Extent Of Seasonal Snow Cover Research Articles

High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data

High-resolution mapping of seasonal snow cover extent in the Pamir Hindu Kush using machine learning-based integration of multi-sensor data

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

Seasonal snow cover regime and historical change in Central Asia from 1986 to 2008

A series of statistics describing seasonal Snow Cover Extent and timing in Central Asia (CA) have been derived from AVHRR satellite images for the time period from 1986 to 2008. Analysis of long term mean snow cover statistics shows that the area weighted mean of long term Snow Covering Days (SCD) for the whole CA is 95.2±65.7days. High elevation mountainous areas above 3000m in Altai, Tien Shan and Pamir, which account for about 2.8% of total area in CA, have SCD >240days. Deserts (Karakorum Desert, Taklamakan Desert, Kumtag Desert) and rain shadow areas of major mountains, accounting for 27.0% of total area in CA, have SCD in the range of 0–30days.Factors affecting snow cover distribution have been analyzed using simple linear regression and segmented regression. For plain regions and windward regions, the SCD rate is +5.9days/100m, while for leeward regions, the rate jumps from +0.7days/100m to +10.0days/100m at about 2335m. Latitude affects the SCD, especially in plain regions with insignificant change of elevation, with rates of 9–10days/degree from south to north.The Mann-Kendal test and the Theil-Sen regression methods have been applied to analyze the spatial heterogeneous trends of change of SCD, Snow Cover Onset Date (SCOD), and Snow Cover Melt Date (SCMD). Area weighed mean SCD in the whole CA does not exhibit significant trend of change from 1986 to 2008. Increase of SCD was observed in the northeastern Kazakh Steppe. Low elevation areas below 2000m in Central Tien Shan and Eastern Tien Shan, as well as mid-elevation areas from 1000m to 3000m in Western Tien Shan, Pamiro-Alai and Western Pamir, also experienced increase of SCD, associated with both earlier SCOD and later SCMD. Decrease of SCD was observed in mountainous areas of Altai, Tien Shan and Pamir, and vast areas in plains surrounding the Aral Sea.

Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing, GIS and ancillary data

Seasonal snow cover is a vital natural resource in the Himalaya. Monitoring of the areal extent of seasonal snow cover is important for both climatological studies as well as hydrological applications. In the present paper, snow cover monitoring was carried out to evaluate the region-wise accumulation and ablation pattern of snow cover in Pir Panjal and Shamshawari ranges of Kashmir valley. The study was carried out for the winter period between November and April of 2004–05, 2005–06 and 2006–07, using multi-temporal WiFS sensor data of IRS-1C/1D satellites. The study shows reduction in the areal extent of seasonal snow cover and rising trend of maximum temperature in three winters for the entire Kashmir valley. This has been validated with 20 years (1988–89 to 2007–08) climatic conditions prevailed in both ranges of Kashmir valley. Region-wise study shows the spatial and temporal variability in seasonal snow cover within Kashmir valley. Advance melting was observed in Banihal and Naugam/Tangdhar regions than Gurez and Machhal regions. Different geographical parameters of these regions were studied to evaluate the influence on snow cover and it was observed that altitude and position of region with respect to mountain range are the deciding factors for retaining the seasonal snow cover for longer duration. Such region-wise study of snow cover monitoring, can provide vital inputs for planning the hydropower projects, development in habitat areas, recreational and strategic planning in the region.

Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements

In the present paper, a methodology has been developed for the mapping of snow cover in Beas basin, Indian Himalaya using AWiFS (IRS-P6) satellite data. The complexities in the mapping of snow cover in the study area are snow under vegetation, contaminated snow and patchy snow. To overcome these problems, field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied. By evaluation and validation of different topographic correction models, it was observed that, the normalized difference snow index (NDSI) values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects. Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique. The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary reflectance), NIR band reflectance and forest/vegetation cover information. The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly, once that pixel is cloud free. The snow cover distribution was estimated using one year (2004–05) cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data.

Snow depth derived from passive microwave remote-sensing data in China

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

Streamflow response to seasonal snow cover extent changes in large Siberian watersheds

This study uses remotely sensed long‐term (1966–1999) weekly snow cover extent data to investigate snowmelt runoff response to seasonal snow cover change in the large Siberian watersheds (Ob, Yenisei, and Lena basins). It quantified the seasonal cycles and variations of snow cover extent and river streamflow and identified a clear correspondence of river streamflow to seasonal snow cover extent change; i.e., an association of low streamflow with high snow cover extent during the cold season and an increase in discharge associated with a decrease of snow cover extent during the melt periods. This study also examined and compared the weekly mean streamflow with the weekly basin snow cover extent for the study period. The results revealed a very strong linkage between the streamflow and snow cover extent change during the spring melt season over the large Siberian watersheds and developed a statistically significant weekly runoff‐snow cover relation. This relation suggests a practical procedure of using remotely sensed snow cover information for snowmelt runoff forecasting over the large northern watersheds. Analyses of extreme (high/low) streamflow cases (years) and the associated snow cover conditions indicate an association of high (low) flood peak with late (early) snowmelt in the Ob and Yenisei basins. Comparisons of snowmelt timing with peak flow show different associations between these two variables among the large Siberian rivers. These results demonstrate that the NOAA weekly snow cover extent data are useful for understanding and predicting streamflow changes in the Arctic regions. Snow cover water equivalent data/products obtained by remote sensing technology and in situ snow observations are currently being examined for what we expect will eventually improve hydrologic forecasts over the large northern watersheds.

Areal Extent of Seasonal Snow Cover in a Changed Climate

In mountain snow basins, a change in climate will likely cause a change in the basin snow cover extent. A procedure for evaluating whether a given climate change scenario will speed up or slow down the seasonal decrease of snow covered area is outlined with hypothetical examples for a simple basin. This procedure has two main purposes. First, it can be used to generate snow covered area data in a new climate for input to runoff models such as the Snowmelt-Runoff Model (SRM). Second, it could potentially be used to provide input to climate models that require knowledge of the land area covered by snow at a given time. A computer program is now operational for use on real basins and is demonstrated on the Rio Grande basin in Colorado and the Illecillewaet River basin in British Columbia.

Evidence of recent changes in global snow and ice cover

Data on recent variations in the seasonal extent of snow cover and sea ice, of the terminal position and volume of alpine glaciers, and of ground temperature profiles in permafrost areas are reviewed. The extent of seasonal snow cover and of sea ice has fluctuated irregularly over the last 15–20 years. There is no apparent response to global warming trends. In contrast, most glaciers retreated and thinned from the late 19th century until the 1960s and Alaskan permafrost temperatures have risen 2°–4° C per century. Recently, some glacier advances have been noted.