Within-day and between-day chlorophyll a dynamics during summer in Guanting Reservoir, Beijing, China

2020 ◽  
Author(s):  
Jiang Weiwei ◽  
Yu Jingshan
Keyword(s):  
Water Temperature ◽  
Chlorophyll A ◽  
Meteorological Factors ◽  
Time Lag ◽  
Principal Component ◽  
Chl A ◽  
Guanting Reservoir ◽  
Vertical Scale ◽  
Concentration Layer ◽  
Beijing China

<p>Recognizing intra- and inter-daily dynamics of Chlorophyll a (Chl-a) and its related environmental variables in consecutive days play an important role in assessing and managing water quality and eutrophication. In this study, the water temperature, nutrients, Chl-a concentration  and meteorological factors were collected at six sampling times in Guanting reservoir during summer. Chl-a concentration generally decreased from last May to primary September. At both test times, thermal stratification and mixing in the water column controlled the variation of maximum Chl-a concentration layer at both temporal and vertical scale. The position of the maximum Chl-a concentration layer between days generally followed the same dynamics as thermocline. Daily stratifications were temporary and maximum Chl-a concentration layer varies downwelling by wind driven; hence, the vertical distribution of Chl-a concentration was homogenized at night. Surface Chl-a concentration decreased during the day and increased at night, except on rainy days. The results of Person correlations and principal component analysis indicated that raw surface and daily average Chl-a concentration generally changed as a negative function of solar radiation, wind speed, water temperature and air temperature. However, when a five hour time lag is considered, the relationship between surface Chl-a concentration, water temperature and all meteorological factors became significantly positive.</p>

scholarly journals THE FLUCTUATION OF CHLOROPHYLL-A CONCENTRATION DERIVED FROM SATELLITE IMAGERY AND CATCH OF OILY SARDINE (SARDINELLA LEMURU0)IN BALI STRAIT

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
J. LUMBAN GAOL ◽  
WUDIANTO ◽  
B. P. PASARIBU ◽  
D. MANURUNG ◽  
R. ENDRIANI
Keyword(s):  
Chlorophyll A ◽  
Satellite Imagery ◽  
Global Climate ◽  
Time Lag ◽  
Wide Field ◽  
Positive Anomaly ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Acoustic Data ◽  
The Relationship

The investigation is aimed to know the relationship between chlorophyll-a (chl-a) concentration and the abundance of Oily sardine (Sardinella lemuru), in Bali Strait. A time series of monthly mean chl-a data derived from Ocean Color Thermal Scanner (OCTS) sensor and Sea-viewing Wide Field-of View Sensor (SeaWiFS) during 1997-1999 are used in this study. Monthly Sardinella lemuru catch during 1997-1999 are obtained from fish landing data. The abundance of Sardinella lemuru is determined from acoustic data conducted in Bali Strait in September 1998 and May 1999. The result shows that the fluctuation of chlorophyll-a concentration in Bali Strait is influenced by monsoon and global climate change phenomena such as Dipole Mode (DM) event. During southeast Monsoon the upwelling process occurred around Bali Strait, so that the chl-a concentration is increased and during DM event occurred positive anomaly of chl-a concentration. The catch of Sardinella lemuru in Bali Strait is fluctuated during 1997-1999. The correlation between chl-a concentration and lemuru catch is positive and significant with certain time lag. Key words: Chlorophyll-a, Sardinella lemuru, Bali Strait, Satellite imagery

Prediction of chlorophyll-a in the Daning River of Three Gorges Reservoir by principal component scores in multiple linear regression models

2013 ◽  
Vol 67 (5) ◽  
pp. 1150-1158 ◽  
Author(s):  
Wang Liping ◽  
Zheng Binghui
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Three Gorges Reservoir ◽  
Prediction Models ◽  
Principal Component ◽  
Three Gorges ◽  
Chl A ◽  
Hydrodynamic Factors ◽  
Component Scores

