Chang Hu, Zuo Ping, Yan Yuru, Qin Yutao
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; Key Laboratory for Coast and Island Development of Ministry of Education, Nanjing University, Nanjing 210023, China.
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; Key Laboratory for Coast and Island Development of Ministry of Education, Nanjing University, Nanjing 210023, China.
Mar Pollut Bull. 2025 Jun;215:117897. doi: 10.1016/j.marpolbul.2025.117897. Epub 2025 Mar 29.
Ulva prolifera blooms, also known as green tides, significantly disrupt coastal ecosystems by altering species balance, energy flow, and nutrient cycling. These blooms, driven by nutrient enrichment, climate change, and human activities, have become a pressing environmental challenge in coastal regions. This review synthesizes current advances in modeling the growth, dispersal, and decline of U. prolifera blooms, emphasizing the roles of environmental drivers such as nutrient availability, temperature, light, and hydrodynamic conditions. We discuss empirical and mechanistic modeling approaches, highlighting their applications, limitations, and potential for predicting bloom dynamics. Special attention is given to model calibration, validation, and the integration of remote sensing and environmental data, which are crucial for ensuring model accuracy and reliability. Despite significant progress, challenges remain in addressing data gaps, incorporating climate variability, and simulating complex ecological interactions. Future research directions include the development of multi-scale, coupled models and the integration of socio-economic impacts to enhance bloom management strategies and inform policy development. The insights presented are intended to advance the understanding of U. prolifera bloom dynamics and contribute to the mitigation of their ecological and socio-economic impacts.
浒苔绿潮通过改变物种平衡、能量流动和养分循环,对沿海生态系统造成显著破坏。这些绿潮由养分富集、气候变化和人类活动引发,已成为沿海地区严峻的环境挑战。本综述综合了浒苔绿潮生长、扩散和衰退建模的当前进展,强调了诸如养分可用性、温度、光照和水动力条件等环境驱动因素的作用。我们讨论了经验模型和机理模型方法,突出了它们在预测绿潮动态方面的应用、局限性和潜力。特别关注模型校准、验证以及遥感和环境数据的整合,这对于确保模型的准确性和可靠性至关重要。尽管取得了重大进展,但在解决数据缺口、纳入气候变异性以及模拟复杂生态相互作用方面仍存在挑战。未来的研究方向包括开发多尺度耦合模型以及整合社会经济影响,以加强绿潮管理策略并为政策制定提供信息。所呈现的见解旨在增进对浒苔绿潮动态的理解,并有助于减轻其生态和社会经济影响。