Simulated Heat Content Variability in the Upper Layers of the Tropical Indian Ocean

Ocean General Circulation Model (OGCM) simulations from 1970–2007 are used to study the upper ocean heat content variability in the Tropical Indian Ocean (TIO). Model computed heat contents up to 50 m (denoted by HC50 m hereafter) representing upper ocean heat content and 300 m (HC300 m) representing heat content up to thermocline depth are first compared with heat contents computed from observations of two buoys in the TIO. It is found that there is good agreement between the model and observations. Fourier analysis of heat content is carried out in different regions of TIO. The amplitudes of semi-annual variability for HC50 m and HC300 m are observed to be greater than those for the annual variability in the Bay of Bengal, while in the Arabian Sea there is a mixed result. Heat content tendency is known to be governed by net surface heat flux and horizontal as well as vertical heat transports. For understanding the relative importance of these processes, a detailed analysis of these terms in the tendency equation is carried out. Rossby wave is observed in the annual mode of heat transport while equatorial jet and Kelvin waves are observed in the semi-annual mode of heart transport. Finally, the correlation between heat content and sea surface temperature (SST) and sea level anomaly (SLA), taken one at a time, is computed. It is found that the correlation improves significantly when both these quantities are together taken into account.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Estimation of structural vulnerability for flooding using geospatial tools in the rural area of Orissa,India

Vulnerability assessment of natural disasters is a crucial input for risk assessment and management. In the light of increasing frequency of disasters, societies must become more disaster resilient. This research tries to contribute to this aim. For risk assessment, insight is needed into the hazard, the elements at risk and their vulnerabilities. This study focused on the estimation of structural vulnerability due to flood for a number of structural elements at risk in the rural area of Orissa, India (Kendrapara), using a community-based approach together with geospatial analysis tools. Sixty-three households were interviewed about the 2003 floods in 11 villages and 166 elements at risk (buildings) were identified. Two main structural types were identified in the study area, and their vulnerability curves were made by plotting the relationships between flood depth and vulnerability for each structural type. The vulnerability ranges from 0 (no damage) to 1 (collapse/total damage). Structural type-1 is characterized by mud wall/floor material and a roof of paddy straw, and structural type-2 is characterized by reinforced cement concrete (RCC) walls/floor and a RCC roof. The results indicate that structural type-1 is most vulnerable for flooding. Besides flood depth, flood duration is also of major importance. Houses from structural type-1 were totally collapsed after 3 days of inundation. Damage of the houses of structural type-2 began after 10 days of inundation.     

Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications.     

Phycoremediation of cyanide from coke–oven wastewater using cyanobacterial consortium

Owing to the presence of several toxic pollutants such as cyanide, phenol, ammonium, coke–oven wastewater is being considered as hazardous stream and needs to be treated properly. In the present study, cyanobacterial consortium of Dinophysis acuminata and Dinophysis caudata, collected from East Kolkata Wetland, was used for the treatment of both synthetic cyanide solution and real coke–oven wastewater. The growth kinetics was studied considering nitrate as substrate. Since consortium showed growth in cyanide solution, a model was proposed considering both nitrate and cyanide as substrates. The simulated data match quite well with experimental ones. Two coke–oven wastewater samples were collected—untreated one from equalization tank and another from secondary clarifier effluent and treated with consortium separately. Lipid was extracted from biomass of native cyanobacterial consortium, biomass treated with raw coke–oven wastewater and biomass treated with secondary clarifier effluents. Fatty acid methyl ester of such lipid samples was analyzed using gas chromatograph.     

Reliability-based robust design for reinforcement of jointed rock slope

Traditional reliability-based design methodologies often involve selection of design which is of lowest cost and satisfies safety requirements. But, this design is sensitive to variation in statistics of input parameters (noise parameters) and might become unsatisfactory if an underestimation of coefficient of variation of input parameters is made. A relatively new design methodology known as robust geotechnical design (RGD) is applied for the case of reinforcement of rock slope using end-anchored rock bolts. This ensures selection of a cost-effective and safe design for which probability of failure (P_f) of reinforced rock slope is least sensitive to the noise parameters. Reliability-based RGD approach involves evaluation of P_f for each design with different possible noise parameters. Finding P_f for the complex geotechnical structure is computationally expensive, and thus an augmented radial basis function-based response surface is used as a surrogate to the finite element model of rock slope. This response surface, being very efficient, also performs well for a range of values of noise parameters. Later, minimum distance algorithm is applied to obtain a cost-effective and robust design. Finally, a comparison is made in the costs between two robust designs obtained for different target probability of failure for the same rock slope.     

Assessing agricultural drought management strategies in the Northern Murray–Darling Basin

Natural Hazards - The Northern Murray–Darling Basin (MDB) is a key Australian agricultural region requiring efficient Agricultural Drought Management (ADM), focused on resilience. Although a...     

Assessing coastal landscape vulnerability using geospatial techniques along Vizianagaram–Srikakulam coast of Andhra Pradesh,India

Vizianagaram–Srikakulam coastal shoreline consisting of beaches, mangrove swamps, tidal channel and mudflats is one of the vulnerable coasts in Andhra Pradesh, India. Five site-specific parameters, namely rate of geomorphology, coastal elevation, coastal slope, shoreline change and mean significant wave height, were chosen for constructing coastal vulnerability index and assessing coastal landscape vulnerability. The findings revealed a shift of 2.5 km in shoreline towards the land surface because of constant erosion and that of 1.82 km towards the sea due to accretion during 1997–2017. The rate of high erosion was found in zones IV and V, and high accretion was found in zones II and III. Coastal vulnerability index analysis revealed constant erosion along shoreline and sea level rise in the study area. Most of the coast in zone V has recorded very high vulnerability due to erosion, high slope, significant wave height and sea level rise. Erosion and accretion, significant wave height, sea level rise and slope are attributed to high vulnerability in zones III and IV. Zone II recorded moderate vulnerability. Relatively lower slope, mean sea wave height and sea level rise have made this zone moderately vulnerable. Very low vulnerability was found in zone I, and low vulnerability was recorded in zone II. Accretion, low slope and low sea level rise were found to be causative factors of lower vulnerability. Thus, zones III, IV and V should be accorded higher priorities for coastal management. The findings can be helpful in coastal land planning and management and preparing emergency plans of the coastal ecosystems.     

An exploratory study on occurrence and impact of climate change on agriculture in Tamil Nadu,India

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture.