Influences of environmental factors on biomass of phytoplankton in the northern part of Tai Lake, China, from 2000 to 2012

Guo, Wenjing; Zhu, Yuanrong; Fu, Zhiyong; Qin, Ning; Wang, Hao; Li, Shasha; Hu, Yan; Wu, Fengchang; Giesy, John P.

doi:10.1007/s10661-017-6318-3

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Other physical environment changes such as WT, hydrodynamics, and stratification also directly affect algae growth (Berger et al 2007, Li et al 2022a. This is coincident with the major bloom-driving factors identified by the method of this study, and the framework also revealed that the overall effect of TP is more significant than TP and qualified the individual effects of the factors (Paerl et al 2011, Guo et al 2017, Li et al 2022b. The overall prediction accuracy of this model is about 0.809 in calibration and 0.736 in validation, and prediction accuracy of the Chl-a concentration is more than 0.8, satisfactory compared to the existing studies (Liang et al 2020, Li et al 2021a.…”

Section: Implications For Lake Functioning and Managementsupporting

confidence: 73%

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

Zhang

Mao²,

Cai³

et al. 2022

Environ. Res. Lett.

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An algal bloom is a complex hydro-biological phenomenon driven by multi-attribute environmental processes and thus is still difficult to predict. In this paper, a comprehensive modelling framework for forecasting algal bloom risks in shallow lakes is presented, which is based on long-term field observation and modelling of eutrophic shallow lakes. In the procedure, the major factors and their suitable ranges are investigated, and the individual influence of various driving factors is evaluated quantitatively, using an integrated approach of orthogonal design and regression analysis. By analyzing the possible combined effects of the major driving factors and the relationship between algal bloom risk and major bloom-driving factors, a cost-effective environmentally driven risk assessment model is developed to forecast the likelihood of algal bloom occurrence, through a parameter optimization and prediction comparison routine. The risk model has been calibrated and validated against long-term field observations of algal blooms in Taihu Lake, with the prediction accuracy higher than 70%, which only requires readily available meteorological and water quality data. It is noted that for the closed shallow lake, the influence of hydrodynamics can be indirectly reflected by the variation of wind speed; and, TP, water temperature (WT), photosynthetically active radiation (PAR), and average wind speed (AWS) could be used as major bloom-driving factors in Taihu Lake generally. This study provides a practical framework for the development of algal bloom early warning schemes for shallow lakes and helps to understand the combined function of complex bloom-driving factors.

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Section: Implications For Lake Functioning and Managementsupporting

confidence: 73%

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

Zhang

Mao²,

Cai³

et al. 2022

Environ. Res. Lett.

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“…Another possible reason was linked with nutrients structure. Guo et al (2017) found that chl‐a had a tendency to be negatively correlated with the nitrogen and phosphorus ratio (N/P). N/P in Mal river was far higher than that in Liangxi River, which may bring about this fact.…”

Section: Resultsmentioning

confidence: 99%

Transverse distribution of cyanobacteria in a regulated urban river

Yan

Wang

et al. 2021

Ecohydrology

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River eutrophication and CyanoHABs are severe problems that are often ignored because of high current speed and strong self‐purification. In this paper, Liangxi River, Taihu Basin, was selected as the research area. Combined with field investigation, a 2D water environment mathematical model was developed to simulate the chl‐a distribution in Liangxi River. An indicator (Transverse Distribution Center, TDC) and its normalized form (NTDC) to quantitatively represent material transverse distribution in rivers is proposed and coupled in the model. The calculation showed that TDC and NTDC had the property of random fluctuation, seasonal consistency, and water transfer dependence. The multiple regression equation with normalized data indicated that the maximum offset, average variation rate, and average reverting rate of Liangxi River chl‐a NTDC were most affected by the chl‐a dry matter flux ratio between tributaries and the mainstream, followed by flow and chl‐a concentration ratio. From the perspective of river morphology, for different river width change modes and river bending directions, when water flows into and out of these river sections, the chl‐a transverse distribution is subject to different specific effects. In addition, the position of nutrients and dissolved oxygen significantly affected the position of chl‐a growth when the N/P ratio was not very high. Conversely, a high N/P ratio may contribute to decrease of the chl‐a concentration. Proliferation with abundant nutrients may cause the settlement of chl‐a resulting in a decrease of chl‐a in the water column.

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“…The primary meteorological factors included air temperature, global radiation, wind speed, precipitation, and sunshine duration. Since prior studies have noted that both nitrogen and phosphorus over-enrichment were responsible for blooms in Lake Taihu [ 35 – 37 ], total nitrogen (TN) and total phosphorus (TP) were included in our study. Because the underwater light climate depends on both incident light and the transparency of the water column [ 38 ], which was estimated by Secchi depth (SD), the product of these two variables has been used as a simple proxy for the under-water light condition.…”

Section: Methodsmentioning

confidence: 99%

Effects of climatically-modulated changes in solar radiation and wind speed on spring phytoplankton community dynamics in Lake Taihu, China

et al. 2018

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Many studies have focused on the interactive effects of temperature increases due to global warming and nutrient enrichment on phytoplankton communities. Recently, non-temperature effects of climate change (e.g., decreases in wind speed and increases in solar radiation) on large lakes have received increasing attention. To evaluate the relative contributions of both temperature and non-temperature effects on phytoplankton communities in a large eutrophic subtropical shallow lake, we analyzed long-term monitoring data from Lake Taihu, China from 1997 to 2016. Results showed that Lake Taihu’s spring phytoplankton biovolume and composition changed dramatically over this time frame, with a change in dominant species. Stepwise multiple linear regression models indicated that spring phytoplankton biovolume was strongly influenced by total phosphorus (TP), light condition, wind speed and total nitrogen (TN) (radj2 = 0.8, p < 0.01). Partial redundancy analysis (pRDA) showed that light condition accounted for the greatest variation of phytoplankton community composition, followed by TP and wind speed, as well as the interactions between TP and wind speed. Our study points to the additional importance of non-temperature effects of climate change on phytoplankton community dynamics in Lake Taihu.

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Influences of environmental factors on biomass of phytoplankton in the northern part of Tai Lake, China, from 2000 to 2012

Cited by 8 publications

References 47 publications

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

Environmentally driven risk assessment for algal bloom occurrence in shallow lakes

Transverse distribution of cyanobacteria in a regulated urban river

Effects of climatically-modulated changes in solar radiation and wind speed on spring phytoplankton community dynamics in Lake Taihu, China

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