شناسایی و تحلیل عدم قطعیت پارامترهای حساس مدل SWAT در برآورد رواناب حوضه کارده

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناشی‌ارشد، گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران.

2 استادیار گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران

3 استادیار گروه مهندسی آب، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران.

4 استادیار پژوهشکده تالاب بین المللی هامون، دانشگاه زابل، زابل، ایران

چکیده

کارآیی مدل‌های هیدرولوژیکی در شبیه‌سازی رفتار یک حوضه به برآورد دقیق و اعتبار داده‌های خروجی مدل بستگی دارد. از آنجایی‌که مدل‌های هیدرولوژیکی توزیعی به دلیل جامعیت در تحلیل پدیده‌های چرخه هیدرولوژی دارای پارامترهای متعددی هستند، در معرض عدم­قطعیت خواهند بود. بنابراین، شناسایی و تجزیه و تحلیل عدم‏قطعیت پارامترهای حساس مدل ضروری می‌باشد. در این مطالعه عدم‌قطعیت ناشی از پارامترهای مدل SWAT در برآورد رواناب حوضه آبخیز کارده بررسی شد و واکنش مدل به تغییر در پارامترهای آن با روش آنالیز حساسیت فراگیر ارزیابی گردید. واسنجی و تحلیل عدم‌قطعیت مدل با استفاده از آمار ماهانه رواناب خروجی حـوضه در بـازه زمانی 2006-2000 انجام و همچنین عملکرد مــدل در بـازه 2012-2008 اعتبارسنجی شد. بـا تـوجـه بـه مقادیر ضریب کارآیی نش- ساتکلیف در دوره­های واسنجی (64/0) و اعتبارسنجی (68/0)، مدل توسعه‌یافته SWAT کارآیی خوبی برای شبیه‌سازی جریان رودخانه کارده نشان داد. همچنین در دوره­های‌ واسنجی و اعتبار‌سنجی به ترتیب 68 و 93 درصد داده‌های مشاهداتی در باند عدم‌قطعیت تولید شده توسط الگوریتم GLUE قرار گرفتند. این نتایج ضمن تأیید نکویی برازش مدل به شرایط حوضه آبخیز سد کارده، نشان داد که بازه عدم‌قطعیت تولید شده در تحلیل اثر عدم‌ایستایی پارامترهای مدل بر رواناب شبیه­سازی شده می­تواند بخش وسیعی از سناریوهای محتمل آبی در حوضه مطالعاتی را در برگیرد. در نتیجه، با شناخت بازه آماری تغییرات قابل قبول پارمترهای حساس مدل و کمّی‌سازی اثر این تغییرات بر خروجی مدل توسط الگوریتم GLUE، قابلیت اجرایی مدل واسنجی شده SWAT در شرایط مختلف عدم­قطعیت در حوضه مطالعاتی تضمین می­گردد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Parameter Identification and Uncertainty Analysis of SWAT in Kardeh Streamflow Simulation

نویسندگان [English]

  • Halime SalimiRad 1
  • ABDOLHAMID DEHVARI 2
  • Hadi Galavi 3
  • MAHBOUBEH EBRAHIMIAN 4
1 Graduate, Department of Range and Watershed Management, Faculty of Water and Soil, University of Zabol, Zabol, Iran.
2 Assistant Professor, Department of Range and Watershed Management, Faculty of Water and Soil, University of Zabol, Zabol, Iran
3 Assistant Professor, Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran.
4 Assistant Professor, Hamoun International Wetland Research Institute, University of Zabol, Zabol, Iran
چکیده [English]

