Algal bloom prediction of the lower Han River, Korea using the EFDC hydrodynamic and water quality model

Kim, Jaeyoung; Lee, Tongeun; Seo, Dongil

doi:10.1016/j.ecolmodel.2017.10.015

Cited by 79 publications

(20 citation statements)

References 26 publications

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“…Complex water quality models have been widely used to understand algal blooms in response to the flow and nutrient discharges and to determine nutrient loading reduction (Thomann and Mueller, 1987;Cerco and Noel, 2004;Shen, 2006). There are many applications for using 2D and 3D water quality models to simulate algae blooms (Wu and Xu, 2011;James, 2016;Kim et al, 2017;Jiang and Xia, 2017). However, it is always a challenge to calibrate a complex model to properly simulate an algal bloom because both physical transport and biological processes modulating algal biomass dynamics are highly variable under different residence times and biological timescales (Lucas et al, 2009;Qin and Shen, 2017).…”

Section: Introductionmentioning

confidence: 99%

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Shen

Qin

Wang

et al. 2019

Ecological Modelling

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Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions 5 between physical transport and algal dynamics, it is challenging to predict interannual variations 6 of bloom correctly using a complex eutrophication model without having a high-resolution model grid to resolve complex geometry and an accurate estimate of nutrient loading to drive the model. In this study, an approach using long-term observational data (from 1990-2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the datadriven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as well.

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Section: Introductionmentioning

confidence: 99%

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Shen

Qin

Wang

et al. 2019

Ecological Modelling

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“…Two-dimensional and three-dimensional numerical simulations have been widely applied in hydrology and water quality simulations of rivers, estuaries, and oceans [37][38][39][40][41][42][43]. MIKE 21(DHI, Denmark) is a professional engineering software to simulate hydrodynamic force, water quality, sediment and wave, which has advanced pre-processing and post-processing function and a friendly customer page and enjoys popularity in simulating water current, wave, sediment, and other environmental variables of river, lake, estuary, bay, coast, and ocean.…”

Section: Numerical Simulation By Mike 21 Software (Dhi Denmark)mentioning

confidence: 99%

Influence of Salinity Gradient Changes on Phytoplankton Growth Caused by Sluice Construction in Yongjiang River Estuary Area

Yuan

Jiang

Weng

et al. 2020

Water

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Though the number of sluices and dams in coastal areas has increased rapidly in recent years, the influence of their construction on phytoplankton in estuary areas is hardly known. This paper aims to provide a reference for quantitative research on the ecological influence of sluice construction and give ecological justifications for the setting of environmental standards in the estuary areas. The survey data gained at the lower reach of the Yongjiang River and its estuarine areas in June 2015 were used in MIKE21 software (Danish Hydraulic Institute (DHI), Denmark)) for establishing a two-dimensional numerical model to simulate the salinity field distribution after sluice construction. Based on the simulation results, the salinity gradient changes caused by the construction were analyzed. The one-dimensional Gaussian model was applied to calculated the phytoplankton’s ecological threshold interval over the salinity changes, which helped predict the influence of salinity changes on phytoplankton cell density. The study shows that salinity in the Yongjiang estuary increases obviously, beyond the phytoplankton ecological threshold, after sluice construction without water discharge. Salinity will become a restriction factor to phytoplankton growth after sluice construction in the study area, which may cause a sharp decrease of certain phytoplankton species.

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“…Gao et al (2018) studied the influence of water age and water temperature on algae in the river by numerical simulation. Kim et al (2017) pointed out that in order to improve the calibration accuracy of chl‐a in the river, more than three algae groups should be added to the Environmental Fluid Dynamics Code (EFDC). Bae and Seo (2018) used four statistics, the coefficient of determination (R 2 ), Nash–Sutcliffe model efficiency (ME), percentage model bias (Pbias), and cost function (CF) to evaluate model prediction accuracy, getting the parallel result that there should be a greater focus on studying multiple algal groups together with EFDC.…”

Section: Introductionmentioning

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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Algal bloom prediction of the lower Han River, Korea using the EFDC hydrodynamic and water quality model

Cited by 79 publications

References 26 publications

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Influence of Salinity Gradient Changes on Phytoplankton Growth Caused by Sluice Construction in Yongjiang River Estuary Area

Transverse distribution of cyanobacteria in a regulated urban river

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