GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality.

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

Co-occurrence patterns and environmental factors associated with rapid onset of Microcystis aeruginosa bloom in a tropical coastal lagoon.

The environmental factors contributing to the Microcystis aeruginosa bloom (hereafter referred to as Microcystis bloom) are still debatable as they vary with season and geographic settings. We examined the environmental factors that triggered Microcystis bloom outbreak in India's largest brackish water coastal lagoon, Chilika. The warmer water temperature (25.31-32.48 °C), higher dissolved inorganic nitrogen (DIN) loading (10.15-13.53 µmol L-1), strong P-limitation (N:P ratio 138.47-246.86), higher water transparency (46.62-73.38 cm), and low-salinity (5.45-9.15) exerted a strong positive influence on blooming process. During the bloom outbreak, M. aeruginosa proliferated, replaced diatoms, and constituted 70-88% of the total phytoplankton population. The abundances of M. aeruginosa increased from 0.89 × 104 cells L-1 in September to 1.85 × 104 cells L-1 in November and reduced drastically during bloom collapse (6.22 × 103 cells L-1) by the late November of year 2017. The decrease in M. aeruginosa during bloom collapse was associated with a decline in DIN loading (2.97 µmol L-1) and N:P ratio (73.95). Sentinel-3 OLCI-based satellite monitoring corroborated the field observations showing Cyanophyta Index (CI) > 0.01 in September, indicative of intense bloom and CI < 0.0001 during late November, suggesting bloom collapse. The presence of M. aeruginosa altered the phytoplankton community composition. Furthermore, co-occurrence network indicated that bloom resulted in a less stable community with low diversity, inter-connectedness, and prominence of a negative association between phytoplankton taxa. Variance partitioning analysis revealed that TSM (16.63%), salinity (6.99%), DIN (5.21%), and transparency (5.15%) were the most influential environmental factors controlling the phytoplankton composition. This study provides new insight into the phytoplankton co-occurrences and combination of environmental factors triggering the rapid onset of Microcystis bloom and influencing the phytoplankton composition dynamics of a large coastal lagoon. These findings would be valuable for future bloom forecast modeling and aid in the management of the lagoon.

Long-term ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance.

The period of disrupted human activity caused by the COVID-19 pandemic, coined the "anthropause," altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Exceptional landscape-wide cyanobacteria bloom in Okavango Delta, Botswana in 2020 coincided with a mass elephant die-off event.

In 2020, nearly 400 elephants died within the Okavango Delta region in Botswana, creating the worst-ever elephant mass die-off event in history. This catastrophic event was widely blamed on toxic cyanobacterial blooms after lab results showed the presence of toxin-forming cyanobacteria in inland waters of the Delta. However, it did not explain why we saw this mass die-off of elephants in 2020 and not in previous years. We conducted a landscape-wide time-series analysis using freely available European Space Agency's Sentinel-2 and NASA's Landsat-8 satellite data. We used existing bio-optical models, Normalized Difference Chlorophyll Index and Green Line Height, as proxies for chlorophyll-a and phycocyanin (cyanobacteria) concentrations. We found that 2020 was an exceptional year for cyanobacteria blooms in the Okavango Delta region compared to the past three years (2017-2019). Bloom phenology indicated that the cyanobacteria blooms initiated in September-October 2019, experienced an exponential growth reaching peak in January-February 2020, and eventually senescing in June 2020. This being a notoriously data-scarce region of the world, we did not have any means to perform site-specific validation of the models. Although magnitude and timeline of the blooms coincided with the timeline of elephant death reports, our study do not confirm it to be the trigger. For the first time, we show the widespread nature of these blooms across the landscape, which may have increased the toxin exposure for elephants. We theorize that 2020 might have been the first year for such a mass die-off event, but it will certainly not be the last because warming trends under changing climate are creating increasingly suitable conditions for these blooms to be pervasive and ubiquitous. Through this preliminary study, we demonstrate the critical need for frequent and comprehensive monitoring of toxic cyanobacterial blooms in the Delta to avoid another such event in the future.

Continuous and Synoptic Assessment of Indian Inland Waters for Harmful Algae Blooms.

