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Research Article

The legacy of STAHY: Milestones, achievements, challenges, and open problems in statistical hydrology

ORCID Icon, , , , , , ORCID Icon, , , , , , ORCID Icon, ORCID Icon, , , , , , , , , , ORCID Icon, , , , ORCID Icon, , & show all
Received 19 Sep 2023, Accepted 01 Jul 2024, Accepted author version posted online: 30 Jul 2024
Accepted author version

References

  • Adarsh, S. Nourani, V. Archana, D. S. & Dharan, D. S. (2020). Multifractal description of daily rainfall fields over India. Journal of Hydrology, 586, 124913.
  • Addor, N. Nearing, G. Prieto, C. Newman, A. J. Le Vine, N. & Clark, M. P. (2018). A ranking of hydrological signatures based on their predictability in space. Water Resources Research, 54(11), 8792-8812.
  • AghaKouchak, A. Bárdossy, A. & Habib, E. (2010). Copula‐based uncertainty modelling: application to multisensor precipitation estimates. Hydrological Processes, 24(15), 2111-2124.
  • AghaKouchak, A. Chiang, F. Huning, L. S. Love, C. A. Mallakpour, I. Mazdiyasni, O. … & Sadegh, M. (2020). Climate extremes and compound hazards in a warming world. Annual Review of Earth and Planetary Sciences, 48, 519-548.
  • AghaKouchak, A. Huning, L. S. Chiang, F. Sadegh, M. Vahedifard, F. Mazdiyasni, O. … & Mallakpour, I. (2018). How do natural hazards cascade to cause disasters?. Nature 561, 458-460.
  • Aksoy, H. & Bayazit, M. (2000). A model for daily flows of intermittent streams. Hydrological Processes, 14(10), 1725-1744.
  • Alexander, L. V. Zhang, X. Peterson, T. C. Caesar, J. Gleason, B. Klein Tank, A. M. G. … & Vazquez‐Aguirre, J. L. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5).
  • Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of climatology, 6(6), 661-675.
  • Alila, Y. (1999). A hierarchical approach for the regionalization of precipitation annual maxima in Canada. Journal of Geophysical Research: Atmospheres, 104(D24), 31645-31655.
  • Allamano, P. Croci, A. & Laio, F. (2015). Toward the camera rain gauge. Water Resources Research, 51(3), 1744-1757.
  • Allamano, P. Laio, F. & Claps, P. (2011). Interactive comment on “Effects of seasonality on the distribution of hydrological extremes” by P. Allamano et al. Hydrol. Earth Syst. Sci. Discuss, 8, C2841-C2851.
  • Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175-185.
  • Archfield, S. A. & Vogel, R. M. (2010). Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments. Water resources research, 46(10).
  • Ashkar, F. & Aucoin, F. (2011). A broader look at bivariate distributions applicable in hydrology. Journal of Hydrology, 405(3-4), 451-461. Ball, J. Babister, M. Retallick, M. Ling, F. & Thyer, M. A. (2019). Fundamental issues. Commonwealth of Australia (Geoscience Australia).
  • Banfi, F. Cazzaniga, G. & De Michele, C. (2022). Nonparametric extrapolation of extreme quantiles: a comparison study. Stochastic Environmental Research and Risk Assessment, 36(6), 1579-1596.
  • Baratti, E. Montanari, A. Castellarin, A. Salinas, J. L. Viglione, A. & Bezzi, A. (2012). Estimating the flood frequency distribution at seasonal and annual time scales. Hydrology and Earth System Sciences, 16(12), 4651-4660.
  • Barber, C. Lamontagne, J. R. & Vogel, R. M. (2020). Improved estimators of correlation and R2 for skewed hydrologic data. Hydrological Sciences Journal, 65(1), 87-101.
  • Bárdossy, A. & Li, J. (2008). Geostatistical interpolation using copulas. Water resources research, 44(7).
  • Bárdossy, A. & Pegram, G. (2013). Interpolation of precipitation under topographic influence at different time scales. Water Resources Research, 49(8), 4545-4565.
  • Bárdossy, A. & Plate, E. J. (1992). Space‐time model for daily rainfall using atmospheric circulation patterns. Water resources research, 28(5), 1247-1259.
  • Bárdossy, A. Seidel, J. & El Hachem, A. (2021). The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrology and Earth System Sciences, 25(2), 583-601.
  • Bárdossy, A. (2023). Changing correlations: a flexible definition of non-Gaussian multivariate dependence. Stochastic Environmental Research and Risk Assessment, 1-11.
  • Basseville, M. & Nikiforov, I. V. (1993). Detection of abrupt changes: theory and application (Vol. 104). Englewood Cliffs: prentice Hall.
  • Bassiouni, M. Vogel, R. M. & Archfield, S. A. (2016). Panel regressions to estimate low‐flow response to rainfall variability in ungaged basins. Water Resources Research, 52(12), 9470-9494.
  • Bayazit, M. & Önöz, B. J. H. S. J. (2007). To prewhiten or not to prewhiten in trend analysis?. Hydrological Sciences Journal, 52(4), 611-624.
  • Ben Aissia, M.A. Chebana, F. & Ouarda, T. B. (2017). Multivariate missing data in hydrology–Review and applications. Advances in Water Resources, 110, 299-309.
  • Benito, G. Lang, M. Barriendos, M. Llasat, M. C. Francés, F. Ouarda, T. … & Bobée, B. (2004). Use of systematic, palaeoflood and historical data for the improvement of flood risk estimation. Review of scientific methods. Natural hazards, 31, 623-643.
  • Benson, M.A. (1950). Use of Historical Data in Flood Frequency Analysis. Eos, Trans. AGU, v. 31(3), p. 419-424.
  • Benson, M. A. & Matalas, N. C. (1967). Synthetic hydrology based on regional statistical parameters. Water Resources Research, 3(4), 931-935.
  • Beran, J. (2017). Statistics for long-memory processes. Routledge.
  • Berezowski, T. Szcześniak, M. Kardel, I. Michałowski, R. Okruszko, T. Mezghani, A. & Piniewski, M. (2016). CPLFD-GDPT5: High-resolution gridded daily precipitation and temperature data set for two largest Polish river basins. Earth System Science Data, 8(1), 127-139.
  • Berg, D. (2009). Copula goodness-of-fit testing: an overview and power comparison. The European Journal of Finance, 15(7-8), 675-701.
  • Berndt, C. & Haberlandt, U. (2018). Spatial interpolation of climate variables in Northern Germany—Influence of temporal resolution and network density. Journal of Hydrology: Regional Studies, 15, 184-202.
  • Berndt, C. Rabiei, E. & Haberlandt, U. (2014). Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. Journal of Hydrology, 508, 88-101.
  • Berne, A. & Krajewski, W. F. (2013). Radar for hydrology: Unfulfilled promise or unrecognized potential?. Advances in Water Resources, 51, 357-366.
  • Bertola, M. Viglione, A. & Blöschl, G. (2019). Informed attribution of flood changes to decadal variation of atmospheric, catchment and river drivers in Upper Austria. Journal of Hydrology, 577, 123919.
  • Bertola, M. Viglione, A. Lun, D. Hall, J. & Blöschl, G. (2020). Flood trends in Europe: are changes in small and big floods different?. Hydrology and Earth System Sciences, 24(4), 1805-1822.
  • Bertola, M. Viglione, A. Vorogushyn, S. Lun, D. Merz, B. & Blöschl, G. (2021). Do small and large floods have the same drivers of change? A regional attribution analysis in Europe. Hydrology and Earth System Sciences, 25(3), 1347-1364.
  • Blöschl, G. Bierkens, M. F. Chambel, A. Cudennec, C. Destouni, G. Fiori, A. … & Renner, M. (2019). Twenty-three unsolved problems in hydrology (UPH)–a community perspective. Hydrological Sciences Journal, 64(10), 1141-1158.
  • Blöschl, G. Hall, J. Parajka, J. Perdigão, R. A. Merz, B. Arheimer, B. … & Živković, N. (2017). Changing climate shifts timing of European floods. Science, 357(6351), 588-590.
  • Blöschl, G. Hall, J. Viglione, A. Perdigão, R. A. Parajka, J. Merz, B. … & Živković, N. (2019). Changing climate both increases and decreases European river floods. Nature, 573(7772), 108-111.
  • Blöschl, G. Kiss, A. Viglione, A. Barriendos, M. Böhm, O. Brázdil, R. … & Wetter, O. (2020). Current European flood-rich period exceptional compared with past 500 years. Nature, 583(7817), 560-566.
  • Blöschl, G. Sivapalan, M. Wagener, T. Savenije, H. & Viglione, A. (Eds.) (2013). Runoff prediction in ungauged basins: synthesis across processes, places and scales. Cambridge University Press.
  • Blum, A. G. Ferraro, P. J. Archfield, S. A. & Ryberg, K. R. (2020). Causal effect of impervious cover on annual flood magnitude for the United States. Geophysical Research Letters, 47(5), no-no.
  • Botero, B. & Francés, F. (2010). Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models. Hydrol. Earth Syst. Sci., 14, 2617-2628.
  • Box, G. E. & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco. Calif: Holden-Day.
  • Box, G. E. Jenkins, G. M. Reinsel, G. C. & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
  • Brahimi, B. & Necir, A. (2012). A semiparametric estimation of copula models based on the method of moments. Statistical Methodology, 9(4), 467-477.
  • Brahimi, B. Chebana, F. & Necir, A. (2015). Copula representation of bivariate L-moments: a new estimation method for multiparameter two-dimensional copula models. Statistics, 49(3), 497-521.
  • Brath, A. Castellarin, A. & Montanari, A. (2003). Assessing the reliability of regional depth‐duration‐frequency equations for gaged and ungaged sites. Water Resources Research, 39(12).
  • Brown, C. Meeks, R. Hunu, K. & Yu, W. (2011). Hydroclimate risk to economic growth in sub-Saharan Africa. Climatic Change, 106(4), 621-647.
  • Burn, D. H. (1990). An appraisal of the “region of influence” approach to flood frequency analysis. Hydrological Sciences Journal, 35(2), 149-165.
  • Burn, D. H. (2014). A framework for regional estimation of intensity–duration–frequency (IDF) curves. Hydrological Processes, 28(14), 4209-4218.
  • Burn, D. H. Hannaford, J. Hodgkins, G. A. Whitfield, P. H. Thorne, R. & Marsh, T. (2012). Reference hydrologic networks II. Using reference hydrologic networks to assess climate-driven changes in streamflow. Hydrological Sciences Journal, 57(8), 1580-1593.
  • Burton, A. Kilsby, C. G. Fowler, H. J. Cowpertwait, P. S. P. & O’connell, P. E. (2008). RainSim: A spatial–temporal stochastic rainfall modelling system. Environmental Modelling & Software, 23(12), 1356-1369.
  • Cabilio, P. Zhang, Y. & Chen, X. (2013). Bootstrap rank tests for trend in time series. Environmetrics, 24(8), 537-549.
  • Calenda, G. Mancini, C. P. & Volpi, E. (2009). Selection of the probabilistic model of extreme floods: The case of the River Tiber in Rome. Journal of Hydrology, 371(1-4), 1-11.
  • Cameron, D.S.; Beven, K.J.; Tawn, J.; Blazkova, S.; Naden, P. (1999). Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty). Journal of Hydrology, 219, 169–187,
  • Camuffo, D. della Valle, A. & Becherini, F. (2020). A critical analysis of the definitions of climate and hydrological extreme events. Quaternary International, 538, 5-13.
