Short-Term Response of Chlorophyll a Concentration Due to Intense Wind and Freshwater Peak Episodes in Estuaries: The Case of Fangar Bay (Ebro Delta)

Estuaries and coastal bays are areas of large spatio-temporal variability in physical and biological variables due to environmental factors such as local wind, light availability, freshwater inputs or tides. This study focuses on the effect of strong wind events and freshwater peaks on short-term chlorophyll a (Chl a) concentration distribution in the small-scale and microtidal, Fangar Bay (Ebro Delta, northwestern Mediterranean). The hydrodynamics of this bay are primarily driven by local wind episodes modulated by stratification in the water column. Results based on field-campaign observations and Sentinel-2 images revealed that intense wind episodes from both NW (offshore) and NE-E (onshore) caused an increase in the concentration of surface Chl a. The mechanisms responsible were horizontal mixing and the bottom resuspension (also linked to the breakage of the stratification) that presumably resuspended Chl a containing biomass (i.e., micropyhtobentos) and/or incorporated nutrients into the water column. On the other hand, sea-breeze was not capable of breaking up the stratification, so the chlorophyll a concentration did not change significantly during these episodes. It was concluded that the mixing produced by the strong winds favoured an accumulation of Chl a concentration, while the stratification that causes a positive estuarine circulation reduced this accumulation. However, the spatial-temporal variability of the Chl a concentration in small-scale estuaries and coastal bays is quite complex due to the many factors involved and deserve further intensive field campaigns and additional numerical modelling efforts.

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Influence of the hydrodynamics in spatio-temporal variability of clorophyll a in a small-scale and microtidal bay: Fangar Bay case (Ebro Delta)

10.5194/egusphere-egu21-7568 ◽

2021 ◽

Author(s):

Marta F-Pedrera Balsells ◽

Manel Grifoll ◽

Margarita Fernández-Tejedor ◽

Manuel Espino ◽

Agustín Sánchez-Arcilla

Keyword(s):

Water Column ◽

Temporal Variability ◽

Light Availability ◽

Ebro Delta ◽

Small Scale ◽

Strong Wind ◽

Chl A ◽

Coastal Bays ◽

Biological Variables ◽

Field Campaigns

<p>Estuaries and coastal bays are areas of large spatial-temporal variability in physical and biological variables due to environmental factors such as local wind, light availability, freshwater inputs or tides. The physical characteristics of an estuary affect its hydrodynamics. These in turn modify the behaviour of biological variables such as the concentration of chlorophyll a (Chl a). In a small-scale, micro tidal bay such as the Fangar Bay (Ebro Delta), hydrodynamics is influenced above all by local winds, as well as by fresh water contributions. The results of two field campaigns and Sentinel-2 images show how the concentration of Chl a is affected by strong wind episodes typical of this area (NW-E winds). With these episodes of strong wind (> 10 m-s-1) mixing occurs in the water column causing an increase in the concentration of Chl a. On the other hand, with sea breezes (< 6 m-s-1) the water column is stratified causing a decrease in the Chl a concentration. However, the spatial-temporal variability of Chl a in small-scale estuaries and coastal bays is quite complex due to the many factors involved and deserves more intensive field campaigns and additional numerical modelling efforts.</p>

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Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration

ICES Journal of Marine Science ◽

10.1093/icesjms/fsp107 ◽

2009 ◽

Vol 66 (7) ◽

pp. 1547-1556 ◽

Cited By ~ 70

Author(s):

V. Vantrepotte ◽

F. Mélin

Keyword(s):

Time Series ◽

Chlorophyll A ◽

Temporal Variability ◽

Global Scale ◽

Wide Field ◽

Seasonal Component ◽

Ocean Colour ◽

Chlorophyll A Concentration ◽

Chl A ◽

The Stability

Abstract Vantrepotte, V., and Mélin, F. 2009. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration. – ICES Journal of Marine Science, 66: 1547–1556. The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) global dataset now offers a 10-year time-series of a consistent, well-calibrated, ocean colour record suitable to analyse temporal variability. The relative importance of the seasonal term in the chlorophyll a (Chl a) concentration signal is first assessed using statistical techniques of temporal decomposition. The emphasis is on the Census method II (X-11) approach, which allows year-to-year variations in the seasonal component. The seasonality detected in the SeaWiFS Chl a record is analysed through a generic province-based classification of marine ecosystems and at global scale and is found very variable spatially. Working with 5′-resolution gridded Chl a products, the contribution of the seasonal component derived from X-11 amounts to 64% of the total variance, compared with only 36% if a fixed annual cycle is assumed. The capacity of X-11 to capture interannual variations in seasonality is used to diagnose the stability of the Chl a seasonal cycle. Finally, linear changes in Chl a concentration observed after a decade of continuous ocean colour record agree globally with previous observations on shorter series. Significant changes of both signs are detected in various regions of the world’s oceans, but primarily a general decrease of Chl a in the mid-ocean gyres.

