تحلیل حساسیت و عدم قطعیت مدل SWAT در شبیه‌سازی دبی جریان، رسوب و فسفر برای یک حوضه آبریز کوهستانی (مطالعه موردی حوضه رودخانه کرج)

نوع مقاله : مقالات پژوهشی

نویسندگان

1 مهندسی محیط زیست، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران، ایران

2 گروه مهندسی محیط زیست، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران، ایران

3 معاون دفتر حفاظت و مدیریت زیست محیطی آب و خاک سازمان حفاظت محیط زیست ایران

چکیده

شبیه‌سازی کمیت و کیفیت رواناب­های سطحی در مناطق کوهستانی، به دلیل ویژگی‌های منحصربه‌­فرد این‌گونه حوضه­های آبریز مانند تأثیر قابل‌توجه برف بر رژیم جریان، تغییرات زیاد پارامترهای هواشناسی به‌واسطه تغییر ارتفاع، نرخ بالای فرسایش و کمبود اطلاعات ناشی از مشکلات دسترسی فیزیکی، یکی از موضوعات چالش‌برانگیز در حوزه مدل‌سازی حوضه­های آبریز محسوب می‌شود. یکی از مواردی که کمتر در خصوص مدل‌سازی این‌گونه حوضه­ها به آن پرداخته شده، تحلیل حساسیت و عدم قطعیت پیش­بینی­های مرتبط با کیفیت آب، به‌ویژه در ارتباط با پارامتر مهم فسفر است. هدف از انجام این تحقیق واسنجی، اعتبارسنجی، تحلیل حساسیت و عدم قطعیت پارامتری مدل SWAT در پیش­بینی دبی جریان، رسوب و فسفر کل در یک حوضه آبریز کوهستانی است. مطالعه موردی در حوضه آبریز رودخانه کرج در بالادست آبگیر بیلقان انجام شده و الگوریتم SUFI2 برای انجام تحلیل عدم قطعیت مورد استفاده قرار گرفته است. بر اساس نتایج تحلیل حساسیت، پارامترهای ضریب جریان پایه آب زیرزمینی، ضریب زبری رواناب سطحی و آهنگ افزایش بارش به ترتیب حساس‌ترین پارامترهای مدل نسبت به دبی جریان، رسوب و فسفر کل بودند. بهترین ضرایب نش-ساتکلیف (NS) مربوط به شبیه‌سازی رواناب، رسوب و فسفر کل در همه ایستگاه‌ها و در مجموع دوره‌های واسنجی و اعتبار‌سنجی، به ترتیب برابر با 76/0، 56/0 و 92/0 بدست آمد. همچنین شبیه‌سازی نقاط اوج نمودار هر سه کمیت مذکور با افزایش عدم قطعیت و کاهش دقت پیش‌بینی مدل همراه بوده، اما با توجه به قرارگیری بالای 70 درصد مقادیر اندازه‌گیری شده رواناب و رسوب و نزدیک به 60 درصد مقادیر اندازه‌گیری شده فسفر کل در بازه عدم قطعیت پیش‌بینی تولید شده توسط الگوریتم SUFI2، محدوده پارامترهای مورد استفاده در واسنجی مدل می‌تواند الگوی مناسبی برای شبیه‌سازی سناریوهای آتی در حوضه‌های آبریز کوهستانی مشابه باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Sensitivity and Uncertainty Analysis of SWAT Model in Flow, Sediment and Phosphorus Simulation for a Mountainous Watershed (Case Study of Karaj River Catchment)

نویسندگان [English]

  • Sh. Nourinezhad 1
  • M.M. Rajabi 2
  • T. Fathi 3
1 Environmental Engineering, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
2 Faculty of Civil and Environmental Engineering,, Tarbiat Modares University
3 Deputy Manager of the Water and Soil Office, Department of Environment, Tehran, Iran.
چکیده [English]

