Dario Del Giudice

Also known as: Del Giudice, D.

https://orcid.org/0000-0002-6375-8527

High-Flow Hydrologic Events, Extreme Water-Quality Impairments, Hydroclimatic Impacts, 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).

Works (16)

2019 journal article

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Science of The Total Environment, 8, 133776.

By: S. Fang, D. Giudice, D. Scavia, C. Binding, T. Bridgeman, J. Chaffin, M. Evans, J. Guinness, T. Johengen, D. Obenour

Source: ORCID
Added: August 10, 2019

2019 article

Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios

ECOLOGICAL APPLICATIONS.

By: D. Del Giudice, V. Matli & D. Obenour

Sources: Web Of Science, ORCID
Added: December 16, 2019

2019 journal article

Modeling biophysical controls on hypoxia in a shallow estuary using a Bayesian mechanistic approach

Environmental Modelling & Software, 7.

By: A. Katin, D. Giudice & D. Obenour

Source: ORCID
Added: July 31, 2019

2018 journal article

Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator

Environmental Modelling & Software, 7.

By: D. Machac, P. Reichert, J. Rieckermann, D. Giudice & C. Albert

Source: ORCID
Added: June 7, 2019

2018 journal article

Long-term phosphorus loading and springtime temperatures explain interannual variability of hypoxia in a large temperate lake

Environmental Science & Technology, 1.

By: D. Giudice, Y. Zhou, E. Sinha & A. Michalak

Source: ORCID
Added: June 7, 2019

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.

By: D. Giudice, R. Muenich, M. Kalcic, N. Bosch, D. Scavia & A. Michalak

Sources: NC State University Libraries, ORCID
Added: August 6, 2018

2017 journal article

Communication: A few words can make a big impact

By: D. Del Giudice & A. Davies

Source: ORCID
Added: June 7, 2019

2016 journal article

Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation

Water Resour. Res., 3, n/a-n/a.

By: D. Giudice, C. Albert, J. Rieckermann & P. Reichert

Source: ORCID
Added: June 7, 2019

2016 journal article

Health

By: D. Del Giudice

Source: ORCID
Added: June 7, 2019

2015 journal article

Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors

Water Resour. Res., 5, n/a-n/a.

By: D. Giudice, R. Löwe, H. Madsen, P. Mikkelsen & J. Rieckermann

Source: ORCID
Added: June 7, 2019

2015 journal article

Model bias and complexity – Understanding the effects of structural deficits and input errors on runoff predictions

Environmental Modelling & Software, 64(1), 205–214.

By: D. Giudice, P. Reichert, V. Bareš, C. Albert & J. Rieckermann

Source: ORCID
Added: June 7, 2019

2015 journal article

The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration

Journal of Hydrology, 9, 241–254.

By: A. Sikorska, D. Giudice, K. Banasik & J. Rieckermann

Source: ORCID
Added: June 7, 2019

2013 journal article

Dynamic time warping improves sewer flow monitoring

Water Research, 47(11), 3803–3816.

By: D. Dürrenmatt, D. Del Giudice & J. Rieckermann

Source: ORCID
Added: June 7, 2019

2013 journal article

Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

Hydrology and Earth System Sciences, 17(10), 4209–4225.

By: D. Del Giudice, M. Honti, A. Scheidegger, C. Albert, P. Reichert & J. Rieckermann

Source: ORCID
Added: June 7, 2019

2012 journal article

Modeling of facade leaching in urban catchments

Water Resources Research, 48(12).

By: S. Coutu, D. Del Giudice, L. Rossi & D. Barry

Source: ORCID
Added: June 7, 2019

2012 journal article

Parsimonious hydrological modeling of urban sewer and river catchments

Journal of Hydrology, 464-465, 477–484.

By: S. Coutu, D. Del Giudice, L. Rossi & D. Barry

Source: ORCID
Added: June 7, 2019