Accuracy of kinematic wave and diffusion wave approximations for time-independent flows

Errors in the kinematic wave and diffusion wave approximations for time-independent (or steady-state) cases of channel flow were derived for three types of boundary conditions: zero flow at the upstream end, and critical flow depth and zero depth gradient at the downstream end. The diffusion wave approximation was found to be in excellent agreement with the dynamic wave approximation, with errors in the range 1–2% for values of KF (? 7.5), where K is the kinematic wave number and F₀ is the Froude number. Even for small values of KF (e.g. KF²₀ = 0.75), the errors were typically less than 15%. The accuracy of the diffusion wave approximation was greatly influenced by the downstream boundary condition. The error of the kinematic wave approximation was found to be less than 13% in the region 0.1 ? x ? 0.95 for KF = 7.5 and was greater than 30% for smaller values of KF (? 0.75). This error increased with strong downstream boundary control.     

Design and Development of Onsite Biofilter Unit for Effective Remediation of Contaminants from Wastewater

Agricultural biomass is proven ecofriendly and effective adsorbent for the remediation of contaminants from wastewater. Here, rice husk biochar (600 °C) prepared by a one-step pyrolysis method is used for the remediation of different contaminants in real samples. An onsite biofilter unit is fabricated with parallel trenches of different layers of coconut coir and biochar and is used as a biofiltration unit. The efficiency of the designed unit is assessed for the removal of different contaminants in pilot-scale experiments. Results show that removal efficiency varies from metal to metal and ranges from 5.52% to 90.76% using the biofilter unit. Fourier-transform infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray analysis, carbon, hydrogen, nitrogen, and Brunauer–Emmett–Teller analysis before and after the adsorption represent the changes in the morphology and surface functionalization of the biochar. Results indicate that the designed biofilter unit could also be used as a promising agent for the remediation of pharmaceutical and other emerging contaminants from wastewater.     

Integration of Microalgae-Based Wastewater Bioremediation–Biorefinery Process to Promote Circular Bioeconomy and Sustainability: A Review

Bioremediation of wastewater using microalgae is inexpensive, energy efficient, and effective in pollutant reduction as compared to conventional wastewater treatment technologies. Wastewater is a huge resource of minerals, nutrients, bioenergy, and valuable organic compounds and can be used for cultivation of microalgae. The microalgal biomass can be further used as biorefinery feedstock to produce biofuels and commercially important high-value products. The potential of microalgae toward bioremediation and biorefinery applications presents the avenues for integrating the two processes to support circular bioeconomy and sustainability. This review presents a holistic view of integration of bioremediation and biorefinery processes using microalgae for deriving multiple benefits like pollutant removal, resource recovery, biofuel production, and generation of high-value commercial products. The current status of high-throughput microalgal screening technologies is also discussed since the selection of suitable microalgal strains is crucial for the application. The review further summarizes various processes involved in bioremediation and biorefinery systems such as cultivation, bioremediation, harvesting, and downstream processing. Recent trends in microalgal strain improvement for bioremediation and biorefinery applications through genetic engineering, bioinformatics, omics technologies, and genome editing tools are highlighted, while addressing the risks, biosafety issues, and regulatory affairs associated with genetically modified algae.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

On the Length-Weight Relationship of the Ganga River Prawn Macro-brachium birmanicum choprai (TIWARI)

A total of 331 male and 237 female individuals of the Ganga river prawn, Macrobrachium birmanicum choprai (TIWARI), were examined to establish relationship between its length and weight. The individuals of both the sexes were placed separately in different size groups. When plotted against corresponding values of weight, the average length computed for each size group exhibited an exponential relationship. However, the logarithimic values of observed lengths when plotted against corresponding values of weight exhibited a linear relationship. Correlation coefficient values for both the sexes were found highly significant (r = 0.9846 for males and 0.9762 for females).     

Long term ionospheric electron content variations over Delhi

Ionospheric electron content (IEC) observed at Delhi (geographic co-ordinates: 28.63°N, 77.22°E; geomagnetic co-ordinates: 19.08°N, 148.91E; dip Latitude 24.8°N), India, for the period 1975/80 and 1986/89 belonging to an ascending phase of solar activity during first halves of solar cycles 21 and 22 respectively have been used to study the diurnal, seasonal, solar and magnetic activity variations. The diurnal variation of seasonal mean of IEC on quiet days shows a secondary peak comparable to the daytime peak in equinox and winter in high solar activity. IEC_max (daytime maximum value of IEC, one per day) shows winter anomaly only during high solar activity at Delhi. Further, IEC_max shows positive correlation with F_10.7 up to about 200 flux units at equinox and 240 units both in winter and summer; for greater F_10.7 values, IEC_max is substantially constant in all the seasons. IECmax and magnetic activity (A_p) are found to be positively correlated in summer in high solar activity. Winter IEC_max shows positive correlation with A_p in low solar activity and negative correlation in high solar activity in both the solar cycles. In equinox IEC_max is independent of A_p in both solar cycles in low solar activity. A study of day-to-day variations in IEC_max shows single day and alternate day abnormalities, semi-annual and annual variations controlled by the equatorial electrojet strength, and 27-day periodicity attributable to the solar rotation.     

Radon Precursory Signals for Some Earthquakes of Magnitude > 5 Occurred in N-W Himalaya: An Overview

The N-W Himalaya was rocked by a few major and many minor earthquakes. Two major earthquakes in Garhwal Himalaya: Uttarkashi earthquake of magnitude M_s= 7.0 (m_b = 6.6) on October 20, 1991 in Bhagirthi valley and Chamoli earthquake of M_s= 6.5 (m_b = 6.8) on March 29, 1999 in the Alaknanda valley and one in Himachal Himalaya: Chamba earthquake of magnitude 5.1 on March 24, 1995 in Chamba region, were recorded during the last decade and correlated with radon anomalies. The helium anomaly for Chamoli earthquake was also recorded and the Helium/Radon ratio model was tested on it. The precursory nature of radon and helium anomalies is a strong indicator in favor of geochemical precursors for earthquake prediction and a preliminary test for the Helium/Radon ratio model.     

A simulation model for unified interrill erosion and rill erosion on hillslopes

A mathematical model was developed for simulating runoff generation and soil erosion on hillslopes. The model is comprised of three modules: one for overland flow, one for soil infiltration, and one for soil erosion including rill erosion and interrill erosion. Rainfall and slope characteristics affecting soil erosion on hillslopes were analysed. The model results show that the slope length and gradient, time distribution rainfall, and distribution of rills have varying influence on soil erosion. Erosion rate increases nonlinearly with increase in the slope length; a long slope length leads to more serious erosion. The effect of the slope gradient on soil erosion can be both positive and negative. Thus, there exists a critical slope gradient for soil erosion, which is about 45° for the rate of erosion at the end of the slope and about 25° for the accumulated erosion. Copyright © 2005 John Wiley & Sons, Ltd.     

Geospatial modelling for sub-watershed prioritization in Western Himalayan Basin using morphometric parameters

Natural Hazards - Geographical information system and remote sensing are proven to be an efficient tool for locating water harvesting and recharge structures, groundwater potential, runoff,...