Variability Of Chl-a Research Articles

Physical forces driving chlorophyll-a variability in the South Java Sea Shelf: a spatio-temporal analysis

The wind and current are two physical forces that strongly influence the biogeochemistry in coastal waters. Both of these forces could enhance the Chlorophyll-a (Chl-a) concentration through the upwelling process. Here we examine the contribution of the wind and current to the Chl-a variability in the South Java upwelling system in terms of wind stress and bottom stress respectively using satellite-derived and reanalysis data from 2002 to 2017. Ten longitudinally cells were used for further analysis. A long-term Chl-a shows a strong longitudinal gradient of Chl-a with the highest value on the shelf. Seasonal and Inter-annual Chl-a analysis shows the evidence of the monsoonal winds and other forcing effects relevant to the previous studies. Wind stress (τwx ) has a strong seasonal variation which is upwelling- favorable during southeast monsoon coincide with higher Chl-a suggesting wind as the main forces during that time, while bottom stress (τbx ) has more complicated variations, but it’s seen that τwx mostly downwelling-favorable or eastward circulations. There were about 39.92- 52.94% of positive Chl anomalous events generated by the combination of upwelling- favourable τwx and downwelling-favorable τbx , higher than other combinations. In terms of Oceanographic drivers, the wind has a higher effect on enhancing Chl-a through a negative correlation. τwx leads the Chl-a anomalies by about 15 – 24 days with a correlation coefficient of more than 0.6.

Long-Term Trends and Impact of SARS-CoV-2 COVID-19 Lockdown on the Primary Productivity of the North Indian Ocean

COrona VIrus Disease (COVID) 2019 pandemic forced most countries to go into complete lockdown and India went on complete lockdown from 24th March 2020 to 8th June 2020. To understand the possible implications of lockdown, we analyze the long-term distribution of Net Primary Productivity (NPP) in the North Indian Ocean (NIO) and the factors that influence NPP directly and indirectly, for the period 2003–2019 and 2020 separately. There exists a seasonal cycle in the relationship between Aerosol Optical Depth (AOD), Chlorophyll-a (Chl-a) and NPP in agreement with the seasonal transport of aerosols and dust into these oceanic regions. In Arabian Sea (AS), the highest Chl-a (0.58 mg/m3), NPP (696.57 mg/C/m2/day) and AOD (0.39) are observed in June, July, August, and September (JJAS). Similarly, maximum Chl-a (0.48 mg/m3) and NPP (486.39 mg/C/m2/day) are found in JJAS and AOD (0.27) in March, April, and May (MAM) in Bay of Bengal. The interannual variability of Chl-a and NPP with wind speed and Sea Surface Temperature (SST) is also examined, where the former has a positive and the latter has a negative feedback to NPP. The interannual variability of NPP reveals a decreasing trend in NPP, which is interlinked with the increasing trend in SST and AOD. The analysis of wind, SST, Chl-a, and AOD for the pre-lockdown, lockdown, and post lockdown periods of 2020 is employed to understand the impact of COVID-19 lockdown on NPP. The assessment shows the reduction in AOD, decreased wind speeds, increased SST and reduced NPP during the lockdown period as compared to the pre-lockdown, post-lockdown and climatology. This analysis is expected to help to understand the impact of aerosols on the ocean biogeochemistry, nutrient cycles in the ocean biogeochemical models, and to study the effects of climate change on ocean ecosystems.

