Saswata Nandi

Postdoctoral Researcher · HydrologyUniversity of California, MercedCalifornia, USA

I study how a warming climate, wildfire and changing forests alter water availability and the risk of floods and droughts. My approach pairs land-surface modelling with satellite observations and climate records. I completed my PhD at IIT Bombay, where I developed IMDLIB, an open-source Python library that is now a widely used tool for working with India’s long-term gridded rainfall and temperature records.

Photo of Saswata Nandi

News

  1. 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

  2. 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

  3. 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).

  4. 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

  5. New paper in Ecohydrology on how forest management changes the water balance of the upper Kings River Basin. Read more about this news item

Selected papers

All publications

Comparing crop evapotranspiration estimation methods to evaluate water-saving potential of alternative land uses in the San Joaquin Valley, California

Kandhway, A., Nandi, S., Conklin, M. H. & Safeeq, M.

Frontiers in Water, 8, 1879797 (2026)

DOI: 10.3389/frwa.2026.1879797
  • Python
  • MIT license
  • v0.3.1

IMDLIB

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

More about IMDLIB

$ 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()

Blog and reading