scholarly journals Snow cover variability and trend over Hindu Kush Himalayan region using MODIS and SRTM data

2021 ◽  
Author(s):  
Nirasindhu Desinayak ◽  
Anup Krishna Prasad ◽  
Hesham El-Askary ◽  
Menas Kafatos ◽  
Ghassem R. Asrar
Keyword(s):  
Snow Cover ◽  
Linear Trend ◽  
Remote Sensing Data ◽  
Monotonic Trend ◽  
Annual Variations ◽  
Hindu Kush ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mk Analysis ◽  
Earth’S Climate ◽  
Melting Season

Abstract. Snow cover changes has a direct bearing on the regional and global energy and water cycles, and the change in Earth's climate condition The study of long term altitudinal (spatial and temporal, 2000–2017) in the coverage of snow and glaciers in one of the world’s largest mountainous region, the Hindu Kush Himalayan (HKH) region including Tibet have been studied using remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra (at 5 km grid resolution). Terra provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (growing season and melting season) and inter-annual variations of snow and glacial cover over the HKH region (2000–2017). The zonal and altitudinal (hypsographic) analyses were carried out for melting-season and accumulating-season. The altitude-wise linear trend analysis (Pearson’s) of snow cover, shown as a hypsographic curve, clearly indicate a major decline in snow cover (average of 5 % or more, at 100 m interval aggregates) between 4000–4500 m and 5500–6000 m altitudes, which is consistent with the median trend (Theil-Sen, TS) and the monotonic trend (Mann-Kendall statistics, MK) analysis. The regions and altitudes where major and statistically significant increase (10 to 30 %) or decrease (−10 to −30 %) in snow cover are identified. The extrapolation of the altitude-wise linear trend shows that it may take between ~74 to 7900 year (for 3001–6000 m and 6000–7000 m altitude zones respectively) for mean snow cover to decline approximately 25 % in the HKH region, assuming no-change in other parameters) that affect the snow cover.

scholarly journals Satellite-based radiative forcing by light-absorbing particles in snow across the Northern Hemisphere

2020 ◽  
Author(s):  
Jiecan Cui ◽  
Tenglong Shi ◽  
Yue Zhou ◽  
Dongyou Wu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Snow Albedo ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Earth’S Climate

Abstract. Snow is the most reflective natural surface on Earth and consequently plays an important role in Earth’s climate. Light-absorbing particles (LAPs) deposited on the snow surface can effectively decrease snow albedo, resulting in positive radiative forcing. In this study, we used remote sensing data from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Snow, Ice, and Aerosol Radiative (SNICAR) model to quantify the reduction in snow albedo due to LAPs, before validating and correcting the data against in situ observations. We then incorporated these corrected albedo reduction data in the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model to estimate Northern Hemisphere radiative forcing in January and February for the period 2003–2018. Our analysis reveals an average corrected reduction in snow albedo of ~0.0246, with instantaneous radiative forcing and daily radiative forcing values of ~5.87 and ~1.69 W m−2, respectively. We also observed significant spatial variations in corrected snow albedo reduction, instantaneous radiative forcing and daily radiative forcing throughout the Northern Hemisphere, with the lowest respective values (~0.0123, ~1.09 W m−2, and ~0.29 W m−2) occurring in the Arctic and the highest (~0.1669, ~36.02 W m−2, and ~10.60 W m−2) in northeastern China. From MODIS retrievals, we determined that the LAP content of snow accounts for 57.6 % and 37.2 % of the spatial variability in Northern Hemisphere albedo reduction and radiative forcing, respectively. We also compared retrieved radiative forcing values with those of earlier studies, including local-scale observations, remote-sensing retrievals, and model-based estimates. Ultimately, estimates of radiative forcing based on satellite-retrieved data are shown to represent true conditions on both regional and global scales.

scholarly journals Satellite-based radiative forcing by light-absorbing particles in snow across the Northern Hemisphere

2021 ◽  
Vol 21 (1) ◽  
pp. 269-288
Author(s):  
Jiecan Cui ◽  
Tenglong Shi ◽  
Yue Zhou ◽  
Dongyou Wu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Remote Sensing Data ◽  
The Arctic ◽  
Snow Albedo ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Earth’S Climate

