Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model

Wang, Xiwen; Wang, Weijia; He, Yejun; Zhang, Shulei; Huang, Wei; Woolway, R. Iestyn; Shi, Kun; Yang, Xiaofan

doi:10.1016/j.jhydrol.2023.129184

Cited by 12 publications

(5 citation statements)

References 64 publications

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“…A typical example of two‐way coupling is given by the models sensu Henderson‐Sellers (1985), and other examples have been implemented in the Community Land surface Model (CLM‐Lake) (Oleson et al., 2013), Common Land Model (CoLM‐Lake) (Dai et al., 2018), Community Earth System Model (CESM‐LISS) (Lawrence et al., 2019; Subin et al., 2012), and the Weather Research and Forecast model (WRF‐Lake) (Gu et al., 2015). Two way coupling has been widely used to study lake/reservoir thermal dynamics, lake ice coverage, lake–atmosphere interactions, and climate change effects on lakes (see e.g., Guo et al., 2022, 2023a; Gu et al., 2015; F. Wang et al., 2019; Wu et al., 2020; X. Wang et al., 2023; Xiao et al., 2016). Another example is the FLake model, which has been coupled in one‐way with ERA40 reanalysis data (Martynov et al., 2010) and WRF (Gula & Peltier, 2012), and incorporated as a lake parameterization scheme into the Consortium for Small‐scale Modeling—COSMO model (Mironov et al., 2010) and coupled in two‐way model configurations with WRF (Mallard et al., 2014).…”

Section: Emerging Modeling Approaches and Future Directionsmentioning

confidence: 99%

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Piccolroaz,

Zhu,

Ladwig

et al. 2024

Reviews of Geophysics

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Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

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Section: Emerging Modeling Approaches and Future Directionsmentioning

confidence: 99%

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Piccolroaz,

Zhu,

Ladwig

et al. 2024

Reviews of Geophysics

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“…GOCI can be used to produce water-quality products in clear weather. In cloudy weather, unmanned aerial vehicle observation results and point ground observation results for an area of interest can be input into hydrodynamic models to generate a simulated numerical water quality dataset [222,223]. The integration of technology can achieve all-weather water quality monitoring of ICWs.…”

Section: Integrating Geostationary Ocean Color Satellites Unmanned Ae...mentioning

confidence: 99%

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Preprint

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In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

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“…Most studies use temperature gradient thresholds to determine the thermal structure of lakes, often employing 0.2 • C/m as the criterion [24,[29][30][31][32]. Some studies define the thermocline as the water layer where the second derivative of temperature with respect to depth is zero [33]; thermocline depth is defined as the depth from the upper part of the metalimnion to the surface [24], while in some studies, it is the distance from the thermocline to the water surface [33].…”

Section: Lake Thermal Stratification Structurementioning

confidence: 99%

“…Different models have their own strengths and weaknesses depending on the research objectives [166], and selecting the appropriate model can address many practical issues [167]. Moreover, coupling various models can compensate for the shortcomings of individual models [32,168]. For example, current models mostly simplify heat transfer at the bottom of lakes.…”

Section: Thermal Stratification Modelsmentioning

confidence: 99%

Challenge to Lake Ecosystems: Changes in Thermal Structure Triggered by Climate Change

Zhang,

Shen,

et al. 2024

Water

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Human activities, global warming, frequent extreme weather events, and changes in atmospheric composition affect the solar radiation reaching the Earth’s surface, affect mass and heat transfer at the air–water interface, and induce oscillations in wind-driven internal waves. This leads to changes in the spatiotemporal characteristics of thermal stratification in lakes, altering lake circulation patterns and vertical mass transfer. However, thermal stratification structures are often overlooked. The intensification of lake thermal stratification due to warming may lead to increased release of bottom pollutants, spreading through the dynamic behavior of the thermocline to the epilimnion. Moreover, the increased heat storage is beneficial for the growth and development of certain phytoplankton, resulting in rapid transitions of the original steady state of lakes. Consequently, water quality deterioration, ecological degradation, and declining biodiversity may occur. Conventional surface water monitoring may not provide comprehensive, accurate, and timely assessments. Model simulations can better predict future thermal stratification behaviors, reducing financial burdens, providing more refined assessments, and thus preventing subsequent environmental issues.

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Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model

Cited by 12 publications

References 64 publications

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Challenge to Lake Ecosystems: Changes in Thermal Structure Triggered by Climate Change

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