Introduction
 Simulation of quantity and quality of surface runoff in mountainous watersheds is one of the most challenging topics in modeling due to its unique features, such as unusual topography and complex hydrological processes. One of the lesser-known aspects of modeling such catchments is the uncertainty analysis of water quality predictions, especially about the vital phosphorus parameter. Phosphorus is one of the important quality variables in water, and its increase in water resources can cause eutrophication phenomena in streams and reservoirs of dams. Due to the importance of the phosphorus parameter and the fact that water quality modeling has not been employed in the Karaj catchment area so far, in this research, total phosphorus has been modeled as a water quality parameter along with the flow and sediment discharge. This study aims to identify the most sensitive parameters of the model to flow, sediment, and total phosphorus discharge and calibrate, validate and analyze the parametric uncertainty of the SWAT model in predicting these three variables in a mountainous catchment. The case study was the catchment area of ​​the Karaj River upstream of Bileqan pond, which is one of the mountainous watersheds in Iran. There are two critical water structures along the Karaj River, namely Amirkabir dam and Bilqan pond. Amirkabir dam (Karaj) is a multi-purpose project that is constructed to supply drinking water to Tehran and regulate water for irrigation and agriculture in the suburbs of Karaj. The Bileqan pond is also the essential point of supply and transfer of drinking water in Tehran. Given the importance of this region in supplying water for different uses, providing a calibrated model for quantitative and qualitative variables of water can be the basis for decisions to apply future management scenarios in this basin.
Materials and Methods
 The case study was the Karaj River catchment area upstream of Bilqan Basin, which with an average height of 2880 meters, is one of the mountainous areas located in the Alborz Mountains. This basin with an area of 1076 square kilometers in the north, includes parts of Mazandaran province. In the east and south of the catchment area includes parts of Tehran province and most of it is located in Alborz province. The average annual temperature and rainfall in this basin are 12.1 °C and 480 mm, respectively, and the average of 117 glacial days during the year is observed in this area. The long-term daily data of synoptic stations adjacent to the study area from the beginning of 1998 to the end of 2018 (21 years in total) was introduced to the model. Also, daily data of relative humidity, rainfall, minimum and maximum temperature, solar radiation hours, and wind speed as meteorological parameters measured at stations in the study area were introduced to the model. It should be noted that there was a lot of missing data in meteorological information, especially for daily temperature data. In addition to the above information, daily flow data discharged from Amirkabir dam and technical specifications of the dam were introduced to the model. In addition, orchard management information, including irrigation periods and information related to phosphate fertilizers used in regional orchards, were presented to the model. The global sensitivity analysis method was used to determine the sensitive parameters of the model. Furthermore, the SUFI2 algorithm was used in SWAT_CUP software to calibrate and analyze the parametric uncertainty of the SWAT model. This algorithm quantifies the output uncertainty by 95% prediction uncertainty boundaries.
Results and Discussion
 According to the results of sensitivity analysis, the parameters Baseflow alpha-factor (ALPHA_BF), Manning’s “n” value for overland flow (OV_N), and Precipitation Laps rate (PLAPS) were the most sensitive parameters to flow, sediment, and total phosphorus, respectively. The best Nash-Sutcliffe (NS) coefficients for runoff, sediment, and total phosphorus simulation obtained in all stations and after full calibration and validation periods were equal to 0.76, 0.56, and 0.92, respectively. Simulation of the peak points of the diagram of all three quantities was also associated with increased uncertainty and decreased model prediction accuracy, but due to the placement of more than 70% of the measured runoff and sediment values and nearly 60% of the measured total phosphorus values in the prediction uncertainty boundaries generated by SUFI2 algorithm the final value of the parameters used in the calibration process can be appropriate for simulating future scenarios in similar mountain catchments. The main weakness of the model is simulating the peak points of flow and sediment discharge. In the case of flow and sediment discharge, the liability of modeling can be generalized due to the lack of accurate prediction of the snowmelt inflow to the river in spring, which begins to increase in February and reaches the peak point in May. A considerable number of missing data in meteorological stations can effectively reflect the lack of accurate model prediction at the peak points. In this region, missing daily temperature data compared to other meteorological parameters has been significant. The dependency of the SWAT model on many experimental and quasi-experimental models such as SCS-CN and MUSLE can be another factor affecting the weakness in predicting the peak points of the sediment discharge, as well.
Conclusion
 According to the uncertainty analysis results, most of observed flow, sediment and total phosphorus discharge values were within the uncertainty prediction boundaries generated by the SUFI2 algorithm. The NS coefficient for all three variables has met the satisfactory modeling threshold. Therefore, it seems that the sensitive parameters identified and used in the calibration process in this study and their final values can be appropriate for modeling future scenarios for this study area and similar mountain catchments. One of the limitations of the present study was a large number of missing data in meteorological stations, especially for the temperature variable. Thus, providing required measured meteorological data to the model may emhance the simulation, especially at peak points.

