Wind-generated flow modeling and future circulation prediction of lakes under complex wind field - A case study of Qinghai Lake

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2025-02-20 DOI:10.1016/j.envsoft.2025.106371
Junyang Li , Yuanfu Zhang , Yuxiu Li , Kai Ma , Zhikang Wang , Xiaohan Zhang , Yuchuan Yi , Pengxiang Lu , Zhiqian Gao , Min Wang
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Abstract

The response mechanism of lake flow under the influence of a complex wind field is a cross-disciplinary hotspot in the fields of hydrology and modern sedimentation research. Qinghai Lake, influenced by multiple wind fields, exhibits complex internal hydrodynamics with largely unknown controlling factors. Current studies predominantly focus on the impact of climate and prevailing northwesterly winds on lake flow in Qinghai Lake, while other factors receive less consideration. Building on previous research, this thesis analyzes the wind-generated flow response under the combined influence of prevailing wind and land-lake breeze. A simulation model of lake flow under water balance conditions was constructed using the shallow water equation, integrating climatic data, multiple wind field conditions, wind speed, blowing range, and water depth. The accuracy of the adopted model is validated by comparison with measured circulation, water level, and flow rate data.Results indicate that the prevailing northwesterly wind in Qinghai Lake predominantly controls the main circulation, while the land-lake breeze governs the secondary circulation. The north wind inhibits the formation of certain secondary circulations, such as the counterclockwise circulation in the Hada Bay area and the clockwise circulation in the Dongnan Bay.On an interannual scale, the wind speed of the land-lake breeze along the north shore of Qinghai Lake is positively correlated with the total water volume and negatively correlated with temperature.These findings facilitate the simulation of circulation under complex wind field conditions and allow inference of future lake current development patterns based on predictions of climate and water level changes.
Currently, the modern sedimentary phenomena along the shore of Qinghai Lake are complex: wind-driven waves and littoral currents form shore sandbars on the southern shore, while wind-driven waves and littoral currents create longshore barriers and lagoons at the mouth of the Hargai River's northern tributary. Therefore, this study can provide a reference for research on modern sandy sedimentation and lake eutrophication in Qinghai Lake. It also offers theoretical support for sedimentary oil and gas reservoirs in similar lake basins.By analyzing the driving mechanisms of complex wind fields on lake circulation, this study can provide new perspectives and data support for the fundamental theoretical research of lake dynamics. It also lays the foundation for predicting the hydrodynamic responses of lakes under future climate change. Additionally, it offers a theoretical framework and practical reference for interdisciplinary research in limnology, sedimentology, ecology, and related fields.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
期刊最新文献
Editorial Board Letting ecosystems speak for themselves: An unsupervised methodology for mapping landscape acoustic heterogeneity Wind-generated flow modeling and future circulation prediction of lakes under complex wind field - A case study of Qinghai Lake Quantifying the impact of different precipitation data sources on hydrological modeling processes in arid basin using transfer entropy Extrapolability improvement of machine learning-based evapotranspiration models via domain-adversarial neural networks
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