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

Water ◽

10.3390/w13050664 ◽

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

Sensors ◽

10.3390/s18082699 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2699 ◽

Cited By ~ 2

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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Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

Remote Sensing ◽

10.3390/rs12162662 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2662 ◽

Cited By ~ 2

Author(s):

Zexi Mao ◽

Zhihua Mao ◽

Cédric Jamet ◽

Marc Linderman ◽

Yuntao Wang ◽

...

Keyword(s):

Time Series ◽

Chlorophyll A ◽

Seasonal Variations ◽

Global Ocean ◽

Boreal Winter ◽

Seasonal Cycles ◽

Chlorophyll A Concentration ◽

Chl A ◽

Global Oceans ◽

Sine Equation

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

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Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

Hydrology Research ◽

10.2166/nh.2017.116 ◽

2017 ◽

Vol 49 (5) ◽

pp. 1608-1617 ◽

Cited By ~ 4

Author(s):

Matias Bonansea ◽

Claudia Rodriguez ◽

Lucio Pinotti

Keyword(s):

Water Quality ◽

Chlorophyll A ◽

Landsat Imagery ◽

Quality Parameters ◽

Landsat 8 ◽

Chlorophyll A Concentration ◽

Chl A ◽

Sensing Applications ◽

Wide Range ◽

Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

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New data on the spatial and temporal variability of the chlorophyll a concentration in the White Sea

Doklady Earth Sciences ◽

10.1134/s1028334x13010170 ◽

2013 ◽

Vol 448 (1) ◽

pp. 120-125 ◽

Cited By ~ 15

Author(s):

M. D. Kravchishina ◽

V. I. Burenkov ◽

O. V. Kopelevich ◽

S. V. Sheberstov ◽

S. V. Vazyulya ◽

...

Keyword(s):

Chlorophyll A ◽

Temporal Variability ◽

White Sea ◽

Spatial And Temporal Variability ◽

The White Sea ◽

Chlorophyll A Concentration

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

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2004.v1.a1325 ◽

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

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Spatio-Temporal Variability of Chlorophyll-A and Environmental Variables in the Panama Bight

Remote Sensing ◽

10.3390/rs12132150 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2150

Author(s):

Andrea Corredor-Acosta ◽

Náyade Cortés-Chong ◽

Alberto Acosta ◽

Matias Pizarro-Koch ◽

Andrés Vargas ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Chlorophyll A ◽

Satellite Data ◽

Temporal Variability ◽

Phytoplankton Growth ◽

Sea Surface ◽

Chl A ◽

Physical Variables ◽

Spatio Temporal

The analysis of synoptic satellite data of total chlorophyll-a (Chl-a) and the environmental drivers that influence nutrient and light availability for phytoplankton growth allows us to understand the spatio-temporal variability of phytoplankton biomass. In the Panama Bight Tropical region (PB; 1–9°N, 79–84°W), the spatial distribution of Chl-a is mostly related to the seasonal wind patterns and the intensity of localized upwelling centers. However, the association between the Chl-a and different physical variables and nutrient availability is still not fully assessed. In this study, we evaluate the relationship between the Chl-a and multiple physical (wind, Ekman pumping, geostrophic circulation, mixed layer depth, sea level anomalies, river discharges, sea surface temperature, and photosynthetically available radiation) and chemical (nutrients) drivers in order to explain the spatio-temporal Chl-a variability in the PB. We used satellite data of Chl-a and physical variables, and a re-analysis of a biogeochemical product for nutrients (2002–2016). Our results show that at the regional scale, the Chl-a varies seasonally in response to the wind forcing and sea surface temperature. However, in the coastal areas (mainly Gulf of Panama and off central-southern Colombia), the maximum non-seasonal Chl-a values are found in association with the availability of nutrients by river discharges, localized upwelling centers and the geostrophic circulation field. From this study, we infer that the interplay among these physical-chemical drivers is crucial for supporting the phytoplankton growth and the high biodiversity of the PB region.

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