Variability Of Chl-a Research Articles

Seasonality of primary productivity affects coastal species more than its magnitude

While the importance of extreme conditions is recognised, patterns in species' abundances are often interpreted through average environmental conditions within their distributional range. For marine species with pelagic larvae, temperature and phytoplankton concentration are key variables. Along the south coast of South Africa, conspicuous spatial patterns in recruitment rates and the abundances of different mussel species exist, with focal areas characterized by large populations. We studied 15 years of sea surface temperature (SST) and chlorophyll-a (chl-a) satellite data, using spectral analyses to partition their temporal variability over ecologically relevant time periods, including seasonal (101 to 365 days) and intra-seasonal cycles (20 to 100 days). Adult cover and mussel recruitment were measured at 10 sites along the south coast and regression models showed that about 70% of the variability in recruitment and adult cover was explained by seasonal variability in chl-a, while mean annual chl-a and SST only explained 30% of the recruitment, with no significant effect for adult cover. SST and chl-a at two upwelling centres showed less predictable seasonal cycles during the second half of the study period with a significant cooling trend during austral autumn, coinciding with one of the mussel reproductive peaks. This likely reflects recent changes in the Agulhas Current, the world's largest western boundary current, which affects coastal ecosystems by driving upwelling. Similar mechanisms probably operate in other marine systems with the potential to affect the distribution patterns of key ecosystem engineers. We propose that variability in the characteristic timescales of environmental fluctuations can explain the spatial patterns of abundance of foundational species by affecting larval recruitment. This is especially important in a context of global and pervasive climate change, as shifts in the periodicity of environmental fluctuations appear to reflect large scale climatic teleconnections driven by anthropogenic forcing.

Relevant factors in the eutrophication of the Uruguay River and the Río Negro

In recent decades, there has been increasing eutrophication of rivers and lagoons in Uruguay and solutions leading to water purification are being sought. The growing pollution has been attributed to nitrogen and phosphorus compounds exported from the river basins with intensification of agricultural production and the absence of tertiary treatment for urban and industrial effluents. Although nitrogen and phosphorus are relevant to eutrophication, there are also other factors that can promote eutrophication and algal blooms. This paper reports a broad analysis of water quality variables recorded over 9 years (2009–2018) at 17 sampling stations on the Uruguay River and 16 sampling stations on the Río Negro, and explores their relationship with the changes of chlorophyll a (Chl-a) concentrations using a generalized linear model and a neural network simulation (NNS). The input variables were total phosphorus; total suspended solids; electrical conductivity of water (ECw); alkalinity; water temperature (T); water pH (pH) and sampling month. The NNS explained 79% of Chl-a variations and showed the most relevant variables to be T, ECw, and pH. Moreover, the NNS showed that replacement of current land uses by natural prairie would not significantly reduce Chl-a concentrations. The results showed that the main factors that drive Chl-a concentrations (i.e., algae) are not directly linked to agriculture land use.

An Improved Model for Chlorophyll-a Concentration Retrieval in Coastal Waters Based on UAV-Borne Hyperspectral Imagery: A Case Study in Qingdao, China

Chlorophyll-a (Chl-a) is an objective biological indicator, which reflects the nutritional status of coastal waters. However, the turbid coastal waters pose challenges to the application of existing Chl-a remote sensing models of case II waters. Based on the bio-optical models, we analyzed the suppression of coastal total suspended matter (TSM) on the Chl-a optical characteristics and developed an improved model using the imagery from a hyper-spectrometer mounted on an unmanned aerial vehicle (UAV). The new model was applied to estimate the spatiotemporal distribution of Chl-a concentration in coastal waters of Qingdao on 17 December 2018, 22 March 2019, and 20 July 2019. Compared with the previous models, the correlation coefficients (R2) of Chl-a concentrations retrieved by the new model and in situ measurements were greatly improved, proving that the new model shows a better performance in retrieving coastal Chl-a concentration. On this basis, the spatiotemporal variations of Chl-a in Qingdao coastal waters were analyzed, showing that the spatial variation is mainly related to the TSM concentration, wind waves, and aquaculture, and the temporal variation is mainly influenced by the sea surface temperature (SST), sea surface salinity (SSS), and human activities.

