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.     

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.     

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.     

Precipitable water as a predictor of LCL height

Based on the precipitable water observations easily available from in situ and remote sensing sensors, a simple approach to define the lifting condensation level (LCL) is proposed in this study. High-resolution radiosonde and microwave radiometer observations over peninsular Indian region during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment Integrated Ground Observational Campaign (CAIPEEX-IGOC) during the monsoon season of 2011 are used to illustrate the unique relationship. The inferences illustrate a linear relationship between the precipitable water (PW) and the LCL temperature. This relationship is especially valuable because PW is easily available as a derived parameter from various remote sensing and ground-based observations. Thus, it could be used to estimate the LCL height and perhaps also the boundary layer height. LCL height and PW correlations are established from historical radiosonde data (1984–2012). This finding could be used to illustrate the boundary layer-cloud interactions during the monsoon and is important for parameterization of boundary layer clouds in numerical models. The relationships are illustrated to be robust and seem promising to get reasonable estimates of the LCL height over other locations as well using satellite observations of PW.

     

Neural network-based methodology for inter-arrival times of earthquakes

In this paper, an artificial neural network (ANN)?Cbased methodology is proposed to determine the probability of inter-arrival time (IAT) of main shock of six broad seismic regions of India. Initially, classical methodology using exponential distribution is applied to IAT of earthquake events computed from earthquake catalog data. From the goodness-of-fit test results, it has been found that exponential distribution is not adequate. In this paper, a more efficient ANN-based methodology is proposed, and two ANN models are developed to determine the probability of IAT of earthquake events for a specified region, specified magnitude range or magnitude greater than the specified value. The performance of ANN models developed is validated with number of examples and found to predict the probability with minimal error compared to exponential distribution model. The methodology developed can be applied to any other region with the database of the respective regions.     

Outlier identification and correction for GRACE aggregated data

Gravity measurements within the Gravity Recovery and Climate Experiment (GRACE) provide a direct measure of monthly changes in mass over the Earth’s land masses. As such changes in mass mainly correspond to water storage changes, these measurements allow to close the continental water balance on large spatial scales and on a monthly time scale within the respective error bounds. When quantifying uncertainties, positive and negative peaks are detected in GRACE aggregated monthly time series (from different data providers) that do not correspond to hydrological or hydro-meteorological signals. These peaks must be interpreted as outliers, which carry the danger of signal degradation. In this paper an algorithm is developed to identify outliers and replace them with hydrologically plausible values. The algorithm is based on a statistical approach in which hydrological and hydro-meteorological signals are used to control the algorithm. The procedure of outlier detection is verified by evaluating catchment based aggregated GRACE monthly signals with ground truth from hydrology and hydro-meteorological signals. The results show improvement in the correlation of GRACE versus hydrometeorological and hydrological signals in most catchments. Also, the noise level is significantly reduced over 255 largest catchments.