Monitoring of Dead Sea water surface variation using multi-temporal satellite data and GIS

Remote sensing (RS) and geographic information systems (GIS) are very useful for environmental-related studies, particularly in the field of surface water studies such as monitoring of lakes. The Dead Sea is exposed to very high evaporating process with considerable scarcity of water sources, thus leading to a remarkable shrinkage in its water surface area. The lake suffers from dry out due to the negative balance of water cycle during the previous four decades. This paper discusses the application of RS, GIS, and Global Positioning System to estimate the lowering and the shrinkage of Dead Sea water surface over the period 1810–2005. A set of multi-temporal remote sensing images were collected and processed to show the lakes aerial extend shrinkage from 1973 up to 2004. Remote sensing data were used to extract spatial information and to compute the surface areas for Dead Sea for various years. The current study aims at estimating the fluctuation of Dead Sea level over the study period with special emphasis on the environmental impact assessment that includes the degradation level of the Dead Sea. The results indicated that there is a decrease of 20 m in the level of the Dead Sea that has occurred during the study period. Further, the results showed that the water surface area of the Dead Sea has shrunk from 934.26 km² in 1973 to 640.62 km² in 2004.     

Seismic performance evaluation and risk assessment of typical reinforced concrete frame buildings with masonry infill and conventional vertical extension in Nepal

Bulletin of Earthquake Engineering - Many reinforced concrete (RC) frame buildings in Nepal were significantly damaged by the 7.8 magnitude (Mw) earthquake in Nepal on April 25, 2015. To contribute...     

A family of exact solutions of Einstein-Maxwell field equations in isotropic coordinates: an application to optimization of quark star mass

In the present paper, we have obtained a class of charged super dense star models, starting with a static spherically symmetric metric in isotropic coordinates for perfect fluid by considering Hajj-Boutros (in J. Math. Phys. 27:1363, 1986) type metric potential and a specific choice of electrical intensity which involves a parameter K. The resulting solutions represent charged fluid spheres joining smoothly with the Reissner-Nordstrom metric at the pressure free interface. The solutions so obtained are utilized to construct the models for super-dense star like neutron stars (ρ _b =2 and 2.7×10¹⁴ g/cm³) and Quark stars (ρ _b =4.6888×10¹⁴ g/cm³). Our solution is well behaved for all values of n satisfying the inequalities \(4 < n \le4(4 + \sqrt{2} )\) and K satisfying the inequalities 0≤K≤0.24988, depending upon the value of n. Corresponding to n=4.001 and K=0.24988, we observe that the maximum mass of quark star M=2.335M _⊙ and radius R=10.04 km. Further, this maximum mass limit of quark star is in the order of maximum mass of stable Strange Quark Star established by Dong et al. (in arXiv:1207.0429v3, 2013). The robustness of our results is that the models are alike with the recent discoveries.     

Runoff modelling of a small watershed using satellite data and GIS

This study was conducted for the Nagwan watershed of the Damodar Valley Corporation (DVC), Hazaribagh, Bihar, India. Geographic Information System (GIS) was used to extract the hydrological parameters of the watershed from the remote sensing and field data. The Digital Elevation Model (DEM) was prepared using contour map (Survey of India, 1:50000 scale) of the watershed. The EASI/PACE GIS software was used to extract the topographic features and to delineate watershed and overland flow-paths from the DEM. Land use classification were generated from data of Indian Remote Sensing Satellite (IRS-1B—LISS—II) to compute runoff Curve Number (CN). Data extracted from contour map, soil map and satellite imagery, viz. drainage basin area, basin shape, average slope of the watershed, main stream channel slope, land use, hydrological soil groups and CN were used for developing an empirical model for surface runoff prediction. It was found that the model can predict runoff reasonably well and is well suited for the Nagwan watershed. Design of conservation structures can be done and their effects on direct runoff can be evaluated using the model. In broader sense it could be concluded that model can be applied for estimating runoff and evaluating its effect on structures of the Nagwan watershed.     

Spit and Inlet Morphodynamics of a Tropical Coastal Lagoon

The morphological changes of spits and inlets of the Chilika lagoon, the largest brackish water tropical coastal lagoon in Asia, are investigated using real-time kinematic GPS observation and numerical models during 2009–2013. The seasonal/interannual variations of the spit and inlet cross-sectional areas with varying widths and depths are recorded in association with different physical processes. The results show significant changes in spit morphology: particularly, the south spit accreted continuously, while the middle and north spits eroded. The cross-sectional depth of inlets becomes narrower and deeper during summer and winter seasons, while they are wider and shallower during the monsoon. The model results show that sediment transport rate is larger during monsoon and summer, while it is relatively less during the winter. Alongshore, sediment transport is predominantly northward throughout the study period. The result shows that gain/loss of the spits and closure/opening of inlets are significantly controlled by the high wave power, longshore drifts, and river discharge. The study demonstrates that the combined use of observational and numerical models is very effective to understand the changes of spit and inlet morphology and their impact on ecological conditions of the lagoon environment.     

Alongshore Sediment Transport Near Tidal Inlets of Chilika Lagoon; East Coast of India

ABSTRACT

Chilika, a lagoon along the east coast of India, is undergoing transformation due to frequent shoreline change near inlet(s). Shoreline change near inlet includes change in position and shape of inlet, inlet channel length, and spit growth/erosion. These variable features of lagoon inlet(s) critically depend on alongshore sediment transport (LST) and discharge (water and sediment) from the lagoon to the sea. The LST and the processes responsible for sand spit growth/erosion, considered as important attributes of inlet stability, are the subject matter of the present investigation and hence the study assumes importance. The study includes integration of observational and modeling framework. Observations include nearshore wave, bathymetry, beach profile, shoreline and sediment grain size of spits while numerical modeling includes simulation of the wave using MIKE 21 Spectral Wave model and LST simulation using LITtoral DRIFT. The results indicate that the predominant wave directions as S and SSE, which induces round the year south to north alongshore transport with significant seasonal variation in magnitude. The estimated LST closely matches with previous studies near Chilika inlet and for other locations along the Odisha coast. Besides temporal variability, the study reveals spatial variability in alongshore transport near Chilika inlet and considers it as one of the important attributes along with northward spit growth for inlet migration/closure/opening.     

Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.     

Branching ratios for the photoionization cross section of atomic oxygen

Branching ratios $\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^2\text{D}{}^0\text{)}\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^4\text{S}{}^0\text{)}$ and $\text{σ(}\text{O}{}^0{}^3\text{P}\text{→}\text{O}{}^{\mathrm{+}}{}^2\text{P}{}^0\text{)}\text{σ (}\text{O}{}^0{}^3\text{P}\text{→}{}^4\text{S}{}^0\text{)}$ are calculated at 584 Å and 304 A employing the close-coupling approximation to compute the photoionization cross section values. The coupled channels include the states dominated by the ground configuration 1s²2s²p³ of O⁺and the next excited configuration ls²2s2p⁴. It is found that the partial c section $\text{σ(}{}^2\text{D}{}^0\text{)}$ decreases more rapidly than $\text{σ(}{}^2\text{P}{}^0\text{)}$, and at the lower wavelength 304 Å, the ratio $\text{σ(}{}^2\text{D}{}^0\text{)}\text{σ(}{}^4\text{S}{}^0\text{)}\text{<}\text{σ(}{}^2\text{P}{}^0\text{)}\text{σ(}{}^4\text{S}{}^0\text{)}$. Present results at 304 Å differ considerably from previous work.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.