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Journal of Arid Land
Journal of Arid Land  2022, Vol. 14 Issue (4): 441-454    DOI: 10.1007/s40333-022-0014-z     CSTR: 32276.14.s40333-022-0014-z
Research article     
Spatial variability between glacier mass balance and environmental factors in the High Mountain Asia
ZHANG Zhen1,2,*(), GU Zhengnan1, Hu Kehong1, XU Yangyang1, ZHAO Jinbiao1
1School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
2State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
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Abstract  

High Mountain Asia (HMA) region contains the world's highest peaks and the largest concentration of glaciers except for the polar regions, making it sensitive to global climate change. In the context of global warming, most glaciers in the HMA show various degrees of negative mass balance, while some show positive or near-neutral balance. Many studies have reported that spatial heterogeneity in glacier mass balance is strongly related to a combination of climate parameters. However, this spatial heterogeneity may vary according to the dynamic patterns of climate change at regional or continental scale. The reasons for this may be related to non-climatic factors. To understand the mechanisms by which spatial heterogeneity forms, it is necessary to establish the relationships between glacier mass balance and environmental factors related to topography and morphology. In this study, climate, topography, morphology, and other environmental factors are investigated. Geodetector and linear regression analysis were used to explore the driving factors of spatial variability of glacier mass balance in the HMA by using elevation change data during 2000-2016. The results show that the coverage of supraglacial debris is an essential factor affecting the spatial heterogeneity of glacier mass balance, followed by climatic factors and topographic factors, especially the median elevation and slope in the HMA. There are some differences among mountain regions and the explanatory power of climatic factors on the spatial differentiation of glacier mass balance in each mountain region is weak, indicating that climatic background of each mountain region is similar. Therefore, under similar climatic backgrounds, the median elevation and slope are most correlated with glacier mass balance. The interaction of various factors is enhanced, but no unified interaction factor plays a primary role. Topographic and morphological factors also control the spatial heterogeneity of glacier mass balance by influencing its sensitivity to climate change. In conclusion, geodetector method provides an objective framework for revealing the factors controlling glacier mass balance.

Key words： geodetector      glacier change      mass balance      climate change      High Mountain Asia     
Received: 30 December 2021      Published: 30 April 2022
Corresponding Authors: *ZHANG Zhen (E-mail: zhangzhen@aust.edu.cn)
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ZHANG Zhen
GU Zhengnan
Hu Kehong
XU Yangyang
ZHAO Jinbiao
Cite this article:

ZHANG Zhen, GU Zhengnan, Hu Kehong, XU Yangyang, ZHAO Jinbiao. Spatial variability between glacier mass balance and environmental factors in the High Mountain Asia. Journal of Arid Land, 2022, 14(4): 441-454.

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http://jal.xjegi.com/10.1007/s40333-022-0014-z     OR     http://jal.xjegi.com/Y2022/V14/I4/441

