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The Interdependence between Rainfall and Temperature: Copula Analyses

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  • Cong, Rong-Gang
  • Brady, Mark

Abstract

Rainfall and temperature are important climatic inputs for agricultural production, especially in the context of climate change. However, accurate analysis and simulation of the joint distribution of rainfall and temperature are difficult due to possible interdependence between them. As one possible approach to this problem, five families of copula models are employed to model the interdependence between rainfall and temperature. Scania is a leading agricultural province in Sweden and is affected by a maritime climate. Historical climatic data for Scania is used to demonstrate the modeling process. Heteroscedasticity and autocorrelation of sample data are also considered to eliminate the possibility of observation error. The results indicate that for Scania there are negative correlations between rainfall and temperature for the months from April to July and September. The student copula is found to be most suitable to model the bivariate distribution of rainfall and temperature based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Using the student copula, we simulate temperature and rainfall simultaneously. The resulting models can be integrated with research on agricultural production and planning to study the effects of changing climate on crop yields.

Suggested Citation

  • Cong, Rong-Gang & Brady, Mark, 2012. "The Interdependence between Rainfall and Temperature: Copula Analyses," MPRA Paper 112149, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:112149
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    References listed on IDEAS

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    Cited by:

    1. Gómez, M. & Ausín Olivera, María Concepción & Domínguez, M. C., 2015. "Seasonal copula models for the analysis of glacier discharge at King George Island, Antarctica," DES - Working Papers. Statistics and Econometrics. WS ws1513, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Domino, Krzysztof & Błachowicz, Tomasz & Ciupak, Maurycy, 2014. "The use of copula functions for predictive analysis of correlations between extreme storm tides," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 489-497.
    3. May Haggag & Ahmad S. Siam & Wael El-Dakhakhni & Paulin Coulibaly & Elkafi Hassini, 2021. "A deep learning model for predicting climate-induced disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 1009-1034, May.
    4. Nelson Christopher Dzupire & Philip Ngare & Leo Odongo, 2019. "Pricing Basket Weather Derivatives on Rainfall and Temperature Processes," IJFS, MDPI, vol. 7(3), pages 1-14, June.
    5. Cong, Rong-Gang & Hedlund, Katarina & Andersson, Hans & Brady, Mark, 2014. "Managing soil natural capital: An effective strategy for mitigating future agricultural risks," MPRA Paper 112155, University Library of Munich, Germany.
    6. Lundin, Erik & Tangerås, Thomas P., 2020. "Cournot competition in wholesale electricity markets: The Nordic power exchange, Nord Pool," International Journal of Industrial Organization, Elsevier, vol. 68(C).
    7. Darren How Jin Aik & Mohd Hasmadi Ismail & Farrah Melissa Muharam, 2020. "Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia," Land, MDPI, vol. 9(10), pages 1-23, October.

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    More about this item

    Keywords

    Copula model; Agricultural economics;

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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