Volume 9, Issue 18 (1-2019)
J Watershed Manage Res 2019, 9(18): 91-110 |
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Memarian H, Pourreza Bilondi M, Zinat Komeh Z K. (2019). Parameters Optimization of KINEROS2 using Particle Swarm Optimization Algorithm for Single Event Rainfall-Runoff Simulation
(Case Study: Tamar Watershed, Golestan, Iran)
. J Watershed Manage Res. 9(18), 91-110. doi:10.29252/jwmr.9.18.91
URL: http://jwmr.sanru.ac.ir/article-1-746-en.html
URL: http://jwmr.sanru.ac.ir/article-1-746-en.html
Abstract: (3725 Views)
Simulation of rainfall-runoff process for planning and management of water resources and watersheds requires the use of a conceptual optimized hydrological model. In this study, the hydroPSO package was employed to optimize KINEROS2 (K2) parameters applied in the Tamar watershed, Iran. Four storm events were utilized in hydrograph simulation. Results indicated better efficiency of K2 based on the event #2 with the coefficient of determination and Nash-Sutcliffe Efficiency (NSE) of 0.9084 and 0.92, respectively. The events #3 and #4 with NSE of 0.89 and 0.86 showed the excellent and very good fitness of simulated flow compared to observed flow, respectively. Sensitivity analysis established that the parameters Ks_p, Ks_c, n_p, n_c, CV_p and Sat were the most effective parameters in K2 calibration, respectively. The posterior distributions of some parameters such as Ks_p and n_c appeared to be more sharply peaked than other parameters which established less uncertainty in hydrological modeling. Visual inspection of boxplots showed that for 6 out of 16 parameters (Ks_c, n_c, G_c, Rock, Dist_c and Smax) the optimum value found during the optimization coincided with the median of all the sampled values confirming that most of the particles converged into a small region of the solution space. Dotty plots showed that the optimum values found for Ks_p, Ks_c, and n_c define a narrow range of the parameter space with high model performance. On the other hand, the model performance was more impacted by the interaction of Ks and n parameters. The parameters CV_p and n_p showed a wider range of the optimized levels. Correlation analysis revealed that the highest linear correlation between the NSE and K2 parameters was obtained for the Ks_p, Ks_c and n_p, followed by CV_p, G_c, Por_p, Dist_p and Smax. Conclusively, this work demonstrated the capability of hydroPSO in optimization of the K2 as an event-based hydrological model.
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