Coastal Zones 2

Coastal pollution poses a major health and environmental hazard, not only for beach goers and coastal communities but for marine organisms as well.  Stormwater runoff is the largest source of environmental pollution in coastal waters of the Southern California Bight (SCB) and is of great concern in increasingly urbanized areas.  Buoyant wastewater plumes also pose a marine environmental risk.  When discharge of treated wastewater during maintenance-driven diversions occurs in comparatively shallower coastal waters, it increases the risk of shoreline contamination and toxicity due to harmful algal blooms. In this study we provide a comprehensive overview of satellite remote sensing capabilities in detecting buoyant coastal pollutants in the form of stormwater runoff and wastewater effluent.

The Southern California Bight is an ecologically thriving area for both marine species and the increasing number of coastal communities that have taken root in the area.  It is also the final destination of four major urban rivers that act as channels for runoff and pollution during and after rainstorms.  Here, we provide a climatology based on satellite SAR imagery of naturally occurring stormwater plumes in coastal waters after rain events, from 1992 to 2014 from four major rivers in the area, Ballona Creek, the Los Angeles River, the San Gabriel River, and the Santa Ana River.  Additionally, we have constructed a heat or intensity map to the primary extent and direction of stormwater plumes, to specify adjacent areas that may be subject to the greatest risk of coastal contamination, by comparing sea surface roughness data from various Synthetic Aperture Radar (SAR) instruments and ocean color data from the Moderate Imaging System (MODIS) sensor on board the Aqua satellite.  Precipitation, wind, river discharge, and bacteria data were also gathered and compared to our satellite findings. 

In conjunction with our efforts to monitor coastal pollution and validate the abilities of satellite remote sensing, a recent Fall 2015 wastewater diversion from the City of Los Angeles Hyperion Treatment Plant (HTP) also provided the opportunity to apply these remote sensing methodologies of plume detection. Multi-sensor satellite data were used to detect plume signatures and correlated with in situ and environmental data.  The HTP is one of the largest wastewater treatment plants in the western United States.  During maintenance of their 5-mile long outfall pipe, they must divert wastewater effluent to a shorter outfall pipe that terminates 1-mile offshore and in shallower waters.  Aqua-MODIS spectral data were used to determine ocean color anomalies associated with wastewater contamination.  Sea surface temperature (SST), chlorophyll-a (chl-a) fluorescence, remote sensing reflectance (Rrs), and particulate backscatter (bbp) signatures were analyzed from MODIS data.  Terra-ASTER and Landsat-8 thermal infrared data were also obtained to determine SST anomalies associated with surfaced wastewater at a higher resolution than MODIS.  SAR data from ALOS-2, and Sentinel-1 were also used to identify surfaced wastewater plumes.  In situ drift, chl-a, water quality?, and SST measurements from the diversion were also compared with those obtained by satellite sensors.

The coastal region in east coast Andhra Pradsh of India has a 525.15-kmlong coastline with low-lying tidal mudflats beaches, mangrove swamp, creek and tidal channels. Recently, the increasing frequency of tropical cyclones in the Bay of Bengal, i.e.,Phylin and Hudhud in Andhra Pradesh coast, and the devastating impact of the 2004 tsunami in India increased the significance in assessing the vulnerability of the coastal lands to inundation and flooding, notably in the context of climate change-induced sea level rise. This study aims to estimate a coastal vulnerability index (CVI) for the coastal region  and to use the calculated index to evaluate the vulnerability of coastal region. This CVI is calculated by using four  geological and four physical parameters characterizing the vulnerability of the study coastal region, including regional slope, coastal elevation, geomorphology, significant wave height, mean tidal range and relative sea level using different conventional and remotely sensed data. In the present study, tsunami run-up has been considered as an additional physical process parameter to calculate the CVI. Using a composite coastal vulnerability index based on the relative risk rating of those parameters, coastal regions was classified according to their vulnerability. . The CVI results depict that coasts are least and most  vulnerable to inundation, flooding and erosion of coastal lands where geological parameters are more efficient to CVI. The paper alerts to decision makers and planners to mitigate the natural disaster and manage the coastal zone and is a primary step toward prioritizing coastal lands for climate change adaptation strategies in the view of increased storminess and projected sea level rise.

The response of marine ecosystems to global change, including the spread of non-indigenous species worldwide, is one of today’s most serious environmental concerns. Due to its large spatial coverage, high temporal resolution and long-lasting archive (now more than 15 years), satellite remote-sensing makes it possible to study the response of marine ecosystems to environmental changes, including phytoplankton decadal variability, harmful algal bloom occurrences, eutrophication of coastal zones, and impact of water quality variability on shellfish farming. Using an original combination of ecophysiological modelling, in situ phytoplankton data and satellite remote sensing, we investigated how global change influences the invasion of European coasts by a non-native marine invertebrate, the Pacific oyster Crassostrea gigas.

