Trichodesmium erythraeum (Ehrenberg) bloom along the southwest coast of India (Arabian Sea) and its impact on trace metal concentrations in seawater

The incidence of a large scale Trichodesmium erythraeum bloom along the southwest coast of India (Arabian Sea) observed in May 2005 is reported. Around 4802 filaments of T. erythraeum ml⁻¹ seawater was observed and a colony consisted of 3.6 × 10⁵ cells. The bloom was predominant off Suratkal (12° 59′N and 74° 31′E) with a depth of about 47 m, covering an area of 7 km in length and 2 km width. The concentrations of Zinc, Cadmium, Lead, Copper, Nickel and Cobalt were determined in samples collected from the bloom and non-bloom sites using stripping voltammetry. The observed hydrographical and meteorological parameters were found to be favorable for the bloom. The concentrations of Zinc, Cadmium and Nickel were found to be higher at bloom stations, while the concentrations of Lead, Copper and Cobalt were found to be very low at bloom stations. Elevated concentrations of Cadmium and Cobalt were observed at Valappad mainly due to the decomposition of detrital material produced in the bloom. Statistically significant differences (P > 0.01) in metal concentrations between the bloom and non-bloom stations were not observed except for Copper. Metals such as Lead, Copper and Cobalt were removed from the seawater at all places where bloom was observed. Cadmium was found to be slowly released during the decaying process of the bloom.     

Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.     

Experimental study on the hydrological performance of green roofs in the application of novel biochar

Biochar has the potential to be a soil amendment in green roofs owing to its water retention, nutrient supply, and carbon sequestration application. The combined effects of biochar and vegetated soil on hydraulic performance (e.g., saturated hydraulic conductivity, retention and detention, and runoff delay) are the crucial factor for the application of the novel biochar in green roofs. Recent studies investigated soil water potential (i.e., suction) either on vegetated soil or on biochar-amended soil but rarely focused on their integrated application. With the purpose of investigating the hydraulic performance of green roofs in the application of biochar, the combined effect of biochar and vegetated soil on hydrological processes was explored. Artificial rainfall experiments were conducted on the four types of experimental soil columns, including natural soil, biochar-amended soil, vegetated natural soil, and vegetated biochar-amended soil. The surface ponding, bottom drainage and the volumetric water content were measured during the rainfall test. Simulation method by using HYDRUS-1D was adopted for estimating hydraulic parameters and developing modelling analysis. The results indicated that the saturated hydraulic conductivity of vegetated soil columns were higher than bare soil columns. The addition of biochar decreased the saturated hydraulic conductivity, and the magnitude of decrease was much significant in the case of vegetated soil. The influence of vegetation on permeability is more prominent than biochar. The vegetated biochar-amended soil has the highest retention and detention capacity, and shows a preferable runoff delay effect under heavy rain among the four soil columns. The results from the present study help to understand the hydrological processes in the green roof in the application of biochar, and imply that biochar can be an alternative soil amendment to improve the hydraulic performance.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Earthquake triggered soft sediment deformational structures (seismites) in the Karewa formations of Kashmir valley–An indicator for palaeo-seismicity

The intermontane Karewa basin contains a wide variety of seismically induced soft sediment deformation structures, interpreted as seismites and occurs in 1300 m thick succession of upper and lower Karewas. The Karewa Formation of Kashmir valley are glacio- fluvial-lacustrine and aeolian loess of Plio-Pleistocene age. The soft sediment deformational structures occurs in various formations and members of Karewas and vary greatly in terms of morphology and pattern. The Karewa Formations were frequently confronted with recurrent seismic activities during differential upliftment of Pir Panjal and Zanaskar ranges which resulted in various deformation structures during their evolution and development. In the present study, an attempt has been made to relate the palaeo-seismicity events in Karewa formations with the deformed structures of various formations. The origin of these deformational structures have been interpreted and analyzed from the field evidences by applying paleo-seismological approach. During and after the deposition of Karewas different soft sediment deformation structures (seismites) like load cast, convolute lamination, pseudonodules, recumbent folds, sand dykes etc. were formed during liquefaction and triggered by tectonic impulsive events. The deformational structures are evidenced by their unique nature, distribution, association, behaviour and deformation, and can be used as vital indicators for palaeo-seismicity.     

