In recent years， the Grid-based distributed Xin’anjiang hydrological Model （GXM） has played an important role in flood forecasting， but when simulating the flooding process， due to the vast amount of data and calculation of the model， the computing time of GXM increases exponentially with the increase of the model warm-up period， which seriously affects the computational efficiency of GXM. Therefore， a parallel computing algorithm of GXM based on grid flow direction division and dynamic priority Directed Acyclic Graph （DAG） scheduling was proposed. Firstly， the model parameters， model components， and model calculation process were analyzed. Secondly， a parallel algorithm of GXM based on grid flow direction division was proposed from the perspective of spatial parallelism to improve the computational efficiency of the model. Finally， a DAG task scheduling algorithm based on dynamic priority was proposed to reduce the occurrence of data skew in model calculation by constructing the DAG of grid computing nodes and dynamically updating the priorities of computing nodes to achieve task scheduling during GXM computation. Experimental results on Dali River basin of Shaanxi Province and Tunxi basin of Anhui Province show that compared with the traditional serial computing method， the maximum speedup ratio of the proposed algorithm reaches 4.03 and 4.11， respectively， the computing speed and resource utilization of GXM were effectively improved when the warm-up period is 30 days and the data resolution is 1 km.

Water level prediction is an auxiliary decision support for flood warning work. For accurate water level prediction and providing scientific basis for natural disaster prevention， a prediction model combining Modified Gray Wolf Optimization （MGWO） algorithm and Temporal Convolutional Network （TCN） was proposed， namely MGWO-TCN. In view of the shortage of premature and stagnation in the original Gray Wolf Optimization （MGWO） algorithm， the idea of Differential Evolution （DE） algorithm was introduced to extend the diversity of the grey wolf population. The convergence factor during update and the mutation operator during mutation of the grey wolf population were improved to adjust the parameters in the adaptive manner， thereby improving the convergence speed and balancing the global and local search capabilities of the algorithm. The proposed MGWO algorithm was used to optimize the important parameters of TCN to improve the prediction performance of TCN. The proposed prediction model MGWO-TCN was used for river water level prediction， and the Root Mean Square Error （RMSE） of the model’s prediction results was 0.039. Experimental results show that compared with the comparison model， the proposed MGWO-TCN has better optimization ability and higher prediction accuracy.