Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers

Wang, Xiaoyue; Wu, Chaoyang; Peng, Dailiang; Gonsamo, Alemu; Liu, Zhengjia

doi:10.1016/j.agrformet.2018.03.004

Cited by 113 publications

(88 citation statements)

References 49 publications

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“…Alpine vegetation plays a crucial role in a mountain ecosystem, so the variability of snow phenology and snow depth have been reported to significantly influence vegetation growth [24,96,97]. For example, a shorter D d would lead to an earlier start and longer growing season on the Tibetan Plateau [10,37]. The snow water equivalent and D d play an equal role in the growth of grassland and sparse vegetation [98].…”

Section: Limitation and Outlookmentioning

confidence: 99%

“…Earlier D e with increased accumulation of seasonal precipitation (P a ) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis. direct implications on the growth of vegetation, snowmelt timing, freshwater supply, and irrigation in snowmelt-dominated basins [5,[9][10][11][12][13][14]. Therefore, it is necessary and meaningful to investigate the spatiotemporal variability of snow phenology.Compared with the sparse observations of meteorological stations, remote sensing is an effective way to monitor snow dynamics of past decades on regional and global scales [4,8,[15][16][17].…”

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confidence: 99%

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Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Yang

Ahmad

et al. 2019

Remote Sensing

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Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of <0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

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Section: Limitation and Outlookmentioning

confidence: 99%

mentioning

confidence: 99%

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Yang

Ahmad

et al. 2019

Remote Sensing

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“…Because SCD was closely related to winter chilling and less chilling accumulation delayed the SOS and further affected EVI max [66], significant correlations between SCE and SOS as well as between SCE and EVI max were mainly concentrated at −5 • C to 0 • C, with a significant positive partial correlation. This relationship may be due to the earlier SCE, which caused the spring phenology to advance and led to drier soil moisture, thereby reducing the EVI max [47]. In other climate gradients, both positive and negative correlations were found, which were not significant.…”

Section: Impact Of Climate In Vegetation Dynamics Response To Snow Comentioning

confidence: 90%

“…Based on the seasonal cycle of snow cover over the boreal region, we used the hydrological year instead of the calendar year to derive the snow cover phenology parameters [22,47]. Here, we defined the hydrological year of the boreal region as the period from September 1st of a given year (t − 1) to August 31st of the next year (t).…”

Section: Snow Cover Phenology Metricsmentioning

confidence: 99%

“…On the other hand, snow cover is also an effective insulator, which can protect the soil from exposure to harsh climatic conditions, such as low temperature and strong winds [43]. Therefore, the soil temperature in snow-covered areas was usually higher than that in snow-free areas [47], which was conducive to the SOS. Similar to SCD, SCE showed an inconsistent correlation with SOS over the boreal region, but the regions that showed significant positive correlations with SOS were larger than the areas that showed significant correlations between SCD and SOS.…”

Section: Influence Of Snow Cover Phenology On Vegetation Dynamicsmentioning

confidence: 99%

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Diverse Responses of Vegetation Dynamics to Snow Cover Phenology over the Boreal Region

Xiong

Zhang

Zhao

et al. 2019

Forests

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Snow cover phenology plays an important role in vegetation dynamics over the boreal region, but the observed evidence of this interaction is limited. A comprehensive understanding of the changes in vegetation dynamics and snow cover phenology as well as the interactions between them is urgently needed. To investigate this, we calculated two indicators, the start of the growing season (SOS) and the annual maximum enhanced vegetation index (EVImax), as proxies of vegetation dynamics using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI). Snow cover duration (SCD) and snow cover end date (SCE) were also extracted from MODIS snow cover datasets. Then, we quantified the spatial-temporal changes in vegetation dynamics and snow cover phenology as well as the relationship between them over the boreal region. Our results showed that the EVImax generally demonstrated an increasing trend, but SOS varied in different regions and vegetation types from 2001 to 2014. The earlier onset of SOS was mainly concentrated in the Siberian boreal region. In the Eurasian boreal region, we observed an advance in the SCE and decrease in the SCD, while in the North American boreal region, the spatial distribution of the trends exhibited substantial heterogeneity. Our results also indicated that the snow cover phenology had significant impacts on the SOS and the EVImax, but the effects varied in different regions, vegetation types, and climate gradients. Our findings provide strong evidence of the interaction between vegetation dynamics and snow cover phenology, and snow cover should be considered when analyzing future vegetation dynamics in the boreal region.

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Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

Liu,

Xiao,

Zhang

et al. 2023

Ecosphere

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Snow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

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Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite observed evidence, impacts of different biomes, and climate drivers

Cited by 113 publications

References 49 publications

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Diverse Responses of Vegetation Dynamics to Snow Cover Phenology over the Boreal Region

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

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