PhD positions

We are looking for prospective PhD students in the following areas for Autumn 2024-25. Interested students are encouraged to email Karthikeyan at karthikl@iitb.ac.in. For more updates, please follow CSRE website and IITB admissions website.

1) Hyper resolution land surface modelling towards generating farm scale soil moisture profile simulations [MoES Project]

Soil moisture is an important variable that is present at the land-atmosphere interface, which is responsible for the partitioning of land surface fluxes into evapotranspiration, runoff, and subsurface contributions. Accurate soil moisture information is critical for agriculture water management, monitoring of floods and droughts, and land surface feedback mechanisms. This project deals with setting up a hyper-resolution land surface model, which has the ability to produce surface and rootzone soil moisture at an unprecedented scale of 30 meters resolution and subsequently ingest satellite data in a data assimilation algorithm to simulate soil moisture fluxes at hyper-resolution scales in an Indian catchment. The outcomes of the project can contribute to the agriculture, hydrology, and climate communities of India.

2) Global terrestrial ecosystem response to dry extreme events

Climate change has a multitude of effects in terms of increase in temperature, vapor pressure deficit, and frequency of dry extreme events such as droughts and heatwaves. It is important to understand how terrestrial ecosystems are responding to these extremes since their sustenance is important in the context of carbon sequestration and other benefits. This project deals with studying the terrestrial ecosystem response using a variety of remote sensing and reanalysis datasets. The focus will be on soil-plant-atmosphere interactions. Candidates will have the freedom to study both past and future responses using GCM simulations.

3) Land-atmosphere interactions during dry extreme events [with Prof. Vishal Dixit]

Soil moisture has a significant role in influencing the feedback from the land surface. There is a growing literature that depicts its influence on the evolution of dry extreme events. This project deals with a thorough examination of soil moisture feedback using numerical weather model Weather Research and Forecasting (WRF) model simulations. Specifically, we would be interested to study the role of land using water and heat tracking schemes when extreme dry events and compound extremes get triggered. It is desirable for the candidates to have experience running the WRF model.

4) Sub-seasonal to seasonal agricultural drought prediction

Sub-seasonal to seasonal predictions are now the frontline research to ensure adequate time for planning and adaptation. There is limited effort that is made to carry out predictions at S2S scales (1-3 months ahead) in India. This work deals with developing models using machine learning or dynamical models to predict various aspects of droughts at S2S scales. Candidates with a good understanding of atmosphere processes and programming background are desirable for this project.