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Mahmood Fooladi

Mahmood Fooladi

PhD Student
Climate Change Research Centre
Biological, Earth & Environmental Sciences

Mahmood received his B.Sc. in Civil Engineering in 2013. He then earned his M.Sc. in Hydrology and Water Resources from Isfahan University of Technology, Iran, in 2021. His research interests and experience include extreme events (droughts and floods), compound extremes, climate change, weather forecasting, remote sensing, surface and subsurface hydrology, multivariate analysis, and machine learning. He began his PhD in September 2024 at the Climate Change Research Centre (CCRC) under the supervision of Dr. Anna Ukkola, Professor Andy Pittman, and Dr. Doug Richardson. His current research focuses on seasonal forecasting of compound extremes across Australia. In his free time, he enjoys bike riding, hiking, playing chess, and swimming.

Supervised by: Dr. Anna Ukkola, Professor Andy Pittman and Dr. Doug Richardson

Project Title: Seasonal forecasting of compound extremes across Australia

Project Description: My PhD is about improving our ability to forecast “compound hot–dry extremes” in Australia, periods when unusually high temperatures and unusually low rainfall occur together. These combination events can be more disruptive than heat or drought alone because they intensify impacts on water availability, agriculture, ecosystems, and the built environment, and they can also contribute to conditions that raise fire risk. I focus on the subseasonal-to-seasonal (S2S) window, meaning forecasts from a few weeks to a few months ahead, which is challenging but highly valuable for planning and early action.
Contact Details

m.fooladi@unsw.edu.au

1. Fooladi, M., Nikoo, M. R., Mirghafari, R., Madramootoo, C. A., Al-Rawas, G., & Nazari, R. (2024). Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis. Journal of Environmental Management, 362, 121259. https://doi.org/10.1016/j.jenvman.2024.121259

2. Kordani, M., Nikoo, M. R., Fooladi, M., Ahmadianfar, I., Nazari, R., & Gandomi, A. H. (2024). Improving long-term flood forecasting accuracy using ensemble deep learning models and an attention mechanism. Journal of Hydrologic Engineering, 29(6), 04024042. https://doi.org/10.1061/JHYEFF.HEENG-6262

3. Khajehali, M., Safavi, H. R., Nikoo, M. R., & Fooladi, M. (2024). A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran. Natural Hazards, 120(9), 8483-8504. https://doi.org/10.1007/s11069-024-06528-x

4. Majnooni, S., Fooladi, M., Nikoo, M. R., Al-Rawas, G., Haghighi, A. T., Nazari, R., ... & Gandomi, A. H. (2024). Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis. Journal of Water Process Engineering, 60, 105187. https://doi.org/10.1016/j.jwpe.2024.105187

5. Majnooni, S., Nikoo, M. R., Nematollahi, B., Fooladi, M., Alamdari, N., Al-Rawas, G., & Gandomi, A. H. (2023). Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning. Hydrological Sciences Journal, 68(14), 1984-2008. https://doi.org/10.1080/02626667.2023.2248112

6. Fooladi, M., Golmohammadi, M. H., Rahimi, I., Safavi, H. R., & Nikoo, M. R. (2023). Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran. Expert Systems with Applications, 221, 119788. https://doi.org/10.1016/j.eswa.2023.119788

7. Mehrab Amiri, S., Fooladi, M., Rahmani, V., & Mirghafari, R. (2022). Assessing scaling behavior of four hydrological variables using combined fractal and statistical methods in Missouri River Basin. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 46(3), 2405-2423. https://doi.org/10.1007/s40996-021-00744-2

8. Fooladi, M., Golmohammadi, M. H., Safavi, H. R., & Singh, V. P. (2021). Application of meteorological drought for assessing watershed health using fuzzy-based reliability, resilience, and vulnerability. International Journal of Disaster Risk Reduction, 66, 102616. https://doi.org/10.1016/j.ijdrr.2021.102616

9. Fooladi, M., Golmohammadi, M. H., Safavi, H. R., & Singh, V. P. (2021). Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis. Journal of environmental management, 297, 113283. https://doi.org/10.1016/j.jenvman.2021.113283

10. Golmohammadi, M. H., Safavi, H. R., Sandoval-Solis, S., & Fooladi, M. (2021). Improving performance criteria in the water resource systems based on fuzzy approach. Water Resources Management, 35(2), 593-611. https://doi.org/10.1007/s11269-020-02739-6

11. Fooladi, M., Golmohammadi, M. H., Safavi, H. R., Mirghafari, R., & Akbari, H. (2021). Trend analysis of hydrological and water quality variables to detect anthropogenic effects and climate variability on a river basin scale: A case study of Iran. Journal of hydro-environment research, 34, 11-23. https://doi.org/10.1016/j.jher.2021.01.001