  • Cancelliere, A. Salas, J.D. (2010) Drought probabilities and return period for annual streamflows series, Journal of Hydrology, 391 (1-2), pp. 77-89.
  • Capéraà, P. A.-L. Fougères, and C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas. Biometrika, 1997. 84(3): p. 567-577.
  • Carsteanu, A.A, & Langousis, A. (2020). Break of temporal symmetry in a stationary Markovian setting: evidencing an arrow of time, and parameterizing linear dependencies using fractional low-order joint moments. Stoch. Environ. Res. Risk Assess. 34, 1–6.
  • Castellarin, A. (2007). Probabilistic envelope curves for design flood estimation at ungauged sites. Water Resources Research, 43(4).
  • Castellarin, A. (2014). Regional prediction of flow-duration curves using a three-dimensional kriging. Journal of Hydrology, 513, 179-191.
  • Castellarin, A. Burn, D. H. & Brath, A. (2008). Homogeneity testing: How homogeneous do heterogeneous cross-correlated regions seem?. Journal of Hydrology, 360(1-4), 67-76.
  • Castellarin, A. Persiano, S. Pugliese, A. Aloe, A. Skøien, J. O. & Pistocchi, A. (2018). Prediction of streamflow regimes over large geographical areas: interpolated flow–duration curves for the Danube region. Hydrological Sciences Journal, 63(6), 845-861.
  • Castiglioni, S. Castellarin, A. Montanari, A. Skøien, J. O. Laaha, G. & Blöschl, G. (2011). Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging. Hydrology and Earth System Sciences, 15(3), 715-727.
  • Ceola, S. Pugliese, A. Ventura, M. Galeati, G. Montanari, A. & Castellarin, A. (2018). Hydro-power production and fish habitat suitability: Assessing impact and effectiveness of ecological flows at regional scale. Advances in Water Resources, 116, 29-39.
  • Chandler, R. E. & Wheater, H. S. (2002). Analysis of rainfall variability using generalized linear models: a case study from the west of Ireland. Water Resources Research, 38(10), 10-1.
  • Charles, S. P. Bates, B. C. & Hughes, J. P. (1999). A spatiotemporal model for downscaling precipitation occurrence and amounts. Journal of Geophysical Research: Atmospheres, 104(D24), 31657-31669.
  • Chebana, F. (2022). Multivariate Frequency Analysis of Hydro-Meteorological Variables: A Copula-Based Approach. Elsevier.
  • Chebana, F. & Ouarda, T. B. (2021). Multivariate non-stationary hydrological frequency analysis. Journal of Hydrology, 593, 125907.
  • Chebana, F. & Ouarda, T.B.M.J. (2011b). Multivariate quantiles in hydrological frequency analysis. Environmetrics, 22(1), 63-78.
  • Chebana, F. & T.B.M.J. Ouarda (2011a), Depth-based multivariate descriptive statistics with hydrological applications, Journal Geophysical Research, 116(D10), D10120.
  • Chebana, F. Ben Aissia, M.A. & Ouarda, T.B.M.J. (2017). Multivariate shift testing for hydrological variables, review, comparison and application. Journal of Hydrology, 548, 88-103
  • Chebana, F. T.B.M.J. Ouarda, and T.C. Duong, (2013) Testing for multivariate trends in hydrologic frequency analysis. Journal of Hydrology, 486: p. 519-530.
  • Chen, J. Brissette, F. P. & Leconte, R. (2012). WeaGETS–a Matlab-based daily scale weather generator for generating precipitation and temperature. Procedia Environmental Sciences, 13, 2222-2235.
  • Chen, L. Qiu, H. Zhang, J. Singh, V. P. Zhou, J. & Huang, K. (2019). Copula-based method for stochastic daily streamflow simulation considering lag-2 autocorrelation. Journal of Hydrology, 578, 123938.
  • Chiang, F. Mazdiyasni, O. & AghaKouchak, A. (2021). Evidence of anthropogenic impacts on global drought frequency, duration, and intensity. Nature communications, 12(1), 1-10.
  • Chiang, F. Greve, P. Mazdiyasni, O. et al. (2021). A multivariate conditional probability ratio framework for the detection and attribution of compound climate extremes. Geophysical Research Letters, 48(15), p.e2021GL094361.
  • Chiang, F. Greve, P. Mazdiyasni, O. et al. (2022). Intensified likelihood of concurrent warm and dry months attributed to anthropogenic climate change. Water Resources Research, 58(6), p.e2021WR030411.
  • Chow, V. T. Maidment, D. R. & Mays, L. W. (1988). Applied hydrology. New York, NY: McGraw-Hill
  • Cipollini, S. Fiori, A. & Volpi, E. (2021). Structure-based framework for the design and risk assessment of hydraulic structures, with application to offline flood detention basins. Journal of Hydrology, 600, 126527.
  • Claps, P. Giordano, A. & Laio, F. (2005). Advances in shot noise modeling of daily streamflows. Advances in water resources, 28(9), 992-1000.
  • Clark, M. Gangopadhyay, S. Hay, L. Rajagopalan, B. & Wilby, R. (2004). The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5(1), 243-262.
  • Clark, M. P. Vogel, R. M. Lamontagne, J. R. Mizukami, N. Knoben, W. J. Tang, G. … & Papalexiou, S. M. (2021). The abuse of popular performance metrics in hydrologic modeling. Water Resources Research, 57(9), e2020WR029001.
  • Cohn, T.A. J.R. Stedinger, (1987). Use of Historical Information in a Maximum Likelihood Framework. Journal of Hydrol. v. 96, p. 215-233.
  • Cohn, T. A. & Lins, H. F. (2005). Nature’s style: Naturally trendy. Geophysical research letters, 32(23).
  • Coles, S. (2001). Classical extreme value theory and models. In An introduction to statistical modeling of extreme values (pp. 45-73). Springer, London.
  • Corbella, S. & Stretch, D. D. (2013). Simulating a multivariate sea storm using Archimedean copulas. Coastal Engineering, 76, 68-78.
  • Costa, J.E. (1978). Holocene Stratigraphy in Flood Frequency Analysis. Water Resour. Res. v. 14(4), p. 626-632.
  • Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.
  • Cowpertwait, P. Isham, V. & Onof, C. (2007). Point process models of rainfall: developments for fine-scale structure. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463(2086), 2569-2587.
  • Cox, D. R. & Isham, V. (1980). Point processes (Vol. 12). CRC Press.
  • Croissant, Y. & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of statistical software, 27(2), 1-43.
  • Cunderlik, J. M. & Burn, D. H. (2003). Non-stationary pooled flood frequency analysis. Journal of Hydrology, 276(1-4), 210-223.
  • Dahlstedt, K. & Jensen, H. J. (2005). Fluctuation spectrum and size scaling of river flow and level. Physica A: Statistical Mechanics and its Applications, 348, 596-610.
  • Dalrymple, T. (1960). Flood-frequency analyses (No. 1543). US Government Printing Office.
  • de Lavenne, A. Skøien, J. O. Cudennec, C. Curie, F. & Moatar, F. (2016). Transferring measured discharge time series: Large‐scale comparison of Top‐kriging to geomorphology‐based inverse modeling. Water Resources Research, 52(7), 5555-5576.
  • de Vos, L. W. Overeem, A. Leijnse, H. & Uijlenhoet, R. (2019). Rainfall estimation accuracy of a nationwide instantaneously sampling commercial microwave link network: Error dependency on known characteristics. Journal of atmospheric and oceanic technology, 36(7), 1267-1283.
  • Debele, S. E. Strupczewski, W. G. & Bogdanowicz, E. (2017). A comparison of three approaches to non-stationary flood frequency analysis. Acta Geophysica, 65, 863-883.
  • DeGaetano, A. T. & Castellano, C. M. (2017). Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Climate Services, 5, 23-35.
  • Dehling, H. Rooch, A. & Taqqu, M. S. (2013). Non‐parametric change‐point tests for long‐range dependent data. Scandinavian Journal of Statistics, 40(1), 153-173.
  • Dehling, H. Vogel, D. Wendler, M. & Wied, D. (2017). Testing for changes in Kendall’s tau. Econometric Theory, 33(6), 1352-1386.
  • Deidda, R. (2000). Rainfall downscaling in a space‐time multifractal framework. Water Resources Research, 36(7), 1779-1794.
  • Deidda, R. (2010). A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series. Hydrology and Earth System Sciences, 14(12), 2559-2575.
  • Deidda, R. Hellies, M. & Langousis, A. (2021). A critical analysis of the shortcomings in spatial frequency analysis of rainfall extremes based on homogeneous regions and a comparison with a hierarchical boundaryless approach. Stochastic Environmental Research and Risk Assessment, 35(12), 2605-2628.
  • Desai, S. & Ouarda, T. B. (2021). Regional hydrological frequency analysis at ungauged sites with random forest regression. Journal of Hydrology, 594, 125861.
  • Di Baldassarre, G. Castellarin, A. & Brath, A. (2006). Relationships between statistics of rainfall extremes and mean annual precipitation: an application for design-storm estimation in northern central Italy. Hydrology and Earth System Sciences, 10(4), 589-601.
  • Di Baldassarre, G. Laio, F. & Montanari, A. (2009). Design flood estimation using model selection criteria. Physics and Chemistry of the Earth, Parts A/B/C, 34(10-12), 606-611.
  • Douglas, E. M. Vogel, R. M. & Kroll, C. N. (2000). Trends in floods and low flows in the United States: impact of spatial correlation. Journal of hydrology, 240(1-2), 90-105.
  • Dung, N. V. Merz, B. Bárdossy, A. & Apel, H. (2015). Handling uncertainty in bivariate quantile estimation–An application to flood hazard analysis in the Mekong Delta. Journal of Hydrology, 527, 704-717.
  • Durocher, M. Chebana, F. & Ouarda, T. B. (2016). On the prediction of extreme flood quantiles at ungauged locations with spatial copula. Journal of Hydrology, 533, 523-532.
  • Eagleson, P.S. (1972). Dynamics Flood Frequency. Water Resources Research, 8, 878–898.
  • Easterling, D.R. Kunkel, K.E. Wehner, M.F. and Sun, L. (2016). Detection and attribution of climate extremes in the observed record. Weather and Climate Extremes, 11, pp.17-27.
  • Ekström, M. Fowler, H. J. Kilsby, C. G. & Jones, P. D. (2005). New estimates of future changes in extreme rainfall across the UK using regional climate model integrations. 2. Future estimates and use in impact studies. Journal of Hydrology, 300(1-4), 234-251.
  • El Adlouni, S. & Ouarda, T. B. M. J. (2010). Frequency analysis of extreme rainfall events. Rainfall: State of the science, 191, 171-188.
  • El Adlouni, S. Ouarda, T. B. Zhang, X. Roy, R. & Bobée, B. (2007). Generalized maximum likelihood estimators for the nonstationary generalized extreme value model. Water Resources Research, 43(3).
  • El Adlouni, S. Bobée, B. & Ouarda, T. B. (2008). On the tails of extreme event distributions in hydrology. Journal of hydrology, 355(1-4), 16-33.
  • Emmanouil, S. Langousis, A. Nikolopoulos, E. I. & Anagnostou, E. N. (2020). Quantitative assessment of annual maxima, peaks-over-threshold and multifractal parametric approaches in estimating intensity-duration-frequency curves from short rainfall records. Journal of Hydrology, 589, 125151.
