ارزیابی خصوصیات واسنجی مدل هیدرولوژیکی SWAT در یک حوضه کوهستانی
محورهای موضوعی :
مدیریت منابع آب
بابک امین نژاد
1
(استادیار، گروه مهندسی عمران، هیئت علمی دانشگاه آزاد اسلامی واحد رودهن. *(مسوول مکاتبات))
سیدبامداد غفوریان
2
(دانشجوی دکتری، گروه مهندسی عمران، هیئت علمی دانشگاه آزاد اسلامی واحد رودهن.)
حسین ابراهیمی
3
(دانشیار، گروه علوم و مهندسی آب، هیئت علمی دانشگاه آزاد اسلامی واحد شهر قدس.)
تاریخ دریافت : 1400/09/19
تاریخ پذیرش : 1400/12/01
تاریخ انتشار : 1401/06/01
کلید واژه:
مدل فیزیکی,
مدل SWAT,
زاگرس,
قره سو,
رواناب,
چکیده مقاله :
زمینه و هدف: اطلاعات دقیق در مورد رواناب آینده در یک حوضه، تصمیمات مربوط به مدیریت و منابع آب تسهیل می‌کند و به حفظ منابع طبیعی برای توسعه پایدار کمک می کند. دو عامل هزینه و زمان دقیق مستقیماً با تخمین دقیق رواناب ارتباط دارند. استفاده از مدل‌های کامپیوتری برای شبیه سازی پدیده‌های طبیعی نظیر چرخه هیدرولوژی یکی از روش‌های کاهش هزینه و افزایش دقت می‌باشد که بسیار مورد توجه بوده است.روش بررسی: در این تحقیق برای بررسی دقت روش های متفاوت واسنجی و اعتبارسنجی در شبیه سازی هیدرولوژیکی حوضه آبریز قره سو، از مدل هیدرولوژیکی SWAT استفاده شد که در آن مقایسه بین روش‌های GLUE و PSO با روش SUFI-2 برای کالیبراسیون مدل مورد بررسی قرار گرفت. برای شبیه سازی، در یک وضعیت یکسان، 13 پارامتر حساس در همه روش‌ها انتخاب شدند. لازم به ذکر است که داده های مورد نیاز برای این تحقیق از داده های جهانی (وبسایت مدل SWAT) و ملی (شرکت مدیریت منابع آب) جمع آوری شدند.یافته‌ها: هر سه روش GLUE و PSO با روش SUFI-2 قادر به شبیه سازی رواناب با R2 و NSE قابل قبول بودند (بالای 7/0) و تجزیه و تحلیل حساسیت نشان داد که Sol_K ، CH_N2 و CN2 حساسیت بیشتری نسبت به سایر پارامترها دارند.بحث و نتیجه گیری: اگرچه الگوریتم‌های SUFI-2، PSO و GLUE می توانند اختلاف بین داده‌های مشاهده ای و شبیه سازی شده را کاهش دهند، اما عملکرد الگوریتم SUFI-2 در شبیه سازی رواناب دقیق تر از الگوریتم های دیگر است. بنابراین پیشنهاد می شود که از این الگوریتم برای پیش بینی رواناب استفاده گردد.
چکیده انگلیسی:
Background and Objective: Accurate data about runoff in the future in a watershed facilitates managers' decisions in water-related decisions and helps conserve natural resources for sustainable development. The two factors of cost and exact time are directly related to accurate runoff estimation. Using computer models to simulate natural phenomena such as the hydrological cycle is one way to reduce costs and increase accuracy.Material and Methodology: The information for this research was obtained through the SWAT model website and global data. This study evaluates the accuracy of different calibration and validation methods in the hydrological simulation of Qarasu watershed, the SWAT (hydrological model) and a comparison between GLUE and PSO methods with the SUFI-2 method were used to calibrate the SWAT model. For simulation, 13 specific parameters were selected in all methods in the same situation. It should be noted that the data required for this research were collected from (SWAT model website) and (Water Resources Management Company).Findings: All three methods were able to simulate runoff with acceptable R2 and NSE results (above 0.7), and the sensitivity analysis showed that Sol_K, CH_N2 and CN2 were more sensitive than other parameters.Discussion and Conclusion: Although the SUFI-2, PSO, and GLUE algorithms can reduce the difference between the observed and simulated data, the performance of the SUFI-2 algorithm in runoff simulation is more accurate than other algorithms. Therefore, it is suggested to use this algorithm to predict runoff.
