year 10, Issue 4 (Winter 2021)                   E.E.R. 2021, 10(4): 90-110 | Back to browse issues page

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Natural Resources Department, Faculty of Agriculture & Natural Resources, University of Hormozgan, BandarAbbas , Iran ,
Abstract:   (285 Views)
Extended abstract
1- Introduction
Simulation of the rainfall-runoff process for planning and management of water resources and watersheds requires using a conceptual optimized hydrological model. Models of different types provide a means of quantitative extrapolation or prediction that will hopefully be helpful in decision-making. Recently, the application of models has become an essential tool for understanding the natural processes that have occurred in the watershed. KINEROS2 (Kinematic runoff and Erosion), or K2, originated at the USDA Agricultural Research Service (ARS) in the late 1960s as a model that routed runoff from hillslopes is represented by a cascade of overland-flow planes using the stream path analogy proposed by Onstad and Brakensiek (1968), laterally into channels. Manual calibration of hydrological models has been used since the early 1960s, but due to its complexity and being time-consuming, automatic calibration has been available since the end of the 1960s. Auto-calibration needs an appropriate objective function, search algorithm, and a criterion to complete the algorithm. The particle swarm optimization (PSO) algorithm, due to its flexibility, easy implementation, and high performance, has been favored by many researchers in recent years. This method has a high rate of convergence and suitable computational cost.
2- Methodology
In this study, the hydroPSO package was employed to optimize KINEROS٢ (K٢) parameters applied in the Bar watershed, Neyshabour, Iran. The hydroPSO package in R software environment was utilized to implement the PSO optimization algorithm. The possibility to develop R capabilities by adding the produced packages by the users is one of the most important specifications of this software. The statistical measures used in model validation analysis were model bias (MB), modified correlation coefficient (rmod), and Nash-Sutcliffe Efficiency (NSE). These metrics are the most common evaluation criteria in the literature. The capability of the model in water discharge estimation can be assessed by MB, while rmod signifies the differences both in hydrograph size and shape. In this work, 16 parameters have been introduced as the effective parameters on flood simulation by K2. These parameters were calibrated using the hydroPSO optimization package within R environment, which benefits from a parallel processing capability and a higher speed of computations, as compared with other software environments like MATLAB. The common parameters in the calibration process involved in the main code of K2 program include Ks, n, CV, G, and In. In this study, by changing some codes in K2 through the FORTRAN programming language, calibration parameters were increased by 16 parameters. Therefore, the response of a watershed to the variations of these parameters, separated for channel and plane, can be well evaluated. Due to semi-distributed simulation of K2, changing the amount of each parameter was done through “relative changes” in the initial value using a multiplier approach. Five storm events were utilized in hydrograph simulation, as well.
3- Results
Results indicated the better efficiency of K2 based on the event 1992/03/31 with the coefficient of determination and Nash-Sutcliffe Efficiency (NSE) of 0.96 and 0.96, respectively. The events dated 1991/03/07, 1991/05/11,1992/03/16,1994/12/04, respectively, with the NSEs of 0.90, 0.90,0.89, and 0.43, showed the excellent, excellent, excellent, and good fitness of simulated flow compared to observed flow, respectively. Sensitivity analysis established that the parameters Ks_c, n_c, Rock, G_p, Ks_p, Por_p, and Sat were the most effective parameters in K2 calibration, respectively. The posterior distributions of some parameters such as n_c and Smax appeared to be more sharply peaked than other parameters which established less uncertainty in hydrological modeling. Visual inspection of boxplots showed that for 8 out of 16 parameters (Ks_p, n_c, G_p, In, Rock, Por_p, Por_c, and Sat), the optimum values 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 n_c define a narrow range of the parameter space with a high model performance. On the other hand, the model performance was more impacted by the interaction of Ks and n parameters. Correlation analysis revealed that the highest linear correlation between NSE and K2 parameters was obtained for the parameters Ks_p, Ks_c, n_p, CV_p, G_c, In, Por_p, Dist_p, Dist_c, Smax, and Sat.
4- Discussion & Conclusions
In comparison with manual calibration, the HydroPSO R package could compensate for the shortage of K2 proficiency, due to the lack of enough observed rainfall records, in hydrologic modeling of semi-arid watersheds. Thus, it can be successfully integrated with the K2 model to harness the combined benefits of a distributed hydrological model and flexible computing capability of the open-source R software. However, the performance of HydroPSO in K2 calibration should be assessed for several semi-arid watersheds which have the similar conditions to Bar watershed. 
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Received: 2020/11/26 | Published: 2021/01/29

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