Arpita Mondal is an Assistant Professor at the Department of Civil Engineering, & Interdisciplinary Program in Climate Studies, IIT Bombay. She obtained a Ph.D. from the Indian Institute of Science, Bangalore (2014). Her research interests include Hydroclimatic Extremes such as Heat Waves, Heavy Rain, Floods, Droughts, Climate Change, Detection and Attribution, Statistical Spatio-Temporal Modeling of Extremes, Uncertainty Analysis, Statistical Downscaling, Regionalization and Prediction in Ungauged Basins, Risk Assessment under Non-stationarity.
Symposium on “Green Energy”
Deciphering the role of climate change in floods
There is growing evidence of human-induced climate change driving global hydroclimatic extremes. However, whether and to what degree individual catastrophic flooding events in India are influenced by climate change is not fully understood. This talk will discuss methods and state-of-art for attributing flooding events to anthropogenic climate change, particularly the Probabilistic Event Attribution (PEA) framework. Three approaches that use observations and physics-based climate and hydrologic model simulations will be discussed, with specific application to the 2018 flooding of Kerala that led to significant damage and loss of lives. Event definition is based on an objective approach that computes the relevant spatiotemporal scale of the largest return period of precipitation over the Periyar River Basin (PRB) during the event. The subsequent flooding event is characterized by the return period of the 1-day maximum streamflow at one of the outlets of the PRB, where the maximum impact was reported. The results from multiple methods are consistent, suggesting that the event is exceptionally less likely to have been caused by anthropogenic climate change. The role of wet antecedent soil moisture conditions, which is the primary driving factor of floods in the PRB, is also found to be unchanged between simulations with and without climate change. Further, we also use a hydraulic flood model to conclude that the inundated area and population exposure change little in these simulations. These results highlight the challenges in unequivocal discerning the climate change signal on regional hydrological events and emphasize the importance of better consideration of local confounding interventions in PEA studies.