Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology

Advances in Water Resources

Volumn 111, Issue , 2018, Pages 132-155

  Serinaldi, Francesco ^a,b   Kilsby, Chris G ^a,b   Lombardo, Federico ^c  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b Towers Watson   (United Kingdom)

^c ROMA TRE UNIVERSITY   (Italy)

Author keywords

Ergodicity; Hypothesis test interpretation; Nonstationarity; Stream flow trends in the United States; Trend attribution; Trend hypothesis tests

Indexed keywords

HYDROLOGY; KALMAN FILTERS; RANDOM PROCESSES; SEMANTICS; STATISTICAL TESTS; STOCHASTIC SYSTEMS; TIME SERIES; TIME SERIES ANALYSIS;

ERGODICITY; HYDROLOGICAL TIME-SERIES; HYPOTHESIS TEST INTERPRETATION; HYPOTHESIS TESTS; NON-STATIONARITIES; NULL HYPOTHESIS; STREAM FLOW TREND IN THE UNITED STATE; TREND ATTRIBUTION; TREND HYPOTHESIS TEST;

STREAM FLOW;

ASSESSMENT METHOD; HUMAN ACTIVITY; HYDROLOGICAL CYCLE; HYPOTHESIS TESTING; STREAMFLOW; TREND ANALYSIS;

UNITED STATES;

EID: 85033557192     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2017.10.015     Document Type: Article

References (142)

