Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions
of multiple dependence, Insurance Math. Econ., 44, 182–198, https://doi.org/10.1016/j.insmatheco.2007.02.001, 2009.

Akaike, H.: A new look at the statistical model identification, IEEE Trans.
Autom. Control, 19, 716–723, 1974.

Balistrocchi, M. and Bacchi, B.: Derivation of flood frequency curves
through a bivariate rainfall distribution based on copula functions:
application to an urban catchment in northern Italy's climate, Hydrol. Res., 48, 749–762, https://doi.org/10.2166/nh.2017.109, 2017.

Bender, J., Wahl, T., and Jensen, J.: Multivariate design in the presence of
non-stationarity, J. Hydrol., 514, 123–130, https://doi.org/10.1016/j.jhydrol.2014.04.017, 2014.

Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A. P., Merz, B., Arheimer, B., and Živkovic, N: Changing
climate shifts timing of European floods, Science, 357, 588–590, 2017.

Bracken, C., Holman, K. D., Rajagopalan, B., and Moradkhani, H.: A Bayesian
hierarchical approach to multivariate nonstationary hydrologic frequency
analysis, Water Resour. Res., 54, 243–255, https://doi.org/10.1002/2017WR020403, 2018.

Bücher, A., Kojadinovic, I., Rohmer, T., and Segers, J.: Detecting changes
in cross-sectional dependence in multivariate time series, J. Multivariate Anal., 132, 111–128, https://doi.org/10.1016/j.jmva.2014.07.012, 2014.

Chow, V. T.: Handbook of Applied Hydrology, McGraw-Hill, New York, 1964.

Department of Comprehensive Statistics of National Bureau of Statistics:
China Compendium of Statistics 1949–2008, China Stat. Press,
Beijing, 2010 (in Chinese).

Engeland, K., Wilson, D., Borsányi, P., Roald, L., and Holmqvist, E.:
Use of historical data in flood frequency analysis: A case study for four
catchments in Norway, Hydrol. Res., 49, 466–486, 2018,

Favre, A. C., El Adlouni, S., Perreault, L., Thiémonge, N., and
Bobée, B.: Multivariate hydrological frequency analysis using copulas,
Water Resour. Res., 40, W01101, https://doi.org/10.1029/2003WR002456, 2004.

Frank, J. and Massey, J. R.: The Kolmogorov-Smirnov test for goodness of
fit, J. Am. Stat. Assoc., 46, 68–78, 1951.

Hawkes, P. J.: Joint probability analysis for estimation of extremes,
J. Hydraul. Res., 46, 246–256, https://doi.org/10.1080/00221686.2008.9521958, 2008.

He, C.: The China Modernization Report 2013, Peking University
Press, Beijing, 2014 (in Chinese).

Hurvich, C. M. and Tsai, C. L.: Regression and time series model selection
in small samples, Biometrika, 76, 297–307, 1989.

Jiang, C., Xiong, L., Xu, C.-Y., and Guo, S.: Bivariate frequency analysis
of nonstationary low-flow series based on the time-varying copula, Hydrol.
Process., 29, 1521–1534, https://doi.org/10.1002/hyp.10288, 2015a.

Jiang, C., Xiong, L., Wang, D., Liu, P., Guo, S., and Xu, C.-Y.: Separating
the impacts of climate change and human activities on runoff using the
Budyko-type equations with time-varying parameters, J. Hydrol., 522,
326–338, https://doi.org/10.1016/j.jhydrol.2014.12.060, 2015b.

Kew, S. F., Selten, F. M., Lenderink, G., and Hazeleger, W.: The simultaneous
occurrence of surge and discharge extremes for the Rhine delta, Nat. Hazards
Earth Syst. Sci., 13, 2017–2029, https://doi.org/10.5194/nhess-13-2017-2013,
2013.

Kobierska, F., Engeland, K., and Thorarinsdottir, T.: Evaluation of design
flood estimates – a case study for Norway, Hydrol. Res., 49, 450–465, 2018.

Kojadinovic, I.: npcp: Some nonparametric CUSUM tests for change-point
detection in possibly multivariate observations, R Package Version 0.1-9,
Vienna, Austria, available at: https://cran.r-project.org/web/packages/npcp/npcp.pdf (last access: 20 March 2019), 2017.

