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The conventional means of flood simulation and prediction using conceptual hydrological model or artificial neural network (ANN) has provided promising results in recent years. However, it is usually difficult to obtain ideal flood reproducing due to the structure of hydrological model. Back propagation (BP) algorithm of ANN may also reach local optimum when training nodal weights. To improve the mapping capability of neural networks, wavelet function was adopted (WANN) to strengthen the nonlinear simulation accuracy and generality. In addition, genetic algorithm is integrated with WANN (GAWANN) to avoid reaching local optimum. Meanwhile, Message Passing Interface (MPI) subroutines are introduced for distributed implement considering the time consumption during nodal weights training. The GAWANN was applied in the flood simulation and prediction in arid area. The test results of 4 independent cases were compared to reveal the relations between historical rainfall and runoff under different time lags. The simulation was also carried out with Xinanjiang model to demonstrate the capability of GAWANN. The numerical experiments in this paper indicated that the parallel GAWANN has strong capability of rain-runoff mapping as well as computational efficiency and is suitable for applications of flood simulation in arid areas. (C) 2011 Elsevier B.V. All rights reserved.