1. Ahn, H., Chen, J.J., Generation of over-dispersed and under-dispersed binomial variates. J. Comput. Graph. Stat. 4:1 (1995), 55–64.
2. Ahn, K.-H., Palmer, R.N., Trend and variability in observed hydrological extremes in the United States. J. Hydrol. Eng. 21:2 (2016), 3518–3532.
3. Archfield, S.A., Hirsch, R.M., Viglione, A., Blöschl, G., Fragmented patterns of flood change across the United States. Geophys. Res. Lett., 2016 2016GL070590.
4. Ayalew, T.B., Krajewski, W.F., Mantilla, R., Wright, D.B., Small, S.J., Effect of spatially distributed small dams on flood frequency: Insights from the soap creek watershed. J. Hydrol. Eng., 22(7), 2017, 10.1061/(ASCE)HE.1943-5584.0001513 04017011–1–04017011–11.
5. Barrett, K.R., Salis, W., Prevalence and magnitude of trends in peak annual flow and 5-, 10-, and 20-year flows in the northeastern United States. J. Hydrol. Eng., 2016, 04016059, 10.1061/(ASCE)HE.1943-5584.0001474.
6. Bayazit, M., Nonstationarity of hydrological records and recent trends in trend analysis: A state-of-the-art review. Environ. Process. 2:3 (2015), 527–542.
7. Bayazit, M., Önöz, B., To prewhiten or not to prewhiten in trend analysis?. Hydrol. Sci. J. 52:4 (2007), 611–624.
8. Bayley, G.V., Hammersley, J.M., The “effective” number of independent observations in an autocorrelated time series. Suppl. J. R. Stat. Soc. 8:2 (1946), 184–197.
9. Beninger, P.G., Boldina, I., Katsanevakis, S., Strengthening statistical usage in marine ecology. J. Exper. Marine Biol. Ecol. 426-427 (2012), 97–108, 10.1016/j.jembe.2012.05.020.
10. Benjamini, Y., Hochberg, Y., Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57:1 (1995), 289–300.
11. Benjamini, Y., Yekutieli, D., The control of the false discovery rate in multiple testing under dependency. Annals Stat. 29:4 (2001), 1165–1188.
12. Berliner, L.M., Statistics, probability and chaos. Statist. Sci. 7:1 (1992), 69–90, 10.1214/ss/1177011444.
13. Briggs, W., Uncertainty: The Soul of Modeling, Probability & Statistics. 2016, Springer, New York, USA, 10.1007/978-3-319-39756-6.
14. Bunde, A., Eichner, J.F., Kantelhardt, J.W., Havlin, S., Long–term memory: A natural mechanism for the clustering of extreme events and anomalous residual times in climate records. Phys. Rev. Lett., 94, 2005, 048701.
15. Busuioc, A., von Storch, H., Changes in the winter precipitation in Romania and its relation to the large–scale circulation. Tellus A 48:4 (1996), 538–552.
16. Chandler, R., Scott, M., Statistical methods for trend detection and analysis in the environmental sciences. 2011, Wiley, Chichester, UK.
17. Cheng, L., AghaKouchak, A., Gilleland, E., Katz, R.W., Non-stationary extreme value analysis in a changing climate. Climatic Change 127:2 (2014), 353–369.
18. Christakos, G., Integrative Problem-Solving in a Time of Decadence. 2011, Springer, London, UK.
19. Clarke, R.T., On the (mis)use of statistical methods in hydro-climatological research. Hydrol. Sci. J. 55:2 (2010), 139–144, 10.1080/02626661003616819.
20. Cohen, J., The Earth is round (p < .05). American Psychologist, 1994, 997–1003.
21. Cooley, D., Return periods and return levels under climate change. AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., Sorooshian, S., (eds.) Extremes in a Changing Climate Water Science and Technology Library, 65, 2013, Springer Netherlands, 97–114, 10.1007/978-94-007-4479-0_4.
22. Daniel, J.S., Portmann, R.W., Solomon, S., Murphy, D.M., Identifying weekly cycles in meteorological variables: The importance of an appropriate statistical analysis. J. Geophys. Res.: Atmos., 117(D13), 2012, D13203.
23. Dickey, D.A., Fuller, W.A., Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74:366 (1979), 427–431.
24. Do, H.X., Westra, S., Leonard, M., A global-scale investigation of trends in annual maximum streamflow. J. Hydrol. 552 (2017), 28–43.
25. Douglas, E., Vogel, R., Kroll, C., Trends in floods and low flows in the United States: impact of spatial correlation. J. Hydrol. 240:1–2 (2000), 90–105.
26. Eco, U., Peirce's notion of interpretant. MLN 91:6 (1976), 1457–1472.
27. Eichner, J.F., Kantelhardt, J.W., Bunde, A., Havlin, S., The statistics of return intervals, maxima, and centennial events under the influence of long-term correlations. Kropp, J., Schellnhuber, H.-J., (eds.) In Extremis, 2011, Springer, Berlin, Heidelberg, 2–43.
28. Ellison, A.M., Gotelli, N.J., Inouye, B.D., Strong, D.R., P values, hypothesis testing, and model selection: it's déjà vu all over again. Ecology 95:3 (2014), 609–610.
