Dario Del Giudice
Also known as: Del Giudice, D.
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).
Works (23)
Updated: April 4th, 2024 10:20
2023 article
SmitomB/Internal_Phosphorus_Loading_Model: Characterizing and Projecting Internal Phosphorus Loading through Bayesian Mass-balance Modeling
(2023, December 23). Zenodo.
Contributors: S. Borah, D. Giudice, M. Aupperle & D. Obenour
Source: ORCID
Added: January 25, 2024
2023 article
SmitomB/Internal_Phosphorus_Loading_Model: Characterizing and Projecting Internal Phosphorus Loading through Bayesian Mass-balance Modeling
(2023, December 23). Zenodo.
Contributors: S. Borah, D. Giudice, M. Aupperle & D. Obenour
Source: ORCID
Added: January 25, 2024
2022 journal article
Temporally resolved coastal hypoxia forecasting and uncertainty assessment via Bayesian mechanistic modeling
HYDROLOGY AND EARTH SYSTEM SCIENCES, 26(4), 1131–1143.
hess.copernicus.org (publisher PDF)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
14. Life Below Water
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: February 28, 2022
2021 journal article
Elucidating controls on cyanobacteria bloom timing and intensity via Bayesian mechanistic modeling
SCIENCE OF THE TOTAL ENVIRONMENT, 755.
author keywords: Eutrophication; Harmful algal blooms; Bayesian inference; Process-based modeling; Great Lakes; Hindcasts and projections
MeSH headings : Bayes Theorem; Chlorophyll A; Cyanobacteria; Eutrophication; Harmful Algal Bloom; Lakes; Phosphorus
TL;DR:
A mechanistic model describing June-October dynamics of chlorophyll a, nitrogen, and phosphorus near the Maumee River outlet, where blooms typically initiate and are most severe, highlights the role of temperature in regulating bloom initiation and subsequent loss rates and suggests that a 2 °C increase could lead to blooms that start about 10 days earlier and grow 23% more intense.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science; OpenAlex)
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: January 11, 2021
2021 journal article
Exploring nutrient and light limitation of algal production in a shallow turbid reservoir
ENVIRONMENTAL POLLUTION, 269.
author keywords: Cyanobacteria; Eutrophic reservoir; Nutrients; Mixing; Modeling
MeSH headings : Bayes Theorem; Environmental Monitoring; Eutrophication; Jordan; Lakes; North Carolina; Nutrients; Phosphorus / analysis
TL;DR:
It is indicated that nutrient reductions, rather than changes in mixing or background turbidity, are critical to controlling cyanobacteria in a shallow eutrophic freshwater system.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science)
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: February 15, 2021
2021 journal article
Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity
ECOLOGICAL MODELLING, 447.
author keywords: Eutrophication; Phytoplankton; Modeling; Bayesian inference; Nutrient management; Neuse River Estuary
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
14. Life Below Water
(Web of Science)
Sources: Web Of Science, NC State University Libraries
Added: April 26, 2021
2020 journal article
Accounting for erroneous model structures in biokinetic process models
RELIABILITY ENGINEERING & SYSTEM SAFETY, 203.
author keywords: Bias description; Kinetic model; Process design; Wastewater treatment; Uncertainty
TL;DR:
This work assesses the strengths and weaknesses of a method to statistically describe complex temporal patterns of residuals with autocorrelated error models and shows that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: September 28, 2020
2020 journal article
Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios
ECOLOGICAL APPLICATIONS, 30(2).
author keywords: Bayesian inference; climate change; dead hypoxic zones; eutrophication; Gulf of Mexico; hindcasts and projections; process-based modeling; riverine nitrogen; uncertainty quantification
MeSH headings : Bayes Theorem; Ecosystem; Environmental Monitoring; Gulf of Mexico; Humans; Hypoxia; Oxygen
TL;DR:
New geostatistical estimates of hypoxia derived from nearly 150 monitoring cruises and a process-based model are used to improve characterization of controlling mechanisms, historic trends, and future responses of Hypoxia while rigorously quantifying uncertainty in a Bayesian framework.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
14. Life Below Water
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: December 16, 2019
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.
author keywords: Harmful algal blooms; Space-time geostatistical model; Lake Erie; Probabilistic estimates; Algal biomass and extent
MeSH headings : Environmental Monitoring / methods; Harmful Algal Bloom; Models, Statistical; Water Pollution / statistics & numerical data
TL;DR:
A space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll data sampled during the HAB season from 2008 to 2017 is developed and comprehensive estimates of overall bloom biomass and surface areal extent with quantified uncertainties are provided.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science)
13. Climate Action
(Web of Science)
14. Life Below Water
(OpenAlex)
15. Life on Land
(Web of Science)
Source: ORCID
Added: August 10, 2019
2019 journal article
Modeling biophysical controls on hypoxia in a shallow estuary using a Bayesian mechanistic approach
Environmental Modelling & Software, 7.
