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A new bivariate Gamma distribution generated from functional scale parameter with application to drought data

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Abstract

Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA.

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References

  • Alley WM (1984) The Palmer Drought Severity Index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109

    Article  Google Scholar 

  • Blom G (1958) Statistical estimates and transformed beta-variables. Wiley, New York

    Google Scholar 

  • Bonaccorso B, Cancelliere A, Rossi G (2003) An analytical formulation of return period of drought severity. Stoch Environ Res Risk Assess 17:157–174

    Article  Google Scholar 

  • Bras RL (1990) Hydrology: an introduction to hydrologic science. Addison-Wesley, Reading

    Google Scholar 

  • Chambers J, Cleveland W, Kleiner B, Tukey P (1983) Graphical methods for data analysis. Chapman and Hall, London

    Google Scholar 

  • Cheng KS, Hou JC, Liou JJ, Wu YC, Chiang JL (2010) Stochastic simulation of bivariate Gamma distribution: a frequency-factor based approach. Stoch Environ Res Risk Assess 25(2):107–122

    Article  Google Scholar 

  • Clarke RT (1980) Bivariate gamma distribution for extending annual stream flow records from precipitation: some large sample results. Water Resour Res 16:863–870

    Article  Google Scholar 

  • Douglas EM, Vogel RM, Kroll CN (2002) Impact of streamflow persistence on hydrologic design. J Hydrol Eng 7(3):220–227

    Article  Google Scholar 

  • Dupuis DJ (2010) Statistical modeling of the monthly Palmer drought severity index. J Hydrol Eng 15(10):796–808

    Article  Google Scholar 

  • Guerrero-Salazar P, Yevjevich V (1975) Analysis of drought characteristics by the theory of runs. Hydrology Paper Nr. 80, Colorado State University, Fort Collins

  • Haan CT (1977) Statistical methods in hydrology. Iowa State University Press, Ames

    Google Scholar 

  • Hallack-Alegria M, Watkins DW Jr (2007) Annual and warm season drought Intensity–Duration–Frequency analysis for Sonora, Mexico. J Clim 20(9):1897–1909

    Article  Google Scholar 

  • Hao Z, Singh VP (2011) Bivariate drought analysis using entropy theory. 2011 Symposium on Data-Driven Approaches to Droughts, Paper 43. http://docs.lib.purdue.edu/ddad2011/43

  • Henningsen A, Toomet O (2011) maxLik: a package for maximum likelihood estimation in R. J Comput Stat 26:443–458

    Article  Google Scholar 

  • Hu Q, Willson GD (2000) Effects of temperature anomalies on the Palmer Drought Severity Index in the central United States. Int J Climatol 20:1899–1911

    Article  Google Scholar 

  • Husak JG, Michaelsen J, Funk C (2007) Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications. Int J Climatol 27:935–944

    Article  Google Scholar 

  • Johnson NL, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, vol 1. Wiley, New York

    Google Scholar 

  • Kim TW, Valdes JB, Yoo C (2006) Nonparametric approach for bivariate drought characterization using Palmer Drought Index. J Hydrol Eng 11(2):134–143

    Article  CAS  Google Scholar 

  • Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull Am Meteor Soc 76:655–668

    Article  Google Scholar 

  • Lloyd EH (1970) Return period in the presence of persistence. J Hydrol 10(3):202–215

    Google Scholar 

  • Loaiciga HA, Leipnik RB (2005) Correlated gamma variables in the analysis of microbial densities in water. Adv Water Resour 28:329–335

    Article  Google Scholar 

  • Loaicigica M, Mariño MA (1991) Recurrence interval of geophysical events. J Water Resour Plan Manag 117(3):367–382

    Article  Google Scholar 

  • Martin AD, Quinn KM, Park JH (2011) MCMCpack: Markov Chain Monte Carlo in R. J Stat Softw 42(9):1–21

    Google Scholar 

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216

    Article  Google Scholar 

  • Mishra AK, Singh VP (2011) Drought modeling: a review. J Hydrol 403:157–175

    Article  Google Scholar 

  • Nadarajah S (2007) A bivariate gamma model for drought. Water Resour Res 43:W08501. doi:10.1029/2006WR005641

    Google Scholar 

  • Nadarajah S (2008) The bivariate F distribution with application to drought data. Statistics 42(6):535–546

    Article  Google Scholar 

  • Nadarajah S (2009a) A bivariate Pareto model for drought. Stoch Environ Res Risk Assess 23:811–822

    Article  Google Scholar 

  • Nadarajah S (2009b) A bivariate distribution with gamma and beta marginals with application to drought data. J Appl Stat 36(3):277–301

