Assessing the Regional Concept with Sub-Sampling Approach to Identify Probability Distribution for at-Site Hydrological Frequency Analysis

The framework of regional analysis allows superior discrimination as well as better identification of the shape of a population distribution in a hydrological frequency analysis. The aim of this study is to incorporate the better of regional concept while performing an at-site frequency analysis. The study proposes a new method in the form of sub-sampling technique with the aid of a regional distribution selection procedure to choose an appropriate probability distribution function for frequency analysis. The technique is evaluated against common distribution selection methods: a widely used goodness-of-fit method in Anderson–Darling (AD) and a popular graphical assessment tool in L-moment ratio diagram (LMRD). The performance is evaluated by applying the technique to gauged annual maximum daily precipitation data series of 24 stations located across China. It is found that the technique accomplished a better performance in discriminating among distributions which or else may not be achievable only by the AD or LMRD test. In general, all results indicate that the proposed technique can be an attractive means in discriminating as well as identifying the best distribution for at-site frequency analysis.

[1]  R. D'Agostino,et al.  Goodness-of-Fit-Techniques , 1987 .

[2]  Michael A. Stephens,et al.  Tests Based on EDF Statistics , 2017 .

[3]  S. Karmakar,et al.  Regionalized Design Rainfall Estimation: an Appraisal of Inundation Mapping for Flood Management Under Data-Scarce Situations , 2018, Water Resources Management.

[4]  J. Kyselý,et al.  Comparison of regional and at‐site approaches to modelling probabilities of heavy precipitation , 2011 .

[5]  Giuliano Di Baldassarre,et al.  Model selection techniques for the frequency analysis of hydrological extremes , 2009 .

[6]  F. Laio Cramer–von Mises and Anderson‐Darling goodness of fit tests for extreme value distributions with unknown parameters , 2004 .

[7]  R. Vogel,et al.  L moment diagrams should replace product moment diagrams , 1993 .

[8]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[9]  J. Heo,et al.  Approximation of modified Anderson–Darling test statistics for extreme value distributions with unknown shape parameter , 2013 .

[10]  T. Jiang,et al.  Regional frequency analysis of observed sub-daily rainfall maxima over eastern China , 2017, Advances in Atmospheric Sciences.

[11]  Alessandro Fassò,et al.  A statistical approach to crowdsourced smartphone-based earthquake early warning systems , 2015, Stochastic Environmental Research and Risk Assessment.

[12]  J. R. Wallis,et al.  Regional Frequency Analysis: An Approach Based on L-Moments , 1997 .

[13]  Richard M. Vogel,et al.  Flood-Flow Frequency Model Selection in Southwestern United States , 1993 .

[14]  C. Cunnane Methods and merits of regional flood frequency analysis , 1988 .

[15]  A. Hutson,et al.  Joint confidence region estimation of L-moment ratios with an extension to right censored data , 2013, Journal of applied statistics.

[16]  Q. J. Wang,et al.  The utility of L-moment ratio diagrams for selecting a regional probability distribution , 2001 .

[17]  Yuan-Fong Su,et al.  Establishing acceptance regions for L-moments based goodness-of-fit tests for the Pearson type III distribution , 2012, Stochastic Environmental Research and Risk Assessment.

[18]  M. Bayazit,et al.  Best-fit distributions of largest available flood samples , 1995 .

[19]  M. J. Hall,et al.  Regional flood frequency analysis for the Gan-Ming River basin in China , 2004 .

[20]  Marco Borga,et al.  Regional frequency analysis of extreme precipitation in the eastern Italian Alps and the August 29, 2003 flash flood , 2007 .

[21]  P. Claps,et al.  A comparison of homogeneity tests for regional frequency analysis , 2007 .

[22]  Christine A. Shoemaker,et al.  Introduction to special section on Uncertainty Assessment in Surface and Subsurface Hydrology: An overview of issues and challenges , 2009 .

[23]  C. Cunnane Statistical distributions for flood frequency analysis , 1989 .

[24]  Ataur Rahman,et al.  Regional flood frequency analysis in arid regions : a case study for Australia , 2012 .

[25]  C. Cunnane,et al.  Performance of flood frequency pooling analysis in a low CV context , 2012 .

[26]  T. Kjeldsen,et al.  A bivariate extension of the Hosking and Wallis goodness‐of‐fit measure for regional distributions , 2015 .

[27]  Samiran Das Goodness-of-Fit Tests for Generalized Normal Distribution for Use in Hydrological Frequency Analysis , 2018, Pure and Applied Geophysics.

[28]  V. Singh,et al.  Precipitation extremes in the Yangtze River Basin, China: regional frequency and spatial–temporal patterns , 2014, Theoretical and Applied Climatology.

[29]  M. Bryson Heavy-Tailed Distributions: Properties and Tests , 1974 .

[30]  C. R. de Mello,et al.  At-Site Flood Frequency Analysis Coupled with Multiparameter Probability Distributions , 2017, Water Resources Management.

[31]  H. Fowler,et al.  A regional frequency analysis of United Kingdom extreme rainfall from 1961 to 2000 , 2003 .

[32]  Q. Shao,et al.  Regional frequency analysis and spatio-temporal pattern characterization of rainfall extremes in the Pearl River Basin, China , 2010 .

[33]  J. R. Wallis,et al.  Regional precipitation-frequency analysis and spatial mapping for 24-hour and 2-hour durations for Washington State , 2007 .

[34]  Samiran Das,et al.  An assessment of using subsampling method in selection of a flood frequency distribution , 2017, Stochastic Environmental Research and Risk Assessment.

[35]  Chandranath Chatterjee,et al.  Regional Flood Frequency Analysis using Soft Computing Techniques , 2015, Water Resources Management.

[36]  Yiping Guo,et al.  L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis , 2017, Water Resources Management.

[37]  Alberto Guadagnini,et al.  Convergence assessment of numerical Monte Carlo simulations in groundwater hydrology , 2004 .