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Hydrological stream flow modeling in the Talar catchment (central section of the Alborz Mountains, north of Iran): Parameterization and uncertainty analysis using SWAT-CUP

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Warianty tytułu
PL
Modelowanie przepływu w zlewni rzeki Talar (środkowa część gór Alborz w północnym Iranie): Parametryzacja i analiza niepewności za pomocą SWAT-CUP
Języki publikacji
EN
Abstrakty
EN
There are several methods and techniques for measuring the parameters and forecasting the errors in the hydrological models. In this study, semi distributed Soil and Water Asseeement Tool (SWAT) model and SWAT-CUP (CUP – Calibration and Uncertainty Programs) have been applied using SUFI2 program. After collection of data, the whole Talar watershed located in the central section of the Alborz Mountains, north of Iran was separated into 219 hydrological response units (HRU) in 23 sub-watersheds. In order to improve the simulation parameters and obtain better correlation of observed and simulated values, the sensitive parameters were validated to obtain finally the acceptable value of both R2 and Nash–Sutcliffe (NS) coefficients equal to 0.93. Final P-value and t-state were also estimated for sensitive parameters. As a result, the CN2 parameter, which was critical in the initial stage of this research was replaced by the SOL-K parameter (electrical conductivity saturated soil layers) as a critical parameter in the later stage. Results of this study show that the SWAT model can be an effective and useful tool for the assessment and optimal management of water and soil resources.
PL
Istnieje kilka metod i technik pomiaru parametrów oraz przewidywania błędów w modelach hydrologicznych. W prezentowanej pracy zastosowano modele SWAT i SWAT-CUP z użyciem programu SUFI2. Po zgromadzeniu danych cała zlewnia rzeki Talar, zlokalizowana w środkowej części gór Alborz w północnym Iranie, została podzielona na 219 jednostek hydrologicznych (HRU) w 23 podzlewniach. W celu usprawnienia parametrów symulacji oraz lepszego powiązania wartości symulowanych i obserwowanych zweryfikowano parametry wrażliwe, co w efekcie doprowadziło wartości R2 i współczynnika Nasha– Sutcliffa (NS) do akceptowalnej wartości 0,93. Dla tych parametrów ustalono także końcowe wartości P i t. W wyniku przeprowadzonej analizy parametr CN2, krytyczny na wstępnym etapie badań, został zastąpiony parametrem SOL-K (przewodnictwo elektrolityczne nasyconej warstwy gleby). Wyniki badań świadczą, że model SWAT może być wydajnym i użytecznym narzędziem w ocenie oraz optymalnym zarządzaniu zasobami wody i gleby.
Wydawca
Rocznik
Tom
Strony
57--69
Opis fizyczny
Bibliogr. 62 poz., rys., tab.
Twórcy
autor
  • Sari University of Agriculture and Natural Resources, Faculty of Natural Resources, Department of Watershed Management, Mazandaran, Farah Abad Road, Iran
  • Sari University of Agriculture and Natural Resources, Faculty of Natural Resources, Department of Watershed Management, Mazandaran, Farah Abad Road, Iran
autor
  • Sari University of Agriculture and Natural Resources, Faculty of Natural Resources, Department of Watershed Management, Mazandaran, Farah Abad Road, Iran
autor
  • Tarbiat Modares University, Faculty of Natural Recourses, Department of Watershed Management, Imam Khomeini Blv, Noor, IRAN
autor
  • Sari University of Agriculture and Natural Resources, Faculty of Natural Resources, Department of Watershed Management, Mazandaran, Farah Abad Road, Iran
Bibliografia
  • ABBASPOUR K.C. 2008. A user manual SWAT-CUP2: SWAT calibration and uncertainty programs. Duebendorf, Switzerland. Swiss Federal, Institute of Aquatic Science and Technology pp. 95.
  • ABBASPOUR K.C., ROUHOLAHNEJAD E., VAGHEFI S., SRINIVASAN R., YANG H., 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 p. 733–752.
  • AHL R.S., WOODS S.W., ZUURING H.R. 2008. Hydrologic calibration and validation of SWAT in a snowdominated rocky mountain watershed, Montana, USA. Journal of American Water Resource Association. Vol. 44. Iss. 6 p. 1411–1430.
  • ANDERTON S., LATRON J., GALLART F. 2002. Sensitivity analysis and multi-response, multi-criteria evaluation of a physically based distributed model. Hydrological Processes. Vol. 16. Iss. 2 p. 333–353.
  • ARNOLD J., ALLEN P. 1996. Estimating hydrologic budgets for three Illinois watersheds. Journal of Hydrology. Vol. 176 p. 57–77.
  • ARNOLD J.G., MORIASI D.N., GASSMAN P.W., ABBASPOUR K.C., WHITE M.J., SRINIVASAN R., SANTHI C., VAN HARMEL R.D., VAN GRIENSVEN A., VAN LIEW M.W., KANNAN N., JHA M.K. 2012. SWAT: model use, calibration, and validation. Transactions of the ASABE. Vol. 55. Iss. 4 p. 1491–1508.
