PNNL

Sundar Niroula is a data scientist at Pacific Northwest National Laboratory (PNNL) whose work focuses on understanding and predicting hydrologic and environmental systems. He develops and applies process-based models, data-driven methods, geospatial workflows, and high-performance computing tools to study complex interactions among landscapes, water, and ecosystems. His research emphasizes integrating mechanistic understanding with AI and machine learning (ML) approaches to improve predictive capabilities, interpretation, and decision support for environmental challenges across scales.

Niroula joined PNNL as a postdoctoral research associate. Before joining PNNL, he earned his PhD in civil engineering from the University of Illinois Urbana-Champaign, where his research focused on hydrology, water quality, and agricultural watershed systems.

• Hydrologic processes and prediction
• Water quality and watershed management
• Earth and environmental systems modeling
• Coupled human and natural systems
• AI/ML for environmental science
• Deep learning and foundation models
• High-performance scientific computing

• Integrated hydrologic modeling
• Hydrobiogeochemical modeling
• Model-data integration
• ML
• Explainable AI
• Geospatial analysis
• Watershed hydrology

• PhD in civil engineering, University of Illinois Urbana-Champaign, 2023
• Master of technology in civil engineering, Indian Institute of Technology Bombay, 2017
• Bachelor of engineering in civil engineering, Institute of Engineering, Pulchowk Campus, 2014

• American Geophysical Union (AGU)
• American Society of Civil Engineers (ASCE)

• Ravindra K. and Kavita Kinra Fellowship, University of Illinois Urbana-Champaign, 2018–2019
• Deutscher Akademischer Austauschdienst (DAAD) Regional Scholarship, Indian Institute of Technology Bombay, 2015–2017
• Merit-Based Government Scholarship, Institute of Engineering, Pulchowk Campus, 2010–2014

Google Scholar Profile

• Niroula, S., X. Cai, and G. F. McIsaac. 2023. “Sustaining crop yield and water quality under climate change in intensively managed agricultural watersheds: The need of both adaptive and conservation measures.” Environmental Research Letters. https://doi.org/10.1088/1748-9326/ad085f
• Li, S., X. Cai, S. Niroula, K. Wallington, B. M. Gramig, R. D. Cusick, V. Singh, G. F. McIsaac, S. Oh, C. V. Kurambhatti, S. A. Emaminejad, and S. John. 2023. “Integrated agricultural practices and engineering technologies enhance synergies of food-energy-water systems in Corn Belt watersheds.” Environmental Science & Technology. https://doi.org/10.1021/acs.est.3c02055
• Niroula, S., K. Wallington, and X. Cai. 2023. “Addressing data challenges in riverine nutrient load modeling of an intensively managed agro-industrial watershed.” Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.13097
• Li, S., K. Wallington, S. Niroula, and X. Cai. 2022. “A modified response matrix method to approximate SWAT for computationally intense applications.” Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2021.105269 
  Authors contributed equally.
• Li, S., X. Cai, S. A. Emaminejad, A. Juneja, S. Niroula, S. Oh, K. Wallington, R. D. Cusick, B. M. Gramig, S. John, G. F. McIsaac, and V. Singh. 2021. “Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds.” Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2021.105083
• Niroula, S., S. Halder, and S. Ghosh. 2018. “Perturbations in the initial soil moisture conditions: Impacts on hydrologic simulation in a large river basin.” Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2018.04.029