After the impoundment of the Three Gorges Reservoir (TGR) since 2003, eutrophication has occurred and has become severe in Daning River. To predict chlorophyll-a (Chl-a) levels, the relationships between Chl-a and 11/13 routine monitoring data on water quality and hydrodynamics in Daning River were studied by principal component scores in the multiple linear regression model (principal component regression (PCR) model). In order to determine the hydrodynamic effect on simulated accuracy, two 0-day ahead prediction models were established: model A without hydrodynamic factors as variables, and model B with hydrodynamic factors (surface water velocity and water residence time) as variables. Based on the results of correlation analysis, score 1 and 2 with significant loads of phosphorus and nitrogen nutrients were omitted in developing model A (R2 = 0.355); while score 2 with significant loads of nitrogen was omitted in developing model B (R2 = 0.777). The results of validation using a new dataset showed that model B achieved a better fitted relationship between the predicted and observed values of Chl-a. It indicated hydrodynamics play an important role in limiting algal growth. The results suggested that a PCR model incorporating hydrodynamics processes has been suitable for the Chl-a concentration simulation and algal blooming prediction in Daning River of TGR.

Differences between Nearshore and Offshore Phytoplankton Communities in Lake Ontario

1987 ◽  
Vol 44 (12) ◽  
pp. 2155-2163 ◽  
Author(s):  
I. M. Gray
Keyword(s):  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Algal Biomass ◽  
Phytoplankton Community ◽  
Seasonal Pattern ◽  
Principal Component ◽  
Lake Ontario ◽  
Dominant Group ◽  
Phytoplankton Communities ◽  
Eutrophic Species

Differences between nearshore and offshore phytoplankton biomass and composition were evident in Lake Ontario in 1982. Phytoplankton biomass was characterized by multiple peaks which ranged over three orders of magnitude. Perhaps as a consequence of the three times higher current velocities at the northshore station, phytoplankton biomass ranged from 0.09 to 9.00 g∙m−3 compared with 0.10 to 2.40 g∙m−3 for the midlake station. Bacillariophyceae was the dominant group at the northshore station until September when Cyanophyta contributed most to the biomass (83%). Although Bacillariophyceae was the principal component of the spring phytoplankton community at the midlake station, phytoflagellates (49%) and Chlorophyceae (25%) were responsible for summer biomass, with the Chlorophyceae expanding to 80% in the fall. The seasonal pattern of epilimnetic chlorophyll a correlated with temperature. While chlorophyll a concentrations were similar to values from 1970 and 1972, algal biomass had declined and a number of eutrophic species (Melosira binderana, Stephanodiscus tenuis, S. hantzschii var. pusilla, and S. alpinus) previously found were absent in 1982.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

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.

scholarly journals Response of Total Suspended Sediment and Chlorophyll-a Concentration to Late Autumn Typhoon Events in the Northwestern South China Sea

2021 ◽  
Vol 13 (15) ◽  
pp. 2863
Author(s):  
Junyi Li ◽  
Huiyuan Zheng ◽  
Lingling Xie ◽  
Quanan Zheng ◽  
Zheng Ling ◽  
...  
Keyword(s):  
South China Sea ◽  
Continental Shelf ◽  
Suspended Sediment ◽  
Chlorophyll A ◽  
South China ◽  
Total Suspended Sediment ◽  
Offshore Area ◽  
Chl A ◽  
Late Autumn ◽  
China Sea

Strong typhoon winds enhance turbulent mixing, which induces sediment to resuspend and to promote chlorophyll-a (Chl-a) blooms in the continental shelf areas. In this study, we find limited Chl-a responses to three late autumn typhoons (typhoon Nesat, Mujigae and Khanun) in the northwestern South China Sea (NWSCS) using satellite observations. In climatology, the Chl-a and total suspended sediment (TSS) concentrations are high all year round with higher value in autumn in the offshore area of the NWSCS. After the typhoon passage, the Chl-a concentration increases slightly (23%), while even TSS enhances by 280% on the wide continental shelf of the NWSCS. However, in the southern area, located approximately 100 km from the typhoon tracks, both TSS and Chl-a concentrations increase 160% and 150% after typhoon passage, respectively. In the deeper area, the increased TSS concentration is responsible for the considerable increase of the Chl-a. An empirical analysis is applied to the data, which reveals the TSS and Chl-a processes during typhoon events. The results of this study suggest a different mechanism for Chl-a concentration increase and thus contribute toward further evaluation of typhoon-induced biological responses.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

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