Interpolation of wave heights

Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output.     

Directional spread parameter at intermediate water depth

The characteristics of directional spread parameters at intermediate water depth are investigated based on a cosine power ‘2s' directional spreading model. This is based on wave measurements carried out using a Datawell directional waverider buoy in 23 m water depth. An empirical equation for the frequency dependent directional spreading parameter is presented. Directional spreading function estimated based on the Maximum Entropy Method is compared with those obtained using a cosine power ‘2s' parameter model. A set of empirical equations relating the directional spreading parameter corresponding to the peak of wave spectrum to other wave parameters like significant wave height and period are obtained. It shows that the wave directional spreading at peak wave frequency can be related to the non-linearity parameter, which allows estimation of directional spreading without reference to wind information.     

A model‐based data‐driven dictionary learning for seismic data representation

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.     

Evaluation of significant sources influencing the variation of physico-chemical parameters in Port Blair Bay,South Andaman,India by using multivariate statistics

Port Blair is the capital city of Andaman & Nicobar Islands, the union territory of India. More than 50% of the population of these islands lives around Port Blair Bay. Therefore the anthropogenic effects in the bay water were studied for monitoring purpose from seven stations. Physico-chemical parameters of seawater were analyzed in samples collected once in every 3 months for 2 years from seven sampling stations located in Port Blair Bay, South Andaman Island to evaluate the spatial and tidal variation. Cluster analysis and factor analysis were applied to the experimental data in an attempt to understand the sources of variation of physico-chemical parameters. In cluster analysis, the stations Junglighat Bay and Phoenix Bay having high anthropogenic influence formed a separate group. The factors obtained from factor analysis indicated that the parameters responsible for physico-chemical variations are mainly related to land run-off, sewage outfall and tidal flow.     

Metamodel-based design of alluvial channels at incipient motion subjected to seepage

Abstract

The design of an alluvial channel affected by seepage requires information about five basic parameters: particle size, water depth, energy slope, seepage velocity, and average velocity. The conventional approach to predicting the incipient motion in an alluvial channel cannot be applied in the case of a channel affected by seepage. Metamodelling techniques are nowadays widely used in engineering design to simulate a complex system. Here, a metamodel is described which employs the radial-basis function (RBF) network to predict the seepage velocity and energy slope based on experimental data under incipient motion conditions. It was found that the model fits experimental data very well and provides predictions for the design. With the help of the metamodel generated by the RBF network, design curves based on the RBF metamodel are presented for use in designing an alluvial channel when it is affected by seepage.

Citation Kumar, B., Sreenivasulu, G. & Ramakrishna Rao, A. (2010) Metamodel-based design of alluvial channels at incipient motion subjected to seepage. Hydrol. Sci. J. 55(3), 459–466.     

Delineating groundwater potential zones in a hard-rock terrain using geospatial tool

Abstract

Geospatial techniques have become one of the leading tools in the field of natural sciences for assessment, monitoring and management of natural resources, particularly in groundwater research. The paper discusses the demarcation and assessment of groundwater potential zones using geospatial techniques in the Deccan Volcanic Province of Maharashtra, India, using multi-criteria analyses. The study incorporates integration of thematic information (geomorphology, lithology, drainage density, slope and lineaments) in a GIS environment in order to identify groundwater potential zones. The methodology adopted can be used as a rapid assessment tool in groundwater exploration and is helpful in predictive groundwater resource management. Multi-criteria evaluation techniques were used to integrate all the thematic layers. Individual themes and their corresponding categories were assigned a knowledge base ranking from 1 to 5, depending on their importance for groundwater potential. Using the Raster calculator tool in Arc GIS software, all thematic maps were integrated to produce a composite groundwater potential map of the study area. The identified groundwater potential zones were classified into four classes, from excellent to poor. The generated groundwater potential zones were validated with field checks and borewell/dugwell yield data, and showed consistency with the observations.

Citation Singh, P., Thakur, J. K., and Kumar, S. (2013) Delineating groundwater potential zones in a hard-rock terrain using geospatial tools. Hydrological Sciences Journal, 58 (1), 1–11.     

A Comprehensive Review on Microalgae-Based Biorefinery as Two-Way Source of Wastewater Treatment and Bioresource Recovery

In the past few years, microalgae have gained huge recognition from the scientific community due to their potential applications in the production of a broad array of bio-based products varying from biofuels to nanoparticles. Due to their elevated growth rate, high tolerance to various types of abiotic stresses, and complex metabolic capacity, microalgae can be used as promising tools for the attainment of a circular bioeconomy. Moreover, they can simply utilize nutrients from wastewater for biorefinery purposes, resulting in resource recovery coupled with wastewater treatment. However, due to their sub-optimal yields and high production costs, microalgae-based bio-products have not yet been commercialized. This review provides insights into the employment of microalgae as an efficient bioresource for the treatment of wastewater with simultaneous enactment as a biorefinery to produce biofuels, biochar, bioplastic, fertilizers, and other high-value bioproducts. Furthermore, the application of microalgal nanoparticles in wastewater treatment and prospects for genetic modification of microalgae for enhanced biorefinery capabilities have also been briefly highlighted.     