  • Emmanouil, S. Langousis, A. Nikolopoulos, E.I. & Anagnostou, E.N. (2022). The spatiotemporal evolution of rainfall extremes in a changing climate: A CONUS-wide assessment based on multifractal scaling arguments. Earth’s Future, 10, e2021EF002539.
  • Emmanouil, S. Langousis, A. Nikolopoulos, E.I. & Anagnostou, E.N. (2023). Exploring the future of rainfall extremes over CONUS: The effects of high emission climate change trajectories on the intensity and frequency of rare precipitation events. Earth’s Future, 11, e2022EF003039.
  • Farmer, W. H. (2016). Ordinary kriging as a tool to estimate historical daily streamflow records. Hydrology and Earth System Sciences, 20(7), 2721-2735.
  • Farmer, W. H. Over, T. M. & Vogel, R. M. (2015). Multiple regression and inverse moments improve the characterization of the spatial scaling behavior of daily streamflows in the Southeast United States. Water Resources Research, 51(3), 1775-1796.
  • Farmer, W. H. & Vogel, R. M. (2016). On the deterministic and stochastic use of hydrologic models. Water Resources Research, 52(7), 5619-5633.
  • Farris, S. Deidda, R. Viola, F. & Mascaro, G. (2021). On the role ofserial correlation and field significance in detecting changes in extreme precipitation frequency. Water Resources Research, 57, e2021WR030172.
  • Faulkner, D. Warren, S. Spencer, P. & Sharkey, P. (2020). Can we still predict the future from the past? Implementing non‐stationary flood frequency analysis in the UK. Journal of Flood Risk Management, 13(1), e12582.
  • Favre, A. C. El Adlouni, S. Perreault, L. Thiémonge, N. & Bobée, B. (2004). Multivariate hydrological frequency analysis using copulas. Water resources research, 40(1).
  • Feng, Y. Shi, P. Qu, S. Mou, S. Chen, C. & Dong, F. (2020). Nonstationary flood coincidence risk analysis using time-varying copula functions. Scientific reports, 10(1), 1-12.
  • Fernandez, B. & Salas, J. D. (1986). Periodic gamma autoregressive processes for operational hydrology. Water Resources Research, 22(10), 1385-1396.
  • Fernández, B. & Salas, J. D. (1999). Return period and risk of hydrologic events. I: Mathematical formulation. Journal of Hydrological Engineering, 4(4), 297-307.
  • Fernandez, W. Vogel, R. M. & Sankarasubramanian, A. (2000). Regional calibration of a watershed model. Hydrological sciences journal, 45(5), 689-707.
  • Fischer, E.M. & Knutti, R. (2016). Observed heavy precipitation increase confirms theory and early models. Nature Climate Change, 6(11), 986-991.
  • Fischer, S. (2018). A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons. Journal of Applied Statistics, 45(15), 2831-2847.
  • Fischer, S. Lun, D. Schumann, A. H. & Blöschl, G. (2023). Detecting flood-type-specific flood-rich and flood-poor periods in peaks-over-threshold series with application to Bavaria (Germany). Stoch Environ Res Risk Assess 37, 1395–1413.
  • Fischer, S. & Schumann, A. H. (2021). Regionalisation of flood frequencies based on flood type-specific mixture distributions. Journal of Hydrology X, 13, 100107.
  • Fischer, S. Schumann, A. & Bühler, P. (2019). Timescale-based flood typing to estimate temporal changes in flood frequencies. Hydrological Sciences Journal, 64(15), 1867-1892.
  • Fitzner, D. & Sester, M. (2015). Estimation of precipitation fields from 1-minute rain gauge time series–comparison of spatial and spatio-temporal interpolation methods. International Journal of Geographical Information Science, 29(9), 1668-1693.
  • Flores, C. (2004, June). Multiplicative cascade models for rain in hydro-meteorological disasters risk management. In ASTIN-Kolloquium, Bergen, Norway (pp. 6-9).
  • Frahm, G. (2006). On the extremal dependence coefficient of multivariate distributions. Statistics & Probability Letters, 76(14): p. 1470-1481.
  • Francés, F. Salas, J. D. & Boes, D. (1994). Flood Frequency Analysis with Systematic and Historical or Paleoflood Data based on the Two-Parameter GEV Models. Water Resources Research, 30(6), 1653-1664.
  • Fu, G. Chiew, F. H. & Shi, X. (2018). Generation of multi-site stochastic daily rainfall with four weather generators: a case study of Gloucester catchment in Australia. Theoretical and Applied Climatology, 134(3-4), 1027-1046.
  • Gaume, E. Mouhous, N. & Andrieu, H. (2007). Rainfall stochastic disaggregation models: Calibration and validation of a multiplicative cascade model. Advances in Water Resources, 30(5), 1301-1319.
  • Gedikli, A. Aksoy, H. & Unal, N. E. (2010). AUG-Segmenter: a user-friendly tool for segmentation of long time series. Journal of Hydroinformatics, 12(3), 318-328.
  • Genest, C. & Nešlehová J. (2012b). Copulas and Copula Models. In: Encyclopedia of Environmetrics, 2nd Edition, Wiley, Chichester, 2, pp. 541-553.
  • Genest, C. & Nešlehová, J. (2012a). Copula modeling for extremes. Encyclopedia of Environmetrics, 2nd Edition, Wiley, Chichester, 2012, 2, p. 530-541.
  • Genest, C. & Chebana, F. (2017). Copula modeling in hydrologic frequency analysis. Chapter 30. Handbook of applied hydrology. McGraw-Hill Education, New York, 30-1.
  • Genest, C. & Favre, A. C. (2007). Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 12(4), 347-368.
  • Genest, C. & Rémillard, B. (2008). Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. In Annales de l’IHP Probabilités et statistiques (Vol. 44, No. 6, pp. 1096-1127).
  • Genest, C. Favre, A. C. Béliveau, J. & Jacques, C. (2007). Metaelliptical copulas and their use in frequency analysis of multivariate hydrological data. Water Resources Research, 43(9).
  • Genest, C. Rémillard, B. & Beaudoin, D. (2009). Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and economics, 44(2), 199-213.
  • Gerstenberger, C. Vogel, D. & Wendler, M. (2020). Tests for scale changes based on pairwise differences. Journal of the American Statistical Association, 115(531), 1336-1348.
  • Gilleland, E. & Katz, R.W. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1-39
  • Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of hydrology, 228(1-2), 113-129.
  • Goudenhoofdt, E. & Delobbe, L. (2009). Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrology and Earth System Sciences, 13(2), 195-203.
  • Gräler, B. Van Den Berg, M. J. Vandenberghe, S. Petroselli, A. Grimaldi, S. De Baets, B. & Verhoest, N. E. C. (2013). Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrology and Earth System Sciences, 17(4), 1281-1296.
  • Grassberger, P. (1983). On the critical behavior of the general epidemic process and dynamical percolation. Mathematical Biosciences, 63(2), 157-172.
  • Graves, T. Gramacy, R. Watkins, N. & Franzke, C. (2017). A brief history of long memory: Hurst, Mandelbrot and the road to ARFIMA, 1951–1980. Entropy, 19(9), 437.
  • Griffis, V. W. & Stedinger, J. R. (2007). Log-Pearson type 3 distribution and its application in flood frequency analysis. I: Distribution characteristics. Journal of Hydrologic Engineering, 12(5), 482-491.
  • Grimaldi, S. & Serinaldi, F. (2006a). Design hyetograph analysis with 3-copula function. Hydrological Sciences Journal, 51(2), 223-238.
  • Grimaldi, S. & Serinaldi, F. (2006b). Asymmetric copula in multivariate flood frequency analysis. Advances in Water Resources, 29(8), 1155-1167.
  • Grimaldi, S. Volpi, E. Langousis, A. Papalexiou, S.M. De Luca, D.L. Piscopia, R. Nerantzaki, S.D. Papacharalampous, G. & Petroselli, A. (2022) Continuous hydrologic modelling for small and ungauged basins: A comparison of eight rainfall models for sub-daily runoff simulations. J. Hydrol. 610.
  • Grønneberg, S. & Hjort, N. L. (2014). The copula information criteria. Scandinavian Journal of Statistics, 41(2), 436-459.
  • Gruber, A. M. & Stedinger, J. R. (2008). Models of LP3 regional skew, data selection, and Bayesian GLS regression. In World Environmental and Water Resources Congress 2008: Ahupua’A (pp. 1-10).
  • Grundmann, J. Hörning, S. & Bárdossy, A. (2019). Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling. Hydrology and Earth System Sciences, 23(1), 225-237.
  • Gumbel, E.J. (1958). Statistics of Extremes, Columbia University Press, New York.
  • Guo, Y. Zhang, Y. Zhang, L. & Wang, Z. (2021). Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review. Wiley Interdisciplinary Reviews: Water, 8(1), e1487.
  • Haan, C. T. Allen, D. M. & Street, J. O. (1976). A Markov chain model of daily rainfall. Water Resources Research, 12(3), 443-449.
  • Haberlandt, U. (2007). Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event. Journal of Hydrology, 332(1-2), 144-157.
  • Haberlandt, U. & Sester, M. (2010). Areal rainfall estimation using moving cars as rain gauges–a modelling study. Hydrology and Earth System Sciences, 14(7), 1139-1151.
  • Haese, B. Hörning, S. Chwala, C. Bárdossy, A. Schalge, B. & Kunstmann, H. (2017). Stochastic reconstruction and interpolation of precipitation fields using combined information of commercial microwave links and rain gauges. Water Resources Research, 53(12), 10740-10756.
  • Hald, A. (2005). A history of probability and statistics and their applications before 1750. John Wiley & Sons.
  • Han, J. C. Zhou, Y. Huang, Y. Wu, X. Liu, Z. & Wang, Y. (2020). Risk assessment through multivariate analysis on the magnitude and occurrence date of daily storm events in the Shenzhen bay area. Stochastic Environmental Research and Risk Assessment, 34(5), 669-689.
  • Hao, Z. & AghaKouchak, A. (2014). A nonparametric multivariate multi-index drought monitoring framework. Journal of Hydrometeorology, 15(1), 89-101.
  • Hao, Z. & Singh, V. P. (2013). Modeling multisite streamflow dependence with maximum entropy copula. Water Resources Research, 49(10), 7139-7143.
  • Hao, Z. & Singh, V. P. (2016). Review of dependence modeling in hydrology and water resources. Progress in Physical Geography, 40(4), 549-578.
  • Hayhoe, H. N. (2000). Improvements of stochastic weather data generators for diverse climates. Climate Research, 14(2), 75-87.
  • Hecht, J. S. & Vogel, R. M. (2020). Updating urban design floods for changes in central tendency and variability using regression. Advances in Water Resources, 136, 103484.
  • Helsel, D. R. & Hirsch, R. M. (1992). Statistical methods in water resources. Elsevier.
  • Helsel, D. R. Hirsch, R. M. Ryberg, K. R. Archfield, S. A. & Gilroy, E. J. (2020). Statistical methods in water resources: US Geological Survey Techniques and Methods. book, 4, 458.
  • Hirsch, R. M. (1979). Synthetic hydrology and water supply reliability. Water Resources Research, 15(6), 1603-1615.
  • Hirsch, R. M. (1982). A comparison of four streamflow record extension techniques. Water Resources Research, 18(4), 1081-1088.
  • Hirschboeck, K. K. (1987). Hydroclimatically-defined mixed distributions in partial duration flood series. In Hydrologic frequency modeling (pp. 199-212). Springer, Dordrecht.
