Near real-time flood inundation and hazard mapping of Baitarani River Basin using Google Earth Engine and SAR imagery.

Flood inundation mapping and satellite imagery monitoring are critical and effective responses during flood events. Mapping of a flood using optical data is limited due to the unavailability of cloud-free images. Because of its capacity to penetrate clouds and operate in all kinds of weather, synthetic aperture radar is preferred for water inundation mapping.

Future projections of worst floods and dam break analysis in Mahanadi River Basin under CMIP6 climate change scenarios.

This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station.

A mini-review on arsenic remediation techniques from water and future trends.

Arsenic contamination is a severe issue because of its toxicity and related health risks. This review article presents an overview of the sources, health hazards, and treatment options for arsenic pollution. Conventional approaches to achieving the permitted level of 10 ppb set by the WHO, such as chemical oxidation, biological oxidation, and coagulation-flocculation, are ineffective and time-consuming.

A simplified modelling framework for real-time assessment of conservative pollutants in ungauged rivers during cloudy periods.

Recently, the optical remote sensing technique is effectively applied to monitor real-time water quality parameters at finer spatiotemporal scales that are mostly based on the surface reflectance of satellite images. However, during the rainy season due to cloudy or hazy satellite images, it is a great challenge to obtain the surface reflectances and to estimate the pollutant concentration. This study is specially focused on developing a novel approach to estimate the daily-scale pollutant concentrations in ungauged rivers during cloudy days.

Mapping of heavy metal pollution in river water at daily time-scale using spatio-temporal fusion of MODIS-aqua and Landsat satellite imageries.

For river water quality monitoring at 30m × 1-day spatio-temporal scales, a spatial and temporal adaptive reflectance fusion model (STARFM) is developed for estimating turbidity (T), total suspended solid (TSS), and six heavy metals (HV) of iron, zinc, copper, chromium, lead and cadmium, by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat (L) spectral bands. A combination of regression analysis and genetic algorithm (GA) techniques are applied to develop spectral relationships between T-L, TSS-T, and each HV-TSS. The STARFM algorithm and all the developed relationship models are evaluated satisfactorily by various performance evaluation measures to develop heavy metal pollution index-based vulnerability maps at 1-km resolution in the Brahmani River in eastern India.