Interannual variability of chlorophyll-a and impact of extreme climatic events in the South Eastern Arabian Sea

South Eastern Arabian Sea (SEAS) is an upwelling dominated region, where the distribution of chlorophyll-a (Chl-a) is mainly controlled by basin-scale to mesoscale processes associated with seasonal reversal of winds and currents. The analysis of long-term satellite-derived Chl-a data revealed interannual variability in association with extreme climatic events. Chl-a data were obtained from Ocean Colour Climate Change Initiative (OC-CCI) which provides high resolution cloud free data for the Arabian Sea during summer monsoon season (June to September). Satellite measurements of Sea Level Anomaly (SLA), Sea Surface Temperature (SST) and sea surface wind data were also compiled from various sources. The monthly data for the above environmental variables during 1998 to 2016 were analysed as representatives of the possible causative physical processes influencing the variability in Chl-a. The time series and Hovmöller analyses shows that the maximum variability occurred during summer monsoon. This study shows that the interannual variability in Chl-a was prominently influenced by the underlying processes involved in coastal upwelling. The relative variability in Chl-a and associated environmental variables with these events was remarkable during strong El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. Cross Correlation Function (CCF) analysis points out that Chl-a content is negatively correlated with IOD to an extent of one month lag or less whereas, ENSO does not indicate such direct relations. Further, the variability associated with IOD was more conspicuous than ENSO. The study elucidates the interannual variability of Chl-a and the overall influence of associated extreme events in the SEAS.

Earth Observation Techniques to Assess Water Quality Monitoring in the Murray Darling Basin of Australia

The Murray Darling Basin Authority (MDBA) currently has been using a discrete field sampling technique for water quality monitoring that is expensive, time consuming and may not adequately represent the spatial variability of water quality relative to the entire water body. A pilot project was executed to assess the effectiveness of using earth observation data, supported by archived field-based observations for quantitative estimation of Water Quality Parameters (WQP) and detection of algal blooms in the River Murray. The selected pilot study area includes a 100km stretch of the River Murray between the Hume Dam and Yarrawonga Weir. The time frame for the archived field samples was between November 2008 and March 2011, when major algal blooms were occurring in this stretch of the Murray River.Analysis of the 2009 data shows that waters in sites in the Murray River downstream of the Hume Dam to the Yarrawonga Weir show more temporal than spatial variability in Chl-a and PC levels. The Chl-a concentration is relatively less in the Yarrawonga Weir than in the Murray River. The scatter plot of PC vs. Turbidity suggests that PC is a more significant parameter for the detection of Cyanobacteria than Chl-a. The field data represents the temporal bio-optical variability across the 2009 algal bloom events by successfully capturing the co-variations among Chlorophyll-a, Chycocyanin and turbidity at pre, during and post bloom conditions. The methodology has proved that the usefulness of an integrated earth observation and field based WQP technique to accurately map algal bloom events. The long term MDBA RMWQMP data for the 2009 bloom event is found partially compatible to the NOW Pilot study data in that only the data for the Heywood site that was used together for testing the WQP monitoring technique. The incompatibility of the RMWQMP data downstream of Yarrawonga Weir may be due to differing techniques used for determining Chlorophyll. The 2010 data was suitable for testing the technique for complex spatial bio-optical variability during the peak of the bloom in a large water storage. Lack of Chlorophyll measurements in 2010 data poses challenges in interpreting the relationship of bio-optical variability with the spatial distributions of bio-optical parameters. As relational parameters are absent, local information and expert advice will be required to develop plausible assumptions between the Chlorophyll - Phycocyanin relationship. The field sampled data for the 2010 bloom event acquired from the Hume Dam was used for comparative investigation of both moderate resolution sensors (MODIS and MERIS) and high resolution sensors (TM/TM+). The 2009 bloom event field samples of sites in the Yarrawonga Weir was used as an input with MODIS and MERIS and the data from all the sites was applied with TM/TM+. This paper will present an integrated earth observation and field based WQP technique to accurately map algal bloom events, and discuss challenges for real time earth observation data initiatives and future collaborative projects.

