Alpine vegetation responses to snow phenology in the Chinese Tianshan mountainous region

Zhang, Bo; Li, Xuemei; Li, Chao; Nyiransengiyumva, Christine; Qin, Qiyong

doi:10.1007/s11629-021-7133-4

Cited by 2 publications

(4 citation statements)

References 56 publications

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“…The SOS of grassland was more sensitive to SCF FMA variations than other vegetation types (Figure 7a). A similar result was also demonstrated in the low-elevation regions of the Ili Valley based on the MODIS snow and vegetation products [42]. Like the Alps [16], the SOS of grassland was influenced primarily by a decline in snow cover, secondarily by snowmelt amount, while the NDVI max was equally affected by SCF FMA and SWE max .…”

Section: Responses Of Different Vegetation Types To Snowsupporting

confidence: 76%

“…Some herb vegetation germination and growth, such as ephemeral plants, heavily depend on the snow conditions around the TS [29]. Large SWE max and Snowmelt FMA suggested that snow provided excellent thermal protection and essential moisture supply for shorter plant types [42,56]. Generally, earlier snowmelt with more snowmelt amount could significantly advance the SOS and prolong LOS for grassland, promoting the increase in NDVI max [16,27].…”

Section: Responses Of Different Vegetation Types To Snowmentioning

confidence: 99%

“…However, the response of land surface phenology to the snowpack changes (snow cover fraction, snow mass, and snow depth, etc.) cannot be adequately revealed by MODIS snow cover due to the limitation of optical sensor [28,42], and its driving mechanisms need to be better explored in the entire TS. In addition, the accuracy and coarse resolution of the existing snow mass datasets also bring a big challenge for finer-scale snow ecology research [43].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Tao

Qiang

et al. 2022

Remote Sensing

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The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

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Section: Responses Of Different Vegetation Types To Snowsupporting

confidence: 76%

Section: Responses Of Different Vegetation Types To Snowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Tao

Qiang

et al. 2022

Remote Sensing

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“…For instance, the timing and dynamics of snow phenology are found closely associated with the onset and intensity of springtime snowmelt‐driven floods (C. G. Pan, Kirchner, et al., 2021). SSD and SED could significantly influence alpine vegetation growth by modulating soil thermal regimes and moisture availability (Xu et al., 2022; B. Zhang, Li, et al., 2022). Additionally, SCD has been recognized as an important factor of the well‐known elevation‐dependent warming over the TP primarily through snow‐albedo feedback mechanisms (You et al., 2020; H. Zhang, Immerzeel, et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection

Zhang,

Sun

et al. 2024

Water Resources Research

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Accurate snow phenology detection, including snow cover days (SCD), snow start date (SSD), and snow end date (SED), is increasingly important for understanding mountain hydrology such as snow heterogeneity and snowmelt seasonality. Multiple cloud‐free daily snow cover products have recently been developed in China, employing diverse retrieval algorithms and cloud‐gap‐filling methods, resulting in varying accuracy levels. However, comprehensive analysis of differences among products and their impact on snow phenology detection is lacking. This study systematically evaluates eight state‐of‐the‐art snow cover products in China, focusing on the challenging Tibetan Plateau (TP). We introduce a novel metric, the consistency‐weighted correlation coefficient (CWR), customized for SSD and SED detection, and propose product‐combining schemes like “ensemble voting” and “sensor preference” to enhance reliability. Our findings highlight the prime influence of retrieval algorithms under clear‐sky conditions on accuracy, surpassing the importance of cloud‐gap‐filling methods. Specifically, a product optimizing normalized difference snow index thresholds for diverse landcover types consistently outperforms others in detecting all three snow phenology parameters, with correlation coefficients for SCD of 0.82 and 0.69, and CWR values for SSD of 0.54 and 0.40, and for SED of 0.53 and 0.37 in both China and the TP, respectively. Moreover, our proposed scheme combining three high‐accuracy products significantly enhances snow cover identification and SCD detection, especially when the best‐performing product alone faces substantial uncertainty. These findings provide immediate, crucial implications for optimizing the use of multiple cloud‐free products to enhance snow phenology detection, ultimately advancing the applicability of derived snow parameters in mountain hydrology research.

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Alpine vegetation responses to snow phenology in the Chinese Tianshan mountainous region

Cited by 2 publications

References 56 publications

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Combined Use of Multiple Cloud‐Free Snow Cover Products in China and Its High‐Mountain Region: Implications From Snow Cover Identification to Snow Phenology Detection

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