Articles | Volume 24, issue 1
https://doi.org/10.5194/hess-24-473-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-473-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2020
Research article |
| 29 Jan 2020
| 29 Jan 2020
Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series
Vincenzo Totaro
CORRESPONDING AUTHOR
Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, Bari, 70125, Italy
Andrea Gioia
Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, Bari, 70125, Italy
Vito Iacobellis
Dipartimento di Ingegneria Civile, Ambientale, del Territorio, Edile e di Chimica (DICATECh), Politecnico di Bari, Bari, 70125, Italy
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Spatially dependent flood probabilities to support the design of civil infrastructure systems
Technical note: Stochastic simulation of streamflow time series using phase randomization
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 2: numerical application
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic
Dealing with uncertainty in the probability of overtopping of a flood mitigation dam
Flood frequency analysis of historical flood data under stationary and non-stationary modelling
Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland
Towards modelling flood protection investment as a coupled human and natural system
A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation
Examination of homogeneity of selected Irish pooling groups
Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models
Design flood hydrographs from the relationship between flood peak and volume
Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function
HESS Opinions "A random walk on water"
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023, https://doi.org/10.5194/hess-27-2075-2023, 2023
Short summary
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Design rainfall volumes at different duration and frequencies are necessary for the planning of water-related systems and facilities. As the procedure for deriving these values is subjected to different sources of uncertainty, here we explore different methods to estimate how precise these values are for different duration, locations and frequencies in Germany. Combining local and spatial simulations, we estimate tolerance ranges from approx. 10–60% for design rainfall volumes in Germany.
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
Short summary
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
Short summary
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This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, https://doi.org/10.5194/hess-25-5535-2021, 2021
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We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850, https://doi.org/10.5194/hess-25-831-2021, https://doi.org/10.5194/hess-25-831-2021, 2021
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In monitoring network design, we have to decide what to measure, where to measure, and when to measure. In this paper, we focus on the question of where to measure. Past literature has used the concept of information to choose a selection of locations that provide maximally informative data. In this paper, we look in detail at the proper mathematical formulation of the information concept as an objective. We argue that previous proposals for this formulation have been needlessly complicated.
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, https://doi.org/10.5194/hess-24-3967-2020, 2020
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Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Phuong Dong Le, Michael Leonard, and Seth Westra
Hydrol. Earth Syst. Sci., 23, 4851–4867, https://doi.org/10.5194/hess-23-4851-2019, https://doi.org/10.5194/hess-23-4851-2019, 2019
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While conventional approaches focus on flood designs at individual locations, there are many situations requiring an understanding of spatial dependence of floods at multiple locations. This research describes a new framework for analyzing flood characteristics across civil infrastructure systems, including conditional and joint probabilities of floods. This work leads to a new flood estimation paradigm, which focuses on the risk of the entire system rather than each system element in isolation.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
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This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 23, 1683–1704, https://doi.org/10.5194/hess-23-1683-2019, https://doi.org/10.5194/hess-23-1683-2019, 2019
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We present the methods addressing the multivariate hydrologic design applied to the engineering practice under nonstationary conditions. A dynamic C-vine copula allowing for both time-varying marginal distributions and a time-varying dependence structure is developed to capture the nonstationarities of multivariate flood distribution. Then, the multivariate hydrologic design under nonstationary conditions is estimated through specifying the design criterion by average annual reliability.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
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A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
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A newly proposed method is applied to solve a stochastic unsteady open-channel flow system (with an uncertain roughness coefficient) in only one simulation. After comparing its results to those of the Monte Carlo simulations, the new method was found to adequately predict the temporal and spatial evolution of the probability density of the flow variables of the system. This revealed the effectiveness, strength, and time efficiency of this new method as compared to other popular approaches.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
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Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
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In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
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A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
B. Guse, Th. Hofherr, and B. Merz
Hydrol. Earth Syst. Sci., 14, 2465–2478, https://doi.org/10.5194/hess-14-2465-2010, https://doi.org/10.5194/hess-14-2465-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Control, 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Beven, K.: Facets of uncertainty: Epistemic uncertainty, non-stationarity,
likelihood, hypothesis testing, and communication, Hydrolog. Sci. J., 61, 1652–1665, https://doi.org/10.1080/02626667.2015.1031761, 2016.