Daily environmental data from the merged SeaWiFs, MODIS and MERIS archive, and from the AVHRR and GHRSST time-series were used as inputs of an oyster bioenergetics growth model following the Dynamical Energy Budget (DEB) theory. Simulation outputs, which were successfully validated against field measurements, allowed us to accurately determine some of the oyster main life-history traits, including growth rate, reproductive effort, and spawning date.

We showed that over the last 15 years the oyster growth rate and reproductive effort were positively correlated to phytoplankton abundance, whereas the spawning date was negatively correlated with late-spring sea surface temperature (SST). From a long-term historical analysis of SST time-series, we report that a phenological shift in the spawning date occurred at the end of the 1980s, with precocious spawning events occurring more frequently since the early 1990s. Upscaling our approach to the scale of the European coasts, we further demonstrated that in the last decades, seawater warming caused a drastic shift of 1400 km northward in the oyster reproductive niche and optimal area for early life-stage development. The combination of mechanistic bioenergetics modelling with ocean color satellite remote-sensing offers a generic approach to analyse the response of marine ecosystems in a climate-changing world.

In this presentation, we showed how Earth Observation (EO) can be merged with oyster ecophysiological responses to provide a consistent spatial picture of the impact of environmental variability between oyster farming sites along more than 200 km of coastline. A special focus was given on the potential of offshore sites, including Offshore Wind Farms (OWF) for shellfish production.   

While the influence of sea surface temperature (SST), chlorophyll a (Chla) concentration and suspended particulate matter (SPM) concentration on the physiological responses of suspension-feeders has mostly been assessed through laboratory experiments, it has rarely been studied at the scale of shellfish-farming ecosystems. As EO satellites have the ability to observe the world ocean at a global scale as well as to zoom in specific coastal areas, the combination of high resolution satellite observations with bivalves ecophysiological model could make it possible to spatially explore the response of cultivated suspension feeders to environmental conditions in every region of the planet, at a spatial scale compatible with the size of shellfish-farming sites. 

Daily Chla and SPM data from the merged SeaWiFs, MODIS and MERIS archive, and daily SST data from the AVHRR and GHRSST time-series were used as inputs of an oyster bioenergetics model following the Dynamical Energy Budget (DEB) theory to simulate oyster growth in both intertidal and off-shore environments. Simulation outputs were successfully validated against field measurements of oyster growth in three contrasted oyster farming sites of the French Atlantic coast. We then applied the approach to the 1998-2013 EO time-series, and analysed the 15-years averaged annual cycle in order to take into account interannual variability. 

While oyster-farming has been historically and traditionally performed in intertidal areas all over the French Atlantic coast, we showed that oyster growth rate was significantly higher offshore than in the intertidal zone. The approach was up-scaled to far field levels to rank the potential of offshore oyster farming in eight water masses (as defined by the Water Framework Directive) located along the French Southern Brittany coastline. Oyster growth potential was also evaluated in two sites where OWF will be deployed in a near-future. The approach presented here opens new perspectives for EO-based aquaculture management, marine spatial planning, and shellfish-farming ecosystems studies.

Dredging projects are subject to strict environmental regulations. One of the environmental concerns is the increase in turbidity during dredging which may have negative effects on the surrounding environment. Turbidity has to be monitored continuously and exceedance of turbidity tresholds may lead to a temporary shut down of the dredging works and thus increasing costs for the dredging contractor. Both Sentinel-2 MSI and Sentinel-3 OLCI can provide valuable and complementary information on the turbidity to be used in the environmental assessment of dredging projects. Whereas Sentinel-3 OLCI can provide an overview of the turbidity for the complete dredging site at high temporal resolution, Sentinel-2 can provide data on the turbidity plume at high spatial resolution and thus a more detailed view on the near field behaviour of the plume.    

Here we present algorithms for atmospheric correction and turbidity retrieval from Sentinel-2 MSI and MERIS (as a precursor of Sentinel-3) and provide insight on how the final products can be used in an operational dredging project. The atmospheric correction model, OPERA, is sensor and scene generic. It can derive both land, inland water and coastal water reflectances. It takes into account specific properties of the air-water interface, the surface altitude and adjacency effects. Aerosol information is either estimated from the land or estimated using a SWIR black pixel assumption for the water. Turbidity is estimated based on red, near-infrared and short-wave infrared spectral bands. The exact bands used depends on the local turbidity of the water (i.e. wavelength switching approach).  OPERA has already been used to process MERIS, Landsat-8 OLI, Sentinel-2 MSI and airborne hyperspectral sensors. Results are provided for Sentinel-2 MSI and MERIS and for several inland and coastal sites. Validation is performed for the reflectance and turbidity products based on the information from Aeronet-OC stations, from fixed turbidity buoys and dedicated in situ sampling.