Climate extrapolations in hydrology: The expanded Bluecat methodology

D. Koutsoyiannis, and A. Montanari, Climate extrapolations in hydrology: The expanded Bluecat methodology, Hydrology, 9, 86, doi:10.3390/hydrology9050086, 2022.

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[English]

Bluecat is a recently proposed methodology to upgrade a deterministic model (D-model) into a stochastic one (S-model), based on the hypothesis that the information contained in a time series of observations and the concurrent predictions made by the D-model is sufficient to support this upgrade. The prominent characteristics of the methodology are its simplicity and transparency, which allow its easy use in practical applications, without sophisticated computational means. In this paper, we utilize the Bluecat methodology and expand it in order to be combined with climate model outputs, which often require extrapolation out of the range of values covered by observations. We apply the expanded methodology to the precipitation and temperature processes in a large area, namely the entire territory of Italy. The results showcase the appropriateness of the method for hydroclimatic studies, as regards the assessment of the performance of the climate projections, as well as their stochastic conversion with simultaneous bias correction and uncertainty quantification.

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Our works referenced by this work:

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14. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, ISBN: 978-618-85370-0-2, 333 pages, Kallipos Open Academic Editions, Athens, 2021.
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Our works that reference this work:

1. E. Rozos, D. Koutsoyiannis, and A. Montanari, KNN vs. Bluecat — Machine Learning vs. Classical Statistics, Hydrology, 9, 101, doi:10.3390/hydrology9060101, 2022.

Tagged under: Climate stochastics, Determinism vs. stochasticity, Uncertainty