29. Ferguson, C.R., Villarini, G., Detecting inhomogeneities in the Twentieth Century Reanalysis over the central United States. J. Geophys. Res. Atmosph., 117(D5), 2012, D05123.
30. Fisher, R.A., Tests of significance in harmonic analysis. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 125:796 (1929), 54–59.
31. Fisher, R.A., The design of experiments. ninth, 1935, Oliver and Boyd, Edinburgh, Scotland.
32. Flueck, J.A., Brown, T.J., Criteria and methods for performing and evaluating solar–weather studies. J. Climate 6:2 (1993), 373–385, 10.1175/1520-0442(1993)006<0373:CAMFPA>2.0.CO;2.
33. Franzke, C., A novel method to test for significant trends in extreme values in serially dependent time series. Geophys. Res. Lett. 40:7 (2013), 1391–1395.
34. Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., Krüger, L., The Empire of Chance: How Probability Changed Science and Everyday Life, New York, USA. 1989.
35. Gill, J., The insignificance of null hypothesis significance testing. Political Res. Q. 52:3 (1999), 647–674, 10.2307/449153.
36. Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N., Altman, D.G., Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur. J. Epidemiol. 31:4 (2016), 337–350.
37. Guerreiro, S.B., Kilsby, C.G., Serinaldi, F., Analysis of time variation of rainfall in transnational basins in Iberia: abrupt changes or trends?. Int. J. Climatol. 34:1 (2014), 114–133.
38. Hamed, K.H., Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. J. Hydrol. 349:3–4 (2008), 350–363.
39. Hamed, K.H., Effect of persistence on the significance of Kendall's tau as a measure of correlation between natural time series. Eur. Phys. J. Special Top. 174:1 (2009), 65–79.
40. Hamed, K.H., Enhancing the effectiveness of prewhitening in trend analysis of hydrologic data. J. Hydrol. 368:1–4 (2009), 143–155.
41. Hamed, K.H., The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data. Hydrol. Sci. J. 56:5 (2011), 841–853.
42. Hamed, K.H., Rao, A.R., A modified Mann–Kendall trend test for autocorrelated data. J. Hydrol. 204:1–4 (1998), 182–196.
43. Hasselmann, K., Optimal fingerprints for the detection of time-dependent climate change. J. Climate 6:10 (1993), 1957–1971, 10.1175/1520-0442(1993)006<1957:OFFTDO>2.0.CO;2.
44. Hasselmann, K., Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dyn. 13:9 (1997), 601–611, 10.1007/s003820050185.
45. Hirsch, R.M., Ryberg, K.R., Has the magnitude of floods across the USA changed with global CO2 levels?. Hydrol. Sci. J. 57:1 (2012), 1–9.
46. Jaynes, E.T., Probability Theory: The Logic of Science. 2003, Cambridge University Press.
47. Johnson, D.H., The insignificance of statistical significance testing. J. Wildlife Manag. 63:3 (1999), 763–772, 10.2307/3802789.
48. Kalra, A., Piechota, T.C., Davies, R., Tootle, G.A., Changes in U.S. streamflow and western U.S. snowpack. J. Hydrol. Eng. 13:3 (2008), 156–163.
49. Katz, R.W., Statistical procedures for making inferences about climate variability. J. Climate 1:11 (1988), 1057–1064.
50. Katz, R.W., Sir Gilbert Walker and a Connection between El Nio and Statistics. Stat. Sci. 17:1 (2002), 97–112.
51. Katz, R.W., Brown, B.G., The problem of multiplicity in research on teleconnections. Int. J. Climatol. 11:5 (1991), 505–513.
52. Kendall, M.G., Rank Correlation Methods. 4th, 1970, Griffin, London.
53. Khaliq, M.N., Ouarda, T.B.M.J., Gachon, P., Identification of temporal trends in annual and seasonal low flows occurring in Canadian rivers: the effect of short- and long-term persistence. J. Hydrol. 369:12 (2009), 183–197.
54. Khaliq, M.N., Ouarda, T.B.M.J., Gachon, P., Sushama, L., St-Hilaire, A., Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers. J. Hydrol. 368:1-4 (2009), 117–130.
55. Kingston, D.G., McGregor, G.R., Hannah, D.M., Lawler, D.M., Large-scale climatic controls on New England river flow. J. Hydrometeorol. 8:3 (2007), 367–379.
56. Koutsoyiannis, D., Climate change, the Hurst phenomenon, and hydrological statistics. Hydrol. Sci. J.l 48:1 (2003), 3–24.
57. Koutsoyiannis, D., HESS Opinions “A random walk on water”. Hydrol. Earth Syst. Sci. 14:3 (2010), 585–601.
58. Koutsoyiannis, D., Hurst-Kolmogorov dynamics and uncertainty. J. Am. Water Resourc. Assoc. 47:3 (2011), 481–495.
59. Koutsoyiannis, D., Generic and parsimonious stochastic modelling for hydrology and beyond. Hydrol. Sci. J. 61:2 (2016), 225–244.