Biomass and physiological responses of green algae and diatoms to nutrient availability differ between the presence and absence of macrophytes

Nutrient enrichment commonly promotes phytoplankton biomass, while the presence of macrophytes can mitigate the promotion effects of nutrient loading. However, the existing studies have seldomly focused on the impacts of macrophytes on the phytoplankton’s physiological responses to nutrient availability. To fill this gap, a 30-day mesocosm experiment was conducted across a four-level nutrient gradient to investigate the variations of the phytoplankton biomass and physiological status (represented by Chl-a and Fv/Fm, respectively) in the absence and presence of macrophytes. Among the eight treatments, the performances of green algae and diatoms were compared as they accounted for >95% of the total phytoplankton biomass. Our results showed that although the presence of macrophytes did not inhibit Chl-a and Fv/Fm of green algae and diatoms, it significantly changed the effects of nutrients on Chl-a and Fv/Fm of green algae and diatoms. Structural equation model (SEM) indicated that the causal relationships between environmental variables and Chl-a/Fv/Fm of green algae and diatoms differed in the absence and presence of macrophytes. When macrophytes was absent, Chl-a and Fv/Fm of green algae and diatoms were significantly affected by electrical conductivity, TN, and Kd/salinity. These explanatory variables accounted for 52% and 84% of the variations in Chl-a of green algae and diatoms, and 10% and 21% of the variations in Fv/Fm of green algae and diatoms. When macrophytes was present, Chl-a and Fv/Fm of green algae and diatoms were significantly affected by electrical conductivity, TN, TP, and pH. These explanatory variables accounted for 39% and 77% of the variations in Chl-a of green algae and diatoms, and 43% and 36% of the variations in Fv/Fm of green algae and diatoms. Overall, nutrient availability and subsequently environmental changes caused more variations in phytoplankton Chl-a than variations in phytoplankton Fv/Fm, and more variations in diatoms than variations in green algae. Our results suggested that the response mechanism of phytoplankton to nutrient availability varied between the presence and absence of macrophytes, as well as between green algae and diatoms. Therefore, simultaneous evaluation of the biomass and physiological status of different phytoplankton groups can provide broader insight into the interaction mechanisms between macrophytes, nutrients and phytoplankton.

Local Drivers Associated to Temporal Spectral Response of Chlorophyll-a in Mangrove Leaves

The pigment content in leaves has commonly been used to characterize vegetation condition. However, few studies have assessed temporal changes of local biotic and abiotic factors on leaf pigments. Here, we evaluated the effect of local environmental variables and tree structural characteristics, in the chlorophyll-a leaf concentration (Chl-a) associated with temporal change in two mangrove species. Rhizophora mangle (R. mangle) and Avicennia germinans (A. germinans) trees of a fringe mangrove forest (FMF) and lower basin mangrove forest (BMF) were visited over a period of one year, to obtain radiometric readings at leaf level to estimate Chl-a. Measurements on tree characteristics included diameter at breast height (DBH), basal area (BA), and maximum height (H). Environmental variables included soil interstitial water temperature (Ti), salinity (Si), and dissolved oxygen (Oi), flood level (fL), ambient temperature (Tamb), and relative humidity (Hrel). Generalized linear models and covariance analysis showed that the variation of Chl-a is mainly influenced by the species, the interaction between species and mangrove forest type, DBH, seasonality and its influence on the species, soil conditions, and fL. Studies to assess spatial and temporal change on mangrove forests using the spectral characteristics of the trees should also consider the temporal variation of leave chlorophyll-a concentration.

Spatio-temporal variability of chlorophyll-a in response to coastal upwelling and mesoscale eddies in the South Eastern Arabian Sea

