Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014

Zhang, Yonghong; Cao, Ting; Kan, Xi; Wang, Jiangeng; Tian, Wei

doi:10.1007/s12524-016-0617-y

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…Additionally, the majority of the 41 remote sensing images obtained during the winter months may influence the accurate extraction of snow cover information. In this study, clouds were removed by fusing MOD10A2 with MYD10A2 (Xie, Wang & Liang, 2009;Zhang et al, 2017). The main principles of removing clouds are as follows: with MOD10A2 and MYD10A2, all pixel values are defined as zero except for clouds (50), snow (200), lake ice (100), land (25) and water (37).…”

Section: Cloud Removalmentioning

confidence: 99%

Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Chen

Ding

Wang

et al. 2020

PeerJ

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Xinjiang, China, is a typical arid and semi-arid region of Central Asia that significantly lacks freshwater resources, and the surface runoff in this region is mainly supplied by mountain glacier and snow cover meltwater. Based on the above background and issues of transnational water resources between Xinjiang and Central Asia along the Silk Road Economic Belt, which were highlighted in the major strategy of “The Belt and Road”, this study analysed the spatial and temporal variations in snow cover and snow cover days in the Xinjiang region from 2001 to 2015. The study area includes four subregions: Northern Xinjiang, Southern Xinjiang, Eastern Xinjiang and the Ili River Valley. Moderate-resolution Imaging Spectroradiometer (MODIS) 8-day snow cover data were used after removing clouds by combining MOD10A2 and MYD10A2. The results showed that seasonal snow cover occurred from October to April in most regions of Xinjiang and that this snow cover consisted of two processes: snow accumulation and snow ablation. The maximum snow cover occurred in January, whereas the minimum snow cover occurred from July to August. During the seasonal snow cover period, the snowfall rates in Northern Xinjiang and the Ili River Valley were higher, while the other regions had a low snowfall probability. To study the relationship between altitude and snow cover, the normalized snow elevation correlation index (NSACI) was calculated. The NSACI showed a significant correlation between snow cover and elevation in most regions of Xinjiang and was classified into five grades. Snow cover days did not fluctuate obviously from 2001 to 2015, and a decreasing trend was observed in the four subregions except for the Ili River Valley (nonsignificant decreasing trend). We also observed a correlation between snow cover and temperature and found that the correlations between monthly snow cover and monthly temperature in the four subregions were strongly related to the underlying land type and global warming background, which also suggests that the special topography of Xinjiang greatly influences both snow cover and climate change.

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Section: Cloud Removalmentioning

confidence: 99%

Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Chen

Ding

Wang

et al. 2020

PeerJ

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“…The current research shows that the climate of the QTP has become warmer and wetter, and the increased precipitation is mainly concentrated in the growing season (Zhang Y. H. et al, 2016). This means that if the rainfall/snowfall increased in late September or early March, as our results suggested, this change would probably aggravate soil N 2 O emissions in dry ecosystems, such as meadow and steppe.…”

Section: The Model Of N 2 O Flux During Freeze-thaw Cycles Under Global Changementioning

confidence: 55%

Soil Moisture but Not Warming Dominates Nitrous Oxide Emissions During Freeze–Thaw Cycles in a Qinghai–Tibetan Plateau Alpine Meadow With Discontinuous Permafrost

Chen

Zhang

et al. 2021

Front. Ecol. Evol.

