Revisiting the stratigraphy of the Mesoproterozoic Chhattisgarh Supergroup,Bastar craton,India based on subsurface lithoinformation

In the last 10 years, several teams of geologists from different institutions in India and abroad have vigorously investigated the Chhattisgarh basin (Bastar craton, India). Based on the new results and the lithologs of more than 350 water wells, resistivity and gamma-ray logs, and extensive geological traverses, we present a revised geological map, relevant cross sections, a new comprehensive stratigraphic column and a discussion of the new findings. Major outcomes of this revision are: (1) confirming the existence of two sub-basins (Hirri and Baradwar) and two depocentres; (2) establishing the age of the basin to be essentially Mesoproterozoic; (3) discarding the ‘unclassified Pandaria Formation’ and classifying the package of Pandaria rock units into Chandi, Tarenga, Hirri and Maniari formations in the Hirri sub-basin; (4) accepting the ‘group’ status of the Singhora Group and the newly proposed Kharsiya Groups in the Baradwar sub-basin; (5) establishing an intrabasinal correlation of formations; (6) reappraising the thicknesses of the different formations; and (7) finding that the geometry of the basin is ‘bowl-shaped’, which is compatible with a sag model for the origin and evolution of the basin.     

Post-2004 mega-earthquake temporal velocity variation at Andaman Islands from GPS measurements

Post-mega-earthquake velocity adjustment at Andaman region was found to vary with time. Coordinate repeatability for 5-year span shows changeover from post-seismic to inter-seismic period in between 900 and 1,000 days from the mega-earthquake. Excluding Havlock Island, all sites move from south to north and from east to west. Velocity vectors gradually rotated from the nearly perpendicular orientation after the mega-earthquake to parallel orientation with the subduction interface in later phase. Velocities in India 2005 reference frame indicate the presence of a structural discontinuity between Bedonabad and Chidiatapu at south Andaman, between Padmanavapuram and Kaushalyanagar at middle Andaman and between Aerial Bay and Radhanagar at north Andaman.     

Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Eight data-driven models and five data pre-processing methods were summarized; the multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition (WD) models were then used in short-term streamflow forecasting at four stations in the East River basin, China. The wavelet–artificial neural network (W-ANN) method was used to predict 1-month-ahead monthly streamflow at Longchuan station (LS). The results indicate better performance of MLR and wavelet–multiple linear regression (W-MLR) in analysing the stationary trained dataset. Four models showed similar performance in 1-day-ahead streamflow forecasting, while W-MLR and W-ANN performed better in 5-day-ahead forecasting. Three reservoirs were shown to have more influence on downstream than upstream streamflow and models had the worst performance at Boluo station. Furthermore, the W-ANN model performed well for 1-month-ahead streamflow forecasting at LS with consideration of a deterministic component.     

Entropy theory for analysing water resources in northeastern region of Brazil

Using the Shannon entropy, the space–time variability of rainfall and streamflow was assessed for daily rainfall and streamflow data for a 10-year period from 189 stations in the northeastern region of Brazil. Mean values of marginal entropy were computed for all observation stations and entropy maps were then constructed for delineating annual and seasonal characteristics of rainfall and streamflow. The Mann-Kendall test was used to evaluate the long-term trend in marginal entropy as well as relative entropy for two sample stations. The marginal entropy values of rainfall and streamflow were higher for locations and periods with the highest amounts of rainfall. The entropy values were higher where rainfall was higher. This was because the probability distributions of rainfall and the resulting streamflow were more uniform and less skewed. The Shannon entropy produced spatial patterns which led to a better understanding of rainfall and streamflow characteristics throughout the northeastern region of Brazil. The total relative entropy indicated that rainfall and streamflow carried the same information content at annual and rainy season time scales.     

Remote Sensing of Turbidity and Phosphate in Creeks and Coast of Mumbai: An Effect of Organic Matter

Geospatial approaches to monitoring and mapping water quality over a wide range of temporal and spatial scales have the potential to save field and laboratory efforts. The present study depicts the estimation of water quality parameters, namely turbidity and phosphate, through regression analysis using the reflectance derived from remote sensing data on the west coast of Mumbai, India. The predetermined coastal water samples were collected using the global positioning system (GPS) and were measured concurrently with satellite imagery acquisition. To study the influence of wastewater, the linear correlations were established between water quality parameters and reflectance of visible bands for either set of imagery for the study area, which was divided into three zones: creek water, shore‐line water and coastal water. Turbidity and phosphate have the correlation coefficients in the range 0.75–0.94 and 0.78–0.98, respectively, for the study area. Negative correlation was observed for creek water owing to high organic content caused by the discharges of domestic wastewater from treatment facilities and non‐point sources. Based on the least square method, equations are formulated to estimate turbidity and phosphate, to map the spatial variation on the GIS platform from simulated points. The applicability of satellite imagery for water quality pattern on the coast is verified for efficient planning and management.     

Effect of climate change on sea water intrusion in coastal aquifers

There is increasing debate these days on climate change and its possible consequences. Much of this debate has focused in the context of surface water systems. In many arid areas of the world, rainfall is scarce and so is surface runoff. These areas rely heavily on groundwater. The consequences of climate change on groundwater are long term and can be far reaching. One of the more apparent consequences is the increased migration of salt water inland in coastal aquifers. Using two coastal aquifers, one in Egypt and the other in India, this study investigates the effect of likely climate change on sea water intrusion. Three realistic scenarios mimicking climate change are considered. Under these scenarios, the Nile Delta aquifer is found to be more vulnerable to climate change and sea level rise. Copyright © 1999 John Wiley & Sons, Ltd.     

Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited

Hardin and Jensen (2011) presented six challenges to using small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenge of the hostile flying environment, challenge of power, challenge of available sensors, challenge of payload weight, challenge of data analysis, and challenge of regulation. Eight years later we revisit each of the challenges in the context of the current sUAS environment. We conclude that technological advances made in the interim (as applied to environmental remote sensing) have either (1) improved practitioner ability to respond to a challenge or (2) decreased the magnitude of the challenge itself. However, relatively short flight time remains a primary challenge to using sUAS in environmental remote sensing.