Temporal Variability Of Snow Cover Research Articles

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

Geothermal monitoring as a tool for observing climate change

The paper examines weather anomalies the first half of 2024 and presents climatic studies at the Lomonosov Moscow State University Meteorological Observatory as a model for the global network of geothermal monitoring systems of permafrost soils. Therefore, within the framework of the state budget topic on the study of the danger and risk of natural processes and phenomena, employees of the research laboratory of snow avalanches and mudflows of the Faculty of Geography of Moscow State University are working on the territory of meteorological observatory, including monitoring the spatial and temporal variability of snow cover, as well as the thermal state of soils.

CRYOLOGICAL STUDY OF SNOW AND SOIL AT THE MSU SITE IN WINTER 2022/2023

The paper presents the results of field studies conducted at the MSU meteorological site for the winter period 2022/2023. The purpose of the observations was to study the development of the snow column and its spatial variability in one winter season. Field research consisted in the analysis of stratigraphic layers of snow and measuring their density. The data obtained made it possible to characterize and evaluate changes in snow layers, their structure and density in spatiotemporal terms. The results of the work are displayed on the graphs of the spatial and temporal variability of the snow cover for 2022/2023, the evolution of the snow column over the winter period is analyzed. The analysis of observations reflects a really high spatial and temporal variability of snow cover in winter, which allows not only to evaluate and compare the data obtained with past studies, but also to supplement and improve the already available information on the heterogeneity of snow cover. The purpose was also to assess the effect of snow cover on the temperature characteristics of the soil.

Investigation of the Spatio-Temporal Heterogeneity of Snow Thickness at the Meteorological Site of the Lomonosov MSU in the Winter of 2022/2023

This paper presents the results of field studies conducted at the MSU meteorological site for the winter period of 2022/2023. The purpose of the observations was to study the development of the snow column and its spatial variability in one winter season. Field research consisted of analyzing stratigraphic layers of snow and measuring their density. The data obtained made it possible to characterize and evaluate changes in snow layers, structure, and density in spatiotemporal terms. The results of the work are displayed on the graphs of the spatial and temporal variability of the snow cover for 2022/2023. The evolution of the snow column over the winter period is analyzed. The analysis of observations reflects a high spatial and temporal variability of snow cover in winter, which allows not only to evaluate and compare the data obtained with past studies but also to supplement and improve the already available information on the heterogeneity of snow cover.

High-Resolution Monitoring of the Snow Cover on the Moroccan Atlas through the Spatio-Temporal Fusion of Landsat and Sentinel-2 Images

Mapping seasonal snow cover dynamics provides essential information to predict snowmelt during spring and early summer. Such information is vital for water supply management and regulation by national stakeholders. Recent advances in remote sensing have made it possible to reliably estimate and quantify the spatial and temporal variability of snow cover at different scales. However, because of technological constraints, there is a compromise between the temporal, spectral, and spatial resolutions of available satellites. In addition, atmospheric conditions and cloud contamination may increase the number of missing satellite observations. Therefore, data from a single satellite is insufficient to accurately capture snow dynamics, especially in semi-arid areas where snowfall is extremely variable in both time and space. Considering these limitations, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8) and Sentinel-2 (S2), with a spatial resolution ranging from 10 to 30 m, provides unprecedented opportunities to enhance snow cover mapping. Hence, the purpose of this study is to examine the effectiveness of the combined use of optical sensors through image fusion techniques for capturing snow dynamics and producing detailed and dense normalized difference snow index (NDSI) time series within a semi-arid context. Three different models include the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatio-temporal data fusion model (FSDAF), and the pre-classification flexible spatio-temporal data fusion model (pre-classification FSDAF) were tested and compared to merge L8 and S2 data. The results showed that the pre-classification FSDAF model generates the most accurate precise fused NDSI images and retains spatial detail compared to the other models, with the root mean square error (RMSE = 0.12) and the correlation coefficient (R = 0.96). Our results reveal that, the pre-classification FSDAF model provides a high-resolution merged snow time series and can compensate the lack of ground-based snow cover data.

Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

Quantification of spatial temporal variability of snow cover and hydro-climatic variables based on multi-source remote sensing data in the Swat watershed, Hindukush Mountains, Pakistan

The northern part of Hindukush Mountains has a perplexing environment due to the influence of adjacent mountains of Himalaya, Karakoram, and Tibetan Plateau. Although reliable evidences of climate change are available; however, a clear knowledge of snow cover dynamics in the context of climate change is missing for this region. In this study, we used various remotely sensed (TRMM precipitation product, while MODIS temperature and snow cover products) and gauge-based datasets to quantify the spatiotemporal variability of climatic variables and their turn effects over the snow cover area (SCA) and river discharge in the Swat watershed, northern Hindukush Mountains, Pakistan. The Mann–Kendall method and Sen’s slope estimator were used to estimate the trends in SCA and hydro-climatic variables, at 5% significant level (P = 0.05). Results show that the winter and springs temperatures have increased (at the rate of 0.079 and 0.059 °C year−1, respectively), while decreasing in the summer and autumn (at the rate of 0.049 and 0.070 °C year−1, respectively). Basin-wide increasing tendency of precipitation was identified with a highest increasing rate of 3.563 mm year−1 in the spring season. A decreasing trend in the winter SCA (at the rate of -0.275% year−1) and increasing trends in other seasons were identified. An increasing tendency of river discharge on annual and seasonal scales was also witnessed. The seasonal variations in discharge showed significant positive and negative relationships with temperature and SCA, respectively. We conclude that the future variations in the temperature and SCA in the higher altitudes of the Swat watershed could substantially affect the seasonality of the river discharge. Moreover, it implies that the effect of ongoing global warming on the SCA in the snowmelt-dominated river basins needs to be considered for sustainable regional planning and management of water resources, hydropower production, and downstream irrigation scheduling.

Case study of spatial and temporal variability of snow cover, grain size, albedo and radiative forcing in the Sierra Nevada and Rocky Mountain snowpack derived from imaging spectroscopy

Abstract. Quantifying the spatial distribution and temporal change in mountain snow cover, microphysical and optical properties is important to improve our understanding of the local energy balance and the related snowmelt and hydrological processes. In this paper, we analyze changes of snow cover, optical-equivalent snow grain size (radius), snow albedo and radiative forcing by light-absorbing impurities in snow and ice (LAISI) with respect to terrain elevation and aspect at multiple dates during the snowmelt period. These snow properties are derived from the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from 2009 in California's Sierra Nevada and from 2011 in Colorado's Rocky Mountains, USA. Our results show a linearly decreasing snow cover during the ablation period in May and June in the Rocky Mountains and a snowfall-driven change in snow cover in the Sierra Nevada between February and May. At the same time, the snow grain size is increasing primarily at higher elevations and north-facing slopes from 200 microns to 800 microns on average. We find that intense snowmelt renders the mean grain size almost invariant with respect to elevation and aspect. Our results confirm the inverse relationship between snow albedo and grain size, as well as between snow albedo and radiative forcing by LAISI. At both study sites, the mean snow albedo value decreases from approximately 0.7 to 0.5 during the ablation period. The mean snow grain size increased from approximately 150 to 650 microns. The mean radiative forcing increases from 20 W m−2 up to 200 W m−2 during the ablation period. The variability of snow albedo and grain size decreases in general with the progression of the ablation period. The spatial variability of the snow albedo and grain size decreases through the melt season while the spatial variability of radiative forcing remains constant.

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

AbstractThe ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station‐based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS‐based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data. Copyright © 2011 John Wiley & Sons, Ltd.

Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.

