Copula based drought frequency analysis considering the spatio-temporal variability in Southwest China

Summary Drought frequency analysis is a prerequisite for drought resistance planning and drought risk management. Drought is a spatio-temporal dynamic process, usually characterized by its duration, spatial extent, and severity. Copula based multivariate frequency analysis has been widely used to calculate drought frequency. However, the spatial extent is scarcely considered in previous studies, due to the fact that drought event is usually identified either for a fixed spatial scale or for a fixed temporal scale. This study develops a regional drought frequency analysis model based on trivariate copulas by considering the spatio-temporal variations of drought events. Drought duration, drought affect area, and drought severity are identified first, and their trivariate joint distribution is constructed later. The model is applied for drought frequency analysis in Southwest China during 1961–2012. A variety of probability distribution functions and copula functions (including elliptical, symmetric and asymmetric Archimedean) are used as candidate choices, and the most appropriate ones are selected based on goodness of fit using different methods. The robustness of drought frequency analysis is then evaluated and discussed. The results show that drought frequency analysis needs to fully consider the three characteristic parameters (duration, affect area, and severity) reflecting drought spatio-temporal variability. And the drought return period estimated by the copula-based trivariate frequency analysis appropriately integrates the effects of drought duration, affect area and severity, which is a reliable drought statistical measurement. The 2009–2010 drought, which has a return period of about 94 years, is the most severe one in Southwest China during the period of 1961–2012. The Joe and Gumbel copulas are found to be more suitable to estimate the joint distribution of drought duration, affect area and severity, and the Asymmetric (nested) function forms perform better than the symmetric functions.

[1]  V. Singh,et al.  A review of drought concepts , 2010 .

[2]  J. Salas,et al.  Drought length properties for periodic‐stochastic hydrologic data , 2004 .

[3]  Quan J. Wang Estimation of the GEV distribution from censored samples by method of partial probability weighted moments , 1990 .

[4]  S. Pecora,et al.  Multivariate assessment of droughts: Frequency analysis and dynamic return period , 2013 .

[5]  Shanhu Jiang,et al.  Multivariate drought characteristics using trivariate Gaussian and Student t copulas , 2013 .

[6]  Marius Hofert,et al.  Nested Archimedean Copulas Meet R: The nacopula Package , 2011 .

[7]  R. Nelsen An Introduction to Copulas , 1998 .

[8]  Benjamin Lloyd-Hughes,et al.  A spatio‐temporal structure‐based approach to drought characterisation , 2012 .

[9]  M. Sklar Fonctions de repartition a n dimensions et leurs marges , 1959 .

[10]  J. Valdes,et al.  Nonparametric Approach for Estimating Return Periods of Droughts in Arid Regions , 2003 .

[11]  R. Stouffer,et al.  Stationarity Is Dead: Whither Water Management? , 2008, Science.

[12]  T. McKee,et al.  THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES , 1993 .

[13]  Vijay P. Singh,et al.  Entropy-Based Method for Bivariate Drought Analysis , 2013 .

[14]  Dennis P. Lettenmaier,et al.  Soil Moisture Drought in China, 1950–2006 , 2011 .

[15]  Vijay P. Singh,et al.  A bivariate mixed distribution with a heavy‐tailed component and its application to single‐site daily rainfall simulation , 2013 .

[16]  Q. J. Wang Using partial probability weighted moments to fit the extreme value distributions to censored samples , 1996 .

[17]  Antonino Cancelliere,et al.  An analytical formulation of return period of drought severity , 2003 .

[18]  Yali Luo,et al.  Regional atmospheric anomalies responsible for the 2009–2010 severe drought in China , 2011 .

[19]  On the use of partial probability weighted moments in the analysis of hydrological extremes , 2007 .

[20]  Kelin Wang,et al.  Is southwestern China experiencing more frequent precipitation extremes? , 2014 .

[21]  Donald A. Wilhite,et al.  Drought : a global assessment , 2000 .

[22]  Jenq-Tzong Shiau,et al.  Return period of bivariate distributed extreme hydrological events , 2003 .

[23]  H. Moradkhani,et al.  Assessment of Climate Change Impacts on Drought Returns Periods Using Copula , 2011 .

[24]  Jenq-Tzong Shiau,et al.  Fitting Drought Duration and Severity with Two-Dimensional Copulas , 2006 .

[25]  G. Marsaglia,et al.  Evaluating the Anderson-Darling Distribution , 2004 .

[26]  Yang Li,et al.  Calculation Higher Probability Weighted Moments for Generalized Extreme Value in Hydrology , 2012 .

[27]  Fadhilah Yusof,et al.  Characterisation of Drought Properties with Bivariate Copula Analysis , 2013, Water Resources Management.

[28]  D. Lettenmaier,et al.  Twentieth-Century Drought in the Conterminous United States , 2005 .

[29]  Shenglian Guo,et al.  Drought Analysis Using Copulas , 2013, Springer Water.

[30]  Jing-shi Tang,et al.  Assessing the recent droughts in Southwestern China using satellite gravimetry , 2014 .

[31]  B. Rémillard,et al.  Goodness-of-fit tests for copulas: A review and a power study , 2006 .

[32]  Yang Hong,et al.  Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data , 2014 .

[33]  H. Joe Multivariate models and dependence concepts , 1998 .

[34]  G. Wong,et al.  Probabilistic analysis of hydrological drought characteristics using meteorological drought , 2013 .

[35]  C. Genest,et al.  Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask , 2007 .

[36]  Jane Qiu,et al.  China drought highlights future climate threats , 2010, Nature.

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

[38]  V. Yevjevich Objective approach to definitions and investigations of continental hydrologic droughts, An , 2007 .

[39]  Jun Niu,et al.  Terrestrial hydrological responses to precipitation variability in Southwest China with emphasis on drought , 2014 .

[40]  Dawen Yang,et al.  Spatio-temporal variation of drought in China during 1961–2012: A climatic perspective , 2015 .

[41]  Fabrizio Durante,et al.  On the return period and design in a multivariate framework , 2011 .

[42]  Eric F. Wood,et al.  Global and Continental Drought in the Second Half of the Twentieth Century: Severity–Area–Duration Analysis and Temporal Variability of Large-Scale Events , 2009 .

[43]  Jun Yan,et al.  Modeling Multivariate Distributions with Continuous Margins Using the copula R Package , 2010 .

[44]  Jun Yan,et al.  Enjoy the Joy of Copulas: With a Package copula , 2007 .

[45]  G. Marsaglia,et al.  Evaluating Kolmogorov's distribution , 2003 .

[46]  Vijay P. Singh,et al.  Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data , 2010 .

[47]  Using higher probability weighted moments for flood frequency analysis , 1997 .