Abstract. Snow is the most reflective natural surface on Earth and consequently plays an important role in Earth's climate. Light-absorbing particles (LAPs) deposited on the snow surface can effectively decrease snow albedo, resulting in positive radiative forcing. In this study, we used remote-sensing data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and the Snow, Ice, and Aerosol Radiative (SNICAR) model to quantify the reduction in snow albedo due to LAPs before validating and correcting the data against in situ observations. We then incorporated these corrected albedo-reduction data in the Santa Barbara DISORT (Discrete Ordinate Radiative Transfer) Atmospheric Radiative Transfer (SBDART) model to estimate Northern Hemisphere radiative forcing except for midlatitude mountains in December–May for the period 2003–2018. Our analysis reveals an average corrected reduction in snow albedo (ΔαMODIS,correctedLAPs) of ∼ 0.021 under all-sky conditions, with daily radiative forcing (RFMODIS,dailyLAPs) values of ∼ 2.9 W m−2, over land areas with complete or near-complete snow cover and with little or no vegetation above the snow in the Northern Hemisphere. We also observed significant spatial variations in ΔαMODIS,correctedLAPs and RFMODIS,dailyLAPs, with the lowest respective values (∼ 0.016 and ∼ 2.6 W m−2) occurring in the Arctic and the highest (∼ 0.11 and ∼ 12 W m−2) in northeastern China. From MODIS retrievals, we determined that the LAP content of snow accounts for 84 % and 70 % of the spatial variability in albedo reduction and radiative forcing, respectively. We also compared retrieved radiative forcing values with those of earlier studies, including local-scale observations, remote-sensing retrievals, and model-based estimates. Ultimately, estimates of radiative forcing based on satellite-retrieved data are shown to represent true conditions on both regional and global scales.

scholarly journals Changes in seasonal snow cover in Hindu Kush-Himalayan region

2011 ◽  
Vol 5 (2) ◽  
pp. 755-777 ◽  
Author(s):  
D. R. Gurung ◽  
A. V. Kulkarni ◽  
A. Giriraj ◽  
K. S. Aung ◽  
B. Shrestha ◽  
...  
Keyword(s):  
Snow Cover ◽  
Linear Trend ◽  
Geographical Area ◽  
Western Himalaya ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Depletion Curve ◽  
Hindu Kush ◽  
Seasonal Snow Cover

Abstract. The changes in seasonal snow covered area in the Hindu Kush-Himalayan (HKH) region have been examined using Moderate – resolution Imaging Spectroradiometer (MODIS) 8-day standard snow products. The average snow covered area of the HKH region based on satellite data from 2000 to 2010 is 0.76 million km2 which is 18.23% of the total geographical area of the region. The linear trend in annual snow cover from 2000 to 2010 is −1.25±1.13%. This is in consistent with earlier reported decline of the decade from 1990 to 2001. A similar trend for western, central and eastern HKH region is 8.55±1.70%, +1.66% ± 2.26% and 0.82±2.50%, respectively. The snow covered area in spring for HKH region indicates a declining trend (−1.04±0.97%). The amount of annual snowfall is correlated with annual seasonal snow cover for the western Himalaya, indicating that changes in snow cover are primarily due to interannual variations in circulation patterns. Snow cover trends over a decade were also found to vary across seasonally and the region. Snow cover trends for western HKH are positive for all seasons. In central HKH the trend is positive (+15.53±5.69%) in autumn and negative (−03.68&plusmn3.01) in winter. In eastern HKH the trend is positive in summer (+3.35±1.62%) and autumn (+7.74±5.84%). The eastern and western region of HKH has an increasing trend of 10% to 12%, while the central region has a declining trend of 12% to 14% in the decade between 2000 and 2010. Snow cover depletion curve plotted for the hydrological year 2000–2001 reveal peaks in the month of February with subsidiary peaks observed in November and December in all three regions of the HKH.

scholarly journals Distribution and Attribution of Terrestrial Snow Cover Phenology Changes over the Northern Hemisphere during 2001–2020

