The testing of a multivariate probabilistic framework for reservoir safety evaluation and flood risks assessment in Slovakia: A study on the Parná and Belá Rivers
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Asquith, W., 2022. lmomco---L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions. R package version 2.4.7.Search in Google Scholar
Blöschl, G., Merz, R., 2008. Bestimmung von Bemessungshochwässern gegebener Jährlichkeit–Aspekte einer zeitgemäßen Strategie. Wasserwirtschaft, 11, 12–18.Search in Google Scholar
Brunner, M., Seibert, J., Favre, A., 2016a. Bivariate return periods and their importance for flood peak and volume estimation. WIREs Water, 3, 819–833. https://doi.org/10.1002/wat2.1173Search in Google Scholar
Brunner, M., Vannier, O., Favre, A., Viviroli, D., Meylan, P., Sikorska, A., Seibert, J., 2016b. Flood volume estimation in Switzerland using synthetic design hydrographs - a multivariate statistical approach. In: Proc. 13th Congress INTERPRAEVENT 2016, Luzern, pp. 468–476. https://doi.org/10.5167/uzh-124430Search in Google Scholar
Brunner, M., Viviroli, D., Sikorska, A., Vannier, O., Favre, A., Seibert, J., 2017. Flood type specific construction of synthetic design hydrographs. Water Resources Research, 53, 1390–1406. https://doi.org/10.1002/2016WR019535Search in Google Scholar
Brunner, M., Furrer, R., Favre, A., 2019. Modeling the spatial dependence of floods using the Fisher copula. Hydrology and Earth System Sciences, 23, 107–124. https://doi.org/10.5194/hess-23-107-2019Search in Google Scholar
Brunner, M., 2023. Floods and droughts: a multivariate perspective on hazard estimation: a multivariate perspective on hazard estimation. Hydrology and Earth System Sciences Discussions, 1–26. https://doi.org/https://doi.org/10.5194/hess-2023-20Search in Google Scholar
Campitelli, E., 2021. metR: tools for easier analysis of meteorological fields: tools for easier analysis of meteorological fields. R Package version 0.13.0. https://doi.org/10.5281/zenodo.2593516Search in Google Scholar
Carril-Rojas, D., Mediero, L., 2023. Bivariate analysis with synthetic hydrograph shapes for hydrological dam safety assessment. Environ. Sci. Proc., 25, 2.Search in Google Scholar
Czado, C., Nagler, T., 2022. Vine copula based modeling. Annual Review of Statistics and Its Application, 9, 453–477. https://doi.org/10.1146/annurev-statistics-040220-101153Search in Google Scholar
Danáčová, Z., Poórová, J., Blaškovičová, L., Liová, S., 2015. Instrumentation for surface water quantity monitoring and discharge measurements by ADCP. Acta Hydrologica Slovaca, 16, Thematic issue, 3–12.Search in Google Scholar
Dissmann, J., Brechmann, E., Czado, C., Kurowicka, D., 2013. Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59, 52–69. https://doi.org/10.1016/j.csda.2012.08.010Search in Google Scholar
Drobot, R., Draghia, A., Ciuiu, D., Trandafir, R., 2021. Design floods considering the epistemic uncertainty. Water, 13. https://doi.org/10.3390/w13111601Search in Google Scholar
DWA, 2012. Merkblatt DWA-M 552: Ermittlung von Hochwasserwahrscheinlichkeiten. Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall e. V.Search in Google Scholar
Földes, G., Labat, M., Kohnová, S., Hlavčová, K., 2022. Impact of changes in short-term rainfall on design floods: Case study of the Hnilec River Basin, Slovakia. Slovak Journal of Civil Engineering, 30, 68–74. https://doi.org/10.2478/sjce-2022-0008Search in Google Scholar
Gaál, L., Szolgay, J., Kohnová, S., Hlavčová, K., Parajka, J., Viglione, A., Merz, R., Blöschl, G., 2015. Dependence between flood peaks and volumes: a case study on climate and hydrological controls. Hydrological Sciences Journal, 60, 968–984. https://doi.org/10.1080/02626667.2014.951361Search in Google Scholar
Gadek, W., Baziak, B., Tokarczyk, T., Szalińska, W., 2022. A novel method of design flood hydrographs estimation for flood hazard mapping. Water, 14, 12, 1856. https://doi.org/10.3390/w14121856Search in Google Scholar
Ganapathy, A., Hannah, D., Agarwal, A., 2022. Flood classification based on hydrograph characteristics. Authorea Preprints.Search in Google Scholar
Ganguli, P., Reddy, M., 2013. Probabilistic assessment of flood risks using trivariate copulas. Theoretical and Applied Climatology, 111, 341–360. https://doi.org/10.1007/s00704-012-0664-4Search in Google Scholar
Genest, C., Favre, A., 2007. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12, 4, 347–368. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347)Search in Google Scholar
