Ph.D. Student

Contact Information:
East Campus (Lincoln)


Area of Research:
Soil and Water Resource Engineering

Water Resources

About Me:

Parisa Sarzaeim was qualified to attend the University of Tehran (UT) and began her Bachelor of Science in Agricultural Engineering-Water Resources Management in 2010. The most important reason to choose this major is her interests in identifying water-related issues, which are considerable all around the world, and look for solutions to tackle them. In 2014, she honored to be selected as an exceptional talented student among undergraduate students and became qualified to receive merit-based admission offer to M.Sc. student without taking national entrance exam in Water Resource Engineering at the University of Tehran. According to various environmental problems and water scarcity, in her MSc, she made decision to work on investigating climate change effects on water resources, particularly rivers, and assessing environmental state compared to other important demanding sectors, especially agriculture. Her thesis titled "Determination of reservoir environmental demand allocation rules under climate change conditions" has been selected as outstanding M.Sc. thesis in the 27th Research Festival-University of Tehran in 2018. Besides her academic career, she has also worked in a consulting engineers company and got involved in several hydrological and water resources projects. These projects provide many valuable experiences for her and make her familiar with the actual water challenges. According to her background, she believes that our water-related issues are widespread and therefore, efficient and multidisciplinary cooperation is required in order to recognize them accurately. she thinks it is the first step to reach solution. Therefore, in 2018 she joined Biological Systems Engineering graduate program at University of Nebraska-Lincoln which is a great opportunity to work and focus on practical areas in her favorite field. Now, she is developing a conceptual model for corn phenotypes prediction with respect to environmental variables.