A. Efstratiadis, P. Dimas, G. Pouliasis, I. Tsoukalas, P. Kossieris, V. Bellos, G.-K. Sakki, C. Makropoulos, and S. Michas, Revisiting flood hazard assessment practices under a hybrid stochastic simulation framework, *Water*, 14 (3), 457, doi:10.3390/w14030457, 2022.

[doc_id=2170]

[English]

We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modelling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms to antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modelling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically-derived quantiles for given return periods. Finally, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.

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https://www.mdpi.com/2073-4441/14/3/457

**Our works referenced by this work:**

1. | H. Tyralis, D. Koutsoyiannis, and S. Kozanis, An algorithm to construct Monte Carlo confidence intervals for an arbitrary function of probability distribution parameters, Computational Statistics, 28 (4), 1501–1527, doi:10.1007/s00180-012-0364-7, 2013. |

2. | A. Efstratiadis, A. D. Koussis, D. Koutsoyiannis, and N. Mamassis, Flood design recipes vs. reality: can predictions for ungauged basins be trusted?, Natural Hazards and Earth System Sciences, 14, 1417–1428, doi:10.5194/nhess-14-1417-2014, 2014. |

3. | A. Efstratiadis, A. Koukouvinos, E. Michailidi, E. Galiouna, K. Tzouka, A. D. Koussis, N. Mamassis, and D. Koutsoyiannis, Description of regional approaches for the estimation of characteristic hydrological quantities, DEUCALION – Assessment of flood flows in Greece under conditions of hydroclimatic variability: Development of physically-established conceptual-probabilistic framework and computational tools, Contractors: ETME: Peppas & Collaborators, Grafeio Mahera, Department of Water Resources and Environmental Engineering – National Technical University of Athens, National Observatory of Athens, 146 pages, September 2014. |

4. | P. Dimitriadis, A. Tegos, A. Oikonomou, V. Pagana, A. Koukouvinos, N. Mamassis, D. Koutsoyiannis, and A. Efstratiadis, Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping, Journal of Hydrology, 534, 478–492, doi:10.1016/j.jhydrol.2016.01.020, 2016. |

5. | E. Michailidi, S. Antoniadi, A. Koukouvinos, B. Bacchi, and A. Efstratiadis, Timing the time of concentration: shedding light on a paradox, Hydrological Sciences Journal, 63 (5), 721–740, doi:10.1080/02626667.2018.1450985, 2018. |

6. | G. Papaioannou, A. Efstratiadis, L. Vasiliades, A. Loukas, S.M. Papalexiou, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, An operational method for Floods Directive implementation in ungauged urban areas, Hydrology, 5 (2), 24, doi:10.3390/hydrology5020024, 2018. |

7. | C. Makropoulos, D. Nikolopoulos, L. Palmen, S. Kools, A. Segrave, D. Vries, S. Koop, H. J. van Alphen, E. Vonk, P. van Thienen, E. Rozos, and G. Medema, A resilience assessment method for urban water systems, Urban Water Journal, 15 (4), 316–328, doi:10.1080/1573062X.2018.1457166, 2018. |

8. | I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019. |

9. | K. Risva, D. Nikolopoulos, and A. Efstratiadis, Distributed hydrological modelling using spatiotemporally varying velocities, European Geosciences Union General Assembly 2020, Geophysical Research Abstracts, Vol. 22, Vienna, EGU2020-13402, doi:10.5194/egusphere-egu2020-13402, 2020. |

10. | I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020. |

11. | I. Tsoukalas, The tales that the distribution tails of non-Gaussian autocorrelated processes tell: Efficient methods for the estimation of the k-length block-maxima distribution, doi:10.1080/02626667.2021.2014056, 2021, (in press). |

12. | G. Papaioannou, L. Vasiliades, A. Loukas, A. Alamanos, A. Efstratiadis, A. Koukouvinos, I. Tsoukalas, and P. Kossieris, A flood inundation modelling approach for urban and rural areas in lake and large-scale river basins, Water, 13 (9), 1264, doi:10.3390/w13091264, 2021. |

13. | P. Kossieris, I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Generic framework for downscaling statistical quantities at fine time-scales and its perspectives towards cost-effective enrichment of water demand records, Water, 13 (23), 3429, doi:10.3390/w13233429, 2021. |

14. | D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 2, ISBN: 978-618-85370-0-2, 346 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2022. |

**Our works that reference this work:**

1. | G. Moraitis, I. Tsoukalas, P. Kossieris, D. Nikolopoulos, G. Karavokiros, D. Kalogeras, and C. Makropoulos, Assessing cyber-physical threats under water demand uncertainty, Environmental Sciences Proceedings, 21 (1), 18, doi:10.3390/environsciproc2022021018, October 2022. |

2. | S. Tsattalios, I. Tsoukalas, P. Dimas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search, Environmental Modelling and Software, 162, 105639, doi:10.1016/j.envsoft.2023.105639, 2023. |

**Other works that reference this work (this list might be obsolete):**

1. | Tegos, A., A. Ziogas, V. Bellos, and A. Tzimas, Forensic hydrology: a complete reconstruction of an extreme flood event in data-scarce area, Hydrology, 9(5), 93, doi:10.3390/hydrology9050093, 2022. |

2. | Afzal, M. A., S. Ali, A. Nazeer, M. I. Khan, M. M. Waqas, R. A. Aslam, M. J. M. Cheema, M. Nadeem, N. Saddique, M. Muzammil, and A. N. Shah, Flood inundation modeling by integrating HEC–RAS and satellite imagery: A case study of the Indus river basin, Water, 14(19), 2984, doi:10.3390/w14192984, 2022. |

3. | Vangelis, H., I. Zotou, I. M. Kourtis, V. Bellos, and V. A. Tsihrintzis, Relationship of rainfall and flood return periods through hydrologic and hydraulic modeling, Water, 14(22), 3618, doi:10.3390/w14223618, 2022. |

**Tagged under:**
Floods,
Hydraulic models,
Hydrological models,
Rainfall models,
Most recent works,
Uncertainty