Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China
The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization met...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/17/3115 |
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| author | Jian Liu Yanyan Li Yuankun Wang Pengcheng Xu |
| author_facet | Jian Liu Yanyan Li Yuankun Wang Pengcheng Xu |
| author_sort | Jian Liu |
| collection | DOAJ |
| description | The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the design of a 43 stations-based network in Huaihe River Basin (HRB), China. The first goal of this study is to improve the accuracy of gauge-related information estimation through the selection and comparison of discretization methods. The second goal of this study is to quantify the impact of trend-caused nonstationarity on optimal network design using the sliding window method. This study compares the divergence of three kinds of discretization methods, including the floor function-based approach, Scott’s equal bin width histogram (EWH-Sc) approach, and Sturges’s equal bin width histogram (EWH-St) approach. The matching degree of the variance and marginal entropy of the observed series is computed to select the most suitable of the above three discretization methods. The trend-caused nonstationarity in 75% of all stations in the HRB could definitely influence the final results of the optimal rain-gauge network design using the sliding window method. Therefore, in future studies of rain-gauge network optimization, it is necessary to carry out uncertainty research according to local conditions in view of climate change and human activities. |
| first_indexed | 2024-03-10T23:10:31Z |
| format | Article |
| id | doaj.art-78fdea028c36418b8b4c19cc8db085d1 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T23:10:31Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-78fdea028c36418b8b4c19cc8db085d12023-11-19T09:02:27ZengMDPI AGWater2073-44412023-08-011517311510.3390/w15173115Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, ChinaJian Liu0Yanyan Li1Yuankun Wang2Pengcheng Xu3Water Resources Research Institute of Shandong Province, Jinan 250014, ChinaWater Resources Research Institute of Shandong Province, Jinan 250014, ChinaSchool of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, ChinaCollege of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225008, ChinaThe nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the design of a 43 stations-based network in Huaihe River Basin (HRB), China. The first goal of this study is to improve the accuracy of gauge-related information estimation through the selection and comparison of discretization methods. The second goal of this study is to quantify the impact of trend-caused nonstationarity on optimal network design using the sliding window method. This study compares the divergence of three kinds of discretization methods, including the floor function-based approach, Scott’s equal bin width histogram (EWH-Sc) approach, and Sturges’s equal bin width histogram (EWH-St) approach. The matching degree of the variance and marginal entropy of the observed series is computed to select the most suitable of the above three discretization methods. The trend-caused nonstationarity in 75% of all stations in the HRB could definitely influence the final results of the optimal rain-gauge network design using the sliding window method. Therefore, in future studies of rain-gauge network optimization, it is necessary to carry out uncertainty research according to local conditions in view of climate change and human activities.https://www.mdpi.com/2073-4441/15/17/3115rain gauge networknonstationaritymulti-objective problemsclimate change |
| spellingShingle | Jian Liu Yanyan Li Yuankun Wang Pengcheng Xu Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China Water rain gauge network nonstationarity multi-objective problems climate change |
| title | Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China |
| title_full | Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China |
| title_fullStr | Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China |
| title_full_unstemmed | Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China |
| title_short | Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China |
| title_sort | utilizing entropy based method for rainfall network design in huaihe river basin china |
| topic | rain gauge network nonstationarity multi-objective problems climate change |
| url | https://www.mdpi.com/2073-4441/15/17/3115 |
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