Connecting European snow cover variability with large scale atmospheric patterns

Abstract. Winter snowfall and its temporal variability are important factors in the development of water management strategies for snow-dominated regions. For example, mountain regions of Europe rely on snow for recreation, and on snowmelt for water supply and hydropower. It is still unclear whether in these regions the snow regime is undergoing any major significant change. Moreover, snow interannual variability depends on different climatic variables, such as precipitation and temperature, and their interplay with atmospheric and pressure conditions. This paper uses the EASE Grid weekly snow cover and Ice Extent database from the National Snow and Ice Data Center to assess the possible existence of trends in snow cover across Europe. This database provides a representation of snow cover fields in Europe for the period 1972–2006 and is used here to construct snow cover indices, both in time and space. These indices allow us to investigate the historical spatial and temporal variability of European snow cover fields, and to relate them to the modes of climate variability that are known to affect the European climate. We find that both the spatial and temporal variability of snow cover are strongly related to the Arctic Oscillation during wintertime. In the other seasons, weaker correlation appears between snow cover and the other patterns of climate variability, such as the East Atlantic, the East Atlantic West Russia, the North Atlantic Oscillation, the Polar Pattern and the Scandinavian Pattern.

Evaluation of spatial and temporal variability of snow cover in a large mountainous basin in Iran

Determination of snow characteristics in mountainous basins is difficult due to the complex spatial and temporal variability of snow cover. Accurate representation of snow cover variations in space and time is an important factor in snowmelt modeling, hydrological forecasts, water resources planning, and drought management. This study demonstrates how remotely sensed data can complement the measurements of ground hydro-meteorological data to simulate the spatial and temporal variations of snow cover characteristics in a mountainous basin. In this paper, we studied Karun basin, located in the south west of Iran, because of its importance in accumulating large snow reserves, and subsequently contributing snowmelt to the total runoff. Snow cover variability was simulated by extraction of maps of snow cover indices using remotely sensed data. Contribution of snowmelt to the runoff was determined using a seasonal water balance model as well as estimations based on indirect approaches by modeling variables such as critical temperature, which is an important variable in snow studies. Agreement between indirect approaches used in this paper is an encouraging result that shows the reliability of the procedure where snow data is scarce. The results of correlation analysis between topographic and meteorological variables with snow cover indices suggested that elevation is the single most important variable on large-scale snow variability.

Use of passive-microwave data to monitor spatial and temporal variations of snow cover at tree line near Churchill, Manitoba, Canada

AbstractWe evaluated the contribution of Special Sensor Microwave/Imager (SSM/I) passive-microwave data to the monitoring of spatial and temporal variability of snow cover in the Churchill area, Manitoba, Canada. Because of the coarse spatial resolution of current passive-microwave sensors, the estimation of snow water equivalent using empirical equations with these instruments is largely compromised in complex areas such as Churchill (forest–tundra ecotone). However, with its high frequency of observations and the availability of a long time series (1988–99), passive-microwave data from the SSM/I radiometer remain a very valuable tool for monitoring the temporal evolution of snow cover at various spatial scales. Through winter 1997/98, we first examined the passive-microwave signatures at the local scale and we identified the major stages of the snow period. Principal-component analysis (PCA) applied on spectral-difference (Tb(19H) - Tb(37H))time series (1988–99) enabled us to identify spatio-temporal effects over a large area. PCA also permitted the extraction of indices of relevance for monitoring climatic variability and climate change (annual snow-cover duration, dates of snow-cover appearance and disappearance).

Application of satellite images for monitoring snow-line in the Yukon and Northwest Territories

Each year, more and more of Canada's vast northern territories are being explored in the search for untapped mineral resources. But the area is covered with snow for seven to ten months a year, which hampers most activities connected with mineral exploration. Snow cover also affects changes in albedo and temperature and hydrologic forecasting. The surface hydrology in most areas is only active for two to five months a year. Historically, snow hydrologists have been limited to the use of ground source measurements for estimating the spatial and temporal variability of snow cover. In the Yukon and Northwest Territories hydrometeorological stations are few and tend to be clustered around settlements. Moreover, the ground source data are subject to error and do not necessarily provide a complete picture of current conditions. The synoptic view provided by satellite images presents an economical way to monitor the retreat and advance of the snow-line and of mapping snow cover in remote and sparsely populated areas. The information may eventually be useful for extrapolating conventional ground source data more effectively over the entire Yukon and Northwest Territories.