2021 ◽  
Vol 13 (9) ◽  
pp. 1843
Author(s):  
Xiaona Chen ◽  
Yaping Yang ◽  
Yingzhao Ma ◽  
Huan Li
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Onset Date ◽  
Driving Factor ◽  
Climate Variables ◽  
Significance Level ◽  
The Past ◽  
Attribution Analysis ◽  
Altitudinal Distributions ◽  
Melting Season

Snow cover phenology has exhibited dramatic changes in the past decades. However, the distribution and attribution of the hemispheric scale snow cover phenology anomalies remain unclear. Using satellite-retrieved snow cover products, ground observations, and reanalysis climate variables, this study explored the distribution and attribution of snow onset date, snow end date, and snow duration days over the Northern Hemisphere from 2001 to 2020. The latitudinal and altitudinal distributions of the 20-year averaged snow onset date, snow end date, and snow duration days are well represented by satellite-retrieved snow cover phenology matrixes. The validation results by using 850 ground snow stations demonstrated that satellite-retrieved snow cover phenology matrixes capture the spatial variability of the snow onset date, snow end date, and snow duration days at the 95% significance level during the overlapping period of 2001–2017. Moreover, a delayed snow onset date and an earlier snow end date (1.12 days decade−1, p < 0.05) are detected over the Northern Hemisphere during 2001–2020 based on the satellite-retrieved snow cover phenology matrixes. In addition, the attribution analysis indicated that snow end date dominates snow cover phenology changes and that an increased melting season temperature is the key driving factor of snow end date anomalies over the NH during 2001–2020. These results are helpful in understanding recent snow cover change and can contribute to climate projection studies.

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals Characterization of Canadian High Arctic glacier surface albedo from MODIS C6 data, 2001–2016

2017 ◽  
Author(s):  
Colleen A. Mortimer ◽  
Martin Sharp
Keyword(s):  
Large Scale ◽  
Surface Albedo ◽  
Temporal Trends ◽  
Principal Component ◽  
High Arctic ◽  
Shortwave Radiation ◽  
Glacier Surface ◽  
Annual Variations ◽  
The Mean ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Inter-annual variations and longer-term trends in the annual mass balance of glaciers in Canada's Queen Elizabeth Islands (QEI) are largely attributable to changes in summer melt. The largest source of melt energy in the QEI in summer is net shortwave radiation, which is modulated by changes in glacier surface albedo. We used measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors to investigate large scale spatial patterns and temporal trends and variability in the summer surface albedo of QEI glaciers and their relationship to observed changes in glacier surface temperature from 2001 to 2016. Mean summer black-sky shortwave broadband albedo (BSA) decreased at a rate of 0.029 ± 0.025 decade-1 over that period. Larger reductions in BSA occurred in July (−0.050 ± 0.031 decade-1). No change in BSA was observed in either June or August. Most of the decrease in BSA, which was greatest at lower elevations around the margins of the ice masses, occurred between 2007 and 2012 when mean summer BSA was anomalously low. The First Principal Component of the 16-year record of mean summer BSA was well correlated with the mean summer North Atlantic Oscillation Index, except in 2006, 2010, and 2016. During this 16-year period, the mean summer LST increased by 0.046 ± 0.036 °C yr-1 and the BSA record was negatively correlated (−0.64, p 

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

2021 ◽  
Author(s):  
Rui Zhang ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Chunguang Ban
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Significant Negative Correlation ◽  
Spatial And Temporal Variation ◽  
Snow Cover Area ◽  
Northern Edge ◽  
Slight Upward Trend

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.

scholarly journals Validation of MODIS snow cover images over Austria

2006 ◽  
Vol 3 (4) ◽  
pp. 1569-1601 ◽  
Author(s):  
J. Parajka ◽  
G. Blöschl
Keyword(s):  
Snow Cover ◽  
Classification Accuracy ◽  
Snow Depth ◽  
Seasonal Pattern ◽  
Acquisition Time ◽  
Spatial And Temporal Variability ◽  
Temporal Shift ◽  
Early Afternoon ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon).

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