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality, socioeconomic, and health hazard worldwide. In India, there are frequent episodes of severe CyanoHABs, which are left untreated due to a lack of awareness and monitoring infrastructure, affecting the economy of the country gravely. In this study, for the first time, we present a country-wide analysis of CyanoHABs in India by developing a novel interactive cloud-based dashboard called "CyanoKhoj" in Google Earth Engine (GEE) which uses Sentinel-3 Ocean and Land Colour Instrument (OLCI) remotely sensed datasets. The main goal of this study was to showcase the utility of CyanoKhoj for rapid monitoring and discuss the widespread CyanoHABs problems across India. We demonstrate the utility of Cyanokhoj by including select case studies of lakes and reservoirs geographically spread across five states: Bargi and Gandhisagar Dams in Madhya Pradesh, Hirakud Reservoir in Odisha, Ukai Dam in Gujarat, Linganamakki Reservoir in Karnataka, and Pulicat Lake in Tamil Nadu. These sites were studied from September to November 2018 using CyanoKhoj, which is capable of near-real-time monitoring and country-wide assessment of CyanoHABs. We used CyanoKhoj to prepare spatiotemporal maps of Chlorophyll-a (Chl-a) content and Cyanobacterial Cell Density (CCD) to study the local spread of the CyanoHABs and their phenology in these waterbodies. A first-ever all-India CCD map is also presented for the year 2018, which highlights the spatial spread of CyanoHABs throughout the country (32 large waterbodies across India with severe bloom: CCD>2,500,000). Results indicate that CyanoHABs are most prevalent in nutrient-rich waterbodies prone to industrial and other nutrient-rich discharges. A clear temporal evolution of the blooms showed that they are dominant during the post-monsoon season (September-October) when the nutrient concentrations in the waterbodies are at their peak, and they begin to decline towards winter (November-December). CyanoKhoj is an open-source tool that can have a significant broader impact in mapping CyanoHABs not only throughout cyanobacteria data-scarce India, but on a global level using archived and future Sentinel-3A/B OLCI data.

Imprints of COVID-19 lockdowns on total petroleum hydrocarbon levels in Asia's largest brackish water lagoon.

India successfully executed one of the strictest lockdowns in the world during the height of the COVID-19 pandemic in early 2020, which provided unique opportunities to analyze the second-largest populous country's anthropogenic footprint on its natural systems. India's first Ramsar site and the world's second-largest brackish water system Chilika lagoon experienced a substantial decline (64%) in the total petroleum hydrocarbon (TPHC) level in water, which was attributed to the massive declines or, at times, an abrupt complete halt of motorized boat operations for fishing and tourism. Using the TPHC values during the lockdown period, our study recommends a TPHC baseline threshold of 2.02 µg L-1 and 0.91 µg g-1 for Chilika waters and sediment, respectively. These baseline values can be used to quantify oil pollution and to formulate policy and management action plans for Chilika lagoon as well as for other similar ecosystems by local environmental agencies.

Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data.

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m-3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.

Tropical cyclones shape mangrove productivity gradients in the Indian subcontinent.

Recent literature on the impact of cyclones on mangrove forest productivity indicates that nutrient fertilizations aided by tropical cyclones enhance the productivity of mangrove forests. We probe the implications of these predictions in the context of Indian mangroves to propose potential future directions for mangrove research in the subcontinent. First, we look at the time series trend (2000-2020) in satellite-derived gross primary productivity (GPP) datasets for seven mangrove forests across the country's coastline. Second, we compare seasonal changes in soil nutrient levels for a specific site to further the arguments proposed in the literature and investigate the role of potential drivers of mangrove productivity. We find overall increasing trends for GPP over the past two decades for all seven mangrove sites with seasonal fluctuations closely connected to the tropical storm activities for three sites (Bhitarkanika, Pichavaram, and Charao). Additionally, organic carbon and nitrogen levels showed no significant trend, but phosphorus levels were higher during the post-monsoon-winter period for Bhitarkanika. Our findings expand the predictions of previous studies that emphasized the role of storm-induced nutrient fluxes and freshwater supply as primary drivers of productivity gradients in mangroves. Our study provides insights on how mangrove productivity may change with fluctuating frequency and magnitude of cyclones under a changing climate, implying the need for more mechanistic studies in understanding the long-term impact on mangrove productivity in the region.

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR-SWIR, 400-2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450-520 nm) and NIR (band 4; 770-900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

BERM: a Belowground Ecosystem Resiliency Model for estimating Spartina alterniflora belowground biomass.

Spatiotemporal patterns of Spartina alterniflora belowground biomass (BGB) are important for evaluating salt marsh resiliency. To solve this, we created the BERM (Belowground Ecosystem Resiliency Model), which estimates monthly BGB (30-m spatial resolution) from freely available data such as Landsat-8 and Daymet climate summaries. Our modeling framework relied on extreme gradient boosting, and used field observations from four Georgia salt marshes as ground-truth data. Model predictors included estimated tidal inundation, elevation, leaf area index, foliar nitrogen, chlorophyll, surface temperature, phenology, and climate data. The final model included 33 variables, and the most important variables were elevation, vapor pressure from the previous four months, Normalized Difference Vegetation Index (NDVI) from the previous five months, and inundation. Root mean squared error for BGB from testing data was 313 g m-2 (11% of the field data range), explained variance (R2 ) was 0.62-0.77. Testing data results were unbiased across BGB values and were positively correlated with ground-truth data across all sites and years (r = 0.56-0.82 and 0.45-0.95, respectively). BERM can estimate BGB within Spartina alterniflora salt marshes where environmental parameters are within the training data range, and can be readily extended through a reproducible workflow. This provides a powerful approach for evaluating spatiotemporal BGB and associated ecosystem function.

