Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China
To investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spat...
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MDPI AG
2024-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/475 |
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| author | Xue Zhou Zhen Zhang Weidong Yang Qingkuan Liu |
| author_facet | Xue Zhou Zhen Zhang Weidong Yang Qingkuan Liu |
| author_sort | Xue Zhou |
| collection | DOAJ |
| description | To investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya’an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya’an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya’an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data. |
| first_indexed | 2024-03-08T09:59:43Z |
| format | Article |
| id | doaj.art-483583090fd843b588f7c6c4908e74c3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T09:59:43Z |
| publishDate | 2024-01-01 |
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| spelling | doaj.art-483583090fd843b588f7c6c4908e74c32024-01-29T13:41:44ZengMDPI AGApplied Sciences2076-34172024-01-0114247510.3390/app14020475Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest ChinaXue Zhou0Zhen Zhang1Weidong Yang2Qingkuan Liu3School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaTo investigate the susceptibility of drifting snow along the Ya’an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya’an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya’an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya’an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data.https://www.mdpi.com/2076-3417/14/2/475drifting snowsouthwest railwayWRFsusceptibilitynumerical simulation |
| spellingShingle | Xue Zhou Zhen Zhang Weidong Yang Qingkuan Liu Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China Applied Sciences drifting snow southwest railway WRF susceptibility numerical simulation |
| title | Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China |
| title_full | Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China |
| title_fullStr | Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China |
| title_full_unstemmed | Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China |
| title_short | Study on the Susceptibility of Drifting Snow in Ya’an–Qamdo Section of the Railway in Southwest China |
| title_sort | study on the susceptibility of drifting snow in ya an qamdo section of the railway in southwest china |
| topic | drifting snow southwest railway WRF susceptibility numerical simulation |
| url | https://www.mdpi.com/2076-3417/14/2/475 |
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