Study on Dynamic Early Warning of Flash Floods in Hubei Province
Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a cas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/17/3153 |
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| author | Yong Tu Yanwei Zhao Lingsheng Meng Wei Tang Wentao Xu Jiyang Tian Guomin Lyu Nan Qiao |
| author_facet | Yong Tu Yanwei Zhao Lingsheng Meng Wei Tang Wentao Xu Jiyang Tian Guomin Lyu Nan Qiao |
| author_sort | Yong Tu |
| collection | DOAJ |
| description | Flash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a case study of riverside villages in Hubei Province. Fully taking into account previous rainfall and assuming different rainfall conditions, this work developed a dynamic early warning threshold chart by determining critical rainfall thresholds at different soil moisture levels (dry, normal, wet, and saturated) through pilot calculations, to support a quick query of the critical rainfall at any soil moisture level. The research results show that of the 74 counties and districts in Hubei Province, more than 50% witnessed higher mean critical rainfall than empirical thresholds when the soil was saturated, and about 90% did so when the soil was dry. In 881 towns, a total of 456 early warnings were generated based on dynamic thresholds from 2020 to 2022, 15.2% more than those based on empirical thresholds. From the perspective of total rainfall, dynamic early warnings were generated more frequently in wet years, while empirical early warnings were more frequent in dry years, and the frequency of two warnings were roughly the same in normal years. There were more early warnings based on empirical thresholds in May each year, but more based on dynamic thresholds in June and July, and early warnings generated based on the two methods were almost equal in August and September. Spatially, after dynamic early warning thresholds were adopted, Shiyan and Xiangyang, both northwestern cities in Hubei Province, witnessed significant increases in early warnings. In terms of the early warning mechanism, dynamic early warning took into account the impact of soil moisture and analyzed the flood discharge capacity of river channels according to the flood stage of the riverside villages. On this basis, the rainfall early warning thresholds under different conditions were determined. This is a refined early warning method that could improve the accuracy of flash flood warnings in Hubei Province. |
| first_indexed | 2024-03-10T23:10:48Z |
| format | Article |
| id | doaj.art-bc3565e3d0954266b898c1bdb8730206 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T23:10:48Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-bc3565e3d0954266b898c1bdb87302062023-11-19T09:02:57ZengMDPI AGWater2073-44412023-09-011517315310.3390/w15173153Study on Dynamic Early Warning of Flash Floods in Hubei ProvinceYong Tu0Yanwei Zhao1Lingsheng Meng2Wei Tang3Wentao Xu4Jiyang Tian5Guomin Lyu6Nan Qiao7China Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaCollege of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, ChinaHubei Water Resources Research Institute, Hubei Water Resources and Hydropower Science and Technology Promotion Center, Wuhan 430064, ChinaDepartment of Water Resources of Hubei Province, Wuhan 430064, ChinaChangjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, ChinaChina Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaChina Institute of Water Resource and Hydropower Research, Beijing 100038, ChinaFlash floods are ferocious and destructive, making their forecasting and early warning difficult and easily causing casualties. In order to improve the accuracy of early warning, a dynamic early warning index system was established based on the distributed spatio-temporally mixed model through a case study of riverside villages in Hubei Province. Fully taking into account previous rainfall and assuming different rainfall conditions, this work developed a dynamic early warning threshold chart by determining critical rainfall thresholds at different soil moisture levels (dry, normal, wet, and saturated) through pilot calculations, to support a quick query of the critical rainfall at any soil moisture level. The research results show that of the 74 counties and districts in Hubei Province, more than 50% witnessed higher mean critical rainfall than empirical thresholds when the soil was saturated, and about 90% did so when the soil was dry. In 881 towns, a total of 456 early warnings were generated based on dynamic thresholds from 2020 to 2022, 15.2% more than those based on empirical thresholds. From the perspective of total rainfall, dynamic early warnings were generated more frequently in wet years, while empirical early warnings were more frequent in dry years, and the frequency of two warnings were roughly the same in normal years. There were more early warnings based on empirical thresholds in May each year, but more based on dynamic thresholds in June and July, and early warnings generated based on the two methods were almost equal in August and September. Spatially, after dynamic early warning thresholds were adopted, Shiyan and Xiangyang, both northwestern cities in Hubei Province, witnessed significant increases in early warnings. In terms of the early warning mechanism, dynamic early warning took into account the impact of soil moisture and analyzed the flood discharge capacity of river channels according to the flood stage of the riverside villages. On this basis, the rainfall early warning thresholds under different conditions were determined. This is a refined early warning method that could improve the accuracy of flash flood warnings in Hubei Province.https://www.mdpi.com/2073-4441/15/17/3153flash flooddynamic early warningcritical rainfalldistributed hydrological modelHubei Province |
| spellingShingle | Yong Tu Yanwei Zhao Lingsheng Meng Wei Tang Wentao Xu Jiyang Tian Guomin Lyu Nan Qiao Study on Dynamic Early Warning of Flash Floods in Hubei Province Water flash flood dynamic early warning critical rainfall distributed hydrological model Hubei Province |
| title | Study on Dynamic Early Warning of Flash Floods in Hubei Province |
| title_full | Study on Dynamic Early Warning of Flash Floods in Hubei Province |
| title_fullStr | Study on Dynamic Early Warning of Flash Floods in Hubei Province |
| title_full_unstemmed | Study on Dynamic Early Warning of Flash Floods in Hubei Province |
| title_short | Study on Dynamic Early Warning of Flash Floods in Hubei Province |
| title_sort | study on dynamic early warning of flash floods in hubei province |
| topic | flash flood dynamic early warning critical rainfall distributed hydrological model Hubei Province |
| url | https://www.mdpi.com/2073-4441/15/17/3153 |
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