An analysis of spectrophotometric observations of comets Austin (1982g) and Bradfield (1980t)

Post-perihelion observed emission fluxes at 388 nm (CN) and 516 nm (C₂) of the coma of comets Austin (1982g) and Bradfield (1980t) are analysed in the framework of the Haser model. Ratios of Haser model CN and C₂ parent production rates with expansion velocity show that each comet behaves normally. For comet Austin (1982g), the Q _CN/v and Q _c2/v values decrease with increase of heliocentric distance of comet. For an assumed %; activity of the total spherical surface area of the nucleus, the water vaporization theory coupled with derived water production rates from the International Ultraviolet Explorer H and OH flux data yields a nuclear diameter of about 6 km for comet Austin (1982g). For comet Bradfield (1980t), the derived nuclear diameter is expected to be of about 1 km. In each comet, the dust mass production rates as well as ratio of dust-to-gas mass production rates decrease with increase of heliocentric distance of comet.     

Megacities – megarisks

We review the definitions, population trends, and characteristics of megacities. Characteristics of megacities are, apart from their size, their complexity in terms of administration, infrastructure, traffic, etc., and at the same time the speed of change. Vulnerabilities and risk potential are discussed using the examples of Mexico City and Mumbai. We present the experience accumulated in the 6 years work of the Earthquakes and Megacities Initiative (EMI, ) with more than 20 large cities around the world, mostly located in the developing world. On this background we analyze obstacles that keep megacities from developing an efficient approach towards disaster mitigation and define a strategy that might overcome these problems. The key element of this strategy is the development of a Disaster Risk Management Master Plan (DRMMP) for cities. Currently the Istanbul Earthquake Master Plan (IEMP) serves as best example for an appropriate strategy for disaster reduction in megacities.     

Optimal pumping from skimming wells from the Yamuna River flood plain in north India

This report examines the problem involving the pumping of groundwater from a group of 90 existing wells along the banks of the Yamuna River, northwest of Delhi (India), underlain with geologically occurring saline water. It is known that unregulated pumping will lead to upconing of saline water and therefore it is necessary to determine optimal rates and associated well locations (from an existing group of candidate wells that supply drinking water to the city of Delhi) that will minimize the total salinity. The nonlinear, non-convex problem is solved by embedding the calibrated groundwater model within a simulation-optimisation (S/O) framework. Optimisation is accomplished by using simulated annealing (SA), a search algorithm. The computational burden is primarily managed by replacing the numerical model with a surrogate simulator-artificial neural network (ANN). The model is applied to the real system to determine the optimal pumping schedule. The results of the operational model suggest that the skimming wells must be operated from optimal locations such that they are staggered in space and time to obtain the least saline water.     

Numerical storm surge model for India and Pakistan

The northeastern sector of the Arabian Sea, which covers the Gujarat coast of India and western coast of Pakistan, is a region vulnerable to extreme sea levels associated with tropical cyclones (TCs). Although the frequency of tropical cyclones in the Arabian Sea is not high, the coastal regions of India and Pakistan suffer in terms of loss of life and property caused by the surges. In view of this a location-specific fine resolution model is developed for the Gujarat coast of India and adjoining Pakistan coast. The east–west and north–south grid distance is about 3.0 km. Using this model, numerical experiments are carried out to simulate the surges generated by 1999 and 2001 cyclones which struck the Pakistan coast. The model computed surges are in agreement with the available observational estimates.     

Measuring similarity between geospatial lifelines in studies of environmental health

