Analysis of Irrigation Needs Using an Approach Based on a Bivariate Copula Methodology
- 247 Downloads
- 1 Citations
Abstract
The problem of drought probability has been investigated by several authors, who have usually analysed droughts using various drought indices such as the Standard Precipitation Index. Various aspects of time series of such indices (intensity, severity and duration) were investigated by several authors using a copula method. Because such analysis is based on only one basic climatic variable, this paper addresses a different approach, i.e., joint analysis of the severity and duration of the most demanding potential annual irrigation periods by a bivariate copula method. Characteristics of these periods are derived from both temperature and precipitation. Maximum annual duration of the potential irrigation period and corresponding rainfall deficit were inferred from these basic variables as inputs to two-dimensional probability analysis by the copula method, because this offers more direct answers to questions of irrigation needs. Results indicate the suitability of the proposed method for analysis of irrigation needs, with greater benefits than the typical one-dimensional analysis of individual climatic variables. A case study for testing the method was done for southwestern Slovakia, for which the frequency of irrigation needs was estimated. Example results indicate that every second year, a one-month period can be expected in which temperatures are >25∘C and there is a moisture deficit of ∼30 mm. Even more significant periods of drought can be expected, for example, with a 5 or 10-year return period. These phenomena significantly damage agriculture yields, so requirements for irrigation structures in the study area are indicated by the proposed method.
Keywords
Drought Irrigation Copula Precipitation TemperatureNotes
Acknowledgments
This work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and Slovak Academy of Sciences, Grant Nos. 1/0665/15 and 1/0625/15.
References
- Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends. Int J Climatol 17:25–34CrossRefGoogle Scholar
- Angelidis P, Maris F, Kotsovinos N, Hrissanthou V (2012) Computation of drought index SPI with alternative distribution functions. Water Resour Manag 26:2453–2473CrossRefGoogle Scholar
- Bacigal T (2013) R Package to handle archimax or any user-defined continuous copula construction: acopula. Adv Intell Syst Comput 26:75–84. Aggregation functions in theory and in practiseCrossRefGoogle Scholar
- Bacigal T, Jagr V, Mesiar R (2011) Non-exchangeable random variables, Archimax copulas and their fitting to real data. Kybernetika 47:519–531Google Scholar
- Cramer H (1928) On the composition of elementary errors. Scand Actuar J 1928:13–74CrossRefGoogle Scholar
- Genest C, Favre A (2007) Everything you always wanted to know about Copula modeling but were afraid to ask. J Hydrol Eng 12:347–368CrossRefGoogle Scholar
- Genest C, Rémillard B (2004) Test of independence and randomness based on the empirical copula process. Test 13:335–369CrossRefGoogle Scholar
- Genest C, Remillard́ B, Beaudoin D (2009) Goodness-of-fit tests for copulas: A review and a power study. Math Econ 44:199–213CrossRefGoogle Scholar
- Gibbs W, Maher J (1967) Rainfall deciles as drought indicators. Bureau of Meteorology, Australia Bulletin No. 48. Commonwealth of Australia, MelbourneGoogle Scholar
- Kao S, Govindaraju R (2007) A bivariate frequency analysis of extreme rainfall with implications for design. J Geophys Res-Atmos 112(D13):119CrossRefGoogle Scholar
- Klementová E, Litschmann T (2001) Evaluation of drought in Hurbanovo region. Paper presented at the workshop: Drought assessment and prediction. Brno (19/11/2001). (Slovak). http://www.cbks.cz/sucho01/Klementova.pdf, accessed: 14 Sep 2015
- Kojadinovic I, Yan J (2010) Modeling multivariate distributions with continuous margins using the copula R package. J Stat Softw 34:1–20CrossRefGoogle Scholar