  • Hodgkins, G. A. Whitfield, P. H. Burn, D. H. Hannaford, J. Renard, B. Stahl, K. … & Wilson, D. (2017). Climate-driven variability in the occurrence of major floods across North America and Europe. Journal of Hydrology, 552, 704-717.
  • Hofert, M. Mächler, M. & Mcneil, A. J. (2012). Likelihood inference for Archimedean copulas in high dimensions under known margins. Journal of Multivariate Analysis, 110, 133-150.
  • Hörning, S. & Bárdossy, A. (2018). Phase annealing for the conditional simulation of spatial random fields. Computers & Geosciences, 112, 101-111.
  • Hosking, J. R. (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society Series B: Statistical Methodology, 52(1), 105-124.
  • Hosking, J. R. & Wallis, J. R. (1987). Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, 29(3), 339-349.
  • Hosking, J. R. M. & Wallis, J. R. (1997). Regional frequency analysis (p. 240).
  • Hrachowitz, M. Savenije, H. H. G. Blöschl, G. McDonnell, J. J. Sivapalan, M. Pomeroy, J. W. … & Cudennec, C. (2013). A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrological sciences journal, 58(6), 1198-1255.
  • Huang, Y. Schmitt, F. G. Lu, Z. & Liu, Y. (2009). Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis. Journal of Hydrology, 373(1-2), 103-111.
  • Hubert, P. (2000). The segmentation procedure as a tool for discrete modeling of hydrometeorological regimes. Stochastic Environmental Research and Risk Assessment, 14, 297-304.
  • Hughes, J. P. & Guttorp, P. (1994). Incorporating spatial dependence and atmospheric data in a model of precipitation. Journal of Applied Meteorology and Climatology, 33(12), 1503-1515.
  • Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American society of civil engineers, 116(1), 770-799.
  • Hutchinson, M. F. (1998). Interpolation of rainfall data with thin plate smoothing splines. Part I: Two dimensional smoothing of data with short range correlation. Journal of Geographic Information and Decision Analysis, 2(2), 139-151.
  • Hutchinson, M. F. (1998). Interpolation of rainfall data with thin plate smoothing splines: II. Analysis of topographic dependence. Journal of Geographic Information and Decision Analysis, 2(2), 168-185.
  • Iliopoulou, T. & Koutsoyiannis, D. (2020). Projecting the future of rainfall extremes: Better classic than trendy. Journal of Hydrology, 588, 125005.
  • Inclan, C. & Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), 913-923.
  • Izady, A. Davary, K. Alizadeh, A. Ghahraman, B. Sadeghi, M. & Moghaddamnia, A. (2012). Application of” panel-data” modeling to predict groundwater levels in the Neishaboor plain, Iran. Hydrogeology Journal, 20(3), 435.
  • Jiang, C. Xiong, L. Xu, C. Y. & Guo, S. (2015). Bivariate frequency analysis of nonstationary low‐flow series based on the time‐varying copula. Hydrological Processes, 29(6), 1521-1534.
  • Joe, H. (2014). Dependence modeling with copulas. Chapman and Hall/CRC.
  • Jothityangkoon, C. Sivapalan, M. & Viney, N. R. (2000). Tests of a space‐time model of daily rainfall in southwestern Australia based on nonhomogeneous random cascades. Water resources research, 36(1), 267-284.
  • Kalai, C. Mondal, A. Griffin, A. & Stewart, E. (2020). Comparison of nonstationary regional flood frequency analysis techniques based on the index-flood approach. Journal of Hydrologic Engineering, 25(7), 06020003.
  • Kantelhardt, J. W. Rybski, D. Zschiegner, S. A. Braun, P. Koscielny-Bunde, E. Livina, V. … & Bunde, A. (2003). Multifractality of river runoff and precipitation: comparison of fluctuation analysis and wavelet methods. Physica A: Statistical Mechanics and its Applications, 330(1-2), 240-245.
  • Kao, S. C. & Govindaraju, R. S. (2007). A bivariate frequency analysis of extreme rainfall with implications for design. Journal of Geophysical Research: Atmospheres, 112(D13).
  • Kao, S. C. and R. S. Govindaraju (2008), Trivariate statistical analysis of extreme rainfall events via the Plackett family of copulas, Water Resources Research, 44(2).
  • Karahacane, H. Meddi, M. Chebana, F. & Saaed, H. A. (2020). Complete multivariate flood frequency analysis, applied to northern Algeria. Journal of Flood Risk Management, 13(4), e12619.
  • Katz, R. W. (2013). Statistical methods for nonstationary extremes. In Extremes in a changing climate (pp. 15-37). Springer, Dordrecht.
  • Katz, R. W. Parlange, M. B. & Naveau, P. (2002). Statistics of extremes in hydrology. Advances in Water Resources, 25(8-12), 1287-1304.
  • Kauffeldt, A. Halldin, S. Rodhe, A. Xu, C. Y. & Westerberg, I. K. (2013). Disinformative data in large-scale hydrological modelling. Hydrology and Earth System Sciences, 17(7), 2845-2857.
  • Kehagias, A. (2004). A hidden Markov model segmentation procedure for hydrological and environmental time series. Stochastic Environmental Research and Risk Assessment, 18, 117-130.
  • Kehagias, A. Nidelkou, E. & Petridis, V. (2006). A dynamic programming segmentation procedure for hydrological and environmental time series. Stochastic Environmental Research and Risk Assessment, 20, 77-94.
  • Kelley, D. (1994). Introduction to probability. Macmillan Publishing Company, London.
  • Keylock, C. J. (2012). A resampling method for generating synthetic hydrological time series with preservation of cross‐correlative structure and higher‐order properties. Water Resources Research, 48(12).
  • Kilsby, C. G. Jones, P. D. Burton, A. Ford, A. C. Fowler, H. J. Harpham, C. … & Wilby, R. L. (2007). A daily weather generator for use in climate change studies. Environmental Modelling & Software, 22(12), 1705-1719.
  • Kim, D. & Onof, C. (2020). A stochastic rainfall model that can reproduce important rainfall properties across the timescales from several minutes to a decade. Journal of Hydrology, 589, 125150.
  • Kim, D. Lee, J. Kim, H. & Choi, M. (2016). Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data. Stochastic Environmental Research and Risk Assessment, 30, 2109-2126.
  • Kim, G. Silvapulle, M. J. & Silvapulle, P. (2007). Comparison of semiparametric and parametric methods for estimating copulas. Computational Statistics & Data Analysis, 51(6), 2836-2850.
  • Kjeldsen, T. R. Ahn, H. & Prosdocimi, I. (2017). On the use of a four-parameter kappa distribution in regional frequency analysis. Hydrological Sciences Journal, 62(9), 1354-1363.
  • Kjeldsen, T. R. & Prosdocimi, I. (2021). Assessment of trends in hydrological extremes using regional magnification factors. Advances in Water Resources, 149, 103852.
  • Klemeš, V. (1974). The Hurst phenomenon: A puzzle?. Water Resources Research, 10(4), 675-688.
  • Klemeš, V. (2000a). Tall tales about tails of hydrological distributions. I. Journal of Hydrologic Engineering, 5(3), 227-231.
  • Klemeš, V. (2000b). Tall tales about tails of hydrological distributions. II. Journal of Hydrologic Engineering, 5(3), 232-239.
  • Kochanek, K. Strupczewski, W. G. & Bogdanowicz, E. (2012). On seasonal approach to flood frequency modelling. Part II: flood frequency analysis of Polish rivers. Hydrological Processes, 26(5), 717-730.
  • Kochanek, K. Strupczewski, W. G. Bogdanowicz, E. & Markiewicz, I. (2020). The bias of the maximum likelihood estimates of flood quantiles based solely on the largest historical records. Journal of Hydrology, 584, 124740.
  • Kochel, R.C. V.R. Baker (1982). Paleoflood Hydrology. Science, v. 215(4531), p. 353-361.
  • Kojadinovic, I. & J. Yan (2010). Comparison of three semiparametric methods for estimating dependence parameters in copula models. Insurance: Mathematics and Economics, 47(1), 52-63.
  • Kojadinovic, I. Yan, J. & Holmes, M. (2011). Fast large-sample goodness-of-fit tests for copulas. Statistica Sinica, 841-871.
  • Kossieris, P. Makropoulos, C. Onof, C. & Koutsoyiannis, D. (2018). A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures. Journal of hydrology, 556, 980-992.
  • Kotz, S. and S. Nadarajah (2000). Extreme Value Distributions. Imperial College Press, London.
  • Koutsoyiannis, D. (2003). Climate change, the Hurst phenomenon, and hydrological statistics. Hydrological Sciences Journal, 48(1), 3-24.
  • Koutsoyiannis, D. (2020). Revisiting the global hydrological cycle: is it intensifying?. Hydrology and Earth System Sciences, 24(8), 3899-3932.
  • Koutsoyiannis, D. (2021). Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, 333 pages, Kallipos Open Academic Editions, Athens, 2021.
  • Koutsoyiannis, D. (2016). Generic and parsimonious stochastic modelling for hydrology and beyond. Hydrological Sciences Journal, 61(2), 225– 244.
  • Koutsoyiannis, D. & Montanari, A. (2015). Negligent killing of scientific concepts: the stationarity case. Hydrological Sciences Journal, 60(7-8), 1174-1183.
  • Koutsoyiannis, D. & Langousis, A. (2011). Precipitation, In: Treaties on Water Sciences: Hydrology, Vol. 2, Edts: P. Wilderer (in chief) and S. Uhlenbrook, Academic Press, Oxford, pp. 27–78.
  • Krajewski, W. F. & Smith, J. A. (2002). Radar hydrology: rainfall estimation. Advances in water resources, 25(8-12), 1387-1394.
  • Kumar, D. N. Lall, U. & Petersen, M. R. (2000). Multisite disaggregation of monthly to daily streamflow. Water Resources Research, 36(7), 1823-1833.
  • Kundzewicz, Z. W. & Robson, A. J. (2004). Change detection in hydrological records—a review of the methodology/revue méthodologique de la détection de changements dans les chroniques hydrologiques. Hydrological sciences journal, 49(1), 7-19.
  • Kundzewicz, Z. W. Graczyk, D. Maurer, T. Pińskwar, I. Radziejewski, M. Svensson, C. & Szwed, M. (2005). Trend detection in river flow series: 1. Annual maximum flow/Détection de tendance dans des séries de débit fluvial: 1. Débit maximum annuel. Hydrological Sciences Journal, 50(5).
  • Kwon, H.-H. & Lall, U. (2016). A copula-based nonstationary frequency analysis for the 2012–2015 drought in California, Water Resources Research, 52, 5662–5675.
  • Laaha, G. Skøien, J. O. & Blöschl, G. (2014). Spatial prediction on river networks: comparison of top‐kriging with regional regression. Hydrological Processes, 28(2), 315-324.
  • Laaha, G. Skøien, J. O. Nobilis, F. & Blöschl, G. (2013). Spatial prediction of stream temperatures using top-kriging with an external drift. Environmental Modeling & Assessment, 18, 671-683.
  • Laio, F. (2004). Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters, Water Resources Research, 40, W09308, 2004.
  • Laio, F. Di Baldassarre, G. and Montanari, A. (2009). Model selection techniques for the frequency analysis of hydrological extremes, Water Resources Research, 45, W07416, 2009.
  • Laio, F. Ganora, D. Claps, P. & Galeati, G. (2011). Spatially smooth regional estimation of the flood frequency curve (with uncertainty). Journal of hydrology, 408(1-2), 67-77.
  • Lall, U. (1995). Recent advances in nonparametric function estimation: Hydrologic applications. Reviews of Geophysics, 33(S2), 1093-1102.
