Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad, India

Hyderabad is one of the fastest growing mega cities in India and it is facing many economic, social and environmental problems due to rapid urban growth. For the better planning of resources and to provide basic amenities to its residents, it is necessary to have sufficient knowledge about its urban growth activities. Also, it is necessary to monitor the changes in land use over time and to detect growth activities in different parts of the city. To accomplish these tasks with greater accuracy and easiest way, remote sensing and geographic information system (GIS) tools proved to be very advantageous. This study makes an attempt towards the mapping of land use classes for different time periods and analysis of apparent changes in land use using the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data for the urban agglomeration of Hyderabad, India. In this study, three different time periods viz. 1989–2000, 2000–2005 and 2005–2011 are chosen for the analysis. The results have shown that high-density urban area had grown during 1989–2011 by encroaching into other land use classes. The urban growth has also affected water resources both, qualitatively and quantitatively in the region. The transformation of other land use types into urban area dynamically continued in the North-East and Southern parts of the city. In the North-East direction, the urban growth was mostly due to growth in industrial and residential area and in Southern part, mostly due to residential growth.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

Implications of vortex initialization and model spin-up in tropical cyclone prediction using Advanced Research Weather Research and Forecasting Model

The roles of vortex initialization and model spin-up in tropical cyclone (TC) prediction using Advanced Research Weather Research and Forecasting (ARW) Model are studied through a case study of NARGIS (2008) cyclone over Bay of Bengal. ARW model is designed to have three two-way interactive nested domains, and a suite of 36 numerical experiments are performed with three values of maximum wind (MW), four of radius of maximum wind (RMW), and three of α and one experiment without vortex initialization. The results indicate that vortex initialization is important toward realistic representation of initial structure and location of cyclone vortex. Model spin-up during the first 18–24 h of model integration lead to faster intensification than of the real atmosphere, thus a weaker initial vortex evolved more realistically. Three experiments from vortex initialization produced MW and RMW nearer to the observations, but none of these produced a good prediction due to unrealistic intensification during model spin-up. A weaker vortex with intensity less than 50 % than observations produced the best forecast in terms of intensity, track, and landfall. The results suggest that slightly larger (~30 %) RMW than observations with α as ?0.5 (for 81 km model resolution) that produces weaker vortex is to be implemented in the design of bogus vortex. This study assesses the merits of TC bogus scheme in ARW model, illustrates the need for vortex initialization, and analyzes the spin-up problem in cold-start model simulations of TC prediction.     

On anomalously high sub-surface dissolved oxygen in the Indian sector of the Southern Ocean

The Southern Ocean (SO) plays a critical role in global ocean productivity and carbon cycling. Bio-Argo floats deployed in the Indian sector of the Southern Ocean provides new insights into the biogeochemical processes. Here we report significantly higher dissolved oxygen(DO) (~?310 μmol/kg) in summer of 2014–2015 for one float (F1) and winter of 2014 in other float (F2) at sub-surface layer in the subantarctic region of the SO. The summer DO peak in F1 was 10% higher than those during the summer of succeeding year, while the winter DO peak in F2 was 20% higher than those during the winter of succeeding year. Temperature and dynamic height structure show that cyclonic eddies play an important role in the observed increase in the dissolved oxygen: the high DO is a manifestation of the co-occurrence of a cold core eddy which transported the cold oxygen rich water from deep to the surface during winter, while, during summer, the high chlorophyll below the mixed layer depth (MLD) also contributed to the elevated DO. Low apparent oxygen utilisation suggests that the observed high oxygen concentration was due to high production rates over the consumption.

     

Spatio-temporal evaluation of event detection and measurement coherence among satellite rainfall products for ensembled dataset generation

Theoretical and Applied Climatology - Selection of a best suited satellite-based gridded rainfall product (SGRP) is challenging due to their significant variations at spatial and temporal scale....     

Leaching behavior of lignite fly ash with shake and column tests

The maximum concentration of the majority of the trace metals in the leachates from shake and column test of lignite fly ash (LFA) was within the prescribed limits; however, total dissolved solids, total hardness, cations and anions (except K⁺), being above the prescribed limits, may lead to the increase in the hardness and salinity in the soil on the disposal of LFA. Present generation of huge amount of fly ash from thermal power plants (TPPs) is a big challenge concerning contamination of soil, crop produce and surface and ground water bodies due to the presence of some of the toxic trace metals in it. The leaching behavior of alkaline LFA (pH, 10.94), from TPP of Neyveli Lignite Corporation (NLC), India, was investigated by shake and column tests using water and sodium acetate buffer. The leaching of trace metals from LFA was governed by their concentrations, association with the ash particles, leaching duration and pH of the leachate (most influencing parameter). The leaching of metals followed the order: buffer column > aqueous column > aqueous shake > buffer shake test.     

Bloom of Hemidiscus hardmannianus (Bacillariophyceae) and Its Impact on Water Quality and Plankton Community Structure in a Mangrove Wetland

An intense monospecies bloom of the centric diatom Hemidiscus hardmannianus (Greville) Mann. (Bacillariophyceae) occurred in the coastal waters of Sundarban mangrove wetland, India on 22 July, 2010. The species was abruptly flourished (mean density 8.86 × 10⁶ cells/L) resulting intense green coloration of water. The greatest impoverishment of diversity and density of phytoplankton, micro‐ and mesozooplankton community was observed during the bloom. The phytoplankton community showed an extreme reduction in diversity (12 species to one) and sharp decrease in standing stock (12.84 × 10³ to 8.86 × 10⁶ cells/L) during this period. The late bloom condition coincided with sudden and huge arrival of a clupoid fish Hilsa ilisha which forms the most potential fisheries in Sundarban deltaic region. An attempt has been made to correlate the satellite remote sensing‐derived information to the bloom conditions. The MODIS‐Aqua derived chlorophyll and TMI satellite derived sea surface temperature and wind speed maps have been interpreted.     

Precipitation-runoff simulation for a Himalayan River Basin, India using artificial neural network algorithms

In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October) during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during the monsoon season, having different soil moisture conditions during period from June to October.