Geomorphological characterisation and interpretation of a mid-latitude glacier-like form: Hellas Planitia, Mars

We describe and interpret the surface terrain types associated with a widely-reported ∼4 km long, mid-latitude martian viscous flow feature (VFF). The feature is located in the southern hemisphere, on the poleward-facing rim of a ∼60 km-diameter crater in eastern Hellas Planitia. High Resolution Imaging Science Experiment (HiRISE) images, analysed in both 2D and 3D, reveal that the upper margin of the feature is bounded by steep (∼30°) headwalls, typically some tens of metres high, that are formed from unconsolidated material and characterised by a series of slope-parallel linear incisions. Below these incised headwalls, the feature flows at a general angle of ∼10° from a broad upper basin to a confined lower tongue that is bounded by a nested sequence of elongate raised ridges. These characteristics are typical of several VFFs in the region and are strikingly similar to moraine-bounded valley glaciers on Earth, and we sub-classify this feature as a ‘glacier-like form’ (GLF)¹. The GLF comprises five distinctive surface terrain types that contrast sharply with surface characteristics outside its bounding moraines. Four of these terrains (scaly terrain, polygonized terrain, linear terrain and mound-and-tail terrain) are located within the GLF’s innermost bounding moraine, while the fifth (rectilinear-ridge terrain) is located between its frontal moraines. These terrains are mapped, characterised and associated with possible mechanisms of formation to draw inferences about the GLF’s glaciology and glacial history. This analysis suggests that the GLF reached its maximal extent in the geologically-recent past, and that it may have been partially wet-based at that time. Subsequent to this phase, the GLF experienced an extended period of general recession that has been punctuated by several episodes of still-stand or advance. Currently, the GLF’s basin appears to be composed of a lower zone that is dominated by an exposed former glacier bed and an upper zone that may still contain a now-degraded and dust-mantled viscous mass, similar to many partially-glacierized basins on Earth.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.     

Assessing Conservation Adoption Decision Criteria Using the Analytic Hierarchy Process: Case Studies from Three Midwestern Watersheds

Land use changes from natural ecosystems to industrial agriculture have impacted water quality and wildlife populations in the Mississippi River basin. Government programs providing technical assistance and monetary incentives have not resulted in adequate adoption rates of conservation practices. While there has been a plethora of research examining the factors associated with conservation adoption, significantly less is understood about the relative importance of these factors. Using the Analytic Hierarchy Process (AHP) with agricultural producers in three Midwestern watersheds, we assess the relative importance of environmental and production decision criteria when making decisions to adopt conservation practices. Although AHP provides insight into how decisions may be made at the watershed scale, this methodology also provides insight into how individuals make conservation decisions and may also provide a method for delivering tailored conservation advice and communications.     

Elemental variations in glacier cryoconites of Indian Himalaya and Spitsbergen,Arctic

Cryoconite samples were collected from two different climatic domains i.e., the Sutri Dhaka glacier, western Himalaya India and Svalbard glaciers, the Spitsbergen, Arctic, to understand the elemental source and elemental deposition patterns. The data of geochemical analysis suggest that the Himalayan cryoconite samples accumulate higher concentrations as compared to the cryoconite samples of the Arctic glaciers. The concentration of lithophile elements (Cs, Li, Rb and U) was recorded higher in the cryoconite holes of the Himalayas, especially, in the lower to the higher parts of the glacier, whereas, lower concentrations were recorded in the Arctic samples. Chalcophile elements in the Himalayan cryoconites are enriched in As and Bi while the Arctic cryoconite samples show a higher concentration of Bi, Pb and As. The higher concentrations are responsible for influencing the ecosystem and in human health related issues. Siderophile elements (Co, Fe, Mn and Ni) show high concentrations in the Himalayan samples, whereas, the Arctic samples show minor variations and low elemental concentration in these elements, respectively. In addition, a few elements, such as Ag, Mg, and Ca show higher concentration in the Himalayan glacier samples. Ca also occurs in high concentrations in Arctic glacier samples. R-mode factor analysis of the Himalayas (Arctic) samples indicate that the elements are distributed in four (three) factors, explaining 89% (90%) of the variance in their elemental distribution. The Factor 1 suggests statistically significant positive loadings for most of the lithophile, chalcophile and siderophile elements of the “Himalayan” and the Arctic cryoconite samples. The sample-wise factor score distribution shows a considerable variation in the sampling locations along the glaciers of both the regions. Factors 2 and 3, demonstrate insignificant loading for most of the elements, except statistically significant positive loading in some of the elements of the both, Himalayan and Arctic “cryoconites”. The higher elemental concentration in the cryoconites of the Himalayan region may be an indicator of the natural processes and/or attributed to the rapid industrialization in the Asian countries.     

