Articles | Volume 28, issue 2
https://doi.org/10.5194/hess-28-375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2024
Research article |
| 31 Jan 2024
| 31 Jan 2024
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Department of Geosciences, University of Padova, Padua, Italy
Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy
Marika Koukoula
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Antonio Canale
Department of Statistical Sciences, University of Padova, Padua, Italy
Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
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Short summary
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Short summary
Short summary
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Mariam Khanam, Giulia Sofia, Marika Koukoula, Rehenuma Lazin, Efthymios I. Nikolopoulos, Xinyi Shen, and Emmanouil N. Anagnostou
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Short summary
Short summary
Compound extremes correspond to events with multiple concurrent or consecutive drivers, leading to substantial impacts such as infrastructure failure. In many risk assessment and design applications, however, multihazard scenario events are ignored. In this paper, we present a general framework to investigate current and future climate compound-event flood impact on coastal critical infrastructures such as power grid substations.
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Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
We present a new physical-based method for estimating extreme sub-hourly precipitation return...