منابع و مأخذ:
Teshome, F. T., Bayabil, H. K., Thakural, L., & Welidehanna, F. G. (2020). Modeling Stream Flow Using SWAT Model in the Bina River Basin, India. Journal of Water Resource and Protection, 12(03), 203.
Tegegne, G., & Kim, Y. O. (2018). Modelling ungauged catchments using the catchment runoff response similarity. Journal of Hydrology, 564, 452-466.
Marques C. A. F., Ferreira J. A., Rocha A., Castanheira J. M., Melo-Goncalves P., Vaz N. and Dias J. M. (2006). Singular spectrum analysis and forecasting of hydrological time series. Phys. Chem. Earth A/B/C., 31(18), 1172–1179.
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Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H. and Kløve, B. 2015. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology Vol. 524, pp. 733-752.
Aalami, M.T., Abbasi, H. and Niksokhan, M.H. 2018. Comparison of two calibration-uncertainty methods for Soil and Water Assessment Tool in stream flow modeling. 28, pp. 53-64. (In Persian)
Narsimlu, B., Gosain, A.K, Chahar, B.R, Singh, S.K and Srivastava, P.K. 2015. SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environ Process, Vol 2(1), pp. 79–95.
Yang, J., Reichert, P., Abbaspour, K.C., Xia, J. and Yang, H. 2018. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of hydrology, Vol. 358(1-2), pp. 1-23.
Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J. and Srinivasan, R. 2007b. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of hydrology, Vol. 333(2-4), pp. 413-430.
Eini, M.R., Javadi, S., Delavar, M., Monteiro, J.A. and Darand, M. 2019. High accuracy of precipitation reanalyses resulted in good river discharge simulations in a semi-arid basin. Ecological engineering, Vol. 131, pp. 107-119.
Zhang, D., Yao, H., James, A., Lin, Q. and Fu, W. 2020. Modifying SWAT-CS for simulating chloride dynamics in a Boreal Shield headwater catchment in south-central Ontario, Canada. Science of The Total Environment, Vol. 717, pp. 137-213.
Abbaspour, K.C., Vejdani, M., Haghighat, S. and Yang, J. 2017. SWAT-CUP calibration and uncertainty programs for SWAT, pp. 1596-1602.
Kumarasamy, K. and Belmont, P. 2018. Calibration Parameter Selection and Watershed Hydrology Model Evaluation in Time and Frequency Domains. Water, Vol. 10(6), pp. 710.
Borah D.K, Arnold J.G, Bera M, Krug C.E, and Liang, X.Z. 2017. Storm event and continuous hydrologic modeling for comprehensive and efficient watershed simulations. Transaction of the ASCE, Vol. 6 (605), pp. 605-617.
Singh, V., Bankar, N., Salunkhe, S.S., Bera, A.K. and Sharma, J. 2013. Hydrological stream flow modelling on Tungabhadra catchment: parameterization and uncertainty analysis using SWAT CUP. Current science, pp. 1187-1199.
Besalatpour, A.A., Ayoubi, S., Hajabbasi, M.A., Gharipour, A., and A. Yousefian Jazi. 2014. Feature selection using parallel genetic algorithm for the prediction of geometric mean diameter of soil aggregates by machine learning methods. Arid Land Research and Management. Vol. 28, pp. 383-394.
Wu, H. and Chen, B. 2015. Evaluating uncertainty estimates in distributed hydrological modeling for the Wenjing River watershed in China by GLUE, SUFI-2 and ParaSol methods. Ecological Engineering, Vol. 76, pp. 110–
Ang, R., & Oeurng, C. (2018). Simulating streamflow in an ungauged catchment of Tonlesap Lake Basin in Cambodia using Soil and Water Assessment Tool (SWAT) model. Water Science, 32(1), 89-101.
