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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Cited articles
Ali, H. and Mishra, V.: Increase in subdaily precipitation extremes in India under 1.5 and 2.0 ∘C warming worlds, Geophys. Res. Lett., 45, 6972–6982, https://doi.org/10.1029/2018GL078689, 2018. a
Ali, H., Peleg, N., and Fowler, H. J.: Global scaling of rainfall with dewpoint temperature reveals considerable ocean–land difference, Geophys. Res. Lett., 48, e2021GL093798, https://doi.org/10.1029/2021GL093798, 2021a. a, b, c
Ali, H., Fowler, H. J., Lenderink, G., Lewis, E., and Pritchard, D.: Consistent large-scale response of hourly extreme precipitation to temperature variation over land, Geophys. Res. Lett., 48, e2020GL090317, https://doi.org/10.1029/2020GL090317, 2021b. a, b
Ban, N., Rajczak, J., Schmidli, J., and Schär, C.: Analysis of Alpine precipitation extremes using generalized extreme value theory in convection-resolving climate simulations, Clim. Dynam., 55, 61–75, https://doi.org/10.1007/s00382-018-4339-4, 2020. a, b, c
Berg, P., Moseley, C., and Haerter, J. O.: Strong increase in convective precipitation in response to higher temperatures, Nat. Geosci., 6, 181–185, https://doi.org/10.1038/ngeo1731, 2013. a
Borga, M., Stoffel, M., Marchi, L., Marra, F., and Jakob, M.: Hydrogeomorphic response to extreme rainfall in headwater system: flash floods and debris flows, J. Hydrol., 518, 194–205, https://doi.org/10.1016/j.jhydrol.2014.05.022, 2014. a
Caillaud, C., Somot, S., Alias, A., Bernard-Bouissières, I., Fumière, Q., Laurantin, O., Seity, Y., and Ducrocq, V.: Modelling Mediterranean Heavy Precipitation Events at Climate Scal.: An Object-Oriented Evaluation of the CNRM-AROME Convection-Permitting Regional Climate Model, Clim. Dynam., 56, 1717–52, https://doi.org/10.1007/s00382-020-05558-y, 2021. a
Cheng, L. and AghaKouchak, A.: Nonstationary Precipitation Intensity–Duration–Frequency Curves for Infrastructure Design in a Changing Climate, Sci. Rep.-UK, 4, 7093, https://doi.org/10.1038/srep07093, 2014. a
Coles, S.: An introduction to statistical modeling of extreme values, Springer-Verlag, Londo., https://doi.org/10.1007/978-1-4471-3675-0, 2001. a
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a
Dallan, E., Borga, M., Zaramella, M., and Marra, F.: Enhanced summer convection explains observed trends in extreme subdaily precipitation in the Eastern Italian Alps, Geophys. Res. Lett., 49, e2021GL096727, https://doi.org/10.1029/2021GL096727, 2022. a, b, c, d
Dallan, E., Marra, F., Fosser, G., Marani, M., Formetta, G., Schär, C., and Borga, M.: How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?, Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, 2023. a
Drobinski, P., Alonzo, B., Bastin, S., Silva, N. D., and Muller, C.: Scaling of precipitation extremes with temperature in the French Mediterranean regio.: What explains the hook shape?, J. Geophys. Res.-Atmos., 121, 3100–3119, https://doi.org/10.1002/2015JD023497, 2016. a
Evin, G., Favre, A. C., and Hingray, B.: Stochastic generators of multi-site daily temperatur.: comparison of performances in various applications, Theor Appl Climatol., 135, 811–824, https://doi.org/10.1007/s00704-018-2404-x, 2019. a
Fatichi, S., Ivanov, V. Y., Paschalis, A., Peleg, N., Molnar, P., Rimkus, S., Kim, J., Burlando, P., and Caporali, E.: Uncertainty partition challenges the predictability of vital details of climate change, Earths Future, 4, 240–251, https://doi.org/10.1002/2015EF000336, 2016. a
Fischer, A. M., Strassmann, K. M., Croci-Maspoli, M., Hama, A. M., Knutti, R., Kotlarski, S., Schär, C., Poberaj, C. S., Ban, N., Bavay, M., Beyerle, U., Bresch, D. N., Brönnimann, S., Burlando, P., Casanueva, A., Fatichi, S., Feigenwinter, I., Fischer, E. M., Hirschi, M., Liniger, M. A., Marty, C., Medhaug, I., Peleg, N., Pickl, M., Raible, C. C., Rajczak, J., Rössler, O., Scherrer, S. C., Schwierz, C., Seneviratne, S. I., Skelton, M., Sørland, S. L., Spirig, C., Tschurr, F., Zeder, J., and Zubler, E. M.: Climate scenarios for Switzerland CH2018–approach and implications, Climate Services, 26, 100288, https://doi.org/10.1016/j.cliser.2022.100288, 2022. a
Fisher, R. A. and Tippett, L. H. C.: Limiting forms of the frequency distribution of the largest or smallest member of a sample, Math. Proc. Cambridge, 24, 180–190, https://doi.org/10.1017/S0305004100015681, 1928.
