Quantitative risk assessment, Parsimonious Process-Based Modeling, Bayesian Statistics, Uncertainty Quantification
Dr. Dario Del Giudice is conducting his research at NCSU, after moving from Stanford where he was working at the Carnegie Institution for Science. He is interested in understanding and predicting the impacts of climate variability and human pressures on the quantity and quality of water resources. In his research at the interface of catchment hydrology and surface water pollution he uniquely combines process-based models, stochastic methods for uncertainty quantification, and statistical inference. Dr. Del Giudice is currently focusing on the complex issue of fertilizer running off agricultural watersheds into surface waters and fostering eutrophication and dead zones in critical systems such as the Great Lakes and the Gulf of Mexico. His goal is to identify the key hydroclimatic and land-management factors responsible for lacustrine, estuarine, and coastal hypoxia and predict how the quality of these aquatic ecosystems will respond to changing patterns in precipitation and temperature. Dr. Del Giudice earned his PhD degree from the Swiss Federal Institute of Technology in Zurich. During his doctorate, he has worked on improving inference of catchment properties, rainfall estimation, and predictions of hydrological and contaminant transport dynamics. In particular, he has developed advanced Bayesian methods to assimilate runoff data and quantify predictive uncertainty coming from errors in model input (related to precipitation) and structure (related to system oversimplifications).
2018 journal article
On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions
Environmental Modelling & Software, 105, 286–295.