Seasonally varying effects of environmental factors on phytoplankton abundance in the regulated rivers

Abstract

This study investigates a seasonally varying response of phytoplankton biomass to environmental factors in rivers. Artificial neural network (ANN) models incorporated with a clustering technique, the clustered ANN models, were employed to analyze the relationship between chlorophyll a ( Chl-a ) and the explanatory variables in the regulated Nakdong River, South Korea. The results show that weir discharge ( Q ) and total phosphorus ( TP ) were the most influential factors on temporal dynamics of Chl-a . The relative importance of both variables increased up to higher than 30% for low water temperature seasons with dominance of diatoms. While, during summer when cyanobacteria predominated, the significance of Q increased up to 45%, while that of TP declined to about 10%. These tendencies highlight that the effects of the river environmental factors on phytoplankton abundance was temporally inhomogeneous. In harmful algal bloom mitigation scenarios, the clustered ANN models reveals that the optimal weir discharge was 400 m 3 /s which was 67% of the value derived from the non-clustered ANN models. At the immediate downstream of confluence of the Kumho River, the optimal weir discharge should increase up to about 1.5 times because of the increase in the tributary pollutant loads attributed to electrical conductivity ( EC ).

Influence of Abiotic Factors on the Growth of Cyanobacteria Isolated from Nakdong River, South Korea1

“…2018b, Kim et al. 2019). These alterations ultimately affect cyanobacterial species composition, frequency, duration, and growth in the aquatic ecosystems worldwide (Hur et al.…”

“…Backer et al (2015) reported 175 animal deaths and 458 human illnesses in the United States because of harmful algal blooms (HABs) during 2007-2011. Over the decades, climate change, global warming, and eutrophication are likely to alter the biological, chemical, and physical characteristics of aquatic ecosystems (Lee et al 2018b, Kim et al 2019). These alterations ultimately affect cyanobacterial species composition, frequency, duration, and growth in the aquatic ecosystems worldwide (Hur et al 2013, Huisman et al 2018.…”

“…Recently, the Nakdong River faces severe issues of eutrophication due to the increased cyanobacterial population. To date, most of the studies focused on cyanobacterial diversity, abundance, and distribution in the Nakdong River (Lee et al 2018a, Kim et al 2019.…”

A Forecasting Method for Harmful Algal Bloom(HAB)-Prone Regions Allowing Preemptive Countermeasures Based only on Acoustic Doppler Current Profiler Measurements in a Large River

“…[38] utilized spatial autocorrelation to relate the spatial correlation of algae observed along a stream in Namdaecheon. Currently, the K-means algorithm has been widely used to predict and classify water quality in rivers and oceans [39][40][41]. As another method of delineating HAB-prone regions, this study also applied a hot-spot analysis based on the Getis-Ord G* method [34,35].…”

Reproductive Biology and Ecology of the Green Mussel Perna viridis: A Multidisciplinary Approach

“…Marine phytoplankton are important food sources for P. viridis that contain a high amount of PUFAs [28]. However, the abundance of plankton is profoundly influenced by the seasonality and environmental factors [29]. P. viridis has the ability to selectively ingest plankton from the water column, depending on the environmental conditions and reproductive cycle [1,30].…”