Snow Cover Index Research Articles

Changes in albedo and its radiative forcing of grasslands in East Asia drylands

BackgroundGrasslands in drylands are increasingly influenced by human activities and climate change, leading to alterations in albedo and radiative energy balance among others. Surface biophysical properties and their interactions change greatly following disturbances. However, our understanding of these processes and their climatic impacts remains limited. In this study, we used multi-year observations from satellites and eddy-covariance towers to investigate the response of albedo to variables closely associated with human disturbances, including vegetation greenness (EVI) and surface soil volumetric water content (VWC), as well as snow cover and clearness index (Ta) for their potential relationships.ResultsEVI and VWC during the growing season were the primary factors influencing albedo. EVI and VWC were negatively correlated with albedo, with VWC’s total direct and indirect impacts being slightly smaller than those of EVI. During the non-growing season, snow cover was the most influential factor on albedo. VWC and Ta negatively affected albedo throughout the year. We estimated the impact of variations in EVI and VWC on climate to be in the range of 0.004 to 0.113 kg CO2 m−2 yr−1 in CO2 equivalent.ConclusionsThis study indicates the significant impacts of climate change and human disturbances on vulnerable grassland ecosystems from the perspective of altered albedo. Changes in vegetation greenness and soil properties induced by climate change and human activities may have a substantial impact on albedo, which in turn feedback on climate change, indicating that future climate policies should take this factor into consideration.

Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile

Abstract. Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32∘ S and ∼ 69 to ∼ 71∘ W). This region has elevations up to 6000 m, is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as “snowmelt hotspots”. Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation.

Evaluation of the Relationship Between Snow Cover Index and Land Surface Temperature for Sulaymaniyah City, Iraq

Changes in land surface reflectance measured by remote sensing data can be useful in climate change studies. This study attempts to Evaluation of the relationship between Snow cover index and Land surface temperature for by used Normalized snow index (S3), Land Surface Temperature (LST), in Sulaymaniyah city, northeastern Iraq. Data from Landsat5 TM and Landsat8 OLI images were used to detect the change in snow cover and land surface temperature during the study period years 1987, 1999, 2009, and 2022. ArcGIS 10.8 is used to determine the variations and calculate S3, NDVI, and LST index indices. All S3 maps have shown a decrease trend from 1987 to 2022 which depicted decreases of snow cover area. Also, LST has increased from 13°C in 1987 to 19 °C in 2022. also the results showed S3 and LST correlation has a strong reverse relationship existed in each year. The reverse relationship found between S3 and LST indicates that areas in which increase land surface temperature it leads to inducing much changes in snow cover.

Surface area change detection of snow/ice cover over Zemu Glacier of Sikkim Himalaya

This study assessed the snow/ice surface area changes over the Zemu Glacier in the Eastern Himalayas. Zemu is considered to be the largest glacier in the Eastern Himalayas located in the Sikkim State of India. Change detection in the snow/ice surface areal extent of the Zemu Glacier was delineated from the year 1945 using US Army Map Service-Topographical Sheets and Landsat imageries available from 1987 to 2020. The results obtained solely focus on surface-change delineation using remote sensing satellite data and GIS software. Landsat imagery of the years 1987, 1997, 2009, 2018, and 2020 was used for snow and ice pixel extraction. Normalized Difference Snow Index (NDSI), Snow Cover Index (S3), and new band ratio index were used to extract the pure snow and ice pixels, fresh snow, and debris-covered snow/ice area and pixels mixed with shadow to demarcate the surface area changes. Manual delineation was required and undertaken for better results. A slope raster image was generated from Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data to delineate the slope and hill shade. The results illustrate that the snow/ice surface covering the glacier had an area of 113.5km2 in the year 1945 which was reduced to 78.31km2 in the year 2020 indicating a total snow/ice surface area loss of 31% in 75years. A loss of 11.45% was observed from 1945 to 1987 in the areal extent, while from 1987 to 2009, a decadal loss of approximately 7% was observed. The surface area loss of 8.46% from 2009 to 2018 led to infer the maximum loss of snow and ice over the glacier body at a rate of 0.94% per year. Between the years 2018 and 2020, the glacier lost 1.08% of its surface area. The Accumulation Area Ratio (AAR) considering the accumulation and ablation areas of the glacier indicated a gradual decline of the accumulation zone in the recent years. Global Land Ice Measurement from Space (GLIMS) program data with RGI version 6.0 was used as a reference to demarcate the areal extent of Zemu Glacier. The study gained above 80% of overall accuracy by generating a confusion matrix in ArcMap. The analysis of this seasonal snow/ice cover showed that there has been a drastic decline in the surface snow/ice cover area over the Zemu Glacier over the years 1987 to 2020 and NDSI; S3 analysis provided improved results to delineate snow/ice cover area over such steep terrain topography of Sikkim Himalaya.

