Dario Del Giudice

ORCID iD
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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Stochastic Hydrology,

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)

Environmental Systems Analysis,

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

Data Assimilation,

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

Water Quality Modeling

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

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Curriculum Vitae

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

ResearchGate

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Dario Del Giudice (2015-12-19)

Google Scholar

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

  • Email
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ddelgiudice@carnegiescience.edu

Sources:
Dario Del Giudice (2016-01-17)

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Scopus Author ID: 55334718300

Sources:
Scopus to ORCID (2015-03-16)

Biography

Dario is a postdoctoral researcher at the Carnegie Institution on the Stanford University campus. He is interested in the impact 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 combines process-based models, stochastic methods for uncertainty quantification, and statistical inference. Dario is currently focusing on the complex issue of fertilizer leaching from agricultural watersheds into surface waters and fostering eutrophication and oxygen depletion. His goal is to identify the key hydroclimatic and land-management factors responsible for lake hypoxia and predict how freshwater quality will respond to changing patterns in precipitation and temperature. Dario earned a 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 environmental (esp. hydrological) models. 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 oversimplifications).
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