spectrometry. We found microplastics at every location, but patterns along the transect were unclear. We observed differences in polymer types at sampling sites closer to the community compared to sites further away suggesting the presence of local inputs. Overall, we highlight the use of snow as a local monitoring tool to assess contamination and sources of microplastics in the Arctic to inform future long-term monitoring programs.

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

AbstractAccurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive.

The challenge of monitoring snow surface sublimation in winter could be resolved with structure-from-motion photogrammetry

Snow sublimation is one of the major water and energy balance processes for the snow water cycle, which induced a significant snow loss. The precise monitoring of snow sublimation is essential for quantifying the snow processes. However, the conventional measurement methods (e.g., pan observation with sporadic data or eddy covariance [EC]) are not feasible for long-term observations in harsh alpine environments). Here, two different one-camera time-lapse structure from motion (O-T-SfM) photogrammetry methods, were adopted to monitor daily snow surface sublimation at two different periods in the cold, harsh conditions at top of the August-one ice cap in the Qilian Mountains of China. The results revealed that the O-T-SfM-based surface sublimation rate estimates has a good agreement with two different methods such as pan observation and EC measurement in snowfall-free conditions and snow bedforms were abundant. The estimated sublimation rate varied from 0.05 to 2.19 mm/day at the study site. The total surface sublimation was about 16.3 mm snow water equivalent in 32 days which is a significant loss of moisture from glacier in a short period. Our results suggest that by apply appropriate estimation methods, remote O-T-SfM photogrammetry derived DEM sequence can be successfully used to monitor the daily snow surface sublimation in winter.

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.

Reconstruction of Snow Cover in Kaidu River Basin via Snow Grain Size Gap-Filling Based on Machine Learning

Fine spatiotemporal resolution snow monitoring at the watershed scale is crucial for the management of snow water resources. This research proposes a cloud removal algorithm via snow grain size (SGS) gap-filling based on a space–time extra tree, which aims to address the issue of cloud occlusion that limits the coverage and time resolution of long-time series snow products. To fully characterize the geomorphic characteristics and snow duration time of the Kaidu River Basin (KRB), we designed dimensional data that incorporate spatiotemporal information. Combining other geographic and snow phenological information as input for estimating SGS. A spatiotemporal extreme tree model was constructed and trained to simulate the nonlinear mapping relationship between multidimensional inputs and SGS. The estimation results of SGS can characterize the snow cover under clouds. This study found that when the cloud cover is less than 70%, the model’s estimation of SGS meets expectations, and snow cover reconstruction achieves good results. In specific cloud removal cases, compared to traditional spatiotemporal filtering and multi-sensor fusion, the proposed method has better detail characterization ability and exhibits better performance in snow cover reconstruction and cloud removal in complex mountainous environments. Overall, from 2000 to 2020, 66.75% of snow products successfully removed cloud coverage. This resulted in a decrease in the annual average cloud coverage rate from 52.46% to 34.41% when compared with the MOD10A1 snow product. Additionally, there was an increase in snow coverage rate from 21.52% to 33.84%. This improvement in cloud removal greatly enhanced the time resolution of snow cover data without compromising the accuracy of snow identification.

Significance and issues of automatic snow cover measurement – case study for the Šumava Mountains region

The study evaluates the contribution of automatic snow measuring stations for snow monitoring in the Šumava region. Using climatological maps, the results of spatial analysis of the maximum depth of the snow cover for six winter seasons were compared, namely first from the measured data of the basic CHMI stations and second from the data of all stations. The biggest differences occurred in the comparison at the highest locations on the windward side of the Šumava (>1 200 m a.s.l.), where there is no corresponding network of basic meteorological stations. In the case of the snow-poor winter season 2020/21, these differences were relatively higher than in the 2018/19 season, with more extensive snow cover in all positions. The evaluation of the entire period (2017/18–2022/23) produced similar results, as snow-poor winter seasons prevailed here. We can therefore assume that the differences in snow characteristics will continue to be rather significant in the Šumava region. Accordingly, the importance of automatic snow measurement will also increase, especially in higher altitudes of the windward border area. In addition to the expansion of the measuring network and the significant refinement of information about the snow cover, especially in the mountains, the main benefit of data on the depth of snow from automatic stations is their operability and immediate use for other applications.