Geomagnetic field variation during winter storm at localized southern and northern high latitude

This paper presents the effect of geomagnetic storm on geomagnetic field components at Southern (Maitri) and Northern (Kiruna) Hemispheres. The Indian Antarctic Station Maitri is located at geom. long. 66.03° S; 53.21° E whereas Kiruna is located at geom. long. 67.52° N; 23.38° E. We have studied all the geomagnetic storms that occurred during winter season of the year 2004–2005. We observed that at Southern Hemisphere the variation is large as compared to the Northern Hemisphere. Geomagnetic field components vary when the interplanetary magnetic field is oriented in southward direction. Geomagnetic field components vary in the main phase of the ring current. Due to southward orientation of vertical component of IMF reconnection takes place all across the dayside that transports plasma and magnetic flux which create the geomagnetic field variation.     

Effect of interplanetary magnetic field and disturb storm time on H component

A fluxgate digital magnetometer is used to study the variation of magnitude of H component during geomagnetic storm events of April, July and November 2004 at southern subauroral localized region at “MAITRI” (geom. lat. 62°S, long. 52.8°E). We also study the effect of vertical component of interplanetary magnetic field (IMF) on the variation of the magnitude of H component during storm time of April, July and November 2004. Results show that before sudden storm commencement (SSC) time magnitude of H component and IMF show smooth variation but after SSC of first storm of 22 July 2004, the magnitude of the H component shows fluctuations and at 09:00 UT it increases, but during second storm of 24 July 2004, the magnitude of H component indicates large fluctuations and it increases rapidly at 04:00 UT.     

Bulk viscous LRS Bianchi-I Universe with variable G and decaying Λ

The present study deals with spatially homogeneous and totally anisotropic locally rotationally symmetric (LRS) Bianchi type I cosmological model with variable G and Λ in presence of imperfect fluid. To get the deterministic model of Universe, we assume that the expansion (θ) in the model is proportional to shear (σ). This condition leads to A=ℓB ⁿ , where A, B are metric potential. The cosmological constant Λ is found to be decreasing function of time and it approaches a small positive value at late time which is supported by recent Supernovae Ia (SN Ia) observations. Also it is evident that the distance modulus curve of derived model matches with observations perfectly.     

Geomatics Based Analysis of Predicted Sea Level Rise and its Impacts in Parts of Tamil Nadu Coast,India

The coastal zones around the world are very densely populated and hence heavily packed with related infrastructures. So, the territorial nations have obvious apprehensions against the IPCC SRES (Intergovernmental Panel on Climate Change, Special Report on Emission Scenario) predicted sea level rise, as it would cause flooding of the low lying coasts and also other related chains of environmental endangers. This has driven these nations to initiate research studies in multiple directions for scientifically evaluating the phenomenon and impacts of sea level rise using all possible technologies including the Geomatics which possesses unique credentials in geosystem mapping. But certain advanced virtues available with Geomatics technology are yet to be capitalized deservingly in this. In addition, almost all the earlier studies have focused only on the impacts of sea level rise (SLR) and not on the predicted shift of high tide line (HTL) and the related inter tidal activities, which would cause a series of environmental disaster. Hence, the present research study was undertaken in a test site of 750 km² in central Tamil Nadu coast to visualize the areas prone to submergence due to predicted SLR and areas prone to environmental disasters/degradation viz. erosion, deposition, salination of agricultural lands, pollution of aquifers, etc. due to predicted shift of HTL, using digital elevation models derived from SRTM data (Shuttle Radar Topographic Mission), geomorphology and land use/cover maps interpreted using IRS P6 LISS IV satellite data. The paper narrates the certain newer concepts and methodologies adopted in the study and the results.     

Bayesian Mixture Modelling in Geochronology via Markov Chain Monte Carlo

In this paper we develop a generalized statistical methodology for characterizing geochronological data, represented by a distribution of single mineral ages. The main characteristics of such data are the heterogeneity and error associated with its collection. The former property means that mixture models are often appropriate for their analysis, in order to identify discrete age components in the overall distribution. We demonstrate that current methods (e.g., Sambridge and Compston, 1994) for analyzing such problems are not always suitable due to the restriction of the class of component densities that may be fitted to the data. This is of importance, when modelling geochronological data, as it is often the case that skewed and heavy tailed distributions will fit the data well. We concentrate on developing (Bayesian) mixture models with flexibility in the class of component densities, using Markov chain Monte Carlo (MCMC) methods to fit the models. Our method allows us to use any component density to fit the data, as well as returning a probability distribution for the number of components. Furthermore, rather than dealing with the observed ages, as in previous approaches, we make the inferences of components from the “true” ages, i.e., the ages had we been able to observe them without measurement error. We demonstrate our approach on two data sets: uranium-lead (U-Pb) zircon ages from the Khorat basin of northern Thailand and the Carrickalinga Head formation of southern Australia.