Kundzewicz, Z. W., Pińskwar, I., and Brakenridge, G. R.: Changes in river
flood hazard in Europe: a review, Hydrol. Res., 49, 294–302, 2018.

Kwon, H.-H., Lall, U., and Kim, S.-J.: The unusual 2013–2015 drought in
South Korea in the context of a multicentury precipitation record: Inferences
from a nonstationary, multivariate, Bayesian copula model, Geophys. Res.
Lett., 43, 8534–8544, https://doi.org/10.1002/2016GL070270, 2016.

Kyselý, J.: A cautionary note on the use of nonparametric bootstrap for
estimating uncertainties in extreme-value models, J. Appl. Meteorol. Clim., 47, 3236–3251, 2009.

Li, T., Guo, S., Liu, Z., Xiong, L., and Yin, J.: Bivariate design flood
quantile selection using copulas, Hydrol. Res., 48, 997–1013, 2017.

Liang, Z., Hu, Y., Huang, H., Wang, J., and Li, B.: Study on the estimation
of design value under non-stationary environment, South-to-North Water
Transfers, Water Sci. Technol., 14, 50–53, 2016 (in Chinese).

López, J. and Francés, F.: 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, 2013.

Loveridge, M., Rahman, A., and Hill, P.: Applicability of a physically based
soil water model (SWMOD) in design flood estimation in eastern Australia,
Hydrol. Res., 48, 1652–1665, 2017.

Milly, P., Betancourt, J., Falkenmark, M., Hirsch, R., Kundzewicz, Z.,
Lettenmaier, D., and Stouffer, R.: Climate change – Stationarity is dead:
Whither water management?, Science, 319, 573–574,
https://doi.org/10.1126/science.1151915, 2008.

Ministry of Water Resources of People's Republic of China: Design Criterion
of Reservoir Management, Chin. Water Resour. and Hydropower Press, Beijing,
1996 (in Chinese).

Niederreiter, H.: Quasi-Monte Carlo methods and pseudo-random numbers, B. Am.
Math. Soc., 197, 957–1041, 1978.

Obeysekera, J. and Salas, J.: Quantifying the uncertainty of design floods
under nonstationary conditions, J. Hydrol. Eng., 19, 1438–1446,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000931, 2014.

Obeysekera, J. and Salas, J.: Frequency of recurrent extremes under
nonstationarity, J. Hydrol. Eng., 21, 04016005,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001339, 2016.

Olsen, J. R., Lambert, J. H., and Haimes, Y. Y.: Risk of extreme events under
nonstationarity conditions, Risk Anal., 18, 497–510,
https://doi.org/10.1111/j.1539-6924.1998.tb00364.x, 1998.

Parey, S., Hoang, T. T. H., and Dacunha-Castelle, D.: Different ways to
compute temperature return levels in the climate change context,
Environmetrics, 21, 698–718, https://doi.org/10.1002/env.1060, 2010.

Qi, W. and Liu, J.: A non-stationary cost-benefit based bivariate extreme
flood estimation approach, J. Hydrol., 557, 589–599,
https://doi.org/10.1016/j.jhydrol.2017.12.045, 2017.

Quessy, J., Saïd, M., and Favre, A. C.: Multivariate Kendall's tau for
change-point detection in copulas, Can. J. Stat., 41, 65–82,
https://doi.org/10.1002/cjs.11150, 2013.

Read, L. K. and Vogel, R. M.: Reliability, return periods, and risk under
nonstationarity, Water Resour. Res., 51, 6381–6398,
https://doi.org/10.1002/2015WR017089, 2015.

Read, L. K. and Vogel, R. M.: Hazard function analysis for flood planning
under nonstationarity, Water Resour. Res., 52, 4116–4131,
https://doi.org/10.1002/2015WR018370, 2016.

Requena, A. I., Mediero, L., and Garrote, L.: A bivariate return period based
on copulas for hydrologic dam design: accounting for reservoir routing in
risk estimation, Hydrol. Earth Syst. Sci., 17, 3023–3038,
https://doi.org/10.5194/hess-17-3023-2013, 2013.

Rootzén, H. and Katz, R. W.: Design Life Level: Quantifying risk in a
changing climate, Water Resour. Res., 49, 5964–5972, https://doi.org/10.1002/wrcr.20425,
2013.

Rosner, A., Vogel, R. M., and Kirshen, P. H.: A risk-based approach to flood
management decisions in a nonstationary world, Water Resour. Res., 50,
1928–1942, https://doi.org/10.1002/2013WR014561, 2014.