Burnham, K. P. and Anderson, D. R.: Model selection and multimodel inference, Springer, New York, 2004.
Cheng, L., AghaKouchak, A., Gilleland, E., and Katz, R. W.: Non-stationary
extreme value analysis in a changing climate, Climatic Change, 127, 353–369, https://doi.org/10.1007/s10584-014-1254-5, 2014.
Chow, V. T. (Ed.): Statistical and probability analysis of hydrologic data, in: Handbook of applied hydrology, McGraw-Hill, New York, 8.1–8.97, 1964.
Cohen, J.: A power primer, Psycholog. Bull., 112, 155–159, 1992.
Cohen, J.: The Earth Is Round (p<.05), Am. Psychol., 49, 997–1003, 1994.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer, London, 2001.
Cooley, D.: Return Periods and Return Levels Under Climate Change, in:
Extremes in a Changing Climate, edited by: AghaKouchak, A., Easterling, D.,
Hsu, K., Schubert, S., and Sorooshian, S., Springer, Dordrecht, 97–113,
https://doi.org/10.1007/978-94-007-4479-0_4, 2013.
Du, T., Xiong, L., Xu, C. Y., Gippel, C. J., Guo, S., and Liu, P.: Return
period and risk analysis of nonstationary low-flow series under climate change, J. Hydrol., 527, 234–250, https://doi.org/10.1016/j.jhydrol.2015.04.041, 2015.
Eagleson, P. S.: Dynamics of flood frequency, Water Resour. Res., 8, 878–898,
https://doi.org/10.1029/WR008i004p00878, 1972.
Fiorentino, M., Gabriele, S., Rossi, F., and Versace, P.: Hierarchical approach for regional flood frequency analysis, in: Regional Flood Frequency
Analysis, edited by: Singh, V. P., D. Reidel, Norwell, Massachusetts, 35-49, 1987.
Gilleland, E. and Katz, R. W.: extRemes 2.0: An Extreme Value Analysis Package in R, J. Stat. Soft., 72, 1–39, 2016.
Gioia, A., Iacobellis, V., Manfreda, S., and Fiorentino, M.: Runoff thresholds in derived flood frequency distributions, Hydrol. Earth Syst. Sci., 12, 1295–1307, https://doi.org/10.5194/hess-12-1295-2008, 2008.
Gocic, M. and Trajkovic, S.: Analysis of changes in meteorological variables
using Mann-Kendall and Sen's slope estimator statistical tests in Serbia,
Global Planet. Change, 100, 172–182, https://doi.org/10.1016/j.gloplacha.2012.10.014, 2013.
Iacobellis, V., Gioia, A., Manfreda, S., and Fiorentino, M.: Flood quantiles estimation based on theoretically derived distributions: regional analysis in Southern Italy, Nat. Hazards Earth Syst. Sci., 11, 673–695, https://doi.org/10.5194/nhess-11-673-2011, 2011.
Jenkinson, A. F.: The frequency distribution of the annual maximum (or minimum) values of meteorological elements, Q. J. Roy. Meteorol. Soc., 81,
158–171, 1955.
Kendall, M. G.: Rank Correlation Methods, 4th Edn., Charles Griffin, London,
UK, 1975.
Khintchine, A.: Korrelationstheorie der stationären stochastischen
Prozesse, Math. Ann., 109, 604–615, https://doi.org/10.1007/BF01449156, 1934.
Kiely, G.: Climate change in Ireland from precipitation and streamflow
observations, Adv. Water Resour., 23, 141–151, https://doi.org/10.1016/S0309-1708(99)00018-4, 1999.
Kochanek, K., Strupczewski, W. G., Bogdanowicz, E., Feluch, W., and Markiewicz, I.: Application of a hybrid approach in nonstationary flood frequency analysis – a Polish perspective, Nat. Hazards Earth Syst. Sci. Discuss., 1, 6001–6024, https://doi.org/10.5194/nhessd-1-6001-2013, 2013.
Kolmogorov, A. N.: Über die analytischen Methoden in der Wahrscheinlichkeitsrechnung. Mathematische Annalen, 104, 415–458 (English translation: On analytical methods in probability theory, in: Kolmogorov, A. N., 1992. Selected Works of A. N. Kolmogorov – Volume 2, Probability Theory and Mathematical Statistics, edited by: Shiryayev, A. N., Kluwer, Dordrecht, the Netherlands, 62–108, https://doi.org/10.1007/BF01457949, 1931.
