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Seminar: Dr. Tirthankar Roy

9:30 am–10:30 am
Scott Engineering Center Link Room: N105 / PKI 250
Additional Info: SLNK
Dr. Roy is a Postdoctoral Research Associate at Princeton University. His seminar is titled “Water Infrastructure in Poorly-Gauged Basins”.

Real-time streamflow monitoring is crucial for a plethora of water management applications. In practice, streamflow is estimated using a hydrologic model, driven by precipitation, temperature, wind-speed, etc. The main challenge in real-time monitoring of streamflow lies in the limited availability of ground-based measurements, and this becomes even more challenging in poorly-gauged basins. Recently, satellite-based estimates of precipitation and other variables have become a popular choice to address the data availability issue. The global/quasi-global coverage, near-real-time availability, and fine spatial and temporal resolutions of the satellite products make them a great alternative to the conventional measurements. However, satellite products are generated based on some estimation model, which introduces errors, causing them to be less reliable for direct use. Thus, accounting for the errors becomes necessary before these products can be used with confidence. Another challenge lies in the selection of the appropriate product, since several of them are available, each with its own pros and cons. Likewise, multiple options are also available for the choice of the hydrologic model. This talk will give a brief overview of a newly developed state-of-the-art real-time streamflow monitoring and forecasting platform (MMSF: Multi-model Multi-product Streamflow Forecasting), which addresses the problems mentioned above. The platform uses multiple satellite-based precipitation products and multiple hydrologic models, to generate streamflow forecasts based on the successive steps of precipitation bias-correction, streamflow simulation using calibrated hydrologic models, streamflow bias-correction, probabilistic forecast representation, and probabilistic forecast averaging. The methodology ensures more accurate forecasts with a meaningful characterization of the forecast uncertainty. The platform is currently operational in some selected pilot river basins in Africa, aiming to support water management decisions addressing human and environmental needs.

Tirthankar Roy is a Postdoctoral Research Associate in the Department of Civil and Environmental Engineering, Princeton University, where he is working on land-atmospheric interactions, droughts, forecasting, and statistical method development. He received his Ph.D. in Hydrology from the Department of Hydrology and Atmospheric Sciences, University of Arizona, in 2017, working on the applications of satellite-based information to improve water management in poorly-gauged river basins, together with some advanced topics in catchment hydrology and climate change impacts assessment. He has a master’s degree in Civil Engineering from the Indian Institute of Technology Kanpur and a bachelor’s degree in Agricultural Engineering from the State Agricultural University, West Bengal, India (BCKV). He received the DAAD Scholarship to work on his master’s thesis at Technische Universität Dresden, Germany, on the topic of optimal water management in coastal aquifers.

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