Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6599-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6599-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Oct 2026
Research article |
| 27 Oct 2026
| 27 Oct 2026
Non-stationary low-flow frequency analysis with mixed Weibull components and Copula-based dependence framework
Farhana Sweeta Fitriana
CORRESPONDING AUTHOR
Institute of Statistics, BOKU University, Vienna, Austria
Svenja Fischer
Hydrology and Environmental Hydraulics Group, Wageningen University and Research, Wageningen, the Netherlands
Gabriele Weigelhofer
Institute of Hydrobiology and Water Management, BOKU University, Vienna, Austria
Johannes Laimighofer
Institute of Statistics, BOKU University, Vienna, Austria
Gregor Laaha
Institute of Statistics, BOKU University, Vienna, Austria
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Preprint withdrawn
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Cited articles
Ahn, K.-H. and Palmer, R. N.: Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin, Hydrol. Process., 30, 3518–3532, 2016. a
Bhatti, S. J., Kroll, C. N., and Vogel, R. M.: Revisiting the probability distribution of low streamflow series in the United States, J. Hydrol. Eng., 24, 04019043, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001844, 2019. a
Casado Rodríguez, J.: CAMELS-ES: Catchment Attributes and Meteorologyfor Large-Sample Studies – Spain, Zenodo [data set], https://doi.org/10.5281/zenodo.8428374, 2023. a
Chebana, F. and Ouarda, T. B.: Multivariate non-stationary hydrological frequency analysis, J. Hydrol., 593, 125907, https://doi.org/10.1016/j.jhydrol.2020.125907, 2021. a, b
Delaigue, O., Guimarães, G. M., Brigode, P., Génot, B., Perrin, C., Soubeyroux, J.-M., Janet, B., Addor, N., and Andréassian, V.: CAMELS-FR dataset: a large-sample hydroclimatic dataset for France to explore hydrological diversity and support model benchmarking, Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, 2025. a
Delgado, J. M., Apel, H., and Merz, B.: Flood trends and variability in the Mekong river, Hydrol. Earth Syst. Sci., 14, 407–418, https://doi.org/10.5194/hess-14-407-2010, 2010. a
Du, T., Xiong, L., Xu, C.-Y., Gippel, C. J., Guo, S., and Liu, P.: Return period and risk analysis of nonstationary low-flow series under climate change, J. Hydrol., 527, 234–250, 2015. a
eHYD: eHYD – der Zugang zu hydrographischen Daten Österreichs, https://ehyd.gv.at/ (last access: 21 November 2025), 2025. a
Fischer, S. and Schumann, A. H.: Temporal changes in the frequency of flood types and their impact on flood statistics, J. Hydrol., 22, 100171, https://doi.org/10.1016/j.hydroa.2024.100171, 2024. a
Fitriana, F. S., Fischer, S., and Laaha, G.: Code to “Non-stationary low-flow frequency analysis with mixed Weibull components and Copula-based dependence framework”, Zenodo [code], https://doi.org/10.5281/zenodo.19406599, 2026. a
Floriancic, M. G., Berghuijs, W. R., Molnar, P., and Kirchner, J. W.: Seasonality and drivers of low flows across Europe and the United States, Water Resour. Res., 57, e2019WR026928, https://doi.org/10.1029/2019WR026928, 2021. a
Graham, D. J., Bierkens, M. F., and van Vliet, M. T.: Impacts of droughts and heatwaves on river water quality worldwide, J. Hydrol., 629, 130590, https://doi.org/10.1016/j.jhydrol.2023.130590, 2024. a
GRDC: Global Runoff Data Centre, https://grdc.bafg.de/ (last access: 21 November 2025), 2025. a
Haslinger, K. and Mayer, K.: Early spring droughts in Central Europe: Indications for atmospheric and oceanic drivers, Atmos. Sci. Lett., 24, e1136, https://doi.org/10.1002/asl.1136, 2023. a
Hodgkins, G. A., Renard, B., Whitfield, P. H., Laaha, G., Stahl, K., Hannaford, J., Burn, D. H., Westra, S., Fleig, A. K., Araújo Lopes, W. T., Murphy, C., Mediero, L., and Hanel, M.: Climate driven trends in historical extreme low streamflows on four continents, Water Resour. Res., 60, e2022WR034326, https://doi.org/10.1029/2022WR034326, 2024. a, b, c
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., and Fenicia, F.: CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland, Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, 2023. a
Ionita, M. and Nagavciuc, V.: Forecasting low flow conditions months in advance through teleconnection patterns, with a special focus on summer 2018, Sci. Rep., 10, 13258, https://doi.org/10.1038/s41598-020-70060-8, 2020. a