  • Lall, U. & Sharma, A. (1996). A nearest neighbor bootstrap for resampling hydrologic time series. Water resources research, 32(3), 679-693.
  • Lamontagne, J. R. Stedinger, J. R. Yu, X. Whealton, C. A. & Xu, Z. (2016). Robust flood frequency analysis: Performance of EMA with multiple Grubbs‐Beck outlier tests. Water Resources Research, 52(4), 3068-3084.
  • Lamontagne, J. R. Barber, C. A. & Vogel, R. M. (2020). Improved estimators of model performance efficiency for skewed hydrologic data. Water Resources Research, 56(9), e2020WR027101.
  • Langousis, A. & Koutsoyiannis, D. (2006). A Stochastic Methodology for Generation of Seasonal Time Series Reproducing Over-year Scaling Behavior. J. Hydrol. 322 (1-4), 138-154
  • Langousis, A. Carsteanu, A. A. & Deidda, R. (2013). A simple approximation to multifractal rainfall maxima using a generalized extreme value distribution model. Stochastic environmental research and risk assessment, 27(6), 1525-1531.
  • Langousis, A. & Kaleris, V. (2014). Statistical framework to simulate daily rainfall series conditional on upper-air predictor variables. Water Resour. Res. 50(5), 3907-3932.
  • Langousis, A. Mamalakis, A. Puliga, M. & Deidda, R. (2016a). Threshold detection for the generalized Pareto distribution: Review of representative methods and application to the NOAA NCDC daily rainfall database. Water Resources Research, 52(4), 2659-2681.
  • Langousis, A. Mamalakis, A. Deidda, R. & Marrocu, M. (2016b). Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results. Water Resour. Res. 52.
  • Langousis, A. & Veneziano, D. (2007). Intensity-Duration-Frequency Curves from Scaling Representations of Rainfall. Water Resour. Res. 43.
  • Langousis, A. Veneziano, D. Furcolo, P. & Lepore, C. (2009). Multifractal Rainfall Extremes: Theoretical Analysis and Practical Estimation. Chaos Solitons and Fractals, 39, 1182-1194.
  • Latif, S. Souaissi, Z. Ouarda, T. B.M.J. & St-Hilaire, A. (2023). Copula-based joint modelling of extreme river temperature and low flow characteristics in the risk assessment of aquatic life, Weather and Climate Extremes, 41: 100586.
  • Latif, S. Ouarda, T. B. St-Hilaire, A. Souaissi, Z. & Rehana, S. (2024). A new nonparametric copula framework for the joint analysis of river water temperature and low flow characteristics for aquatic habitat risk assessment. Journal of Hydrology, 634, 131079.
  • Lawrance, A. J. & Kottegoda, N. T. (1977). Stochastic modelling of riverflow time series. Journal of the Royal Statistical Society: Series A (General), 140(1), 1-31.
  • Lebrenz, H. & Bárdossy, A. (2019). Geostatistical interpolation by quantile kriging. Hydrology and Earth System Sciences, 23(3), 1633-1648.
  • Leclerc, M. & Ouarda, T. B. (2007). Non-stationary regional flood frequency analysis at ungauged sites. Journal of hydrology, 343(3-4), 254-265.
  • Lee, T. Modarres, R. & Ouarda, T.B.M.J. (2013). Data-based analysis of bivariate copula tail dependence for drought duration and severity. Hydrological Processes, 27(10), 1454-1463,
  • Lee, T. & Ouarda, T. B. (2012). Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition. Water Resources Research, 48(2).
  • Lee, T. Salas, J. D. & Prairie, J. (2010). An enhanced nonparametric streamflow disaggregation model with genetic algorithm. Water Resources Research, 46(8).
  • Leese, M.N. (1973). Use of Censored Data in the Estimation of Gumbel Distribution Parameters for Annual Maximum Flood Series. Water Res. Res. v. 9, p. 1534-1542
  • Lekina, A. Chebana, F. & Ouarda, T. B. (2015). On the tail dependence in bivariate hydrological frequency analysis. Dependence Modeling, 3(1).
  • Li, J. Evans, J. Johnson, F. & Sharma, A. (2017). A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM. Journal of Hydrology, 547, 413-427.
  • Libertino, A. Allamano, P. Laio, F. & Claps, P. (2018). Regional-scale analysis of extreme precipitation from short and fragmented records. Advances in Water Resources, 112, 147-159.
  • Libiseller, C. & Grimvall, A. (2002). Performance of partial Mann–Kendall tests for trend detection in the presence of covariates. Environmetrics: The official journal of the International Environmetrics Society, 13(1), 71-84.
  • Liguori, S. & Rico-Ramirez, M. A. (2014). A review of current approaches to radar-based quantitative precipitation forecasts. International Journal of River Basin Management, 12(4), 391-402.
  • Lilienthal, J. Fried, R. & Schumann, A. (2018). Homogeneity testing for skewed and cross-correlated data in regional flood frequency analysis. Journal of Hydrology, 556, 557-571.
  • Lins, H. F. & Cohn, T. A. (2011). Stationarity: wanted dead or alive? 1. JAWRA Journal of the American Water Resources Association, 47(3), 475-480.
  • Liu Y.R. Li Y.P. Ma Y. Jia Q.M. and Su Y.Y. (2020). Development of a Bayesian-copula-based frequency analysis method for hydrological risk assessment–The Naryn River in Central Asia, Journal of Hydrology 580, 124349.
  • López, J. & 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.
  • Luke, A. Vrugt, J. A. AghaKouchak, A. Matthew, R. & Sanders, B. F. (2017). Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the United States. Water Resources Research, 53, 5469– 5494.
  • Lun, D. Fischer, S. Viglione, A. & Blöschl, G. (2020). Detecting flood‐rich and flood‐poor periods in annual peak discharges across Europe. Water Resources Research, 56(7), e2019WR026575.
  • Lun, D. Viglione, A. Bertola, M. Komma, J. Parajka, J. Valent, P. & Blöschl, G. (2021). Characteristics and process controls of statistical flood moments in Europe–a data-based analysis. Hydrology and Earth System Sciences, 25(10), 5535-5560.
  • Ma, M. Song, S. Ren, L. Jiang, S. & Song, J. (2013). Multivariate drought characteristics using trivariate Gaussian and Student t copulas. Hydrological processes, 27(8), 1175-1190.
  • Machado, M. J. Botero, B. A. López, J. Francés, F. Díez-Herrero, A. & Benito, G. (2015). Flood frequency analysis of historical flood data under stationary and non-stationary modelling. Hydrology and Earth System Sciences, 19(6), 2561-2576.
  • Madsen, H. P. F. Rasmussen, and D. Rosbjerg (1997), Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events: 1. At-site modeling, Water Resources Research, 33(4), 747–757.
  • Madsen, H. Lawrence, D. Lang, M. Martinkova, M. & Kjeldsen, T. R. (2014). Review of trend analysis and climate change projections of extreme precipitation and floods in Europe. Journal of Hydrology, 519, 3634-3650.
  • Mallakpour, I. & Villarini, G. (2017). Analysis of changes in the magnitude, frequency, and seasonality of heavy precipitation over the contiguous USA. Theoretical and Applied Climatology, 130(1), 345-363.
  • Mandelbrot, B. B. & Mandelbrot, B. B. (1982). The fractal geometry of nature (Vol. 1). New York: WH freeman.
  • Mandelbrot, B. B. & Wallis, J. R. (1968). Noah, Joseph, and operational hydrology. Water Resources Research, 4(5), 909-918.
  • Mangini, W. Viglione, A. Hall, J. Hundecha, Y. Ceola, S. Montanari, A. … & Parajka, J. (2018). Detection of trends in magnitude and frequency of flood peaks across Europe. Hydrological Sciences Journal, 63(4), 493-512.
  • Marani, M. & Ignaccolo, M. (2015). A metastatistical approach to rainfall extremes. Advances in Water Resources, 79, 121-126.
  • Maraun, D. (2013). Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue. Journal of Climate, 26(6), 2137-2143.
  • Markonis, Y. Moustakis, Y. Nasika, C. Sychova, P. Dimitriadis, P. Hanel, M. … & Papalexiou, S. M. (2018). Global estimation of long-term persistence in annual river runoff. Advances in Water Resources, 113, 1-12.
  • Marra, F. Amponsah, W. & Papalexiou, S. M. (2023). Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation. Advances in Water Resources, 173, 104388.
  • Martins, E. S. & Stedinger, J. R. (2000). Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data. Water Resources Research, 36(3), 737-744.
  • Mascaro, G. (2018). On the distributions of annual and seasonal daily rainfall extremes in central Arizona and their spatial variability. Journal of Hydrology, 559, 266-281.
  • Matalas, N. C. (1967). Mathematical assessment of synthetic hydrology. Water Resources Research, 3(4), 937-945.
  • Matalas, N. C. (2012). Comment on the announced death of stationarity. Journal of Water Resources Planning and Management, 138(4), 311-312.
  • Mazdiyasni, O. Sadegh, M. Chiang, F. & AghaKouchak, A. (2019). Heat wave intensity duration frequency curve: A multivariate approach for hazard and attribution analysis. Scientific reports, 9(1), 1-8.
  • McNeil, A. J. Frey, R. & Embrechts, P. (2015). Quantitative risk management: Concepts, techniques and tools (2nd ed.). Princeton, NJ: Princeton University Press.
  • Medda, S. & Bhar, K. K. (2019). Comparison of single-site and multi-site stochastic models for Streamflow generation. Applied Water Science, 9, 1-14.
  • Mehrotra, R. & Sharma, A. (2007). A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability. Journal of Hydrology, 335(1-2), 180-193.
  • Mejia, J. M. Rodriguez‐Iturbe, I. & Dawdy, D. R. (1972). Streamflow simulation: 2. The broken line process as a potential model for hydrologic simulation. Water Resources Research, 8(4), 931-941.
  • Menabde, M. Harris, D. Seed, A. Austin, G. & Stow, D. (1997). Multiscaling properties of rainfall and bounded random cascades. Water Resources Research, 33(12), 2823-2830.
  • Merz, R. & Blöschl, G. (2004). Regionalisation of catchment model parameters. Journal of hydrology, 287(1-4), 95-123.
  • Merz, R. & Blöschl, G. (2005). Flood frequency regionalisation—spatial proximity vs. catchment attributes. Journal of hydrology, 302(1-4), 283-306.
  • Merz, R. Blöschl, G. & Humer, G. (2008). National flood discharge mapping in Austria. Natural Hazards, 46, 53-72.
  • Merz, B. Basso, S. Fischer, S. Lun, D. Blöschl, G. Merz, R. … & Schumann, A. (2022). Understanding heavy tails of flood peak distributions. Water Resources Research, 58(6), e2021WR030506.
  • Michiels, F. & De Schepper, A. (2008). A copula test space model how to avoid the wrong copula choice. Kybernetika, 44(6), 864-878.
  • Milly, P. C. Betancourt, J. Falkenmark, M. Hirsch, R. M. Kundzewicz, Z. W. Lettenmaier, D. P. & Stouffer, R. J. (2008). Stationarity is dead: Whither water management?. Science, 319(5863), 573-574.
  • Milly, P. C. Betancourt, J. Falkenmark, M. Hirsch, R. M. Kundzewicz, Z. W. Lettenmaier, D. P. … & Krysanova, V. (2015). On critiques of “Stationarity is dead: Whither water management?”. Water Resources Research, 51(9), 7785-7789.