Koutsoyiannis, D. and Montanari A.: Negligent killing of scientific concepts: the stationarity case, Hydrolog. Sci. J., 60, 1174–1183,
https://doi.org/10.1080/02626667.2014.959959, 2015.
Kundzewicz, Z. W. and Robson, A. J.: Change detection in hydrological records – a review of the methodology, Hydrolog. Sci. J., 49, 7–19,
https://doi.org/10.1623/hysj.49.1.7.53993, 2004.
Laio, F., Baldassarre, G. D., and Montanari, A.: Model selection techniques
for the frequency analysis of hydrological extremes, Water Resour. Res.,
45, W07416, https://doi.org/10.1029/2007wr006666, 2009.
Lehmann, E. L.: Nonparametrics, Statistical Methods Based on Ranks, Holden-Day, Oxford, England, 1975
Lieber, R. L.: Statistical significance and statistical power in hypothesis
testing, J. Orthop. Res., 8, 304–309, https://doi.org/10.1002/jor.1100080221, 1990.
Madsen, H., Rasmussen, P., and Rosbjerg, D.: Comparison of annual maximum
series and partial duration series for modelling exteme hydrological events:
1. At-site modeling, Water Resour. Res., 33, 747–757, https://doi.org/10.1029/96WR03848, 1997.
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., Stouffer, R. J., Dettinger, M. D., and Krysanova,
V.: On Critiques of “stationarity is Dead: Whither Water Management?”, Water Resour. Res., 51, 7785–7789, https://doi.org/10.1002/2015WR017408, 2015.
Montanari, A. and Koutsoyiannis, D.: Modeling and mitigating natural hazards: Stationarity is immortal!, Water Resour. Res., 50, 9748–9756,
https://doi.org/10.1002/2014wr016092, 2014.
Montanari, A., Young, G., Savenije, H. H. G., Hughes, D., Wagener, T., Ren, L. L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S. J., Di Baldassarre, G., Yu, B., Hubert, P.,
Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S.,
Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and
Belyaev, V.: “Panta Rhei—Everything Flows”: Change in hydrology and
society – The IAHS Scientific Decade 2013–2022, Hydrolog. Sci. J., 58, 1256–1275, 2013.
Muraleedharan, G., Guedes Soares, C., and Lucas, C.: Characteristic and moment generating functions of generalised extreme value distribution (GEV),
in: Sea Level Rise, Coastal Engineering, Shorelines and Tides, Nova Science, New York, 2010.
Olsen, J. R., Lambert, J. H., and Haimes, Y. Y.: Risk of extreme events under nonstationary conditions, Risk Anal., 18, 497–510,
https://doi.org/10.1111/j.1539-6924.1998.tb00364.x, 1998.
Parey, S., Malek, F., Laurent, C., and Dacunha-Castelle, D.: Trends and climate evolution: statistical approach for very high temperatures in
France, Climatic Change, 81, 331–352, https://doi.org/10.1007/s10584-006-9116-4, 2007.
Parey, S., Hoang, T. T. H., and Dacunha-Castelle, D.: Different ways to compute temperature return levels in the climate change context, Environmetrics, 21, 698–718, https://doi.org/10.1002/env.1060, 2010.
Pettitt, A. N.: A non-parametric approach to the change-point problem, Appl.
Stat., 28, 126–135, https://doi.org/10.2307/2346729, 1979.
Read, L. K. and Vogel, R. M.: Reliability, return periods, and risk under
nonstationarity, Water Resour. Res., 51, 6381–6398,
https://doi.org/10.1002/2015WR017089, 2015.
Rosbjerg, D., Blöschl, G., Burn, D., Castellarin, A., Croke, B., Di Baldassarre, G., Iacobellis, V., Kjeldsen, T. R., Kuczera, G., Merz, R.,
Montanari, A., Morris, D., Ouarda, T. B. M. J., Ren, L., Rogger, M., Salinas, J. L., Toth, E., and Viglione, A.: Prediction of floods in ungauged basins, in: Runoff Prediction in Ungauged Basins: Synthesis across Processes, Places and Scales, edited by: Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H., Cambridge University Press, Cambridge, 189–226, https://doi.org/10.1017/CBO9781139235761.012, 2013.