Mengistu, A. G., van Rensburg, L. D., & Woyessa, Y. E. (2019). Techniques for calibration and validation of SWAT model in data scarce arid and semi-arid catchments in South Africa. Journal of Hydrology: Regional Studies, 25, 100621 .
Yang, X., Magnusson, J & ,.Xu, C.-Y. (2019). Transferability of regionalization methods under changing climate. Journal of Hydrology, 568, 67-81.
Leye, I., Sambou, S., Sané, M. L., Ndiaye, I., Ndione, D. M., Kane, S., . . . Cissé, M. T. (2020). Hydrological Modeling of an Ungauged River Basin Using SWAT Model for Water Resource Management Case of Kayanga River Upstream Niandouba Dam. Journal of Water Resources and Ocean Science, 9(1), 29-41 .
Ashrafi, S.M., Gholami, H. and Najafi, M.R. 2020. Uncertainties in runoff projection and hydrological drought assessment over Gharesu basin under CMIP5 RCP scenarios. Journal of Water and Climate Change, Vol. 11(S1), pp. 145-163.
Cameron, D., Beven, K. and Naden, P., 2000a. Flood frequency estimation by continuous simulation under climate change (with uncertainty). Hydrology and Earth System Sciences, Vol. 4:3, pp. 393–405.
Blazkova, S., Beven, K., Tacheci, P. and Kulasova, A. 2002. Testing the distributed water table predictions ofTOPMODEL (allowing for uncertainty in model calibration): the death of TOPMODEL? Water Resources Research, Vol. 38:11, pp. 1257.
Zhang, X., Srinivasan, R., Zhao, K. and Liew, M.V. 2009. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrological Processes, Vol. 23(3), pp. 430-441.
Eini, M.R. 2019. Discussion of Intra-and interannual streamflow variations of Wardha watershed under changing climate. ISH Journal of Hydraulic Engineering, 27(4), pp. 474–475.
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Teshome, F. T., Bayabil, H. K., Thakural, L., & Welidehanna, F. G. (2020). Modeling Stream Flow Using SWAT Model in the Bina River Basin, India. Journal of Water Resource and Protection, 12(03), 203.
Tegegne, G., & Kim, Y. O. (2018). Modelling ungauged catchments using the catchment runoff response similarity. Journal of Hydrology, 564, 452-466.
Marques C. A. F., Ferreira J. A., Rocha A., Castanheira J. M., Melo-Goncalves P., Vaz N. and Dias J. M. (2006). Singular spectrum analysis and forecasting of hydrological time series. Phys. Chem. Earth A/B/C., 31(18), 1172–1179.
Sokolowski J. and Banks C. (2011). Principles of modeling and simulation: a multidisciplinary approach. John Wiley and Sons, Hoboken, New Jersey, USA.
Arnold, J., Moriasi, D., Gassman, P., Abbaspour, K.C., White, M., Srinivasan, R., Santhi, C., Harmel, R., Van Griensven, A. and Van Liew, M. 2012. SWAT: Model use, calibration, and validation. Transactions of the ASABE Vol. 55(4), pp. 1491-1508.
Abbaspour, K.C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H. and Kløve, B. 2015. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology Vol. 524, pp. 733-752.
Aalami, M.T., Abbasi, H. and Niksokhan, M.H. 2018. Comparison of two calibration-uncertainty methods for Soil and Water Assessment Tool in stream flow modeling. 28, pp. 53-64. (In Persian)
Narsimlu, B., Gosain, A.K, Chahar, B.R, Singh, S.K and Srivastava, P.K. 2015. SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environ Process, Vol 2(1), pp. 79–95.
Yang, J., Reichert, P., Abbaspour, K.C., Xia, J. and Yang, H. 2018. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of hydrology, Vol. 358(1-2), pp. 1-23.
Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J. and Srinivasan, R. 2007b. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of hydrology, Vol. 333(2-4), pp. 413-430.