60. Koutsoyiannis, D., Montanari, A., Statistical analysis of hydroclimatic time series: Uncertainty and insights. Water Resources Res., 43(5), 2007, W05429.
61. Koutsoyiannis, D., Montanari, A., Negligent killing of scientific concepts: the stationarity case. Hydrol. Sci. J. 60:7-8 (2015), 1174–1183.
62. Kugiumtzis, D., Test your surrogate data before you test for nonlinearity. Phys. Rev. E 60 (1999), 2808–2816.
63. Kulkarni, A., von Storch, H., Monte Carlo experiments on the effect of serial correlation on the Mann–Kendall test of trend. Meteorologische Zeitschrift 4:2 (1995), 82–85.
64. Kundzewicz, Z.W., Robson, A.J., Change detection in hydrological records–a review of the methodology. Hydrol. Sci. J. 49:1 (2004), 7–19.
65. Kwiatkowski, D., Phillips, P., Schmidt, P., Shin, Y., Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. J. Econ. 54:1–3 (1992), 159–178.
66. Laplace, P.S.M., A Philosophical Essay on Probabilities. 1, 1814, Dover Publications, New York, US 1995, translated from the sixth French edition.
67. Lettenmaier, D.P., Wood, E.F., Wallis, J.R., Hydro-climatological trends in the continental United States, 1948-88. J. Climate 7:4 (1994), 586–607.
68. Levine, T.R., Weber, R., Hullett, C., Park, H.S., Lindsey, L.L.M., A critical assessment of null hypothesis significance testing in quantitative communication research. Human Commun. Res. 34:2 (2008), 171–187, 10.1111/j.1468-2958.2008.00317.x.
69. Lins, H., 2012. Hydro-Climatic Data Network 2009 (HCDN-2009). U.S. Geological Survey Fact Sheet 2012–3047.
70. Lins, H.F., Cohn, T.A., Stationarity: wanted dead or alive?. J. Am. Water Resourc. Assoc. 47:3 (2011), 475–480.
71. Lins, H.F., Slack, J.R., Streamflow trends in the United States. Geophys. Res. Lett. 26:2 (1999), 227–230.
72. Livezey, R.E., Chen, W.Y., Statistical field significance and its determination by monte carlo techniques. Monthly Weather Rev. 111:1 (1983), 46–59, 10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.
73. Lombardo, F., Volpi, E., Koutsoyiannis, D., Papalexiou, S.M., Just two moments! A cautionary note against use of high-order moments in multifractal models in hydrology. Hydrol. Earth Syst. Sci. 18:1 (2014), 243–255.
74. Lorenz, E.N., Deterministic nonperiodic flow. J. Atmos. Sci. 20:2 (1963), 130–141, 10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.
75. Luke, A., Vrugt, J.A., AghaKouchak, A., Matthew, R., Sanders, B.F., Predicting nonstationary flood frequencies: evidence supports an updated stationarity thesis in the United States. Water Resources Research, 53, 2017.
76. Mallakpour, I., Villarini, G., The changing nature of flooding across the central United States. Nat. Climate Change 5:3 (2015), 250–254.
77. Mallakpour, I., Villarini, G., Analysis of changes in the magnitude, frequency, and seasonality of heavy precipitation over the contiguous USA. Theor. Appl. Climatol., 2016, 1–19.
78. Mandelbrot, B.B., The Fractal Geometry of Nature. 1982, Macmillan, New York, US.
79. Mann, H.B., Nonparametric tests against trend. Econometrica 13:3 (1945), 245–259.
80. Matalas, N.C., Sankarasubramanian, A., Effect of persistence on trend detection via regression. Water Resources Res., 39(12), 2003, 1342 1342.
81. McBride, G.B., Loftis, J.C., Adkins, N.C., What do significance tests really tell us about the environment?. Environ. Mana. 17:4 (1993), 423–432.
82. McCabe, G.J., Wolock, D.M., A step increase in streamflow in the conterminous United States. Geophys. Res. Lett. 29:24 (2002), 38.1–38.4 2185.
83. McCuen, R., Modeling Hydrologic Change: Statistical Methods. 2003, CRC Press.
84. McLeod, A.I., Hipel, K.W., Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon. Water Resources Res. 14:3 (1978), 491–508.
85. McLeod, A.I., Yu, H., Krougly, Z.L., Algorithms for linear time series analysis: with R package. J. Stat. Softw. 23:5 (2007), 1–26.
86. Meehl, P.E., The Problem is Epistemology, Not Statistics: Replace Significance tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions. 1997, Erlbaum, Mahwah, NJ (USA), 393–425.
87. Merz, B., Vorogushyn, S., Uhlemann, S., Delgado, J., Hundecha, Y., HESS Opinions “More efforts and scientific rigour are needed to attribute trends in flood time series”. Hydrol. Earth Syst. Sci. 16:5 (2012), 1379–1387.
88. Milly, P.C.D., Betancourt, J., Falkenmark, M., Hirsch, R.M., Kundzewicz, Z.W., Lettenmaier, D.P., Stouffer, R.J., Dettinger, M.D., Krysanova, V., On critiques of “Stationarity is dead: Whither water management?”. Water Resources Res.h 51:9 (2015), 7785–7789.
