Data base management system for erosivity values

In order to check the premature siltation of the reservoirs and guard against the drop in the irrigation potential, the Government of India has launched the schemes of soil conservation and integrated watershed management in the catchments of RVPs and Flood Prone rivers. Owing to the large financial and manpower commitments needed to implement and execute soil conservation measures over vast catchment areas, a priority approach for treatment was identified. The methodology developed for prioritization of watersheds of a catchment area conceptualizes sedimentation of the reservoirs as a multiplicative function of erosivity value and the delivery ratio. This paper deals with the development of a computerized data base software module ‘WEIGHT’ for determination of erosivity values for the mapping units comprising assemblages of the varying combinations of climate, physiography and slope, land use and cover conditions, soil characteristics (texture, solumn thickness, permeability and pH) and the existing erosion and soil conservation measures. The WEIGHT software package is coded in FORTRON-4 for PDP 11/83 operating system. the data base comprises storage of the attributes of the different erosivity determinants of the mapping units with predetermined erosivity values sequentially on a disk and comparing the attributes of a new mapping unit to get the most probabilities erosivity value. The objective has been to eliminate the personal bias and bring about the objectivity in the process of assigning erosivity values to the different mapping units. The data base design, design logic and operational sequence of the data base are discussed in the paper.     

Solubility of alumina in orthopyroxene plus spinel as a geobarometer in complex systems. Applications to spinel-bearing alpine-type peridotites

The addition of Fe and Cr to the simple system MgO-SiO₂-Al₂O₃ markedly affects the activities of phases involved in the equilibrium

X-ray transient sources

The galactic distribution and physical nature of X-ray transient sources is investigated. Two types of transients are considered. The observational data on 41 X-ray transient sources are given, and the average parameters of hard and soft X-ray transients are estimated.     

The Upper Middle Rhine Valley as a risk area

The Upper Middle Rhine Valley, granted the status of a World Heritage site, is well known for its unique inner narrow valley of Quaternary age with its historical legacy of numerous medieval castles and old towns. Less known is that this has always been a risk area of floods and gravitative mass movements. Up to the recent past, mainly ice floods caused enormous damage. The inhabitants of the valley were well aware that they lived in a risk area, but they had learned to handle the flood hazard. With the demise of ice floods over the last 40 years, due to climate change and because of the additional heating of the river water by power plants, the awareness of flood hazards has been much diminished, in contrast to that of potential damage by rockfalls and landslides which were also much feared in the past, though at the local level only. Still in the people’s memory is the Kaub catastrophe of March 10, 1876, when 28 persons were killed by a landslide. Nowadays, even minor rockfalls are a major threat, as they will affect the much-used traffic lines on both banks of the river, in particular the railroads. Therefore, since 2002, on behalf of German Rail (Deutsche Bahn, DB), all problematic slopes have been protected by costly steel-ring nets, although they are an aesthetic problem by UNESCO standards. The feeling of absolute safety created among the public is only subjective, though, as planners are well aware of. Moreover, the impact of modern climate change on slope stability is nearly unknown. Therefore, it is still necessary to develop a risk map for the narrow valley, with emphasis on gravitational hazards.     

A Long-Term Decrease of the Mid-Size Segmentation Lengths Observed in the He <Emphasis Type="SmallCaps">ii</Emphasis> (30.4 nm) Solar EUV Emission

Power spectra of segmentation-cell length (a dominant length scale of EUV emission in the transition region) from full-disk He?ii extreme ultraviolet (EUV) images observed by the Extreme ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO) and the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) during periods of quiet-Sun conditions for a time interval from 1996 to 2015 were analyzed. The spatial power as a function of the spatial frequency from about 0.04 to 0.27 (EIT) or up to 0.48 (AIA) Mm^?1 depends on the distribution of the observed segmentation-cell dimensions – a structure of the solar EUV network. The temporal variations of the spatial power reported by Didkovsky and Gurman (Solar Phys. 289, 153, 2014) were suggested as decreases at the mid-spatial frequencies for the compared spectra when the power curves at the highest spatial frequencies of 0.5 pix^?1 were adjusted to match each other. This approach has been extended in this work to compare spectral ratios at high spatial frequencies expressed in the solar spatial frequency units of Mm^?1. A model of EIT and AIA spatial responses allowed us to directly compare spatial spectral ratios at high spatial frequencies for five years of joint operation of EIT and AIA, from 2010 to 2015. Based on this approach, we represent these ratio changes as a long-term network transformation that may be interpreted as a continuous dissipation of mid-size network structures to the smaller-size structures in the transition region. In contrast to expected cycling of the segmentation-cell dimension structures and associated spatial power in the spectra with the solar cycle, the spectra demonstrate a significant and steady change of the EUV network. The temporal trend across these structural spectra is not critically sensitive to any long-term instrumental changes, e.g. degradation of sensitivity, but to the change of the segmentation-cell dimensions of the EUV network structure.