Research

Research

Dr. Parajuli is a recipient of multiple USDA/NIFA as well as USDA/FAS Nationally Competitive Grants as Principal Investigator (PI). He is an investigator of over $6 million federal and local research funding. Dr. Parajuli served on nationally competitive grant review panels (e.g., NSF, USDA/NIFA) for over 7 times and led the USDA/NIFA review panel as Panel Manager. Dr. Parajuli published over 190 peer-reviewed journal articles including book chapter, conference proceedings and technical presentations. For his pioneering research achievements, Dr. Parajuli was distinguished as a world’s top 2% scientists in 2024.

Dr. Parajuli’s research areas include: monitoring, machine learning, modeling and assessment of surface and ground water quality and quantity (sediment, nutrients, pesticide, bacteria) at field, watershed, and regional scales; evaluation of water conservation practices, crop systems and management through modeling; land use and climate change impact on water resources; nutrient and pathogen transport process; application of geographic information systems and remote sensing; and modeling natural resources and decision support systems. Dr. Parajuli promotes international collaboration on water management, sustainability, food security, and policy.

Project

We are currently implementing a project funded through USDA/NIFA. The project goals include evaluation of surface-ground water quality (suspended solids, phosphorus, nitrogen, bacteria loads) and quantity impacts driven by implementation of conservation and crop management practices within the Big Sunflower River watershed (BSRW) in the Mississippi delta using a modeling approach with field verification. Both field observed and geospatial data as shown below will be utilized with biophysical predictive tools (surafce-ground water models) to assess the effects of mitigation strategies on water quality.

Big Sunflower River Watershed - Monitoring and Modeling
Big Sunflower River Watershed - Water Quality Monitoring and Modeling
Commonly used geospatial data input in the model.
Commonly used geospatial data input in the model.