89. Mitchell, J.F.B., Karoly, D.J., Hegerl, G.C., Zwiers, F.W., Allen, M.R., Marengo, J., Detection of Climate Change and Attribution of Causes. 2001, Cambridge University Press, Cambridge, UK, 393–425.
90. Myers, D.E., To be or not to be.. stationary? That is the question. Math. Geol. 21:3 (1989), 347–362.
91. Neyman, J., Pearson, E.S., On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. A: Math., Phys. Eng. Sci. 231:694–706 (1933), 289–337, 10.1098/rsta.1933.0009.
92. Nicholls, N., commentary and analysis: The insignificance of significance testing. Bull. Am. Meteorol. Soc. 82:5 (2001), 981–986, 10.1175/1520-0477(2001)082<0981:CAATIO>2.3.CO;2.
93. Nilsen, I.B., Stagge, J.H., Tallaksen, L.M., A probabilistic approach for attributing temperature changes to synoptic type frequency. Int. J. Climatol., 2016, 10.1002/joc.4894.
94. Nuzzo, R., Statistical errors: P-values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume. Nature 506:7487 (2014), 150–152.
95. Olsen, J.R., Lambert, J.H., Haimes, Y.Y., Risk of extreme events under nonstationary conditions. Risk Anal. 18:4 (1998), 497–510, 10.1111/j.1539-6924.1998.tb00364.x.
96. Ouarda, T.B.M.J., El-Adlouni, S., Bayesian nonstationary frequency analysis of hydrological variables. J. Am. Water Resources Assoc. 47:3 (2011), 496–505.
97. Papoulis, A., Probability, Random Variables, and Stochastic Processes. Third, 1991, McGraw Hill, New York.
98. Pathak, P., Kalra, A., Ahmad, S., Bernardez, M., Wavelet-aided analysis to estimate seasonal variability and dominant periodicities in temperature, precipitation, and streamflow in the midwestern United States. Water Resources Manag.t 30:13 (2016), 4649–4665.
99. Pettitt, A.N., A non-parametric approach to the change-point problem. J. R. Stat. Soc. Series C (Appl. Stat.) 28:2 (1979), 126–135.
100. Pollard, P., Richardson, J.T., On the probability of making Type I errors. Psychol. Bull. 102:1 (1987), 159–163, 10.1037/0033-2909.102.1.159.
101. Poppick, A., Moyer, E.J., Stein, M.L., Estimating trends in the global mean temperature record. Adv. Stat. Climatol., Meteorol. Oceanogr. 3:1 (2017), 33–53, 10.5194/ascmo-3-33-2017.
102. Prosdocimi, I., Kjeldsen, T.R., Svensson, C., Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK. Nat. Hazards Earth Syst. Sci. 14:5 (2014), 1125–1144.
103. R Development Core Team, R: A Language and Environment for Statistical Computing. 2016, R Foundation for Statistical Computing, Vienna, Austria ISBN 3-900051-07-0.
104. Rice, J.S., Emanuel, R.E., Vose, J.M., The influence of watershed characteristics on spatial patterns of trends in annual scale streamflow variability in the continental U.S. J. Hydrol. 540 (2016), 850–860.
105. Rice, J.S., Emanuel, R.E., Vose, J.M., Nelson, S.A.C., Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics. Water Resour. Res. 51:8 (2015), 6262–6275.
106. Rootzén, H., Katz, R.W., Design Life Level: Quantifying risk in a changing climate. Water Resour. Res. 49:9 (2013), 5964–5972.
107. Rougé, C., Ge, Y., Cai, X., Detecting gradual and abrupt changes in hydrological records. Adv. Water Resour. 53 (2013), 33–44.
108. Sagarika, S., Kalra, A., Ahmad, S., Evaluating the effect of persistence on long-term trends and analyzing step changes in streamflows of the continental United States. J. Hydrol. 517 (2014), 36–53.
109. Salas, J.D., Obeysekera, J., Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J. Hydrol. Eng. 19:3 (2014), 554–568, 10.1061/(ASCE)HE.1943-5584.0000820.
110. Schreiber, T., Schmitz, A., Improved surrogate data for nonlinearity tests. Phys. Rev. Lett. 77 (1996), 635–638.
111. Schreiber, T., Schmitz, A., Surrogate time series. Phys. D: Nonlinear Phen. 142:3–4 (2000), 346–382.
112. Serinaldi, F., Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series. Physica A: Stat. Mech. Appl. 389:14 (2010), 2770–2781.
113. Serinaldi, F., Dismissing return periods!. Stoch. Environ. Res. Risk Assess. 29:4 (2015), 1179–1189, 10.1007/s00477-014-0916-1.
114. Serinaldi, F., Kilsby, C.G., Stationarity is undead: Uncertainty dominates the distribution of extremes. Adv. Water Resourc. 77 (2015), 17–36.
115. Serinaldi, F., Kilsby, C.G., The importance of prewhitening in change point analysis under persistence. Stochastic Environmental Research and Risk Assessment 30:2 (2016), 763–777.
