Danial Golbaz
PhD Candidate
I am a PhD student in coastal and hydrologic modeling, with a background in civil and environmental engineering. My research focuses on compound flooding, nearshore wave dynamics, rainfall–runoff processes, and the interaction of surge, waves, tides, rainfall, and river discharge. I work with numerical models such as WRF-Hydro, FUNWAVE-TVD, SCHISM, and machine-learning surrogates to improve flood predictions. My goal is to connect physics-based modeling, data analysis, and practical coastal engineering applications for better assessment and decision-making.
Education
- MS Coastal Engineering, University of Tehran, 2021
Currently working on
- Developing Engineering practices using Ecosystem Design Solutions (DEEDS)
DEEDS is a collaborative project that uses nature-based solutions, engineering, modeling, and community engagement to strengthen coastal resilience.
- Machine-Learning Surrogates for Hydrologic Prediction
CNN–LSTM surrogate models that emulate WRF-Hydro streamflow to accelerate scenario testing and explainable hydrologic analysis.
- Skew Surge and Extreme Sea-Level Analysis
Estimating extreme skew-surge probabilities from tide-gauge and atmospheric data, with nonstationarity detection and storm clustering.