Testing a numerically-analytical method for prediction design maxima discharges of floods using plotting position formulas: the river Uzh case, the “Uzhhorod” gauging station data

Authors

DOI:

https://doi.org/10.32347/2411-4049.2023.2.138-162

Keywords:

annual probability of exceedance, divergence indicator, extrapolation, floods, numerically-analytical method, plotting position formulas, probability distributions, prediction, return period

Abstract

There are a lot of analytical probability distributions that might be used to predict peak discharges of floods. However, there is no proper theoretical or another similar justification for choosing an appropriate parametric probability distribution to predict peak discharges of floods by using observed data. As a permissible hypothesis, any of recommended probability distributions can be considered providing it meets the given statistical criteria and other considerations for the adequacy of simulation are taken into account. In turn, more than seventeen plotting position formulas have been proposed. They provide a non-parametric means to estimate the observed data probability distribution. Using a plotting position formula, a plot of the estimated values from a theoretical parametric probability distribution can be compared with the observed data.
The choice of a better plotting position formula for fitting the different probability distributions has been discussed many times in hydrology and statistical literature. However, no specific criterion for choosing these formulas has been proposed yet. Perhaps there is no need for such a criterion. Maybe, the diversity of estimates that can be obtained due to these formulas matters more. Due to the diversity of the different plotting position estimates, from the point of view of informational entropy, different plotting position formulas enable revealing epistemic (non-stochastic or subjective) uncertainty in predictions of hydrological extremes.
Results of calculating empirical annual probabilities of exceedance observed maxima discharge employing various plotting position formulas show that increasing the predicting horizon toward low probable and more extreme events increases the divergence between the estimates obtained using the different plotting position formulas. Therefore, it is reasonable to assume that this divergence may be extrapolated to predict design maxima discharges of floods based on empirical estimates of plotting position probabilities.
This paper proposes a numerically-analytical method using such an extrapolation. It is based on using different plotting position formulas, numerical calculations of plotting position probabilities, and extrapolation of the divergence between the obtained estimates. The method is tested in predicting the maxima discharges of 0.5% and 1% annual probability of exceedance for the Uzh River flowing in the Transcarpathia region, the hydrological station “Uzhhorod” data.

References

Ukraine – Vulnerability. Climate Change Knowledge Portal. Available from https://climateknowledgeportal.worldbank.org/country/ukraine/vulnerability.

Flood protection of territories. United Nations Development Programme. UNDP in Ukraine. Available from https://www1.undp.org/content/dam/ukraine/docs/EE/Flood.

Stefanyshyn, D.V. (2022). What could we have learnt from the previous flood data to predict losses caused by the 1980, 1986, and 1998 catastrophic floods in Ukrainian Transcarpathian? Environmental safety and natural resources, 43(3), 81–109; https://doi.org/10.32347/2411-4049.2022.3.81-109.

Ryabchenko, O., Snizhko, S., and Trypolska, G. (2020). Ukraine. Technology needs assessment for climate change adaptation. Barrier analysis and enabling framework. Report. Project: Technological Needs Assessment under the United Nations Framework Convention on Climate Change (UNFCCC), 171 p.

Danko, K., Nabyvanets, Yu., Filippova, Yu., Korniienko, V., Lobodzinskyi, O., Surai, K., Malyshev, A., and Kostiantyn Sokolchuk, K. (2019). Steps to implement Directive 2007/60/EC in Ukraine and preliminary flood risk assessment. Available from https://uhmi.org.ua/conf/danube_conference_2019/presentations/.

Didovets, I., Lobanova, A., Bronstert, A., Snizhko, S, Fox Maule, C, and Krysanova, V. (2017). Assessment of Climate Change Impacts on Water Resources in Three Representative Ukrainian Catchments Using Eco-Hydrological Modelling. Water, 9, 204; https://doi.org/10.3390/w9030204.