Physical processes controlling chlorophyll-a variability on the Mid-Atlantic Bight along northeast United States

We employed empirical orthogonal function (EOF) analysis to examine the spatial and temporal pattern changes in the surface chlorophyll a distribution (chl-a) on the Mid-Atlantic Bight (MAB) using Moderate Resolution Imaging Spectroradiometer Aqua (MODISA) chl-a data (2003–2016) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chl-a data (1998–2007), and interpreted the underlying environmental determinants. A coupled physical-biogeochemical model was used to explore the primary physical factors determining the chl-a variability on the shelf. Model sensitivity studies identified wind mixing, net heat flux, and river discharge as the dominant factors influencing the MAB water column stability and consequent phytoplankton growth. The primary feature of chl-a indicated spring peaks on the outer shelf during the MODISA period, while fall-winter high during the SeaWiFS period in the same area. The observed increase in wind mixing and heat loss during winter and pre-spring were responsible for the delay in the phytoplankton bloom to spring on the outer shelf. The secondary chl-a peak occurred in the fall on the New Jersey shelf during MODISA period, and in the fall-winter in the Delaware Bay estuary for chl-a during SeaWiFS period. The Hudson River discharge was associated with the chl-a anomalies on the New Jersey shelf in the fall and winter during the MODISA period. Both the MODISA and SeaWiFS chl-a concentrations peaked during the fall-winter on the southern part of the MAB (in the EOF mode 3 region), but the MODISA chl-a peak area was north of the SeaWiFS chl-a peak area. The variation of chl-a concentration in the southern region of the MAB was most likely associated with the Chesapeake Bay rivers' discharge. In our study, the regional associations between chl-a and multiple climate-sensitive environmental parameters suggest that basin-scale forcing plays an important role in the underlying chl-a variabilities on the MAB. • The MAB outer shelf experienced more spring chl-a peaks during the MODISA period. • There are high chl-a concentrations in fall-winter during the SeaWiFS period in the outer shelf. • Wind, net heat flux, and river discharge influenced the potential energy anomaly on the MAB. • The outer shelf chl-a patterns were associated with wind mixing and net heat flux. • The inner shelf chl-a patterns were associated with river discharges.

STUDY ON CYCLONE INDUCED PHYTOPLANKTON BLOOM IN THE ARABIAN SEA THROUGH GAP-FREE RECONSTRUCTED CHLOROPHYLL-A DATA

Abstract. Ocean surface phytoplankton responses to the tropical cyclone (TC)/storms have been extensively studied using satellite observations by aggregating the data into a weekly or bi-weekly composite. The reason behind is the significant limitations found in the satellite-based observation is the missing of valid data due to cloud cover, especially at the time of cyclone track passage. The data loss during the cyclone is found to be a significant barrier to efficiently investigate the response of chl-a and SST during cyclone track passage. Therefore it is necessary to rectify the above limitation to effectively study the impact of TC on the chlorophyll-a concentration (chl-a) and the sea surface temperature (SST) to achieve a complete understanding of their response to the TC prevailed in the Arabian Sea. Intending to resolve the limitation mentioned above, this study aims to reconstruct the MODIS-Aqua chl-a, and SST data using Data Interpolating Empirical Orthogonal Function (DINEOF) for all the 31 cyclonic events occurred in the Arabian Sea during 2003-2018 (16 years). Reconstructed satellite retrieved data covering all the cyclonic events were further used to investigate the chl-a and SST dynamics during TC. From the results, the exciting fact has been identified that only two TC over the eastern-AS were able to induce phytoplankton bloom. On investigating this scenario using sea surface temperature, it was disclosed that the availability of nutrients decides the suitable condition for the phytoplankton to proliferate in the surface ocean. Relevant to the precedent criterion, the results witnessed that the 2 TC (Phyan and Ockhi cyclone) prevailed in the eastern AS invoked a suitable condition for phytoplankton bloom. Other TC found to be less provocative either due to less intensity, origination region or the unsuitable condition. Thereby, gap-free reconstructed daily satellite-derived data efficiently investigates the response of bio-geophysical parameters during cyclonic events. Moreover, this study sensitised that though several TC strikes the AS, only two could impact phytoplankton productivity and SST found to highly consistent with the chl-a variability during the cyclone passage.

