Biostratigraphy and floristic evolution of coal swamp floras of a part of Talcher Basin,India: a window on a Permian temperate ecosystem

The present megafloral assemblage recorded from the Barakar sediments of Dholpahar section along Singda rivulet near Gopal Prasad Village in Talcher Basin comprises of equisetaceous stems, Gangamopteris buriadica, Palaeovittaria kurzii and 19 species of the genus Glossopteris. Record of Gangamopteris, Palaeovittaria and many narrow mesh forms of Glosspteris viz., G. angustifolia, G. churiensis, G. communis, G. recurva, G. spatulata, G. stenoneura, G. tenuifolia, G. vulgaris and G. zeilleri from two older fossiliferous horizons demonstrates that these fossils were preserved during Lower Barakar sedimentation. The report of middle and broad mesh forms of Glossopteris viz., G. barakarensis, G. browniana, G. indica, G. intermittens, G. karharbariensis, G. nakkarea, G. oldhamii, G. taeniensis and G. retifera in the youngest fossiliferous horizons reveals that these fossils were preserved during the deposition of Upper Barakar sediments. The continuation of some of the Karharbari plant fossils in the early phase of Barakar Formation and their disappearance in the flora of Late Barakar suggests a shift in the climatic setup. Palaeoclimate and palaeovegetation of this area are also summarised in this study. Moreover, the fossil assemblages of different fossiliferous beds of Dholpahar section demonstrate the evolution of midrib and meshes in different reticulate leaves.     

Assessing flood inundation mapping through estimated discharge using GIS and HEC-RAS model

Water discharge is the main parameter in hydraulic modeling for flood hazard assessment. However, the unavailability of data on discharge and observed river morphologies resulted in erroneous calculations and irregularities in flood inundation mapping. The objectives of this study are (i) to investigate uncertainties of hydraulic parameters (width, cross-sectional depth, and channel slope) used in discharge equation and (ii) to examine the influence of estimate discharge on water extent and flood depth with different boundary conditions on interferometric synthetic aperture radar (IFSAR) and modified IFSAR DEMs. Sensitivity analysis was conducted with the Monte Carlo simulation method to generate random data combinations. Bjerklie’s equation was used to calculate discharge based on the three variables, and Manning’s n was substituted into the Hydrologic Engineering Center River Analysis System (HEC-RAS) model. TerraSAR-X was used to distinguish existing flood water bodies and normal water extent. The uncertainty of the combined variables was assessed with the likelihood measures such as F-statistic, mean absolute error, root mean square error, and Nash–Sutcliffe efficiency which compares observed and predicted inundated area as well as flood water depth simulated using the HEC-RAS model.     

A review on the effect of rubber membrane in triaxial tests

This paper presents a review on the effect of rubber membranes on the measured stress and volume change data during the triaxial tests. In many instants, the error associated due to membrane penetration is measured to be close to the total sample volumetric strains. Hence, in addition to precise measurements, adopting an appropriate correction to the measured data is pivotal. This paper provides a detailed review on the methods used to estimate and reduce the influence of rubber membrane on the measured triaxial test results. The discussion therefore clearly highlights the stiffness, thickness, and diameter of the membrane used, to have significant influence on the measured deviatoric stress and the volume change data. The mean grain size of sample has an equally significant influence on the volume change. The methods in general are limited to test conditions, and therefore, it is practically uncertain to have a generalized correction procedure. Effectively, the outcome from this review work enhances the understanding of the reader towards the effect, methods, and corrections to be considered in triaxial testing due to rubber membrane and membrane penetration effects.     

Geospatial modeling to identify the effects of anthropogenic processes on landscape pattern change and biodiversity

This research used geospatial data to quantify biodiversity changes and landscape pattern change to track anthropogenic impacts of such changes at the Mouteh Wildlife Refuge (MWR), Isfahan, Iran. Satellite image duration of four decades, LandSat_1-5, and IRS-P6 data were used to develop land cover classification maps for 1971, 1987, 1998, and 2011. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated and compared for a 40-year period. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. The number and size of land cover patches, the degree of naturalness, and the diversity indices were calculated. Overall changes in natural land use between 1971 and 1998 at MWR showed that the number of patches for natural land use has increased, but it also showed a decrease in 2011. Similar changes were observed for seminatural land use. Within the artificial classes, the number and area of patches were higher and the largest patch occurred in 2011. The maximum variation of diversity is related to the year 2011. The results showed an increasing concern with regard to unplanned human activities. Some improvements of the natural landscape also occurred in the core protected zone of the study area. Remote sensing and geographic information system offers an important means of detecting and analyzing temporal changes occurring in our landscape.     

Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin,Egypt

Natural Hazards - Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in...     

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district,Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\)), differences of circular vertical and horizontal \(\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})\) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (\({\hbox {RMS}}_{\mathrm{height}}\)). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., \(\sigma ^{\mathrm{o}}\). Near surface SM measurements were related to \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\) derived using 5.35 GHz (C-band) image of RISAT-1 and \({\hbox {RMS}}_{\mathrm{height}}\). The roughness component derived in terms of \({\hbox {RMS}}_{\mathrm{height}}\) showed a good positive correlation with \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)\). By considering all the major influencing factors (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\)), an SEM was developed where SM (volumetric) predicted values depend on \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\). This SEM showed \(R^{2}\) of 0.87 and adjusted \(R^{2}\) of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (\({\hbox {SM}}_{\mathrm{Observed}}\)) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (\({\approx } 1\)), index of agreement (d) = 1, coefficient of determination \((R^{2}) = 0.87\), mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences \(({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004\). The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on \(\sigma ^{\mathrm{o}}\). By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.     

Remote sensing-based studies coupled with field data reveal urgent solutions to avert the risk of flash floods in the Wadi Qus (east of Jeddah) Kingdom of Saudi Arabia

Natural Hazards - On November 25, 2009, heavy amount of rainfall precipitated in the city of Jeddah that led to floods causing unexpected loss of life (113 person died), and damaged public and...     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

Natural Hazards - Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the...     

Land suitability analysis for cereal production in Himachal Pradesh (India) using Geographical Information System

Land suitability analysis is prerequisite for sustainable agriculture and it plays a pivotal role in the niche based agricultural planning in mountain regions. In this paper different parameters viz. climatic (precipitation and temperature), topographic (elevation), soil type and land cover/land use have been used in order to perform land suitability evaluation for cereals food-grain crops in Himachal Pradesh using Geographic Information System (GIS). The suitability analysis was performed by digital processing of geo-referenced data (elevation, climate, soil and landcover) and calculating potential production areas by combining different types of geographical data through decision rules framed for each crop in ArcView spatial analyst. Suitable areas have been delineated for cereal crops in the form of land suitability maps. In comparison to the actual area under cereal crops, the possibility of further expansion under each cereal crop was determined. These discriminated areas appear suitable for growing these crops and can be harnessed efficiently for achieving long term sustainability and food security.