The research of my group focuses on the following aspects of non-structural approaches for flood management, specifically for data scarce regions:
(i) Flood inundation modeling, hazard and risk assessment: Methodologies are being developed to address data scarcity issues in developing countries like lack of high resolution DEM, cross-section data, and sufficient and accurate calibration and validation data sets. Also, methodology is being developed for optimal allocation of rice varieties for floodplains by maximizing net benefits and considering the flood risk. These studies are being carried out using MIKE models in different river basins like Mahanadi in Odisha, Elbe in Germany and Bharathapuzha in Kerala etc.
(ii) Flood forecasting: Poor raingauge network as well as unavailability of rainfall data in real-time hinders the accuracy of flood forecasting at different lead times. We are focusing on the use of real-time satellite-based rainfall products for flood forecasting as they are now increasingly becoming available for the data-scarce regions. Their integration with the data-driven models (like neural networks with the use of wavelets) as well as physically based models (like the MIKE models) could be effectively used for real-time flood forecasting. The idea is to improve the accuracy of flood forecasts at higher lead times.
(iii) Impact of climate change on flood risk: The idea is to analyze the impact of projected climate change on flood hazard/risk in a river basin, and to develop management scenarios to minimize the impact of climate change on flood hazard/risk in the river basin.
(iv) Flood estimation: Regional flood formulae are being developed by integrating L-moments based approach with soft computing techniques for small size gauged and ungauged catchments of India covering different hydro-meteorological regions. The focus is on estimation of floods for small catchments where adequate runoff data are generally not available.
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Development of an accurate and reliable hourly flood forecasting model using waveletbootstrapANN (WBANN) hybrid approach Tiwari M. K., Chatterjee C. By Journal of Hydrology 394 458-470 (2010)
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Comparison of hydrodynamic models of different complexities to model floods with emergency storage areas Chatterjee C., Förster S. , Bronstert A. By Hydrological Processes 22 4695-4709 (2008)
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Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) Tiwari M. K., Chatterjee C. By Journal of Hydrology 382 20-33 (2010)
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Hydrodynamic modelling of a large flood prone river system in India with limited data Patro S., Chatterjee C. , Singh R. , Raghuwanshi N. S. By Hydrological Processes 23 2774-2791 (2009)
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Regional flood frequency analysis using L-moments for North Brahmaputra region of India Kumar R., Chatterjee C. By Journal of Hydrologic Engineering 10 1-7 (2005)
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Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi) Kneis D., Chatterjee C. , Singh R. By Hydrology and Earth System Sciences 18 2493-2502 (2014)
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A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products Nanda T., Sahoo B. , Beria H. , Chatterjee C. By Journal of Hydrology 539 57-73 (2016)
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Enhancing the applicability of Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in hydrometeorological timeseries data Nanda T., Sahoo B. , Chatterjee C. By Journal of Hydrology 549 133-147 (2017)
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Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework Bisht D. S., Mohite A. R., Jena P. P., Khatun A. , Chatterjee C. , Raghuwanshi N. S., Singh R. , Sahoo B. By Journal of Hydrology 590 - (2020)
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Enhancing real-time streamflow forecasts with wavelet-neural network based error-updating schemes and ECMWF meteorological predictions in Variable Infiltration Capacity model Nanda T., Sahoo B. , Chatterjee C. By Journal of Hydrology 575 890-910 (2019)
Principal Investigator
- Development and Management of Surface Water Resources and Soil Moisture in Different Agro-Ecological Regions of India using Geoinformatics and Nano Technology
Co-Principal Investigator
- AI Based Imaging Solution for Detection of Pest attack in Tea Leaves using Aerial Imaging NATIONAL TEA RESEARCH FOUNDATION,KOLKATA
- AI for Agriculture and Food Sustainability Ministry of Electronics and Information Technology
- Development and Field Implementation of a Conceptual
Hydrological Model for Efficient River Basin Planning and Management Science and Engineering Research Board (SERB)
Ph. D. Students
Krishna Mondal
Area of Research: Hydrological Modeling
Rituparna Saha
Area of Research: Network applications
Roshan Suryakant Mohanty
Area of Research: Hydrological Modeling
Saidutta Mohanty
Area of Research: Hydrological modeling
Saikat Karmakar
Area of Research: Hydrology and Climate Change
Somrita Sarkar
Area of Research: Image Processing and Machine Learning
Sudarsan Biswal
Area of Research: Development of image based analytics for crop water management.
