Cluster analysis of rainfall stations of the Indian peninsula

Principal component analysis is applied to derive patterns of temporal variation of the rainfall at fifty-three stations in peninsular India. The location of the stations in the coordinate space determined by the amplitudes of the two leading eigenvectors is used to delineate them into eight clusters. The clusters obtained seem to be stable with respect to variations in the grid of stations used. Stations within any cluster occur in geographically contiguous areas.

Relationship between annual rainfall and tree mortality in a tropical dry forest: Results of a 19-year study at Mudumalai, southern India

Variability in rainfall is known to be a major influence on the dynamics of tropical forests, especially rates and patterns of tree mortality. In tropical dry forests a number of contributing factors to tree mortality, including dry season fire and herbivory by large herbivorous mammals, could be related to rainfall patterns, while loss of water potential in trees during the dry season or a wet season drought could also result in enhanced rates of death. While tree mortality as influenced by severe drought has been examined in tropical wet forests there is insufficient understanding of this process in tropical dry forests. We examined these causal factors in relation to inter-annual differences in rainfall in causing tree mortality within a 50-ha Forest Dynamics Plot located in the tropical dry deciduous forests of Mudumalai, southern India, that has been monitored annually since 1988. Over a 19-year period (1988-2007) mean annual mortality rate of all stems >1 cm dbh was 6.9 +/- 4.6% (range = 1.5-17.5%); mortality rates broadly declined from the smaller to the larger size classes with the rates in stems >30 cm dbh being among the lowest recorded in tropical forest globally. Fire was the main agent of mortality in stems 1-5 cm dbh, elephant-herbivory in stems 5-10 cm dbh, and other natural causes in stems > 10 cm dbh. Elephant-related mortality did not show any relationship to rainfall. On the other hand, fire-related mortality was significantly negatively correlated to quantity of rainfall during the preceding year. Mortality due to other causes in the larger stem sizes was significantly negatively correlated to rainfall with a 2-3-year lag, suggesting that water deficit from mild or prolonged drought enhanced the risk of death but only with a time lag that was greater than similar lags in tree mortality observed in other forest types. In this respect, tropical dry forests growing in regions of high rainfall variability may have evolved greater resistance to rainfall deficit as compared to tropical moist or temperate forests but are still vulnerable to drought-related mortality.

Geographical Distribution Patterns of Amphibians in the Western Ghats, India

The Western Ghats of India are very rich in amphibian species with 117 species of frogs, toads and caecilians. Eighty-nine species are endemic to this biogeographical region. Analysis of ranges and patterns of geographical distribution of amphibians on the Western Ghats suggest that the southern half of the Western Ghats and the low-medium elevation hills are more diverse in species than the northern half and higher hills. This is attributed to the more widespread rainfall and the less variable climatic conditions in the south. About half the species are apparently localized. Of those species with wider ranges, a majority show patchy distribution. Species preferring the moist evergreen forests as habitats tend to have patchy distributions. This appears to be a result of habitat destruction and fragmentation. The overall patterns of species richness and local endemism are rather different from those of the angiosperms and birds. In birds and angiosperms, a significant proportion of endemics are found on the higher hills. On the contrary, endemic amphibian species are found in the lower altitudinal range of 0-1000 m, with a majority between 800 and 1000 m.

A novel neural network design for long range prediction of rainfall pattern

The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Seasonal variability of rainfall recorded in growth bands of the Giant African Land Snail Lissachatina fulica (Bowdich) from India

Seasonal rainfall patterns in Bangalore, India, have been reconstructed using stable isotopic ratios in the growth bands of Giant African Land Snail shells. The present study was conducted at Bangalore, India which receives rain during the summer months. The oxygen isotopic record in the rainwater samples collected during different months covering the period of the summer monsoon of the year 2008 is compared with the isotopic ratio in the gastropod growth bands deposited simultaneously. The chronology of the shell growth band is independently established assuming the growth rate observed in a chamber experiment maintaining similar relative humidity and temperature conditions. A consistent pattern observed in the isotopic ratio in the gastropod growth bands and rainwater is demonstrated and provides a novel approach for precipitation reconstruction at seasonal and weekly time scales. This approach of using isotopic ratios in the gastropod growth bands for rainfall can serve as a substitute for filling gaps in rainfall data and for cases where no rain records are available. In addition, they can be used to determine the frequencies and magnitudes of dry spells from the past records. (C) 2013 Elsevier B.V. All rights reserved.

Characterising weather patterns associated with fire in a seasonally dry tropical forest in southern India

Anthropogenic fires in seasonally dry tropical forests are a regular occurrence during the dry season. Forest managers in India, who presently follow a fire suppression policy in such forests, would benefit from a system of assessing the potential risk to fire on a particular day. We examined the relationship between weather variables (seasonal rainfall, relative humidity, temperature) and days of fire during the dry seasons of 2004-2010, based on MODIS fire incident data in the seasonally dry tropical forests of Mudumalai in the Western Ghats, southern India. Logistic regression analysis showed that high probabilities of a fire day, indicating successful ignition of litter and grass fuel on the forest floor, were associated with low levels of early dry season rainfall, low daily average relative humidity and high daily average temperatures. These weather conditions are representative of low moisture levels of fine fuels, suggesting that the occurrence of fire is moderated by environmental conditions that reduce the flammability of fine fuels in the dry tropics. We propose a quantitative framework for assessing risk of a fire day to assist forest managers in anticipating fire occurrences in this seasonally dry tropical forest, and possibly for those across South Asia.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

On the variety of electron diffraction patterns from quasicrystals

A new exciting era in the study of rapidly solidified alloys has been ushered in by the discovery of a quasicrystalline phase in an Al-1O%Mn alloy by Shechtman et al. (l). The fact that a quasicrystal diffracts electrons and X-rays like a single crystal provides a powerful approach for exploring the atomic configuration in these alloys. Shechtman et al deduced the icosahedral point group symmetry exhibited by quasicrystals on the basis of a set of three electron diffraction patterns showing 5-fold, 3-fold and 2-fold axes of symmetry with appropriate angular relationships. The exotic crystallography of quasicrystals has been recently reviewed by Nelson and Halperin (2).