کلیدواژه‌ها [English]

  • Calibration
  • Prediction
  • SUFI2
  • Semi-distributed Model
  • Water Quality
1- Abbaspour K.C., Johnson C.A., and Genuchten M.T van. 2004. Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure 1352: 1340–1352. https://doi.org/10.2113/3.4.1340.
2- Abbaspour K.C., Rouholahnejad E., Vaghefi S.R.I.N.I.V.A.S.A.N.B., 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 524: 733-752. https://doi.org/10.1016/j.jhydrol.2015.03.027.
3- Abbaspour K.C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J., and Srinivasan R. 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology 333(2–4): 413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014.
4- Andrianaki M., Shrestha J., Kobierska F., Nikolaidis N.P., and Bernasconi S.M. 2019. Assessment of SWAT spatial and temporal transferability for a high-altitude glacierized catchment. Hydrology and Earth System Sciences 23(8): 3219-3232. https://doi.org/10.5194/hess-23-3219-2019.
5- Arnold JG., Kiniry JR., Srinivasan R., Williams JR., Haney EB., and Neitsch SL. 2012. Soil & Water Assessment Tool.
6- Arnold J.G., Srinivasan R., Muttiah R.S., and Williams J.R. 1998. Large area hydrologic modeling and assessment part I: model development 1. JAWRA Journal of the American Water Resources Association 34(1): 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x.
7- Azimi M., Heshmati G.A., Farahpour M., Faramarzi M., and Abbaspour K.C. 2013. Modeling the impact of rangeland management on forage production of sagebrush species in arid and semi-arid regions of Iran. Ecological Modelling 250: 1-14. https://doi.org/10.1016/j.ecolmodel.2012.10.017.
8- Bagnold RA. 1977. Bed load transport by natural rivers. Water Resources Research 13(2): 303–312. https://doi.org/10.1029/WR013i002p00303.
9- Bhatta B., Shrestha S., Shrestha PK., and Talchabhadel R. 2020. Modelling the impact of past and future climate scenarios on streamflow in a highly mountainous watershed: A case study in the West Seti River Basin, Nepal. Science of the Total Environment. Elsevier B.V 740: 140156. https://doi.org/10.1016/j.scitotenv.2020.140156.
10- Carpenter SR. 2008. Phosphorus control is critical to mitigating eutrophication. Proceedings of the National Academy of Sciences of the United States of America 105(32): 11039–11040. https://doi.org/10.1073/pnas.080611210.
11- D’Ambrosio E., De Girolamo AM., Barca E., Ielpo P., and Rulli MC. 2017. Characterising the hydrological regime of an ungauged temporary river system: a case study. Environmental Science and Pollution Research 24(16): 13950–13966. https://doi.org/10.1007/s11356-016-7169-0.
12- Engebretsen A., Vogt R.D., and Bechmann M. 2019. SWAT model uncertainties and cumulative probability for decreased phosphorus loading by agricultural Best Management Practices. Catena 175: 154-166. https://doi.org/10.1016/j.catena.2018.12.004.
13- Faramarzi M., Abbaspour KC., Adamowicz WLV., Lu W., Fennell J., Zehnder AJB., and Goss GG. 2017. Uncertainty based assessment of dynamic freshwater scarcity in semi-arid watersheds of Alberta, Canada. Journal of Hydrology Regional Studies 9: 48-68. https://doi.org/10.1016/j.ejrh.2016.11.003.
14- Flynn K.F., and Van Liew M.W. 2011. Evaluation of SWAT for sediment prediction in a mountainous snowmelt-dominated catchment. Transactions of the ASABE 54(1): 113-122. https://doi.org/10.13031/2013.36265.
15- Grusson Y., Sun X., Gascoin S., Sauvage S., Raghavan S., Anctil F., and Sáchez-Pérez J.M. 2015. Assessing the capability of the SWAT model to simulate snow, snow melt and streamflow dynamics over an alpine watershed. Journal of Hydrology 531: 574-588. https://doi.org/10.1016/j.jhydrol.2015.10.070.