Fig. 1 Principle of geodetector. x, independent variable; y, dependent variable; h, the strata of x or y (classification or partition); ${{\bar{Y}}_{h}}$, the mean value of strata h; σ2h, the variance of y at strata h; σ2, the variance of y at the whole region.
Fig. 2 Spatial distribution of the glacier mass balance change in the High Mountain Asia. Mts, Mountains.
Region Factors arranged from the largest to the smallest by the q value Dominant interaction (q)
West Himalaya S (0.20), NDG (0.08), Zmax (0.07), DG (0.06), P (0.05), Pc (0.03), Tc (0.03), Zmed (0.03), PDG (0.03), T (0.03), Lmax (0.02), HI (0.02), A (0.02) S∩P (0.25)
Central Himalaya NDG (0.18), S (0.18), DG (0.10), DCR (0.08), Zmed (0.08), HI (0.07), Zmax (0.07), Zmin (0.07), Tc (0.04), PDG (0.03), T (0.03), A (0.02), Lmax (0.02), Pc (0.02) NDG∩DCR (0.33)
East Himalaya S (0.25), Zmed (0.17), Zmax (0.17), DCR (0.16), NDG (0.16), Zmin (0.15), P (0.13), DG (0.12), T (0.11), PDG (0.09) S∩Zmed (0.45)
Karakoram DCR (0.05), Zmed (0.04), Zmax (0.04), Tc (0.04), Zmin (0.04), P (0.03), T (0.03), S (0.03), Lmax (0.02), HI (0.02), A (0.02), NDG (0.01), Pc (0.01), DG (0.01) DCR∩Zmed (0.13)
Pamir Plateau S (0.13), Zmed (0.09), DCR (0.07), Pc (0.06), NDG (0.06), As (0.06), P (0.05), Zmax (0.05), DG (0.04), T (0.03), HI (0.03), Tc (0.03), Zmin (0.03), PDG (0.02) S∩Pc (0.22)
Hissar Alay P (0.16), HI (0.15), Tc (0.15), S (0.14), T (0.12), Zmin (0.11), Pc (0.10), Lmax (0.09) Tc∩T (0.41)
Hindu Kush S (0.41), DG (0.35), NDG (0.35), Zmax (0.24), PDG (0.17), Zmed (0.13), P (0.11), Pc (0.10), Zmin (0.09), Tc (0.08), T (0.08), Lmax (0.07), HI (0.06) S∩NDG (0.55)
Western Tianshan S (0.18), Zmed (0.17), Zmax (0.16), T (0.15), NDG (0.13), P (0.12), DG (0.11), Tc (0.09), PDG (0.05), DCR (0.04), Zmin (0.04), Pc (0.04), As (0.04), HI (0.03) Zmed∩T (0.28)
Eastern Tianshan Zmed (0.30), S (0.28), Zmax (0.27), NDG (0.25), DG (0.22), HI (0.15), Pc (0.11), T (0.08), Zmin (0.06) S∩HI (0.46)
Western Kunlun P (0.09), Lmax (0.07), A (0.07), Zmed (0.05), DCR (0.04), Zmin (0.03), Tc (0.03), Zmax (0.03), As (0.02), S (0.02) P∩Lmax (0.18)
Eastern Kunlun Zmed (0.28), Zmax (0.28), Zmin (0.26), T (0.23), Pc (0.23), P (0.15), DG (0.11), S (0.09), NDG (0.08), DCR (0.06) Zmed∩Pc (0.52)
Tibetan Interior Mountains S (0.22), Zmed (0.21), Pc (0.21), P (0.20), Zmin (0.18), DCR (0.18), T (0.13),
Lmax (0.09)		 S∩DCR (0.46)
Tanggula Zmed (0.44), P (0.28), DCR (0.26), Zmin (0.25), Zmax (0.24), NDG (0.23), Tc (0.22), T (0.18), Pc (0.18) Zmed∩A (0.61)
Qilian Tc (0.17), P (0.15), T (0.14), Zmed (0.14), Zmax (0.08) Zmed∩T (0.34)
Eastern Tibetan Plateau Zmed (0.75), Zmax (0.72), PDG (0.71), DG (0.65) Zmed∩T (0.99)
Gangdise Mountains Zmed (0.52), Zmax (0.50), S (0.44), Pc (0.43), Zmin (0.38) Zmin∩T (0.79)
Nyainqêntanglha S (0.12), Lmax (0.10), P (0.08), Tc (0.08), A (0.08), Zmed (0.07), T (0.05), Zmax (0.04), PDG (0.04), Zmin (0.04), Pc (0.04), As (0.04), HI (0.03), DG (0.03), DCR (0.02) S∩Lmax (0.23)
Hengduan DG (0.49), Zmin (0.49), S (0.46), PDG (0.45), NDG (0.44), Zmax (0.38), Pc (0.29), Tc (0.21), T (0.18), Zmed (0.18), HI (0.18), DCR (0.17), Lmax (0.16) Zmin∩As (0.68)