  • Montanari, A. Rosso, R. Taqqu, M.S. (1997). Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation. Water Resources Research, 33 (5), pp. 1035-1044.
  • Naghettini, M. (Ed.). (2017). Fundamentals of statistical hydrology. Cham: Springer International Publishing.
  • Naulet, R. Lang, M. Ouarda, T. B. Coeur, D. Bobée, B. Recking, A. & Moussay, D. (2005). Flood frequency analysis on the Ardèche river using French documentary sources from the last two centuries. Journal of Hydrology, 313(1-2), 58-78.
  • Nelsen, R. B. (2007). An introduction to copulas. Springer Science & Business Media.
  • Nerantzaki, S. D. & Papalexiou, S. M. (2022). Assessing extremes in hydroclimatology: A review on probabilistic methods. Journal of Hydrology, 605, 127302.
  • Nowak, K. Prairie, J. Rajagopalan, B. & Lall, U. (2010). A nonparametric stochastic approach for multisite disaggregation of annual to daily streamflow. Water Resources Research, 46(8).
  • O’Brien, N. L. & Burn, D. H. (2014). A nonstationary index-flood technique for estimating extreme quantiles for annual maximum streamflow. Journal of Hydrology, 519, 2040-2048.
  • O’Brien, N. L. Burn, D. H. Annable, W. K. & Thompson P. J. (2020). Trend detection in the presence of positive and negative serial correlation: a comparison of block maxima and peaks-over-threshold data. Water Resources Research, 57, e2020WR028886.
  • O’Shea, Nathan, R. Sharma, A. Wasko, C. (2023). Improved Extreme Rainfall Frequency Analysis Using a Rwo-Step Kappa Approach. Water Resources Research,59(4), e2021WR031854.
  • Ochoa‐Rodriguez, S. Wang, L. P. Willems, P. & Onof, C. (2019). A review of radar‐rain gauge data merging methods and their potential for urban hydrological applications. Water Resources Research, 55(8), 6356-6391.
  • Oliveira, B. & Maia, R. (2018). Stochastic generation of streamflow time series. Journal of Hydrologic Engineering, 23(10), 04018043.
  • Oriani, F. Mehrotra, R. Mariethoz, G. Straubhaar, J. Sharma, A. & Renard, P. (2018). Simulating rainfall time-series: how to account for statistical variability at multiple scales?. Stochastic environmental research and risk assessment, 32(2), 321-340.
  • Ostrowski, P. Falkowski, T. & Kochanek, K. (2023). Reconstructing parameters of the Holocene paleofloods in alluvial lowland river valleys–An example from the Bug valley (East Poland). Journal of Hydrology, 624, 129930.
  • Ouali, D. Chebana, F. & Ouarda, T. B. (2017). Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites. Journal of Advances in Modeling Earth Systems, 9(2), 1292-1306.
  • Ouarda, T. B. & Charron, C. (2019). Changes in the distribution of hydro-climatic extremes in a non-stationary framework. Scientific reports, 9(1), 1-8.
  • Ouarda, T. B. Charron, C. & St‐Hilaire, A. (2020). Uncertainty of stationary and nonstationary models for rainfall frequency analysis. International Journal of Climatology, 40(4), 2373-2392.
  • Ouarda, T. B. Girard, C. Cavadias, G. S. & Bobée, B. (2001). Regional flood frequency estimation with canonical correlation analysis. Journal of Hydrology, 254(1-4), 157-173.
  • Ouarda, T. B. Yousef, L. A. & Charron, C. (2019). Non‐stationary intensity‐duration‐frequency curves integrating information concerning teleconnections and climate change. International Journal of Climatology, 39(4), 2306-2323.
  • Pan, X. Rahman, A. Haddad, K. & Ouarda, T.B.M.J. (2022). Peaks-over-threshold model in flood frequency analysis: a scoping review. Stochastic Environmental Research and Risk Assessment, 36(9): 2419-2435.
  • Papaioannou, G. Kohnová, S. Bacigál, T. Szolgay, J. Hlavčová, K. & Loukas, A. (2016). Joint modelling of flood peaks and volumes: A copula application for the Danube River. Journal of Hydrology and Hydromechanics, 64(4), 382-392.
  • Papalexiou, S. M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in water resources, 115, 234-252.
  • Papalexiou, S. M. & Montanari, A. (2019). Global and regional increase of precipitation extremes under global warming. Water Resources Research, 55(6), 4901-4914.
  • Papalexiou, S. M. & Serinaldi, F. (2020). Random fields simplified: Preserving marginal distributions, correlations, and intermittency, with applications from rainfall to humidity. Water Resources Research, 56(2), e2019WR026331.
  • Papalexiou, S. M. Koutsoyiannis, D. & Makropoulos, C. (2013). How extreme is extreme? An assessment of daily rainfall distribution tails. Hydrology and Earth System Sciences, 17(2), 851-862.
  • Papalexiou, S. M. Serinaldi, F. & Porcu, E. (2021). Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466.
  • Papalexiou, S. M. Serinaldi, F. & Clark, M. P. (2023). Large‐Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites. Water Resources Research, 59(3), e2022WR034094.
  • Parajka, J. Merz, R. Skøien, J. O. & Viglione, A. (2015). The role of station density for predicting daily runoff by top-kriging interpolation in Austria. Journal of Hydrology and Hydromechanics, 63(3), 228-234.
  • Parzen, E. (1962). On estimation of a probability density function and mode. The annals of mathematical statistics, 33(3), 1065-1076.
  • Persiano, S. Ferri, E. Antolini, G. Domeneghetti, A. Pavan, V. & Castellarin, A. (2020). Changes in seasonality and magnitude of sub-daily rainfall extremes in Emilia-Romagna (Italy) and potential influence on regional rainfall frequency estimation. Journal of Hydrology: Regional Studies, 32, 100751.
  • Persiano, S. Salinas, J. L. Stedinger, J. R. Farmer, W. H. Lun, D. Viglione, A. … & Castellarin, A. (2021). A comparison between generalized least squares regression and top-kriging for homogeneous cross-correlated flood regions. Hydrological Sciences Journal, 66(4), 565-579.
  • Pettitt, A. N. (1979). A non‐parametric approach to the change‐point problem. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(2), 126-135.
  • Piantadosi, J. Howlett, P. Borwein, J. & Henstridge, J. (2012). Maximum entropy methods for generating simulated rainfall. Numerical Algebra, Control & Optimization, 2(2), 233.
  • Poulin, A. Huard, D. Favre, A. C. & Pugin, S. (2007). Importance of tail dependence in bivariate frequency analysis. Journal of hydrologic engineering, 12(4), 394-403.
  • Price, K. Purucker, S. T. Kraemer, S. R. Babendreier, J. E. & Knightes, C. D. (2014). Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrological Processes, 28(9), 3505-3520.
  • Prieto, C. Le Vine, N. Kavetski, D. García, E. & Medina, R. (2019). Flow prediction in ungauged catchments using probabilistic random forests regionalization and new statistical adequacy tests. Water Resources Research, 55(5), 4364-4392.
  • Prieto, C. Le Vine, N. Kavetski, D. Fenicia, F. Scheidegger, A. & Vitolo, C. (2022). An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments. Water Resources Research, 58(3), e2021WR030705.
  • Prosdocimi, I. Kjeldsen, T. R. & Svensson, C. (2014). Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK. Natural Hazards and Earth System Sciences, 14(5), 1125-1144.
  • Prosdocimi, I. Kjeldsen, T. R. & Miller, J. D. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood‐frequency models. Water resources research, 51(6), 4244-4262.
  • Prosdocimi, I. & Kjeldsen, T. (2021). Parametrisation of change-permitting extreme value models and its impact on the description of change. Stochastic Environmental Research and Risk Assessment, 35(2), 307-324.
  • Pugliese, A. Castellarin, A. & Brath, A. (2014). Geostatistical prediction of flow–duration curves in an index-flow framework. Hydrology and Earth System Sciences, 18(9), 3801-3816.
  • Pugliese, A. Farmer, W. H. Castellarin, A. Archfield, S. A. & Vogel, R. M. (2016). Regional flow duration curves: Geostatistical techniques versus multivariate regression. Advances in water resources, 96, 11-22.
  • R Core Team, (2020). R Core Team R: a language and environment for statistical computing. Foundation for Statistical Computing.
  • Rabiei, E. & Haberlandt, U. (2015). Applying bias correction for merging rain gauge and radar data. Journal of Hydrology, 522, 544-557.
  • Ragno, E. AghaKouchak, A. Cheng, L. & Sadegh, M. (2019). A generalized framework for process-informed nonstationary extreme value analysis. Advances in Water Resources, 130, 270-282.
  • Ragno, E. AghaKouchak, A. Love, C. A. Cheng, L. Vahedifard, F. & Lima, C. H. (2018). Quantifying changes in future intensity‐duration‐frequency curves using multimodel ensemble simulations. Water Resources Research, 54(3), 1751-1764.
  • Ragno, E. Hrachowitz, M. & Morales-Nápoles, O. (2022). Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges. Hydrology and Earth System Sciences, 26(6), 1695-1711.
  • Rajagopalan, B. & Lall, U. (1995). A kernel estimator for discrete distributions. Journal of Nonparametric Statistics, 4(4), 409-426.
  • Rajagopalan, B. Lall, U. & Tarboton, D. G. (1997). Evaluation of kernel density estimation methods for daily precipitation resampling. Stochastic Hydrology and Hydraulics, 11, 523-547.
  • Rao, A. R. & Hamed, K. H. (2019). Flood frequency analysis. CRC press.
  • Reed, D. W. (2002). Reinforcing flood–risk estimation. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 360(1796), 1373-1387.
  • Reis Jr, D. S. & Stedinger, J. R. (2005). Bayesian MCMC flood frequency analysis with historical information. Journal of hydrology, 313(1-2), 97-116.
  • Renard, B. Lang, M. Bois, P. Dupeyrat, A. Mestre, O. Niel, H. … & Gailhard, J. (2008). Regional methods for trend detection: Assessing field significance and regional consistency. Water Resources Research, 44(8).
  • Requena, A. I. Burn, D. H. & Coulibaly, P. (2019). Estimates of gridded relative changes in 24-h extreme rainfall intensities based on pooled frequency analysis. Journal of Hydrology, 577, 123940.
  • Requena, A. I. Burn, D. H. & Coulibaly, P. (2021). Technical guidelines for future intensity–duration–frequency curve estimation in Canada. Canadian Water Resources Journal/revue Canadienne Des Ressources Hydriques, 46(1-2), 87-104.
  • Requena, A. I. 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(8), 3023-3038.
  • Requena, A.I. F. Chebana, and L. Mediero (2016), A complete procedure for multivariate index-flood model application. Journal of Hydrology, 535, 559-580.
  • Richardson, C. W. (1981). Stochastic simulation of daily precipitation, temperature, and solar radiation. Water resources research, 17(1), 182-190.
  • Rigby, R. A. & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society Series C: Applied Statistics, 54(3), 507-554.
  • Rodriguez‐Iturbe, I. De Power, B. F. & Valdes, J. B. (1987). Rectangular pulses point process models for rainfall: analysis of empirical data. Journal of Geophysical Research: Atmospheres, 92(D8), 9645-9656.
  • Rodriguez‐Iturbe, I. Mejia, J. M. & Dawdy, D. R. (1972). Streamflow simulation: 1. A new look at Markovian models, fractional Gaussian noise, and crossing theory. Water Resources Research, 8(4), 921-930.
