Optimization Algorithm Comparison and Application to Parameter Calibration in a Distributed Hydrological Model

The distributed hydrological model is so advantageous that the spatial and temporal variability of hydrological processes can be restored and hydrological elements and inhomogeneities of spatial and temporal distribution can be simulated. However, there are many parameters in model simulation, especially in cases with many sub-basins, and manually adjusting parameters is cumbersome and complex. Optimization algorithms, which can automatically adjust parameters, are a preferred approach to counter these issues. After selecting and analyzing runoff data from 1988 to 2005 for the Jiutiaoling Station, Shiyang River, distributed time-varying gain model parameters(Distribute Time Variant Gain Model, DTVGM) were calibrated by applying the SCE-UA algorithm, genetic algorithm(GA) and particle swarm optimization(PSO) algorithm. We compared performance by looking at convergence rates, iteration number and the stability of the three algorithms. We found that after optimization using the three algorithms, the water balance coefficients were well controlled at around 0.0; the correlation coefficient reached 0.90, and the efficiency factor was more than 0.84. All model simulation results had much better precision and accuracy; however, the global search ability and convergence rates of PSO are superior to the SCE-UA and genetic algorithms. The PSO algorithm had the best performance in the minimum number of iteration processes and showed little sensitivity to initial values. This algorithm is more suitable for parameter optimization of the time-varying gain model.