An integrated buoy-satellite based coastal water quality nowcasting system: India's pioneering efforts towards addressing UN ocean decade challenges

The Indian coastal waters are stressed due to a multitude of factors, such as the discharge of industrial effluents, urbanization (municipal sewage), agricultural runoff, and river discharge. The coastal waters along the eastern and western seaboard of India exhibit contrasting characteristics in terms of seasonality, the magnitude of river influx, circulation pattern, and degree of anthropogenic activity. Therefore, understanding these processes and forecasting their occurrence is highly necessary to secure the health of coastal waters, habitats, marine resources, and the safety of tourists. This article introduces an integrated buoy-satellite based Water Quality Nowcasting System (WQNS) to address the unique challenges of water quality monitoring in Indian coastal waters and to boost the regional blue economy. The Indian National Centre for Ocean Information Services (INCOIS) has launched a first-of-its-kind WQNS, and positioned the buoys at two important locations along the east (Visakhapatnam) and west (Kochi) coast of India, covering a range of environmental conditions and tourist-intensive zones. These buoys are equipped with different physical-biogeochemical sensors, data telemetry systems, and integration with satellite-based observations for real-time data transmission to land. The sensors onboard these buoys continuously measure 22 water quality parameters, including surface current (speed and direction), salinity, temperature, pH, dissolved oxygen, phycocyanin, phycoerythrin, Coloured Dissolved Organic Matter, chlorophyll-a, turbidity, dissolved methane, hydrocarbon (crude and refined), scattering, pCO2 (water and air), and inorganic macronutrients (nitrite, nitrate, ammonium, phosphate, silicate). This real-time data is transmitted to a central processing facility at INCOIS, and after necessary quality control, the data is disseminated through the INCOIS website. Preliminary results from the WQNS show promising outcomes, including the short-term changes in the water column oxic and hypoxic regimes within a day in coastal waters off Kochi during the monsoon period, whereas effluxing of high levels of CO2 into the atmosphere associated with the mixing of water, driven by local depression in the coastal waters off Visakhapatnam. The system has demonstrated its ability to detect changes in the water column properties due to episodic events and mesoscale processes. Additionally, it offers valuable data for research, management, and policy development related to coastal water quality.

An improved method for quality control of in situ data from Argo floats using α convex hulls

An improved method for detecting abnormal oceanic in situ temperature and salinity (T/S) profiles is developed. This procedure extends previous method developed by Udaya Bhaskar et al. [2017].

This method utilizes World Ocean Atlas 2013 gridded climatology which is on 0.25° x 0.25° resolution to build α convex hulls. These α shapes are then used to categorize good and bad in situ T/S data profiles. This extended method classify the entire profiles instead of data for standard depths to avoid any errors introduced by interpolation to standard depths. Like in previous method, an 'n' sided polygon (convex hull) encompassing the T/S profile data is constructed using Jarvis March algorithm and Points In Polygon (PIP) principle is employed to judge the profile as good or bad. Extensive sensitivity experiments were done for arriving at the optimal α value such that false positives and true negatives are minimized. All types of issues associated with the in situ oceanographic data are identified and quality flag assigned. Examples of this improved method as applied to few Argo floats are presented.•

The T/S profiles corresponding to region of interest are used to build α convex hulls.

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This extended method can be effectively used for quality control of entire profile and clearly demarcate the profile as good/bad.

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This method has the advantage of treating bulk of oceanographic in situ profiles data in a single go which filters out erroneous profile data from the good.

Persistence of marine heat waves for coral bleaching and their spectral characteristics around Andaman coral reef

Coral reefs are fragile and endangered ecosystems in the tropical marine and coastal environment. Thermal stress due to marine heat waves (MHW) could cause significantly negative impacts on the health conditions, i.e., bleaching of the coral ecosystem. The current study is an attempt to quantify the intensity of coral bleaching in the Andaman region in recent decades using the intensity of marine heat wave (IMHW) estimated from satellite measured sea surface temperature (SST). A linear regression model was developed between IMHW and in situ observations of percent coral bleaching (PCB) which has the slope 7.767 (of IMHW unit) and intercept (- 141.7). Further, an attempt was also made to establish the relationship between PCB and the ratio between the remote sensing reflectance (Rrs) at 443 and 531 nm to upscale the percentage of coral bleaching at synoptic scales. A significant positive correlation between the PCB and band ratio index was found (R² = 0.72). This approach can be used for the operational monitoring of coral reef beaching in this region.

Quality control of oceanographic in situ data from Argo floats using climatological convex hulls

A new method of identifying anomalous oceanic temperature and salinity (T/S) data from Argo profiling floats is proposed. The proposed method uses World Ocean Database 2013 climatology to classify good against anomalous data by using convex hulls. An n-sided polygon (convex hull) with least area encompassing all the climatological points is constructed using Jarvis March algorithm. Subsequently Points In Polygon (PIP) principle implemented using ray casting algorithm is used to classify the T/S data as within or without acceptable bounds. It is observed that various types of anomalies associated with the oceanographic data viz., spikes, bias, sensor drifts etc can be identified using this method. Though demonstrated for Argo data it can be applied to any oceanographic data.

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The patterns of variation of the parameter (temperature or salinity) corresponding to a particular depth, along the longitude or latitude can be used to build convex hulls.

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This method can be effectively used for quality control by building Convex hulls for various observed depths corresponding to biogeochemical data which are sparsely observed.

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This method has the advantage of treating the bulk of oceanographic in situ data in a single iteration which filters out anomalous data.