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.
A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products by Nanda, T., Sahoo, B., Beria, H., Chatterjee, C. Journal of Hydrology 539 57-73 (2016)
Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study by Bisht, D. S., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., and Panda, A. Natural Hazards DOI 10.1007/s11069-016-2455-1 1-28 (2016)
Assessment of Cartosat-1 DEM for modeling floods in data scarce regions by Jena, P. P., Panigrahi, P., Chatterjee, C. Water Resources Management 30(3) 1293-1309 (2016)
Flood risk modeling for optimal rice planning for delta region of Mahanadi river basin in India by Samantaray, D., Chatterjee, C., Singh, R., Kumar, P., and Panigrahy, S. Natural Hazards 76(1) 347-372 (2015)
Regional flood frequency analysis using soft computing techniques by Kumar, R., Goel, N. K., Chatterjee, C., and Nayak, P. C. Water Resources Management 29(6) 1965-1978 (2015)
Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi) by Kneis, D., Chatterjee, C., and Singh, R. Hydrology and Earth System Sciences 18(7) 2493-2502 (2014)
Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls? by Jena, P. P., Chatterjee, C., Pradhan, G., and Mishra, A. Journal of Hydrology 517 847-862 (2014)
Uncertainty assessment and ensemble flood forecasting using bootstrap based artificial neural networks (BANNs) by Tiwari, M. K. and Chatterjee, C. Journal of Hydrology 382(1) 20-33 (2010)
Hydrodynamic modelling of a large flood prone river system in India with limited data by Patro, S., Chatterjee, C., Singh, R., and Raghuwanshi, N. S. Hydrological Processes 23(19) 2774-2791 (2009)
Comparison of hydrodynamic models of different complexities to model floods with emergency storage areas by Chatterjee, C., Förster, S., and Bronstert, A Hydrological Processes 22(24) 4695-4709 (2008)
Impact of Climate Change on Flood Risk Department of Science and Technology(DST)
An Integrated Autonomous UAV and WSN - Based System for Crop Management and Crop Condition Monitoring MHRD
Development and Testing of a Large Scale Conceptual Hydrological Model National Institute of Hydrology
Effect of Climate Change & Land Use/Land Cover Changes on Spatial and Temporal Water Availability in Subarnarekha Basin Ministry of Water Resources
Climate Change Impact and Adaptation Options for Sustaining Rice-Wheat Crop Production in India Department of Science and Technology (DST), Govt. of West Bengal
Vulnerability and Risk Assessment due to Various Environmental Drivers in a Climate Change Scenario over Eastern India Department of Science and Technology, Climate Change Programme (SPLICE)
Prachi Pratyasha Jena
Area of Research: Hydrological Modeling
Deepak Singh Bisht
Area of Research: Hydrological Modeling
Area of Research: Networks
Area of Research: Impact of Climate Change on Floods
Area of Research: Network applications
Sushree Swagatika Swain
Area of Research: Hydrology and Climate Change
Area of Research: Development of image based analytics for crop water management.