Dr. Amina Khatun

 

Brief Details:

I have pursued my Ph.D. from the Agricultural and Food Engineering (AgFE) Department, Indian Institute of Technology (IIT) Kharagpur, India under the supervision of Prof. Chandranath Chatterjee (AgFE Department, IIT Kharagpur) and Prof. Bhabagrahi Sahoo (School of Water Resources, IIT Kharagpur). My Ph.D. thesis is entitled “Flood Forecasting and Risk Assessment in Mahanadi River Basin considering Climate Change”. My Ph.D. work focuses on the analysis of the aggravated flood problems and a way to tackle them in the Mahanadi River basin, a highly flood-prone river basin in eastern India. Currently, I am working as an Assistant Professor in the College of Horticulture & Farming System Research, Assam Agricultural University, Nalbari.

I have 4 publications from my Ph.D. thesis work in reputed journals including ‘Journal of Hydrology’, ‘Hydrological Processes’ and ‘Hydrological Sciences Journal’. A part of my M.Tech thesis work is also published in the ‘Sustainability’ journal.

I have also worked as a Junior Research Fellow for 2 years (2017-2019) and as a Senior Research Fellow for 3 years (2019-2022) in the project ‘Impact of climate change on flood risk’ sponsored by the Department of Science and Technology (DST), Govt. of India. It is during this project that I have co-authored two research papers. Apart from my bench work, I have also worked as a Teaching Assistant for an undergraduate (Land and Water Resources Engineering Lab) and a postgraduate (Geo-informatics for Land and Water Resources Lab) course. As a Ph.D. scholar, I have mentored three postgraduate, one undergraduate and one dual-degree students for their thesis work. Currently, I am mentoring one dual degree and three undergraduate students at AgFE, IIT Kharagpur.  I am also co-supervising one postgraduate student at AgFE, IIT Kharagpur along with Prof. Chandranath Chatterjee. In addition to my scientific writing, I also have experience in delivering presentations at various conferences. I have received the prestigious ‘AGU Virtual Student Travel Grant’ to participate in the 2021 American Geophysical Union (AGU) Fall Meeting. I have also received the ‘Best Presentation Award’ in HYDRO-2020, International Conference on Hydraulics, Water Resources and Coastal Engineering.

During my Ph.D., I was extensively involved in other lab duties such as purchasing and installation of high-end softwares like ArcGIS, MIKE FLOOD and ERDAS Imagine. I could accomplish all this due to my high work ethic combined with my targeted approach and interpersonal skills. I would also like to highlight that I have an excellent academic track record holding the first position from matriculation to postgraduation. I was awarded the ‘University Gold Medal’ and the ‘Merit Prize’ for my outstanding performance during the undergraduate course. I have qualified the Graduate Aptitude Test in Engineering (GATE) in the year 2015 with an All India Rank (AIR) 44. I have also qualified the National Eligibility Test (NET) twice in the years 2018 and 2021. 

Research

Flood accounts for about one-third of all natural disasters around the globe. Due to changing climate, extreme floods have recently become more frequent in the tropical region. Keeping in view the role of precipitation driver as a proxy of catchment wetness on flood generation, no study has evaluated the relationship between the lagged predecessor rain events and the sub-catchment scale runoff. In India, the India Meteorological Department provides daily rainfall forecasts (IMD-MME) for up to 5-days lead-time. However, limited studies so far have evaluated these forecasts for analyzing floods in any Indian river basin. Most recently, the Long Short-Term Memory (LSTM) have emerged as the state-of-the-art machine learning models in rainfall-runoff simulations from a catchment. But, the possibility of the LSTM network-based models as the error-updating schemes integrated with a conceptual hydrological model (e.g., MIKE11-NAM-HD) for daily streamflow forecasting have not been explored in the literature. These daily streamflow forecasts along with the contribution from the reservoir releases may be used for forecasting the flood inundations in the downstream reaches. Further, with the evident increase in extreme flood risk in the tropical region, it is found that none of the studies have assessed and attempted to propose any adaptation measure to the flood risk as a function of crop damage due to flood inundation depth and duration under a changing climate in a dam-regulated Indian River basin. To address these issues, my study assessed the causal mechanism of recurring high floods in the Mahanadi River basin considering the retrospective and future projected climate change scenarios; developed a hybrid Copula–Enhanced Kohonen Self-Organizing Map (Cop-SOM) based bias-correction method for improving daily ensemble rainfall forecasts and compared its performance with the conventional Quantile Mapping (QM), Copula-based and enhanced Kohonen Self-Organizing Map (eKSOM) approaches; developed a short-to-medium range streamflow forecasting framework by integrating the standalone MIKE11-NAM-HD (MIKE) with a novel nested smoothing-based LSTM (sLSTM) and the recently developed Wavelet-based Nonlinear AutoRegressive neural network with eXogenous inputs (WNARX) error-updating sub-models; developed a flood inundation forecasting framework for the Mahanadi River delta considering reservoir outflow forecasts; assessed flood risk in the Mahanadi River delta in terms of agricultural production in a changing climate considering the most probable design floods; and recommended the potential rice varieties to be cultivated in the projected flood inundation areas in the Mahanadi delta as a probable flood adaptation strategy.

Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, considering extreme rainfall as the covariate, floods in larger sub-catchments shows an increase in compound flood hazard in the projected period. Among the rainfall bias-correction techniques adopted herein, the hybrid Cop-SOM approach outperforms in correcting the highly biased daily raw IMD-MME rainfall forecasts with the least systematic errors. Overall, the sLSTM proves to be a robust error-forecasting model at 1–5 days lead-times with reliable reproduction of peak flows; and the MIKE-sLSTM framework forced with the Cop-SOM based bias-corrected rainfall forecasts has the lowest model prediction uncertainty. The MIKE FLOOD model is able to accurately simulate the inundation forecasts with reasonable accuracy up to 5-days lead-time in the delta region. Overall, the findings reveal an increased flood risk in the future projected scenarios in the Mahanadi River delta, which can be best-adapted with an alternate rice planning.

Teaching: 

Geoinformatics can be defined as the science and technology that handles the information related to geographic data. It deals with the acquisition, creation, storage, processing, presentation and dissemination of datasets associated with a geographic location. While an information system is a set of processes, executed on raw data, to produce information which will be useful in decision-making, a Geographic Information System (GIS) uses geographically referenced data as well as non-spatial data and includes operations which support spatial analysis. In GIS, the common purpose is decision-making, for managing use of land, resources, transportation, retailing, oceans or any spatially distributed entities. The connection between the elements of the system is geography, e.g. location, proximity, and spatial distribution. In this context, GIS can be seen as a system of hardware, software and procedures designed to support the capture, management, manipulation, analysis, modeling and display of spatially-referenced data for solving complex planning and management problems. Although many other computer programs can use spatial data (e.g. AutoCAD and statistics packages), GISs include the additional ability to perform spatial operations. The classroom presentation for Teaching will be based on the topic ‘Introduction to GIS’ which would include some basic concepts (with example), components and applications of GIS, types of data etc.

Google Scholar Link:

https://scholar.google.com/citations?user=p0SWSCAAAAAJ&hl=en