Attenuation of ultraviolet radiation and photosynthetically active radiation in six Yunnan Plateau lakes of China based on seasonal field investigations
Solar radiation is a primary driver affecting several physical, chemical and biological processes in lake ecosystems. The attenuation of sunlight in water is directly controlled by optically active substances. Here, the seasonal and interlake heterogeneities of the diffuse attenuation coefficients (Kd(λ)) of ultraviolet radiation (UVR) and photosynthetically active radiation (PAR) were studied based on field investigations in six Yunnan Plateau lakes (i.e., Chenghai, Dianchi, Erhai, Fuxian, Lugu and Yangzong) of China, October 2014‒July 2016. The results revealed that Kd(λ) generally increased with decreasing wavelength and increasing trophic state and that Kd(UVR) presented higher interlake heterogeneity than Kd(PAR). The interlake heterogeneity surpassed the seasonal heterogeneity of Kd(λ), whereas the intralake seasonal heterogeneity, which is related to the lake trophic state and solar spectrum, was obvious. Although the main factors affecting Kd(λ) were chromophoric dissolved organic matter (CDOM) and phytoplankton in general, the interlake heterogeneity was found. In eutrophic, turbid shallow Lake Dianchi, CDOM primarily affected UV-B, whereas total suspended solids (TSS) and/or phytoplankton had important effects on Kd(UV-B), Kd(UV-A) and Kd(PAR). CDOM, TSS and phytoplankton influenced the Kd(UV-B), Kd(UV-A) and Kd(PAR) in the deep mesotrophic Lake Chenghai and Lake Erhai, but the main particulate factors were different between these two lakes. In the deep, oligotrophic clear Lake Fuxian and Lake Lugu, only the significant effect of CDOM on Kd(UVR) in Lake Fuxian was detected. Additionally, the factors affecting Kd(λ) in Lake Yangzong were atypical, possibly due to the artificial addition of massive amounts of ferric chloride.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2020 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.