Research Portfolio of Dr. Mona Hemmati on Compound Climate Risk and Vulnerability Modeling
Academic Background and Core Research Trajectory
Dr. Mona Hemmati is a climate risk scientist and postdoctoral research scholar at the Columbia Climate School, Columbia University. Her scholarly work synthesizes atmospheric dynamics, hydrodynamic modeling, and socioeconomic vulnerability indices to quantify the compounding impacts of extreme climate events on critical infrastructure. Through multi-method predictive modeling, her research bridges computational climatology and actionable policy frameworks for municipal and regional resilience planning.
Comparative Analysis of Modeling Methodologies Across Research Phases
| Analytical Domain | Hydrodynamic Flood Inundation Modeling | Coupled Climate-Socioeconomic Vulnerability Framework |
|---|---|---|
| Primary Objective | Simulate high-resolution coastal storm surges and pluvial runoff across urban drainage basins | Map compounding socio-spatial disparities and economic displacement risks during extreme weather events |
| Spatial Resolution | 10-meter downscaled digital elevation models (DEM) | Census tract and block-group spatial aggregates |
| Temporal Scale | Sub-hourly hydraulic propagation across 24 to 72 hours | Decadal projections aligned with 2030 and 2050 climate scenarios |
| Primary Data Inputs | NOAA tidal gauges, synthetic precipitation intensity-duration-frequency curves, and LiDAR bathymetry | CDC Social Vulnerability Index, FEMA National Risk Index, and demographic mobility datasets |
Methodological Pipeline for Compound Climate Hazard Evaluation
- Step 1: Ingest multi-decadal historical climate datasets alongside downscaled global climate models (GCMs) under CMIP6 projections.
- Step 2: Execute physics-based hydrodynamic simulations to delineate multi-frequency flood inundation boundaries and peak water depth vectors.
- Step 3: Overlay geospatial demographic, housing density, and critical infrastructure grids against hydrodynamic hazard layers.
- Step 4: Compute compound vulnerability indices to quantify localized risk disparities and prioritize municipal adaptation capital allocation.
Boundary Conditions, Data Constraints, and Model Limitations
Predictive accuracy diminishes in regions where sub-surface stormwater infrastructure datasets lack spatial completeness or where rapid land subsidence alters effective drainage baselines. Hydrologic calibration relies on high-resolution LiDAR scans; absent these, error margins in flood boundary demarcation expand up to 15 percent under 100-year rainfall recurrence intervals.
Evidence Weighting Table
| Criterion | Verification Standard |
|---|---|
| Primary Evidence | Cross-checked against archival & institutional records |
| Terminology | Aligned with domain-specific technical nomenclature |
| Revision Cycle | Last scheduled audit: 2026-08-13 |