Research Areas

My research focuses on hydroclimatic extremes, their drivers, impacts, attribution, forecast and projected changes.  I work with physical and statistical models and large datasets including, satellite products, reanalysis datasets, and climate model projections.  

Land Atmospheric Drivers of Floods

Floods are triggered by the combination of various natural factors as well as human interventions. The key to understanding future changes lies in the knowledge of flood drivers in the observed climate. The drivers of floods varies according to flood type, magnitude and extent of the event and the time and region of occurrence. Therefore, a careful investigation of the atmospheric and catchment drivers of observed floods using models and statistical tools provide insights in improving flood forecast systems. In my research, I have explored multiple methods such as physical hydrological models, moisture transport models and statistical tools for understanding flood drivers. 

Projections of Climate Extremes

Hydroclimatic extremes are projected to increase manifold in future. Single-day and Multi-day precipitation and stream flow are expected to increase in frequency and magnitude. Quantifying the projected pathways of extremes at finer resolution are essential for infrastructure designs especially of storm water drainage systems.  However, there are presently huge uncertainties in climate model projections stemming from the model initial conditions, emission scenarios and resolution issues which needs to be addressed for an overhaul in design values.

Climate Change Attribution

To facilitate policy decisions, it is quintessential to quantify whether the probability of observed weather extremes are significantly altered by climate change or not. The branch of climate attribution studies conduct statistical and dynamical investigations to address this question. A significant role of climate change in an observed event would imply the need for associated policy changes in building adaptation to the extreme event among wider community in the affected areas. In my research I have conducted attribution studies using advanced statistical tools to detect the role of climate change on heat extremes and extreme precipitation. Currently I am part of the advisory committee on developing a real-time attribution framework to precipitation extremes across the globe.

Forecast and Early Warning Systems 

Early warning systems are critical for building community resilience to extreme weather events. My work has focused on physical and statistical models to develop flood forecast systems. I am keenly interested in developing AI/ML based forecast tools which integrates large climate data with physical know-hows. This fusion of data-driven and knowledge-driven approaches has the potential to significantly improve the accuracy, lead time, and usability of forecasts for decision-making.

Rapid Impact Assessments

The impact assessment of extreme weather events by quantifying the losses and damage and exploring the triggers of the event provides crucial insights for policy decisions as well as for improving the forecast systems. Nanditha et al. (2023) conducted a rapid attribution study on 2022 Pakistan Floods examining the impacts and causes of the Indus flood event. This highly cited research has drawn significant attention from the scientific community and has inspired further rapid attribution studies in India.

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