Prasad K. Bhaskaran

Performance and validation of a coupled parallel ADCIRC–SWAN model for THANE cyclone in the Bay of Bengal

An accurate prediction of near-shore sea-state is imperative during extreme events such as cyclones required in an operational centre. The mutual interaction between physical processes such as tides, waves and currents determine the physical environment for any coastal region, and hence the need of a parallelized coupled wave and hydrodynamic model. The present study is an application of various state-of-art models such as WRF, WAM, SWAN and ADCIRC used to couple and simulate a severe cyclonic storm Thane that developed in the Bay of Bengal during December 2011. The coupled model (ADCIRC–SWAN) was run in a parallel mode on a flexible unstructured mesh. Thane had its landfall on 30 December, 2011 between Cuddalore and Pondicherry where in-situ observations were available to validate model performance. Comprehensive experiment on the impact of meteorological forcing parameters with two forecasted tracks derived from WRF model, and JTWC best track on the overall performance of coupled model was assessed. Further an extensive validation experiment was performed for significant wave heights and surface currents during Thane event. The significant wave heights measured along satellite tracks by three satellites viz; ENVISAT, JASON-1 and JASON-2, as well in-situ near-shore buoy observation off Pondicherry was used for comparison with model results. In addition, qualitative validation was performed for model computed currents with HF Radar Observation off Cuddalore during Thane event. The importance of WRF atmospheric model during cyclones and its robustness in the coupled model performance is highlighted. This study signifies the importance of coupled parallel ADCIRC–SWAN model for operational needs during extreme events in the North Indian Ocean.

Coastal vulnerability assessment studies over India: a review

Coastal areas are one of the key systems for global sustainability. These are the transition areas between land and sea. Coastal regions gained importance because of multiple uses, like high productivity of the ecosystem, highly concentrated population, industrial friendly, waste disposal, tourism, transportation, strategic planning in military and many more. These coasts are always in a dynamic state trying to change, and nature always works for maintaining the equilibrium. India, with most diverse ecosystem, high productivity and thickly populated over coastal region, has gained its very own importance. Despite all of these, Indian coasts are under threat due to multiple stresses like global climate change and human intervention. These stresses are driving vulnerabilities like sea-level rise, coastal erosion, frequent extreme events, and saltwater encroachment. In this critical scenario, coastal management has become one of the very important issues in the last two decades. Thus, coastal vulnerability assessment methods have been developed to identify and manage the vulnerable areas over the coast. In the present review, we focussed on different vulnerabilities to coast of India and one of the assessment methods, coastal vulnerability index methodology, applied over India. Vulnerability assessment is the process where we identify the problem, quantify it, and assess the risk rate in formulating development strategies to reduce the risk and vulnerabilities. Proper planning and protection strategies for Indian coast must be taken swiftly by the coastal management and policy makers to safeguard coastal ecosystem and livelihoods. In recent years, there has been much focus on coastal vulnerability assessments using various kinds of data. Most of the reported studies over Indian coast are based on remote sensing and GIS methods.

Wind-wave Climate Projections for the Indian Ocean from Satellite Observations

The oceans play a key role in climate change and their impact has profound implications on the marine ecosystem and multitude activities around the globe. The effects due to climate change can have long-term repercussion. The latest report on the Intergovernmental Panel on Climate Change had identified the importance of wind-wave climate and its key role in global climate models. The present study investigates the impact of climate change on variability of maximum significant wave height and wind speeds over the Indian Ocean basin. The study is based on analysis from the daily observation of satellite altimeter measured wind and waves derived from eight satellite missions covering a period of 21 years from 1992 until 2012. The results signify that the Southern Ocean belt encompassing latitudinal belts between 40°S – 55°S experienced the highest variability due to impact from climate change. Both wind and wave activity has shown an increasing trend in the Southern Ocean, and this rise is more conspicuous in the current decade. The implications from increased wave activity in the Southern Ocean results in swell field that can influence the local wind-generated waves in the North Indian Ocean basin. The wind-wave activity in certain sectors of the tropical North Indian Ocean also increased from impact of climate change.