Distributed hydrological models have a large number of parameters in their structure. Such models produce different results using different sets of parameters’ value resulting in model uncertainty. This study, therefore, analyzes the model uncertainty induced by its parameters instability. The soil and water assessment tool (SWAT)—a powerful semi-distributed hydrological model—is employed here to simulate the Kardeh river flow, in Iran. The model parameter sensitivity was assessed using the global sensitivity analysis method; and the generalized likelihood uncertainty estimation (GLUE) technique was used for uncertainty analysis. GLUE performs the uncertainty analysis and calibration of the model through inverse modeling. Thus, observed streamflow data of 2000-2006 and 2008-2012 were respectively used at uncertainty-analysis/calibration and validation periods. Consulting the Nash-Sutcliffe efficiency coefficient values obtained in calibration (0.64) and validation (0.68) steps, the developed SWAT model showed good performance for simulating Kardeh river flow. The produced uncertainty band was also able to encompass 68 % and 93 % of the observations during calibration and validation steps, respectively. Results, while confirming the model goodness of fit, showed that the generated uncertainty interval by GLUE covers a large spectrum of the probable streamflow scenarios in the study area. Therefore, the model applicability in the study area is confirmed under different uncertainty scenarios and it can be applied for its river basin planning and management. Then, the calibrated model can be reliably forced by climate change scenario driven data to assess the hydrological impacts of climate change in the study area.

کلیدواژه‌ها [English]