ABSTRACT The influence of mesoscale dynamics on variability of phytoplankton biomass in terms of chlorophyll-a (chl-a) concentration was studied in the coastal waters of the South Eastern Arabian Sea (SEAS) using long-term satellite data. Satellite-derived chl-a, sea level anomaly, sea surface temperature, and sea surface wind data for the period 1998–2016 were compiled from various sources and analysed to investigate the chl-a variability associated with coastal upwelling and mesoscale eddies. The Empirical Orthogonal Function and Morlet wavelet analyses were performed to estimate the quantitative variability and the result showed strong seasonal and interannual modulation in chl-a concentration and associated environmental variables. The Okubo–Weiss criterion was applied for the identification of mesoscale eddies. The results indicated the presence of cyclonic (cold core) eddies during the summer monsoon season (June–September). The wind-induced upwelling and the cyclonic eddies were most intense during the summer monsoon season, causing higher values of chl-a compared with other season. It is revealed that the variability of chl-a, which might be attributed to seasonal and interannual differences in the surface and sub-surface nutrients, is caused either by coastal upwelling or cyclonic eddies. In particular, the wind-induced upwelling strongly controls the spatial and temporal variability of chl-a compared with mesoscale eddies along the SEAS. The regression model we adopted points out the dominant role played by the wind and its forcing bring about variability in chl-a. The occurrence of extreme climatic events such as El Niño, La Niña and Indian Ocean Dipole (IOD) was noticed during the study period and particularly taken into account to understand the interannual fluctuations in chl-a and associated environmental variables. The relative variability in chl-a concentration was prominent during strong El Niño, La Niña, and IOD. We have attempted to determine the relationship between chl-a with coastal upwelling and mesoscale eddies, the overall importance of such physical forcings, and their influence on bio-production in the SEAS.

Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity

The Neuse River Estuary (North Carolina, USA) is a valuable ecosystem that has been affected by the expansion of agricultural and urban watershed activities over the last several decades. Eutrophication, as a consequence of enhanced anthropogenic nutrient loadings, has promoted high phytoplankton biomass, hypoxia, and fish kills. This study compares and contrasts three models to better understand how nutrient loading and other environmental factors control phytoplankton biomass, as chl-a, over time. The first model is purely statistical, while the second model mechanistically simulates both chl-a and nitrogen dynamics, and the third additionally simulates phosphorus. The models are calibrated to a multi-decadal dataset (1997–2018) within a Bayesian framework, which systematically incorporates prior information and accounts for uncertainties. All three models explain over one third of log-transformed chl-a variability, with the mechanistic models additionally explaining the majority of the variability in bioavailable nutrients (R2 > 0.5). By disentangling the influences of riverine nutrient concentrations, flows, and loadings on estuary productivity we find that concentration reductions, rather than total loading reductions, are the key to controlling estuary chl-a levels. The third model indicates that the estuary, even in its upstream portion, is rarely phosphorus limited, and will continue to be mostly nitrogen limited even under a 30% phosphorus reduction scenario. This model also predicts that a 10% change in nitrogen loading (flow held constant) will produce an approximate 4.3% change in estuary chl-a concentration, while the statistical model suggests a larger (10%) effect. Overall, by including a more detailed representation of environmental factors controlling algal growth, the mechanistic models generate chl-a forecasts with less uncertainty across a range of nutrient loading scenarios. Methodologically, this study advances the use of Bayesian methods for modeling the eutrophication dynamics of an estuarine system over a multi-decadal period.

The Research on the Variation of Chlorophyll-a in Bohai Sea Based on MODIS Data

Chlorophyll-a (Chl-a) is an important parameter to characterize the degree of eutrophication in water. It has become an important index for water quality evaluation and is also one of the important water quality parameters that can be directly retrieved by remote sensing method. In this study, MODIS L1B data and OC3M Chl-a inversion model are used to invert Chl-a in the Bohai Sea from 2014 to 2019, the spatial and temporal variations of Chl-a in the Bohai Sea are investigated, and the influencing factors are analyzed. Results demonstrate that (1) the variations of Chl-a in different bays of the Bohai Sea have different trends. The Bohai bay and Laizhou bay show an upward trend, while the Liaodong bay and the central Bohai Sea show a downward trend. The concentration of Chl-a is related to the distance from the coast. The closer to the coast, the higher the concentration of Chl-a. (2) From 2014 to 2019, the overall concentration of Chl-a in Bohai Sea show a downward trend, the seasonal variation trend is relatively small, and the variation of Chl-a in spring is larger than that in summer and autumn. (3) River runoff and sea surface temperature will affect the variation of Chl-a, so the conc.