Temporal variability in wind–wave climate and its validation with ESSO-NIOT wave atlas for the head Bay of Bengal

The head Bay region bordering the northern Bay of Bengal is a densely populated area with a complex geomorphologic setting, and highly vulnerable to extreme water levels along with other factors like sea level rise and impact of tropical cyclones. The influence of climate change on wind–wave regime from this region of Bay of Bengal is not known well and that requires special attention, and there is a need to perform its long-term assessment for societal benefits. This study provides a comprehensive analysis on the temporal variability in domain averaged wind speed, significant wave height (SWH) utilizing satellite altimeter data (1992–2012) and mean wave period using ECMWF reanalysis products ERA-Interim (1992–2012) and ERA-20C (1992–2010) over this region. The SWH derived from WAVEWATCH III (WW3) model along with the ERA-Interim reanalysis supplements the observed variability in satellite altimeter observations. Further, the study performs an extensive error estimation of SWH and mean wave period with ESSO-NIOT wave atlas that shows a high degree of under-estimation in the wave atlas mean wave period. Annual mean and wind speed maxima from altimeter show an increasing trend, and to a lesser extent in the SWH. Interestingly, the estimated trend is higher for maxima compared to the mean conditions. Analysis of decadal variability exhibits an increased frequency of higher waves in the present decade compared to the past. Linear trend analysis show significant upswing in spatially averaged ERA-20C mean wave period, whereas the noticed variations are marginal in the ERA-Interim data. A separate trend analysis for the wind-seas, swell wave heights and period from ERA-20C decipher the fact that distant swells governs the local wind–wave climatology over the head Bay region, and over time the swell activity have increased in this region.

NUMERICAL MODELING OF SUSPENDED SEDIMENT CONCENTRATION AND ITS VALIDATION FOR THE HOOGHLY ESTUARY, INDIA

Suspended sediment transport plays an important role in the evolution of coastal environment. Hence, observation and modeling of suspended sediment concentration (SSC) is of immense interest to coastal oceanographers, engineers and coastal zone management authorities. The variation of SSC in an estuary is expected both in space and time scales governed by factors such as freshwater influx, tidal variation and basin geometry. The present study reports on development of SSC model for the tidal dominated Hooghly basin, a tributary of the Ganges River having its discharge in the Bay of Bengal, India. The Hooghly estuary experiences high rates of sedimentation related problems requiring periodic maintenance dredging at various locations in the basin. Being a tidal dominated estuary, varying loads of suspended sediments is distributed along various channels in the estuarine zone. The existing SSC models developed earlier for this region had inherent limitations that tend to over-predict computed SSC. These issues critically investigated in the present study thereby developing an upgraded SSC model. It includes better physical parametrizations compared to existing SSC models developed for this region. Efficacy and robustness of the developed SSC model substantiated with measurements conducted in the Hooghly basin. Validation of model computed SSC performed with all available data that comprises of 15 location specific observations of SSC. The validation results signify that upgraded SSC model performs reasonably well compared to field measurements.

Neural-Network-Based Data Assimilation to Improve Numerical Ocean Wave Forecast

This paper demonstrates the skill level of a wavelet neural network in improving numerical ocean wave predictions of significant wave height (H8) and peak wave period (Tp) having practical applications in operational centers. The study uses data of H8 and Tp for a coastal region off Puducherry located in the east coast of India, and obtained from a high-resolution wave model resulting from nesting of the SWAN model with the WW3 model. A wave rider buoy located off Puducherry provided data for a period of 25 months during the period from June 2007 until July 2009 used in this study. The time series of error between numerical and corresponding measured values was first constructed, and using a wavelet neural network, the errors were predicted for future time steps. The predicted errors when incorporated into the model values provided the updated prediction of H8 and Tp. The study signifies that numerical estimations could be significantly improved using this procedure. The results provide quite satisfactory predictions with a lead time varying from 3 to 24 h. The study points out that adequate training of the neural network is an essential prerequisite to obtain good performance and skill levels. A comparison between the suggested prediction method with the standalone neural network model trained with measured data off Puducherry showed that the former approach is preferred over the latter in obtaining a sustained prediction performance.