Rossi, F., Fiorentino, M., and Versace, P.: Two-Component Extreme Value
Distribution for Flood Frequency Analysis, Water Resour. Res., 20, 847–856,
https://doi.org/10.1029/WR020i007p00847, 1984.
Salas, J. D. and Obeysekera, J.: Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events, J. Hydrol. Eng., 19, 554–568, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820, 2014.
Schwarz, G.: Estimating the dimension of a model, Ann. Stat., 6, 461–464,
1978.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.2307/2285891, 1968.
Serinaldi, F. and Kilsby, C. G.: Stationarity is undead: Uncertainty dominates the distribution of extremes, Adv. Water Resour., 77, 17–36,
https://doi.org/10.1016/j.advwatres.2014.12.013, 2015.
Serinaldi, F., Kilsby, C. G., and Lombardo, F.: Untenable nonstationarity:
An assessment of the fitness for purpose of trend tests in hydrology, Adv.
Water Resour., 111, 132–155, https://doi.org/10.1016/J.ADVWATRES.2017.10.015, 2018.
Sivapalan, M., Prediction in Ungauged Basins: A Grand Challenge for Theoretical Hydrology, Hydrol. Process., 17, 3163–3170, 2003.
Smadi, M. M. and Zghoul A.: A sudden change in rainfall characteristics in
Amman, Jordan during the mid 1950s, Am. J. Environ. Sci., 2, 84–91,
https://doi.org/10.3844/ajessp.2006.84.91, 2006.
Strupczewski, W. G., Kochanek, K., Bogdanowicz, E., Markiewicz, I., and Feluch, W.: Comparison of two nonstationary flood frequency analysis methods
within the context of the variable regime in the representative polish rivers, Acta Geophys., 64, 206–236, https://doi.org/10.1515/acgeo-2015-0070, 2016.
Sugiura, N.: Further analysis of the data by Akaike's information criterion
and the finite corrections, Commun. Stat. Theor. Meth., A7, 13–26,
https://doi.org/10.1080/03610927808827599, 1978.
Todorovic, P. and Zelenhasic, E.: A Stochastic Model for Flood Analysis, Water Resour. Res., 6, 1641–1648, https://doi.org/10.1029/WR006i006p01641, 1970.
Tramblay, Y., Neppel, L., Carreau, J., and Najib, K.: Non-stationary frequency analysis of heavy rainfall events in southern France, Hydrolog. Sci. J., 58, 280–194, https://doi.org/10.1080/02626667.2012.754988, 2013.
Vogel, R. M., Rosner, A., and Kirshen, P. H.: Brief Communication: Likelihood of societal preparedness for global change: trend detection, Nat. Hazards Earth Syst. Sci., 13, 1773–1778, https://doi.org/10.5194/nhess-13-1773-2013, 2013.
Wang, W., Van Gelder, P. H., and Vrijling, J. K.: Trend and stationarity
analysis for streamflow processes of rivers in Western Europe in the 20th Century, in: IWA International Conference on Water Economics, Statistics, and Finance, 8–10 July 2005, Rethymno, Greece, 2005.
Yilmaz, A. G. and Perera, B. J. C.: Extreme rainfall nonstationarity
investigation and intensity–frequency–duration relationship, J. Hydrol. Eng., 19, 1160–1172, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000878, 2014.
Yilmaz, A. G., Hossain, I., and Perera, B. J. C.: Effect of climate change
and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne, Hydrol. Earth Syst. Sci., 18,
4065–4076, https://doi.org/10.5194/hess-18-4065-2014, 2014.
Yue, S., Pilon, P., and Cavadias, G.: Power of the Mann–Kendall and
Spearman's rho tests for detecting monotonic trends in hydrological series,
J. Hydrol., 259, 254–271, https://doi.org/10.1016/S0022-1694(01)00594-7, 2002a.
Yue, S., Pilon, P., Phinney, B., and Cavadias, G.: The influence of autocorrelation on the ability to detect trend in hydrological series, Hydrol. Process., 16, 1807–1829, https://doi.org/10.1002/hyp.1095, 2002b.
Short summary
We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.
We highlight the need for power evaluation in the application of null hypothesis significance...