DTEx: A dynamic urban thermal exposure index based on human mobility patterns.

BACKGROUND: Extreme heat in light of climate change is increasingly threatening the health and comfort of urban residents. Understanding spatio-temporal patterns of heat exposure is a critical factor in directing mitigation measures. Current heat vulnerability indices provide insight into heat sensitivities within given communities but do not account for the dynamic nature of the human movement as people travel for different activities. Here, we present a new Dynamic urban Thermal Exposure index (DTEx) that captures the varying heat exposure within urban environments. METHODS: We developed the DTEx to understand human heat exposure patterns in a mid-sized city. This index incorporates the human movement pattern and the heat hazard pattern obtained via novel and advanced techniques. We generated the human movement pattern from large-scale, anonymized smartphone location data. The heat hazard patterns were extrapolated via machine learning models from air temperature data measured through vehicle-mounted sensors. The exposure index was then developed by combining the two parameters using their standard-deviation-classified indices. RESULTS: Our exposure index varied between 2 and 12, indicating low to high thermal exposures. Several high-temperature spots associated with a large volume of foot traffic are successfully identified through this DTEx. We observed the hottest spots at shopping plazas but not specifically in the urban center. During the selected football gameday, the exposure index surged across most places near the football stadium but was reduced considerably further away. DISCUSSION: The proposed DTEx is novel because it provides dynamic heat monitoring capability to facilitate heat mitigation strategies at vulnerable locations in urban environments. Combining the mobility data and extensive sensor data generates rich details on the most heat-exposed areas due to human congregation. Such information will be critical for risk communication and urban planning for policymakers. DTEx could also help smart route planning in sustainable cities to avoid heat hazards risks.

Responses of phytoplankton community structure and association to variability in environmental drivers in a tropical coastal lagoon.

Spatial and seasonal heterogeneity in phytoplankton communities are governed by many biotic and abiotic drivers. However, the identification of long-term spatial and temporal trends in abiotic drivers, and their interdependencies with the phytoplankton communities' structure is understudied in tropical brackish coastal lagoons. We examined phytoplankton communities' spatiotemporal dynamics from a 5-year dataset (n = 780) collected from 13 sampling stations in Chilika Lagoon, India, where the salinity gradient defined the spatial patterns in environmental variables. Generalized additive models showed a declining trend in phytoplankton biomass, pH, and dissolved PO4 in the lagoon. Hierarchical modelling of species communities revealed that salinity (44.48 ± 28.19%), water temperature (4.37 ± 5.65%), and season (4.27 ± 0.96%) accounted for maximum variation in the phytoplankton composition. Bacillariophyta (Indicator Value (IV): 0.74) and Dinophyta (IV: 0.72) emerged as top indicators for polyhaline regime whereas, Cyanophyta (IV: 0.81), Euglenophyta (IV: 0.79), and Chlorophyta (IV: 0.75) were strong indicators for oligohaline regime. The responses of Dinophyta and Chrysophyta to environmental drivers were much more complex as random effects accounted for ~70-75% variation in their abundances. Prorocentrum minimum (IV: 0.52), Gonyaulax sp. (IV: 0.52), and Alexandrium sp. (IV: 0.51) were potential indicators of P-limitation. Diploneis weissflogii (IV: 0.43), a marine diatom, emerged as a potential indicator of N-limitation. Hierarchical modelling revealed the positive association between Cyanophyta, Chlorophyta, and Euglenophyta whereas, Dinophyta and Chrysophyta showed a negative association with Cyanophyta, Chlorophyta, and Euglenophyta. Landsat 8-Operational Land Imager satellite models predicted the highest and lowest Cyanophyta abundances in northern and southern sectors, respectively, which were in accordance with the near-coincident field-based measurements from the lagoon. This study highlighted the dynamics of phytoplankton communities and their relationships with environmental drivers by separating the signals of habitat filtering and biotic interactions in a monsoon-regulated tropical coastal lagoon.

Landfall season is critical to the impact of a cyclone on a monsoon-regulated tropical coastal lagoon.