Variability in the Sea Surface Temperature Gradient and Its Impacts on Chlorophyll-a Concentration in the Kuroshio Extension

Sixteen years of satellite observational data in the Northwestern Pacific Ocean are used to describe the variability in the sea surface temperature (SST) gradient and its impact on chlorophyll-a concentrations (Chl-a). Spatially, a meridional dependence is identified in which the SST gradient increases to the north in association with elevated Chl-a. Temporally, the seasonal variability shows a large SST gradient and high Chl-a in winter and spring, while the SST gradient and Chl-a are much lower in summer. The seasonal variability in Chl-a leads the variability in the SST gradient by one month. A significant correlation between the SST gradient and Chl-a in the anomalous field is obtained only in the western section of the Kuroshio extension (KE) and the highest correlation is identified without any lags. An index for the section is defined as the proportion of the number of times that the SST gradient magnitude is anomalously large in each year, and the index is highly related to the stability of the KE and has a prominent influence on Chl-a in the region. An anomalously large positive (negative) SST gradient magnitude occurs when the KE is unstable (stable) and the corresponding Chl-a is high (low).

Spatio-temporal distribution of chlorophyll-a concentration, sea surface temperature and wind speed using aqua-modis satellite imagery over the Savu Sea, Indonesia

The climatological means and annual variation of chlorophyll-a (Chl-a) concentration, sea surface temperature (SST) and sea surface wind (SSW) from 2002 to 2016 in the Savu Sea (SS) within Indonesia were analysed. Chl-a variability and SST information was used as a reference in determining potential fishing grounds. The research aimed to investigate the variability of Chl-a and SST along with the influencing factor of the Australian-Indonesian Monsoon (AIM) winds. The study was conducted in SS waters, in East Nusa Tenggara (NTT), Indonesia, using 15 years of monthly Aqua MODIS Level 3 data. The retrieved data were corrected geometrically and radiometrically and then analysed to produce mean climatology data. The results indicated that the spatial and temporal AIM winds had a significant influence on the Chl-a concentration and the SST in SS waters. There was a two-month delay in the peak concentration of Chl-a/SST; moreover, the AIM wind speed, which peaks in June, was not followed by either the SST minima or the Chl-a maxima, which occur in August. The level of Chl-a in the SS waters showed seasonal patterns, with the highest concentration occurring during the east season (June–August), the peak concentration being 0.52 mg m−3, alongside a wind speed of 5.42 m s−1 and an SST of 22.89 °C in August. Meanwhile, the lowest concentration of Chl-a 0.17 mg m−3 occurred in February during the west season (December–February), with a wind speed of 2.10 m s−1 and an SST of 29.57 °C. Three specific areas showed characteristics that differed from the general pattern of the monsoon winds’ effects on Chl-a and SST in the SS. This study provides the latest description of Chl-a, SST and wind speed distribution at seasonal and spatial scales and their variability across the Savu Sea. The results of this study are essential for fishing catching activities and fisheries management in the region.

Reconstruction of Gap-Free OCM-2 Chlorophyll-a Concentration Using DINEOF

Primary productivity plays a pivotal role in the global carbon cycle and the marine food chain. Chlorophyll-a concentration (chl-a) is considered as a proxy for the biomass, which requires reliable estimation. Remote sensing of ocean colour provides a valuable source of chl-a over the global oceans. However, the ocean colour measurements are often hindered by the presence of clouds, thereby denying information on various spatial scales. Data Interpolating Empirical Orthogonal Function (DINEOF) is a robust technique to reconstruct the data in gaps. The present study demonstrates the application of DINEOF-based reconstruction of the chl-a concentration data derived from the Ocean Colour Monitor-2 (OCM-2) onboard Oceansat-2 satellite for the period 2016–2019 over the northern Indian Ocean. The reconstructed and raw chl-a (Level-2) from OCM-2 are compared and found to be comparing well with a correlation of 0.93 and 0.9 in the Arabian Sea and Bay of Bengal, respectively. The seasonal and inter-annual variability of chl-a over the study region is examined to showcase the application of reconstructed data for spatio-temporal analysis on different scales.