Many epidemiological studies involve analysis of clusters of diseases to infer locations of environmental hazards that could be responsible for the disease. This approach is however only suitable for sedentary populations or diseases with small latency periods. For migratory populations and diseases with long latency periods, people may change their residential location between time of exposure and onset of ill health. For such situations, clusters are diffused and diluted by in- and out-migration and may become very difficult to detect. One way to address the problem of diffused clusters is to include in analyses not only current residential locations, but all past locations at which cases might have been exposed to environmental hazardous. In this paper, we assume that a persons residential history provides such information and represent it through a discrete geospatial lifeline data model. Clusters of similar geospatial lifelines represent individuals who have similar residential histories—and therefore represent people who are more likely to have had similar environmental exposure histories. We therefore introduce a lifeline distance (dissimilarity) measure to detect clusters of cases, providing a basis for revealing possible regions in space-time where environmental hazards might have existed in the past. The ability of the measure to distinguish cases from controls is tested using two sets of synthetically generated cases and controls. Results indicate that the measure is able to consistently distinguish between populations of cases and controls with statistically significant results. The lifeline distance measure consistently outperforms another measure which uses only the distance between subjects residences at time of diagnosis. However, the advantages of using the entire residential history are only partly realized, since the ability to distinguish between cases and controls is only moderately better for the lifeline distance function. Future work is needed to investigate modifications to the inter-lifeline distance measure in order to enhance the potential of this approach to detect locations of environmental hazards over the lifespan.This project is supported by grant number 1 R01 ES09816-01 from the National Institute of Environmental Health Sciences, NIH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS or NIH. We wish to thank Peter Rogerson for helpful discussions of the migration models, and the anonymous reviewers for pointing out areas where the paper could be improved.     

Sea Levels and Coastal Inundation Due to Tropical Cyclones in Indian Coastal Regions of Andhra and Orissa

Coastal inundation associated with extreme sea levels is the main factor which leads to the loss of life and property whenever a severe tropical cyclonic storm hits the Indian coasts. The Andhra and Orissa coasts are most vulnerable for coastal inundation due to extreme rise in sea levels associated with tropical cyclones. Loss of life may be minimized if extreme sea levels and associated coastal flooding is predicted well in advance. Keeping this in view, location specific coastal inundation models are developed and applied for the Andhra and Orissa coasts of India. Several numerical experiments are carried out using the data of past severe cyclones that struck these regions. The simulated inland inundation distances are found to be in general agreement with the reported flooding.     

Extreme Sea Levels Associated With Severe Tropical Cyclones Hitting Orissa Coast of India

Extreme sea levels associated with severe cyclonic storms are common occurrences along the east coast of India. The coastal districts of Orissa have experienced major surges in the past. The recent Paradip super cyclone is one of the most severe cyclones, causing extensive damage to property and loss of lives. Extreme sea levels are major causes for coastal flooding in this region. Damages can be minimized if the extreme sea levels are forecast well in advance. In the present study, we develop a location specific, fine resolution model for the Orissa coast on the lines similar to that of IIT-D storm surge model (Dube et al. 1994). The model runs on a personal computer. The bathymetry for the model is extracted from very fine resolution naval hydrographic charts for the region extending from the south of Orissa to south of West Bengal. A simple drying scheme has also been included in the model in order to avoid the exposure of land near the coast due to strong negative sea surface elevations. An attempt was made in this study to simulate extreme sea levels along the Orissa coast using the data of past severe cyclones. The model results reported in the present study are in good agreement with available observations or estimates.     

Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.     

Discrimination between Babul plantations and Mustard crop using Polarimetric C- and L- band data

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscattering coefficients in different polarizations and wavelength bands. C-band space borne SAR is widely used for the classification of agricultural crops, but can only perform a limited discrimination of various tree species. This paper presents the results of discrimination between mustard crop and babul plantation (Prosopis sp.) using quad polarisation Radarsat 2 and ALOS PALSAR data. Study area is comprised of dense babul plantation along the canal, mustard crop on one side of the canal and Fallow land near to Ramgarh village of Jaisalmer district. Three bands of Radarsat (HH, HV and VV) acquired during peak mustard crop growth stage were integrated with four polarizations (HH, HV, VH and VV) of ALOS PALSAR acquired when crop cover was absent. Using only Radarsat data Jefferies-Matusita (JM) separability between mustard crop and babul plantation was found to be poor (710). Where as in the seven band combination the separability was observed to be high (1374). Among the different polarizations three layer combination, highest separability was observed using cross polarizations (HV and VH) of L-band with any one of the Radarsat Polarisation (HH/HV/VV). This combination of C- and L-band resulted in easy separation of mustard and babul plantation which was otherwise difficult using only Radarsat data.