Albedo reduction as an important driver for glacier melting in Tibetan Plateau and its surrounding areas

Zhang, Yulan; Gao, Tanguang; Kang, Shichang; Shangguan, Donghui; Luo, Xi

doi:10.1016/j.earscirev.2021.103735

Cited by 66 publications

(30 citation statements)

References 86 publications

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“…Numerous studies have demonstrated that glacier ablation is strongly influenced by climate and particularly sensitive to air temperature changes [1,2]. In this study, two climate factors, the air temperature and the precipitation, were considered due to their physical significance in the fields of glacier hydrology and climate change [16,36].…”

Section: Discussionmentioning

confidence: 99%

“…Glaciers, as one of the main elements of the cryosphere, have been widely studied owing to their high sensitivity to climate changes [1][2][3][4] and their significance in the multitude of water usages and ecosystem goods in the glacier-fed downstream areas [5][6][7]. Approximately 75% of the world's freshwater resources are stored in the cryosphere [8], and glacial meltwater production provides an extremely important water source for the development of oases agroecosystems in drylands globally.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

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The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

View full text Add to dashboard Cite

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“…Snowpack is a crucial component of the cryosphere, serving as a huge water reservoir for river catchments, and it is especially important for the regional sustainability of ecosystems and communities (Barnett et al., 2005; Hugonnet et al., 2021; Sturm et al., 2017). The surface energy budget of snow‐covered regions, and the ablation rate of the snowpack in particular, are significantly affected by snow albedo (Flanner et al., 2011; Jakobs et al., 2021; Riihelä et al., 2021; Zhang et al., 2021). Numerous observations and model simulations have shown that light‐absorbing particles (LAPs; e.g., black carbon (BC) and mineral dust (hereafter referred to as dust)) within the snowpack can reduce snow albedo and accelerate snow melting by enhancing the absorption of solar radiation (Chylek et al., 1983; Dumont et al., 2020; Hadley & Kirchstetter, 2012; Liou et al., 2014; Shi et al., 2020; Skiles & Painter, 2019), which has important implications for regional climate, hydrology, and ecological systems (Hansen & Nazarenko, 2004; Matt et al., 2018; Oaida et al., 2015; Qian et al., 2011; Yasunari et al., 2012).…”

Section: Introductionmentioning

confidence: 99%

Opposite Effects of Mineral Dust Nonsphericity and Size on Dust‐Induced Snow Albedo Reduction

Shi

Zhang

et al. 2022

Geophysical Research Letters

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Snowpack is a crucial component of the cryosphere, serving as a huge water reservoir for river catchments, and it is especially important for the regional sustainability of ecosystems and communities (Barnett et al., 2005;Hugonnet et al., 2021;Sturm et al., 2017). The surface energy budget of snow-covered regions, and the ablation rate of the snowpack in particular, are significantly affected by snow albedo (Flanner et al., 2011;Jakobs et al., 2021;Riihelä et al., 2021;Zhang et al., 2021). Numerous observations and model simulations have shown that light-absorbing particles (LAPs; e.g., black carbon (BC) and mineral dust (hereafter referred to as dust)) within the snowpack can reduce snow albedo and accelerate snow melting by enhancing the absorption of solar radiation (

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“…Conventional means of mountain glacier monitoring are usually performed through field survey measurements, including field stereo photogrammetry [ 12 ], Global Navigation Satellite System (GNSS) technology [ 13 ], LiDAR, and ground-penetrating radar measurement techniques [ 14 ]. On that basis, the glacier surface elevation of monitoring points can be directly measured, and monitoring results with high accuracy can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

Yang

et al. 2022

Sensors

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The morphological changes in mountain glaciers are effective in indicating the environmental climate change in the alpine ice sheet. Aiming at the problems of single monitoring index and low prediction accuracy of mountain glacier deformation at present, this study takes Meili Mountain glacier in western China as the research object and uses InSAR technology to construct the mountain glacier deformation time series and 3D deformation field from January 2020 to December 2021. The relationship between glacier deformation and elevation, slope, aspect, glacier albedo, surface organic carbon content, and rainfall was revealed by grey correlation analysis. The GA-BP neural network prediction model is established from the perspective of multiple factors to predict the deformation of Meili Mountain glacier. The results showed that: The deformation of Meili Mountain glacier has obvious characteristics of spatio-temporal differentiation; the cumulative maximum deformation quantity of glaciers in the study period is −212.16 mm. After three-dimensional decomposition, the maximum deformation quantity of glaciers in vertical direction, north–south direction and east–west direction is −125.63 mm, −77.03 mm, and 107.98 mm, respectively. The average annual deformation rate is between −94.62 and 75.96 mm/year. The deformation of Meili Mountain glacier has a gradient effect, the absolute value of deformation quantity is larger when the elevation is below 4500 m, and the absolute value of deformation quantity is smaller when it is above 4500 m. The R2, MAPE, and RMSE of the GA-BP neural network to predict the deformation of Meili glacier are 0.86, 1.12%, and 10.38 mm, respectively. Compared with the standard BP algorithm, the prediction accuracy of the GA-BP neural network is significantly improved, and it can be used to predict the deformation of mountain glaciers.

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Albedo reduction as an important driver for glacier melting in Tibetan Plateau and its surrounding areas

Cited by 66 publications

References 86 publications

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Opposite Effects of Mineral Dust Nonsphericity and Size on Dust‐Induced Snow Albedo Reduction

Monitoring and Prediction of Glacier Deformation in the Meili Snow Mountain Based on InSAR Technology and GA-BP Neural Network Algorithm

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