Analysis of the Monthly and Spring-Neap Tidal Variability of Satellite Chlorophyll-a and Total Suspended Matter in a Turbid Coastal Ocean Using the DINEOF Method

Missing spatial data is one of the major concerns associated with the application of satellite data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has been proven to be an effective tool for filling spatial gaps in various satellite data products. The Ariake Sea, which is a turbid coastal sea, shows the large spatial and temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM). However, ocean color satellite data for this region usually have large gaps, which affects the accurate analysis of Chl-a and TSM variability. In this study, we applied the DINEOF method to fill the missing pixels from the regionally tuned Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua (hereafter, MODIS) Chl-a and MODIS-derived TSM datasets for the period 2002–2017. The validation results showed that the DINEOF reconstructed data were accurate and reliable. Furthermore, the Empirical Orthogonal Functions (EOF) analysis based on the reconstructed data was used to quantitatively analyze the spatial and temporal variability of Chl-a and TSM at both monthly and individual events of spring-neap tidal scales. The first three EOF modes of Chl-a showed seasonal variability mainly caused by precipitation, the sea surface temperature (SST), and river discharge for the first EOF mode and the sea level amplitude for the second. The first three EOF modes of TSM exhibited both seasonal and spring-neap tidal variability. The first and second EOF modes of TSM displayed spring-neap tidal variability caused by the sea level amplitude. The second EOF mode of TSM also showed seasonal variability caused by the sea level amplitude. In this study, we first applied the DINEOF method to reconstruct the satellite data and to capture the major spatial and temporal variability of Chl-a and TSM for the Ariake Sea. Our results demonstrate that the DINEOF method can reconstruct patchy oceanic color datasets and improve spatio-temporal variability analysis.

Strengthened ocean-desert process in the North Pacific over the past two decades

North Pacific ocean desert (NPOD) refers to the subtropical North Pacific Ocean of low chlorophyll-a (Chl-a) concentrations, as the largest ocean desert globally. Studies have suggested a development of NPOD over recent decades based on limited evidences from in-field measurements and yet elusive mechanism. In this study, we characterize intensity, area and position of the NPOD from year 1998 to 2018, and investigate its control by the coherent climate processes, based on an available, longest satellite observations of Chl-a concentration. Our results suggested that NPOD oligotrophication and expansion processes were correlated with warming upper oceans in most part of the NPOD, except for the SW NPOD area where the Chl-a variations were linked with regional change in sea surface heights. Moreover, based on our analysis, insignificant shift but only NW-SE variability of the NPOD mean position was likely controlled by the Pacific decadal oscillation processes.

Two-Decade Variability of Sea Surface Temperature and Chlorophyll-a in the Northern South China Sea as Revealed by Reconstructed Cloud-Free Satellite Data

Monthly sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration during 1997–2018 in the Northern South China Sea (NSCS) were gap-filled using the Data INterpolating Empirical Orthogonal Functions (DINEOFs) approach. Good accuracy of the reconstructed data was verified based on an independent cross-validation data set. Significant spatial heterogeneities and seasonal variations were observed in the cloud-free SST and Chl-a data. An apparent decreasing trend in SST was found along the coast of Guangdong, east of Hainan Island, and in the Taiwan Strait with an average decreasing rate amounting to 0.01 °C per year. Increase in SST was observed in Beibu Gulf and the deep offshore region in the NSCS at an average rate as high as 0.009 °C per year. An increasing trend in Chl-a was detected in the northern Beibu Gulf, near the mouth of the Pearl River estuary, and around Shantou with an average rate of 0.005 mg <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot \text{m}^{-3}$ </tex-math></inline-formula> per year. EOF analysis of SST and Chl-a time series showed that the first three modes of both SST and Chl-a can explain most of their variances with accumulative contributions of 99.1% and 82.3%, respectively. The effects of surface net heat flux (NHF), Oceanic Niño Index (ONI), wind speed, and river flux on the first three modes of SST and Chl-a were quantitatively elucidated. Factors that affected the temporal and spatial variations of Chl-a and SST in the NSCS were qualitatively discussed, including El Niño-Southern Oscillation (ENSO), monsoon, upwelling, typhoon, internal waves, and Kuroshio intrusion.