  • Rutkowska, A. (2013). Statistical methods for trend investigation in hydrological non-seasonal series. Acta Scientiarum Polonorum-Formatio Circumiectus, 12(4), 85-94.
  • Sadegh, M. Moftakhari, H. Gupta, H. V. Ragno, E. Mazdiyasni, O. Sanders, B. … & AghaKouchak, A. (2018). Multihazard scenarios for analysis of compound extreme events. Geophysical Research Letters, 45(11), 5470-5480.
  • Saint Criq, L. Gaume, E. Hamdi, Y. & Ouarda, T.B.M.J. (2022). Extreme Sea Level Estimation Combining Systematic Observed Skew Surges and Historical Record Sea Levels. Water Resources Research, 58(3): e2021WR030873.
  • Salas, J. D. (1980). Applied modeling of hydrologic time series. Water Resources Publication.
  • Salas, J. D. & Fernandez, B. (1993). Models for data generation in hydrology: univariate techniques. Stochastic hydrology and its use in water resources systems simulation and optimization, 47-73.
  • Salas, J. D. & Obeysekera, J. (2014). Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. Journal of Hydrologic Engineering, 19(3), 554-568.
  • Salas, J. D. & Obeysekera, J. T. B. (1992). Conceptual basis of seasonal streamflow time series models. Journal of Hydraulic Engineering, 118(8), 1186-1194.
  • Salas, J. D. Obeysekera, J. & Vogel, R. M. (2018). Techniques for assessing water infrastructure for nonstationary extreme events: a review. Hydrological Sciences Journal, 63(3), 325-352.
  • Salas, J. D. Anderson, M. L. Papalexiou, S.M. & Francés, F. (2020). PMP and Climate Variability and Change. Journal of Hydrologic Engineering, 25(12): 03120002.
  • Salinas, J. L. Laaha, G. Rogger, M. Parajka, J. Viglione, A. Sivapalan, M. & Blöschl, G. (2013). Comparative assessment of predictions in ungauged basins–Part 2: Flood and low flow studies. Hydrology and Earth System Sciences, 17(7), 2637-2652.
  • Salvadori, G. & C. De Michele, (2011). Estimating strategies for multiparameter multivariate extreme value copulas. Hydrology and Earth System Sciences. 15(1): p. 141-150.
  • Salvadori, G. & De Michele, C. (2004). Frequency analysis via copulas: Theoretical aspects and applications to hydrological events. Water resources research, 40(12).
  • Salvadori, G. & De Michele, C. (2010). Multivariate multiparameter extreme value models and return periods: A copula approach. Water resources research, 46(10).
  • Salvadori, G. De Michele, C. Kottegoda, N. T. & Rosso, R. (2007). Extremes in nature: an approach using copulas (Vol. 56). Springer Science & Business Media.
  • Salvadori, G. Durante, F. De Michele, C. & Bernardi, M. (2018). Hazard Assessment under Multivariate Distributional Change-Points: Guidelines and a Flood Case Study. Water, 10(6), 751.
  • Salvadori, G. Durante, F. De Michele, C. Bernardi, M. & Petrella, L. (2016). A multivariate copula‐based framework for dealing with hazard scenarios and failure probabilities. Water Resources Research, 52(5), 3701-3721.
  • Samaniego, L. Bárdossy, A. & Kumar, R. (2010). Streamflow prediction in ungauged catchments using copula‐based dissimilarity measures. Water Resources Research, 46(2).
  • Schertzer, D. & Lovejoy, S. (1987). Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes. Journal of Geophysical Research: Atmospheres, 92(D8), 9693-9714.
  • Seibert, J. & McDonnell, J. J. (2010). Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 55(3), 316-332.
  • Seidou, O. Asselin, J.J. & T.B.M.J. Ouarda (2007). Bayesian multivariate linear regression with application to changepoint models in hydrometeorological variables, Water Resources Research. 43, W08401.
  • Seo, D. J. Krajewski, W. F. & Bowles, D. S. (1990). Stochastic interpolation of rainfall data from rain gages and radar using cokriging: 1. Design of experiments. Water Resources Research, 26(3), 469-477.
  • Seo, D. J. Krajewski, W. F. Azimi‐Zonooz, A. & Bowles, D. S. (1990). Stochastic interpolation of rainfall data from rain gages and radar using cokriging: 2. Results. Water Resources Research, 26(5), 915-924.
  • Seo, D. J. Siddique, R. Zhang, Y. & Kim, D. (2014). Improving real-time estimation of heavy-to-extreme precipitation using rain gauge data via conditional bias-penalized optimal estimation. Journal of hydrology, 519, 1824-1835.
  • Serago, J. M. & Vogel, R. M. (2018). Parsimonious nonstationary flood frequency analysis. Advances in Water Resources, 112, 1-16.
  • Serinaldi, F. (2009). A multisite daily rainfall generator driven by bivariate copula‐based mixed distributions. Journal of Geophysical Research: Atmospheres, 114(D10).
  • Serinaldi, F. Bonaccorso, B. Cancelliere, A. Grimaldi, S. (2009) Probabilistic characterization of drought properties through copulas, Physics and Chemistry of the Earth, 34 (10-12), pp. 596-605.
  • Serinaldi, F. (2013). An uncertain journey around the tails of multivariate hydrological distributions, Water Resources Research, 49, 6527–6547.
  • Serinaldi, F. (2015). Dismissing return periods!. Stochastic environmental research and risk assessment, 29(4), 1179-1189.
  • Serinaldi, F. & Kilsby, C. G. (2015). Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17-36.
  • Serinaldi, F. & Kilsby, C. G. (2016). The importance of prewhitening in change point analysis under persistence. Stochastic Environmental Research and Risk Assessment, 30(2), 763-777.
  • Serinaldi, F. & Kilsby, C. G. (2018). Unsurprising surprises: The frequency of record‐breaking and overthreshold hydrological extremes under spatial and temporal dependence. Water Resources Research, 54(9), 6460-6487.
  • Serinaldi, F. & Lombardo, F. (2020). Probability distribution of waiting time of the kth extreme event under serial dependence. Journal of Hydrologic Engineering.
  • Serinaldi, F. & Kilsby, C. (2015). Stationarity is undead: Uncertainty dominates the distribution of extremes. Advances in Water Resources, 77, 17– 36.
  • Serinaldi, F. Kilsby, C. G. & Lombardo, F. (2018). Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology. Advances in Water Resources, 111, 132-155.
  • Sharma, A. & Mehrotra, R. (2010). Rainfall generation. Washington DC American Geophysical Union Geophysical Monograph Series, 191, 215-246.
  • Sharma, A. Wasko, C. & Lettenmaier, D. P. (2018). If precipitation extremes are increasing, why aren’t floods?. Water resources research, 54(11), 8545-8551.
  • Sharma, S. & Mujumdar, P. P. (2019). On the relationship of daily rainfall extremes and local mean temperature. Journal of Hydrology, 572, 179-191.
  • Shu, C. & Ouarda, T. B. (2007). Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resources Research, 43(7).
  • Sinclair, S. & Pegram, G. (2005). Combining radar and rain gauge rainfall estimates using conditional merging. Atmospheric Science Letters, 6(1), 19-22.
  • Singh, V. P. & Strupczewski, W. G. (2002). On the status of flood frequency analysis. Hydrological Processes, 16(18), 3737-3740.
  • Sklar, M. (1959), Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de statistique de l’Université de Paris. 8: p. 229-231.
  • Skøien, J. O. & Blöschl, G. (2007). Spatiotemporal topological kriging of runoff time series. Water Resources Research, 43(9).
  • Skøien, J. O. Merz, R. & Blöschl, G. (2006). Top-kriging-geostatistics on stream networks. Hydrology and Earth System Sciences, 10(2), 277-287.
  • Slater, L. Villarini, G. Archfield, S. Faulkner, D. Lamb, R. Khouakhi, A. & Yin, J. (2021a). Global changes in 20-year, 50-year, and 100-year river floods. Geophysical Research Letters, 48, e2020GL091824.
  • Slater, L. J. Anderson, B. Buechel, M. Dadson, S. Han, S. Harrigan, S. … & Wilby, R. L. (2021b). Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management. Hydrology and Earth System Sciences, 25(7), 3897-3935.
  • Snelder, T. H. Datry, T. Lamouroux, N. Larned, S. T. Sauquet, E. Pella, H. & Catalogne, C. (2013). Regionalization of patterns of flow intermittence from gauging station records. Hydrology and Earth System Sciences, 17(7), 2685-2699.
  • Srikanthan, R. & McMahon, T. A. (2001). Stochastic generation of annual, monthly and daily climate data: A review. Hydrology and earth system sciences, 5(4), 653-670.
  • Srinivas, V. V. & Srinivasan, K. (2005). Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows. Journal of Hydrology, 302(1-4), 307-330.
  • Srivastav, R. K. & Simonovic, S. P. (2014). An analytical procedure for multi-site, multi-season streamflow generation using maximum entropy bootstrapping. Environmental Modelling & Software, 59, 59-75.
  • Stedinger, J.R. R.M. Vogel and E. Foufoula-Georgiou, (1993) Frequency Analysis of Extreme Events, Chapter 18, Handbook of Hydrology, McGraw-Hill Book Company, David R. Maidment, Editor-inChief. Stedinger, J. R. & Cohn, T. A. (1986). Flood frequency analysis with historical and paleoflood information. Water resources research, 22(5), 785-793.
  • Steinschneider, S. Yang, Y. C. E. & Brown, C. (2013). Panel regression techniques for identifying impacts of anthropogenic landscape change on hydrologic response. Water Resources Research, 49(12), 7874-7886.
  • Steinschneider, S. Ray, P. Rahat, S. H. & Kucharski, J. (2019). A weather‐regime‐based stochastic weather generator for climate vulnerability assessments of water systems in the western United States. Water Resources Research, 55(8), 6923-6945.
  • Stern, R. D. & Coe, R. (1984). A model fitting analysis of daily rainfall data. Journal of the Royal Statistical Society: Series A (General), 147(1), 1-18.
  • Strupczewski, W. G. Kochanek, K. Bogdanowicz, E. & Markiewicz, I. (2012). On seasonal approach to flood frequency modelling. Part I: Two‐component distribution revisited. Hydrological processes, 26(5), 705-716.
  • Strupczewski, W. G. Kochanek, K. Bogdanowicz, E. Markiewicz, I. & Feluch, W. (2016). Comparison of two nonstationary flood frequency analysis methods within the context of the variable regime in the representative polish rivers. Acta Geophysica, 64, 206-236.
  • Svensson, C. & Jones, D. A. (2010). Review of rainfall frequency estimation methods. Journal of Flood Risk Management, 3(4), 296-313.
  • Szilagyi, J. Balint, G. & Csik, A. (2006). Hybrid, Markov chain-based model for daily streamflow generation at multiple catchment sites. Journal of Hydrologic Engineering, 11(3), 245-256.
  • Szolgayova, E. Laaha, G. Blöschl, G. & Bucher, C. (2014). Factors influencing long range dependence in streamflow of European rivers. Hydrological Processes, 28(4), 1573-1586.
  • Tessier, Y. Lovejoy, S. Hubert, P. Schertzer, D. & Pecknold, S. (1996). Multifractal analysis and modeling of rainfall and river flows and scaling, causal transfer functions. Journal of Geophysical Research: Atmospheres, 101(D21), 26427-26440.
  • Thiemig, V. Rojas, R. Zambrano-Bigiarini, M. & De Roo, A. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324-338.
