Tropospheric biennial oscillation and south Asian summer monsoon rainfall in a coupled model

In this study Tropospheric Biennial Oscillation (TBO) and south Asian summer monsoon rainfall are examined in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. High correlation between the observations and model TBO index suggests that the model is able to capture most of the TBO years. Spatial patterns of rainfall anomalies associated with positive TBO over the south Asian region are better represented in the model as in the observations. However, the model predicted rainfall anomaly patterns associated with negative TBO years are improper and magnitudes are underestimated compared to the observations. It is noted that positive (negative) TBO is associated with La Niña (El Niño) like Sea surface temperature (SST) anomalies in the model. This leads to the fact that model TBO is El Niño-Southern Oscillation (ENSO) driven, while in the observations Indian Ocean Dipole (IOD) also plays a role in the negative TBO phase. Detailed analysis suggests that the negative TBO rainfall anomaly pattern in the model is highly influenced by improper teleconnections allied to IOD. Unlike in the observations, rainfall anomalies over the south Asian region are anti-correlated with IOD index in CFSv2. Further, summer monsoon rainfall over south Asian region is highly correlated with IOD western pole than eastern pole in CFSv2 in contrast to the observations. Altogether, the present study highlights the importance of improving Indian Ocean SST teleconnections to south Asian summer rainfall in the model by enhancing the predictability of TBO. This in turn would improve monsoon rainfall prediction skill of the model.     

Evaluation of indicators for desertification risk assessment in part of Thar Desert Region of Rajasthan using geospatial techniques

Desertification has emerged as a major economic, social and environmental problem in the western part of India. The best way of dealing with desertification is to take appropriate measures to arrest land degradation, especially in areas prone to desertification. This requires an early warning system for desertification based on scientific inputs. Hence, in the present study, an attempt has been made to develop a comprehensive model for the assessment of desertification risk in the Jodhpur district of Rajasthan, India, using 23 desertification indicators. Indicators including soil, climate, vegetation and socio-economic parameters were integrated into a GIS environment to get environmental sensitive areas (ESAs) to desertification. Desertification risk index (DRI) was calculated based on ESAs to desertification, the degree of land degradation and significant desertification indicators obtained from the stepwise multiple regression model. DRI was validated by using independent indicators such as soil organic matter content and cation exchange capacity. Multiple regression analysis shows that 16 indicators out of 23 were found to be significant for assessing desertification risk at a 99% confidence interval with \(R^{2}=0.83\). The proposed methodology provides a series of effective indicators that would help to identify where desertification is a current or potential problem, and what could be the actions to alleviate the problem over time.     

Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification

The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction.     

Lead isotopic ratios in source apportionment of heavy metals in the street dust of Kolkata,India

Street dust from 29 locations, in some of the busiest parts of north and south Kolkata, was analysed for heavy metal composition. The decreasing order of average metal concentrations (mg kg^?1) found was Mn (390) > Pb (380) > Zn (300) > As (96) > Cu (61) > Cr (40) > Co (13) > Ag (2.1). The heavy metal composition of the Kolkata dust was compared with reported data for other cities. Enrichment factors of Pb and As were high. Multivariate statistical analysis of the heavy metals and analysis of lead isotopic ratios of the dust revealed a predominant anthropogenic influence in the contamination. The range of lead isotopic ratios found in the dust was between 0.8789 and 0.8998 with a mean Pb concentration of 383 mg kg^?1. The three Pb isotope plots of street dust, diesel and rainwater clustered linearly, while coal did not fit into this trend. The highest 207/206 lead isotopic ratio obtained was from diesel with a mean value of 0.9015, followed by the rainwater sample. The application of the binary mixing model showed that about 66.86% of lead contamination in the street dust was sourced from the atmosphere. The two components extracted by the principal component analysis explained 64.34% of the total variance. Vehicular and industrial emissions appeared to be an important contributor to the accumulation of heavy metals in the dust. The health risk assessment study of the dust indicated carcinogenic risk associated with As and Cr.     

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.     

Uncertainty Analysis for Assessing Leakage Through Water Tunnels: A Case from Nepal Himalaya

Water leakage problems in unlined or shotcrete lined water tunnels are not new issues. In many occasions severe water leakage problems have been faced that not only have reduced the stability of the rock mass, but also have caused valuable water to be lost from it, causing safety risk as well as huge economic loss to the projects. Hence, making tunnels water tight plays an important role in improving stability and safety of underground excavations. The real challenge is however accurate prediction and quantification of possible water leakage, so that cost consequences can be incorporated during planning of a water conveying tunnel project. The main purposes of this paper are to analyze extensive data on leakage test carried out through exploratory drillhole used to define the need for pre-injection grouting of Khimti headrace tunnel and to carry out probabilistic approach of uncertainty analysis based on relationship established between leakage, hydrostatic head and selected Q-value parameters. The authors believe that the new approach regarding uncertainty analysis of leakage presented in this paper will improve the understanding of leakage characteristics of the rock mass, and hope this will lead to a better understanding concerning quantification of possible water leakage from unlined and shotcrete lined water tunnels.     

