Multi‐site rainfall simulation at tropical regions: a comparison of three types of generators

Rainfall modelling is an essential component of different hydrological studies. However, rainfall modelling in tropical regions, especially urban areas, remains inadequate. To determine the applicability of different types of rainfall modelling approaches, simulations by two Markov models (Matlab‐based weather generator ( MulGETS ) and multi‐site rainfall simulator ( MRS )) and a Neyman–Scott based Poisson cluster model ( RainSim ) were compared with a proposed modified k ‐nearest neighbour ( KNN ) model for 30 years rainfall in Singapore. The MRS model was determined to be suitable for single‐site applications in tropical regions. However, for multi‐site conditions, RainSim was adjudged the most suitable given its accuracy in preserving observed spatial information, despite its performance lagging in few statistical indicators. The KNN model was found to perform satisfactorily during the wet seasons, and was the only model that could repeat the extreme precipitation values closely. Although typical studies evaluate the performance of models based on a set of criteria, there exists a lacuna in understanding the quality of simulations by these models. Therefore, uncertainty analysis based on two different criteria was implemented to understand the performance of the stochastic processes within. Although the MulGETS model exhibited the lowest differences between Prediction Intervals, RainSim 's Prediction Intervals were found to subsume observed data more often. The proposed study would be useful for users examining rainfall models for differing objectives.