Data Paper Zone II Versions EN1 Vol 3 (4) 2018
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A dataset of glacier mass balance of Hailuogou catchment in Mount Gongga, southeastern Tibetan Plateau, during 1952–2009
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Abstract & Keywords
Abstract: Glacier mass balance is one of sensitive indicators of ongoing climate change and is important for the assessment of water resources and sea-level rise. However, few maritime glaciers in the southeastern Tibetan Plateau have carried out continued mass balance measurements. In addition, debris cover is widely present in the ablation areas of these glaciers in the southeastern Tibetan Plateau, which affects the melt rate of underlying ice and consequently influences glacier mass balance. Accordingly, the overall characteristics of glacier mass balance in this region are not yet clear. Hailuogou catchment is located on the eastern side of Mount Gongga, southeastern Tibetan Plateau. The dataset of glacier mass balance of Hailuogou catchment for the period 1952–2009 is reconstructed by an energy-mass balance model that accounts for the significance of debris cover and its effect on the ice melt rate, based on observed meteorological data and gridded climate data. These data are stored in the TXT format. The model performance is then validated against observed ablation and river runoff in the catchment. This dataset includes glacier mass balance and equilibrium line altitude (ELA) datasets, which can reflect overall variation in the glacier mass changes of Hailuogou catchment during the period 1952–2009. This dataset can be used as basic data for studying maritime glacier change and its response to climate change in the southeastern Tibetan Plateau.
Keywords: maritime glacier; mass balance; energy-mass balance model; debris cover; Hailuogou catchment
Dataset Profile
TitleA dataset of glacier mass balance of Hailuogou catchment in Mount Gongga, southeastern Tibetan Plateau, during 1952–2009
Data corresponding authorZhang Yong (yong.zhang@hnust.edu.cn)
Data authorsZhang Yong, Liu Shiyin, Liu Qiao
Time range1952–2009
Geographical scope29°30′–29°40′ N, 101°50′–102°1′ E
Data volume1.7 KB
Data format*.txt
Data service system< http://www.sciencedb.cn/dataSet/handle/623>
Sources of fundingNational Natural Science Foundation of China (41671057; 41761144075; 41771075); Fundamental Program of the Ministry of Science and Technology of China (MOST) (2013FY111400); Research Funds for New Talents of Yunnan University (YJRC3201702).
Dataset compositionThis dataset consists of 2 subsets in total: (1) Mass_balance_data.txt is made up of time series of glacier mass balance in the Hailuogou catchment of Mount Gongga, southeastern Tibetan Plateau; (2) Equilibrium_line_atitude_data.txt is made up of time series of glacier equilibrium line altitude in the catchment.
1.   Introduction
Glacier mass balance is one of sensitive indicators of climate change, and forms a vital link of energy-mass-water exchange in the glacierized region.1.2. Glacier mass balance and its spatio-temporal variation are not only closely related to climate change, but also the mass foundation of changes in glacier property and area, regional water resources and global sea level.2.3. Therefore, glacier mass balance has become an important monitoring and simulation object in the global climate system.3.4. Under the global climate warming, most mountain glaciers have generally been retreating and acceleratedly losing mass, leading to sea-level rise, water cycle and ecological environment problems, which have raised more and more concerns.3.4.5. Till now, a total of 126 glaciers in the world have continued mass balance observation, and the area of these glaciers is relatively small.6.As well-known, the glaciers in different regions have different physical properties and areas, leading to large difference in the response of glaciers to climate change.7.For example, maritime glaciers are largely sensitive to climate change, while continental glaciers are relatively slow. Such differences may be due to the difference in the sensitivity of glacier mass balance to climate change.1.8. However, observed continuous mass balance are scarce worldwide. Therefore, to study glacier mass balance and its response to climate change in different regions, model simulation is an effective way to solve this problem.
The southeastern Tibetan Plateau is the dominant distribution region of maritime glaciers in China.7.These glaciers are largely sensitive to climate change, especially to temperature change.1.2.7. Furthermore, the ablation zones of some maritime glaciers are widely covered by debris mantles, which makes the responses of these glaciers to climate change more complicated relative to debris-free glaciers.9.10. However, only a few glaciers in the southeastern Tibetan Plateau have carried out continued mass balance observations.4.11.12. To reconstruct the time series of glacier mass balance, a surface energy-mass balance model is developed, which not only considers the refreezing process and snow densification, but also takes account for the spatial distribution of debris cover and its influence on melt rates.10.In this study, Hailuogou catchment is chosen as the study area (Figure 1), which is located on the eastern slope of Mount Gongga, southeastern Tibetan Plateau. The catchment covers an area of 80.5 km2, and contains seven glaciers with an area of 36.44 km2, which represents 45.3% of the total catchment area. Among these glaciers, three glaciers are debris-covered glaciers, on which debris-covered area accounts for 8.2% of the total glacier area of the catchment.10.Here the time series of glacier mass balance in the Hailuogou catchment is reconstructed using the surface energy-mass balance model mentioned above. The model performance has been validated in previous studies,9.10.13. which can obtain reliable data of glacier mass balance. This dataset can provide necessary data support for further study of the regional characteristics and differences in maritime glacier change and its response to climate change.


