“…(2003) and Kim et al . (2018a) reported that centric diatoms, Stephanodiscus usually occupy about 85% of total phytoplankton population in the Nakdong River from late fall to spring (about 5–15 °C) 18,30 . Thus, the ANN models adequately explained the seasonal behavior of Chl-a in the low water temperature below about 15 °C as the concentration of Chl-a in Cluster 31 was governed by the population of the single diatom assemblage.…”
Section: Resultsmentioning
confidence: 99%
“…In the regulated Nakdong River, toxic cyanobacteria such as Microcystis , Aphanizomenon and Anabaena have been abundant, and herein resulted in the significant level of cyanobacterial cell counts around 10,000–20,000 cells/ml during summer owing to the water temperature higher than 25 °C and the extended water residence time arisen from the artificial flow control by the weirs 29 . From winter to early spring, diatoms with the prevalence of Stephanodiscus have usually predominated in the study area 30 .Figure 1Location of target water quality monitoring stations in the Nakdong River.…”
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
).
“…(2003) and Kim et al . (2018a) reported that centric diatoms, Stephanodiscus usually occupy about 85% of total phytoplankton population in the Nakdong River from late fall to spring (about 5–15 °C) 18,30 . Thus, the ANN models adequately explained the seasonal behavior of Chl-a in the low water temperature below about 15 °C as the concentration of Chl-a in Cluster 31 was governed by the population of the single diatom assemblage.…”
Section: Resultsmentioning
confidence: 99%
“…In the regulated Nakdong River, toxic cyanobacteria such as Microcystis , Aphanizomenon and Anabaena have been abundant, and herein resulted in the significant level of cyanobacterial cell counts around 10,000–20,000 cells/ml during summer owing to the water temperature higher than 25 °C and the extended water residence time arisen from the artificial flow control by the weirs 29 . From winter to early spring, diatoms with the prevalence of Stephanodiscus have usually predominated in the study area 30 .Figure 1Location of target water quality monitoring stations in the Nakdong River.…”
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
).
“…In the last decades, researchers began to focus on the effect of the upcoming climate change and natural phenomena on the water quality and the eutrophication problem using field investigations or numerical models, which are widely accepted in complex domains such as coastal areas. For instance, Kim et al, ( 2018 ), Liu et al ( 2019 ), Sepulveda-Jauregui et al ( 2018 ), and Trombetta et al ( 2019 ) investigated the effect of increasing water temperature on different water quality parameters. They found out that increasing water temperature played a major role in reducing water quality in water systems.…”
Eutrophication problem in El Gouna shallow artificial coastal lagoons in Egypt was investigated using 2D TELEMAC-EUTRO-WAQTEL module. Eight reactive components were presented, among them dissolved oxygen (DO), phosphorus, nitrogen, and phytoplankton biomass (PHY). The effect of warmer surface water on the eutrophication problem was investigated. Also, the spatial and temporal variability of the eutrophication was analyzed considering different weather conditions: tide wave, different wind speeds and directions. Moreover, effect of pollution from a nearby desalination plant was discussed considering different pollution degrees of brine discharge, different discharge quantities and different weather conditions. Finally, new precautions for better water quality were discussed. The results show that tide wave created fluctuations in DO concentrations, while other water quality components were not highly influenced by tide’s fluctuations. Also, it was found that high water temperatures and low wind speeds highly decreased water quality producing low DO concentrations and high nutrients rates. High water quality was produced beside inflow boundaries when compared to outflow boundaries in case of mean wind. Moreover, the results show that the average water quality was not highly deteriorated by the nearby desalination operation, while the area just beside the desalination inflow showed relatively strong effects. Different weather conditions controlled the brine’s propagation inside the lagoons. Moreover, increasing the width of the inflow boundaries and injecting tracer during tide and mean wind condition are new precautions which may help to preserve the water quality in a future warmer world. This study is one of the first simulations for eutrophication in manmade lagoons.
“…Such high expectation output from the advanced and coupled WQM models is limited due to over-parameterization and associated assumptions in the process [ 28 , 29 ]. Opinions remain divided on whether higher dimensional complex models (two-dimensional or three-dimensional) or simple models based on appropriate theories and logic is the best approach to water quality modeling [ 30 , 31 , 32 ].…”
Section: Introductionmentioning
confidence: 99%
“…It can capture reactions and the decay process of phytoplankton, nitrate, ammonia, phosphate, DO, BOD, organic phosphorous, and nitrogen concentrations [ 34 , 40 , 41 , 42 ]. The dynamic interactions between nutrient loads from various sources and the consequent longitudinal and lateral water quality pattern in the recipient water body are best described by the WQMs [ 27 , 31 ].…”
A river water quality spatial profile has a diverse pattern of variation over different climatic regions. To comprehend this phenomenon, our study evaluated the spatial scale variation of the Water Quality Index (WQI). The study was carried out over four main climatic classes in Asia based on the Koppen-Geiger climate classification system: tropical, temperate, cold, and arid. The one-dimensional surface water quality model, QUAL2Kw was selected and compared for water quality simulations. Calibration and validation were separately performed for the model predictions over different climate classes. The accuracy of the water quality model was assessed using different statistical analyses. The spatial profile of WQI was calculated using model predictions based on dissolved oxygen (DO), biological oxygen demand (BOD), nitrate (NO3), and pH. The results showed that there is a smaller longitudinal variation of WQI in the cold climatic regions than other regions, which does not change the status of WQI. Streams from arid, temperate, and tropical climatic regions show a decreasing trend of DO with respect to the longitudinal profiles of main river flows. Since this study found that each climate zone has the different impact on DO dynamics such as reaeration rate, reoxygenation, and oxygen solubility. The outcomes obtained in this study are expected to provide the impetus for developing a strategy for the viable improvement of the water environment.
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