Monsoonal Influence and Variability of Water Quality, Phytoplankton Biomass in the Tropical Coastal Waters – A Multivariate Statistical Approach

Rushikulya estuary is rich in biodiversity facing significant changes in recent periods due to pollution/anthropogenic impacts from the industries and growing urbanization along the banks of the river. This estuary caters mass nesting of Olive Ridley sea turtles and one of the world's largest rookery in India. In view of the above the present study examined, the seasonal variability of water quality parameters (water temperature, pH, salinity, dissolved oxygen, total suspended matter, inorganic nutrients (NO2-N, NO3-N, NH4-N, PO4-P and SiO4-Si) and the phytopigment i.e. Chlorophyll-a (chl-a) from the seawater samples of three different seasons pre-monsoon, monsoon and post-monsoon.Time-series observations were made at five locations off Rushikulya estuary, Bay of Bengal from March 2011 to February 2013. A wide range of nutrient concentrations except for NO2-N varied from 0.89 - 3.62 µmol/l in the NO3-N, from 1.36 - 6.81 µmol/l in the NH4-N, from 0.66 - 3.45 µmol/l in the PO4- P and from 0.89 -7.97 µmol/l in the SiO4-Si. The highest chl-a (3.72 mg/m3) was recorded during pre-monsoon than monsoon and post monsoon. Factor analysis (FA), showed that three underlying factors, each during pre-monsoon, monsoon and post-monsoon influencing the water quality to the extent of 75.02 %, 67.33 % and 66.37 % respectively. The significant result from a statistical view of non-metric multidimensional scaling (nm-MDS) and cluster analysis (CA) revealed that the chl-a variability was due to the direct influence of nutrients than the physical parameters.Correlation analysis revealed that chl-a was positive correlation with DO, NO2, NO3, PO4 and SiO4, while negative with salinity in pre-monsoon and monsoon. The composite results indicated that the study area is well oxygenated, rich in nutrients and chl-a distribution represents typical upper ocean dynamics and food chain linked to the pristine coastal and ecologically rich ecosystem.

Effect of Antarctic sea ice on chlorophyll concentration in the Southern Ocean

Sea-ice extent is very sensitive to climate change and its minor variations can significantly affect the regional biota of the Southern Ocean (SO). Chlorophyll-a concentration (Chl-a) is a primary proxy for the understanding of phytoplankton distribution and primary productivity in the oceans. Therefore, analysing the relation between Chl-a and sea-ice extent could be significant in understanding the role of SO sea-ice extent on regional Chl-a variability. Local Chl-a variability in the SO was analysed in five major sectors, utilizing 39 years of remotely sensed sea ice and 15 years of Chl-a and Sea Surface Temperature (SST) datasets. Katabatic winds blowing offshore from the Antarctic coast were found to enhance the sea-ice extent during winter and diminish sea-ice melting in the austral summer in the SO. Thus, cold polar wind enhanced the sea-ice extent whereas warm subtropical winds reduced its formation. The Weddell Sea region showed the highest monthly averaged sea-ice extent of 6.2 × 106km2 during September (austral spring), whereas monthly averaged sea-ice extent was less than 2.9 × 106km2 for any other region. The largest sea-ice extent of 2 × 104km2 was observed in the Ross Sea during the austral summer (December, January, and February) owing to the katabatic winds. All other regions reported sea-ice extents below 1.15 × 104km2. Melting of sea ice during the austral summer was responsible for enhanced Chl-a in the SO through increased availability of nutrients in the near surface waters. Considering Chl-a as a proxy for productivity in the SO, the western sector (Weddell, Bellam and Ross Sea regions) was found to be more productive than the eastern sector (Indian and Pacific Ocean regions) during the study period. To understand the vertical variation of physical and biogeochemical variables and their influence on Chl-a variability, data from a few active Biogeochemical-Argo floats in the region were analysed. It was concluded that while the surface Chl-a was directly influenced by SST variations and sea-ice melting, the nutricline influenced the depth of Deep Chlorophyll Maximum (DCM) and Chl-a vertical distribution in the SO.