Fowler, H. J., Ali, H., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Cabi, N. S., Chan, S., Dale, M., Dunn, R. J. H., Ekström, M., Evans, J. P., Fosser, G., Golding, B., Guerreiro, S. B., Hegerl, G. C., Kahraman, A., Kendon, E. J., Lenderink, G., Lewis, E., Li, X., O'Gorman, P. A., Orr, H. G., Peat, K. L., Prein, A. F., Pritchard, D. Schär, C., Sharma, A., Stott, P. A., Villalobos-Herrera, R., Villarini, G., Wasko, C., Wehner, M. F., Westra, S., and Whitford, A.: Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes, Philos. T. R. Soc. A, 37, 20190542, https://doi.org/10.1098/rsta.2019.0542, 2021a. a, b
Fowler, H. J., Wasko, C., and Prein, A. F.: Intensification of short-duration rainfall extremes and implications for flood ris.: current state of the art and future directions, Philos. T. R. Soc. A, 37, 20190541, https://doi.org/10.1098/rsta.2019.0541, 2021b. a
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter, J. O., Kendon, E. J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., and Zhang, X.: Anthropogenic intensification of short-duration rainfall extremes, Nature Reviews Earth and Environment, 2, 107–122, https://doi.org/10.1038/s43017-020-00128-6, 2021c. a, b
Gnedenko, B.: Sur la distribution limite du terme maximum d'une serie aleatoire, Ann. Math., 44, 423–453, https://doi.org/10.2307/1968974, 1943.
Hardwick Jones, R., Westra, S., and Sharma, A.: Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity, Geophys. Res. Lett., 37, L22805, https://doi.org/10.1029/2010GL045081, 2010. a
Huang, J., Fatichi, S., Mascaro, G., Manoli, G., and Peleg, N.: Intensification of sub-daily rainfall extremes in a low-rise urban area, Urban Climate, 42, 101124, https://doi.org/10.1016/j.uclim.2022.101124, 2022. a
Iliopoulou T. and Koutsoyiannis, D.: Projecting the future of rainfall extreme: Better classic than trendy, J. Hydrol.,, 588, 125005, https://doi.org/10.1016/j.jhydrol.2020.125005, 2020. a
Katz, R. W., Parlange, and M. B., Naveau P.: Statistics of extremes in hydrology, Adv. Water Resour., 25, 1287–1304, https://doi.org/10.1016/S0309-1708(02)00056-8, 2002. a
Landl, B., Roulet, Y. A., and Calpini, B.: SwissMetNe: operational quality control on raw data of the new automatic meteorological ground-based network of Switzerland, 9th EMS Annual Meeting, 28 September–2 October 2009, Toulouse, France, EMS2009-453, 2009. a
Lenderink, G. and van Meijgaard, E.: Increase in hourly precipitation extremes beyond expectations from temperature changes, Nat. Geosci. 1, 511–514, https://doi.org/10.1038/ngeo262, 2008. a
Lengfeld, K. and Marra, F.: Improving estimations of design storms from weather radar observations using a simplified metastatistical extreme value approach, EGU General Assembly 2023, Vienna, Austria, 24–28 April 2023, EGU23-7371, https://doi.org/10.5194/egusphere-egu23-7371, 2023.
Libertino, A., Ganora, D., and Claps, P.: Evidence for increasing rainfall extremes remains elusive at large spatial scale: The case of Italy, Geophys. Res. Lett., 46, 7437–7446, https://doi.org/10.1029/2019GL083371, 2019. a
Maity, S. S. and Maity, R.: Changing Pattern of Intensity–Duration–Frequency Relationship of Precipitation due to Climate Change, Water Resour. Manag., 36, 5371–5399, https://doi.org/10.1007/s11269-022-03313-y, 2022. a
Marani, M. and Ignaccolo, M.: A metastatistical approach to rainfall extremes, Adv. Water Resour., 79, 121–126, https://doi.org/10.1016/j.advwatres.2015.03.001, 2015. a
Marra, F.: A unified framework for extreme sub-daily precipitation frequency analyses based on ordinary events – data & codes (Version v1), Zenodo, https://doi.org/10.5281/zenodo.3971558, 2020.
Marra, F. and Peleg, N.: TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX) (version 1.1), Zenodo, https://doi.org/10.5281/zenodo.8345905, 2023. a
Marra, F., Zoccatelli, D., Armon, M., and Morin, E.: A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes, Adv. Water Resour., 127, 280–290, https://doi.org/10.1016/j.advwatres.2019.04.002, 2019. a, b
Marra, F., Borga, M., and Morin, E.: A unified framework for extreme subdaily precipitation frequency analyses based on ordinary events, Geophys. Res. Lett., 47, e2020GL090209, https://doi.org/10.1029/2020GL090209, 2020 (data available at: https://doi.org/10.5281/zenodo.3971558). a, b, c, d, e, f, g, h, i
Marra, F., Armon, M., Adam, O., Zoccatelli, D., Gazal, O., Garfinkel, C. I., Rostkier-Edelstein, D., Dayan, U., Enzel, Y., and Morin, E.: Towards narrowing uncertainty in future projections of local extreme precipitation, Geophys. Res. Lett., 48, e2020GL091823, https://doi.org/10.1029/2020GL091823, 2021. a, b, c
Marra, F., Armon, M., and Morin, E.: Coastal and orographic effects on extreme precipitation revealed by weather radar observations, Hydrol. Earth Syst. Sci., 26, 1439–1458, https://doi.org/10.5194/hess-26-1439-2022, 2022. a, b
Marra F., Amponsah, W., and Papalexiou, S. M.: Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation, Adv. Water Resour., 173, 104388, https://doi.org/10.1016/j.advwatres.2023.104388, 2023. a
Molnar, P., Fatichi, S., Gaál, L., Szolgay, J., and Burlando, P.: Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature, Hydrol. Earth Syst. Sci., 19, 1753–1766, https://doi.org/10.5194/hess-19-1753-2015, 2015. a, b
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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...