Stefanyshyn D.V., Korbutiak V.M., Stefanyshyna-Gavryliuk Y.D. (2019). Situational predictive modelling of the flood hazard in the Dniester river valley near the town of Halych. Environmental safety and natural resources, Issue 1 (29), 16–27; https://doi.org/10.32347/2411-4049.2019.1.16-27.

Susidko, M.M., and Lukyanets, O.I. (2004). Zoning of the territory of Ukraine according to the degree of hydrological danger. UkrNDGMI, Issue 253, 196–204. (In Ukrainian) [Сусідко М.М., Лук’янець, О.І. (2004). Районування території України за ступенем гідрологічної небезпеки. УкрНДГМІ, Вип. 253, 196–204].

Didovets, Iu., Krysanova, V., Bürger, G., Sergiy Snizhko, S., Balabukh, V., and Bronstert, A. (2019). Climate change impact on regional floods in the Carpathian region. Journal of Hydrology: Regional Studies, 22, 100590; https://doi.org/10.1016/j.ejrh.2019.01.002.

Climate Landscape Analysis for Children (CLAC) in Ukraine. (2021). UNICEF, Hydroconseil, 156 p. Available from https://www.unicef.org/ukraine/media/15766.

Munich Re’s NatCatSERVICE – The natural catastrophe loss database. Data on natural disasters since 1980. Available from https://www.munichre.com/en/solutions/reinsurance-property-casualty/natcatservice.html.

McBain, W., Wilkes, D., and Retter M. (2010). Flood resilience and resistance for critical infrastructure. CIRIA C688. London, 134 p.

Joyce, J., Chang, N. Bin, Harji, R., Ruppert, T. (2018). Coupling infrastructure resilience and flood risk assessment via copulas analyses for a coastal green-grey-blue drainage system under extreme weather events. Environ. Modelling Software. 100, 82–103; https://doi.org/10.1016/j.envsoft.2017.11.008.

Sahani, J., Kumar, P., Debele, S., Spyrou, Ch., Loupis, M., Aragão, L., Porcù, F., Rahman Shah, M.A., and Di Sabatino, S. (2019). Hydro-meteorological risk assessment methods and management by nature-based solutions. Science of The Total Environment, Vol. 696, 133936; https://doi.org/10.1016/j.scitotenv.2019.133936.

Debele, S.E., Kumar, P., Sahani, J., et al. (2019). Nature-based solutions for hydro-meteorological hazards: Revised concepts, classification schemes and databases. Env. Research, Vol. 179, Part B, 108799; https://doi.org/10.1016/j.envres.2019.108799.

Directive 2007/60/EC on the assessment and management of flood risks. (2007). Official Journal of the European Union, L288/27, 8 p. Available from https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32007L0060&from=EN.

Effectiveness of flood management measures. (2015). Integrated flood management tools series, Issue 21. World Meteorological Organization and the Global Water Partnership, 66 p. Available from https://www.floodmanagement.info/publications/tools.

Salazar, S., Francés, F., Komma, J., Blume, T., Francke, T., Bronstert, A., and Blöschl, G. (2012). A comparative analysis of the effectiveness of flood management measures based on the concept of “retaining water in the landscape” in different European hydro-climatic regions. Nat. Hazards Earth Syst. Sci., 12, 3287–3306; https://doi.org/10.5194/nhess-12-3287-2012.

Hudson, P., Botzen, W.J.W., Kreibich, H., Bubeck, P., and Aerts, J.C.J.H. Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching (2014). Nat. Hazards Earth Syst. Sci., 14, 1731–1747; https://doi.org/10.5194/nhess-14-1731-2014.

Kron, W., and Müller, O. (2019). Efficiency of flood protection measures: selected examples. Water Policy 21(6), 449–467; https://doi.org/10.2166/wp.2019.023.