Tsunami Early Warning System: An Indian Ocean Perspective

Tsunamis are considered the most devastating natural hazard on coastal environments ever known. Densely populated cities on coastal belts are the engines of economic growth and the centers of innovation for global economy and hinterlands of respective nations. As we know most of global cities are located near the coast facilitating trade and commerce. They are also located near the mouths of major perennial rivers which serve as conduits for commerce connecting rest of the world. These locations place major cities at a greater risk of natural hazards viz., cyclones, flooding, sea-level rise, tsunamis, etc. With the increasing intensity of economic exploitation in coastal belts, there is also an increase in socio-economic consequences resulting from the hazardous action of tsunami waves generated from submarine seismic activity and other causes. On 26 December 2004, the countries within the vicinity of East Indian Ocean experienced and witnessed the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4° N, 95.7° E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT).

The application of low-rank and sparse decomposition method in the field of climatology

The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such as image processing, web data ranking, and bioinformatics data analysis. In climate science, this method exactly separates the original data into a set of low-rank and sparse components, wherein the low-rank components depict the linearly correlated dataset (expected or mean behavior), and the sparse component represents the variation or perturbation in the dataset from its mean behavior. The study attempts to verify the efficacy of this proposed technique in the field of climatology with two examples of real world. The first example attempts this technique on the maximum wind-speed (MWS) data for the Indian Ocean (IO) region. The study brings to light a decadal reversal pattern in the MWS for the North Indian Ocean (NIO) during the months of June, July, and August (JJA). The second example deals with the sea surface temperature (SST) data for the Bay of Bengal region that exhibits a distinct pattern in the sparse component. The study highlights the importance of the proposed technique used for interpretation and visualization of climate data.

High Frequency Tail Characteristics in the Coastal Waters off Gopalpur, Northwest Bay of Bengal: A Nearshore Modelling Study

Over the years, continued uncertainty amid − 4 and − 5 frequency exponent representation observed in the slope of the high-frequency tail of a wind-wave frequency spectrum is a major concern. To comprehend the nature of the high-frequency tail an effort has been made to assess the slope of the high-frequency tail with measured data recorded for 3 years off Gopalpur. The study demonstrates that the high-frequency slope of the spectra varied seasonally in the range of n = − 2.13 to − 3.48. The swell and wind sea parameters calculated by separation frequency method, shows that 64.6% of waves were dominant by swell and the rest 34.9% by sea annually. Single, double and multi-peaked spectra occur 12.23, 71.80 and 15.37% annually. To simulate wave spectra, the nested WAM-SWAN model is forced with ERA-Interim winds and 1D wave spectra comparisons, when performed, proved to be encouraging. From the comparisons of measured and theoretical spectra it is concluded that JONSWAP model could not describe the high-frequency tail of measured spectrum, as indicated by the very high Scatter Index ranging from 0.24 to 1.44. Whether there exists a correct slope for the high-frequency tail is still a question. Moreover, the philosophy of a unique slope at any coastal location remains uncertain for the wave modelling community.

Multi-hazard risk assessment of coastal vulnerability from tropical cyclones – A GIS based approach for the Odisha coast

The coastal region bordering the East coast of India is a thickly populated belt exposed to high risk and vulnerability from natural hazards such as tropical cyclones. Tropical cyclone frequencies that develop over the Bay of Bengal (average of 5-6 per year) region are much higher as compared to the Arabian Sea thereby posing a high risk factor associated with storm surge, inland inundation, wind gust, intense rainfall, etc. The Odisha State in the East coast of India experiences the highest number of cyclone strikes as compared to West Bengal, Andhra Pradesh, and Tamil Nadu. To express the destructive potential resulting from tropical cyclones the Power Dissipation Index (PDI) is a widely used metric globally. A recent study indicates that PDI for cyclones in the present decade have increased about six times as compared to the past. Hence there is a need to precisely ascertain the coastal vulnerability and risk factors associated with high intense cyclones expected in a changing climate. As such there are no comprehensive studies attempted so far on the determination of Coastal Vulnerability Index (CVI) for Odisha coast that is highly prone to cyclone strikes. With this motivation, the present study makes an attempt to investigate the physical, environmental, social, and economic impacts on coastal vulnerability associated with tropical cyclones for the Odisha coast. The study also investigates the futuristic projection of coastal vulnerability over this region expected in a changing climate scenario. Eight fair weather parameters along with storm surge height and onshore inundation were used to estimate the Physical Vulnerability Index (PVI). Thereafter, the PVI along with social, economic, and environmental vulnerability was used to determine the overall CVI using the GIS based approach. The authors believe that the comprehensive nature of this study is expected to benefit coastal zone management authorities.