EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimation of extreme flooding based on stochastic weather generators supported by the use of non-systematic flood data

Carles Beneyto1, José Ángel Aranda1, Gerardo Benito2, and Félix Francés1
Carles Beneyto et al.
  • 1Universitat Politècnica de València, Research Institute of Water and Environmental Engineering, Valencia, Spain (
  • 2Department of Geology, National Museum of Natural Sciences, Madrid, Spain

An adequate characterization of extreme floods is key for the correct design of the infrastructures and for the flood risk estimation. Traditionally, these studies have been carried out based on the design storm. However, we now know that this approach is uncertain since peak discharges and hydrographs are strongly dependant on the initial conditions of the basin and on the spatio-temporal distribution of the precipitation.
One of the possible solutions that has recently been better welcomed between the scientific community is the continuous simulation. This combination of statistical and deterministic methods consist of the generation of extended synthetic data series of discharges by combining the use of a stochastic weather generator and a hydrological model. Nevertheless, weather generators still need robust data series of observed precipitation in order to perform adequately, especially when trying to capture extremes. To date, however, the length of both available precipitation and discharge records are still not sufficient to guarantee an adequate estimation of extreme discharges, presenting these high uncertainty.
In the present study, the same approach is taken (i.e. continuous simulation). However, in order to deal with the short length of the data records and to improve the estimations of extreme discharges, non-systematic information (i.e. historical and Palaeoflood) is integrated in the methodology, extending the length of the flow records and giving extra information of the higher tail of the distribution function, thus reducing the uncertainty of these estimations.
This methodology was implemented in a Spanish Mediterranean ephemeral catchment, Rambla de la Viuda (Castello, Valencia). The study area comprises an approximate area of 1,500 km2 and presents a mean rainfall of 615 mm, most of them falling within the autumn months (SON) as a consequence of medicanes. The weather generator used was GWEX, which was designed to focus on extremes, and the hydrological model implemented was TETIS, which is a conceptual model and spatially distributed. Both of them were implemented at a daily scale. Non-systematic information was obtained from previous studies, having information at two locations and, therefore, being able to validate the results in more than one point.
The results, in terms of precipitation, showed that weather generators using heavy-tailed marginal distribution functions outperform those using light-tailed distributions (e.g. Exponential or Gamma), especially when extra information is incorporated, as in this study, where regional maxima precipitation studies were integrated for the parametrisation of the weather generator.
With regards to discharges, the incorporation of non-systematic information clearly gave extra information of the higher tail of the distribution function (up to approx. T=600 years in this study), allowing to validate the generated discharges up to larger return periods and, therefore, reducing the uncertainty of the extreme discharge estimations

How to cite: Beneyto, C., Aranda, J. Á., Benito, G., and Francés, F.: Estimation of extreme flooding based on stochastic weather generators supported by the use of non-systematic flood data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16934,, 2020

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Presentation version 2 – uploaded on 03 May 2020
Remove the term "Medicane"
  • CC1: Comment on MCS vs Medicane, Félix Francés, 04 May 2020

    Carmen post a comment/correction to us and actually it is funny: long time ago I asked Carmen how to name these phenomena and she told me the same. But Mesoscale Convective Storms is a long name and medicane is shorter and appealing. Sorry for the mistake and I will never forget!

  • AC1: Answer to Dr. Llasat's questions, Carles Beneyto, 04 May 2020

    Q: As far as I understood, you have used Spain02-v4 to validate your weather generator. If I am not wrong Spain02 has a resolution of 20x20 km.? At which spatial resolution have you developed your weather generator to create rainfall series? Have you applied a downscaling to Spain02? How have you reproduced the spatial pattern of precipitation?

    A: We directly used the gridded Spain02-v4 dataset to both calibrate and validate the weather generator, however, the upper tail of the distribution function was clearly underestimated and this is the reason why we decided to do a second calibration/validation with the information of the regional analysis undertook by CEDEX

    The resolution of Spain02-v4 (rotated) is 0.11º, which equals to some 12x12 km.

    The weather generator was thus implemented with the Spain02-v4 dataset, a total of 20 rain gauges spread across our study area at a distance of 0.11º

    We have not applied any downscaling to these rainfall series as considered that the resolution was good enough and, actually, to avoid adding even more uncertainty to the methodology.

    With regards to the spatial pattern of the precipitation, this is captured by the weather generator; the spatial dependence of the precipitation states (dry/wet) is modelled using an unobserved Gaussian stochastic process and: spatial dependence of precipitation amounts is represented using a multivariate autoregressive model of order 1 (MAR(1)). For more information see Evin et al. (2018)

    Hope to have answered your questions, please let me know should you have any further question

    • AC2: Sorry, I missed this bit, Carles Beneyto, 04 May 2020

      Dear Dr. Llasat, thanks for your interesting question and sorry for having missed it during the chat session.

Presentation version 1 – uploaded on 27 Apr 2020
  • CC1: Comment on EGU2020-16934, Maria-Carmen Llasat, 02 May 2020

    Dear friends

    It is a very interesting communication. Congratulations! Only a little think. These events are not a consequence of Medicanes. Medicanes are not so much frequent in the Mediterranean basin (less in Valencia) and usually are not so much efficient in precipitation. They are produced by heavy convective events, in some occasions, Mesoscale Convective Systems (MCS)

    • AC1: Reply to CC1, Carles Beneyto, 03 May 2020

      Dear M. Carmen LLasat,

      Thanks a lot for your comments. Yes, you are right, we were trying to find a translation for the spanish term "gota fria" and wrongly ended up with medicane. I will amend the presentation accordingly and submit the new revision.

      Again, thanks for your comment, it is much appreciated