Tariq, M.A.U.R., Farooq, R., and van de Giesen, N. (2020). A Critical Review of Flood Risk Management and the Selection of Suitable Measures. Appl. Sci., 10, 8752; https://doi.org/10.3390/app10238752.

Munich Re. Flood risks on the rise. Underestimated natural hazard, devastating damage. Available from https://www.munichre.com/en/risks/natural-disasters-losses-are-trending-upwards/floods-and-flash-floods-underestimated-natural-hazards.html.

De Ruig, L.T., Haer, T., de Moel, H., Brody, S.D., Wouter Botzen, W.J., Czajkowski, J., and Aerts, J.C.J.H. (2022). Climate-proofing the National Flood Insurance Program. Policy brief, Climate adaptation; https://doi.org/10.1038/s41558-022-01502-6.

Paprotny, D., Sebastian, A., Morales-Nápoles, O., and Jonkman, S.N. (2018). Trends in flood losses in Europe over the past 150 years. Nature communications, 9:1985; https://doi.org/10.1038/s41467-018-04253-1.

Flood issues and climate changes – Integrated Report for Tisza River Basin. (2018). Danube Transnational Programme JOINTISZA. Deliverable 5.1.2. project co-funded by the EU (ERDF, IPA funds), 136 p. Available from https://www.interreg-danube.eu/uploads/media/approved_project_output/0001/36/49d50d0b2429884b0a1f2eafc8c158b70bc31679.pdf.

Climate Landscape Analysis for Children (CLAC) in Ukraine. (2021). UNICEF, Hydroconseil, 156 p. Available from https://www.unicef.org/ukraine/media/15766.

Kikwasi, G.J. (2018). Critical Success Factors for Effective Risk Management. From the Edited Volume “Risk Management Treatise for Engineering Practitioners”. Edited by Ch.F. Oduoza. Chapter 4, 73-94; https://doi.org/10.5772/intechopen.74419.

Savage, L.J. (1954). The foundations of statistics. New York: Wiley, 294 p.; https://doi.org/10.1002/nav.

Stedinger, J.R., Vogel, R.M. and Foufoula-Georgia, E. (1993). Frequency Analysis of Extreme Events. Chapter 18. In Maidment, D.R., Ed., Handbook of Hydrology, McGraw Hill, New York, 18.1-18.66.

Schröter, K., Falter, D., Nguyen, D., Kreibich, H., Vorogushyn, S., Hundecha, Y., Apel, H., and Merz, B. (2014). Is probability of peak discharge a suitable proxy for probability of damage in flood risk analysis? International Conference “Analysis and Management of Changing Risks for Natural Hazards”, Padua, Italy, Abstract code: D02.

Integrated flood risk management. Bulletin 156. (2014). CIGB, ICOLD. Commission Internationale des Grands Barrages, International commission on large dams. Paris, 288 p.

National Disaster Risk Assessment. Governance System, Methodologies, and Use of Results. (2017). Words into Action Guidelines. Consultative version. United Nations Office for Disaster Risk Reduction (UNISDR), Geneva - Switzerland, 101 p.

Okoli, K., Breinl, K., Mazzoleni, M., and Di Baldassarre, G. (2019). Design Flood Estimation: Exploring the Potentials and Limitations of Two Alternative Approaches. Water, 11, 729; https://doi.org/10.3390/w11040729.

Statistical distributions for flood frequency analysis. (1989). World meteorological organization. Operational hydrology report No. 33, Geneva, Switzerland, 128 p.

Maity, R. (2018). Statistical Methods in Hydrology and Hydroclimatology. Springer Transactions in Civil and Environmental Engineering, 451 p.; https://doi.org/10.1007/978-981-10-8779-0_1.

HEC-RAS River Analysis System. User’s Manual V. 6.0. (2021). US Army Corps of Engineers. Inst. for Water Resources. Hydrologic Eng. Center. Available from https://www.hec.usace.army.mil/software/hec-ras/documentation/HEC-RAS_6.0_Users_Manual.pdf.

