Dr Madhumita Sahoo (she/her)
School of Mechanical, Aerospace and Civil Engineering
Marie Skłodowska-Curie Actions Postdoctoral Fellow
Full contact details
School of Mechanical, Aerospace and Civil Engineering
D01
Pam Liversidge Building
Mappin Street
Sheffield
S1 3JD
- Profile
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Dr. Madhumita Sahoo completed her Ph.D. in Water Resources Engineering and Management from Indian Institute of Technology Kharagpur (INDIA) in December 2017. During her doctoral research, she examined the efficiency of remotely sensed data which can help in identifying the groundwater level variation for small basins. Her devised methodologies can be helpful groundwater flow modeling under data-scarce conditions.
She has been actively involved with research dealing with water resources management through state-funded and the World Bank-funded projects. She has published her research findings in reputed international journals and conferences.Dr. Sahoo received the prestigious Fulbright fellowship to pursue research on soil nutrient migration under a warming climate trend at the University of Alaska Fairbanks, USA. Her work on soil nutrient movement under climate change scenarios will be continued in her present study at the University of Sheffield. She received the Marie Skłodowska-Curie Actions (MSCA) postdoctoral fellowship in 2023. She is currently working with Prof. Steven F. Thornton and Prof. Domenico Bau. Her research will focus on warming of winter seasons and their impact on soil nitrogen cycle. Warming of winter seasons may increase nitrate fluxes from soil, particularly in temperate zones and high latitudes, with negative impacts on the quality of receiving waters (both surface and subsurface water). Effects of temperature response on the soil-nutrient dynamics and its future impact as a result of warming climate regime will help the agrarian community in efficient and sustainable management of nutrients in soil.
- Publications
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Journal articles
- Winter soil temperature and its effect on soil nitrate Status: A Support Vector Regression-based approach on the projected impacts. CATENA, 211, 105958-105958.
- Space-Time Cokriging Approach for Groundwater-Level Prediction with Multiattribute Multiresolution Satellite Data. Journal of Hydrologic Engineering, 23(7).
- On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations. Water Resources Management, 32(4), 1225-1244.
- Space–time forecasting of groundwater level using a hybrid soft computing model. Hydrological Sciences Journal, 62(4), 561-574.
- Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context. Journal of Hydrology, 541, 1303-1315.
- Identification of groundwater potential zones considering water quality aspect. Environmental Earth Sciences, 74(7), 5663-5675.
- Evaluation of Recharge and Groundwater Dynamics of a Shallow Alluvial Aquifer in Central Ganga Basin, Kanpur (India). Natural Resources Research, 23(4), 409-422.
- Runoff and Sediment Yield Processes in a Tropical Eastern Indian River Basin: A Multiple Machine Learning Approach. Land, 12(8), 1565-1565.
Chapters
- Chapter 5 Evaluation of machine learning-based modeling approaches in groundwater quantity and quality prediction, Advances in Remediation Techniques for Polluted Soils and Groundwater (pp. 87-103). Elsevier
- Evaluation of machine learning-based modeling approaches in groundwater quantity and quality prediction, Advances in Remediation Techniques for Polluted Soils and Groundwater (pp. 87-103). Elsevier
Other
- Research group