摘要 : The development of statistical equations for meteorological simulation is germane to water resources planning and management in all catchments around the world. Several locations of agricultural and economic importance in developi... 展开 The development of statistical equations for meteorological simulation is germane to water resources planning and management in all catchments around the world. Several locations of agricultural and economic importance in developing countries, lack location-specific models for their water resources management. To address these gaps, daily rainfall data were obtained from the Cocoa Research Institute of Nigeria (CRIN) and National Aeronautics and Space Administration (NASA) website (https://power.larc.nasa.gov/data-access-viewer/) for periods between 1977 and 2020. Runoff rates were also estimated from rainfall and land use data of the study area, using the Natural Resources Conservation (NRCS) method. Annual maxima of daily rainfall and runoff data were fitted to nine probability plotting positions and six probability density functions. Non-linear regression models were thus developed. Peak rainfall intensities and runoff rates were simulated from developed models. Performance and goodness-of-fit of developed models were tested. Performances of developed models were also compared with five previously established models. Rainfall data were best fitted to Hazen and Gumbel probability plotting positions. Weibull distribution fitted both rainfall intensities and runoff rates best while lognormal and Generalized Extreme Value (GEV) distributions were least efficient for simulation of rainfall intensities and runoff rates, respectively. All developed models outperformed all previously established model compared. Equations developed from best-performed probability plotting positions and density functions are therefore adequate and efficient for rainfall intensities and runoff rates predictions for the agrarian suburban area investigated. 收起