Stefanyshyn, D.V., Khodnevich, Y.V., Korbutiak, V.M. (2021). Estimating the Chezy roughness coefficient as a characteristic of hydraulic resistance to flow in river channels: a general overview, existing challenges, and ways of their overcoming. Env. safety and natural resources, 39(3), 16–43; URL: https://doi.org/10.32347/2411-4049.2021.3.16-43.

Blöschl, G., Bierkens, M.F.P., Chambel, A., Cudennec, Ch., Destouni, G., Fiori, A., Kirchner, J.W. , McDonnell, J. J., Savenije, H. H.G., Sivapalan, M., Stumpp, Ch., Toth, E., Volpi, E., and al. (2019). Twenty-three unsolved problems in hydrology (UPH) – a community perspective. Hydrological Sciences Journal, 64:10, 1141-1158; https://doi.org/10.1080/02626667.2019.1620507.

Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P., and Stouffer, R.J. (2008). Stationarity is dead: whither water management? Science, 319, 573-574.

López, J., and Francés, F. (2013) Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrol. Earth Syst. Sci., 17, 3189-3203; https://doi.org/10.5194/hess-17-3189-2013.

Debele, S.E., Strupczewski, W.G., and Bogdanowicz, E. (2017). A comparison of three approaches to non-stationary flood frequency analysis. Acta Geophys., 65, 863-883; https://doi.org/10.1007/s11600-017-0071-4.

Villarini, G., Taylor, S., Wobus, C., Vogel, R., Hecht, J., White, K.D., Baker, B., Gilroy, K., Olsen, J.R., and Raff, D. (2018). Floods and Nonstationarity: A Review, CWTS 2018-01, U.S. Army Corps of Engineers: Washington, DC, 88 p.

Apel, H., Merz, B., Thieken, and A.H. (2008). Quantification of uncertainties in flood risk assessments. International Journal of River Basin Management (JRBM), Vol. 6, No. 2, 149-162; https://doi.org/10.1080/15715124.2008.9635344.

Serinaldi, F., and Kilsby, Ch.G. (2015). Stationarity is undead: Uncertainty dominates the distribution. Advances in Water Resources, 77, 17–36; http://dx.doi.org/10.1016/j.advwatres.2014.12.013.

Stefanyshyna-Gavryliuk, Yu.D., and Stefanyshyn, D.V. (2013). The use of fuzzy measure to overcome the uncertainty of long-term predictions based on extrapolations. System Research and Information Technologies, No. 4, 99–110; https://ela.kpi.ua/handle/123456789/7021. (in Ukrainian) [Стефанишина-Гаврилюк Ю.Д., Стефанишин Д.В. (2013). Використання нечіткої міри для подолання невизначеності довгострокових прогнозів на основі екстраполяцій. Системні дослідження та інформаційні технології, № 4, 99–110].

Apel, H., Thieken, A.H., Merz, B. and Blöschl, G. (2006). A Probabilistic Modelling System for Assessing Flood Risks. Natural Hazards, 38, 79-100; https://doi.org/10.1007/s11069-005-8603-7.

Stefanyshyn, D.V. (2018). On the use of the type I Gumbel distribution to assess risks given floods. Mathematical modeling in economy [Математичне моделювання в економіці], №1, 74–83.

Korbutiak, V., Stefanyshyn, D., Lahodniuk, O., and Lahodniuk, A. (2020). The combined approach to solving issues of the flood hazard assessment using water gauge records and spatial data. Acta Sci. Pol. Architectura 19 (1), 111–118; https://doi.org/10.22630/ASPA.2020.19.1.12.

Stefanyshyn, D.V. (2021). Probability assessment of the Kyiv reservoir overflow. Environmental safety and natural resources, 40 (4), 73–99; https://doi.org/10.32347/2411-4049.2021.4.73-99.

