An assessment of Varuna river basin of India was performed to study the various drainage parameters in GIS platform. The delineation of drainage network is possible either physically from topographic sheets or with the help data of Digital Elevation Model (DEM) by methods for calculation techniques. Extraction of the basin and sub-basins, stream network has been produced to evaluate the drainage characteristics in the study zone. The entire Varuna river basin has been subdivided into 3 sub-watersheds and 41 morphometric parameters have been computed under four broad categories i.e. drainage network, basin geometry, drainage texture, and relief characteristics. The morphometric analysis has been performed and different parameters have been correlated with each other to understand their underlying connection and their role over the basin hydro geomorphology. The study discloses different types of morphometric analysis and how they influence the soil and topography of the basin. The investigation and estimation of basin morphometry and relief parameters in GIS will be of massive utility in catchment area advancement, understanding the watershed for natural resource evaluation, planning and administration at any scale. The outcomes thus generated equip us with significant knowledge and may also provide an input that are essential in decision making for watershed planning and drainage development of the watershed.

Land use and land cover change (LULCC) is among the most apparent natural landscape processes impacted by anthropogenic activities, particularly in fast-growing regions. In India, at present, due to the impacts of anthropogenic climate change, supplemented by the fast pace of developmental activities, the areas providing the highest agricultural yields are facing the threat of either extinction or change in land use. This study assesses the LULCC in the fastest-changing landscape region of the Indian state of Bihar, District Muzaffarpur. This district is known for its litchi cultivation, which, over the last few years, has been observed to be increasing in acreage at the behest of a decrease in natural vegetation. In this study, we aim to assess the past, present and future changes in LULC of the Muzaffarpur district using support vector classification and CA-ANN (cellular automata-artificial neural network) algorithms. For assessing the present and past LULC of the study area, we used Landsat Satellite data for 1990, 2000, 2010, and 2020. It was observed that between 1990 and 2020, the area under vegetation, wetlands, water body, and fallow land decreased by 44.28%, 34.82%, 25.56%, and 5.63%, respectively. At the same time, the area under built-up, litchi plantation, and cropland increased by 1451.30%, 181.91%, and 5.66%, respectively. Extensive ground truthing was carried out to assess the accuracy of the LULC for 2020, whereas historical google earth images were used for 1990, 2000, and 2010, through the use of overall accuracy and kappa coefficient indices. The kappa coefficients for the final LULC for the years 1990, 2000, 2010, and 2020 were 0.79, 0.75, 0.87, and 0.85, respectively. For forecasting the future LULC, first, the LULC of 1990 and 2010 were used to predict the landscape for 2020 using the CA-ANN model. After calibrating and validating the CA-ANN outputs, LULC for 2030 and 2050 were generated. The generated future LULC scenarios were validated using kappa index statistics by comparing the forecast outcomes with the original LULC data for 2020. It was observed that in both 2030 and 2050, built-up and vegetation would be the major transitioning LULC. In 2030 and 2050, built-up will increase by 13.15% and 108.69%, respectively, compared to its area in 2020; whereas vegetation is expected to decrease by 14.30% in 2030 and 32.84% in 2050 compared to its area in 2020. Overall, this study depicted a decline in the natural landscape and a sudden increase in the built-up and cash-crop area. If such trends continue, the future scenario of LULC will also demonstrate the same pattern. This study will help formulate better land use management policy in the study area, and the overall state of Bihar, which is considered to be the poorest state of India and the most vulnerable to natural calamities. It also demonstrates the ability of the CA-ANN model to forecast future events and comprehend spatiotemporal LULC dynamics.

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Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies.