Regional ombrian curves: Design rainfall estimation for a spatially diverse rainfall regime

T. Iliopoulou, N. Malamos, and D. Koutsoyiannis, Regional ombrian curves: Design rainfall estimation for a spatially diverse rainfall regime, Hydrology, 9 (5), 67, doi:10.3390/hydrology9050067, 2022.

[doc_id=2188]

[English]

Ombrian curves, i.e., curves linking rainfall intensity to return period and time scale, are well-established engineering tools crucial to the design against stormwaters and floods. Though the at-site construction of such curves is considered a standard hydrological task, it is a rather challenging one when large regions are of interest. Regional modeling of ombrian curves is particularly complex due to the need to account for spatial dependence together with the increased variability of rainfall extremes in space. We develop a framework for the parsimonious modeling of the extreme rainfall properties at any point in a given area. This is achieved by assuming a common ombrian model structure, except for a spatially varying scale parameter which is itself modeled by a spatial smoothing model for the 24 h average annual rainfall maxima that employs elevation as an additional explanatory variable. The fitting is performed on the pooled all-stations data using an advanced estimation procedure (K-moments) that allows both for reliable high-order moment estimation and simultaneous handling of space-dependence bias. The methodology is applied in the Thessaly region, a 13 700 km² water district of Greece characterized by varying topography and hydrometeorological properties.

PDF Full text (9357 KB)

PDF Additional material:

Our works referenced by this work:

1. M. Mimikou, and D. Koutsoyiannis, Extreme floods in Greece: The case of 1994, U.S. - ITALY Research Workshop on the Hydrometeorology, Impacts, and Management of Extreme Floods, Perugia, Italy, doi:10.13140/RG.2.1.1945.8802, 1995.
2. D. Koutsoyiannis, D. Kozonis, and A. Manetas, A mathematical framework for studying rainfall intensity-duration-frequency relationships, Journal of Hydrology, 206 (1-2), 118–135, doi:10.1016/S0022-1694(98)00097-3, 1998.
3. D. Koutsoyiannis, Broken line smoothing: A simple method for interpolating and smoothing data series, Environmental Modelling and Software, 15 (2), 139–149, 2000.
4. D. Koutsoyiannis, Statistics of extremes and estimation of extreme rainfall, 2, Empirical investigation of long rainfall records, Hydrological Sciences Journal, 49 (4), 591–610, doi:10.1623/hysj.49.4.591.54424, 2004.
5. D. Koutsoyiannis, An entropic-stochastic representation of rainfall intermittency: The origin of clustering and persistence, Water Resources Research, 42 (1), W01401, doi:10.1029/2005WR004175, 2006.
6. S.M. Papalexiou, and D. Koutsoyiannis, Ombrian curves in a maximum entropy framework, European Geosciences Union General Assembly 2008, Geophysical Research Abstracts, Vol. 10, Vienna, 00702, doi:10.13140/RG.2.2.23447.98720, European Geosciences Union, 2008.
7. D. Koutsoyiannis, N. Mamassis, A. Efstratiadis, N. Zarkadoulas, and Y. Markonis, Floods in Greece, Changes of Flood Risk in Europe, edited by Z. W. Kundzewicz, Chapter 12, 238–256, IAHS Press, Wallingford – International Association of Hydrological Sciences, 2012.
8. N. Malamos, and D. Koutsoyiannis, Broken line smoothing for data series interpolation by incorporating an explanatory variable with denser observations: Application to soil-water and rainfall data, Hydrological Sciences Journal, doi:10.1080/02626667.2014.899703, 2015.
9. N. Malamos, and D. Koutsoyiannis, Bilinear surface smoothing for spatial interpolation with optional incorporation of an explanatory variable. Part 1:Theory, Hydrological Sciences Journal, 61 (3), 519–526, doi:10.1080/02626667.2015.1051980, 2016.
10. N. Malamos, and D. Koutsoyiannis, Bilinear surface smoothing for spatial interpolation with optional incorporation of an explanatory variable. Part 2: Application to synthesized and rainfall data, Hydrological Sciences Journal, 61 (3), 527–540, doi:10.1080/02626667.2015.1080826, 2016.
11. D. Koutsoyiannis, and S.M. Papalexiou, Extreme rainfall: Global perspective, Handbook of Applied Hydrology, Second Edition, edited by V.P. Singh, 74.1–74.16, McGraw-Hill, New York, 2017.
12. N. Malamos, and D. Koutsoyiannis, Field survey and modelling of irrigation water quality indices in a Mediterranean island catchment: A comparison between spatial interpolation methods, Hydrological Sciences Journal, 63 (10), 1447–1467, doi:10.1080/02626667.2018.1508874, 2018.
13. T. Iliopoulou, D. Koutsoyiannis, and A. Montanari, Characterizing and modeling seasonality in extreme rainfall, Water Resources Research, 54 (9), 6242–6258, doi:10.1029/2018WR023360, 2018.
14. D. Koutsoyiannis, Knowable moments for high-order characterization and modelling of hydrological processes for sustainable management of water resources, Invited Lecture, Bologna, Italy, doi:10.13140/RG.2.2.35109.86248, University of Bologna, 2019.
15. K. Glynis, T. Iliopoulou, P. Dimitriadis, and D. Koutsoyiannis, Stochastic investigation of daily air temperature extremes from a global ground station network, Stochastic Environmental Research & Risk Assessment, doi:10.1007/s00477-021-02002-3, 2021.
16. T. Iliopoulou, and D. Koutsoyiannis, PythOm: A python toolbox implementing recent advances in rainfall intensity (ombrian) curves, EGU General Assembly 2021, online, EGU21-389, doi:10.5194/egusphere-egu21-389, European Geosciences Union, 2021.
17. P. Dimitriadis, D. Koutsoyiannis, T. Iliopoulou, and P. Papanicolaou, A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes, Hydrology, 8 (2), 59, doi:10.3390/hydrology8020059, 2021.
18. D. Koutsoyiannis, and P. Dimitriadis, Towards generic simulation for demanding stochastic processes, Sci, 3, 34, doi:10.3390/sci3030034, 2021.
19. D. Koutsoyiannis, An open letter to the Editor of Frontiers, doi:10.13140/RG.2.2.34248.39689/1, December 2021.
20. D. Koutsoyiannis, and T. Iliopoulou, Ombrian curves advanced to stochastic modeling of rainfall intensity (Chapter 9), Rainfall Modeling, Measurement and Applications, 261–283, Elsevier, 2022.
21. D. Koutsoyiannis, Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, Edition 3, ISBN: 978-618-85370-0-2, 391 pages, doi:10.57713/kallipos-1, Kallipos Open Academic Editions, Athens, 2023.

Our works that reference this work:

1. N. Malamos, D. Koulouris, I. L. Tsirogiannis, and D. Koutsoyiannis, Evaluation of BOLAM fine grid weather forecasts with emphasis on hydrological applications, Hydrology, 10 (8), 162, doi:10.3390/hydrology10080162, 2023.
2. E. Dimitriou, A. Efstratiadis, I. Zotou, A. Papadopoulos, T. Iliopoulou, G.-K. Sakki, K. Mazi, E. Rozos, A. Koukouvinos, A. D. Koussis, N. Mamassis, and D. Koutsoyiannis, Post-analysis of Daniel extreme flood event in Thessaly, Central Greece: Practical lessons and the value of state-of-the-art water monitoring networks, Water, 16 (7), 980, doi:10.3390/w16070980, 2024.