Extreme Hydrological Events: New Concepts for Security (NATO Science Series: IV: Earth and Environmental Sciences). (2007). Paperback: Editors: O. F. Vasiliev, P. H. A. J. M. van Gelder, E. J. Plate, M. V. Bolgov. Springer; 1 edition, 480 p. Available from https://link.springer.com/book/10.1007%2F978-1-4020-5741-0.

Koutsoyiannis, D. (2008). Probability and statistics for geophysical processes. National Tech. University of Athens. Available from https://www.itia.ntua.gr/en/docinfo/1322/.

Review of Applied-Statistical Methods for Flood-Frequency Analysis in Europe. (2012). Editors: Castellarin, A., Kohnová, S., Gaál, L., Fleig, A., Salinas, J.L., Toumazis, A., Kjeldsen, T.R., and Macdonald, N. NERC/Centre for Ecology & Hydrology, 122 p. Available from https://nora.nerc.ac.uk/id/eprint/19286/.

Ren, M., He, X., Kan, G., Wang, F., Zhang, H., Li, H., Cao, D., Wang, H., Sun, D., Jiang, X., Wang, G., and Zhang, Z. (2017). A Comparison of Flood Control Standards for Reservoir Engineering for Different Countries. Water, 9, 152; https://doi.org/10.3390/w9030152.

Stefanyshyn, D.V. (2008). Application of risk analysis to support safety of dams and flooded territories against floods. Proc. of Int. Scientific School “Modelling and Analysis of Safety and Risk in Complex Systems”. June 24–28, Saint-Petersburg, Russia, 371–376.

Cunnane, C. (1978). Unbiased plotting positions – A review. Journal of Hydrology, Vol. 37, Issues 3-4, 205-222; https://doi.org/10.1016/0022-1694(78)90017-3.

Ahmad Shukri Yahaya, Norlida Md. Nor, Nor Rohashikin Mat Jali, Nor Azam Ramli, Fauziah Ahmad, and Ahmad Zia Ul-Saufie (2012). Determination of the Probability Plotting Position for Type I Extreme Value Distribution. Journal of Applied Sciences, 12, 1501–1506; https://doi.org/10.3923/jas.2012.1501.1506.

Harter, H.L. (1984). Another look at plotting positions. Communications in Statistics – Theory and Methods, Vol. 13, Issue 13, 1613-1633; https://doi.org/10.1080/03610928408828781.

Shabri, A. (2002). A comparison of plotting formulas for the Pearson type III distribution. Jurnal Teknologi, 36(C), 61–74.

Makkonen, L. (2006). Plotting Positions in Extreme Value Analysis. Journal of Applied Meteorology and Climatology, Vol. 45, 334–340; https://doi.org/10.1175/JAM2349.1.

Mehdi, F., and Mehdi, J. (2011). Determination of Plotting Position Formula for the Normal, Log-Normal, Pearson (III), Log-Pearson (III) and Gumble Distributional Hypotheses Using The Probability Plot Correlation Coefficient Test. World Applied Sciences Journal, 15 (8), 1181–1185.

Ologhadien, I. (2021). Study of Unbiased Plotting Position Formulae for the Generalized Extreme Value (GEV) Distribution. European J. of Eng. and Technology Research, Vol. 6, Issue 4, 94–99; http://dx.doi.org/10.24018/ejers.2021.6.4.2468.

Ologhadien, I. (2021). Evaluation of Plotting Position Formulae for Pearson Type 3 Distribution in Three Hydrological Stations on the Niger River. Int. Journal of Environment and Climate Change, 11(9), 117–128; https://doi.org/10.9734/IJECC/2021/v11i930485.

Miklanek, P., and Pavla Pekarová, P. (2009). 100-year Flood Event Scenario and Flood Risk Assessment for Uzh River at Lekarovce (Slovakia). International Symposium on Water Management and Hydraulic Engineering. Ohrid/Macedonia, Paper A108, 749–760.