Giani, G., Tarasova, L., Woods, R.A., Rico-Ramirez, M.A., 2022. An objective time-series-analysis method for rainfall-runoff event identification. Water Resources Research, 58, 2, e2021WR031283.Search in Google Scholar
Gómez, M., Ausín, M., Domínguez, M., 2018. Vine copula models for predicting water flow discharge at King George Island, Antarctica. Stochastic Environmental Research and Risk Assessment, 32, 2787–2807. https://doi.org/10.1007/s00477-018-1599-9Search in Google Scholar
Gräler, B., van den Berg, M., Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B., Verhoest, N., 2013. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Sciences, 17, 1281–1296. https://doi.org/10.5194/hess-17-1281-2013Search in Google Scholar
Grimaldi, S., Petroselli, A., Salvadori, G., De Michele, C., 2016. Catchment compatibility via copulas: A non-parametric study of the dependence structures of hydrological responses. Advances in Water Resources, 90, 116–133. https://doi.org/10.1016/j.advwatres.2016.02.003Search in Google Scholar
Größer, J., Okhrin, O., 2022. Copulae: An overview and recent developments. WIREs Computational Statistics, 14. https://doi.org/10.1002/wics.1557Search in Google Scholar
Hosking, J., 1990. L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society: Series B (Methodological), 52, 105–124. https://doi.org/10.1111/j.2517-6161.1990.tb01775.xSearch in Google Scholar
Hu, C., Ran, G., Li, G., Yu, Y., Wu, Q., Yan, D., Jian, S., 2021. The effects of rainfall characteristics and land use and cover change on runoff in the Yellow River basin, China. Journal of Hydrology and Hydromechanics, 69, 1, 29–40. https://doi.org/10.2478/johh-2020-0042Search in Google Scholar
Hundecha, Y., Parajka, J., Viglione, A., 2017. Flood type classification and assessment of their past changes across Europe. Hydrology and Earth System Sciences Discussions, 1–29. https://doi.org/https://doi.org/10.5194/hess-2017-356Search in Google Scholar
Cheng, S., Tong, X., Illman, W., 2022. Evaluation of baseflow separation methods with real and synthetic streamflow data from a watershed. Journal of Hydrology, 613, Part A, 128279. https://doi.org/10.1016/j.jhydrol.2022.128279Search in Google Scholar
Jafry, N., Suhaila, J., Yusof, F., Mohd Nor, S., Alias, N., 2022. Preliminary study on flood frequency analysis in Johor River basin using vine copula. Proceedings of Science and Mathematics, 7, 52–55.Search in Google Scholar
Latif, S., Simonovic, S., 2022. Parametric vine copula framework in the trivariate probability analysis of compound flooding events. Water, 14, 14, 2214. https://doi.org/10.3390/w14142214Search in Google Scholar
Le Clerc, S., Sauquet, E., Lang, M., 2003. Scaling properties of flood hydrographs and their use to derive design flood hydro-graphs. WIT Transactions on Ecology and the Environment, 60.Search in Google Scholar
LfU BW, 2005. Festlegung des Bemessungshochwassers für Anlagen des technischen Hochwasserschutzes. Leitfaden. Karlsruhe, 92 p.Search in Google Scholar
Liová, A., Valent, P., Hlavčová, K., Kohnová, S., Bacigál, T., Szolgay, J., 2022. A methodology for the estimation of control flood wave hydrographs for the Horné Orešany reservoir. Acta Hydrologica Slovaca, 23, 52–61. https://doi.org/10.31577/ahs-2022-0023.01.0006Search in Google Scholar
Lorenz, P., Gattermayr, W., Kölbl, C., Krammer, C., Maracek, K., Mathis, C., Moser, J., Schatzl, R., Wiesenegger, H., Wimmer, M., Lorenz, P. (Eds.), 2011. Leitfaden: Verfahren zur Abschätzung von Hochwasserkennwerten, 113 p.Search in Google Scholar
Mediero, L., Jiménez-Álvarez, A., Garrote, L., 2010. Design flood hydrographs from the relationship between flood peak and volume. Hydrology and Earth System Sciences, 14, 2495–2505. https://doi.org/10.5194/hess-14-2495-2010Search in Google Scholar
Nagler, T., Schepsmeier, U., Stoeber, J., Brechmann, E., Graeler, B., Erhardt, T., 2022. VineCopula: Statistical Inference of VinCopulas. R package version 2.4.4.Search in Google Scholar
Narasimhan, B., Johnson, S., Hahn, T., Bouvier, A., Kiêu, K., 2023. cubature: Adaptive Multivariate Integration over Hypercubes. R package version 2.0.4.6.Search in Google Scholar
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C., Mirabbasi, R., 2022. Trivariate joint frequency analysis of water resources deficiency signatures using vine copulas. Applied Water Science, 12, 67. https://doi.org/10.1007/s13201-022-01589-4Search in Google Scholar