Urban environment monitoring in industrial city using remote sensing of snow cover

The dynamic development of modern cities requires new solutions to urban planning and management by local regional authorities. The paper focuses on ecological indicators based on Earth Remote Sensing Data (ERSD) of the snow cover with the purpose to evaluate the city and to plan ecological environment protection strategy. The paper deals with the method of using space images to assess the snow cover pollution of Chelyabinsk, a large Russian industrial city. The assessment of the snow cover of Chelyabinsk was carried out by comparing the heavy metals concentrations with the Landsat 8 data. The spectral indices were calculated for fourteen sites evenly distributed over the urban area of four types: courtyards, car parks, industrial zones and roads. We found a statistically significant difference between the Swirl/Green index and the site type and a correlation with the concentrations of dissolved and suspended forms of heavy metals in snow cover. Snow cover indices can be used as ecological indicators of urban environment.

Investigating the Effects of Snow Cover and Vegetation on Soil Temperature Using Remote Sensing Indicators in the Three River Source Region, China

Soil temperature is an important physical variable that characterises geothermal conditions and influences geophysical, biological and chemical processes in the earth sciences. Soil temperature is not only affected by climatic and geographical factors; it is also modulated by local factors such as snow cover and vegetation. This paper investigates the relationship between snow cover and vegetation and soil temperature with the help of two classical remote sensing indicators, the Snow Cover Days (SCD) based Advanced Very High Resolution Radiometer and the Normalized Difference Vegetation Index (NDVI)-based Global Inventory Modelling and Mapping Studies, to analyse the influence of local factors on soil temperature in the Three River Source Region (TRSR). Combing multi-layer geothermal observations from 23 stations in the TRSR with meteorological dataset, soil properties datasets, snow cover and vegetation indices, a non-linear model, the Random Forest model, is used to establish a multi-layer soil temperature dataset to analyse the influence of surface cover factors in each depth. The results showed that the annual SCD had a decreasing trend during 1982–2015 and was negatively correlated with the annual mean soil temperature; the annual NDVI had no significant trend, but it was positively correlated with the annual mean soil temperature. Regionally, there was a significant decrease in SCD in the mountainous areas bordering the source areas of the three rivers, and there was a trend of increasing NDVI in the northwest and decreasing vegetation in the southwest in the TRSR. The stronger the correlation with soil temperature in areas with a larger SCD, the more the snow has a cooling effect on the shallower soil temperatures due to the high albedo of the accumulated snow and the repeated melting and heat absorption of the snow in the area. The snow has an insulating effect on the 40 cm soil layer by impeding the cooling effect of the atmosphere in winter. In sparsely vegetated areas, vegetation lowers ground albedo and warms the soil, but in July and August, in areas with more vegetation, NDVI is negatively correlated with soil temperature, with heavy vegetation intercepting summer radiant energy and having a cooling effect on the soil.

Analysis of environmental factors using AI and ML methods

The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.