Competing impact of anthropogenic emissions and meteorology on the distribution of trace gases over Indian region

The spatial distribution of trace gases exhibit large spatial heterogeneity over the Indian region with an elevated pollution loading over densely populated Gangetic Plains (IGP). The contending role and importance of anthropogenic emissions and meteorology in deciding the trace gases level and distribution over Indian region, however, is poorly investigated. In this paper, we use an online regional chemistry transport model (WRF/Chem) to simulate the spatial distribution of trace gases over Indian region during one representative month of only three meteorological seasons namely winter, spring/summer and monsoon. The base simulation, using anthropogenic emissions from SEAC⁴RS inventory, is used to simulate the general meteorological conditions and the realistic spatial distribution of trace gases. A sensitivity simulation is conducted after removing the spatial heterogeneity in the anthropogenic emissions, i.e., with spatially uniform emissions to decouple the role of anthropogenic emissions and meteorology and their role in controlling the distribution of trace gases over India. The concentration levels of Ozone, CO, SO₂ and NO₂ were found to be lower over IGP when the emissions are uniform over India. A comparison of the base run with the sensitivity run highlights that meteorology plays a dominant role in controlling the spatial distribution of relatively longer-lived species like CO and secondary species like Ozone while short-lived species like NO_X and SO₂ are predominantly controlled by the spatial variability in anthropogenic emissions over the Indian region.     

Early In-orbit Performance of Scanning Sky Monitor Onboard AstroSat

We report the in-orbit performance of Scanning Sky Monitor (SSM) onboard AstroSat. The SSM operates in the energy range 2.5 to 10 keV and scans the sky to detect and locate transient X-ray sources. This information of any interesting phenomenon in the X-ray sky as observed by SSM is provided to the astronomical community for follow-up observations. Following the launch of AstroSat on 28th September, 2015, SSM was commissioned on October 12th, 2015. The first power ON of the instrument was with the standard X-ray source, Crab in the field-of-view. The first orbit data revealed the basic expected performance of one of the detectors of SSM, SSM1. Following this in the subsequent orbits, the other detectors were also powered ON to find them perform in good health. Quick checks of the data from the first few orbits revealed that the instrument performed with the expected angular resolution of 12’ \(\times \) 2.5\(^\circ \) and effective area in the energy range of interest. This paper discusses the instrument aspects along with few on-board results immediately after power ON.     

A model of the late universe with viscous Zel’ldovich fluid and decaying vacuum

Many have speculated about the presence of a stiff fluid in very early stage of the universe. Such a stiff fluid was first introduced by Zel’dovich. Recently the late acceleration of the universe was studied by taking bulk viscous stiff fluid as the dominant cosmic component, but the age predicted by such a model is less than the observed value. We consider a flat universe with viscous stiff fluid and decaying vacuum energy as the cosmic components and found that the model predicts a reasonable background evolution of the universe with de Sitter epoch as end phase of expansion. More over, the model also predicts a reasonable value for the age of the present universe. We also performed a dynamical system analysis of the model and found that the end de Sitter phase predicted by the model is stable.     

Simple Approaches to Oil Spill Detection Using Sentinel Application Platform (SNAP)-Ocean Application Tools and Texture Analysis: A Comparative Study

Effective and efficient monitoring of oil spills that originate from ships, offshore platforms and any accidents are of immense importance from the viewpoint of public safety and environmental protection. Detection of spilled oil is also essential to estimate the potential spread and drift from the source to the nearby coastal areas. In this regard, utilization of SAR data for the detection and monitoring of oil spills has received considerable attention in recent times, due to their wide area coverage, day-night and all-weather capabilities. In this paper, two oil spills incidents along the coast of Mumbai, India are investigated; (1) The 2010 oil spill that occurred after the MV MSC Chitra and MV Khalijia-3 collided and (2) the oil spill caused due to sinking of MV RAK carrier in 2011. Two simple and relatively quick approaches for oil spill detection have been applied to VV polarized Radarsat-2 imagery of the incidents and a comparison is made of the results obtained. The first approach utilizes the oil spill detection tool of Sentinel Application Platform (SNAP) and the second explores texture analysis using Grey Level co-occurrence matrix (GLCM). The results of the study show that texture analysis proves to be an efficient method for oil spill detection as compared to the SNAP oil spill detection tool. Nevertheless, both the proposed methodologies are useful for detection of oil spills and for subsequent utilization of the results, timely and cost effectively, for the calibration and validation of numerical models that predict oil spill dispersion trajectories.     

The polar form of the spherical harmonic spectrum: implications for filtering <Emphasis Type="BoldSmallCaps">grace</Emphasis> data

Representing the spherical harmonic spectrum of a field on the sphere in terms of its amplitude and phase is termed as its polar form. In this study, we look at how the amplitude and phase are affected by linear low-pass filtering. The impact of filtering on amplitude is well understood, but that on phase has not been studied previously. Here, we demonstrate that a certain class of filters only affect the amplitude of the spherical harmonic spectrum and not the phase, but the others affect both the amplitude and phase. Further, we also demonstrate that the filtered phase helps in ascertaining the efficacy of decorrelation filters used in the grace community.