Eini, M.R., Javadi, S., Delavar, M., Monteiro, J.A. and Darand, M. 2019. High accuracy of precipitation reanalyses resulted in good river discharge simulations in a semi-arid basin. Ecological engineering, Vol. 131, pp. 107-119.
Zhang, D., Yao, H., James, A., Lin, Q. and Fu, W. 2020. Modifying SWAT-CS for simulating chloride dynamics in a Boreal Shield headwater catchment in south-central Ontario, Canada. Science of The Total Environment, Vol. 717, pp. 137-213.
Abbaspour, K.C., Vejdani, M., Haghighat, S. and Yang, J. 2017. SWAT-CUP calibration and uncertainty programs for SWAT, pp. 1596-1602.
Kumarasamy, K. and Belmont, P. 2018. Calibration Parameter Selection and Watershed Hydrology Model Evaluation in Time and Frequency Domains. Water, Vol. 10(6), pp. 710.
Borah D.K, Arnold J.G, Bera M, Krug C.E, and Liang, X.Z. 2017. Storm event and continuous hydrologic modeling for comprehensive and efficient watershed simulations. Transaction of the ASCE, Vol. 6 (605), pp. 605-617.
Singh, V., Bankar, N., Salunkhe, S.S., Bera, A.K. and Sharma, J. 2013. Hydrological stream flow modelling on Tungabhadra catchment: parameterization and uncertainty analysis using SWAT CUP. Current science, pp. 1187-1199.
Besalatpour, A.A., Ayoubi, S., Hajabbasi, M.A., Gharipour, A., and A. Yousefian Jazi. 2014. Feature selection using parallel genetic algorithm for the prediction of geometric mean diameter of soil aggregates by machine learning methods. Arid Land Research and Management. Vol. 28, pp. 383-394.
Wu, H. and Chen, B. 2015. Evaluating uncertainty estimates in distributed hydrological modeling for the Wenjing River watershed in China by GLUE, SUFI-2 and ParaSol methods. Ecological Engineering, Vol. 76, pp. 110–
Ang, R., & Oeurng, C. (2018). Simulating streamflow in an ungauged catchment of Tonlesap Lake Basin in Cambodia using Soil and Water Assessment Tool (SWAT) model. Water Science, 32(1), 89-101.
Mengistu, A. G., van Rensburg, L. D., & Woyessa, Y. E. (2019). Techniques for calibration and validation of SWAT model in data scarce arid and semi-arid catchments in South Africa. Journal of Hydrology: Regional Studies, 25, 100621 .
Yang, X., Magnusson, J & ,.Xu, C.-Y. (2019). Transferability of regionalization methods under changing climate. Journal of Hydrology, 568, 67-81.
Leye, I., Sambou, S., Sané, M. L., Ndiaye, I., Ndione, D. M., Kane, S., . . . Cissé, M. T. (2020). Hydrological Modeling of an Ungauged River Basin Using SWAT Model for Water Resource Management Case of Kayanga River Upstream Niandouba Dam. Journal of Water Resources and Ocean Science, 9(1), 29-41 .
Ashrafi, S.M., Gholami, H. and Najafi, M.R. 2020. Uncertainties in runoff projection and hydrological drought assessment over Gharesu basin under CMIP5 RCP scenarios. Journal of Water and Climate Change, Vol. 11(S1), pp. 145-163.
Cameron, D., Beven, K. and Naden, P., 2000a. Flood frequency estimation by continuous simulation under climate change (with uncertainty). Hydrology and Earth System Sciences, Vol. 4:3, pp. 393–405.
Blazkova, S., Beven, K., Tacheci, P. and Kulasova, A. 2002. Testing the distributed water table predictions ofTOPMODEL (allowing for uncertainty in model calibration): the death of TOPMODEL? Water Resources Research, Vol. 38:11, pp. 1257.
Zhang, X., Srinivasan, R., Zhao, K. and Liew, M.V. 2009. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrological Processes, Vol. 23(3), pp. 430-441.
Eini, M.R. 2019. Discussion of Intra-and interannual streamflow variations of Wardha watershed under changing climate. ISH Journal of Hydraulic Engineering, 27(4), pp. 474–475.
