Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China

Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements...

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Published in:Hydrological processes 2022-08, Vol.36 (8), p.n/a
Main Authors: Feng, Ting, Zhu, Shuzhen, Huang, Farong, Hao, Jiansheng, Mind'je, Richard, Zhang, Jiudan, Li, Lanhai
Format: Article
Language:English
Subjects:
Accumulation
Correlation analysis
Density
Elevation
Learning algorithms
Machine learning
middle Tianshan Mountains
Modelling
Mountain regions
Mountainous areas
Mountains
Oases
regression model
Regression models
Seasons
Snow
Snow density
Snow-water equivalent
Snowpack
Spatial analysis
Spatial variability
Spatial variations
Surface temperature
Variability
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container_start_page
container_title Hydrological processes
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creator Feng, Ting
Zhu, Shuzhen
Huang, Farong
Hao, Jiansheng
Mind'je, Richard
Zhang, Jiudan
Li, Lanhai
description Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p 
doi_str_mv 10.1002/hyp.14644
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To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p &lt; 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources. Spatial variability of snow density in melt period of snow season was slightly smaller than that in accumulation and stable periods in the middle Tianshan Mountains with a continental snow climate. Elevation, latitude and ground surface temperature had critical impacts on the spatial variability of snow density in the study area. 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To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in‐situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm−3) was generally greater than that in the stable (0.20 g cm−3) and accumulation periods (0.18 g cm−3), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p &lt; 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources. Spatial variability of snow density in melt period of snow season was slightly smaller than that in accumulation and stable periods in the middle Tianshan Mountains with a continental snow climate. Elevation, latitude and ground surface temperature had critical impacts on the spatial variability of snow density in the study area. Machine learning models, especially Random Forest, performed better than multiple linear regression model for simulating spatial distribution of snow density in three periods of snow season.</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; Sons, Inc</pub><doi>10.1002/hyp.14644</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9792-1532</orcidid><oa>free_for_read</oa></addata></record>
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recordid cdi_proquest_journals_2709793636
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subjects Accumulation
Correlation analysis
Density
Elevation
Learning algorithms
Machine learning
middle Tianshan Mountains
Modelling
Mountain regions
Mountainous areas
Mountains
Oases
regression model
Regression models
Seasons
Snow
Snow density
Snow-water equivalent
Snowpack
Spatial analysis
Spatial variability
Spatial variations
Surface temperature
Variability
title Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China
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