استفاده از hydropso در واسنجی مدل هیدرولوژی kineros2 جهت شبیه‌سازی رواناب در حوزه‌های آبخیز مناطق نیمه خشک (مطالعه موردی: حوزه آبخیز بار نیشابور)

شبیه ‌سازی بارش رواناب به ‌منظور برنامه ریزی و مدیریت منابع آب در حوضه ‌های آبخیز صورت می ‌گیرد که نیازمند استفاده از مدل ‌های هیدرولوژیکی بهینه است. در این تحقیق از بسته ‌ی بهینه ‌سازی hydropso به ‌منظور واسنجی و بهینه ‌سازی پارامترهای مدل بارش رواناب (2k) 2kineros در حوزه آبخیز بار نیشابور در استان خراسان رضوی استفاده شد. به‌ همین منظور، پنج واقعه ‌ی رگباری با هیدروگراف ‌های متناظر انتخاب شد. براساس نتایج، این مدل کارآیی بهتری در شبیه‌ سازی هیدرولوژیک حوضه بر اساس واقعه‌ ی رگباری 01.71.11 نشان داد. در این شبیه ‌سازی، ضریب تبیین (2r) و نش (nse) مدل در شبیه ‌سازی دبی آب برابر با 96/0 و 0.96 به ‌دست آمد. مدل‌ سازی براساس وقایع رگباری تاریخ های 12.69.16، 02.70.21، 12.70.26 و 09.73.13 به ترتیب با ضرایب nse برابر با 90/0، 0.90، 89/0 و 43/0، توانست به نیکویی برازش عالی، عالی، عالی و خوب هیدروگراف شبیه‌ سازی شده بر هیدروگراف مشاهداتی منجر شود. تحلیل حساسیت مدل نشان داد که پارامترهای هدایت هیدرولیکی اشباع کانال (ks_c)، ضریب زبری مانینگ کانال(n_c)، ضریب حجمی سنگ و سنگ ریزه (rock)، میانگین نیروی کاپیلاری دامنه (g_p)، هدایت هیدرولیکی اشباع دامنه (ks_p) و تخلخل دامنه (por_p)، به ترتیب مهم ترین و موثرترین پارامترها در فرآیند واسنجی مدل 2k به شمار می ‌رود. توزیع فراوانی برخی از پارامترها مانند n_c و smax، دارای شکل زنگوله ای با اوج تیزتر است که این خود فقدان قطعیت کمتر تحمیل شده از جانب این پارامترها را بر مدل نشان می ‌دهند؛ اما برخی از پارامترها مانند por_c و dist_، توزیع فراوانی یکنواختی را نشان می ‌دهند. تفسیر بصری نمودارهای جعبه ‌ای نشان می ‌دهد که در پارامترهایی مثل ks_p، n_c، g_p، in، rock، por_p، por_c وsat ، ارزش بهینه ‌ی پارامتر به‌ دست آمده در فرایند واسنجی تقریباً منطبق بر میانه ارزش های نمونه ‌برداری شده ‌است و بیشتر ذرات به سمت یک منطقه ‌ی کوچک از فضای پاسخ هدایت شده ‌اند. نمودار نقطه‌ ای نشان می‌ دهد که کارآیی مدل بیشتر تحت ‌تاثیر اندرکنش پارامترهای ks وn است. بر اساس تحلیل همبستگی نیز بالاترین ضریب همبستگی nse با پارامترهای ks_p، ks_c، n_p، cv_p، g_c، in، cov،por_p ،dist_p ، dist_c، smax و sat به دست آمد. مقایسه‌ ی نتایج این تحقیق با یافته ‌های حاصل از واسنجی دستی نشان داد که بسته‌ ی بهینه‌ سازی hydropso می ‌تواند با موفقیت با مدل 2k تلفیق شود و با سرعت و کارآیی بالایی پارامترهای مدل را واسنجی و بهینه ‌سازی کند.

Use of HydroPSO in calibration of KINEROS2 hydrologic model to simulate runoff in semi-arid watersheds (Case study: Bar watershed, Neyshabour, Iran)

Extended abstract1 IntroductionSimulation of the rainfallrunoff 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 decisionmaking. 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 overlandflow 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 timeconsuming, automatic calibration has been available since the end of the 1960s. Autocalibration 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 MethodologyIn 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 NashSutcliffe 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 semidistributed simulation of K2, changing the amount of each parameter was done through ldquo;relative changes rdquo; 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 NashSutcliffe 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 ConclusionsIn 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 semiarid 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 opensource R software. However, the performance of HydroPSO in K2 calibration should be assessed for several semiarid watersheds which have the similar conditions to Bar watershed.