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Fabrizio Fenicia

Researcher at Swiss Federal Institute of Aquatic Science and Technology

Publications -  102
Citations -  5189

Fabrizio Fenicia is an academic researcher from Swiss Federal Institute of Aquatic Science and Technology. The author has contributed to research in topics: Computer science & Streamflow. The author has an hindex of 31, co-authored 79 publications receiving 4296 citations. Previous affiliations of Fabrizio Fenicia include Delft University of Technology.

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A decade of Predictions in Ungauged Basins (PUB)—a review

TL;DR: The Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS) launched in 2003 and concluded by the PUB Symposium 2012 held in Delft (23-25 October 2012), set out to shift the scientific culture of hydrology towards improved scientific understanding of hydrological processes, as well as associated uncertainties and the development of models with increasing realism and predictive power as discussed by the authors.
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Pursuing the method of multiple working hypotheses for hydrological modeling

TL;DR: In this paper, the authors advocate using the method of multiple working hypotheses for systematic and stringent testing of model alternatives in hydrology and discuss how the multiple-hypothesis approach provides the flexibility to formulate alternative representations describing both individual processes and the overall system.
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Elements of a flexible approach for conceptual hydrological modeling : 1. Motivation and theoretical development

TL;DR: In this paper, a flexible framework for conceptual hydrological modeling is proposed, which allows the hydrologist to hypothesize, build, and test different model structures using combinations of generic components.
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Understanding catchment behavior through stepwise model concept improvement

TL;DR: In this article, the authors proposed a methodology where they systematically update the model structure, progressively incorporating new hypotheses of catchment behavior, and applied this methodology to the Alzette river basin in Luxembourg, showing how stepwise model improvement helps to identify the behavior of this catchment.
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Learning from model improvement: On the contribution of complementary data to process understanding

TL;DR: In this paper, the authors present a flexible approach to model development where the model structure is adapted progressively based on catchment characteristics and the data described by the experimentalist, and demonstrate this approach with the Maimai catchment in New Zealand, a location with a large availability of data, including stream discharge, groundwater levels, and stream isotope measurements.