Geomagnetic field variation during winter storm at localized southern and northern high latitude

This paper presents the effect of geomagnetic storm on geomagnetic field components at Southern (Maitri) and Northern (Kiruna) Hemispheres. The Indian Antarctic Station Maitri is located at geom. long. 66.03° S; 53.21° E whereas Kiruna is located at geom. long. 67.52° N; 23.38° E. We have studied all the geomagnetic storms that occurred during winter season of the year 2004–2005. We observed that at Southern Hemisphere the variation is large as compared to the Northern Hemisphere. Geomagnetic field components vary when the interplanetary magnetic field is oriented in southward direction. Geomagnetic field components vary in the main phase of the ring current. Due to southward orientation of vertical component of IMF reconnection takes place all across the dayside that transports plasma and magnetic flux which create the geomagnetic field variation.     

Effect of interplanetary magnetic field and disturb storm time on H component

A fluxgate digital magnetometer is used to study the variation of magnitude of H component during geomagnetic storm events of April, July and November 2004 at southern subauroral localized region at “MAITRI” (geom. lat. 62°S, long. 52.8°E). We also study the effect of vertical component of interplanetary magnetic field (IMF) on the variation of the magnitude of H component during storm time of April, July and November 2004. Results show that before sudden storm commencement (SSC) time magnitude of H component and IMF show smooth variation but after SSC of first storm of 22 July 2004, the magnitude of the H component shows fluctuations and at 09:00 UT it increases, but during second storm of 24 July 2004, the magnitude of H component indicates large fluctuations and it increases rapidly at 04:00 UT.     

Copula entropy coupled with artificial neural network for rainfall–runoff simulation

The rainfall–runoff relationship is not only nonlinear and complex but also difficult to model. Artificial neural network (ANN), as a data-driven technique, has gained significant attention in recent years and has been shown to be an efficient alternative to traditional methods for hydrological modeling. However, for different input combinations, ANN models can yield different results. Therefore, input variables and ANN types need to be carefully considered, when using an ANN model for stream flow forecasting. This study proposes the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial MI directly which avoids calculating the marginal and joint probability distributions. Three different ANN models, namely multi-layer feed (MLF) forward networks, radial basis function networks and general regression neural network, were applied to predict stream flow of Jinsha River, China. Results showed that the inputs selected by the CE method were better than those by the traditional linear correlation analysis, and the MLF ANN model with the inputs selected by CE method obtained the best predicted results for the Jinsha River at Pingshan gauging station.     

Changing structure of the precipitation process during 1960–2005 in Xinjiang, China

Using daily precipitation data spanning 1960–2005 from 51 meteorological stations in Xinjiang province, China, spatial and temporal changes in consecutive maximum wet days in the year, summer, and winter were investigated. Fifteen precipitation extreme indices, which reflect the attributes of consecutive maximum wet days, were defined, and the modified Mann–Kendall test was applied to detect the tendencies, and changes in the indices were evaluated through linear regression with the F test. Results showed that: (1) two consecutive wet days occurred most frequently in the year and summer, and the fractional contributions and precipitation intensities decreased as the duration increased; in winter, one wet day had the maximum possibility, fractional contributions decreased and intensities increased as the duration increased. (2) The possibility of consecutive wet days which had short durations reduced, while those of long durations increased; annual fractional contributions of short durations decreased, while those of long durations increased; summer and winter fractional contribution of all durations decreased first and then increased; the intensities of all durations increased. (3) The wet tendency was identified in Xinjiang; the wet trend in Southern Xinjiang was more significant than Northern Xinjiang in summer, while in winter the wet tendency in Northern Xinjiang was more pronounced.     

