Local studies aimed at assessing the impact of climate variability on crop yield at the individual farm level require the use of weather and climate data. These are often collected at points known as meteorological stations. In West Africa, meteorological stations are sparsely distributed and as a result, are often unable to satisfy the data requirements for such studies. One major problem arising from this is how to estimate values for locations where primary data is not available. General Circulation Models (GCMs) have recently been deployed for weather forecasting and climate change projections but the resolution of their outputs is low requiring downscaling. This article describes a GIS-based procedure for downscaling GCMs' outputs for use in studies assessing the impacts of climate variability on crop yield at the farm level. The procedure is implemented with the Hadley Centre's GCM (HadCM2) data, although any other GCM can be used. Results in this study show that the model works best when representing drier months as compared to wet months in all three domains tested. For example, it estimated the rainfall for January (the driest month) better than that of July which is the peak of the rainy season in West Africa. There is also a north-south pattern influencing the accuracy of estimated rainfall distribution, with stations in the south better represented than those in the north. For the greater part of West Africa where similar climatic conditions persist as in Nigeria, this procedure can be considered suitable for interpolation and downscaling.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)