Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Marra, Francesco; Koukoula, Marika; Canale, Antonio; Peleg, Nadav

doi:https://doi.org/10.5194/hess-28-375-2024

Articles | Volume 28, issue 2

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Articles | Volume 28, issue 2

https://doi.org/10.5194/hess-28-375-2024

Articles | Volume 28, issue 2

Article
Assets
Peer review
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Research article

31 Jan 2024

Research article |

| 31 Jan 2024

Predicting extreme sub-hourly precipitation intensification based on temperature shifts

Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg

Francesco Marra

CORRESPONDING AUTHOR

francesco.marra@unipd.it

https://orcid.org/0000-0003-0573-9202

Department of Geosciences, University of Padova, Padua, Italy

Institute of Atmospheric Sciences and Climate, National Research Council, Bologna, Italy

Marika Koukoula

https://orcid.org/0000-0002-8437-7272

Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland

Antonio Canale

Department of Statistical Sciences, University of Padova, Padua, Italy

Nadav Peleg

CORRESPONDING AUTHOR

nadav.peleg@unil.ch

https://orcid.org/0000-0001-6863-2934

Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland

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Total article views: 3,617 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
2,978	583	56	3,617	184	57	50

HTML: 2,978
PDF: 583
XML: 56
Total: 3,617
Supplement: 184
BibTeX: 57
EndNote: 50

Views and downloads (calculated since 20 Sep 2023)

Cumulative views and downloads (calculated since 20 Sep 2023)

Total article views: 2,776 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
2,389	354	33	2,776	89	44	39

HTML: 2,389
PDF: 354
XML: 33
Total: 2,776
Supplement: 89
BibTeX: 44
EndNote: 39

Views and downloads (calculated since 31 Jan 2024)

Month	HTML	PDF	XML	Total
Jan 2024	111	12	1	124
Feb 2024	392	79	6	477
Mar 2024	95	20	2	117
Apr 2024	82	33	2	117
May 2024	166	49	9	224
Jun 2024	95	18	2	115
Jul 2024	301	11	2	314
Aug 2024	85	14	2	101
Sep 2024	325	32	1	358
Oct 2024	93	9	1	103
Nov 2024	79	13	0	92
Dec 2024	68	7	0	75
Jan 2025	78	10	0	88
Feb 2025	70	10	0	80
Mar 2025	69	10	1	80
Apr 2025	172	16	4	192
May 2025	108	11	0	119

Cumulative views and downloads (calculated since 31 Jan 2024)

Month	HTML	PDF	XML	Total
Jan 2024	111	12	1	124
Feb 2024	392	79	6	477
Mar 2024	95	20	2	117
Apr 2024	82	33	2	117
May 2024	166	49	9	224
Jun 2024	95	18	2	115
Jul 2024	301	11	2	314
Aug 2024	85	14	2	101
Sep 2024	325	32	1	358
Oct 2024	93	9	1	103
Nov 2024	79	13	0	92
Dec 2024	68	7	0	75
Jan 2025	78	10	0	88
Feb 2025	70	10	0	80
Mar 2025	69	10	1	80
Apr 2025	172	16	4	192
May 2025	108	11	0	119

Total article views: 841 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
589	229	23	841	95	13	11

HTML: 589
PDF: 229
XML: 23
Total: 841
Supplement: 95
BibTeX: 13
EndNote: 11

Views and downloads (calculated since 20 Sep 2023)

Month	HTML	PDF	XML	Total
Jan 2024	111	12	1	124
Feb 2024	392	79	6	477
Mar 2024	95	20	2	117
Apr 2024	82	33	2	117
May 2024	166	49	9	224
Jun 2024	95	18	2	115
Jul 2024	301	11	2	314
Aug 2024	85	14	2	101
Sep 2024	325	32	1	358
Oct 2024	93	9	1	103
Nov 2024	79	13	0	92
Dec 2024	68	7	0	75
Jan 2025	78	10	0	88
Feb 2025	70	10	0	80
Mar 2025	69	10	1	80
Apr 2025	172	16	4	192
May 2025	108	11	0	119

Cumulative views and downloads (calculated since 20 Sep 2023)

Month	HTML	PDF	XML	Total
Jan 2024	111	12	1	124
Feb 2024	392	79	6	477
Mar 2024	95	20	2	117
Apr 2024	82	33	2	117
May 2024	166	49	9	224
Jun 2024	95	18	2	115
Jul 2024	301	11	2	314
Aug 2024	85	14	2	101
Sep 2024	325	32	1	358
Oct 2024	93	9	1	103
Nov 2024	79	13	0	92
Dec 2024	68	7	0	75
Jan 2025	78	10	0	88
Feb 2025	70	10	0	80
Mar 2025	69	10	1	80
Apr 2025	172	16	4	192
May 2025	108	11	0	119

Viewed (geographical distribution)

Show all Final revised paper only Preprint only

Total article views: 3,617 (including HTML, PDF, and XML) Thereof 3,522 with geography defined and 95 with unknown origin.