  • Thomas, J. & Fiering, M. (1962). Mathematical Synthesis of Streamflow Sequences for the Analysis of River Basins by Simulation. In Design of Water-Resource Systems: New Techniques for Relating Economic Objectives, Engineering Analysis, and Governmental Planning (pp. 459-493). Harvard University Press.
  • Thorndahl, S. Nielsen, J. E. & Rasmussen, M. R. (2014). Bias adjustment and advection interpolation of long-term high resolution radar rainfall series. Journal of Hydrology, 508, 214-226.
  • Todini, E. (2001). A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements. Hydrology and Earth System Sciences, 5(2), 187-199.
  • Tootoonchi, F. Haerter, J. O. Räty, O. Grabs, T. Sadegh, M. & Teutschbein, C. (2020). Copulas for hydroclimatic applications–a practical note on common misconceptions and pitfalls. Hydrology and Earth System Sciences Discussions, 1-31.
  • Tosunoglu, F. & Singh, V. P. (2018). Multivariate modeling of annual instantaneous maximum flows using copulas. Journal of Hydrologic Engineering, 23(3), 04018003.
  • Treiber, B. & Plate, E. J. (1977). A stochastic model for the simulation of daily flows/Modèle stochastique pour la simulation des débits journaliers. Hydrological Sciences Journal, 22(1), 175-192.
  • Tyralis, H. Papacharalampous, G. & Tantanee, S. (2019). How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset. Journal of Hydrology, 574, 628-645.
  • Vazifehkhah, S. Tosunoglu, F. & Kahya, E. (2019). Bivariate risk analysis of droughts using a nonparametric multivariate standardized drought index and copulas. Journal of Hydrologic Engineering, 24(5), 05019006.
  • Veilleux, A.G. Cohn, T.A. Flynn, K.M. Mason, R.R. Jr. and Hummel, P.R. (2014). Estimating magnitude and frequency of floods using the PeakFQ 7.0 program: U.S. Geological Survey Fact Sheet 2013-3108, 2 p.
  • Velghe, T. Troch, P. A. De Troch, F. P. & Van de Velde, J. (1994). Evaluation of cluster‐based rectangular pulses point process models for rainfall. Water Resources Research, 30(10), 2847-2857.
  • Veneziano, D. & Langousis, A. (2005a). The maximum of multifractal cascades: exact distribution and approximations. Fractals, 13(04), 311-324.
  • Veneziano, D. & Langousis, A. (2005b). The Areal Reduction Factor a Multifractal Analysis. Water Resour. Res. 41.
  • Veneziano, D. & Langousis, A. (2010) Scaling and Fractals in Hydrology, In: Advances in Data-based Approaches for Hydrologic Modeling and Forecasting, Edited by: B. Sivakumar and R. Berndtsson, World Scientific, 145p.
  • Veneziano, D. Langousis, A. & Furcolo, P. (2006). Multifractality and rainfall extremes: A review. Water resources research, 42(6).
  • Veneziano, D. Langousis, A. & Lepore, C. (2009). New Asymptotic and Pre-Asymptotic Results on Rainfall Maxima from Multifractal Theory. Water Resour. Res. 45.
  • Veneziano, D. Lepore, C. Langousis, A. & Furcolo, P. (2007). Marginal Methods of Intensity-duration-frequency Estimation in Scaling and Nonscaling Rainfall. Water Resour. Res. 43.
  • Veneziano, D. & Yoon, S. (2013). Rainfall extremes, excesses, and intensity–duration–frequency curves: a unified asymptotic framework and new non-asymptotic results based on multifractal measures. Water Resour. Res. 49 (7), 4320–4334.
  • Vermeer, M. & Rahmstorf, S. (2009). Global sea level linked to global temperature. Proceedings of the national academy of sciences, 106(51), 21527-21532.
  • Villarini, G. Smith, J. A. Baeck, M. L. Vitolo, R. Stephenson, D. B. & Krajewski, W. F. (2011). On the frequency of heavy rainfall for the Midwest of the United States. Journal of Hydrology, 400(1-2), 103-120.
  • Visessri, S. & McIntyre, N. (2016). Regionalisation of hydrological responses under land-use change and variable data quality. Hydrological Sciences Journal, 61(2), 302-320.
  • Vogel, R. M. (2010). Regional calibration of watershed models. In Watershed models (pp. 71-96). CRC Press.
  • Vogel, R. M. (2017). Stochastic watershed models for hydrologic risk management. Water Security, 1, 28-35.
  • Vogel, R. M. & Fennessey, N. M. (1993). L moment diagrams should replace product moment diagrams. Water resources research, 29(6), 1745-1752.
  • Vogel, R.M. A. Rosner, and P.H. Kirshen, (2013) Likelihood of Societal Preparedness for Global Change –Trend Detection, Natural Hazards and Earth System Science, Brief Communication. 13. 1-6.
  • Vogel, R. M. & Castellarin, A. (2017). Risk, reliability, and return periods and hydrologic design. Handbook of Applied Hydrology; Singh, VP, Ed.; McGraw-Hill Book Company: New York, NY, USA.
  • Volpi, E. (2019). On return period and probability of failure in hydrology. Wiley Interdisciplinary Reviews: Water, 6(3), e1340.
  • Volpi, E. & Fiori, A. (2012). Design event selection in bivariate hydrological frequency analysis. Hydrological Sciences Journal, 57(8), 1506-1515.
  • Volpi, E. & Fiori, A. (2014). Hydraulic structures subject to bivariate hydrological loads: Return period, design, and risk assessment. Water Resources Research, 50(2), 885-897.
  • Volpi, E. Fiori, A. Grimaldi, S. Lombardo, F. & Koutsoyiannis, D. (2015). One hundred years of return period: Strengths and limitations. Water Resources Research, 51(10), 8570-8585.
  • Volpi, E. Fiori, A. Grimaldi, S. Lombardo, F. & Koutsoyiannis, D. (2019). Save hydrological observations! Return period estimation without data decimation. Journal of Hydrology, 571, 782-792.
  • Vormoor, K. Skaugen, T. Langsholt, E. Diekkrüger, B. & Skøien, J. O. (2011). Geostatistical regionalization of daily runoff forecasts in Norway. Intl. J. River Basin Management, 9(1), 3-15.
  • Vorogushyn, S. & Merz, B. (2012). What drives flood trends along the Rhine River: climate or river training?. Hydrology & Earth System Sciences Discussions, 9(12).
  • Wahl, T. Jain, S. Bender, J. Meyers, S. D. & Luther, M. E. (2015). Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nature Climate Change, 5(12), 1093-1097.
  • Wang, W. & Ding, J. (2007). A multivariate non‐parametric model for synthetic generation of daily streamflow. Hydrological Processes: An International Journal, 21(13), 1764-1771.
  • Wang, W.P. Chen, Y.F. Becker, S. & Liu, B. (2015). Variance correction prewhitening method for trend detection in autocorrelated data. Journal of Hydrologic Engineering, 20(12), 04015033.
  • Westra, S. Fowler, H. J. Evans, J. P. Alexander, L. V. Berg, P. Johnson, F. … & Roberts, N. (2014). Future changes to the intensity and frequency of short‐duration extreme rainfall. Reviews of Geophysics, 52(3), 522-555.
  • Whitfield, P. H. Burn, D. H. Hannaford, J. Higgins, H. Hodgkins, G. A. Marsh, T. & Looser, U. (2012). Reference hydrologic networks I. The status and potential future directions of national reference hydrologic networks for detecting trends. Hydrological Sciences Journal, 57(8), 1562-1579.
  • Wilks, D. S. (1998). Multisite generalization of a daily stochastic precipitation generation model. journal of Hydrology, 210(1-4), 178-191.
  • Wilks, D. S. (1999). Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and forest meteorology, 93(3), 153-169.
  • Wilks, D. S. & Wilby, R. L. (1999). The weather generation game: a review of stochastic weather models. Progress in physical geography, 23(3), 329-357.
  • Wilson, L. L. Lettenmaier, D. P. & Skyllingstad, E. (1992). A hierarchical stochastic model of large‐scale atmospheric circulation patterns and multiple station daily precipitation. Journal of Geophysical Research: Atmospheres, 97(D3), 2791-2809.
  • Worthington, A. C. Higgs, H. & Hoffmann, M. (2009). Residential water demand modeling in Queensland, Australia: a comparative panel data approach. Water Policy, 11(4), 427-441.
  • Xu, P. Wang, D. Singh, V. P. Lu, H. Wang, Y. Wu, J. … & Zhang, J. (2020). Copula-based seasonal rainfall simulation considering nonstationarity. Journal of Hydrology, 590, 125439.
  • Xu, Z. Schumann, A. & Li, J. (2003). Markov cross-correlation pulse model for daily streamflow generation at multiple sites. Advances in Water Resources, 26(3), 325-335.
  • Yadav, M. Wagener, T. & Gupta, H. (2007). Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins. Advances in water resources, 30(8), 1756-1774.
  • Yaglom, A. M. (1966). The influence of fluctuations in energy dissipation on the shape of turbulence characteristics in the inertial interval. In Sov. Phys. Dokl. (Vol. 11, pp. 26-29).
  • Yevjevich, V. (1984). Extremes in hydrology. Statistical Extremes and Applications, 197-220.
  • Yilmaz, M. & Tosunoglu, F. (2019). Trend assessment of annual instantaneous maximum flows in Turkey. Hydrological Sciences Journal, 64(7), 820-834.
  • Yue, S. Ouarda, T. B. Bobée, B. Legendre, P. & Bruneau, P. (1999). The Gumbel mixed model for flood frequency analysis. Journal of hydrology, 226(1-2), 88-100.
  • Yue, S. Pilon, P. & Phinney, B. O. B. (2003). Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrological Sciences Journal, 48(1), 51-63.
  • Zaghloul, M. Papalexiou, S. M. Elshorbagy, A. & Coulibaly, P. (2020). Revisiting flood peak distributions: A pan-Canadian investigation. Advances in Water Resources, 145, 103720.
  • Zhang, L. S. V. P. & Singh, V. P. (2006). Bivariate flood frequency analysis using the copula method. Journal of hydrologic engineering, 11(2), 150-164.
  • Zhang, L. & Singh, V. P. (2019). Copulas and their applications in water resources engineering. Cambridge University Press.
  • Zhang, L. & Singh, V.P. (2007a), Gumbel-Hougaard copula for trivariate rainfall frequency analysis, Journal of Hydrologic Engineering, 12(4), 409-419.
  • Zhang, L. & Singh, V.P. (2007b), Trivariate flood frequency analysis using the Gumbel-Hougaard copula, Journal of Hydrologic Engineering, 12(4), 431-439
  • Zhang, Q. Gu, X. Singh, V. P. & Xiao, M. (2014). Flood frequency analysis with consideration of hydrological alterations: Changing properties, causes and implications. Journal of hydrology, 519, 803-813.
  • Zhu, D. Xuan, Y. & Cluckie, I. (2014). Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures. Hydrology and Earth System Sciences, 18(1), 257-272.
  • Zorzetto, E. Botter, G. & Marani, M. (2016). On the emergence of rainfall extremes from ordinary events. Geophysical Research Letters, 43(15), 8076-8082.
  • Zscheischler, J. Westra, S. Van Den Hurk, B. J. Seneviratne, S. I. Ward, P. J. Pitman, A. … & Zhang, X. (2018). Future climate risk from compound events. Nature Climate Change, 8(6), 469-477.
  • Zucchini, W. & Guttorp, P. (1991). A hidden Markov model for space‐time precipitation. Water Resources Research, 27(8), 1917-1923.

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