A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes

Landsat-8 Operational Land Imager (OLI) provides an opportunity to map chlorophyll-a (Chla) in lake waters at spatial scales not feasible with ocean color missions. Although state-of-the-art algorithms to estimate Chla in lakes from satellite-borne sensors have improved, there are no robust and reliable algorithms to generate Chla time series from OLI imageries in turbid lakes due to the absence of a red-edge band and issues with atmospheric correction. Here, a machine learning approach termed the extreme gradient boosting tree (BST) was employed to develop an algorithm for Chla estimation from OLI in turbid lakes. This model was developed and validated by linking Rayleigh-corrected reflectance to near-synchronous in situ Chla data available from eight lakes in eastern China (N = 225) and three coastal and inland waters in SeaWiFS Bio-optical Archive and Storage System (N = 97). The BST model performed well on a subset of data (N = 102, R2 = 0.79, root mean squared difference = 7.1 μg L−1, mean absolute percentage error = 24%, mean absolute error = 1.4, Bias = 0.9), and had better Chla retrievals than several band-ratio algorithms and a random forest approach. The performance of BST model was judged as appropriate only for the range of conditions in the training data. Given these limitations, spatial and temporal variations of Chla in hundreds of lakes larger than 1 km2 in eastern China for the period of 2013–2018 were mapped using the BST model. OLI-derived Chla indicated that small lakes (<50 km2) had greater Chla than the larger lakes. This research suggests that machine-learning models provide practical approaches to estimate Chla in turbid lakes via broadband instruments like OLI and that extending to other regions requires training with a representative dataset.

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

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.

A case study of Chlorophyll a response to tropical cyclone Wind Pump considering Kuroshio invasion and air-sea heat exchange

New evidences provided that the tropical cyclone (TC) Linfa in 2015 induced looping path of Kuroshio invasion into the northeastern South China Sea (NESCS) through the northwestern Luzon Strait (LS), based on the in-situ measurements, satellite data and model output data. This TC-enhanced Kuroshio invasion with low nutrients and denser waters suppressed the TC “Wind Pump” induced upwelling and nutrients uptake, and therefore inhibited the Chlorophyll a concentration (Chl-a) increase from surface to ~50 m in the open ocean of the NESCS. The TC-induced Kuroshio invasion promoted the generation of the strong cyclonic eddy to its left side where weak Ekman Pumping Velocity was observed. This enhancing cyclonic eddy then dominated the nutrients uplift and increased the surface and subsurface (0–50 m) Chl-a through eddy pumping rather than Ekman Pumping. The TC-declined anti-cyclonic eddy, which shoaled the Mixed Layer Depth (MLD), benefited to the nutrient uptake through TC-induced upwelling and thereby increased the surface Chl-a and raised the Chl-a Maximum Layer (CML) to ~20 m over the southwestern LS. The temporal Chl-a variations were also influenced by TC intensities and biochemical processes. The air-sea heat budget analysis indicated that, the air-sea heat exchange contributed to nearly 80% of the sea surface cooling (SST cooling) over the northwestern LS with Kuroshio invasion, while eddy-induced upwelling dominated the SST cooling over the western LS, and the wind-driven upwelling (and mixing) controlled the SST cooling over the southwestern LS. These different formations of SST cooling then played important role in Chl-a variations. This study is the first case of TC “Wind Pump” induced Chl-a variations considering air-sea heat exchange, Kuroshio invasion and mesoscale eddies over LS, which would help to evaluate the influence of TCs over the other major heat transport arteries of the world ocean: The Gulf Stream area.

Effects of Spring–Neap Tidal Cycle on Spatial and Temporal Variability of Satellite Chlorophyll-A in a Macrotidal Embayment, Ariake Sea, Japan

We investigated the spatio-temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) associated with spring–neap tidal cycles in the Ariake Sea, Japan. Our study relied on significantly improved, regionally-tuned datasets derived from the ocean color sensor Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua over a 16-year period (2002–2017). The results revealed that spring–neap tidal variations in Chl-a and TSM within this macrotidal embayment (the Ariake Sea) are clearly different regionally and seasonally. Generally, the spring–neap tidal variability of Chl-a in the inner part of the Ariake Sea was controlled by TSM for seasons other than summer, whereas it was controlled by river discharge for summer. On the other hand, the contribution of TSM to the variability of Chl-a was not large for two areas in the middle of Ariake Sea where TSM was not abundant. This study demonstrates that ocean color satellite observations of Chl-a and TSM in the macrotidal embayment offer strong advantages for understanding the variations during the spring–neap tidal cycle.

