Characterizing and modeling hydroclimatic extremes
Floods, droughts, extreme precipitation and heatwaves: the processes behind them and how to model them, with a focus on the southeastern United States.
PhD student · University of Florida
I study hydroclimate extremes (floods, droughts, extreme precipitation and heatwaves) and how to keep infrastructure resilient to them, in the Climate Resilience Lab at the University of Florida. The globe shows the last five days of the atmosphere behind extreme rain.
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Scroll ↓My research focuses on hydroclimate extremes and risk: how floods, droughts, extreme precipitation and heatwaves arise, and how infrastructure and ecosystems can stay resilient to them under deep uncertainty.
Floods, droughts, extreme precipitation and heatwaves: the processes behind them and how to model them, with a focus on the southeastern United States.
Stochastic decision frameworks that improve the resilience of infrastructure and ecosystems when the future climate cannot be pinned down.
Process-based and ensemble approaches to assess climate vulnerability across water, energy, transportation, agriculture and public health.
Notes and interactive pieces on extreme rainfall, hydroclimate risk, and the data and tools behind them.
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My latest posts on LinkedIn, newest first. Scroll the feed for older ones.
Recent papers, abstracts and reports. The full, up-to-date list is on Google Scholar.
A Comprehensive Geospatial Dataset for Risk Assessments of Natural Hazards in the United States
M. Hatami Goloujeh, E. C. Kurter, J. Imran · Geoscience Data Journal 13(4), e70105
Estimating opium use prevalence at the national and provincial levels in Iran: a modelling study
S. Nemati, M. Hatami Goloujeh, H. Poustchi, M. Hadji, H. Rashidian, et al. · Scientific Reports 16, 8430
Projected Global Lung Cancer Burden Linked to Waterpipe Use: Estimates for 2025–2050
S. Nemati, M. Hatami Goloujeh, S. Abdi, N. Taheri, N. D. Freedman, et al. · International Journal of Cancer 159, 122
Advancing Camera-Based Monitoring for Operational Hydrologic Applications
E. Goharian, S. N. Young, J. S. Horsburgh, S. Khan, S. Neupane, R. Issa, et al. · AGU Fall Meeting 2024, H13C-1039
Harnessing Heterogeneous Sources of Data and Artificial Intelligence for Hydrologic Monitoring
E. Goharian, S. M. H. Erfani, M. Hatami Goloujeh · EGU General Assembly 2024, EGU24-4390
Applications of Machine Learning and Remote Sensing in Disaster Management Cycle
M. Hatami · MSc thesis, Politecnico di Milano
M. Hatami Goloujeh, A. G. M. E. Salem, A. I. Y. S. Elmahdy, A. A. A. M. Hassanien, et al. · Project report, Politecnico di Milano
Geo-Engineering Techniques for Unstable Slopes: Shallow Landslide Simulator Case
M. Hatami Goloujeh, A. G. M. E. Salem, Q. L. T. Nhu, K. Zhengyun, S. Korucu, S. Neisani · Project report, Politecnico di Milano
M. Hatami Goloujeh, S. Rahmati
Software, datasets and reports I have built. More are listed in the projects section of my LinkedIn profile.
What I am building now, for understanding and assessing hydroclimate extremes and their risks.
The globe on this site: the last five days of vapor transport, moisture, wind and rain from NOAA's GFS analyses, refreshed every six hours, with past events such as Hurricane Helene to replay.
An hour-by-hour replay of the winds, moisture and rain behind Helene's inland flooding, and a look at which ingredients were already unusual weeks before landfall and what the forecasts said.
A comprehensive geospatial dataset for risk assessments of natural hazards across the United States, published in Geoscience Data Journal (2026).
Extracts flood extents from Sentinel-1 SAR before and after events with the SPIDER framework, then cuts georeferenced 256×256 chips into balanced training sets for flood detection models.
Transfer learning with the Prithvi foundation model compared with UNet and SegFormer for mapping floods from SAR imagery (manuscript in preparation).
Interactive dashboard and StoryMap for coastal vulnerability from South Carolina to the East Coast, using multi-criteria decision-making and probabilistic principal component analysis.
Cloud-based tool that computes land cover shares (wetlands, forests, developed land and more) for states, counties or tracts in minutes instead of hours.
From my MSc at Politecnico di Milano and other work: earthquakes, landslides, risk assessment and statistics.
Geospatial data processing to support seismic emergency management: choosing emergency shelter locations in Amatrice, Italy.
Geo-engineering techniques for unstable slopes: geological, photogrammetric and geophysical methods applied to a shallow landslide simulator experiment.
Systematic assessment of hazards, vulnerability and exposure for a whole valley in Tuscany.
Web app for exploring probability distributions in real time: adjust parameters, compare distributions and see the mathematics behind them.
Recorded talks, newest first. Click a video to play it here.
AGU Fall Meeting 2024 · poster
EGU General Assembly 2024 · poster
GIS Day MicroConference, University of South Carolina
SCDOT Research and Innovation 2024 · pitch
I am a PhD student in hydrology at the University of Florida, in the Climate Resilience Lab of Dr. Nasser Najibi’s research group in the Department of Agricultural and Biological Engineering.
I began my PhD in Civil Engineering at the University of South Carolina in the iWERS lab with Dr. Erfan Goharian, alongside graduate coursework in computer science, and moved to UF when my advisor left. Before that, I spent four months at the University of Groningen building an agent-based model of human behavior during cascading disasters.
I earned an MSc in Civil Engineering for Risk Mitigation at Politecnico di Milano, with a thesis on deep learning and remote sensing in disaster management, and a BSc in Civil Engineering at Imam Khomeini International University, where river hydrology and hydraulics started my interest in flood risk.
More about me, my education and awards →
Web GIS applications in ArcGIS Experience Builder, with custom React widgets and real-time data APIs.
A QGIS plugin that finds unstable ground in mining areas, with machine learning to automate routine processing.