Cyclones can produce a wide variety of short-term and long-term ecological impacts on coastal lagoons depending on cyclone's physical-meteorological characteristics and the lagoon's geographic, geomorphic, and bathymetric characteristics. Here, we theorized that in monsoon regulated tropical coastal lagoons, another important factor that could determine the impact of a cyclone is the landfall season or time of the year with reference to the monsoon season. We analyzed the impact of two cyclones which made landfall near Chilika, Asia's largest brackish water lagoon in different seasons, Cyclone Fani and Titli before and after the monsoon season. We compared field measured and satellite-derived water quality parameters including nutrient, salinity, water temperature, transparency, Chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) before and after the cyclones. We found that although both the cyclones were of similar intensities, after their land interaction, their impact on the lagoon's water quality was contrasting. The post-monsoon cyclone produced a substantial increase in total nitrogen (TN) and total phosphorous (TP), a large drop in salinity, CDOM, and Chl-a. In contrast, after the pre-monsoon cyclone, TN and TP did not show any such hike, no substantial change in salinity and CDOM either, and only a slight increase in Chl-a was observed. We found that the controlling factor in determining the impact of a cyclone is the rate and duration of freshwater discharge to the lagoon, which is normally a strong pulse for pre-monsoon and a continued high flow for post-monsoon cyclones. We conclude that the antecedent conditions of the lagoon and the watershed at the time of a cyclone's landfall is a key criterion in determining the impact. The combined use of satellite data and field data was proved critical to capture the overall impact of cyclones on the hydrological characteristics of the monsoon-regulated coastal lagoon.

CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms.

Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as "CyanoTRACKER" for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

Sea-level rise thresholds for stability of salt marshes in a riverine versus a marine dominated estuary.

We studied the ecological resilience of salt marshes by deriving sea level rise (SLR) thresholds in two estuaries with contrasting upland hydrological inputs in the north-central Gulf of Mexico: Grand Bay National Estuarine Research Reserve (NERR) with limited upland input, and the Pascagoula River delta drained by the Pascagoula River, the largest undammed river in the continental United States. We applied a mechanistic model to account for vegetation responses and hydrodynamics to predict salt marsh distributions under future SLR scenarios. We further investigated the potential mechanisms that contribute to salt marsh resilience to SLR. The modeling results show that salt marshes in the riverine dominated estuary are more resilient to SLR than in the marine dominated estuary with SLR thresholds of 10.3 mm/yr and 7.2 mm/yr respectively. This difference of >3 mm/yr is mainly contributed by larger quantities of riverine-borne mineral sediments in the Pascagoula River. In both systems, sediment trapping by the above-ground vegetation appears to contribute more to marsh platform accretion than organic matter from below-ground biomass based on the medians of the accretion rates. However, below-ground biomass could contribute up to 90% of accretion in the marine dominated estuary compared to only 60% of accretion in the riverine dominated estuary. SLR thresholds of salt marshes are more sensitive to vegetation biomass in the marine dominated estuary while biomass and sediment similarly affect SLR thresholds of salt marshes in the riverine dominated estuary. This research will likely help facilitate more informed decisions on conservation/restoration policies for these two types of systems in the near-term needed to minimize future catastrophic loss of these coastal marsh habitats once SLR thresholds are exceeded.

Risks for cyanobacterial harmful algal blooms due to land management and climate interactions.

The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.

Spatio-Temporal Dynamics of Inherent Optical Properties in Oligotrophic Northern Gulf of Mexico Estuaries.

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3's Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m-1 (13.95% NRMSE) for total absorption (a t ), 0.21 m-1 (7.61% NRMSE) for detritus-gelbstoff absorption (a dg ), and 0.09 m-1 (22.77% NRMSE) for phytoplankton pigment absorption (aphi). Results showed that absorption by detritus and gelbstoff (adg) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.

Interannual and cyclone-driven variability in phytoplankton communities of a tropical coastal lagoon.

One of the main challenges in phytoplankton ecology is to understand their variability at different spatiotemporal scales. We investigated the interannual and cyclone-derived variability in phytoplankton communities of Chilika, the largest tropical coastal lagoon in Asia and the underlying mechanisms in relation to environmental forcing. Between July 2012 and June 2013, Cyanophyta were most prolific in freshwater northern region of the lagoon. A category-5 very severe cyclonic storm (VSCS) Phailin struck the lagoon on 12th October 2013 and introduced additional variability into the hydrology and phytoplankton communities. Freshwater Cyanophyta further expanded their territory and occupied the northern as well as central region of the lagoon. Satellite remote sensing imagery revealed that the phytoplankton biomass did not change much due to high turbidity prevailing in the lagoon after Phailin. Modeling analysis of species-salinity relationship identified specific responses of phytoplankton taxa to the different salinity regime of lagoon.