Nelsen, R., 2006. An Introduction to Copulas. 2nd ed. Springer, New York.Search in Google Scholar
O’Connor, K., Goswami, M., Faulkner, D., 2014. Flood Studies Update, Technical Research Report: Vol. III - Hydrograph Analysis.Search in Google Scholar
Okhrin, O., Ristig, A., Xu, Y. F., 2017. Copulae in high dimensions: an introduction. Applied Quantitative Finance, 247–277. https://doi.org/10.1007/978-3-662-54486-0_19Search in Google Scholar
Oppel, H., Mewes, B., 2020. On the automation of flood event separation from continuous time series. Frontiers in Water, 2, 18. https://doi.org/10.3389/frwa.2020.00018Search in Google Scholar
Pandi, G., 2010. The analysis of flood waves. Aerul si Apa. Componente ale Mediului, 35–44.Search in Google Scholar
Pramanik, N., Panda, R., Sen, D., 2009. Development of design flood hydrographs using probability density functions. Hydrological Processes, 24, 415–428. https://doi.org/10.1002/hyp.7494Search in Google Scholar
Pekárová, P., Mészáros, J., Miklánek, P., Pekár, J., Siman, C., Podolinská, J., 2021. Post-flood field investigation of the June 2020 flash flood in the upper Muráň River basin and the catastrophic flash flood scenario. Journal of Hydrology and Hydromechanics, 69, 3, 288–299. https://doi.org/10.2478/johh-2021-0015Search in Google Scholar
R Core Team, 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing.Search in Google Scholar
Requena, A., Mediero, L., Garrote, L., 2013. A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation. Hydrology and Earth System Sciences, 17, 3023–3038. https://doi.org/10.5194/hess-17-3023-2013Search in Google Scholar
Rizwan, M., Guo, S., Yin, J., Xiong, F., 2019. Deriving design flood hydrographs based on copula function: A case study in Pakistan. Water, 11. https://doi.org/10.3390/w11081531Search in Google Scholar
Segers, J., Sibuya, M., Tsukahara, H., 2017. The Empirical Beta Copula. Journal of Multivariate Analysis, 155, 35–51. https://doi.org/10.1016/j.jmva.2016.11.010Search in Google Scholar
Schirmacher, D., Schirmacher, E., 2008. Multivariate dependence modeling using pair-copulas. Technical report.Search in Google Scholar
Szolgay, J., Gaál, L., Kohnová, S., Hlavčová, K., Výleta, R., Bacigál, T., Blöschl, G., 2015. A process-based analysis of the suitability of copula types for peak-volume flood relationships. Proceedings of the International Association of Hydrological Sciences, 370, 183–188. https://doi.org/10.5194/piahs-370-183-2015Search in Google Scholar
Škvarka, J., Bednárová, E., Miščík, M., Uhorščák, Ľ., 2021. The Domaša reservoir in the spectrum of climate change. Slovak Journal of Civil Engineering, 29, 9–15. https://doi.org/10.2478/sjce-2021-0009Search in Google Scholar
Thiesen, S., Darscheid, P., Ehret, U., 2019. Identifying rainfall-runoff events in discharge time series: a data-driven method based on information theory. Hydrology and Earth System Sciences, 23, 2, 1015–1034. https://doi.org/10.5194/hess-23-1015-2019Search in Google Scholar
Tootoonchi, F., Sadegh, M., Haerter, J., Räty, O., Grabs, T., Teutschbein, C., 2022. Copulas for hydroclimatic analysis: A practice‐oriented overview. WIREs Water, 9, 2, e1579. https://doi.org/10.1002/wat2.1579Search in Google Scholar
Tosunoglu, F., Gürbüz, F., İspirli, M., 2020. Multivariate modeling of flood characteristics using Vine copulas. Environmental Earth Sciences, 79, 459. https://doi.org/10.1007/s12665-020-09199-6Search in Google Scholar
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D. A., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K., Yutani, H., 2019. Welcome to the Tidyverse. Journal of open source software, 4, 43, 1686. https://doi.org/10.21105/joss.01686Search in Google Scholar
Xiao, Y., Guo, S., Liu, P., Yan, B., Chen, L., 2009. Design flood hydrograph based on multicharacteristic synthesis index method. Journal of Hydrologic Engineering, 14, 1359–1364.Search in Google Scholar
Yue, S., Ouarda, T., Bobée, B., Legendre, P., Bruneau, P., 2002. Approach for describing statistical properties of flood hydro-graph. Journal of Hydrologic Engineering, 7, 147–153.Search in Google Scholar
Zhang, Q., Zhang, L., She, D., Wang, S., Wang, G., Zeng, S., 2021. Automatic procedure for selecting flood events and identifying flood characteristics from daily streamflow data. Environmental Modelling & Software, 145, 105180. https://doi.org/10.1016/j.envsoft.2021.105180Search in Google Scholar