The Evaluation of Snow Depth Simulated by Different Land Surface Models in China Based on Station Observations

Snow plays an important role in catastrophic weather, climate change, and water recycling. In order to analyze the ability of different land surface models to simulate snow depth in China, we used atmospheric forcing data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) to drive the CLM3.5 (the Community Land Model version 3.5), Noah (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model), and Noah-MP (the community Noah land surface model with multi-parameterization options) land surface models. We also used 2380 daily snow-depth site observations of CMA to analyze the simulation effects of different models on the snow depth in China and different regions during the periods of snow accumulation and snowmelt from 2015 to 2019. The results show that CLM3.5, Noah, and Noah-MP can simulate the spatial distribution of the snow depth in China, but there are some differences between the models. In particular, the snow depth and snow cover simulated by CLM3.5 are lower than those simulated by Noah and Noah-MP in Northwest China and the Tibetan Plateau. From the overall quantitative assessment results for China, the snow depth simulated by CLM3.5 is underestimated, while that simulated by Noah is overestimated. Noah-MP has the best overall performance; for example, the biases of the three models during the snow-accumulation periods are −0.22 cm, 0.27 cm, and 0.15 cm, respectively. Furthermore, the three models perform differently in the three snowpack regions of Northeast China, Northwest China, and the Tibetan Plateau; Noah-MP has the best snow-depth performance in Northeast China, while CLM3.5 has the best snow-depth performance in the Tibetan Plateau region. Noah-MP performs best in the snow-accumulation period, and Noah performs best in the snowmelt period for Northwest China. In conclusion, no single model can perform optimally for snow simulations in different regions of China and at different times of the year, and the multi-model integration of snow may be an effective way to obtain high-quality snow simulation results. So this study provides some scientific references for the spatiotemporal evolution of snow in the context of climate change, monitoring and analysis of snow, the study of land surface models for snow, and the sustainable development and utilization of snow resources in China and other regions.

Use of Webcams in Support of Operational Snow Monitoring

Ultrasonic sensors are one of the most common automatic monitoring methods in operational snow depth monitoring with reliable results. On the other hand, there is significant uncertainty when measuring small snow depths (<2 cm), thus it cannot provide binary snow presence (on/off) information. The use of webcams in monitoring snow cover has proven to be successful in recent studies and applications. In this study, we applied an adaptive thresholding technique on images from webcams to obtain reliable snow on/off information to complement the ultrasonic snow depth measurements. Camera and ultrasonic sensor data from two weather stations in Finland were studied. The webcam data was processed using FMIPROT (Finnish Meteorological Institute Image Processing Tool) software, operating in a cloud computing environment, which can generate near real-time data. Our results indicate that webcam-derived data can be successfully used for quality control or as auxiliary data to support operational ultrasonic sensor measurements and provide a cost-effective improvement to operational monitoring capabilities. Webcam monitoring is especially useful during the melting season when the snow depth is below 15 mm, with accuracy values between 72% and 94%.

Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends

Abstract. Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided. The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.

SNOTEL, the Soil Climate Analysis Network, and water supply forecasting at the Natural Resources Conservation Service: Past, present, and future

AbstractThe Snow Survey and Water Supply Forecasting (SSWSF) Program and the Soil Climate Analysis Network (SCAN) of the United States Department of Agriculture's Natural Resources Conservation Service (NRCS) generate key observational and predictive information for water managers. Examples include mountain climate and snow monitoring through manual snow surveys and the SNOw TELemetry (SNOTEL) and SNOtel LITE networks, in situ soil moisture data acquisition through the SCAN and SNOTEL networks, and water supply forecasting using river runoff prediction models. The SSWSF Program has advanced continuously over the decades and is a major source of valuable water management information across the western United States, and the SCAN network supports agricultural and other water users nationwide. Product users and their management goals are diverse, and use‐cases range from guiding crop selection to seasonal flood risk assessment, drought monitoring and prediction, avalanche and fire prediction, hydropower optimization, tracking climate variability and change, environmental management, satisfying international treaty and domestic legal requirements, and more. Priorities going forward are to continue innovating to enhance the accuracy and completeness of the observational and model‐generated data products these programs deliver, including expanded synergies with the remote sensing community and uptake of artificial intelligence while maintaining long‐term operational reliability and consistency at scale.

Statistical evaluation of snow accumulation and depletion from remotely sensed MODIS snow time series data using the SARIMA model

AbstractIn the remote and challenging terrain of the Himalayan region, accurate measurement of cyclic snow accumulation and depletion is a significant challenge. To overcome this, an attempt has been made in the present study by applying a statistical analysis of MODIS snow time series data with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model from 2003 to 2018 over the Beas river basin. The Box–Jenkins methodology of forecasting is based on the identification using seasonality, stationarity, ACF, and PACF plots; and estimation based on maximum likelihood techniques; and the last diagnostic checking based on the residual and error values have been used. Later, forecasting models have been proposed separately for the snow accumulation period (October–February) as (1,1,1) (0,1,3)19 and for the snow depletion period (March–September) as (1,1,1) (1,1,2)27 after calibration of the data (2003–2015) and the same were then validated using data (2016–2018). The accuracy assessment of the models has been checked using performance criteria like AIC, MSE, and RSS. The comparison of the forecasting models with the observed data showed a good agreement with R2 of 0.83 and 0.89 for snow accumulation and snow depletion, respectively. This research highlights the potential of utilizing satellite data and statistical modeling to address the challenges of monitoring snow cover in remote and inaccessible regions.