Figure 1   Map of the Hailuogou catchment located on the eastern side of Mt. Gongga, southeastern Tibetan Plateau
2.   Data collection and processing
2.1   Data collection
We use various datasets, including meteorological observations at the Gongga Alpine Ecosystem Observation and Research Station (hereafter, Gongga station; Figure 1), observed glacier melt rates and monthly river runoff, daily gridded climate data with a resolution of 0.5°, glacier boundaries derived from different periods, ASTER-derived thermal resistance of the debris layer and digital elevation model. A summary of these datasets is listed in Table 1.
Table 1   A summary of datasets used in this study
No.DatasetPeriodReference
1Daily temperature, precipitation, wind speed, relative humidity and solar radiation1988–2009http://ggf.cern.ac.cn
20.5° gridded temperature data1951–2007[15]
30.5° APHRODITE precipitation data1951–2007[16]
4Monthly river runoff1994–2007http://ggf.cern.ac.cn
5Glacier boundary1966, 1975, 1994 & 2002[14]
6Glacier melt rate1982–1983, 1990–1994 & 2008[17-19]
7Thermal resistance (90 m resolution)2009[18]
8DEM (30 m resolution)1989[20]
Meteorological observations and river runoff records are obtained from the Gongga station at the glacier terminus (3000m a.s.l., http://ggf.cern.ac.cn). Meteorological observations include daily temperature, precipitation, wind speed, relative humidity and solar radiation, which are used to force the model and bias-correct gridded climate data, whereas monthly river runoff data are used to calibrate model parameters and validate the model performance, as well as glacier melt rates observed during different periods. 0.5° gridded temperature and precipitation data are derived from the grid cell nearest to the catchment, which are bias-corrected using the corresponding observations at the glacier terminus.10.Glacier boundaries of different periods are derived from the topographic maps and remote sensing images,14.which are used to calculate glacier area in the mass balance calculation. ASTER-derived thermal resistances of debris layers are used to force the model used in this study, which considers the spatial distribution of debris thickness and its impact on melting rates and its spatial patterns, while DEM is used to discretize the catchment and distribute the spatial distribution of meteorological data.
2.2   Data processing
We implement a surface energy-mass balance model to reconstruct glacier mass balance time series in the Hailuogou catchment. The model consists of two components: one module that computes the energy available for melting from the exchange of energy between the debris-covered/-free surfaces and the atmosphere, and the other module that treats snow densification and the refreezing process.10.In particular, the model considers the spatial distribution of debris thickness and its influence on melting rates.10.
Based on the spatial distribution of the thermal resistance of the debris layer, we first divide the glaciers into debris-covered/-free surfaces (Figure 1). Then we follow the method shown in Figure 2 to reconstruct time series of glacier mass balance and equilibrium line altitude (ELA), which is forced by various datasets mentioned above. The temperature threshold is used to discriminate rain from snow in the catchment.4.The detailed description of the calculation methods of energy balance and the optimization process of model parameters can be found in the previous study.10.


Figure 2   Schematic diagram of the surface energy-mass balance model used in this study.10 SEB denotes the surface energy-balance model.
3.   Sample description
The storage format of this dataset is TXT format, which includes two datasets: glacier mass balance and equilibrium line altitude (ELA). They are respectively named Mass_balance_data.txt and Equilibrium_line_altitude_data.txt, in which the units are m w.e. and m a.s.l., respectively. The sample is shown in Figure 3.


Figure 3   Variation in glacier mass balance (MB) and equilibrium line altitude (ELA) in the Hailuogou catchment during the period of 1952–2009
4.   Quality control and assessment
In this study, we reconstruct glacier mass balance time series of the Hailuogou catchment using the surface energy-mass balance model forced by various datasets shown in Table 2. First, gridded temperature and precipitation data are evaluated using the corresponding observations at the glacier terminus (Table 2). Overall, daily, monthly, and annual gridded temperature and precipitation values correlate well with the corresponding observations at the terminus, although correlation coefficients for precipitation are lower than those for temperature. Similarly, the correlations between gridded data and observations during the spring, summer and autumn are higher than that during the winter. Although the correlation between gridded precipitation data and observations during the winter is relatively low, the glaciers in the Hailuogou catchment are the summer-accumulation type glaciers, where summer precipitation represents about 80% of the annual total.10.Thus, precipitation in the winter may have little influence on the model performance in the catchment. Then gridded temperature and precipitation data are bias-corrected by linear regression equations established between meteorological observations and gridded datasets.10.The correlation coefficients between the bias-corrected and observed daily temperature and precipitation are 0.96 and 0.87,10.respectively.
