Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis
Precipitation is an important variable affecting regional climate characteristics. Accurately identifying trends in precipitation is essential for understanding the evolution of the water cycle in the context of climate change. This study uses an innovative trend analysis (ITA), an innovative polygo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22003399 |
| _version_ | 1818006172189327360 |
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| author | Shuqi Wu Wenji Zhao Jiaqi Yao Jiannan Jin Miao Zhang Guofu Jiang |
| author_facet | Shuqi Wu Wenji Zhao Jiaqi Yao Jiannan Jin Miao Zhang Guofu Jiang |
| author_sort | Shuqi Wu |
| collection | DOAJ |
| description | Precipitation is an important variable affecting regional climate characteristics. Accurately identifying trends in precipitation is essential for understanding the evolution of the water cycle in the context of climate change. This study uses an innovative trend analysis (ITA), an innovative polygon trend analysis (IPTA), and wavelet analysis to analyze precipitation at multiple timescales (annual, seasonal, and monthly), and the influencing factors at 30 meteorological stations in the Tai Lake Basin (TLB) from 1971 to 2018. The main conclusions are as follows: 1) the annual precipitation had a significantly increasing trend, while high precipitation had the largest increasing trend, leading to a further increase in the flood risk in the TLB. The precipitation trend mainly decreased in spring and autumn, whereas it mainly increased in summer and winter. Precipitation in different months played crucial but varying roles in the corresponding seasons; there was a sharp transition trend from August to September, whereas the transition from January to February was relatively stable. 2) There was a complex non-linear relationship between precipitation and atmospheric teleconnection. The dominant tele-correlation alone could not explain the relationship between precipitation and large-scale circulation. The highest percentage of significant power includes the optimal combination of variables meant to explain the precipitation variations. 3) The detection results from the ITA method and classic trend analysis methods (Linear regression analysis, Mann Kendall, and Modified Mann Kendall) were consistent; the non-monotonic trends masked by these methods were detectable. IPTA can systematically identify consecutive seasons and monthly transition characteristics as a supplement to ITA. This study can, therefore, provide a reference for water resource management and the prevention and control of droughts and floods. |
| first_indexed | 2024-04-14T04:56:47Z |
| format | Article |
| id | doaj.art-357d2a9176c24ed0b19d9621bb4d0885 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-14T04:56:47Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-357d2a9176c24ed0b19d9621bb4d08852022-12-22T02:11:07ZengElsevierEcological Indicators1470-160X2022-06-01139108868Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysisShuqi Wu0Wenji Zhao1Jiaqi Yao2Jiannan Jin3Miao Zhang4Guofu Jiang5School of Resource, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaSchool of Resource, Environment and Tourism, Capital Normal University, Beijing 100048, China; Corresponding author.College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Resource, Environment and Tourism, Capital Normal University, Beijing 100048, ChinaKey Laboratory of Remote Sensing Monitoring for Natural Disasters of Henan Universities, School of Geography and Tourism, Nanyang Normal University, Nanyang 473061, ChinaKey Laboratory of Remote Sensing Monitoring for Natural Disasters of Henan Universities, School of Geography and Tourism, Nanyang Normal University, Nanyang 473061, ChinaPrecipitation is an important variable affecting regional climate characteristics. Accurately identifying trends in precipitation is essential for understanding the evolution of the water cycle in the context of climate change. This study uses an innovative trend analysis (ITA), an innovative polygon trend analysis (IPTA), and wavelet analysis to analyze precipitation at multiple timescales (annual, seasonal, and monthly), and the influencing factors at 30 meteorological stations in the Tai Lake Basin (TLB) from 1971 to 2018. The main conclusions are as follows: 1) the annual precipitation had a significantly increasing trend, while high precipitation had the largest increasing trend, leading to a further increase in the flood risk in the TLB. The precipitation trend mainly decreased in spring and autumn, whereas it mainly increased in summer and winter. Precipitation in different months played crucial but varying roles in the corresponding seasons; there was a sharp transition trend from August to September, whereas the transition from January to February was relatively stable. 2) There was a complex non-linear relationship between precipitation and atmospheric teleconnection. The dominant tele-correlation alone could not explain the relationship between precipitation and large-scale circulation. The highest percentage of significant power includes the optimal combination of variables meant to explain the precipitation variations. 3) The detection results from the ITA method and classic trend analysis methods (Linear regression analysis, Mann Kendall, and Modified Mann Kendall) were consistent; the non-monotonic trends masked by these methods were detectable. IPTA can systematically identify consecutive seasons and monthly transition characteristics as a supplement to ITA. This study can, therefore, provide a reference for water resource management and the prevention and control of droughts and floods.http://www.sciencedirect.com/science/article/pii/S1470160X22003399Precipitation trendInnovation trend analysisInnovation polygon trend analysisMulti-wavelet coherenceWater resources management |
| spellingShingle | Shuqi Wu Wenji Zhao Jiaqi Yao Jiannan Jin Miao Zhang Guofu Jiang Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis Ecological Indicators Precipitation trend Innovation trend analysis Innovation polygon trend analysis Multi-wavelet coherence Water resources management |
| title | Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis |
| title_full | Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis |
| title_fullStr | Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis |
| title_full_unstemmed | Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis |
| title_short | Precipitation variations in the Tai Lake Basin from 1971 to 2018 based on innovative trend analysis |
| title_sort | precipitation variations in the tai lake basin from 1971 to 2018 based on innovative trend analysis |
| topic | Precipitation trend Innovation trend analysis Innovation polygon trend analysis Multi-wavelet coherence Water resources management |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22003399 |
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