IMPROVED ON SNOW COVER EXTRACTION IN MOUNTAINOUS AREAS BASED ON MULTI-FACTOR NDSI DYNAMIC THRESHOLD

Abstract. Snow cover is one of the most active elements of the cryosphere and plays an important role in the surface radiation budget and water balance. Optical satellite remote sensing has become an important tool for snow identification and monitoring. The Sentinel-2 A/B satellite has become an important data source for snow cover extraction because of its high radiation resolution, which reduces the problem of snow/ice saturation in remote sensing observations. The normalized differential snow cover index (NDSI) and the snow cover index considering the forest cover area (NDFSI) are important methods for snow cover extraction, but due to the strong spatial heterogeneity and fast change speed of snow cover in mountainous areas, using the classical fixed threshold method to extract snow cover will result in a large omission error. In this paper, a dynamic threshold method is constructed by synthesizing the effects of the type of snow cover period, aspect and snow underlying land coverage type on the snow cover NDSI/NDFSI. Compared with the high-resolution GF-2 snow map results, the dynamic threshold method has higher accuracy in extracting snow cover, and the overall classification accuracy, omission error, commission error and Kappa coefficient are 97.70%, 0.20%, 11.17% and 0.93 respectively. The dynamic threshold method is used to extract snow cover in a snow cover period in the Babao River Basin. The snow cover rate in the basin fluctuates greatly with time, and the spatial distribution of snow cover is uneven, with more snow cover in mountainous areas and rapid changes in snow cover in river valleys.

Analysis of the Potential Impact of Climate Change on Climatic Droughts, Snow Dynamics, and the Correlation between Them

Climate change is expected to increase the occurrence of droughts, with the hydrology in alpine systems being largely determined by snow dynamics. In this paper, we propose a methodology to assess the impact of climate change on both meteorological and hydrological droughts, taking into account the dynamics of the snow cover area (SCA). We also analyze the correlation between these types of droughts. We generated ensembles of local climate scenarios based on regional climate models (RCMs) representative of potential future conditions. We considered several sources of uncertainty: different historical climate databases, simulations obtained with several RCMs, and some statistical downscaling techniques. We then used a stochastic weather generator (SWG) to generate multiple climatic series preserving the characteristics of the ensemble scenario. These were simulated within a cellular automata (CA) model to generate multiple SCA future series. They were used to calculate multiple series of meteorological drought indices, the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and a novel hydrological drought index (Standardized Snow Cover Index (SSCI)). Linear correlation analysis was applied to both types of drought to analyze how they propagate and the time delay between them. We applied the proposed methodology to the Sierra Nevada (southern Spain), where we estimated a general increase in meteorological and hydrological drought magnitude and duration for the horizon 2071–2100 under the RCP 8.5 emission scenario. The SCA droughts also revealed a significant increase in drought intensity. The meteorological drought propagation to SCA droughts was reflected in an immediate or short time (1 month), obtaining significant correlations in lower accumulation periods of drought indices (3 and 6 months). This allowed us to obtain information about meteorological drought from SCA deficits and vice versa.

Variation of snow cover in the Nyang River basin of southeastern Tibetan Plateau, China

Abstract Snow cover is highly sensitive to global climate change and strongly influences the climate at global and regional scales. Because of limited in situ observations, snow cover dynamics in the Nyang River basin (NRB) have been examined in few studies. Five snow cover indices derived from observation and remote sensing data from 2000 to 2018 were used to investigate the spatial and temporal variation of snow cover in the NRB. There was clear seasonality in the snow cover throughout the entire basin. The maximum snow-covered area was 8,751.35 km2, about 50% of the total basin area, and occurred in March. The maximum snow depth (SD) was 5.35 cm and was found at the northern edge of the middle reaches of the basin. Snow cover frequency, SD, and fraction of snow cover area increased with elevation. The decrease in SD was the most marked in the elevation range of 5,000–6,000 m. Above 6,000 m, the snow water equivalent showed a slight upward trend. There was a significant negative correlation between snow cover and temperature. The results of this study could improve our understanding of changes in snow cover in the NRB from multivariate perspectives. It is better for water resources management.

