The value of large-scale climate variables in climate change assessment: The case of Botswana’s rainfall |
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| Institution: | 1. School of Civil Engineering, Purdue University, West Lafayette, IN, United States;2. Department of Civil Engineering, Indian Institute of Technology, Kanpur, India;1. Department of Production Animal Studies, Faculty of Veterinary Science, University of Pretoria, Section Reproduction, Private Bag X04, Onderstepoort 0110, South Africa;2. Department of Chemistry, Faculty of Natural and Agricultural Sciences, University of Pretoria, Department of Chemistry, Lynnwood Road, Hatfield 0002, South Africa;3. Exotic Leather Research Centre, Private Bag X04, Onderstepoort 0110, South Africa |
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| Abstract: | Climate change is expected to alter rainfall regimes across most parts of the world. The implications of this could be more severe in arid environments where rainfall is limited and highly variable in space and time. However, lack of good quality data, of sufficient record length and spatial coverage usually restricts model development and performance geared towards assessing the effects of climate change in these areas. This paper presents an analysis of rainfall and climate data in order to determine the time of change in rainfall series and identify possible correlations between rainfall and temperature. In addition, the paper aims to make predictions of future rainfall patterns in Botswana. This is achieved by using historical rainfall and climate data from rainfall stations spread across Botswana from 1965 to 2008. In addition, large scale reanalysis data from NCAR/NCEP and El Nino Southern Oscillation (ENSO) data were used to augment the limited observed spatial climate data series when developing a rainfall model. Temperature and ENSO indices have been used to predict rainfall regimes for the present climate. Based on these, the effects of climate change were quantified using a stochastic generalised linear rainfall model (GLM) driven by outputs of global climate models (GCMs). The results indicate that temperature is a significant rainfall predictor in Botswana compared to ENSO indices. |
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