Importance of Epilwimnion Phosphorus Loading and Wind-Induced Flow for Phytoplankton Growth in Ěímov Reservoir

1993 ◽  
Vol 28 (6) ◽  
pp. 5-14 ◽  
Author(s):  
J. Hejzlar ◽  
M. Balejová ◽  
D. Kafková ◽  
M. Růžička
Keyword(s):  
Chlorophyll A ◽  
Phosphorus Loading ◽  
Density Currents ◽  
Chlorophyll A Concentration ◽  
Dynamical Simulation ◽  
Variable Surface ◽  
Surface Discharges ◽  
Induced Flow ◽  
The One ◽  
Wind Induced Flow

Total reservoir phosphorus loading and epilimnion phosphorus loading were estimated and their relations to phytoplankton quantity examined for spring-summer periods during 1984-1991 in Ěímov Reservoir, a stratified, moderately eutrophic reservoir with variable surface and deep outlets operation. Epilimnion phosphorus loading was quantified from the volumes of water entering the epilimnion due to the inflow temperature-density currents and due to the surface discharges that were determined by the one-dimensional dynamical simulation model of reservoir hydrodynamics DYRESM 5. The fraction of total phosphorus input into the reservoir entering the epilimnion varied between 12 and 31% in different years. Chlorophyll a concentration measured near the dam correlated only with the epilimnion phosphorus loading due to surface discharges. The phosphorus input by river water entering directly the epilimnion caused an increase of the chlorophyll a concentration at the dam only occasionally if the wind was of sufficient velocity and favourable direction to induce mixing and transport of epilimnion water masses from the headwater part of the reservoir to the dam.

scholarly journals Evaluating Primary Productivity Based on Chlorophyll-a in Ciburuy Lake, West Java Indonesia

2021 ◽  
pp. 1-8
Author(s):  
Regy Pratama Rusdiansyah ◽  
. Zahidah ◽  
Dedi Supriadi ◽  
Herman Hamdani
Keyword(s):  
Chlorophyll A ◽  
Primary Productivity ◽  
Management Plan ◽  
Lake Area ◽  
West Java ◽  
Chlorophyll A Concentration ◽  
Natural Lakes ◽  
Ph Range ◽  
The One ◽  
Depth Water

Ciburuy Lake is the one of many natural lakes in Padalarang, West Java. Padalarang is industries area and pruduce huge waste water to contamination Ciburuy Lake. Ciburuy Lake area has many functions such as tourism, settlement, agriculture and industry. This study aim to evaluate velue of primary productivity based on chlrophyill-a. Chlorophyll-a is one of the indicator to creat management plan of water resources in Ciburuy Lake. This research was conducted from January to March 2021. Sampling carried out in 4 station with deferent water situation. This research used chlorophyll-a concentration measurment method to determine primary productivity. Result obtained in terms of water quality are: temperature  range of 24 - 28 oC; depth water range of 151,83 – 190,83 cm; Transparation range of 22,83 – 27,67 cm; pH range of 5,94 – 8,00; CO2 range of 6,98 – 33,52 mg/L; BOD range of 7,84 – 12,14 mg/L; nitrate range of 0,235 – 0,312 mg/L; fosfate range of 0,142 --0,156 mg/L, and chlorophyll-a concentration range of 0.024 - 0.065 mg/L . This research showed that Ciburuy Lake has water condition eutrophic to hypertrophic because primary productivity in Ciburuy Lake has middle to high condition based on chlorophyll-a concentration.

Etude sur la teneur en pigment de frênes qui croissent dans des conditions différentes

1973 ◽  
Vol 37 ◽  
Author(s):  
N. Lust
Keyword(s):  
Chlorophyll A ◽  
Strong Influence ◽  
The Other ◽  
Pigment Content ◽  
Chlorophyll B ◽  
Light Quantity ◽  
Yellow Pigments ◽  
The One ◽  
Green Pigments

Pigment content of ashes grown up under different circumstances - The pigment content (chlorophyll a, chlorophyll b,  xanthophyll and carotene) has been researched with ashes grown up under  different light circumstances and varying in age and height.     The results prove that the general laws concerning the influence of light  on the pigment content, don’t always work.     The phenomen is very complex. The light quantity is very important in some  cases, but insignificant in others. It seems origin and height of plants have  a strong influence. The results prove also the influence of the environment  is much higher on small plants as on big ones.     The research indicates finally the correlation between the green pigments,  the yellow pigments, and between the green pigments on the one side and the  yellow ones on the other side.

scholarly journals The Variation on Sediment Reworking with Influencing Factors by a Sand Bubbler Crab, Scopimera globosa, in Intertidal Sediments of the Anmyeon Island, Korea

2021 ◽  
Vol 13 (10) ◽  
pp. 5703
Author(s):  
Jaehwan Seo ◽  
Bon Joo Koo
Keyword(s):  
Chlorophyll A ◽  
Influencing Factors ◽  
Surface Sediment ◽  
Intertidal Sediments ◽  
Daily Production ◽  
Chlorophyll A Concentration ◽  
Sediment Reworking ◽  
Pellet Production ◽  
Crab Density ◽  
Annual Sediment

Though biological and ecological characteristics of Scopimera globosa have been intensively investigated, little has been understood on bioturbation, especially sediment reworking. This study was designed to evaluate variation on sediment reworking of S. globosa based on feeding pellet production (FP) and burrowing pellet production (BP) with influencing factors and estimating the chlorophyll content reduction within the surface sediment by its feeding. The FP and BP largely fluctuated according to chlorophyll a concentration and crab density, but both were not influenced by temperature. The FP was enhanced by chlorophyll a concentration, whereas both FP and BP were restricted by crab density. The daily individual production was highest in spring, followed by fall and summer, with values of 25.61, 20.70 and 3.90 g ind.−1 d−1, respectively, while the total daily production was highest in fall, followed by summer and spring 2150, 1660 and 660 g m−2 d−1, respectively. The daily sediment reworking based on the FP and BP of Scopimera was highest in fall, followed by summer and spring, with values of 1.91, 1.70 and 0.77 mm d-1 and the annual sediment reworking rate of this species was calculated 40 cm year−1 based on its density in this study area. The chlorophyll a reduction ratio was estimated from 11 to 24% in one day by its feeding. These results imply that the sediment reworking of S. globosa is regulated by food abundance and its density, and Scopimera is an important bioturbator, greatly influencing biogeochemical changes in the intertidal sediments.

scholarly journals Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

2021 ◽  
Vol 18 (9) ◽  
pp. 4419
Author(s):  
Yuequn Lai ◽  
Jing Zhang ◽  
Yongyu Song ◽  
Zhaoning Gong
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Supply Function ◽  
Estimation Model ◽  
Nitrogen And Phosphorus ◽  
Chlorophyll A Concentration ◽  
Guanting Reservoir

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

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