Validation of a Primary Production Algorithm of Vertically Generalized Production Model Derived from Multi-Satellite Data around the Waters of Taiwan

Basin-scale sampling for high frequency oceanic primary production (PP) is available from satellites and must achieve a strong match-up with in situ observations. This study evaluated a regionally high-resolution satellite-derived PP using a vertically generalized production model (VGPM) with in situ PP. The aim was to compare the root mean square difference (RMSD) and relative percent bias (Bias) in different water masses around Taiwan. Determined using light–dark bottle methods, the spatial distribution of VGPM derived from different Chl-a data of MODIS Aqua (PPA), MODIS Terra (PPT), and averaged MODIS Aqua and Terra (PPA&amp;T) exhibited similar seasonal patterns with in situ PP. The three types of satellite-derived PPs were linearly correlated with in situ PPs, the coefficients of which were higher throughout the year in PPA&amp;T (r2 = 0.61) than in PPA (r2 = 0.42) and PPT (r2 = 0.38), respectively. The seasonal RMSR and bias for the satellite-derived PPs were in the range of 0.03 to 0.09 and −0.14 to −0.39, respectively, which suggests the PPA&amp;T produces slightly more accurate PP measurements than PPA and PPT. On the basis of environmental conditions, the subareas were further divided into China Coast water, Taiwan Strait water, Northeastern upwelling water, and Kuroshio water. The VPGM PP in the four subareas displayed similar features to Chl-a variations, with the highest PP in the China Coast water and lowest PP in the Kuroshio water. The RMSD was higher in the Kuroshio water with an almost negative bias. The PPA exhibited significant correlations with in situ PP in the subareas; however, the sampling locations were insufficient to yield significant results in the China Coast water.

Daily Variation of Chlorophyll-A Concentration Increased by Typhoon Activity

The chlorophyll-a (Chla) concentration product of the Himawari-8 geostationary meteorological satellite is used to show the temporal variation of Chla owing to the passage of typhoons, namely, tropical cyclones in the western North Pacific Ocean. The daily Chla variation shows that Chla usually increases along the paths of typhoons, whereas the same observations are almost impossible when using the data of polar-orbiting satellites as shown in previous studies. This is because the temporal resolution of Himawari-8 is ten times more than that of polar-orbiting satellites, and the daily Chla distribution contains a few disturbances attributed to clouds after compositing cloud-free data. Chla usually increases on the day of typhoon arrival, but mostly, the ratio of Chla increased by a typhoon to the background Chla, R Chla HIMA , is less than 2. Only a few typhoons considerably increased Chla. As a whole, R Chla HIMA is proportional to the maximum 10-min sustained wind speed up to 85 knots (44 m s−1), namely, 0.01 mg m−3 knot−1 (0.019 mg s m−4). However, there is no clear dependence between Chla and the wind speed in seas with higher Chla, such as the South China Sea. The result that typhoons are usually cultivating the ocean is important for studies of primary ocean productivity and carbon flux between the atmosphere and ocean.

Temporal Variation and Spatial Structure of the Kuroshio-Induced Submesoscale Island Vortices Observed from GCOM-C and Himawari-8 Data

Dynamics of ocean current-induced island wake has been an important issue in global oceanography. Green Island, a small island located off southeast of Taiwan on the Kuroshio path was selected as the study area to more understand the spatial structure and temporal variation of well-organized vortices formed by the interaction between the Kuroshio and the island. Sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration data derived from the Himawari-8 satellite and the second generation global imager (SGLI) of global change observation mission (GCOM-C) were used in this study. The spatial SST and Chl-a variations in designed observation lines and the cooling zone transitions on the left and right sides of the vortices were investigated using 250 m spatial resolution GCOM-C data. The Massachusetts Institute of Technology general circulation model (MITgcm) simulation confirmed that the positive and negative vortices were sequentially detached from each other in a few hours. In addition, totals of 101 vortexes from July 2015 to December 2019 were calculated from the 1-h temporal resolution Himawari-8 imagery. The average vortex propagation speed was 0.95 m/s. Totals of 38 cases of two continuous vortices suggested that the average vortex shedding period is 14.8 h with 1.15 m/s of the average incoming surface current speed of Green Island, and the results agreed to the ideal Strouhal-Reynolds number fitting curve relation. Combined with the satellite observation and numerical model simulation, this study demonstrates the structure of the wake area could change quickly, and the water may mix in different vorticity states for each observation station.

