document: A spatial rainfall simulator for crop production modeling in Southern Africa This paper describes a methodology for simulating rainfall in dekads across a set of spatial units in areas where long-term meteorological records are available for a small number of sites only . The work forms part of a larger simulation model of the food system in a district of Zimbabwe , which includes a crop production component for yields of maize , small grains and groundnuts . Only a limited number of meteorological stations are available within or surrounding the district that have long time series of rainfall records . Preliminary analysis of rainfall data for these stations suggested that intra-seasonal temporal correlation was negligible , but that rainfall at any given station was correlated with rainfall at neighbouring stations . This spatial correlation structure can be modeled using a multivariate normal distribution consisting of 30 related variables , representing dekadly rainfall in each of the 30 wards . For each ward , log-transformed rainfall for each of the 36 dekads in the year was characterized by a mean and standard deviation , which were interpolated from surrounding meteorological stations . A covariance matrix derived from a distance measure was then used to represent the spatial correlation between wards . Sets of random numbers were then drawn from this distribution to simulate rainfall across the wards in any given dekad . Cross-validation of estimated rainfall parameters against observed parameters for the one meteorological station within the district suggests that the interpolation process works well . The methodology developed is useful in situations where long-term climatic records are scarce and where rainfall shows pronounced spatial correlation , but negligible temporal correlation
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