Student researcher

Antara Dasgupta Research Leader

Climate change has resulted in an observable increase in the number of extreme weather events leading to widespread and recurring flood events. In this scenario, it is critical to efficiently utilise the growing volume of hydrological data available through satellite remote sensing and crowd-sourcing to improve our flood forecasting skill. As the number of diverse data sources grow, more advanced techniques need to be employed in order to quantify and reduce the associated uncertainties. Since images represent only an instant in time, the knowledge on temporal flood dynamics given by process models is irreplaceable in any flood study. The problem of hydrologic data assimilation is then best posed as a synergistic combination of spatially distributed flood observations with hydrodynamic model predictions to improve model forecasting accuracy. 

Our research will focus on integrating remote sensing derived water levels with a 2D hydrodynamic model using data assimilation. The water levels will be calculated by combining flood maps derived from optical and SAR imagery with topography, and additionally from crowd sourced images. The effect of the inclusion of crowd-sourced information on the modelling will also be evaluated.  The project involves investigation of flood events in Australia and India.  As the proposed research is a proof of concept study, it is imperative that all the requisite data be available. Most Indian catchments are ungauged and for the others, hydro-meteorological information is often classified this informaton is often more readily available for Australian catchments.  

A particularly novel component of this's work will be the use of crowd-sourced data to improve the modelling of flood levels and extent.

This project is being undertaken by PhD candidate Antara Dasgupta as part of the IITB - a Monash Universtity Reserch program that encourages academic collaboration between the scientific community of India and Australia. This program requires the students to spend a minimum of one year of their Ph. D. at Monash.  The time spent at Monash, which would be in the second half of the PhD, will focus on the data assimilation and performance analysis.