Susanta Das

Our study involved visualizing and analyzing the Overall scores and key statistics of the top five universities ranked by the Times Higher Education (THE) World University Rankings for the years 2020-2023. The key statistics included full-time equivalent students, students per staff, international students, and the ratio of female-to-male students. We collected the data from THE website and calculated the average values for each year across the universities. This analysis revealed significant variations in the data over the years. Surprisingly, lower-ranked universities occasionally outperformed higher-ranked ones in certain cases. We also assessed the impact of including or excluding a university, which had a significant effect on the key statistics, leading to fluctuations in the average values. Lastly, we investigated the effect of the worldwide lockdown, imposed due to the coronavirus (COVID-19) pandemic on the key statistics. Our assessment suggested that COVID-19 played a mixed role in influencing the key statistics, thereby affecting the rankings.

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET₀) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET₀) using limited meteorological inputs are critical in data-constrained circumstances, and the preferred FAO-56 Penman-Monteith (PM) equation cannot be used. To overcome the complexity of calculation, the present study is focused on developing a Random Forest-based ET₀ model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the readily available data at the farm level. For comparative study Hargreaves–Samani model was also modified and used to estimate the ET₀. Further, ET₀ was also estimated using existing models like Hargreaves–Samani model and the Modified Panman model. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (2090 − 2010) and nine years (2011–2019), respectively, and the PM FAO-56 model was taken as a standard model. The developed RF-based model's mean absolute error and root-mean-square deviation were found to be better than the other models, and it was obtained as 0.95 mm/d and 1.32, respectively, with an r² value of 0.92. The developed RF-based model was used to predict the ET₀, and further, predicted ET0 values were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The result of the field experiment also shows that there was no significant yield reduction in the crop. Hence, This developed study model can be used for the irrigation in the semiarid region of the Punjab India as well as other part of world. Also, it can be used as a replacement FAO-56 model.

Over the globe, efforts are being made to collect data and develop an adequate water resource information system for optimising its use. India is the largest consumer of water, with an estimated usage of around 300 cubic kilometers per year. Punjab, a north-western state of India, is an example of severe crises aquifer depletion due to unconstrained consumption of groundwater, leading to degradation of its quantity as well as quality. Thus it is of great importance to compile up-to-date information about the water requirement for its appropriate and sustainable use. Remote sensing and geographic information system (GIS) are the technologies that can provide efficient and effective information system to tackle the water quality & water supply planning parameters. Thus, under the present study, a web-enabled water resource information system has been developed in the GIS environment for the SAS (Sahibzada Ajit Singh) Nagar and Patiala districts of Punjab by using the open source software MS4W and pmapper. This system provides digital information of natural, such as drainage, and man-made features like roads, canals, and tube wells with their location and so on, and also provide information related to water level, water quality of wells, and well depth for the study area. Such an information system can be very helpful for the administrators and can serve as a decision support system for planners and policy makers so that the areas where problems related to water quality can be identified and focused upon. The system can provide an effective and meaningful direction for the planning and development of both districts.

The novel coronavirus disease (COVID-19) halted almost all the industrial scale anthropogenic activities across the globe, resulting in improvements in water and air quality of megacities. Here, using Sentinel-2A data, we quantified impact of COVID-19 lockdown on the water quality parameters in one of the largest perennial creeks i.e., the Buddha Nala located in District Ludhiana in India. This creek has long been considered as a dumping ground for industrial wastes and has resulted in surface and ground water pollution in the entire lower Indus Basin. Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Chlorophyll Index (NDCI), Nitrogen Content Index (NI), Normalized Difference Turbidity Index (NDTI), and Total Suspended Matter (TSM) were compared prior (2019) and during (2020) lockdown in the creek. There was a significant enhancement in NDVI, NDWI, NDCI, and NI values, and reduction in NDTI and TSM values during the lockdown period. When compared with prior year (2019), the values of indices suggested an improvement in water quality and an indicative change in aquatic ecology in the creek. The impact of the COVID-19 lockdown on the improvement in water quality of Buddha Nala was more evident in the upstream and downstream sections than the middle section. This is intriguing since the middle section of the creek was continually impacted by domestic household effluents. The earth observation inspired methodology employed and findings are testament to the discriminatory power to employ remote sensing data and to develop protocols to monitor water quality in regions where routine surveillance of water remains cost prohibitive.

Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan–Morgan–Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).

In absence of soil erosion plots for determination of erodibility index (K) for erosion models like Universal Soil Loss Equation (USLE) or Revised Universal Soil Loss Equation (RUSLE) to estimate soil erosion, empirical relations are used. In the present study, soil erodibility index was determined for entire Ri-bhoi district of Meghalaya based on soil physical and chemical properties through empirical relationship and presented in a map form. Dominant land uses of the district were identified through geo-spatial tools which were viz. agriculture, forest, jhum land and wasteland. Soil samples from surface depth (01–15 cm) were collected from areas of different dominant land uses. Twenty five sampling points were selected under each land use type and geo-coded them on the base map of Ri-bhoi district. Apart from K-index, Clay Ratio, Modified Clay Ratio and Critical Soil Organic Matter were also determined for understanding the effect of primary soil particles on erodibility. In agriculture land use system K-index values were found in the range of 0.08–0.41 with an average of 0.25 ± 0.02. In case of jhum, forest and wasteland these were in the range of 0.08–0.42 with an average of 0.20 ± 0.01; 0.09–0.40 with an average of 0.22 ± 0.02, and 0.10–0.34 with an average value of 0.23 ± 0.02, respectively. Clay ratio (2.74) and Modified clay ratio (2.41) were observed to be higher in forest LUS, lower clay ratio (1.97) and modified clay ratio (1.81) were found in the wasteland indicating erosion susceptibility in forested area. The values of Critical Level of Organic Matter (CLOM) for the district ranged from 4.72 to 16.56. Out of 100 samples, only one sample had CLOM value less than 5 and rest 99 samples had values more than 5 indicating that the soils of the district had moderate to stable soil structure and offer resistance to erosion. All the indices values of geo-coded points were then interpolated in the Arc-GIS environment to produce land use based maps for Ri-bhoi district of Meghalaya. As K-index is a quantitative parameter which is used in models, the index can be then interpolated for estimation of soil erosion through USLE or RUSLE for any given situation.