Spatio-temporal change delineation and forecasting of snow/ice-covered areas of Sikkim Himalaya using multispectral and thermal band combinations of landsat imagery

The widespread retreat of snow/ice-covered areas from the past century has given rise to the sea level. The snow/ice-covered area of Sikkim Himalaya is not an exception. Snow-covered areas are disappearing at an alarming rate, which would give rise to water scarcity especially to the population dwelling in the mountainous regions. Changes in the snow/ice-covered areas indicate a huge threat to the changing climate. Therefore, snow cover area monitoring is currently an important aspect of climate change studies. Landsat data are widely accepted and immensely valued for snow cover mapping. An assessment through change delineation has been performed for the images acquired during the accumulation period of snow for the years 1998, 2008, and 2018 and another three acquired during the ablation period for the years 2000, 2009, and 2017. Snow Cover Index (S3) and Normalized Difference Snow Thermal Index (NDSTI) techniques have been applied to the images. A newly proposed snow index called Perennial Snow Index (PSI) has been applied to delineate the perennial snow-covered regions using the difference of reflectance observed in a visible band (RED) and the Thermal infrared (TIR) band divided by the sum of the two reflectances of Landsat 8 after resampling to 15meter resolution through merging with panchromatic data. The acquired results demarcated the thick or perennial snow accumulated areal boundaries from the surrounding lesser snow deposited regions. The temporal change analysis of two decades shows a difference of 10% in the extent of the snow cover region in Sikkim Himalayas. S3 index values were taken into consideration while performing a Time series analysis. The observed values show a declining trend line along with forecasts till the year 2030. Global Land Ice Measurement from Space (GLIMS) program and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) data have been used for reference.

Методика прогноза водности высокогорных рек Центральной Азии на основе снимков MODIS

The article investigates the possibility of using snow cover satellite data for short-term hydrological forecasting for Central Asian rivers with high-altitude catchments. The research aimed to elaborate a methodology for forecasting mean 10-day (decadal) river discharge (MDD) of the Varzob and Tar Rivers based on MODSNOW-processed MODIS satellite imagery. The objectives of the study were to calculate the snow cover index (SCI) for high-altitude zones in 200 m increments for selected river basins, and to analyze the closeness of dependency between the corresponding SCIs and MDDs. The research resulted in equations applicable for producing operational river water content forecasts. Timely and reliable information on expected river water content during the forthcoming 10-day period allows decision makers (water management and hydropower agencies, emergency authorities) plan water supply for various economic sectors, as well as take measures to prevent hazardous hydrological phenomena on the rivers of Tajikistan and Kyrgyzstan.

Patterns of Vegetation and Climatic Conditions Derived from Satellite Images Relevant for Sub-Antarctic Rangeland Management

Sub-Antarctic rangelands are characterized by weather seasonality and abundant winter snowfall. These climate factors determine most livestock management decisions. Nonetheless, data on these weather patterns are only available for some areas of the region, and management is entirely empirically-derived. The aim of this study was twofold: to compile spatial and temporal environmental information of rangelands in the Magallanes Region of Chile by using satellite images and to provide an example for local purposes through a simple evaluation of current grazing systems of four ranches in central Tierra del Fuego using satellite-derived image products and a known thermal comfort zone for sheep. To determine the environmental conditions of the region, we processed a 12-year (2000–2011) series of three MODIS-Terra platform products: Normalized Difference Vegetation Index (NDVI) as an index of photosynthetic activity, snow cover index, and Land Surface Temperature (LST). Results indicate that data obtained from satellite images follow the known seasonality of the region and deliver spatial and temporal environmental information (e.g., temperature at large scale) for most ranching areas of the region where it was formerly unavailable. The determined grazing period of analyzed ranches for winter range was May 8 to September 29. These tools show promise to encourage management innovation from simple applications to combine platforms and models for forage monitoring and ranch management in Sub-Antarctic rangelands.

Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow maps, derived from MODIS imagery, provide managers and researchers with vital snow cover information, but only at spatial scales of 500 m or more. Finer resolution time series maps, however, retain large temporal gaps, particularly during recurrent cloud cover. This paper’s authors have developed the novel algorithm MODSAT-NDSI to harness the strengths of both coarse and finer spatial resolution imagery by fusing MODIS and Landsat normalized difference snow index (NDSI) data. Daily 30 m snow cover maps were thus generated for 2000 – 2017 with an overall accuracy of 90%, using 33 validation sites distributed throughout south-central British Columbia. Snow cover trends were analyzed across stratified elevation bands and land cover types, revealing that snow cover persists under lower elevation forests for an average of 23.5 d longer than in adjacent open areas during spring. We conclude that the MODSAT-NDSI approach captures temporal and spatial advantages of freely available snow cover datasets and can be modified to suit a variety of novel investigations relating to snow cover or other spectral indices.

Globally scalable alpine snow metrics

Horizontal and altitudinal redistribution of snow by wind transport and avalanches can be important controls on small- and large-scale snow accumulation patterns that control meltwater supply in alpine environments. Redistribution processes control the spatial variability of snow accumulation, which not only controls meltwater supply, but also regulates snowmelt timing, duration, and rates, as well as snow-covered area depletion and the variable contributing area for meltwater runoff generation. However, most hydrological models and land surface schemes do not consider snow redistribution processes, and those that do are difficult to verify without spatially distributed snow depth measurements. These are rarely available in both high resolution and covering large scales. As an increased number of hydrological models include snow redistribution processes there is a need for additional snowcover metrics to verify snow redistribution schemes over large areas using readily available data.This study develops novel high-resolution (20 m), snowcover indices from remotely sensed imagery (Landsat-8 and Sentinel-2) to evaluate snow redistribution models over alpine areas without in-situ or airborne snow observations. A snowcover absence (SA) index, calculated from snow-free areas during the winter, identifies areas of wind erosion or avalanche source areas. A snowcover persistence (SP) index, calculated from snow-covered areas during the summer, identifies snow deposition in drifts and avalanche deposits.The snowcover indices captured the relative differences in surface observations of snow presence and absence between exposed and sheltered sites on an intensely instrumented ridge in the Canadian Rockies Hydrological Observatory. Within the Tuolumne River Basin in central California (1100 km2), the SP index captured roughly half of the spatial variability (R2 = 0.49 to 0.56) in peak SWE as estimated from airborne LiDAR-derived snow depths. At the individual mountain ridge scale (~800 m), variability in both ablation and snow redistribution controlled the SP patterns over 7979 ridges. Differences in shortwave irradiance explained 76% of the SP variance across ridges, but could not explain smaller-scale (~100 m) SP peaks that are associated with snowdrifts and avalanche deposits. The snowcover indices can be used to evaluate snow redistribution models of the finer scale impacts of snow redistribution by wind and gravity as long as the larger scale influences of spatially variable solar irradiance effects are also simulated.

Monitoring and projecting snow on Hawaii Island

AbstractThe highest mountain peaks on Hawaii Island are snow covered for part of almost every year. This snow has aesthetic and recreational value as well as cultural significance for residents and visitors. Thus far there have been almost no systematic observations of snowfall, snow cover, or snow depth in Hawaii. Here we use satellite observations to construct a daily index of Hawaii Island snow cover starting from 2000. The seasonal mean of our index displays large interannual variations that are correlated with the seasonal mean freezing level and frequency of trade wind inversions as determined from nearby balloon soundings. Our snow cover index provides a diagnostic for monitoring climate variability and trends within the extensive area of the globe dominated by the North Pacific trade wind meteorological regime. We have also conducted simulations of the Hawaii climate with a regional atmospheric model. Retrospective simulations for 1990–2015 were run with boundary conditions prescribed from gridded observational analyses. Simulations for the end of 21st century employed boundary conditions based on global climate model projections that included standard scenarios for anticipated anthropogenic climate forcing. The future projections indicate that snowfall will nearly disappear by the end of the current century.

Influence of shadow on the thermal and optical snow indices and their interrelationship

Abstract In snow-covered mountainous terrains, topography-induced different illumination conditions would cause varying influence on the snow characteristics in the sunlit and shadowed sites owing to albedo variation. Since the thermal bands suffer less by the shadows as compared to the optical bands, it is imperative to investigate the effect of shadow on the optical and thermal snow indices. The investigation comprises determination of (a) difference of means between the sunlit snow cover and snow cover under shadow for thermal snow indices, optical snow cover indices and optical snow cover characteristic indices and (b) correlation coefficient of the thermal snow indices with the respective optical snow cover indices and optical snow cover characteristic indices. The study was conducted in the test sites of the Chenab basin, western Himalayas using the Landsat-8 Operational Land Imager and Thermal Infrared Sensor data. The mean values of different snow indices exhibit significant difference between the sunlit and shadow test sites with Z-values ranging between 68.92 and 1220.39 (p r ≥ 0.81, while r -values lie below 0.29 in the sunlit test site (Student's t -test, p r = − 0.37 to − 0.62, p

Development of a new thermal snow index and its relationship with snow cover indices and snow cover characteristic indices

In this study, we have developed a new thermal snow index viz. S3 thermal snow index (S3TSI) and determined its relationship with two existing thermal snow indices (i.e., normalized difference snow thermal index (NDSTI) and normalized difference thermal snow index (NDTSI)) and with snow cover indices (i.e., normalized difference snow index (NDSI) and S3) and snow cover characteristic indices (i.e., snow grain size index (SGI) and snow contamination index (SCI)) for two different sites such as bare snow cover and snow cover mixed with vegetation, respectively, based on Pearson’s correlation coefficients. Whereas NDSTI uses blue band, NDTSI incorporates NDSI. With this analogy, we have developed “S3TSI” by substituting S3 for NDSI since S3 better demarcates snow cover mixed with vegetation. The study was conducted in snow cover test sites of the Chenab basin, Western Himalayas using LANDSAT-8 Operational Land Imager and Thermal Infrared Sensor data of November 2013. The investigation revealed that S3TSI exhibits considerably higher correlation with NDTSI and also with NDSI and S3 in either site except in the case of snow cover mixed with vegetation where NDTSI correlates better with NDSI. All the three thermal snow indices exhibit very high correlation with the snow cover indices in snow cover mixed with vegetation as compared to bare snow cover. However, the thermal snow indices show weak correlation with the snow cover characteristic indices as compared to the snow cover indices in either site. The findings demonstrate the significance of the new thermal snow index as compared to NDSTI and NDTSI.

Estimating Snow Cover From Publicly Available Images

In this paper we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year, the fraction of visible area covered by snow, possibly at different elevations. We created a manually labelled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.

Observed Trends in Canada’s Climate and Influence of Low-Frequency Variability Modes

Abstract Trends in Canada’s climate are analyzed using recently updated data to provide a comprehensive view of climate variability and long-term changes over the period of instrumental record. Trends in surface air temperature, precipitation, snow cover, and streamflow indices are examined along with the potential impact of low-frequency variability related to large-scale atmospheric and oceanic oscillations on these trends. The results show that temperature has increased significantly in most regions of Canada over the period 1948–2012, with the largest warming occurring in winter and spring. Precipitation has also increased, especially in the north. Changes in other climate and hydroclimatic variables, including a decrease in the amount of precipitation falling as snow in the south, fewer days with snow cover, an earlier start of the spring high-flow season, and an increase in April streamflow, are consistent with the observed warming and precipitation trends. For the period 1900–2012, there are sufficient temperature and precipitation data for trend analysis for southern Canada (south of 60°N) only. During this period, temperature has increased significantly across the region, precipitation has increased, and the amount of precipitation falling as snow has decreased in many areas south of 55°N. The results also show that modes of low-frequency variability modulate the spatial distribution and strength of the trends; however, they alone cannot explain the observed long-term trends in these climate variables.