- Lana X, Martnez M, Burgueno~ A, Serra C, Martin-Vidé J, Gomeź L (2006) Distributions of long dry spells in the Iberian peninsula, years 1951-1990. Int J Climatol 26:1999–2021CrossRefGoogle Scholar
- McKee T, Doesken N, Kleist J (1993) The relationship of drought frequency and duration on time scales. Paper presented at the 8th conf on Applied Climatology, Anaheim CAGoogle Scholar
- Michele CD, Salvadori G (2003) A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-copulas. J Geophys Res-Atmos 108:4067CrossRefGoogle Scholar
- Mirabbasi R, Fakheri-Fard A, Dinpashoh Y (2012) Bivariate drought frequency analysis using the copula method. Theor Appl Climatol 108:191–206CrossRefGoogle Scholar
- Mirakbari M, Ganji A, Fallah S (2010) Regional bivariate frequency analysis of meteorological droughts. J Hydrol Eng 15:985–1000CrossRefGoogle Scholar
- Morid S, Smakhtin V, Moghaddasi M (2006) Comparison of seven meteorological indices for drought monitoring in Iran. Int J Climatol 26:971–985CrossRefGoogle Scholar
- Nadarajah S (2009) A bivariate distribution with gamma and beta marginals with application to drought data. J Appl Stat 36:277–301CrossRefGoogle Scholar
- Nelsen R (2006) An introduction to copulas. Springer, New YorkGoogle Scholar
- Palmer W (1965) Meteorological drought. Tech. Rep. 45, US Department of Commerce, Weather bureau, http://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf, accessed: 14 Sep 2015
- R-Core Team (2012) R: A language and environment for statistical computing. Vienna, AustriaGoogle Scholar
- Rauf U, Zeephongsekul P (2014) Analysis of rainfall severity and duration in Victoria, Australia using non-parametric copulas and marginal distributions. Water Resour Manag 28:4835–4856CrossRefGoogle Scholar
- Reddy MJ, Ganguli P (2012) Bivariate flood frequency analysis of upper Godavari River flows using Archimedean copulas. Water Resour Manag 26(14):3995–4018CrossRefGoogle Scholar
- Saghafian B, Mehdikhani H (2014) Drought characterization using a new copula-based trivariate approach. Nat Hazards 72:1391–1407CrossRefGoogle Scholar
- Salvadori G, Michele CD (2004) Frequency analysis via copulas: Theoretical aspects and applications to hydrological events. Water Resour Res 40(W12):511Google Scholar
- Salvadori G, Michele CD (2015) Multivariate real-time assessment of droughts via copula-based multi-site hazard trajectories and fans. J Hydrol 526:101–115CrossRefGoogle Scholar
- Santos J, Portela M, Pulido-Calvo I (2011) Regional frequency analysis of droughts in Portugal. Water Resour Manag 25:3537–3558CrossRefGoogle Scholar
- Shiau J, Modarres R (2009) Copula-based drought severity-duration-frequency analysis in Iran. Meteorol Appl 16:481–489CrossRefGoogle Scholar
- Sklar A (1959) Fonctions de répartition á n dimensions et leurs marges. Publications de l’Institut de Statistique de l’Universite de Paris vol. 8Google Scholar
- Stephens M (1974) EDF statistics for goodness of fit and some comparisons. J Am Stat Assoc 69:730–737CrossRefGoogle Scholar
- Vergni L, Todisco F, Mannocchi F (2015) Analysis of agricultural drought characteristics through a two-dimensional copula. Water Resour Manag 29:2819–2835CrossRefGoogle Scholar
- Wang X, Gebremichael M, Yan J (2010) Weighted likelihood copula modeling of extreme rainfall events in Connecticut. J Hydrol 390:108–115CrossRefGoogle Scholar
- Woli P, Jones J, Ingram K, Hoogenboom G (2014) Predicting crop yields with the agricultural reference index for drought. J Agron Crop Sci 200(3):163–171CrossRefGoogle Scholar
- Yusof F, Hui-Mean F, Suhaila J, Yusof Z (2013) Characterisation of drought properties with bivariate copula analysis. Water Resour Manag 27:4183–4207CrossRefGoogle Scholar
- Zhai J, Liu B, Hartmann H, Su B, Jiang T, Fraedrich K (2009) Dryness/wetness variations in China during the first 50 years of the 21st century. Hydrol Earth Syst Sci Discuss 6:1385–1409CrossRefGoogle Scholar
- Zhang Q, Singh V, Chen Y, Xiao M (2015) Regional frequency analysis of droughts in China: A multivariate perspective. Water Resour Manag 29:1767–1787CrossRefGoogle Scholar