Application ID: ASWRD23021001
Brief Details:
Dr.
Kasiviswanathan K S is an Assistant Professor at the Department of Water
Resources Development and Management, Indian Institute of Technology (IIT)
Roorkee, India. He graduated with BE in Civil
Engineering from Anna University, MTech in Water Resources Development from IIT
Roorkee, and Ph.D. in Quantification of uncertainty in hydrological models from
IIT Madras. His current research focuses on reservoir
operation, flood forecasting, geospatial data analysis, machine learning
approaches for hydrological modeling, and uncertainty quantification. Before
joining IIT Roorkee, he worked as an Assistant Professor (Grade I) at IIT Mandi
for two years. After the Ph.D., he worked as a Postdoctoral researcher for
about two and half years at the University of Calgary, Canada, and Heriot-Watt
University, UK. He has published more than 20 papers (of
which 14 papers as the corresponding author) in high-impact factor peer-reviewed
journals during the last four
years of tenure at IIT Roorkee. He has edited two books published
in Elsevier and MDPI. He has
research projects funded by DST, Swedish Research Council, and Tehri Hydropower
Corporation, worth approximately three crores INR in collaboration with reputed
institutes worldwide. He is the recipient of several prestigious awards such as
the Berkner fellowship from the American Geophysical Union, USA, the Sivapalan
Young Scientist award from the International Association of Hydrological
Sciences, United Kingdom, Early Career Research award, SERB, India, and the Eyes
High Postdoctoral Fellowship, University of Calgary, Canada.
Title of Talk: Development of a web-based rainfall intensity duration frequency (IDF) curves over India under present and future climatic conditions
Research Abstract
Design rainfall estimate is crucial for stormwater management, the design of various hydraulic structures, and risk and reliability assessments in disaster management. This study developed grid-based rainfall Intensity Duration Frequency (IDF) curves for India through the modeling framework, which integrates statistical methods, computational algorithms, and mathematical models. As obtaining the observed fine-resolution rainfall data across India is challenging, satellite-based rainfall products were used to develop the modeling framework. IMERG, a satellite-based sub-hourly rainfall data with a spatial resolution of 10x10 km, was extracted for this endeavor. The long-term IMD gridded rainfall data collected from 1901 to 2019 at a daily scale was utilized to bias correct the satellite rainfall data and then disaggregate the daily scale IMD data into sub-hourly data. It is obvious that the uncertainty arising from the model structure, parameters, and data affects the estimate of design rainfall. Therefore, methods have been developed to quantify the uncertainty at different levels to improve the reliability of the estimate. Several bias correction methods were investigated to identify the best method specific to the region. Different statistical and machine learning models were developed at every grid level to disaggregate the IMD daily rainfall data into sub-hourly data. Further, the modeling framework was extended to future projected rainfall data for developing the IDF curves under the climate change scenarios.
The
outcome of the modeling framework was integrated with a web-based tool, which
can be easily used by the end users, including researchers, academicians, city
planners, and policymakers. With this tool, the user can select any region of
interest to obtain the design rainfall of desired return period from the IDF
curves developed for the present and future periods.
Google Scholar Link:
https://scholar.google.ca/citations?user=J7AMGk8AAAAJ&hl=en
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