Kendall, M. G.: Rank Correlation Methods, 4th edn., Griffin, London, ISBN 978-0-85264-199-6, 1976. a
Klein, B., Schumann, A. H., and Pahlow, M.: Copulas–new risk assessment methodology for dam safety, in: Flood risk assessment and management: How to specify hydrological loads, their consequences and uncertainties, 149–185, Springer, https://doi.org/10.1007/978-90-481-9917-4_8, 2010. a, b
Laaha, G.: Modelling summer and winter droughts as a basis for estimating river low flows, International Association of Hydrological Sciences, Publication, 289–296, ISBN 978-1-901502-81-7, 2002. a
Laaha, G.: Process based regionalisation of low flows, PhD thesis, Technische Universität Wien, https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-13474 (last access: 25 May 2025), 2003. a
Laaha, G., Gauster, T., Tallaksen, L. M., Vidal, J.-P., Stahl, K., Prudhomme, C., Heudorfer, B., Vlnas, R., Ionita, M., Van Lanen, H. A. J., Adler, M.-J., Caillouet, L., Delus, C., Fendekova, M., Gailliez, S., Hannaford, J., Kingston, D., Van Loon, A. F., Mediero, L., Osuch, M., Romanowicz, R., Sauquet, E., Stagge, J. H., and Wong, W. K.: The European 2015 drought from a hydrological perspective, Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, 2017. a, b
Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., Trisos, C., Romero, J., Aldunce, P., Barrett, K., Blanco, G., Cheung, W. W. L., Connors, S. L., Denton, F., Diongue-Niang, A., Dodman, D., Garschagen, M., Geden, O., Hayward, B., Jones, C., Jotzo, F., Krug, T., Lasco, R., Lee, J.-Y., Masson-Delmotte, V., Meinshausen, M., Mintenbeck, K., Mokssit, A., Otto, F. E. L., Pathak, M., Pirani, A., Poloczanska, E., Pörtner, H.-O., Revi, A., Roberts, D. C., Roy, J., Ruane, A. C., Skea, J., Shukla, P. R., Slade, R., Slangen, A., Sokona, Y., Sörensson, A. A., Tignor, M., van Vuuren, D., Wei, Y.-M., Winkler, H., Zhai, P., and Zommers, Z.: Synthesis report of the IPCC Sixth Assessment Report (AR6), Longer report, IPCC, https://doi.org/10.59327/IPCC/AR6-9789291691647, 2023. a
Loritz, R., Dolich, A., Acuña Espinoza, E., Ebeling, P., Guse, B., Götte, J., Hassler, S. K., Hauffe, C., Heidbüchel, I., Kiesel, J., Mälicke, M., Müller-Thomy, H., Stölzle, M., and Tarasova, L.: CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany, Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, 2024. a
Milly, P. C., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: Whither water management?, Science, 319, 573–574, 2008. a
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D. A., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V.: “Panta Rhei – Everything Flows”: Change in hydrology and society – The IAHS Scientific Decade 2013–2022, Hydrol. Sci. J., 58, 1256–1275, https://doi.org/10.1080/02626667.2013.809088, 2013. a
Mosley, L. M.: Drought impacts on the water quality of freshwater systems; review and integration, Earth-Sci. Rev., 140, 203–214, 2015. a
Önöz, B. and Bayazit, M.: Block bootstrap for Mann–Kendall trend test of serially dependent data, Hydrol. Process., 26, 3552–3560, 2012. a
Patakamuri, S. K., O'Brien, N., and Patakamuri, M. S. K.: Package “modifiedmk”, Cran, R-project, https://doi.org/10.32614/CRAN.package.modifiedmk, 2020. a, b
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau, J. Am. Stat. Assoc., 63, 1379–1389, 1968. a
Stahl, K., Hisdal, H., Hannaford, J., Tallaksen, L. M., van Lanen, H. A. J., Sauquet, E., Demuth, S., Fendekova, M., and Jódar, J.: Streamflow trends in Europe: evidence from a dataset of near-natural catchments, Hydrol. Earth Syst. Sci., 14, 2367–2382, https://doi.org/10.5194/hess-14-2367-2010, 2010. a
Stedinger, J. R.: Frequency analysis of extreme events, Handbook of hydrology, ISBN 978-0-07-039732-3, 1993. a
Tallaksen, L. M. and Laaha, G.: Frequency analysis, in: Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, 2nd edn., edited by: Tallaksen, L. M. and van Lanen, H. A. J., Chapter 6, 233–304, Elsevier, https://doi.org/10.1016/B978-0-12-819082-1.00003-5, 2023. a
Tallaksen, L. M. and van Lanen, H. A. J. (Eds.): Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater, 2nd edn., Elsevier, 684 pp., ISBN 978-0-12-819082-1, https://doi.org/10.1016/C2017-0-03464-X, 2023. a, b