116. Serinaldi, F., Kilsby, C.G., Irreversibility and complex network behavior of stream flow fluctuations. Phys. A: Stat. Mech. Appl. 450 (2016), 585–600.
117. Serinaldi, F., Kilsby, C.G., Understanding persistence to avoid underestimation of collective flood risk. Water, 8(4), 2016, 152.
118. Serinaldi, F., Lombardo, F., General simulation algorithm for autocorrelated binary processes. Phys. Rev. E, 95, 2017, 023312, 10.1103/PhysRevE.95.023312.
119. Sivakumar, B., Chaos in Hydrology: Bridging Determinism and Stochasticity. 2016, Springer, Dordrecht, Netherlands.
120. Skellam, J.G., A probability distribution derived from the binomial distribution by regarding the probability of success as variable between the sets of trials. J. R. Stat. Soc. Ser. B (Methodol.) 10:2 (1948), 257–261.
121. von Storch, H., Zwiers, F.W., Statistical Analysis in Climate Research. 2003, Cambridge University Press, New York, US.
122. Tananaev, N.I., Makarieva, O.M., Lebedeva, L.S., Trends in annual and extreme flows in the Lena River basin, Northern Eurasia. Geophys. Res. Lett. 43:20 (2016), 10764–10772.
123. Tramblay, Y., El Adlouni, S., Servat, E., Trends and variability in extreme precipitation indices over Maghreb countries. Nat. Hazards Earth Syst. Sci. 13:12 (2013), 3235–3248.
124. Tyralis, H., Koutsoyiannis, D., Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process. Stoch. Environ. Res. Risk Assess. 25:1 (2011), 21–33.
125. Venema, V., Ament, F., Simmer, C., A stochastic iterative amplitude adjusted fourier transform algorithm with improved accuracy. Nonlinear Process. Geophys. 13:3 (2006), 321–328.
126. Venema, V., Bachner, S., Rust, H.W., Simmer, C., Statistical characteristics of surrogate data based on geophysical measurements. Nonlinear Processes in Geophysics 13:4 (2006), 449–466.
127. Viglione, A., Merz, B., Viet Dung, N., Parajka, J., Nester, T., Blschl, G., Attribution of regional flood changes based on scaling fingerprints. Water Resourc. Res. 52:7 (2016), 5322–5340.
128. Villarini, G., Serinaldi, F., Smith, J.A., Krajewski, W.F., On the stationarity of annual flood peaks in the continental United States during the 20th century. Water Resour. Res., 45(8), 2009, W08417.
129. Villarini, G., Smith, J.A., Flood peak distributions for the eastern United States. Water Resour. Res., 46(6), 2010, W06504.
130. Villarini, G., Smith, J.A., Baeck, M.L., Krajewski, W.F., Examining flood frequency distributions in the Midwest U.S. J. Am. Water Resourc. Assoc. 47:3 (2011), 447–463.
131. Villarini, G., Smith, J.A., Serinaldi, F., Bales, J., Bates, P.D., Krajewski, W.F., Flood frequency analysis for nonstationary annual peak records in an urban drainage basin. Adv. Water Resourc. 32:8 (2009), 1255–1266, 10.1016/j.advwatres.2009.05.003.
132. Villarini, G., Smith, J.A., Serinaldi, F., Ntelekos, A.A., Analyses of seasonal and annual maximum daily discharge records for central Europe. J. Hydrol. 399:3-4 (2011), 299–312.
133. Vogel, R.M., Yaindl, C., Walter, M., Nonstationarity: flood magnification and recurrence reduction factors in the United States. J. Am. Water Resourc. Assoc. 47:3 (2011), 464–474.
134. Volpi, E., Fiori, A., Grimaldi, S., Lombardo, F., Koutsoyiannis, D., One hundred years of return period: Strengths and limitations. Water Resourc. Res. 51:10 (2015), 8570–8585.
135. von Storch, H., Misuses of statistical analysis in climate research. von Storch, H., Navarra, A., (eds.) Analysis of Climate Variability, 1999, Springer, Dordrecht, 11–26.
136. Wasserstein, R.L., Lazar, N.A., The ASA's statement on p-values: context, process, and purpose. Am. Stat. 70:2 (2016), 129–133.
137. Wilks, D.S., Resampling hypothesis tests for autocorrelated fields. J. Climate 10:1 (1997), 65–82.
138. Wilks, D.S., On “field significance” and the false discovery rate. J. Appl. Meteorol. Climatol. 45:9 (2006), 1181–1189.
139. Yevjevich, V., Determinism and stochasticity in hydrology. J. Hydrol. 22:3 (1974), 225–238 https://doi.org/10.1016/0022-1694(74)90078-X.
140. Yue, S., Pilon, P., Phinney, B., Cavadias, G., The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Process 16:9 (2002), 1807–1829.
141. Yue, S., Wang, C., Applicability of prewhitening to eliminate the influence of serial correlation on the Mann–Kendall test. Water Resour. Res. 38:6 (2002), 41–47.
142. Yue, S., Wang, C.-Y., The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour. Manag. 18:3 (2004), 201–218.