Relationships Between Chlorophyll‐a and Suspended Sediment Concentration in a High‐Nutrient Load Estuary: An Observational and Idealized Modeling Approach

AbstractThe Guadalquivir estuary is a high‐nutrient load environment, yet it has a reduced primary production because the high suspended sediment concentration (SSC) causes light attenuation. High‐resolution observations revealed relationships between the SSC and Chlorophyll‐a (Chla) concentration from seasonal to intratidal time scales. Local maxima of time‐averaged Chla levels occurred at locations with relatively low SSC. In the upper (lower) part of the estuary, larger Chla concentrations were observed during the wet (dry) season. In contrast to longer time scales, SSC and Chla exhibited in‐phase oscillations during the spring‐neap cycle. Both SSC and Chla were found to peak at maximum ebb and flood. There was no positive correlation at certain apogean neap tides, and instead, Chla exhibited daily variations. An idealized model was developed, which mimicked and helped to explain SSC‐Chla relationships at different time scales. The model accounts for the vertical advection of Chla and SSC, tidal resuspension, radiation‐mediated growth, and effects of the change in stratification stemming from tides and SSC. The specific growth rate of the Chla biomass is inhibited due to high SSC throughout the year. The in‐phase relationship at spring‐neap and tidal scales seems to be due to resuspension of algal microorganisms attached to sediments. Daily variability of Chla during apogean neap tides emerges because tidal shear stress is low and the strong decline in the suspended particulate matter reduces the light attenuation. The light‐mediated growth of part of the biomass, still remaining in the upper layers of the water column, is then driven by the day‐night cycle.

Multidecadal water quality deterioration in the largest freshwater lake in China (Poyang Lake): Implications on eutrophication management

Poyang Lake is the largest freshwater lake in China and a globally important wetland with various functions. Exploring the multidecadal trend of water quality and hydroclimatic conditions is important for understanding the adaption of the lake system under the pressure from multiple anthropogenic and meteorological stressors. The present study applied the Mann–Kendall trend analysis and Pettitt test to detect the trend and breakpoints of hydroclimatic, and water quality parameters (from the 1980s to 2018) and the trend of monthly–seasonal ammonia (NH4-N) and total phosphorus (TP)concentrations (from 2002 to 2018) in Poyang Lake. Results showed that Poyang Lake had undergone a highly significant warming trend from 1980 to 2018, with a warming rate of 0.44 °C/decade in terms of annual daily mean air temperature. The wind speed and water level of the lake presented a highly significant decreasing trend, whereas no notable trend was detected for precipitation variations. The annual mean total nitrogen (TN), NH4-N, TP, and permanganate index (CODMn) concentrations showed significant upward trends from the 1980s to 2018. Remarkable abrupt shifts were detected for TN, NH4-N, and CODMn in around 2003. They were in accordance with the water level breakpoint of the lake, thus implying the important role of hydrological conditions in water quality variations in floodplain lakes. A significant increasing trend has been detected for Chl-a variations during wet season from 2008 to 2018, which could be attributed to the increasing trend of nutrient concentration during the nutrient-limited phase of Poyang Lake. These hydroclimatic and water quality trends suggest a high risk of increasing phytoplankton growth in Poyang Lake. This study thus emphasizes the need for adaptive lake eutrophication management for floodplain lakes, particularly the consideration of the strong trade-off and synergies between hydroclimatic conditions and water quality variations.

Satellite-Observed Chlorophyll-a Concentration Variability and Its Relation to Physical Environmental Changes in the East Sea (Japan Sea) from 2003 to 2015

The spatio-temporal variability of the chlorophyll-a (chl-a) concentration in the East Sea (Japan Sea) (EJS) was investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) data over 13 years from 2003 to 2015 to understand the impact of physical environmental changes on low-trophic level marine ecosystems. The chl-a images were composited to produce gridded monthly chl-a images using a weighted average method after applying a speckle removal algorithm to each path image. Missing pixels without any satellite observations because of cloud coverage were objectively filled using three-dimensional optimal interpolation. The first empirical orthogonal function (EOF) mode of the monthly chl-a images, accounting for 14% of the total variance, showed higher eigenvectors values in the eastern part of the EJS and distinct annual peaks each spring. The amplitudes of the first EOF mode, despite the dominant year-to-year variations in chl-a, tended to increase during spring over time. The recent 13-year trend of chl-a showed differences between the southwestern part and the northeastern part of the EJS, and this difference clearly appeared in the monthly trend maps for March and April. The eastern coast of Korea showed a highly positive trend during summer, particularly during August. The changes in chl-a over the decade were related to the physical environmental changes in sea surface wind, mixed layer depth, and stratification of upper sea water in the EJS.