Chlorophyll-a concentrations in 82 large alpine lakes on the Tibetan Plateau during 2003–2017: temporal–spatial variations and influencing factors

ABSTRACT As essential parts of the unique ecosystem of Tibetan Plateau (TP), the sizes and associated physical properties of alpine lakes have long been investigated. However, little is known about one of the most critical biogeochemical properties, i.e. the Chlorophyll-a (Chl-a) concentrations. Here, for the first time, we presented a comprehensive investigation of the temporal–spatial variations in Chl-a in 82 lakes (>50 km2) across the entire TP region, based on MODIS observations in the period of 2003–2017. The results showed that the 82 lakes exhibited an average long-term mean Chl-a of 3.3 ± 4.3 mg m−3, with high Chl-a lakes concentrated in the eastern and southern inner TP basin and northeastern parts of the TP. An interannual trend analysis revealed that lakes exhibiting (significantly) decreasing Chl-a trends and (significantly) increasing Chl-a trends were comparable in numbers but differed in distribution patterns. A correlation analysis indicated that at least 70% of the interannual variability in Chl-a values of lakes was significantly correlated with one of the four environmental factors (wind speed, ice cover duration, lake water surface temperature and surface runoff) and lake size. In addition, glacier meltwater tended to reduce lake Chl-a while salinity levels showed minor influences.

Numerical Study of the Three Gorges Dam Influences on Chlorophyll-a in the Changjiang Estuary and the Adjacent East China Sea

The impoundment of the Three Gorges Dam (TGD) has altered downstream hydrological characteristics and sediment discharge, and it has caused ecological impacts, such as changes in chlorophyll-a (Chl-a) in estuaries and coastal oceans. To investigate the TGD’s influence on Chl-a’s interannual and seasonal variations in the Changjiang Estuary and the adjacent coastal East China Sea, a physical-biogeochemical model was developed with numerical experiments covering a decade, including TGD’s pre-period (pre-TGD, 1999–2003) and post-period (post-TGD, 2004–2008). The modeling results demonstrate an annual increase in the regional average Chl-a from pre- to post-TGD, with the largest increase reaching up to 20.8% in spring in the nearshore region beyond the Changjiang mouth. The spatial variations in Chl-a are high, with the largest variation being observed around the 20–40 m isobaths. The increase in Chl-a is influenced by improved light and nutrient conditions, which is attributed to dam construction and fertilization by human activities. A decline in nitrogen-phosphorus fertilizer usage ratio along the Changjiang River watershed after the TGD’s impoundment is another factor that influences the Chl-a’s variation. The modeling results also show severe NO3 and PO4 imbalances with a larger N/P ratio during the post-TGD period, especially in regions with large Chl-a increases. Moreover, the greater increase in the usage of phosphorus fertilizer than nitrogen fertilizer influences Chl-a’s variation and has potential promotion effects on the outbreak of harmful algal bloom events.

