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Benjamin Renard
Researcher at University of Newcastle
Publications - 103
Citations - 4482
Benjamin Renard is an academic researcher from University of Newcastle. The author has contributed to research in topics: Bayesian probability & Stage (hydrology). The author has an hindex of 32, co-authored 94 publications receiving 3722 citations. Previous affiliations of Benjamin Renard include Institut national de la recherche agronomique & University of Adelaide.
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Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors
TL;DR: In this article, the authors focus on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios, and highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall runoff data with large unknown errors.
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Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis
TL;DR: In this paper, a case study presented a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients, and BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization.
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Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology
Benjamin Renard,Michel Lang +1 more
TL;DR: In this paper, the Gaussian copula has been used for field significance determination, regional risk analysis, discharge-duration-frequency (QdF) models with design hydrograph derivation and regional frequency analysis.
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Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation
TL;DR: It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors.
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Regional methods for trend detection: Assessing field significance and regional consistency
Benjamin Renard,Michel Lang,Philippe Bois,Anne Dupeyrat,O. Mestre,Hélène Niel,Eric Sauquet,Christel Prudhomme,S. Parey,Emmanuel Paquet,Luc Neppel,Joël Gailhard +11 more
TL;DR: In this article, the authors describe regional methods for assessing field significance and regional consistency for trend detection in hydrological extremes, using daily discharge data arising from 195 gauging stations in France.