Dario Del Giudice

ORCID iD
https://orcid.org/0000-0002-6375-8527
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Del Giudice, D.

Sources:
Dario Del Giudice (2015-03-16)

  • Country
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United States

Sources:
Dario Del Giudice (2016-01-24)

  • Keywords
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High-Flow Hydrologic Events,

Sources:
Dario Del Giudice (2015-03-16)

Extreme Water-Quality Impairments,

Sources:
Dario Del Giudice (2017-01-18)

Hydroclimatic Impacts,

Sources:
Dario Del Giudice (2018-01-25)

Parsimonious Process-Based Modeling,

Sources:
Dario Del Giudice (2015-03-16)

Bayesian Statistics,

Sources:
Dario Del Giudice (2015-03-16)

Uncertainty Quantification

Sources:
Dario Del Giudice (2015-03-16)

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Curriculum Vitae

Sources:
Dario Del Giudice (2016-09-12)

ResearchGate

Sources:
Dario Del Giudice (2015-12-19)

Google Scholar

Sources:
Dario Del Giudice (2017-05-31)

Linkedin

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Dario Del Giudice (2018-03-11)

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Sources:
Dario Del Giudice (2018-01-25)

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Scopus Author ID: 55334718300

Sources:
Scopus - Elsevier (2015-03-16)

Biography

Dr. Dario Del Giudice recently started 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).
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