A data-driven approach to simulate the spatiotemporal variations of chlorophyll-a in Chesapeake Bay

Phytoplankton biomass, indicated by chlorophyll-a (Chl-a) concentration, is fundamentally important for aquatic ecosystems. However, accurately simulating Chl-a is always challenging even when using state-of-the-art numerical models. We propose a data-driven modeling framework that combines Empirical Orthogonal Function (EOF) analysis and machine-learning technique to tackle this problem, using Chesapeake Bay as an example. Through the dimension reduction using EOF, the three-dimensional (3D) problem can be decomposed into multiple one-dimensional (1D) problems. The non-linearity of these 1D problems will be modeled with machine learning using an artificial neural network. Model performance in terms of spatiotemporal Chl-a variations with both seasonal and interannual signals is evaluated. The model performance is comparable or higher than 3D numerical models previously applied in Chesapeake Bay. Sensitivity tests reveal the necessity of forcing transformations to improve the model predictive skill. Instead of manually applying a transformation for each input forcing variable, an auto-selection procedure is adopted to choose an appropriate transformation from a variety of transformation options. While it is unlikely the data-approach can replace the traditional numerical models, we argue that data-driven approaches provide a promising way for future studies in coastal and estuarine systems considering the fast accumulation of observational data.

Remote Sensing of Spatial-Temporal Variation of Chlorophyll-a in the Jiaozhou Bay Using 32 Years Landsat Data

Wu, M.; Zhao, Y.F.; Sun, L.E.; Huang, J.; Wang, X.H., and Ma, Y., 2020. Remote sensing of spatial-temporal variation of chlorophyll-a in the Jiaozhou Bay using 32 years Landsat data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T.W. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 271-279. Coconut Creek (Florida), ISSN 0749-0208.The chlorophyll-a concentration (Chla, µg/L) is a vital indicator of water quality and eutrophication, yet optical complexity and significant variability of coastal waters make the accurate estimation of Chla challenging. Monitoring spatial-temporal distribution and variation of Chla and comprehending the correlation between Chla and environmental factors are necessary for long-term water quality assessment. This study calibrated and validated the Chla estimation model with satisfactory performance (R2, RMSE, and MRE values are 0.77, 0.64 µg/L, and 32.5 %) and further characterized the spatial-temporal variation of Chla in Jiaozhou Bay (JZB) based on 381 cloud-free Landsat images of 32 years (1986-2017). The annual mean Chla in JZB reached the highest value in 1997 and decreased gradually in the following two decades. The seasonal variation of Chla is obvious: the highest value of Chla appeared in summer, followed by spring, autumn and winter. Accordingly, the monthly averaged Chla peaked in July, while the minimum occurred in January. The spatial distribution of Chla on different time scales shared a similar pattern. High Chla appeared in the northwestern part of JZB and decreased gradually to the southwest, resulting in the lowest Chla near the water channel connected to the open sea. The spatial heterogeneity mainly arose from river discharge, while the temporal heterogeneity may be caused by seasonal variations in precipitation, temperature, and river discharge. This study indicated that the empirical models for the Landsat data could effectively monitor the long-term Chla variation in JZB.

Caspian Sea is eutrophying: the alarming message of satellite data

The competition over extracting the energy resources of the Caspian Sea together with the major anthropogenic changes in the coastal zones have resulted in increased pollution and environmental degradation of the sea. We provide the first evaluation of the spatiotemporal variation of chlorophyll-a (Chl-a) across the Caspian Sea. Using remotely sensed data from 2003 to 2017, we found that the Caspian Sea has suffered from a growing increase in Chl-a, especially in warmer months. The shallow parts of the sea, near Russia and Kazakhstan, especially where the Volga and Terek rivers discharge large nutrient loads (nitrogen- and phosphorus-rich compounds) into the sea, have experienced the highest variations in Chl-a. The Carlson’s trophic state index showed that during the study period, on average, about 12%, 26%, and 62% of the Caspian Sea’s area was eutrophic, mesotrophic, and oligotrophic, respectively. The identified trends reflect an increasing rate of environmental degradation in the Caspian Sea, which has been the subject of conflict among its littoral states that since the collapse of the Soviet Union have remained unable to agree on a legal regime for governing the sea and its resources.