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Drivers to spatial and temporal dynamics of column integrated phytoplankton biomass in the shallow lake of Chaohu, China

机译:巢湖浅湖浮游植物总生物量的时空动态驱动因素

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摘要

Eutrophication of inland freshwater bodies is a major threat to the ecosystem services. Many studies have focused on using surface or near surface phytoplankton biomass to determine trophic status or bloom conditions. However, surface phytoplankton biomass can change quickly due to the vertical migration of phytoplankton. A more appropriate indicator is column integrated biomass which considers the vertical distribution of the phytoplankton. In this study, we estimated the spatial and temporal dynamics of column integrated biomass in a shallow eutrophic lake, Lake Chaohu in China, using a biomass estimation algorithm and fourteen years of satellite data. The built algorithm was validated by in situ datasets with a significant correlation with the coefficient of determination (R-2) of 0.89, mean absolute relative difference, MARD = 25.97%, the root-mean-square error, RMSE = 20.17 mg.m(-2).We decomposed the temporal dynamics of the satellite-based time series into inter-annual trends, seasonal and irregular behaviors of biomass in different lake sections. We compared these individual dynamics to nutrients, meteorological and climate variables, in particular with respect to ongoing effort to manage nutrients in this complex lake and catchment. Nutrient concentrations were shown to be determinant in the inter-annual trends. Irregular variation of biomass was found to be sensitive to global climate change events (ENSO) which influence regional conditions of precipitation and temperature. By taking the vertical profile of phytoplankton into consideration, the derived temporal and spatial distribution of phytoplankton biomass, rather than surface biomass, provided new sights into lake conditions and were seen to be a good support for lake management efforts.
机译:内陆淡水水体的富营养化是对生态系统服务的主要威胁。许多研究集中在利用表层或近表层浮游生物的生物量来确定营养状态或开花条件。然而,由于浮游植物的垂直迁移,浮游植物的表面生物量可以快速变化。一个更合适的指标是综合考虑浮游植物垂直分布的柱生物量。在这项研究中,我们使用生物量估算算法和14年的卫星数据,估算了中国浅水富营养化湖泊巢湖中柱综合生物量的时空动态。通过现场数据集验证了构建的算法,该算法与确定系数(R-2)为0.89,平均绝对相对差MARD = 25.97%,均方根误差RMSE = 20.17 mg.m有显着相关性。 (-2)。我们将基于卫星的时间序列的时间动态分解为不同湖区生物量的年际趋势,季节和不规则行为。我们将这些个体动态与养分,气象和气候变量进行了比较,特别是在管理这种复杂湖泊和流域的养分方面正在进行的工作方面。营养浓度在年际趋势中是决定性因素。发现生物量的不规则变化对影响区域降水和温度条件的全球气候变化事件(ENSO)敏感。通过考虑浮游植物的垂直剖面,得出的浮游植物生物量而不是表面生物量的时空分布为湖泊状况提供了新的视角,并被视为对湖泊管理工作的良好支持。

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