494 International Water Resources Association Water International, Volume 32, Number 3, Pg. 494-502, September 2007 © 2007 International Water Resources Association INTRODUCTION Precipitation data are important to many applications in hydrology or agriculture. However, no matter how dense the network of measuring stations in a region, there will always be many locations with no available precipitation data. Therefore it is necessary to estimate point rainfall at unrecorded locations from values at surrounding sites (Goovaerts, �999). A number of methods have been used in the past for the spatial interpolation of precipitation. Spatial interpolation methods are techniques that predict the value at a given location by using values from sample points. For each computation the values of measured points are weighted depending on their locations. There are many spatial interpolation methods including density estimation, inverse distance weighted method, thin-plate splines method, etc. All methods share the underlying assumption that sample points that are closer to the interpolated location will infuence the interpolated value more strongly than sample points which are further away. A key difference among these approaches is the criterion which is used to weigh the values of the sample points. Criteria may include simple distance relations (e.g., inverse distance methods), minimization of variance (e.g., kriging and co-kriging), minimization of curvature, and enforcement of smoothness criteria (splining) (Hartkamp et al., �999). The interpolation methods can be classifed in two ma�or groups depending on the nature of the function that is used to interpolate the values. A frst group of spatial interpolation methods uses mathematical formulas and sample point values to estimate unmeasured values at any point across a given surface. The weight that is assigned to each known value for the interpolation of unknown values depends only on the distance between sample point and location of the interpolated point. These methods are grouped as deterministic and they can be global (making use of all of the sample points) or local (using a limited number of sample points in the vicinity of estimated value’s location). When it comes to interpolating precipitation data, the simplest approach consists of assigning the record of the closest gauge to the unsampled location (Thiessen, �9��� Goovaerts, �999). This method, also referred to as the �nearest neighbor� method (Hartkamp Spatial Interpolation of Annual Precipitation in South Africa - Comparison and Evaluation of Methods M. Coulibaly and S. Becker, University of Wisconsin Oshkosh, Department of Geography and Urban Planning, Abstract: Data from �4� rainfall gauges were used to interpolate the spatial distribution of annual rainfall in South Africa. Several spatial interpolation methods (inverse distance weighting (IWD), ordinary kriging, universal kriging, cokriging) were tested by variation analyses and cross-validation to determine the most suitable one. The best results were achieved by ordinary kriging, whereby the setting of the parameters was determined through sensitivity analyses. The median of the errors turned out to be 61 mm (11%). The interpolation errors were generally small for the interior of the country and high for coastal and mountainous regions. Keywords: Spatial interpolation, data, evaluation, South Africa