Comparing crop evapotranspiration estimation methods to evaluate water-saving potential of alternative land uses in the San Joaquin Valley, California
Frontiers in Water, 8, 1879797 (2026)
Postdoctoral Researcher · HydrologyUniversity of California, MercedCalifornia, USA
I am a postdoctoral researcher working on hydrology and water resources management. My work connects a physical understanding of the water cycle with the decisions made about water, using land-surface models, satellite observations and climate data. My current research focuses on how forests, fire and a changing climate reshape our water. I received my PhD in Water Resources Engineering from IIT Bombay. I also develop open-source tools for hydroclimate data, including IMDLIB, a Python library for India Meteorological Department gridded data, used widely in hydroclimate research in India.

Released IMDLIB 0.3, with one function to load IMD gridded data and named regions of India (states, districts, basins and cities). Read more about this news item
New paper in Frontiers in Water on crop water use in the San Joaquin Valley, and how much water other land uses could save. Read more about this news item
At AGU Fall Meeting 2025 with work on how drought affects ecosystem water-use efficiency across the US (with A. Chakraborty, J. Abatzoglou and M. Safeeq).
New paper in Journal of Water and Climate Change on how climate change may affect runoff and drought risk in the Upper Bhima basin. Read more about this news item
New paper in Ecohydrology on how forest management changes the water balance of the upper Kings River Basin. Read more about this news item
Frontiers in Water, 8, 1879797 (2026)
Ecohydrology, 18(1), e2753 (2025)
Environmental Modelling & Software, 171, 105869 (2024)
Journal of Hydrology, 610, 127842 (2022)
Open-source Python package for retrieval, processing, and spatiotemporal analysis of gridded India Meteorological Department (IMD) observational datasets.
284,147 PyPI downloadsas of 28 Sept 2026
$ pip install imdlib
# daily rainfall over India, 2020 to 2023
>>> import imdlib as imd
>>> data = imd.load('rain', 2020, 2023)
>>> ds = data.get_xarray()
>>> ds['rain'].mean('time').plot()