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Large quantities of organic matter are stored in frozen soils (permafrost) within the Qinghai–Tibetan Plateau (QTP). The most of QTP regions in particular have experienced significant warming and wetting over the past 50 years, and this warming trend is projected to intensify in the future. Such climate change will likely alter the soil freeze–thaw pattern in permafrost active layer and toward significant greenhouse gas nitrous oxide (N2O) release. However, the interaction effect of warming and altered soil moisture on N2O emission during freezing and thawing is unclear. Here, we used simulation experiments to test how changes in N2O flux relate to different thawing temperatures (T5–5°C, T10–10°C, and T20–20°C) and soil volumetric water contents (VWCs, W15–15%, W30–30%, and W45–45%) under 165 F–T cycles in topsoil (0–20 cm) of an alpine meadow with discontinuous permafrost in the QTP. First, in contrast to the prevailing view, soil moisture but not thawing temperature dominated the large N2O pulses during F–T events. The maximum emissions, 1,123.16–5,849.54 μg m–2 h–1, appeared in the range of soil VWC from 17% to 38%. However, the mean N2O fluxes had no significant difference between different thawing temperatures when soil was dry or waterlogged. Second, in medium soil moisture, low thawing temperature is more able to promote soil N2O emission than high temperature. For example, the peak value (5,849.54 μg m–2 h–1) and cumulative emissions (366.6 mg m–2) of W30T5 treatment were five times and two to four times higher than W30T10 and W30T20, respectively. Third, during long-term freeze–thaw cycles, the patterns of cumulative N2O emissions were related to soil moisture. treatments; on the contrary, the cumulative emissions of W45 treatments slowly increased until more than 80 cycles. Finally, long-term freeze–thaw cycles could improve nitrogen availability, prolong N2O release time, and increase N2O cumulative emission in permafrost active layer. Particularly, the high emission was concentrated in the first 27 and 48 cycles in W15 and W30, respectively. Overall, our study highlighted that large emissions of N2O in F–T events tend to occur in medium moisture soil at lower thawing temperature; the increased number of F–T cycles may enhance N2O emission and nitrogen mineralization in permafrost active layer.

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“…Many studies have also conducted research in this area. The current cloud removal method mainly uses the combination of MOD10A1, MYD10A1, and 8-day products MOD10A2 and MYD10A2, and uses the Terra and the Aqua to remove the cloud from the observation results of the same area at different time periods [9,80]. Using this method, the cloud cover of the TP was reduced to 25.2% [81], the total accuracy of the snow cover measured in Xinjiang was 94.2%, and the cloud cover was reduced by 45.99% in Northeast China [82].…”

Section: Discussionmentioning

confidence: 99%

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

Zou

Sun

et al. 2022

Remote Sensing

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In the context of global warming, relevant studies have shown that China will experience the largest temperature rise in the Qinghai–Tibet Plateau and northwestern regions in the future. Based on MOD10A2 and MYD10A2 snow products and snow depth data, this study analyzes the temporal and spatial evolution characteristics of the snow cover fraction, snow depth, and snow cover days in the three stable snow cover areas in China, and combines 15 modes in CMIP6 snow cover data in four different scenarios with three kinds of variables, predicting the spatiotemporal evolution pattern of snow cover in China’s three stable snow cover areas in the future. The results show that (1) the mean snow cover fraction, snow depth, and snow cover days in the snow cover area of Northern Xinjiang are all the highest. Seasonal changes in the snow cover areas of the Qinghai–Tibet Plateau are the most stable. The snow cover fraction, snow depth, and snow cover days of the three stable snow cover areas are consistent in spatial distribution. The high values are mainly distributed in the southeast and west of the Qinghai–Tibet Plateau, the south and northeast of Northern Xinjiang, and the north of the snow cover area of Northeast China. (2) The future snow changes in the three stable snow cover areas will continue to decline with the increase in development imbalance. Snow cover fraction and snow depth decrease most significantly in the Qinghai–Tibet Plateau and the snow cover days in Northern Xinjiang decrease most significantly under the SSPs585 scenario. In the future, the southeast of the Qinghai–Tibet Plateau, the northwest of Northern Xinjiang, and the north of Northeast China will be the center of snow cover reduction. (3) Under the four different scenarios, the snow cover changes in the Qinghai–Tibet Plateau and Northern Xinjiang are the most significant. Under the SSPs126 and SSPs245 scenarios, the Qinghai–Tibet Plateau snow cover has the most significant change in response. Under the SSPs370 and SSPs585 scenarios, the snow cover in Northern Xinjiang has the most significant change.

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Spatial and Temporal Variation Analysis of Snow Cover Using MODIS over Qinghai-Tibetan Plateau during 2003–2014

Cited by 16 publications

References 29 publications

Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Soil Moisture but Not Warming Dominates Nitrous Oxide Emissions During Freeze–Thaw Cycles in a Qinghai–Tibetan Plateau Alpine Meadow With Discontinuous Permafrost

Snow Cover in the Three Stable Snow Cover Areas of China and Spatio-Temporal Patterns of the Future

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