Data Stream Approach for Exploration of Droughts and Floods Driving Forces in the Dongting Lake Wetland

Yeqing Zhai, Jie Liang (), Zhenyu An, Xin Li, Ziqian Zhu, Wanting Wang, Yuru Yi and Suhang Yang
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Yeqing Zhai: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Jie Liang: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Zhenyu An: Hunan Water Resources and Hydropower Survey, Design, Planning and Research Co., Ltd., Changsha 410007, China
Xin Li: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Ziqian Zhu: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Wanting Wang: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Yuru Yi: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China
Suhang Yang: College of Environmental Science and Engineering, Hunan University, Changsha 410082, China

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: Wetlands are important environmental resources that are vulnerable to droughts and floods. Studying drought-flood events and their driving factors is essential for wetland resource planning and management. However, climate change and human activities present dynamic challenges that traditional approaches are unable to simulate dynamically in a rapidly changing environment. This makes quantitative analysis difficult. Our research focused on the innovative use of the data stream model, namely online bagging of Hoeffding adaptive trees, to quantify drought and flood drivers in response to climate change and human activity. The proposed approach was applied to a river-lake system, the Dongting Lake wetland. The frequency and duration characteristics of drought-flood events were analyzed. In addition, the cyclical changes of droughts and floods were analyzed by wavelet analysis. Then, drought-flood indicators as well as climatic and hydrological factors were entered into a dynamic data stream model for quantitative calculations. The results showed that the water conservancy projects largely reduced flood events while aggravating droughts. The frequency of floods decreased by 4.91% and the frequency of droughts increased by 6.81% following the construction of the Gezhouba Hydro-project and the Three Gorges Dam. Precipitation and Sankou streamflow were two dominant factors in the Dongting Lake drought and flood events, both of which had a feature importance value of approximately 0.3. This research showed how the data stream model can be used in a changing environment and the applicability of the conclusions reached through real-world instances. Moreover, these quantitative outputs can help in the sustainable utilization of Dongting Lake wetland resources.

Keywords: drought-flood; data stream; driving forces; standardized precipitation index; standardized streamflow index; human influence; wetland (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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