Modeling spatially anisotropic nonstationary processes in coastal environments based on a directional geographically neural network weighted regression

Quantifying the spatial association between ecological indicators (e.g., chlorophyll-a) and environmental parameters is crucial for explaining the ecological status in coastal ecosystems. Although global and local regression models have been widely used to estimate spatial relationships in marine environmental processes, spatial anisotropy caused by strong coastal-inland environmental gradients has not been investigated. This is very likely to result in incomprehensive characterization of the coastal ecological status. To better quantify the spatially anisotropic nonstationary relationship in coastal environments, a spatial proximity neural network (SPNN) was proposed in this paper to address the nonlinear effects of spatial anisotropy. A directional geographically neural network weighted regression (DGNNWR) model was accordingly developed by combining a geographically neural network weighted regression (GNNWR) with SPNN to incorporate anisotropic impacts into spatial nonstationarity. Modeling of chlorophyll-a in Zhejiang coastal areas of China in the spring over 2015–2017 was conducted to examine its performance. The results demonstrated that DGNNWR achieved a better fitting accuracy and a more adequate prediction ability than ordinary linear regression (OLR), geographically weighted regression (GWR), GNNWR, and anisotropic-based GWR models. Notably, compared to the best comparison model, the fitting error indicators were declined for more than 30% and the fitted R2 was considerably increased from 0.83 to 0.92 using our proposed DGNNWR. The spatial mapping of parameter estimates confirmed that DGNNWR successfully handled the anisotropic nonstationarity in coastal environments and quantified the main driven parameters of Chl-a. Based on the spatially refined relationship between Chl-a and environmental parameters, we further characterized the spatial and temporal distributions of Chl-a in Zhejiang coastal areas in the spring of 2015–2017, and then investigated the impacts of riverine discharges and ocean currents on the spatiotemporal variations of Chl-a. The findings are crucial to formulate appropriate mitigation strategies for eutrophication and are meaningful for the management of coastal ecosystems.

Towards the comprehensive water quality control in Lake Taihu: Correlating chlorphyll a and water quality parameters with generalized additive model

In this study, the generalized additive model (GAM) was used to analyze seasonal monitoring data from Lake Taihu, collected from 2010 to 2014, with the aim to explore the correlation between chlorophyll a (Chla) and other water quality parameters. The selected optimal multivariable GAM could effectively explain the concentration variation of Chla occurring during each season, and the interpretation degree followed the order: summer > autumn > spring > winter. The fitting results indicated that the concentration variation of Chla could reflect that of biochemical oxygen demand and chemical oxygen demand in all seasons. In addition, the total phosphorus showed strong ability to explain the concentration change of Chla in spring and summer, as the growth of algae would be affected when the concentration of phosphorus shifted high or low. Nitrogen showed strong ability to explain the variations in Chla concentration in autumn. The conclusions of the optimal multivariable GAM could provide decision basis for the eutrophication control. In other words, the prevention of eutrophication outbreaks could be carried out via the targeted control of key water pollutants. According to these results, the concentration of Chla was higher in northern and western lake during summer and autumn, the management should focus on nutrient input of adjacent rivers.

Variabilidade Temporal das Concentrações de Clorofila -A e da Temperatura Superficial do Mar na Costa Central de Santa Catarina

O fenômeno das florações de algas é conhecido mundialmente devido aos impactos diretos na saúde dos ecossistemas aquáticos, nos recursos pesqueiros e aquícolas, nas atividades recreacionais e na saúde humana. O presente estudo avaliou o uso de sensores satelitais na quantificação dos parâmetros de temperatura superficial do mar (TSM) e concentração de clorofila-a na superfície do mar (Chl-a) na costa de Santa Catarina, durante o período entre 2002 e 2019, objetivando identificar a variabilidade temporal da TSM e da Chl-a a partir da decomposição das séries temporais em tendência, sazonalidade e resíduos. Para atingir este objetivo foram adquiridas estimativas de TSM e Chl-a oriundas dos sensores orbitais Moderate-Resolution Imaging Spectroradiometer (MODIS) a bordo dos satélites (Aqua e Terra) e Visible Infrared Imaging Radiometer Suite (VIIRS) a bordo dos satélites (SNPP e NOAA-20). Através da extração dos valores de TSM e Chl-a para um ponto ao largo da costa central de Santa Catarina foram geradas séries temporais para o período especificado e aplicada a metodologia de decomposição de séries temporais para identificar oscilações de baixa frequência. Foi possível identificar que os eventos com maior Chl-a estão relacionados com eventos com menores valores de TSM. As componentes encontradas poderão ser utilizadas para modelos preditivos auxiliando assim a minimizar os impactos associados às florações de algas.