Wang Sheng, Zhao Liang, Wang Yuheng, Zhang Haiyan, Li Fei, Zhang Yijie
Key Laboratory of Marine Resource Chemistry and Food Technology (TUST), Ministry of Education, Tianjin University of Science and Technology, Tianjin, China; Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
Key Laboratory of Marine Resource Chemistry and Food Technology (TUST), Ministry of Education, Tianjin University of Science and Technology, Tianjin, China; Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China.
Mar Environ Res. 2022 Nov;181:105756. doi: 10.1016/j.marenvres.2022.105756. Epub 2022 Sep 24.
Large-scale outbreaks of green tides in the Yellow Sea affect the development of local tourism and aquaculture and significantly damage the coastal ecological environment. To study the distribution characteristics of green tides and to explore their impact on the environment, a coupled physical-ecological model (LTRANS-GT) based on the Lagrangian TRANSport model (LTRANS) was constructed in this paper to simulate the growth and dissipation process of Ulva prolifera and to obtain its drift trajectory and biomass. The results show that the tracks of the green tide are mainly divided into three categories, namely, northwestward, northward, and eastward. The green tide biomass showed a single-peak with seasonal variation in most years (entering a rapid proliferation period in May-June, reaching a peak biomass after developing for approximately 30 days, then dying out rapidly and basically disappearing by August), and showed a double-peak only in a few years due to extreme weather effects. In 2017, the biomass of U. prolifera was the lowest, with a maximum wet weight of only 24600 tons, while the largest biomass occurred in 2013, with a maximum wet weight of more than 560,000 tons. The interannual difference in the biomass of U. prolifera was mainly due to its initial biomass and the difference in nutrient concentration in the area where it was located. The year in which U. prolifera absorbed the most dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) was 2013, with absorption values of 3973 tons and 114 tons, respectively; and the year in which U. prolifera absorbed the least DIN and DIP was 2017, with absorption values of 172 tons and 5 tons, respectively. During the period of U. prolifera outbreak, the consumption of DIN and DIP in the sea area where it occurred accounted for approximately 43.65% and 0.6% of the total discharge of the rivers, and 26.86% and 6.1% of the atmospheric deposition, respectively. The impact of green tide outbreaks on the annual nitrogen and phosphorus nutrient budget of the entire Yellow Sea was relatively small, but the impact on the area where U. prolifera grows and decays can not be ignored. In dissipation period, the decay of U. prolifera may make DON and DOP double near Shandong coast.
黄海大规模绿潮爆发影响当地旅游业和水产养殖业发展,并对沿海生态环境造成严重破坏。为研究绿潮分布特征并探索其对环境的影响,本文基于拉格朗日输运模型(LTRANS)构建了物理-生态耦合模型(LTRANS-GT),以模拟浒苔的生长和消亡过程,并获取其漂移轨迹和生物量。结果表明,绿潮轨迹主要分为三类,即向西北、向北和向东。多数年份绿潮生物量呈现季节性单峰变化(5-6月进入快速增殖期,生长约30天后生物量达到峰值,随后迅速消亡,8月基本消失),少数年份因极端天气影响呈现双峰。2017年浒苔生物量最低,最大湿重仅24600吨,而2013年生物量最大,最大湿重超过560000吨。浒苔生物量的年际差异主要源于其初始生物量及其所在区域营养盐浓度差异。浒苔吸收溶解无机氮(DIN)和溶解无机磷(DIP)最多的年份是2013年,吸收量分别为3973吨和114吨;吸收DIN和DIP最少的年份是2017年,吸收量分别为172吨和5吨。浒苔爆发期间,其所在海域DIN和DIP的消耗量分别约占河流总排放量的43.65%和0.6%,以及大气沉降量的26.86%和6.1%。绿潮爆发对整个黄海年度氮磷营养盐收支的影响相对较小,但对浒苔生长消亡区域的影响不容忽视。在消亡期,浒苔的腐烂可能使山东近岸溶解有机氮(DON)和溶解有机磷(DOP)加倍。