Salas, J. D. and Obeysekera, J.: Revisiting the concepts of return period and
risk for nonstationary hydrologic extreme events, J. Hydrol. Eng., 19,
554–568, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820, 2014.

Salvadori, G. and De Michele, C.: Frequency analysis via copulas: theoretical
aspects and applications to hydrological events, Water Resour. Res., 40,
W12511, https://doi.org/10.1029/2004WR003133, 2004.

Salvadori, G. and De Michele, C.: Multivariate multiparameter extreme value
models and return periods: A copula approach, Water Resour. Res., 46, W10501,
https://doi.org/10.1029/2009WR009040, 2010.

Salvadori, G., De Michele, C., Kottegoda, N. T., and Rosso, R.: Extremes in
Nature: An Approach Using Copulas, Springer, Dordrecht, the Netherlands, 2007.

Salvadori, G., De Michele, C., and Durante, F.: On the return period and
design in a multivariate framework, Hydrol. Earth Syst. Sci., 15, 3293–3305,
https://doi.org/10.5194/hess-15-3293-2011, 2011.

Salvadori, G., Durante, F., and De Michele, C.: Multivariate return period
calculation via survival functions, Water Resour. Res., 49, 2308–2311,
https://doi.org/10.1002/wrcr.20204, 2013.

Salvadori, G., Durante, F., Tomasicchio, G. R., and D'Alessandro, F.:
Practical guidelines for the multivariate assessment of the structural risk
in coastal and off-shore engineering, Coastal Eng., 95, 77–83,
https://doi.org/10.1016/j.coastaleng.2014.09.007, 2015.

Salvadori, G., Durante, F., De Michele, C., Bernardi, M., and Petrella, L.: A
multivariate Copula-based framework for dealing with Hazard Scenarios and
Failure Probabilities, Water Resour. Res., 52, 3701–3721,
https://doi.org/10.1002/2015WR017225, 2016.

Salvadori, G., Durante, F., Michele, C. D., and Bernardi, M.: Hazard
assessment under multivariate distributional change-points: Guidelines and a
flood case study, Water, 10, 751–765, https://doi.org/10.3390/w10060751, 2018.

Sarhadi, A., Burn, D. H., Ausín, M. C., and Wiper, M. P.: Time-varying
nonstationary multivariate risk analysis using a dynamic Bayesian copula,
Water Resour. Res., 52, 2327–2349, https://doi.org/10.1002/2015WR018525, 2016.

Serinaldi, F.: Dismissing return periods!, Stoch. Env. Res. Risk. A., 29,
1179–1189, https://doi.org/10.1007/s00477-014-0916-1, 2015.

Serinaldi, F. and Kilsby, C. G.: Stationarity is undead: Uncertainty
dominates the distribution of extremes, Adv. Water Resour., 77, 17–36,
https://doi.org/10.1016/j.advwatres.2014.12.013, 2015.

Shafaei, M., Fakheri-Fard, A., Dinpashoh, Y., Mirabbasi, R., and De Michele,
C.: Modeling flood event characteristics using D-vine structures, Theor. Appl. Climatol., 130, 713–724, https://doi.org/10.1007/s00704-016-1911-x, 2017.

Sklar, M.: Fonctions de Répartition a n Dimensions et Leurs Marges, 8
pp., Univ. Paris, Paris, 1959.

Strupczewski, W. G., Singh, V. P., and Feluch, W.: Non-stationary approach
to at-site flood frequency modeling I. Maximum likelihood estimation, J.
Hydrol., 248, 123–142, https://doi.org/10.1016/S0022-1694(01)00397-3, 2001.

Vandenberghe, S., Verhoest, N. E. C., Onof, C., and De Baets, B.: A
comparative copula – based bivariate frequency analysis of observed and
simulated storm events: A case study on Bartlett – Lewis modeled rainfall,
Water Resour. Res., 47, W07529, https://doi.org/10.1029/2009WR008388, 2011.

Vezzoli, R., Salvadori, G., and De Michele, C.: A distributional
multivariate approach for assessing performance of climate-hydrology models,
Sci. Rep., 7, 12071, https://doi.org/10.1038/s41598-017-12343-1,
2017.