Biogeochemistry and paleoclimate variability during the Holocene: a record from Mansar Lake,Lesser Himalaya

Investigation of biogeochemistry and amino acids on a 30-m-long core from Mansar Lake has thrown light on palaeoclimate variability during the Holocene period. The C/N ratio between 6 and 10 with some deviations and δ13C mostly between −20 and −22‰ in the shallow core, as well as a C/N ratio greater than 13 reaching from 19 to 20 and correspondingly lower δ13C of –28‰ in the deeper core suggest an aquatic source of carbon in the former and a cellulose-rich land plant source in the latter. This is supported by the abrupt increase in organic carbon content in the deeper core compared to the shallow core, which indicates a hot and wet climatic regime during the early Holocene and dry and cold during the late Holocene period. The amino acid data AA-C/C% and AA-N/N% are higher in shallow sediments compared to deeper sediments, indicating an aquatic plant source in the shallow core and greater supply of land plant sources in the deeper core. The lower percentage of the non-protein amino acids β-alanine (ALA) and γ-amino-butyric acid in the shallow core compared to the deeper core indicates different sources of organic matter in the lake basin. The higher amino acid ratio Asp/β-Ala (16.99 av.) and Glu/γ-Aba (18.18 av.) in the shallow core and lower ratios (10.32 and 12.41 av.) in the deeper core, and Asp/Glu (1.52 av.) and β-Ala/γ-Aba (1.61 av.) ratios in the former, which are potential indicators of the nature of the organic matter, are higher in the shallow core relative to the deeper core (1.33 and 1.23 av.), indicating relative biodegradation of organic matter in deeper sediments. It has been observed that the organic matter associated with the dry season is relatively less biodegraded, as evidenced from their higher ratios, and is more biodegraded in the wet season as their ratios are lower in the river sediments. In the absence of a bacterial contribution of organic matter from the soil source in this lake, since Mansar Lake is a non-drainage type, it is envisaged that the climatic variation may be responsible for biodegradation in the deeper core sediments. Therefore, the C/N ratio and δ13C values supported by amino acid data, the latter being significant in revealing primary productivity and a terrestrial source of organic matter, suggest a hot and wet climatic regime during the early Holocene (ca. 7580 bp) and a dry and cold in the late Holocene period (ca. 4050 bp).     

Variation of Emission Line Width in Mid- and High-Latitude Corona

Spectroscopic studies of the solar corona, using the high spatial and spectral resolution 25-cm coronagraph at the Norikura Solar Observatory for equatorial off-limb observations, indicated that the variation of radiance and line width with height is different for different temperature lines. The line width of the forbidden red emission line [Fe x] 6374 Å was found to increase with height, and that of the green emission line [Fe xiv] 5303 Å decreased with height. This had been interpreted in terms of the interaction between different temperature plasmas but needed to be confirmed. Further observations were made on several days during 2004, in two emission lines simultaneously covering the mid-latitude and polar regions to investigate the existence of the observed variation in other parts of the solar corona. In this study, we have analysed several raster scans that cover mid- and high-latitude regions of the off-limb corona in all four bright emission lines [Fe x] 6374 Å, [Fe xi] 7892 Å, [Fe xiii] 10747 Å, and [Fe xiv] 5303 Å. We find that the FWHM of the red line increases with height and that of the green line decreases with height, similar to the observations in the equatorial regions. The line widths are higher in the polar regions for all of the observed emission lines except the green line. Higher values of FWHM in polar regions may imply higher non-thermal velocities, which could be further linked to a non-thermal source powering the solar-wind acceleration, but the reason for the behaviour of the green emission line remains to be explored.     

Dynamics of Coronal Bright Points as Seen by Sun Watcher Using Active Pixel System Detector and Image Processing (SWAP), Atmospheric Imaging Assembly (AIA), and Helioseismic and Magnetic Imager (HMI)

The Sun Watcher using Active Pixel system detector and Image Processing (SWAP) onboard the PRoject for OnBoard Autonomy-2 (PROBA2) spacecraft provides images of the solar corona in EUV channel centered at 174 Å. These data, together with the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO), are used to study the dynamics of coronal bright points. The evolution of the magnetic polarities and associated changes in morphology are studied using magnetograms and multi-wavelength imaging. The morphology of the bright points seen in low-resolution SWAP images and high-resolution AIA images show different structures, whereas the intensity variations with time show similar trends in both SWAP 174 Å and AIA 171 Å channels. We observe that bright points are seen in EUV channels corresponding to a magnetic flux of the order of 10¹⁸ Mx. We find that there exists a good correlation between total emission from the bright point in several UV–EUV channels and total unsigned photospheric magnetic flux above certain thresholds. The bright points also show periodic brightenings, and we have attempted to find the oscillation periods in bright points and their connection to magnetic-flux changes. The observed periods are generally long (10?–?25 minutes) and there is an indication that the intensity oscillations may be generated by repeated magnetic reconnection.