16- Hasan M.A., and Pradhanang S.M. 2017. Estimation of flow regime for a spatially varied Himalayan watershed using improved multi-site calibration of the Soil and Water Assessment Tool (SWAT) model. Environmental Earth Sciences. Springer Berlin Heidelberg 76(23). https://doi.org/10.1007/s12665-017-7134-3.
17- Jalowska A.M., and Yuan Y. 2019. Evaluation of SWAT impoundment modeling methods in water and sediment simulations. JAWRA Journal of the American Water Resources Association 55(1): 209-227. https://doi.org/10.1111/1752-1688.12715.
18- Jeong J., Yang J., Han S., Seo Y.S., and Hong Y. 2020. Assessment of coupled hydrologic and biogeochemical Hg cycles in a temperate forestry watershed using SWAT-Hg. Environmental Modelling & Software 126: 104644. https://doi.org/10.1016/j.envsoft.2020.104644.
19- Kalra A., and Ahmad S. 2011. Evaluating changes and estimating seasonal precipitation for the Colorado River Basin using a stochastic nonparametric disaggregation technique. Water Resources Research 47(5): 1–26. https://doi.org/10.1029/2010WR009118.
20- Kheiri S., Solak C.N., Edlund M.B., Spaulding S., Nejadsattari T., Asri Y., and Hamdi S.M.M. 2018. Biodiversity of diatoms in the Karaj River in the Central Alborz, Iran. Diatom Research 33(3): 355–380. https://doi.org/10.1080/0269249X.2018.1557747.
21- Kulkarni A.V., Rathore B.P., Singh S.K., and Ajai. 2010. Distribution of seasonal snow cover in central and western Himalaya. Annals of Glaciology 51(54): 123–128. https://doi.org/10.3189/172756410791386445.
22- Lamba J., Thompson A.M., Karthikeyan K.G., Panuska J.C., and Good L.W. 2016. Effect of best management practice implementation on sediment and phosphorus load reductions at subwatershed and watershed scale using SWAT model. International Journal of Sediment Research 31(4): 386-394. https://doi.org/10.1016/j.ijsrc.2016.06.004.
23- Li X., and Williams M.W. 2008. Snowmelt runoff modelling in an arid mountain watershed, Tarim Basin, China. Hydrological Processes 22(19): 3931-3940. https://doi.org/10.1002/hyp.7098.
24- Liu R., Xu F., Zhang P., Yu W., and Men C. 2016. Identifying non-point source critical source areas based on multi-factors at a basin scale with SWAT. Journal of Hydrology 533: 379-388. https://doi.org/10.1016/j.jhydrol.2015.12.024.
25- Mengistu A.G., van Rensburg L.D., and 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. https://doi.org/10.1016/j.ejrh.2019.100621.
26- Moriasi D.N., Arnold J.G., Van Liew M.W., Bingner R.L., Harmel R.D., and Veith TL. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50(3): 885-900. https://doi.org/10.13031/2013.23153.
27- Moriasi D.N., Gitau M.W., Pai N., and Daggupati P. 2015. Hydrologic and water quality models: Performance measures and evaluation criteria. Transactions of the ASABE 58(6): 1763–1785. https://doi.org/10.13031/trans.58.10715.
28- Nash J., and Sutcliffe I. 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology 10(3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6.
29- Navratil O., Esteves M., Legout C., Gratiot N., Nemery J., Willmore S., and Grangeon T. 2011. Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment. Journal of Hydrology 398(3-4): 246-259. https://doi.org/10.1016/j.jhydrol.2010.12.025.
30- Neitsch S.L. 2005. Soil and Water Assessment Tool. User’s Manual Version 2005 476.
31- Neitsch S.L., Arnold J.G., Kiniry J.R., and Williams J. 2011. Soil & Water Assessment Tool Theoretical Documentation Version 2009. Texas Water Resources Institute.