    Article  Google Scholar 

  • Nadarajah S, Gupta AK (2006a) Cherian’s bivariate gamma distribution as a model for drought data. Agrociencia 40:483–490

    Google Scholar 

  • Nadarajah S, Gupta AK (2006b) Intensity-duration models based on bivariate gamma distribution. Hiroshima Math J 36:387–395

    Google Scholar 

  • Nadarajah S, Kotz S (2006) A note on the correlated gamma distribution of Loaiciga and Leipnik. Adv Water Resour 30:1053–1055

    Article  Google Scholar 

  • Porporato A, Laio F, Ridolfi L, Rodriguez-Iturbe I (2001) Plants in water-controlled ecosystem: active role in hydrologic processes and response to water stress: III. Vegetation water stress. Adv Water Resour 24:725–744

    Article  Google Scholar 

  • Prekopa A, Szantai T (1978) New multivariate gamma distribution and its fitting to empirical stream flow data. Water Resour Res 14:19–24

    Article  Google Scholar 

  • Prudnikov AP, Brychkov YA, Marichev OI (1986) Integrals and series, vols 1–3. Gordon and Breach Science Publishers, Amsterdam

  • Shiau JT (2003) Return period of bivariate distributed hydrological events. Stoch Environ Res Risk Assess 17:42–57

    Article  Google Scholar 

  • Shiau J, Shen HW (2001) Recurrence analysis of hydrologic droughts of differing severity. J Water Res Plan Manag 127(1):30–40

    Article  Google Scholar 

  • Song SB, Singh VP (2010) Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stoch Environ Res Risk Assess 24:783–805

    Article  Google Scholar 

  • Vogel RM (1987) Reliability indices for water supply systems. J Water Resour Plan Manag 113(4):645–654

    Article  Google Scholar 

  • Willeke G, Hosking JRM, Wallis JR, Guttman NB (1994) The National Drought Atlas. Institute for Water Resources Rep. 94-NDS-4. U.S. Army Corps of Engineers, Fort Belvoir, VA, 587 pp

  • Yevjevich V (1967) An objective approach to definitions and investigations of continental hydrologic drought. Hydrol. Pap., 23, Colorado State University, Fort Collins

  • Yue S (2001) A bivariate gamma distribution for use in multivariate flood frequency analysis. Hydrol Process 15:1033–1045

    Article  Google Scholar 

  • Yue S, Ouarda TBMJ, Bobee B (2001) A review of bivariate Gamma distribution for hydrological application. J Hydrol 246:1–18

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to the Associate editor and the two referees for their valuable comments and suggestions which significantly helped to improve the paper. The first author is also thankful to the Higher Education Commission of Pakistan for their financial support for this project.

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Correspondence to Muhammad Mohsin.

Appendix

Appendix

The Fisher information matrix \( Q\left( {\hat{g}} \right) \) is given by:

$$ \begin{aligned} Q\left( {\hat{g}} \right) & = - E\left[ {\begin{array}{*{20}c} {\frac{{\partial^{2} L\left( g \right)}}{{\partial \alpha^{2} }}} & {\frac{{\partial^{2} L\left( g \right)}}{\partial \alpha \,\partial \beta }} & 0 & 0 \\ {} & {\frac{{\partial^{2} L\left( g \right)}}{{\partial \beta^{2} }}} & 0 & 0 \\ {} & {} & {\frac{{\partial^{2} L\left( g \right)}}{{\partial \gamma^{2} }}} & {\frac{{\partial^{2} L\left( g \right)}}{\partial \gamma \,\partial \delta }} \\ {} & {} & {} & {\frac{{\partial^{2} L\left( g \right)}}{{\partial \delta^{2} }}} \\ \end{array} } \right] \\ & = n\left[ {\begin{array}{*{20}c} {\psi^{\prime}\left( \alpha \right)} & { - \frac{1}{\beta }} & 0 & 0 \\ { - \frac{1}{\beta }} & {\frac{\alpha }{{\beta^{2} }}} & 0 & 0 \\ 0 & 0 & {\psi^{\prime}\left( \gamma \right)} & { - \frac{1}{\delta }} \\ 0 & 0 & { - \frac{1}{\delta }} & {\frac{\gamma }{{\delta^{2} }}} \\ \end{array} } \right] \\ \end{aligned} $$

Here \( \,\psi^{\prime}\left( . \right) \) is the first derivative of the Psi function (also called trigamma function).

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Mohsin, M., Gebhardt, A., Pilz, J. et al. A new bivariate Gamma distribution generated from functional scale parameter with application to drought data. Stoch Environ Res Risk Assess 27, 1039–1054 (2013). https://doi.org/10.1007/s00477-012-0641-6

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