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Theoretical and Applied Climatology

, Volume 115, Issue 3–4, pp 411–426 | Cite as

Rainfall-derived growing season characteristics for agricultural impact assessments in South Africa

  • Chiara Ambrosino
  • Richard E. Chandler
  • Martin C. Todd
Original Paper

Abstract

Precipitation variability imposes significant pressure in areas where agricultural practice is dominated by smallholder farmers who are dependent on subsistence farming. Advances in the understanding of this variability, in both time and space, have an important role to play in increasing the resilience of agricultural systems. The need is particularly pressing in regions of the world such as the African continent, which is already affected by multiple stresses including poverty and economic and political instability. In this paper, we explore the use of generalised linear models (GLMs) for this purpose, via a case study from north-east South Africa. A GLM is used to link the local rainfall variability to large-scale climate drivers identified from previous subcontinental-scale analyses, and the ability of the resulting model to simulate precipitation features that are relevant in agricultural applications is evaluated. We focus in particular on a set of growing season indices, proposed for the investigation of intraseasonal characteristics relevant for maize production in the region. Seven indices were computed from spatially averaged daily rainfall series from nine stations in the study area. As a first attempt to use GLMs for this type of application, the results are encouraging and suggest that the models are able to reproduce a range of agriculture-relevant indices. However, further research into spatial correlation structure is recommended to improve the multisite generation of the rainfall-derived characteristics.

Keywords

Daily Rainfall Rainfall Variability Rainfall Series Rainfall Occurrence Spatial Correlation Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partly funded by the University College London Environment Institute and partly by grant number NE/I006621/1 under the NERC Changing Water Cycle programme.

Supplementary material

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Chiara Ambrosino
    • 1
  • Richard E. Chandler
    • 1
  • Martin C. Todd
    • 2
  1. 1.Department of Statistical ScienceUniversity College LondonLondonUK
  2. 2.Department of GeographyUniversity of SussexBrightonUK

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