Total article views: 2,776 (including HTML, PDF, and XML) Thereof 2,707 with geography defined and 69 with unknown origin.

Total article views: 841 (including HTML, PDF, and XML) Thereof 815 with geography defined and 26 with unknown origin.

Country	#	Views	%
United States of America	1	900	24
China	2	418	11
Italy	3	353	9
Germany	4	247	6
Switzerland	5	230	6

Country	#	Views	%
United States of America	1	680	24
China	2	378	13
Italy	3	252	9
Switzerland	4	177	6
Germany	5	156	5

Country	#	Views	%
United States of America	1	220	26
Italy	2	101	12
Germany	3	91	10
Switzerland	4	53	6
France	5	49	5


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900


Total:	0
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680


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PDF:	0
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220

Cited

Show all Final revised paper only Preprint only

11 citations as recorded by crossref.

Urbanization Enhances Shorter‐Duration Precipitation Intensity in the Yangtze River Delta Region X. Xie et al. 10.1029/2024JD043300
A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard N. Peleg et al. 10.1016/j.advwatres.2024.104823
Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh A. Dey & M. Patwary 10.1007/s00477-025-02969-3
Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. 10.5194/nhess-24-2025-2024
Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. 10.5194/hess-28-5443-2024
A non-stationary climate-informed weather generator for assessing future flood risks V. Nguyen et al. 10.5194/ascmo-10-195-2024
Predictive modeling of daily precipitation occurrence using weather data of prior days in various climates G. Mahtabi et al. 10.1007/s12145-024-01289-4
Deriving Future Rainfall Depth-Duration-Frequency Curves from Hourly Regional Climate Projections and Simple Scaling in Sicily G. Buonacera et al. 10.1007/s11269-025-04219-1
Climate change shifts risk of soil salinity and land degradation in water-scarce regions I. Kramer et al. 10.1016/j.agwat.2024.109223
Modelling non-stationarity in extreme rainfall using large-scale climate drivers L. Jayaweera et al. 10.1016/j.jhydrol.2024.131309
Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods K. Lengfeld & F. Marra 10.1016/j.ejrh.2024.101952

11 citations as recorded by crossref.

Urbanization Enhances Shorter‐Duration Precipitation Intensity in the Yangtze River Delta Region X. Xie et al. 10.1029/2024JD043300
A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard N. Peleg et al. 10.1016/j.advwatres.2024.104823
Utilizing non-stationary extreme value model to quantify extreme rainfall in two major cities in Bangladesh A. Dey & M. Patwary 10.1007/s00477-025-02969-3
Estimation of future rainfall extreme values by temperature-dependent disaggregation of climate model data N. Ebers et al. 10.5194/nhess-24-2025-2024
Enhancing generalizability of data-driven urban flood models by incorporating contextual information T. Cache et al. 10.5194/hess-28-5443-2024
A non-stationary climate-informed weather generator for assessing future flood risks V. Nguyen et al. 10.5194/ascmo-10-195-2024
Predictive modeling of daily precipitation occurrence using weather data of prior days in various climates G. Mahtabi et al. 10.1007/s12145-024-01289-4
Deriving Future Rainfall Depth-Duration-Frequency Curves from Hourly Regional Climate Projections and Simple Scaling in Sicily G. Buonacera et al. 10.1007/s11269-025-04219-1
Climate change shifts risk of soil salinity and land degradation in water-scarce regions I. Kramer et al. 10.1016/j.agwat.2024.109223
Modelling non-stationarity in extreme rainfall using large-scale climate drivers L. Jayaweera et al. 10.1016/j.jhydrol.2024.131309
Estimation of design precipitation using weather radar in Germany: A comparison of statistical methods K. Lengfeld & F. Marra 10.1016/j.ejrh.2024.101952

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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...