Simple new PWM patterns for thyristor three-phase AC/DC convertors

Thyristor forced commutated AC/DC convertors are useful for improving the power factor and waveform of AC-side line current. These are controlled through pulse-width modulation schemes for best performance. However, the 3-phase versions impose restrictions on the PWM strategies that can be implemented for excellent harmonic rejection. This paper presents new PWM control strategies for the 3-phase converters and compares them along with the conventional 4-pulse PWM strategy for harmonic elimination. Finally, two new PWM strategies are shown to be the best, for which oscillograms are presented from actual implementation.

Rural energy consumption patterns - A field study

This paper is a condensed version of the final report of a detailed field study of rural energy consumption patterns in six villages located west of Bangalore in the dry belt of Karnataka State in India. The study was carried out in two phases; first, a pilot study of four villages and second, the detailed study of six villages, the populations of which varied from around 350 to about 950. The pilot survey ended in late 1976, and most of the data was collected for the main project in 1977. Processing of the collected data was completed in 1980. The aim was to carry out a census survey, rather than a sample study. Hence, considerable effort was expended in production of both a suitable questionnaire, ensuring that all respondents were contacted, and devising methods which would accurately reflect the actual energy use in various energy-utilising activities. In the end, 560 households out of 578 (97%) were surveyed. The following ranking was found for the various energy sources in order of average percentage contribution to the annual total energy requirement: firewood, 81·6%; human energy, 7·7%; animal energy, 2·7%; kerosene, 2·1%; electricity, 0·6% and all other sources (rice husks, agro-wastes, coal and diesel fuel), 5·3%. In other words commercial fuels made only a small contribution to the overall energy use. It should be noted that dung cakes are not burned in this region. The average energy use pattern, sector by sector, again on a percentage basis, was as follows: domestic, 88·3%; industry, 4·7%; agriculture, 4·3%; lighting, 2·2% and transport, 0·5%. The total annual per capita energy consumption was 12·6 ± 1·2 GJ, giving an average annual household consumption of around 78·6 GJ.

Rural energy consumption patterns - A field study

This paper is a condensed version of the final report of a detailed field study of rural energy consumption patterns in six villages located west of Bangalore in the dry belt of Karnataka State in India. The study was carried out in two phases; first, a pilot study of four villages and second, the detailed study of six villages, the populations of which varied from around 350 to about 950. The pilot survey ended in late 1976, and most of the data was collected for the main project in 1977. Processing of the collected data was completed in 1980. The aim was to carry out a census survey, rather than a sample study. Hence, considerable effort was expended in production of both a suitable questionnaire, ensuring that all respondents were contacted, and devising methods which would accurately reflect the actual energy use in various energy-utilising activities. In the end, 560 households out of 578 (97%) were surveyed. The following ranking was found for the various energy sources in order of average percentage contribution to the annual total energy requirement: firewood, 81A·6%; human energy, 7A·7%; animal energy, 2A·7%; kerosene, 2A·1%; electricity, 0A·6% and all other sources (rice husks, agro-wastes, coal and diesel fuel), 5A·3%. In other words commercial fuels made only a small contribution to the overall energy use. It should be noted that dung cakes are not burned in this region. The average energy use pattern, sector by sector, again on a percentage basis, was as follows: domestic, 88A·3%; industry, 4A·7%; agriculture, 4A·3%; lighting, 2A·2% and transport, 0A·5%. The total annual per capita energy consumption was 12A·6 A± 1A·2 GJ, giving an average annual household consumption of around 78A·6 GJ.

Stochastic modelling of monthly rainfall

A transformation is suggested which can transform a non-Gaussian monthly hydrological time series into a Gaussian one. The suggested approach is verified with data of ten Indian rainfall time series. Incidentally, it is observed that once the deterministic trends are removed, the transformation leads to an uncorrelated process for monthly rainfall. The procedure for normalization is general enough in that it should be also applicable to river discharges. This is verified to a limited extent by considering data of two Indian river discharges.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Variability of Indian summer monsoon rainfall in daily data from gauge and satellite

It has long been thought that tropical rainfall retrievals from satellites have large errors. Here we show, using a new daily 1 degree gridded rainfall data set based on about 1800 gauges from the India Meteorology Department (IMD), that modern satellite estimates are reasonably close to observed rainfall over the Indian monsoon region. Daily satellite rainfalls from the Global Precipitation Climatology Project (GPCP 1DD) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) are available since 1998. The high summer monsoon (June-September) rain over the Western Ghats and Himalayan foothills is captured in TMPA data. Away from hilly regions, the seasonal mean and intraseasonal variability of rainfall (averaged over regions of a few hundred kilometers linear dimension) from both satellite products are about 15% of observations. Satellite data generally underestimate both the mean and variability of rain, but the phase of intraseasonal variations is accurate. On synoptic timescales, TMPA gives reasonable depiction of the pattern and intensity of torrential rain from individual monsoon low-pressure systems and depressions. A pronounced biennial oscillation of seasonal total central India rain is seen in all three data sets, with GPCP 1DD being closest to IMD observations. The new satellite data are a promising resource for the study of tropical rainfall variability.