Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China
Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estima...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/23/5989 |
| _version_ | 1797462285212450816 |
|---|---|
| author | Xiqiang Wang Rensheng Chen Chuntan Han Yong Yang Junfeng Liu Zhangwen Liu Shuhai Guo |
| author_facet | Xiqiang Wang Rensheng Chen Chuntan Han Yong Yang Junfeng Liu Zhangwen Liu Shuhai Guo |
| author_sort | Xiqiang Wang |
| collection | DOAJ |
| description | Soil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth. |
| first_indexed | 2024-03-09T17:34:21Z |
| format | Article |
| id | doaj.art-f2f33313f02549198099c5b12a3c338a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:34:21Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f2f33313f02549198099c5b12a3c338a2023-11-24T12:03:49ZengMDPI AGRemote Sensing2072-42922022-11-011423598910.3390/rs14235989Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, ChinaXiqiang Wang0Rensheng Chen1Chuntan Han2Yong Yang3Junfeng Liu4Zhangwen Liu5Shuhai Guo6Qilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaQilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaSoil freeze depth variations greatly affect energy exchange, carbon exchange, ecosystem diversity, and the water cycle. Given the importance of these processes, obtaining freeze depth data over large scales is an important focus of research. This paper presents a simple empirical algorithm to estimate the maximum seasonally frozen depth (MSFD) of seasonally frozen ground (SFG) in snowy regions. First, the potential influences of driving factors on the MSFD variations were quantified in the baseline period (1981–2010) based on the 26 meteorological stations within and around the SFG region of Heilongjiang province. The three variables that contributed more than 10% to MSFD variations (i.e., air freezing index, annual mean snow depth, and snow cover days) were considered in the analysis. A simple multiple linear regression to estimate soil freeze depth was fitted (1981–2010) and verified (1975–1980 and 2011–2014) using ground station observations. Compared with the commonly used simplified Stefan solution, this multiple linear regression produced superior freeze depth estimations, with the mean absolute error and root mean square error of the station average reduced by over 20%. By utilizing this empirical algorithm and the ERA5-Land reanalysis dataset, the multi-year average MSFD (1981–2010) was 132 cm, ranging from 52 cm to 186 cm, and MSFD anomaly exhibited a significant decreasing trend, at a rate of −0.38 cm/decade or a net change of −28.14 cm from 1950–2021. This study provided a practical approach to model the soil freeze depth of SFG over a large scale in snowy regions and emphasized the importance of considering snow cover variables in analyzing and estimating soil freeze depth.https://www.mdpi.com/2072-4292/14/23/5989seasonally frozen groundsnow coverair temperatureempirical algorithm |
| spellingShingle | Xiqiang Wang Rensheng Chen Chuntan Han Yong Yang Junfeng Liu Zhangwen Liu Shuhai Guo Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China Remote Sensing seasonally frozen ground snow cover air temperature empirical algorithm |
| title | Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China |
| title_full | Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China |
| title_fullStr | Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China |
| title_full_unstemmed | Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China |
| title_short | Estimation of Soil Freeze Depth in Typical Snowy Regions Using Reanalysis Dataset: A Case Study in Heilongjiang Province, China |
| title_sort | estimation of soil freeze depth in typical snowy regions using reanalysis dataset a case study in heilongjiang province china |
| topic | seasonally frozen ground snow cover air temperature empirical algorithm |
| url | https://www.mdpi.com/2072-4292/14/23/5989 |
| work_keys_str_mv | AT xiqiangwang estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT renshengchen estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT chuntanhan estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT yongyang estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT junfengliu estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT zhangwenliu estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina AT shuhaiguo estimationofsoilfreezedepthintypicalsnowyregionsusingreanalysisdatasetacasestudyinheilongjiangprovincechina |