Every facet of life, including human habitation, economic development, food security, etc., depends on water as a valuable resource. Due to the burgeoning population and rapid urbanization, water availability needs to be simulated and measured using hydrologic models and trustworthy data. To fulfill this aim, the SWAT model was processed in this work. The SWAT model was formulated to estimate the hydrological parameters of Yeralwadi using meteorological data from IMD (India Meteorological Department) for the period 1995-2020. The observed discharge data was collected from the HDUG Nasik group and used in the calibration and validation of the Model. The SWAT model was corrected & validated through the SUFI-II algorithm in SWAT-CUP to get a better result. The model’s sensitivity is checked by using statistical parameters like Nash-Sutcliffe Efficiency (NSE) and a coefficient of determination (R2). NSE values were 0.72 and 0.80 in calibration and validation, and R2 were 0.80 & 0.76 in calibration and validation, respectively, indicating the acceptance of the model. Results show that 40.6% of the total yearly precipitation was lost by evapotranspiration. The estimated total discharge from the Yeralwadi catchment was 55.6%, out of which 41.2% was surface runoff and 14.4% was baseflow. The other 17.8% was made up of percolation into confined and unconfined aquifers, which served as soil and groundwater storages. The surface runoff is influenced by Curve number (CnII), SOL_AWC, ESCO, and base flow was influenced by ALPHA-BF and GW_REVAP. This study will be useful to water managers and researchers to develop sustainable water resource management and to alleviate the water scarcity issues in the study basin.

With the rapid advancement of Remote Sensing Technology, monitoring the agricultural land has become a facile task. To surveil the growth of paddy crops and provide detailed information regarding monitoring soil, drought, crop type, crop growth, crop health, crop yield, irrigation, and fertilizers, different types of remote sensing satellites are used like Landsat 8, Sentinel 2, and MODIS satellite. The main aim of Landsat 8, Sentinel 2 and MODIS satellites is to monitor the land and vegetation area and to provide data regarding agricultural activities. Each of these satellites possesses a different spectral band, resolution, and revisit period. By using the remote sensing spectral indices, different types of vegetation indices are calculated. This survey paper provides comprehensive about Remote Sensing and the major parameters that influence for growth of paddy crops, like soil and water, and the future scope of agriculture and its demand in research is discussed.

Drought assessment using drought indices has been widely carried out for drought monitoring. Remote sensing-based indices use remotely sensed data to map drought conditions in a particular area or region. Therefore, the objective of the present study is to make a study on drought risk based on the calculation of an indicator from biophysical parameters extracted from NOAA/AVHRR satellite data, namely TCI and VCI, to obtain a better understanding of the differentiation between each index, and their application for drought monitoring in the High Atlas of Marrakech on the Chchaoua Morocco watershed during 1980-2020. Landsat oli7 and8 data were used to construct the indices. The result showed that each index proved to be a useful, fast, sufficient, and inexpensive tool for drought monitoring. However, each index has its differences. The TCI was found to be drought sensitive during the dry season or in months when high temperatures occurred. While VCI detected drought more sensitively in the rainy season as well (December-January-February to May) than TCI and VCI. Meanwhile, VCI, including the improved TCI, combined two indicators to better understand drought occurrence. These indices were calculated using GIS, QGis, ArcGis satellite imagery scenes, and Landsat. After a comparative study of these years, from 1984 to 2020, the evolution of the VCI and TCI was highlighted.

This study aims to analyze the association between the share of agriculture in GDP and changes in climatic variables, notably per capita CO2 emissions and temperature change, using time series data of India, Bangladesh, and Nepal for the period 1961-2018. The ARDL bounds testing method was applied to analyze the relationships among the research variables for both short-term and long-term. The results revealed that in the long run, per capita CO2 emissions and temperature change have no statistically significant relationship with India and Nepal’s share of agriculture in GDP. However, temperature change has demonstrated a positive and statistically significant relationship with the share of agriculture in Bangladesh’s GDP. Temperature change has a significant and adverse impact on the share of agriculture in India’s GDP in the short run, whereas CO2 has no significant effect. In the short run, CO2 shows a positive and significant connection with the share of agriculture in Bangladesh’s GDP. Still, temperature change is negatively and significantly associated with the proportion of agriculture in the nation’s GDP. Different lag values of both CO2 and temperature change have significant relationships with the share of GDP in agriculture in the short run in Nepal. As agriculture is a key source of GDP for all three countries, it is vital to implement suitable policies and make plans and strategies to mitigate climate change’s harmful consequences in agriculture.