Abstract：
Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.
摘要：
叶绿素a(Chla)浓度可作为藻类生物量的指标,水柱中藻类生物量的积累对地表水华的出现至关重要。通过使用中分辨率成像光谱仪（MODIS）数据，以前开发了一种机器学习算法来评估富营养深度（Beu）内的藻类生物量。这里,生成了2003年至2020年太湖的长期Beu数据集，以检查其时空动态，对环境因素的敏感性，以及与地表藻华面积相比的变化。在此期间,每日Beu(整个湖中的总Beu)表现出40到90tChla之间的时间波动，年平均Chla为63.32±5.23t。值得注意的是,它在2007年(72.34tChla)和2017年(73.57tChla)达到最高水平。此外，从2003年至2007年，它表现出明显的增加趋势，为0.197tChla/y，随后在2017年后略有下降，为0.247tChla/y。季节变化表现为双峰年周期，特点是3月~4月有一个小高峰，7月~9月有一个大高峰。空间上,基于像素的平均Beu（单位水柱的总Beu）范围为21.17至49.85mgChla，高值主要分布在西北地区，低值分布在中部地区。Beu对环境因素的敏感性因地区和时间尺度而异。温度对月变化有显著影响(65.73%),而养分浓度水平影响年变化(55.06%)。风速,温度,和水动力条件共同影响Beu在整个湖泊中的空间分布。与表面藻华面积相比，藻华生物量可以捕获两个突变年的趋势变化以及双峰物候变化。该研究可为科学评价水环境提供依据,为其他类似富营养化湖泊藻类生物量监测提供参考。