Tramblay, Y., Rutkowska, A., Sauquet, E., Sefton, C., Laaha, G., Osuch, M., Albuquerque, T., Alves, M. H., Banasik, K., Beaufort, A., Brocca, L., Camici, S., Csabai, Z., Dakhlaoui, H., DeGirolamo, A. M., Dörflinger, G., Gallart, F., Gauster, T., Hanich, L., Kohnová, S., Mediero, L., Ninov, P., Parry, S., Quintana-Seguí, P., Tzoraki, O., and Datry, T.: Trends in flow intermittence for European rivers, Hydrol. Sci. J., 66, 37–49, https://doi.org/10.1080/02626667.2020.1849708, 2021. a
Turner, S., Hannaford, J., Barker, L. J., Suman, G., Killeen, A., Armitage, R., Chan, W., Davies, H., Griffin, A., Kumar, A., Dixon, H., Albuquerque, M. T. D., Almeida Ribeiro, N., Alvarez-Garreton, C., Amoussou, E., Arheimer, B., Asano, Y., Berezowski, T., Bodian, A., Boutaghane, H., Capell, R., Dakhlaoui, H., Daňhelka, J., Do, H. X., Ekkawatpanit, C., El Khalki, E. M., Fleig, A. K., Fonseca, R., Giraldo-Osorio, J. D., Goula, A. B. T., Hanel, M., Horton, S., Kan, C., Kingston, D. G., Laaha, G., Laugesen, R., Lopes, W., Mager, S., Rachdane, M., Markonis, Y., Medeiro, L., Midgley, G., Murphy, C., O’Connor, P., Pedersen, A. I., Pham, H. T., Piniewski, M., Renard, B., Saidi, M. E., Schmocker-Fackel, P., Stahl, K., Thyer, M., Toucher, M., Tramblay, Y., Uusikivi, J., Venegas-Cordero, N., Visessri, S., Watson, A., Westra, S., and Whitfield, P. H.: ROBIN: Reference observatory of basins for international hydrological climate change detection, Sci. Data, 12, 654, https://doi.org/10.1038/s41597-025-04907-y, 2025. a
Van Vliet, M. T. H., Thorslund, J., Strokal, M., Hofstra, N., Flörke, M., Ehalt Macedo, H., Nkwasa, A., Tang, T., Kaushal, S. S., Kumar, R., van Griensven, A., Bouwman, L., and Mosley, L. M.: Global river water quality under climate change and hydroclimatic extremes, Nat. Rev. Earth Environ., 4, 687–702, https://doi.org/10.1038/s43017-023-00472-3, 2023. a
Vogel, R. M. and Kroll, C. N.: Low-flow frequency analysis using probability-plot correlation coefficients, J. Water Res. Pl., 115, 338–357, 1989. a
Vogel, R. M. and Kroll, C. N.: A comparison of estimators of the conditional mean under non-stationary conditions, Adv. Water Res., 143, 103672, https://doi.org/10.1016/j.advwatres.2020.103672., 2020. a
Vogel, R. M. and Kroll, C. N.: On the need for streamflow drought frequency guidelines in the US, Water Policy, 23, 216–231, 2021. a
Vogel, R. M. and Sankarasubramanian, A.: Spatial scaling properties of annual streamflow in the United States, Hydrolog. Sci. J., 45, 465–476, 2000. a
Volpi, E., Grimaldi, S., Aghakouchak, A., Castellarin, A., Chebana, F., Papalexiou, S. M., Aksoy, H., Bárdossy, A., Cancelliere, A., Chen, Y., Deidda, R., Haberlandt, U., Eris, E., Fischer, S., Francés, F., Kavetski, D., Kjeldsen, T. R., Kochanek, K., Langousis, A., Mediero Orduña, L., Montanari, A., Nerantzaki, S. D., Ouarda, T. B. M. J., Prosdocimi, I., Ragno, E., Rajulapati, C. R., Requena, A. I., Ridolfi, E., Sadegh, M., Schumann, A., and Sharma, A.: The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology, Hydrol. Sci. J., 69, 1913–1949, https://doi.org/10.1080/02626667.2024.2385686, 2024. a, b
Wang, M., Jiang, S., Ren, L., Xu, C.-Y., Shi, P., Yuan, S., Liu, Y., and Fang, X.: Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates, J. Hydrol., 612, 128266, https://doi.org/0.1016/j.jhydrol.2022.128266, 2022. a
Yılmaz, M. and Tosunoğlu, F.: Non-stationary low flow frequency analysis under climate change, Theor. Appl. Climatol., 155, 7479–7497, 2024. a
Yu, X., Cohn, T. A., and Stedinger, J. R.: Flood frequency analysis in the context of climate change, in: World Environmental and Water Resources Congress 2015, 2376–2385, https://doi.org/10.1061/9780784479162.233, 2015. a
Zeder, J. and Fischer, E. M.: Observed extreme precipitation trends and scaling in Central Europe, Weather and Climate Extremes, 29, 100266, https://doi.org/10.1016/j.wace.2020.100266, 2020. a
Short summary
This study improves the estimation of design low flows in rivers under changing climate conditions. We analysed records from more than 700 near-natural European catchments and developed a framework that allows low-flow risk to change over time and differ between summer and winter, including how the seasons are linked. Results show that assuming a constant climate can under-overestimate low-flow events. Accounting for change gives more reliable design values for planning under a warming climate.
This study improves the estimation of design low flows in rivers under changing climate...
