Snow depth reconstruction over last century: Trend and distribution in the Tianshan Mountains, China

Li, Qian; Yang, Tao; Zhang, Feiyun; Qi, Zhiming; Li, Lanhai

doi:10.1016/j.gloplacha.2018.12.008

Cited by 28 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have investigated the impact of glacier change on water resources in the TS [42,51-53], but only a few studies have addressed the snow cover changes with optical remote sensing [54,55]. In addition, different moisture sources cause diverse climate zones in the TS [56,57], but most studies have focused on only a part of the TS, especially on the areas within China [43,58]. Research on the snow phenology concerning the entire TS and its response to the different climate types in the sub-regions is lacking.…”

mentioning

confidence: 99%

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

Yang

Ahmad

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

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.

show abstract

mentioning

confidence: 99%

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

Yang

Ahmad

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Limited by the forcing data period, the variability of snow mass in this study was described during a relatively short time period. A point‐scale in‐situ historical snow depth reconstruction was performed based on the corrected reanalysis data (Q. Li, Yang, et al., 2018). Machine learning provided a reasonable approach so as to estimate the historical snow depth in a grid cell (J. Yang, Jiang, Luojus, et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

Tao

Chen

et al. 2021

JGR Atmospheres

Self Cite

View full text Add to dashboard Cite

The seasonal snowpack plays an essential role in the water resources budget of the global mountainous area and provides freshwater supply for over 1/6 of the world's population (Barnett et al., 2005;, which has a profound effect on the food production of irrigated agriculture and snowmelt runoff regimes in the snow-dominated basin (Qin et al., 2020). It also strongly affects regional climate system, alpine phenology, and biogeochemical processes through regulation of the land-atmospheric exchanges of water and energy (Arndt et al., 2020;Tomaszewska et al., 2020;Zhang, 2005). In addition, snow attracts recreational activities and is an important resource of winter tourism (Deng et al., 2019), but it also causes snow-related disasters, such as snow avalanches and snowmelt flooding (Ballesteros-Cánovas et al., 2018;Schweizer et al., 2003). Although the role of snow is irrefutable, present approaches show a large uncertainty regarding snow mass estimation in a global mountainous area due to the presence of orographic barriers, its strong vertical and horizontal variability, diverse vegetation cover, and representative sites for snow measurement (Dong, 2018;Dozier et al., 2016;Mudryk et al., 2015). The accuracy of snow mass map based on in-situ observations interpolation methods depends on the number and representativeness of the ground observations, while a sparse network of snow observations usually exists in the mountainous environment, especially in the area with dense vegetation and a complex topography (Dozier et al., 2016;Mortimer et al., 2020). The passive microwave sensors could provide a nearly real-time global snow mass

show abstract

“…It is one of the most developed mountains with modern glaciers in the world and one of the most sensitive areas for global climate change [29]. The part of it in China, with coordinates of 39.34 • -45.43 • N and 73.8 • -96.37 • E, stretches 1700 km from east to west, and spans the whole territory of Xinjiang, covering an area of 5.7 × 10 5 km 2 [30][31][32]. It is composed of a series of parallel mountains running east to west and surrounded by the Taklimakan and Gurbantunggut Deserts in north and south [33] (Figure 1).…”

Section: Study Areamentioning

confidence: 99%

Simulation of the Potential Distribution of the Glacier Based on Maximum Entropy Model in the Tianshan Mountains, China

Wang

Zhang

Liu

et al. 2021

Water

View full text Add to dashboard Cite

Under the background of global climate change, the variation in the spatial distribution and ice volume of mountain glaciers have a profound influence on regional economic development and ecological security. The development of glaciers is like biological succession; when climate change approaches or exceeds the threshold of suitable conditions for glacier development, it will lead to changes in potential distribution pattern. Therefore, from the perspective of the "biological" characteristics of glaciers, it is a beneficial exploration and attempt in the field of glaciology to explore its potential distribution law with the help of the niche model. The maximum entropy model (MaxEnt) can explain the environmental conditions suitable for the survival of things by analyzing the mathematical characteristics and distribution laws of samples in space. According to glacier samples and the geographical environment data screened by correlation analysis and iterative calculation, the potential distribution pattern of Tianshan glaciers in China in reference years (1970–2000) was simulated by MaxEnt. This paper describes the contribution of geographical environmental factors to distribution of glaciers in Tianshan Mountains, quantifies the threshold range of factors affecting the suitable habitat of glaciers, and predicts the area variation and distribution pattern of glaciers under different climate scenarios (SSP1-2.6, SSP5-8.5) in the future (2040–2060, 2080–2100). The results show that the MaxEnt model has good adaptability to simulate the distribution of glaciers. The spatial heterogeneity of potential distribution of glaciers is caused by the spatio-temporal differences of hydrothermal combination and topographic conditions. Among the environmental variables, precipitation during the wettest month, altitude, annual mean temperature, and temperature seasonality have more significant effects on the potential distribution of glaciers. There is significant spatial heterogeneity in the potential distribution of glaciers in different watersheds, altitudes, and aspects. From the forecast results of glacier in various climatic scenarios in the future, about 18.16–27.62% of the total reference year glacier area are in an alternating change of melting and accumulation, among which few glaciers are increasing, but this has not changed the overall retreat trend of glaciers in the study area. Under the low emission scenario, the glacier area of the Tianshan Mountains in China decreased by 18.18% and 23.73% respectively in the middle and end of the 21st century compared with the reference years and decreased by 20.04% and 27.63%, respectively, under the high emission scenario, which showed that the extent of glacier retreat is more intense under the high emission scenario. Our study offers momentous theoretical value and practical significance for enriching and expanding the theories and analytical methods of the glacier change.

show abstract

Snow depth reconstruction over last century: Trend and distribution in the Tianshan Mountains, China

Cited by 28 publications

References 40 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

Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia

Simulation of the Potential Distribution of the Glacier Based on Maximum Entropy Model in the Tianshan Mountains, China

Contact Info

Product

Resources

About