Kovalets, I.V., Kivva, S.L., and Udovenko, O.I. (2015). Usage of the WRF/DHSVM model chain for simulation of extreme floods in mountainous areas: a pilot study for the Uzh River Basin in the Ukrainian Carpathians. Natural Hazards, 75, 2049–2063; https://doi.org/10.1007/s11069-014-1412-0.

Ecosystem services and hydropower: pilot application of European tools in the river basin of the EaP countries. (2021). Policy paper / compiled by: R. Havryliuk, O. Cazanteva, I. Trombitsky [et al.]. Chişinău: Eco-TIRAS, 68 p.

Velychko, S., and Dupliak, O. (2021). Hydrological Assessment of the Water Replenishment Possibility of the Uzh River Urbanized Floodplain on the Example of Bozdosky Park, Ukraine. Ecological Engineering and Environmental Technology, 22 (4), 30–38; https://doi.org/10.12912/27197050/137871.

Obodovskyi, O.G., Surai, K.S., Pochayevets, O.O. (2018). Estimation of the minimum water flow of the rivers of the Uzh sub-basin (the Tisza river basin). Hidrolohiiа, hidrokhimiiа i hidroekolohiiа, 2 (49), 6–15. (In Ukrainian) [Ободовський О.Г., Сурай К.С., Почаєвець О.О. (2018). Оцінка мінімального стоку води річок суббасейну Ужа (басейн річки Тиса). Гідрологія, гідрохімія і гідроекологія, 2 (49), 6–15].

Stoyko, S.M. (2002). The causes of catastrophic floods in the Transcarpathian region and the system of ecological prophylactic measures for their prevention. TISCIA monograph series, 6, 17–28.

Babych, M. (2009). Flood management in Transcarpathia Region of Ukraine. State Committee of Ukraine for Water management. Available from https://unece.org/fileadmin/DAM/env/water/meetings/flood/workshop%202009/presentations/session%202/Babich.pdf.

Bálint, M.Z. (2017). Multilateral efforts towards basin wide flood control along the Tisza River: The Hungarian-Ukrainian joint Upper-Tisza flood development program. Hungarian Journal of Hydrology, Vol. 97, No. 3, 73-80.

Central Geophysical Observatory named after Boris Sreznevsky. Available from http://cgo-sreznevskyi.kyiv.ua/index.php?lang=en&dv=main.

Brezinski, C., and Redivo-Zaglia, M. (2020). Extrapolation and Rational Approximation. The Works of the Main Contributors. Springer Nature, Cham, Switzerland, 406 p.; doi:10.1007/978-3-030-58418-4.

Fishburn, P.C. (1970). Utility Theory for Decision Making. New York, John Wiley & Sons, Inc., 234 p.

Fishburn, P.C. (1989). Non-transitive measurable utility for decision under uncertainty. Journal of Mathematical Economics, Vol. 18, Issue 2, 187-207; https://doi.org/10.1016/0304-4068(89)90021-9.

De Rocquigny, E. (2012). Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods. Wiley series in probability and statistics, 484 p.

Kochenderfer, M.J. (2015). Decision-making under uncertainty. Theory and Application. With Ch. Amato, G. Chowdhary, J.P. How, H.J. Davison Reynolds, J.R.Thornton, P.A. Torres-Carrasquillo, N. Kemal Üre, and J. Vian. Massachusetts Institute of Technology, The MIT Press, Cambridge, Massachusetts, London, England, 323 p.

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2023-06-28

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Stefanyshyn, D. V. (2023). Testing a numerically-analytical method for prediction design maxima discharges of floods using plotting position formulas: the river Uzh case, the “Uzhhorod” gauging station data. Environmental Safety and Natural Resources, 46(2), 138–162. https://doi.org/10.32347/2411-4049.2023.2.138-162

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Information systems and mathematical modeling