Search for the near infrared counterparts to the IRAS sources

As part of a program to search for the near infrared counterparts to the far infrared sources discovered by the Infrared Astronomical Satellite (IRAS) we have scanned two areas including the IRAS error boxes for the sources IRAS 0453+444 and IRAS 1717-087 at 2.2 m down to the flux density limit of 1 Jy. Results on the IRAS sources and the detection of two new bright near infrared sources are reported and discussed.     

Spatial variations in chemical compositions along Langtang–Narayani river system in central Nepal

Surface water samples were collected from Langtang Lirung glacier outlet point to the Narayani river system in central Nepal in order to investigate the role of elevation in the variation of chemistry along the drainage networks. The chemistry of Langtang–Narayani river system was dominated by sulfide oxidation coupled with carbonate dissolution and weathering of silicate minerals. Calcium and magnesium concentrations were relatively higher than other cations and the sum of both species strongly correlated with alkalinity, supporting the dissolution of carbonate and dolomite as the dominant source for these ions. Aluminosilicate minerals primarily as albite and anorthite appeared as dominant silicate minerals within the drainage basin. Bisiallitization was the dominant type of weathering within the entire drainage system. Hydrogen ion concentration was lower in the low elevation sites than in high elevation sites reflecting the more consumption of carbon dioxide in the low elevation sites due to enhanced chemical weathering rates. Furthermore, major solutes like sum of base cations, silicon as well as alkalinity increased in concentration in the lower elevation sites. All regulating factors appeared to be directly related to elevation and hence elevation appeared to be the prime factor for the variation in chemical species along the Langtang–Narayani river system. Toshiyuki Masuzawa: deceased.     

Late Quaternary stratigraphic development in the lower Luni,Mahi and Sabarmati river basins,western India

This study reviews the Quaternary alluvial stratigraphy in three semi-arid river basins of western India i.e., lower Luni (Rajasthan), and Mahi and Sabarmati (Gujarat alluvial plains). On the basis of OSL chronologies, it is shown that the existing intra-valley lithostratigraphic correlations require a revision. The sand, gravel and mud facies are present during various times in the three basins, however, the fluvial response to climate change, and the resulting facies associations, was different in the Thar desert as compared to that at the desert margin; this makes purely lithostratigraphic correlations unviable. It is further shown that the rivers in the Thar desert were more sensitive to climate change and had small response times and geomorphic thresholds as compared to the desert-margin rivers. This is illustrated during the early OIS 1, when the Luni river in the Thar desert was dynamic and showed frequent variations in fluvial styles such as gravel bedload braided streams, sand-bed ephemeral streams and meandering streams, all followed by incision during the early Holocene. The coeval deposits in Sabarmati, however, only show a meandering, floodplain-dominated river. Late Quaternary alluvial deposits in these basins unconformably overlie some older deposits that lack any absolute chronology. Based on the facies types and their associations, and the composition and architecture of the multistoried gravel sheets in the studied sections, it is suggested that older deposits are of pre-Quaternary age. This hypothesis implies the presence of a large hiatus incorporating much of the Quaternary period in the exposed sections     

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite‐based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst flood‐affected districts lie in this region.     

Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground‐based point rainfall data from sparsely positioned rain‐gauge stations in a rain‐gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging [OK], ordinary cokriging [OCK], kriging with an external drift [KED]), and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain‐gauge stations of the catchments during 1980–2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and kriging with an external drift methods. The prediction performance of the adopted interpolation methods is assessed through cross‐validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment.     

A framework for risk-based assessment of urban floods in coastal cities

Natural Hazards - Many coastal cities in developing countries are at the risk of flooding due to a progressive increase in the built-up areas and poor management of storm water. The flooding...     

Prediction of the Maximum Amplitude and Timing of Sunspot Cycle 24

Precursor techniques, in particular those using geomagnetic indices, often are used in the prediction of the maximum amplitude for a sunspot cycle. Here, the year 2008 is taken as being the sunspot minimum year for cycle 24. Based on the average aa index value for the year of the sunspot minimum and the preceding four years, we estimate the expected annual maximum amplitude for cycle 24 to be about 92.8±19.6 (1-sigma accuracy), indicating a somewhat weaker cycle 24 as compared to cycles 21 – 23. Presuming a smoothed monthly mean sunspot number minimum in August 2008, a smoothed monthly mean sunspot number maximum is expected about October 2012±4 months (1-sigma accuracy).     

North Atlantic warming: patterns of long-term trend and multidecadal variability

Climate fluctuations in the North Atlantic Ocean have wide-spread implications for Europe, Africa, and the Americas. This study assesses the relative contribution of the long-term trend and variability of North Atlantic warming using EOF analysis of deep-ocean and near-surface observations. Our analysis demonstrates that the recent warming over the North Atlantic is linked to both long-term (including anthropogenic and natural) climate change and multidecadal variability (MDV, ~50–80 years). Our results suggest a general warming trend of 0.031 ± 0.006°C/decade in the upper 2,000 m North Atlantic over the last 80 years of the twentieth century, although during this time there are periods in which short-term trends were strongly amplified by MDV. For example, MDV accounts for ~60% of North Atlantic warming since 1970. The single-sign basin-scale pattern of MDV with prolonged periods of warming (cooling) in the upper ocean layer and opposite tendency in the lower layer is evident from observations. This pattern is associated with a slowdown (enhancement) of the North Atlantic thermohaline overturning circulation during negative (positive) MDV phases. In contrast, the long-term trend exhibits warming in tropical and mid-latitude North Atlantic and a pattern of cooling in regions associated with major northward heat transports, consistent with a slowdown of the North Atlantic circulation as evident from observations and confirmed by selected modeling results. This localized cooling has been masked in recent decades by warming during the positive phase of MDV. Finally, since the North Atlantic Ocean plays a crucial role in establishing and regulating the global thermohaline circulation, the multidecadal fluctuations discussed here should be considered when assessing long-term climate change and variability, both in the North Atlantic and at global scales.     

Porosity and permeability prediction from wireline logs using artificial neural networks: a North Sea case study

Estimations of porosity and permeability from well logs are important yet difficult tasks encountered in geophysical formation evaluation and reservoir engineering. Motivated by recent results of artificial neural network (ANN) modelling offshore eastern Canada, we have developed neural nets for converting well logs in the North Sea to porosity and permeability. We use two separate back-propagation ANNs (BP-ANNs) to model porosity and permeability. The porosity ANN is a simple three-layer network using sonic, density and resistivity logs for input. The permeability ANN is slightly more complex with four inputs (density, gamma ray, neutron porosity and sonic) and more neurons in the hidden layer to account for the increased complexity in the relationships. The networks, initially developed for basin-scale problems, perform sufficiently accurately to meet normal requirements in reservoir engineering when applied to Jurassic reservoirs in the Viking Graben area. The mean difference between the predicted porosity and helium porosity from core plugs is less than 0.01 fractional units. For the permeability network a mean difference of approximately 400 mD is mainly due to minor core-log depth mismatch in the heterogeneous parts of the reservoir and lack of adequate overburden corrections to the core permeability. A major advantage is that no a priori knowledge of the rock material and pore fluids is required. Real-time conversion based on measurements while drilling (MWD) is thus an obvious application.