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Spatial Variability of Snow Density and Its Estimation in Different Periods of Snow Season in the Middle Tianshan Mountains, China
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  • Ting FENG,
  • Shuzhen Zhu,
  • Farong Huang,
  • Jiansheng Hao,
  • Richard Mind’je,
  • Jiudan Zhang,
  • Lanhai Li
Ting FENG
Chinese Academy of Sciences

Corresponding Author:[email protected]

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Shuzhen Zhu
Chinese Academy of Sciences
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Farong Huang
Chinese Academy of Sciences
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Jiansheng Hao
Institute of Geographic Sciences and Natural Resources Research CAS
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Richard Mind’je
Chinese Academy of Sciences
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Jiudan Zhang
Chinese Academy of Sciences
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Lanhai Li
Chinese Academy of Sciences
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Abstract

Snow density is one of the essential properties to describe snowpack characteristics. To obtain the spatial variability of snow density and estimate it accurately in different periods of snow season still remain as challenges, particularly in the mountains. This study analyzed the spatial variability of snow density with in-situ measurements in three different periods (i.e. accumulation, stable, melt period) of snow seasons 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The performance of multiple linear regression model (MLR) and three machine learning models (i.e. Random Forest (RF), Extreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM)) to simulate snow density has been evaluated. It was found that the snow density in melt period (0.27 g cm-3) was generally greater than that in stable (0.20 g cm-3) and accumulation period (0.18 g cm-3), and the spatial variability of snow density in melt period was slightly smaller than that in other two periods. The snow density in mountainous areas was generally higher than that in plain or valley areas, and snow density increased significantly (p < 0.05) with elevation in the accumulation and stable periods. Besides elevation, latitude and ground surface temperature also had critical impacts on the spatial variability of snow density in the middle Tianshan Mountains, China. In this work, the machine learning model, especially RF model, performed better than MLR on snow density simulation in three periods. Compared with MLR, the determination coefficients of RF promoted to 0.61, 0.51 and 0.58 from 0.50, 0.1 and 0.52 in accumulation period, stable period and melt period respectively. This study provide a more accurate snow density simulation method for estimating regional snow mass and snow water equivalent, which allows us to achieve a better understanding of regional snow resources.
24 Sep 2021Submitted to Hydrological Processes
25 Sep 2021Submission Checks Completed
25 Sep 2021Assigned to Editor
27 Sep 2021Reviewer(s) Assigned
28 Nov 2021Review(s) Completed, Editorial Evaluation Pending
13 Dec 2021Editorial Decision: Revise Major
24 Feb 20221st Revision Received
24 Feb 2022Submission Checks Completed
24 Feb 2022Assigned to Editor
24 Feb 2022Reviewer(s) Assigned
06 Apr 2022Review(s) Completed, Editorial Evaluation Pending
18 Apr 2022Editorial Decision: Revise Major
03 May 20222nd Revision Received
10 May 2022Submission Checks Completed
10 May 2022Assigned to Editor
10 May 2022Reviewer(s) Assigned
26 May 2022Review(s) Completed, Editorial Evaluation Pending
15 Jun 2022Editorial Decision: Accept
Aug 2022Published in Hydrological Processes volume 36 issue 8. 10.1002/hyp.14644