Prof. Dr. Orhan GÜNDÜZ

Environmental Engineering Department

Professor Doctor 

Contact Info:

  • +90 232 750 6857
  • Engineering Building – C, Room: D-204
 
To access our Faculty Member’s academic work, please click the GCRIS information button below.

Education:

  • Ph.D., Georgia Institute of Technology, Atlanta, GA, ABD
  • M.S., Georgia Institute of Technology, Atlanta, GA, ABD & Middle East Technical University, Ankara, Turkey
  • B.S., Middle East Technical University, Ankara, Turkey

Research Interests:

  • Surface and subsurface water quality
  • Environmental modeling
  • Numerical methods in environmental engineering
  • Flow and contaminant transport in surface and subsurface waters
  • Dynamic interactions of surface and subsurface systems
  • Source/sink terms in environmental modeling
  • Environmental impact assessment
  • Geographic information systems

Recent Publications:

  • Marijuan, R., Díez, B., Peláez-Sánchez, S., Sánchez, C., Iglesias, J., Şirin, B., Baba, A., Gündüz, O. & Sanchez R. (2024). Evaluating the impact of nature-based solutions on the provision of water-related and water-dependent ecosystem services. Nature-Based Solutions, 6, 100194. https://doi.org/10.1016/j.nbsj.2024.100194 
  • Khorrami, B., Sahin, O. G., & Gunduz, O. (2024). Comprehensive comparison of different gridded precipitation products over geographic regions of Türkiye. Journal of Applied Remote Sensing, 18(3), 1-25. https://doi.org/10.1117/1.JRS.18.034503
  • Şahin O.G., Gündüz O. (2024), A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning, Journal of Hydrology, 634,131051https://doi.org/10.1016/j.jhydrol.2024.131051 
  • Khorrami, B., Ali, S., & Gündüz, O. (2023). An appraisal of the local-scale spatio-temporal variations of
    drought based on the integrated GRACE/GRACE-FO observations and fine-resolution FLDAS model. Hydrological Processes. 37:e15034. https://doi.org/10.1002/hyp.15034
  • Khorrami, B., Ali, S., & Gündüz, O. (2023). Investigating the local-scale fluctuations of groundwater storage by using downscaled GRACE/GRACE-FO JPL mascon product Based on Machine Learning (ML) Algorithm. Water Resources Management, 1-18. https://doi.org/10.1007/s11269-023-03509-w