  • Parameter Uncertainty
  • GLUE
  • Sensitivity Analysis
  • Inverse Modeling
Aalami MT, Abbasi H, and Niksokhan MH (2018) Comparison of two calibration-uncertainty methods for soil and water assessment tool in stream flow and total suspended solids modeling. Water and Soil Science 28(3):53-64 (In Persian)
Abbaspour KC (2007) User Manual for SWAT-CUP, SWAT calibration and uncertainty analysis programs. Swiss Federal Institute of Aquatic Science and Technology, Eawag Dübendorf, Switzerland, 325p
Abedini ME, Ziai AN, Shafi'i M, Ghahraman B, Ansari H and Meshkini C (2017) Uncertainty assessment of groundwater flow modeling by using generalized likelihood uncertainty estimation method (Case study: Bojnourd Plain). Iranian Journal of Irrigation and Drainage 10(6):755-769 (In Persian)
Ahmadi A and Nasseri M (2020) Do direct and inverse uncertainty assessment methods present the same results? Journal of Hydroinformatics 22(4):842–855
Akhoun S, Shahverdi M, and Zare-Abyaneh H (2018) Modeling the spatial changes of blue and green water (case study: Hamadan province). In: Proc. of First National Conference on Water Resources Management Strategies and Environmental Challenges (In Persian)
Amini-Zad A, Galavi H and MohammadRezaPoor OB (2018) Hydrological modeling of Pishin dam watershed using SWAT. In: Proc. of the First National Conference on SWAT Applications in Iran, Water and Wastewater Research Institute, Isfahan University of Technology, Isfahan
Beven KJ and Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrological Process 6(3):279-298
Campbell EP, Fox DR, and Bates BC (1999) A Bayesian approach to parameter estimation and pooling in nonlinear flood event models. Water Resource Research 35(1):211-220
EmamiFar S, Davary K, Ansari H, Ghahraman B, Hosseini SM, and Naseri M (2016) Uncertainty assessment DWB model by using GLUE method (Case study: Andrabi and Farvbrman catchments). Journal of Soil and Water Resources Conservation 6(1):125-142 (In Persian)
Galavi H, Kamal MR, Mirzaei M, and Ebrahimian M (2019) Assessing the contribution of different uncertainty sources in streamflow projections. Theoretical and Applied Climatology 137(1-2):1289-1303
Galavi H and Lee TS (2012) Uncertainty analysis of climate change impacts on runoff. In: Proc. of International Conference on Future Environment and Energy 28:235-239
Golshan M, Esmali-Ouri A, Shahedi K, and Jahanshahi A (2016) Performance evaluation of SWAT and IHACRES models to simulate runoff in Khorramabad watershed. Water and Soil Science 26(2):29-42 (In Persian)
Hamraz BS, Akbarpour A, and Pourreza-bilondi M (2016) Assessment of parametric uncertainty of MODFLOW model using GLUE method (Case study: Birjand plain). Journal of Water and Soil Conservation 22(6):61-79 (In Persian)
Ja'farzadeh M and Rouhani H (2016) Sensitivity analysis of SWAT model in runoff simulation. In: Proc. of First National Conference on Natural Resources and Sustainable Development in Central Zagros, Shahrekord University, Iran (In Persian)
Jalavand M, Dehwari AH, and HaghNazari F (2016) Sensitivity analysis of effective parameters on input runoff to the Latian dam using SWAT model. In: Proc. of Third International Conference on New Findings in Agricultural Sciences, Natural Resources and Environment, Tehran, Iran (In Persian)
Judi-Hamzehabadi A, Kadkhodosseini M, Akhavan S, and Nozari H (2016) Evaluation of SWAT and SVM models to simulate the runoff of Lighvanchay river. Water and Soil Science 26:137-150 (In Persian)
Kavian A, Namdar M, Golshan M, and Bahri M (2017) Hydrological modeling of climate changes impact on flow discharge in Haraz river basin. Journal of Natural Environmental Hazards 6(12):89-104 (In Persian)
Kobarfard M, Fazloula R, Zarghami M, Akbarpour A (2019) Assessment uncertainty of SWMM urban flood model using GLUE method case study: 2nd district municipality of Tabriz. Iran-Water Resources Research 14(5):103-117 (In Persian)
Lee TS, Galavi H, and Huang YF (2014) Uncertainty in climate change impact studies: a general picture. International Journal Climate Chang Impacts Responses 6(1):1-10
Mirzaei M, Galavi H, Faghih M, Huang YF, Lee TS, and El-Shafie A (2013) Model calibration and uncertainty analysis of runoff in the Zayanderood river basin using generalized likelihood uncertainty estimation (GLUE) method. Journal of Water Supply: Research and Technology-AQUA 62(5):309-320
Nasiri S, Ansari H, and NaghiZiaei A (2020) Simulation of stream flow in samalqan watershed using SWAT hydrological model. Journal of Water Resources Engineering 13(45):39-56 (In Persian)
Neitsch SL, Arnold JG, Kiniry JR, and Williams JR (2011) Soil and water assessment tool theoretical documentation. Texas Water Resources Institute
Rostamian R, Jaleh A, Afyuni M, Mousavi SF, Heidarpour M, Jalalian A, and Abbaspour KC (2008) Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal 53(5):977-988
Roodaki S and Azizian A (2020) Uncertainty analysis due to the application of different infiltration methods on the performance of HEC-HMS model using GLUE algorithm. Iran-Water Resources Research 16(2):50-66 (In Persian)
Sepúlveda N and Doherty J (2015) Uncertainty analysis of a groundwater flow model in east-central Florida. Groundwater 53(3):464-474
Shafiei M, Ghahraman B, Saghafian B, Davary K, and Vazifedoust M (2014) Calibration and uncertainty analysis of SWAP model by using GLUE method. Water Research in Agriculture 28(2):447-448 (In Persian)
Shafi'I M, Bazrafshan C, and Iran-nejhad P (2018) Uncertainty analysis for simulation of river flow applied by GLUE method. Geography (Iranian Journal of Geography) 58:82-99 (In Persian)
Stedinger JR, Vogel RM, Lee SU, and Batchelder R (2008) Appraisal of the generalized likelihood uncertainty estimation (GLUE) method. Water Resources Research 44:1-17
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 76:110–121
Yang J, Reichert P, Abbaspour KC, Xia J, and Yang H (2008) Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology 358:1-23
Zuo D, Xu Z, Zhao J, Abbaspour KC, and Yang H (2014) Response or runoff to climate change in the Wei River basin, china. Hydrological Sciences Journal 60(3):508-522