Table 2   Correlation coefficients between gridded temperature and precipitation data and corresponding observations from the Gongga station at the glacier terminus.10 All significance levels are p < 0.001.
Temperature aPrecipitation b
Daily0.900.70
Monthly0.980.96
Annual0.710.53
Spring (May, April, May)0.980.90
Summer (June, July, August)0.910.70
Autumn (September, October, November)0.990.95
Winter (December, January, Februray)0.640.53
a Data were used for the period 1988–2007, and b Data were used for the period 1988–2004.
The model performance is comprehensively verified against ice melt rates observed during different periods and monthly river runoff observed at the terminus.10.As shown in Figure 4a and 4b, the model performs well for melt rates during different periods. Overall, the correlation coefficient and RMSE between observed and modelled melt rates are 0.78 and 0.003 m w.e. d-1. In particular, the correlation coefficient between observed and modelled melt rates underlying different debris thicknesses is about 0.82 (Figure 4b), and the relative error between modelled and observed values is only 10%. Compared to observed monthly river runoff, the Nash-Sutcliffe efficiency is 0.84 (Figure 4c).10.The cumulative runoff simulated using observed meteorological data is underestimated by 10% over the study period, whereas that simulated using gridded climate data is overestimated by 4% at the same period (Figure 4d). Overall, the model used in this study performs well and can simulate the process of mass transformation in the catchment. Therefore, the model performance is reliable for reconstruction the time series of glacier mass balance in the Hailuogou catchment.


Figure 4   Comparisons of modelled and observed melt rates and river runoff in the Hailuogou catchment
5.   Value and significance
Glacier mass balance data is the basic dataset for studying energy-mass exchange on the glaciers. Better understanding of trend, difference and control mechanism of glacier mass balance in different regions can systematically reveal the scientific knowledge of the response mechanism of maritime glaciers to climate change and associated impacts, then further evaluate the impacts of regional glacier changes on water resources, climate, ecology and global sea level. This dataset includes glacier mass balance and ELA time series of the Hailuogou catchment in the southeastern Tibetan Plateau for the period 1952–2009, which can reflect the mass change characteristics of typical maritime glaciers in this region. Hence, the dataset provides data support for studying the response of maritime glaciers to climate change in the southeastern Tibetan Plateau.
6.   Usage notes
Data storage format for the time series of glacier mass balance and ELA of the Hailuogou catchment in the southeastern Tibetan Plateau during 1952–2009 is TXT format. We can use the common office software to read, edit, and operate this dataset. This dataset has high reliability and representativeness, which can be used as the reference dataset for studying the mass balance of maritime glaciers in the southeastern Tibetan Plateau. Therefore, this dataset can provide data support for the study of regional climate change and glacier changes.
Acknowledgments
We thank the Gongga Alpine Ecosystem Observation and Research Station of the Chinese Academy of Sciences for providing the meteorological and river runoff data.
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Data citation
1. Zhang Y, Liu S & Liu Q, A dataset of glacier mass balance of Hailuogou catchment in Mount Gongga, southeastern Tibetan Plateau, during 1952–2009. Science Data Bank, DOI: 10.11922/sciencedb.623 (2018).
Article and author information
How to cite this article
Zhang Y, Liu S & Liu Q, A dataset of glacier mass balance of Hailuogou catchment in Mount Gongga, southeastern Tibetan Plateau, during 1952–2009.China Scientific Data 3(2018). DOI: 10.11922/csdata.2018.0042.zh
Zhang Yong
Contribution: data calculation and analysis.
yong.zhang@hnust.edu.cn
PhD, Professor, research area: glacier modelling.
Liu Shiyin
Contribution: data processing design.
PhD, Professor, research area: glacier change.
Liu Qiao
Contribution: data analysis.
PhD, Associate Professor, research area: glacier change.
National Natural Science Foundation of China (41671057; 41761144075; 41771075); Fundamental Program of the Ministry of Science and Technology of China (MOST) (2013FY111400); Research Funds for New Talents of Yunnan University (YJRC3201702).
Publication records
Published: Nov. 28, 2018 ( VersionsEN1
Released: Aug. 2, 2018 ( VersionsZH2
Published: Nov. 28, 2018 ( VersionsZH3
References
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