Probabilistic Assessment of Tsunami Recurrence in the Indian Ocean

The Indian Ocean is one of the most tsunamigenic regions of the world and recently experienced a mega-tsunami in the Sumatra region on 26 December 2004 (M _W 9.2 earthquake) with tsunami intensity I (Soloviev-Imamura intensity scale) equal to 4.5, causing heavy destruction of lives and property in the Indian Ocean rim countries. In this study, probabilities of occurrences of large tsunamis with tsunami intensities I ≥ 2.0 and I ≥ 3.0 (average wave heights H ≥ 2.83 m and H ≥ 5.66 m, respectively) during a specified time interval were calculated using three stochastic models, namely, Weibull, gamma and lognormal. Tsunami recurrence was calculated for the whole Indian Ocean and the special case of the Andaman-Sumatra-Java (ASJ) region, excluding the 1945 Makran event from the main data set. For this purpose, a reliable, homogeneous and complete tsunami catalogue with I ≥ 2.0 during the period 1797–2006 was used. The tsunami hazard parameters were estimated using the method of maximum likelihood. The logarithm of likelihood function (ln L) was estimated and used to test the suitability of models in the examined region. The Weibull model was observed to be the most suitable model to estimate tsunami recurrence in the region. The sample mean intervals of occurrences of tsunamis with intensity I ≥ 2.0 and I ≥ 3.0 were calculated for the observed data as well as for the Weibull, gamma and lognormal models. The estimated cumulative and conditional probabilities in the whole Indian Ocean region show recurrence periods of about 27–30 years (2033–2036) and 35–36 years (2039–2040) for tsunami intensities I ≥ 2.0 and I ≥ 3.0, respectively, while it is about 31–35 years (2037–2041) and 41–42 years (2045–2046) for a tsunami of intensity I ≥ 2.0 and I ≥ 3.0, respectively, in the ASJ region. A high probability (>0.9) of occurrence of large tsunamis with I ≥ 2.0 in the next 30–40 years in the Indian Ocean region was revealed.     

Solar Photocatalytic Decolorization and Detoxification of Industrial Batik Dye Wastewater Using P(3HB)‐TiO2 Nanocomposite Films

Solar photocatalytic decolorization and detoxification of batik dye wastewater using titanium dioxide (TiO₂) immobilized on poly‐3‐hydroxybutyrate (P(3HB)) film was studied. The effects of initial dye concentration, catalyst concentration, P(3HB) film thickness, and fabrication methods of the nanocomposite films were evaluated against methylene blue, a standard organic dye. It was observed that 0.4 g of P(3HB)‐40 wt% TiO₂ removed 96% of the color under solar irradiation. P(3HB) and TiO₂, mixed concurrently in chloroform followed by stirring for 24 h showed a more even distribution of the photocatalyst on the polymer surface and yielded almost 100% color removal. The photocatalytic films were able to completely decolorize real industrial batik dye wastewater in 3 h and induced a chemical oxygen demand (COD) reduction of 80%. Reusability of the 0.4 g P(3HB)‐40 wt% TiO₂ film in decolorizing the batik dye wastewater was also possible as it gave a high consistent value of decolorization percentage (>80%) even after the sixth repeated usage. Recovery step of the photocatalysts was also not required in this simple treatment system. The decolorized batik dye wastewater had less/no toxic effects on mosquito larvae, Aedes aegypti, and microalgae, Scenedesmus quadricauda indicating simultaneous detoxification process along with the decolorization process.     

Oxygen and hydrogen isotopic characterization of rainfall and throughfall in four South Korean cool temperate forests

Understanding the isotopic composition of precipitation in a forested catchment is critical for ecohydrological studies. Changes in the water isotopes of rainfall were assessed during its passage through the canopy in throughfall, and the effect of different forest stands on the isotope composition of throughfall. In a cool temperate forest in Korea, rainfall and throughfall samples collected under Pinus densiflora (red pine), Castanea crenata (chestnut), Robinia pseudoacacia (black locust) and mixed stands (mix of these three species) were analysed for oxygen and hydrogen isotopes. Throughfall δ¹⁸O and δD were enriched compared to rainfall. A difference of δ¹⁸O and δD among throughfall may be related to the difference in interception–storage capacity of different species due to dissimilar canopy characteristics. Since isotopic composition of throughfall and rainfall are different due to canopy isotopic effects, use of rainfall isotopic signatures for ecohydrological studies in forested ecosystem can lead to biases.