32- Niraula R., Kalin L., Srivastava P., and Anderson C.J. 2013. Identifying critical source areas of nonpoint source pollution with SWAT and GWLF. Ecological Modelling 268: 123-133. https://doi.org/10.1016/j.ecolmodel.2013.08.007.
33- Noor H., Vafakhah M., Taheriyoun M., and Moghadasi M. 2014. Hydrology modelling in Taleghan mountainous watershed using SWAT. Journal of Water and Land Development 20(1): 11–18. https://doi.org/10.2478/jwld-2014-0003.
34- Prasannakumar V., Vijith H., Abinod S., and Geetha N.J.G.F. 2012. Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers 3(2): 209-215. https://doi.org/10.1016/j.gsf.2011.11.003.
35- Rahman K., Maringanti C., Beniston M., Widmer F., Abbaspour K., and Lehmann A. 2013. Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland. Water Resources Management 27(2): 323–339. https://doi.org/10.1007/s11269-012-0188-9.
36- Rostamian R., Jaleh A., Afyuni M., Mousavi S., Heidarpour M., Jalalian A., and Abbaspour K. 2008. Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal 53(5): 977–988. https://doi.org/10.1623/hysj.53.5.977.
37- Singh V., and Goyal M.K. 2016. Analysis and trends of precipitation lapse rate and extreme indices over north Sikkim eastern Himalayas under CMIP5ESM-2M RCPs experiments. Atmospheric Research 167: 34-60. https://doi.org/10.1016/j.atmosres.2015.07.005.
38- Talebizadeh M., Morid S., Ayyoubzadeh S.A., and Ghasemzadeh M. 2010. Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resources Management 24(9): 1747–1761. https://doi.org/10.1007/s11269-009-9522-2.
39- Viviroli D., Du HH., Messerli B., Meybeck M., and Weingartner R. 2007. Mountains of the world , water towers for humanity: Typology, Mapping, and Global Significance 43:1–13. https://doi.org/10.1029/2006WR005653.
40- Wang W., Xie Y., Bi M., Wang X., Lu Y., and Fan Z. 2018. Effects of best management practices on nitrogen load reduction in tea fields with different slope gradients using the SWAT model. Applied Geography 90: 200-213. https://doi.org/10.1016/j.apgeog.2017.08.020.
41- Williams J.R. 1975. Sediment routing for agricultural watersheds 1. JAWRA Journal of the American Water Resources Association 11(5): 965-974. https://doi.org/10.1111/j.1752-1688.1975.tb01817.x.
42- Wu F., Zhan J., Wang Z., and Zhang Q. 2015. Streamflow variation due to glacier melting and climate change in upstream Heihe River Basin, Northwest China. Physics and Chemistry of the Earth, Parts A/B/C 79: 11-19. https://doi.org/10.1016/j.pce.2014.08.002.
43- Wu L., Long T yu., Liu X., and Ma X. 2013. Modeling impacts of sediment delivery ratio and land management on adsorbed non-point source nitrogen and phosphorus load in a mountainous basin of the three Gorges reservoir area, China. Environmental Earth Sciences 70(3): 1405–1422. https://doi.org/10.1007/s12665-013-2227-0.
44- Zeiger S.J., and Hubbart J.A. 2016. A SWAT model validation of nested-scale contemporaneous stream flow, suspended sediment and nutrients from a multiple-land-use watershed of the central USA. Science of The Total Environment 572: 232-243. https://doi.org/10.1016/j.scitotenv.2016.07.178.
45- Zhang G., Su X., Ayantobo O.O., Feng K., and Guo J. 2021. Spatial interpolation of daily precipitation based on modified ADW method for gauge-scarce mountainous regions: A case study in the Shiyang River Basin. Atmospheric Research 247: 105167. https://doi.org/10.1016/j.atmosres.2020.105167.
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