This is an open access article distributed under the CC BY 4.0
Volume 19 article 810 pages: 432-438
Flood flow frequency analysis (FFA) plays one of the key roles in many fields of hydraulic engineering and water
resources management. The reliability of FFA results depends on many factors, an obvious one being the reliability
of the input data - datasets of the annual peak flow. In practice, however, engineers often encounter the problem of
incomplete datasets (missing data, data gaps and/or broken records) which increases the uncertainty of FFA results.
In this paper, we perform at-site focused analysis, and we use a complete dataset of annual peak flows from 1931 to
2016 at the hydrologic station Senta of the Tisa (Tisza) river as the reference dataset. From this original dataset we
remove some data and thus we obtain 15 new datasets with one continuous gap of different length and/or location.
Each dataset we further subject to FFA by using the USACE HEC-SSP Bulletin 17C analysis, where we apply perception
thresholds for missing data representation. We vary perception threshold lower bound for all missing flows
in one dataset, so that we create 56 variants of the input HEC-SSP datasets. The flood flow quantiles assessed from
the datasets with missing data and different perception thresholds we evaluate by two uncertainty measures. The
results indicate acceptable flood quantile estimates are obtained, even for larger return periods, by setting a lower
perception threshold bound at the value of the highest peak flow in the available - incomplete dataset.
The authors are grateful to the Republic Hydrometeorological
Service of Serbia for making data available for
this research. The work for this study is financed from
the research project TR37005 'Climate change impact
on the rivers in Serbia' for the Ministry of Education, Science
and Technological Development of the Republic of
1.Tencaliec, P., Favre, A. C., Prieur, C., Mathevet, T. (2015). Reconstruction of missing daily streamflow data using dynamic regression models. Water Resources Research, vol. 51, no. 12, 9447–9463, DOI:10.1002/2015WR017399
2. Pappas, C., Papalexiou, S. M., Koutsoyiannis, D. (2014). A quick gap filling of missing hydrometeorological data. Journal of Geophysical Research: Atmospheres, vol. 119, no. 15, 9290–9300, DOI:10.1002/ 2014JD021633
3. England, J.F., Jr., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Jr., Veilleux, A.G., Kiang, J.E., and Mason, R.R., Jr. (2018). Guidelines for determining flood flow frequency—Bulletin 17C (ver. 1.1, May 2019). U.S. Geological Survey Techniques and Methods, Book 4, chap. B5, p. 1-168. https://doi. org/10.3133/tm4B5
4. Slater, L., Villarini, G. (2017). On the impact of gaps on trend detection in extreme streamflow time series. International Journal of Climatology, vol. 37, no. 10, 3976-3983, DOI: 10.1002/joc.4954
5. USWRC. (1982). Guidelines for Determining Flood Flow Frequency, Bulletin No. 17B. U.S. Water Resources Council, Subcommittee on Hydrology, Washington, D.C.
6. U.S. Army Corps of Engineers. (2019). Statistical Software Package HEC-SSP User’s Manual, Version 2.2, US Army Corps of Engineers – Hydrologic Engineering Center
7. Blagojevic B., Mihailovic V., Plavsic J. (2014). Outlier treatment in the flood flow statistical analysis. Proc. Int. Conf. On Contemporary Achievements in Civil Eng., Facity of Civil Engineering Subotica, University of Novi Sad, Subotica, Serbia, p. 603-609, DOI: 10.14415/konferencijaGFS2014.081
8. Hircsh, R.M., Stedinger, J. (1987). Plotting positions for historical floods and their precision. Water Resources Research, Vol. 23, No.4, 715-727.
9. Vukmirovic, V. (1990). Analiza verovatnoce pojave hidroloskih velicina. Naucna knjiga. Beograd
10. Hanak, T., & Korytarova, J. . Zone rizika sa aspekta osiguranja - poredjenje poplava, sneznih lavina, olujnog vetra i oluja sa gradom. Journal of Applied Engineering Science, 12(2), 137-144.