Subsurface modelling, often, suffers from lack of continuous field observations. Unavailability of data at both spatial and temporal scales poses difficulties for the preparation of initial conditions of the subsurface hydrological modeling. Anthropogenic impact assessment on the groundwater beneath is important for sustainable food security. The presentation discusses solving the problems of spatial and temporal data scarcity in groundwater modeling by the use geostatistics, statistical methods and machine learning techniques with remotely-sensed hydrometeorological datasets. The mathematical and GIS-based methods for identifying potential groundwater sources along with their usability and vulnerability status are the significant research outcomes. The studies have taken care of the qualitative and quantitative aspects of groundwater together to produce maps which will come handy for water managers prior to start any groundwater experiment in an area. All the methods used in these studies are scientifically relevant. The methodologies are simulation-based studies and fit with field data with good amount of success.The presentation provides generic methods which are essential a priori taking up any specific groundwater research.
Bio: Dr. Madhumita Sahoo is a Fulbright Scholar at the University of Alaska Fairbanks. She graduated from Indian Institute of Technology Kharagpur in 2017. She has remained actively involved in studying various problems in subsurface hydrology. Dr. Sahoo has devised methodologies and studied the efficiency of alternate data sources that could be helpful in subsurface hydrology modeling under data-scarce conditions. The methods developed by her can be taken up a priori in any groundwater development study. She has published her findings in several high-impact international journals. Presently, Dr. Sahoo is working with Prof. D. Misra on studying the impact of warming climate trend on the movement of soil nutrient.
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Meeting ID: 102 179 088