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61.
In this paper, we use the conductivity-temperature-depth (CTD) observation data and a three-dimensional ocean model in a seasonally-varying
forcing field to study the barrier layer (BL) in the PN section in the East China Sea (ECS). The BL can be found along the
PN section with obviously seasonal variability. In winter, spring and autumn, the BL occurs around the slope where the cold
shelf water meets with the warm Kuroshio water. In summer, the BL can also be found in the shelf area near salinity front
of the Changjiang (Yangtze) River Dilution Water (YRDW). Seasonal variations of BL in the PN section are caused by local hydrological
characteristics and seasonal variations of atmospheric forcing. Strong vertical convection caused by sea surface cooling thickens
the BL in winter and spring in the slope area. Due to the large discharge of Changjiang River in summer, the BL occurs extensively
in the shelf region where the fresh YRDW and the salty bottom water meet and form a strong halocline above the seasonal thermocline.
The formation mechanism of BL in the PN section can be explained by the vertical shear of different water masses, which is
called the advection mechanism. The interannual variation of BL in summer is greatly affected by the YRDW. In the larger YRDW
year (such as 1998), a shallow but much thicker BL existed on the shelf area.
Supported by National Basic Research Program of China (973 Program, No. 2005CB422303 and 2007CB411804), the Key Project of
the International Science and Technology Cooperation Program of China (No. 2006DFB21250), the “111 Project” of the Ministry
of Education (No. B07036), the Program for New Century Excellent Talents in University, China (No. NECT-07-0781) 相似文献
62.
饮用水源地藻类增殖监测和预测对于改善水生态系统环境和保护人类健康具有重要意义。利用多源遥感数据能够获取高时空分辨率的藻类动态信息,结合长时序遥感监测与机器学习算法能够适应藻类生长复杂的影响机制和非线性特征,实现藻类增殖风险时空变化的预测。本文利用Landsat与MODIS长时间序列卫星遥感数据,采用FAI与NDVI两种方法提取2000—2020年丹江口水库藻类浓度的时空变化信息,在此基础上分析藻类增殖对气象因子(气温、气压、相对湿度、风速和累计日照时间)的时间滞后效应。利用支持向量机、朴素贝叶斯与随机森林3种机器学习算法预测藻类增殖风险,并对3种算法的预测性能进行评价和对比。结果表明:丹江口水库藻类浓度年际变化呈现出先增后降的趋势,呈现出明显的季节性周期变化,春末夏初是藻类快速增殖时期。空间上入库支流和库湾等局部地区藻类浓度相对较高,为藻类增殖高风险区,丹江口水库藻类增殖风险预测模型能够较为准确地确定藻类增殖高风险区位置并反映短期内的空间变化情况,3种算法的预测结果呈现出整体上的一致性,其中支持向量机与朴素贝叶斯算法表现出更高的精度,提前4～5天是最佳预测时间。 相似文献