Faculty Arfan Arshad

Arfan Arshad

Assistant Professor
  • Institute of Geographical Information Systems
  • 5190854412
Summary

I currently serve as an Assistant Professor of Geoinformatics Engineering at the Institute of Geographical Information Systems (IGIS), National University of Sciences and Technology (NUST), Pakistan. Prior to this appointment, I worked as an Associate Researcher (Postdoc I) from Jan 2025 to Dec 2025 at the Research Applications Laboratory (RAL), NSF National Center for Atmospheric Research (NCAR), USA, where my research focused on fire–hydrology interactions and climate-driven water resources dynamics. I hold a Ph.D. in Biosystems and Water Resources Engineering (2021-2024) from Oklahoma State University, with advanced doctoral training in GIS and Remote Sensing (2018-2020) from the Chinese Academy of Sciences (CAS), China. My academic background also includes an undergraduate degree (2016) and master’s degree (2018) in Agricultural Engineering from the University of Agriculture Faisalabad, Pakistan. My research focuses on using different research tools including remote sensing, hydrology, and artificial intelligence, with a strong emphasis on integrating satellite observations (e.g., GRACE, MODIS, Landsat, Sentinel), land surface and hydrological models, and machine learning techniques to support decision-relevant assessment of water resources management. My work addresses critical challenges related to climate change impacts, droughts and floods, agricultural water use, and transboundary water security, particularly in data-scarce regions.

Academic Background
PhD (Remote sensing and water resources ) Oklahoma State University July 01, 2021 - July 31, 2024
Honours and Awards
Publications
Quantifying water resource changes across natural and managed landscapes in the Indus Basin drainage catchments using satellite-informed machine learning–based downscaled GRACE data December 01, 2026 Science of Remote Sensing - Volume:14, Article Number 100479
Data-Driven Prediction of Fertility Outcomes Through Machine Learning Models November 01, 2026 Earth Systems and Environment - Volume:10, Issue:7, Page:8873-8886
Reconstructing accurate long-term PM2.5 across China: Bridging data gaps and revealing emission-meteorological drivers in a global context October 01, 2026 Earth Systems and Environment - Volume:10, Issue:6, Page:7265-7280
Artificial intelligence-driven drought prediction using long-term meteorological and vegetation indices August 15, 2026 Geology, Ecology, and Landscapes - Volume 10, Issue 3, Pages 880-904
Advancing aquifer recharge forecasting through hybrid explainable AI and hydrological modeling July 01, 2026 Geoscience Frontiers - Volume:17, Issue:4, Article Number 102336
Deep learning-based aided spatial mapping of local scale hydrologic soil groups in the Upper Oum Er-Rbia Basin, Morocco April 29, 2026 Applied Water Science - Volume:16, Issue:5, Article Number 157
Investigating Effects of Dynamic Debris Cover Variations on Glacio-Hydrology under Projected Climate Change in High Mountain Asia April 22, 2026 Earth Systems and Environment - Pages 30
Quantifying Methane–Climate Interactions in Eastern Saudi Arabia Using Geospatial and Machine Learning Modeling March 12, 2026 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing - Volume:19, Page:8504-8517
Sustainable Tourism Pathways in the Lolab Valley, Kashmir: Integrating Community Livelihoods With the UN Sustainable Development Goals February 23, 2026 Sustainable Development - Pages 16
Conferences
Research Associate NSF National Center for Atmospheric Research January 01, 2025 - January 10, 2026
Research Associate OKLAHOMA STATE UNIVERSITY August 01, 2024 - December 31, 2025
Research Assistant OKLAHOMA STATE UNIVERSITY August 02, 2021 - July 31, 2024