Villarini, G., Serinaldi, F., Smith, J. A., and Krajewski, W. F.: On the
stationarity of annual flood peaks in the Continental United States during
the 20th Century, Water Resour. Res., 45, W08417, https://doi.org/10.1029/2008WR007645, 2009.

Vogel, R. M.: Reliability indices for water supply systems, J. Water Res. Pl., 113, 563–579, https://doi.org/10.1061/(ASCE)0733-9496(1987)113:4(563), 1987.

Vogel, R. M., Yaindl, C., and Walter, M.: Nonstationarity: Flood
magnification and recurrence reduction factors in the United States, J. Am.
Water Resour. As., 47, 464–474, https://doi.org/10.1111/j.1752-1688.2011.00541.x, 2011.

Volpi, E. and Fiori, A.: Design event selection in bivariate hydrological
frequency analysis, Hydrolog. Sci. J., 57, 1506–1515, https://doi.org/10.1080/02626667.2012.726357, 2012.

Xiao, Y., Guo, S., Liu, P., Yan, B., and Chen, L.: Design flood hydrograph
based on multicharacteristic synthesis index method, J. Hydrol. Eng.,
14, 1359–1364, https://doi.org/10.1061/(ASCE)1084-0699(2009)4:12(1359),
2009.

Xiong, L. and Guo, S.: Trend test and change-point detection for the annual
discharge series of the Yangtze River at the Yichang hydrological station,
Hydrolog. Sci. J., 49, 99–112, https://doi.org/10.1623/hysj.49.1.99.53998,
2004.

Xiong, L., Jiang, C., Xu, C.-Y., Yu, K.-X., and Guo, S.: A framework of
changepoint detection for multivariate hydrological series, Water Resour.
Res., 51, 8198–8217, https://doi.org/10.1002/2015WR017677, 2015.

Xu, B., Xie, P., Tan, Y., Li, X., and Liu, Y.: Analysis of flood returning
to main channel influence on the flood control ability of Xijiang River, Journal of Hydroelectric Engineering, 33, 65–72, 2014 (in Chinese).

Yan, L., Xiong, L., Guo, S., Xu, C.-Y., Xia, J., and Du, T.: Comparison of
four nonstationary hydrologic design methods for changing environment, J.
Hydrol., 551, 132–150, https://doi.org/10.1016/j.jhydrol.2017.06.001, 2017.

Yang, T., Shao, Q., Hao, Z., Chen, Xi., Zhang, Z., Xu, C.-Y., and Sun, L.:
Regional frequency analysis and spatio-temporal pattern characterization of
rainfall extremes in the Pearl River Basin, China, J. Hydrol., 380,
386–405, https://doi.org/10.1016/j.jhydrol.2009.11.013, 2010.

Yin, J., Guo, S., Liu, Z., Chen, K., Chang, F., and Xiong, F.: Bivariate
seasonal design flood estimation based on copulas, J. Hydrol. Eng., 22,
05017028, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001594, 2017.

Zhang, L. and Singh, V. P.: Trivariate flood frequency analysis using the
Gumbel–Hougaard copula, J. Hydrol. Eng., 12, 431–439, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(431), 2007.

Zheng, F., Westra, S., and Sisson, S. A.: Quantifying the dependence between
extreme rainfall and storm surge in the coastal zone, J. Hydrol., 505,
172–187, https://doi.org/10.1016/j.jhydrol.2013.09.054, 2013.

Zheng, F., Westra, S., Leonard, M., and Sisson, S. A.: Modeling dependence
between extreme rainfall and storm surge to estimate coastal flooding risk,
Water Resour. Res., 50, 2050–2071, https://doi.org/10.1002/2013WR014616,
2014.

Zheng, F., Leonard, M., and Westra, S.: Efficient joint probability analysis
of flood risk, J. Hydroinform., 17, 584–597, 2015.

Zheng, F., Leonard, M., and Westra, S.: Application of the design variable
method to estimate coastal flood risk, J. Flood Risk Manag., 10,
522–534, https://doi.org/10.1111/jfr3.12180, 2017.

Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., Chen, O., Assumpção, T. H., Yang, P., Heidari, B.,
Rickermann, J., Minsker, B., Bi, W., Cai, X.,
Solomatine, D., and Popescu, I.:
Crowdsourcing methods for data collection in geophysics: State of the art,
issues, and future directions, Rev. Geophys., 56, 698–740, https://doi.org/10.1029/2018RG000616,
2018.