HMA DCR (0.14), T (0.10), Pc (0.10), P (0.08), S (0.08), Zmed (0.06), Zmax (0.05), NDG (0.05), DG (0.03), HI (0.03), Tc (0.02), Zmin (0.02), PDG (0.02), Lmax (0.01) DCR∩S (0.30)
Table 1 Geodetector's results for different mountain regions
Region A Zmin Zmax Zmed S As Lmax DCR
West Himalaya -0.11** -0.04 0.21** 0.11** 0.45** 0.03 -0.11** -0.07
Central Himalaya -0.09* -0.09* 0.22** 0.20** 0.43** 0.02 -0.13** -0.15**
East Himalaya -0.00 0.03 0.34** 0.25** 0.48** -0.02 -0.03 -0.08
Karakoram -0.10** -0.06* -0.08** -0.05* 0.14** 0.04 -0.13** -0.12**
Pamir Plateau -0.01 0.08* 0.22** 0.25** 0.34** 0.11** -0.03 -0.06
Hissar Alay -0.20* -0.13 -0.07 0.03 0.21* 0.06 -0.22** -0.17*
Hindu Kush 0.05 -0.19** 0.43** 0.27** 0.63** 0.02 0.12 -0.09
Western Tianshan -0.04 0.06 0.31** 0.40** 0.42** -0.05 -0.05 0.05
Eastern Tianshan 0.03 0.25** 0.49** 0.54** 0.52** 0.08 0.07 0.12
Western Kunlun -0.24** -0.10* -0.17** -0.14** 0.10** 0.16** -0.24** -0.10*
Eastern Kunlun -0.13 0.39** 0.37** 0.53** 0.28** -0.06 -0.05 0.19**
Tibetan Interior Mountains -0.01 -0.30** -0.16 -0.18 -0.10 0.04 -0.02 -0.16
Tanggula -0.02 0.19* 0.44** 0.64** 0.20* 0.03 -0.01 -0.31**
Qilian -0.04 0.02 0.26** 0.28** 0.21* 0.05 0.02 -0.03
Eastern Tibetan Plateau 0.18 -0.04 0.82** 0.85** 0.17 0.24 0.30 -0.24
Gangdise Mountains 0.01 0.52** 0.60** 0.70** 0.59** 0.26 -0.16 -0.35*
Nyainqêntanglha -0.24** 0.01 -0.06 0.06 0.34** -0.01 -0.31** 0.02
Hengduan Shan 0.26** -0.58** 0.47** -0.02 0.63** 0.14 0.32** 0.32**
HMA -0.00 0.11** 0.19** 0.20** 0.27** 0.01 -0.01 -0.19**
Table 2 Correlation matrix analyzing glacier mass changes and environmental factors
Region DG PDG NDG HI P T Pc Tc
West Himalaya 0.23** 0.15** 0.25** -0.10** 0.03 0.07* 0.06 -0.09*
Central Himalaya 0.26** 0.10* 0.38** -0.25** -0.03 0.06 0.04 0.00
East Himalaya 0.29** 0.19** 0.31** -0.13* 0.21** 0.04 0.05 -0.07
Karakoram -0.00 -0.03 0.03 -0.09** 0.05* -0.07** -0.03 -0.11**
Pamir Plateau 0.12** 0.05 0.17** -0.14** -0.13** -0.12** -0.17** 0.11**
Hissar Alay 0.04 -0.10 0.21* -0.37** -0.32** -0.26** -0.20* -0.29**
Hindu Kush 0.47** 0.35** 0.50** -0.20** 0.24** 0.16* 0.25** 0.08
Western Tianshan 0.22** 0.15** 0.27** -0.17** -0.21** -0.31** 0.11** -0.04
Eastern Tianshan 0.40** 0.17* 0.50** -0.33** 0.02 -0.09 0.33** 0.06
Western Kunlun -0.04 -0.03 -0.04 0.01 -0.12** -0.08* -0.03 0.10*
Eastern Kunlun 0.15* 0.07 0.20** -0.02 -0.10 -0.21** -0.23** 0.05
Tibetan Interior Mountains 0.13 0.04 0.16 -0.09 -0.30** -0.17 -0.16 -0.11
Tanggula 0.18* 0.14 0.18* -0.16 0.23** -0.20* 0.10 -0.00
Qilian 0.17 0.08 0.23* -0.10 0.10 0.05 0.08 -0.20*
Eastern Tibetan Plateau 0.65** 0.62** 0.56* 0.11 0.12 -0.24 0.22 0.06
Gangdise Mountains 0.18 0.10 0.22 -0.06 0.14 -0.18 0.33* -0.32*
Nyainqêntanglha -0.05 -0.14** 0.06 -0.15** 0.09* 0.06 0.14** 0.14**
Hengduan 0.61** 0.55** 0.58** -0.11 0.04 0.20* 0.46** -0.28**
HMA 0.08** 0.01 0.14** -0.15** -0.23** -0.29** 0.10** -0.05**
Table 3 Correlation matrix analyzing glacier mass changes and environmental factors
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