Paleomires of Eocene lignites of Bhavnagar,Saurashtra basin (Gujarat), western India: Petrographic implications

The Tertiary basins of Gujarat have always been a potential target for their hydrocarbon resources. The lignite resources of the region have also been an important field of research. The present paper presents the results of the petrological study carried out on the lignites of the Saurashtra basin. For this purpose samples were collected from lower and upper lignite seams from the Surkha lignite mine of Bhavnagar, Saurashtra. These samples were subjected to detailed petrographic analysis (both maceral and microlithotype). The study reveals that these lignites are dominantly composed of huminite group macerals while liptinite and inertinite group macerals occur in subordinate amounts. These lignites have attained a thermal maturity up to 0.28-0.30 percent vitrinite reflectance (VRr) which classifies them as ‘low rank C’ coals. Moreover, Bhavnagar lower lignite seam shows relatively less gelification as compared to the upper seam which suffered relatively more biochemical degradation. These lignites are characterized by high gelification index (GI) and low tissue preservation index (TPI).With the help of petrography based facies models an attempt has been made to reconstruct the environment of the paleomire of these lignites.     

Performance evaluation of a reverse-gradient artificial recharge system in basalt aquifers of Maharashtra,India

Hydrogeology Journal - Drinking water scarcity in rural parts of central India in basaltic terrain is common. Most of the rural population depends on groundwater sources located in the fractured...     

Spatiotemporal trends of aridity index in Shiyang River basin of northwest China

Drought/wetness conditions are fundamental not only for agricultural production but also ecology, human health, and economic activity. Dryness/wetness is a function of precipitation, temperature, vegetation and potential evapotranspiration. Regions with low moisture are often characterized by aridity which, in turn, reflects the degree of meteorological drought. Observed climatic data from eleven meteorological stations in and around Shiyang River basin, China, were used to calculate the aridity index (AI) which was defined as the ratio of potential evapotranspiration (ET₀) to precipitation (P). ET₀ was calculated using the Penman–Monteith method. The ordinary kriging method was used to interpolate the spatial variability of ET₀, P and AI. The Mann–Kendall test with a pre-whitening method was employed using the Yue and Wang autocorrelation correction to detect temporal trends. The Theil–Sen estimator was used to estimate the slopes of trend lines. Results showed a higher AI in the north basin and a lower AI in the Qilian Mountain region. Annual ET₀ and P had increasing trends with a slope of 0.672 and 0.459 mm per year, respectively, but trends were not statistically significant for most stations. While annual AI had a slight decreasing trend with a slope of ?0.01 per year, the trend was not statistically significant for all stations. The decreasing trends in winter AI (at a rate of ?0.313/a) was more significant than that in other seasons. The study indicates that the Shiyang River basin is getting slightly wetter, especially in winter.     

Improvement in predictability of waves over the Indian Ocean

Accurate prediction of ocean surface waves is a challenging task with many associated difficulties. Availability of good quality wind and wave information from satellite platforms inspired the scientific community to assimilate such data in various spectral wave models for enhancing the accuracy of prediction. Over the Indian Ocean, which is the region of interest for the present study, wave heights in extreme situation can go up to 12–14 m, thereby increasing the probability of coastal hazards. This region is further governed by the southern ocean swells that propagate thousands of kilometers. These are, in general, not well captured by the spectral wave models. Therefore, assimilation of altimeter data in open ocean wave model WAM has been attempted with the aim of enhancing the quality of prediction of significant wave height. Further, simulated wave spectra have been assimilated in a coastal wave model SWAN. This assimilation has been found to significantly improve the prediction of the height of wind waves as well as swell waves. V. Bhatt and S. Surendran are former students of Meteorology and Oceanography Group, Space Applications Centre, ISRO, Ahmedabad.     

A geomorphologic kinematic‐wave (GKW) model for estimation of floods from small alpine watersheds

A geomorphologic kinematic‐wave (GKW) model was developed for simulation of extreme floods from small alpine catchments. The GKW model couples the kinematic‐wave theory and the geomorphologic representation of the catchment based on the Horton–Strahler ordering scheme. The model was tested on two small alpine catchments in Switzerland, and the agreement between simulated and observed floods was good. Care must however be taken with the computation of slope and roughness parameters. Copyright © 1999 John Wiley & Sons, Ltd.