author keywords: Hypoxia; Dissolved oxygen modeling; Bayesian inference; Neuse River estuary; Stratification; Oxygen demand
TL;DR:
Results indicate that the bottom layer dissolved oxygen (BLDO) concentration in the Neuse River Estuary is more responsive to nutrient loading reductions than previously thought, though it may take multiple years to produce measurable declines in hypoxia due to hydro-meteorological variability.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
14. Life Below Water
(Web of Science; OpenAlex)
Source: ORCID
Added: July 31, 2019
2018 journal article
Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator
Environmental Modelling & Software, 7.
TL;DR:
This didactic example uses a model implemented with the hydrological simulator SWMM that allows us to compare the authors' inference results against those derived with the full model, and demonstrates that iterative improvements lead to reasonable results with a very small design data set.
(via Semantic Scholar)
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.
MeSH headings : Environmental Monitoring; Eutrophication; Humans; Hypoxia; Lakes; Phosphorus; Temperature
TL;DR:
It is found that a linear combination of decadal total phosphorus loading from tributaries and springtime air temperatures explains a high proportion of the interannual variability in average summertime hypoxic extent, which suggests that the lake responds primarily to long-term variations in phosphorus inputs, rather than springtime or annual loading as previously assumed.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
14. Life Below Water
(OpenAlex)
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.
author keywords: Uncertainty assessment; Mechanistic modeling; Surface hydrology; Water quality; Least squares; Statistical inference
TL;DR:
It is found that least squares performs similarly to more sophisticated approaches such as a Bayesian autoregressive error model in terms of both accuracy and reliability if calibration periods are long or if the input data and the model have minimal bias.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(Web of Science; OpenAlex)
13. Climate Action
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018
2017 journal article
MeSH headings : Communication; Journal Impact Factor; Publishing; Research Personnel / standards
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.
Source: ORCID
Added: June 7, 2019
2016 journal article
TL;DR:
For students entering the PGA Golf Management degree program, a certified golf handicap or written ability equivalent to a 12 or better handicap by a PGA professional or high school golf coach is required.
(via Semantic Scholar)
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.
author keywords: Bayesian uncertainty analysis; hydrological simulator; stochastic differential equations; statistical error model; urban drainage; model discrepancy
TL;DR:
The results show that both approaches can provide probabilistic predictions of wastewater discharge in a similarly reliable way, both for periods ranging from a few hours up to more than 1 week ahead of time.
(via Semantic Scholar)
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.
Contributors: * , P. Reichert *, V. Bareš *, C. Albert * & J. Rieckermann *
author keywords: Model structural deficits; Rainfall errors; Stochastic uncertainty analysis; Bayesian bias description; Hydrodynamic simulations; Model comparison
TL;DR:
The method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process, and shows that a single bias description produces reliable predictions for all models.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(OpenAlex)
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.
Contributors: A. Sikorska*, * , K. Banasik * & J. Rieckermann *
author keywords: TSS; Uncertainty analysis; Bayesian inference; Multivariate calibration; Model bias; Autocorrelated errors
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(OpenAlex)
Source: ORCID
Added: June 7, 2019
2013 journal article
Dynamic time warping improves sewer flow monitoring
Water Research, 47(11), 3803–3816.
Contributors: D. Dürrenmatt*, * & J. Rieckermann *
author keywords: Dynamic time warping; Sensor diagnosis; Sewer flow monitoring; Signal processing
MeSH headings : Computer Simulation; Models, Theoretical; Sewage; Switzerland; Temperature; Time Factors; Waste Disposal, Fluid / instrumentation; Waste Disposal, Fluid / methods; Wastewater / analysis; Water Quality
TL;DR:
This paper improves sewer flow monitoring by creating redundant information on sewer velocity from natural wastewater tracer data, and shows that DTW outperforms XCORR, because it provides more accurate velocity estimates, with an error of about 7% under typical conditions, at a higher temporal resolution.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(OpenAlex)
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.
Contributors: * , M. Honti *, A. Scheidegger *, C. Albert *, P. Reichert * & J. Rieckermann *
TL;DR:
A structured approach to select, among five variants, the optimal bias de- scription for a given urban or natural case study and results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected.
(via Semantic Scholar)
Source: ORCID
Added: June 7, 2019
2012 journal article
Modeling of facade leaching in urban catchments
Water Resources Research, 48(12).
Contributors: S. Coutu*, * , 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.
Contributors: S. Coutu*, * , L. Rossi * & D. Barry *
author keywords: Urban hydrology; Modeling; Sewer catchment; Case study
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation
(OpenAlex)
Source: ORCID
Added: June 7, 2019
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