  • ARNOLD J., SRINIVASAN R., MUTTIAH R., WILLIAMS J. 1998. Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association. Vol. 34. Iss. 1 p. 73–89.
  • BANASIK K., WOOWORD D. 2010. Empirical determination of runoff curve number for a small agricultural watershed in Poland [online]. 2nd Joint Federal Interagency Conference. Las Vegas, NV, June 27–July 1, 2010. [Access 10.01.2014]. Available at: http://acwi.gov/sos/pubs/2ndJFIC/Contents/10E_Banasik_28_02_10.pdf
  • BEKELE E., NICKLOW J. 2007. Multi-objective automatic calibration of SWAT using NSGA-II. Journal of Hydrology. Vol. 341 p. 165–176.
  • BELYAEH A., ADAMOWSKI J. 2013. Drought forecasting using new machine learning methods. Journal of Water and Land Development. No. 18 p. 3–12.
  • BEVEN K. 1989. Changing ideas in hydrology – the case of physically-based models. Journal of Hydrology. Vol. 105 p. 157–172.
  • BEVEN K. 2002. Towards an alternative blueprint for a physically based digitally simulated hydrologic response modelling system. Hydrological Processes. Vol. 16. Iss. 2 p. 189–206.
  • BEVEN K. 2006. A manifesto for the equifinality thesis. Journal of Hydrology. Vol. 320 p. 18–36.
  • BEVEN K., BINLEY A. 1992. The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes. Vol. 6. Iss. 3 p. 279–298.
  • BEVEN K., FREER J. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modeling of complex environmental systems using the GLUE methodology. Journal of Hydrology. Vol. 249 p. 11–29.
  • BOYLE D., GUPTA H., SOROOSHIAN S. 2000. Towards improved calibration of hydrologic models: combining the strengths of manual and automatic methods. Water Resources Research. Vol. 36. Iss. 12 p. 3663–3674.
  • CAO W., BOWDEN W., DAVIE T., FENEMOR A. 2006. Multivariable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability. Hydrological Processes. Vol. 20. Iss. 5 p. 1057–1073.
  • CHEN J., WU Y. 2012. Advancing representation of hydrologic processes in the Soil and Water Assessment Tool (SWAT) through integration of the Topographic Mode (TOPMODEL) features. Journal of Hydrology. Vol. 420–421 p. 319–328.
  • CHU T.W., SHIRMOHAMMADI A. 2004. Evaluation of the SWAT model’s hydrology component in the piedmont physiographic region of Maryland. American Society of Agricultural and Biological Engineers. Vol. 47. Iss. 4 p. 1057–1073.
  • DUAN Q., SOROOSHIAN S., GUPTA V. 1992. Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resources Research. Vol. 28. Iss. 4 p. 1015–1031.
  • DUAN Q., SOROOSHIAN S., GUPTA V. 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. Journal of Hydrology. Vol. 158 p. 265–284.
  • ECKHARDT K., ARNOLD J. 2001. Automatic calibration of a distributed catchment model. Journal of Hydrology. Vol. 251 p. 103–109.
  • FARAMARZI M., ABBASPOUR K.C., SCHULIN R., YANG H. 2009. Modeling blue and green water availability in Iran. Hydrological Process.Vol. 23. Iss. 3 p. 486–501.
  • GASSMAN P.W., REYES M.R., GREEN C.H., ARNOLD J.G. 2007. The soil and water assessment tool: Historical development, applications, and future research directions. Transactions of the American Society of Agricultural Engineers. Vol. 50. Iss. 4 p. 1211–1250.
  • GUPTA H., SOROOSHIAN S., YAPO P.O. 1998. Toward improved calibration of hydrologic model: multiple and non commensurable measures of information. Water Resources Research. Vol. 34. Iss. 4 p. 751–763.
  • HUANG Q., ZHANG W. 2004. Improvement and application of GIS-based distributed SWAT hydrological modeling on high altitude, cold, semi-arid catchment of Heihe river basin, China. Journal of Nanjing Forestry University. Vol. 28 p. 22–26.
  • HUANG Q., ZHANG W. 2010. Application and parameters sensitivity analysis of SWAT model. Arid Land Geography. Vol. 33. No. 1 p. 8–15.
  • KANNAN N., SANTHI C., ARNOLD J. 2008. Development of an automated procedure for estimation of the spatial variation of runoff in large river basins. Journal of Hydrology. Vol. 359 p. 1–15.
  • KLØVE B., ALA-AHO P., BERTRAND G., GURDAK J.J., KUPFERSBERGER H., KVOERNER J., MUOTKA T., MYKRÄ H., PREDA E., ROSSI P., BERTACCHI UVO C., VELASCO E., WACHNIEW P., PULIDO-VELÁZQUEZ M. 2014. Climate change impacts on groundwater and dependent ecosystems. Journal of Hydrology. Vol. 518 p. 250–266.
  • LI Z., SHAO Q., XU Z., CAI X. 2010. Analysis of parameter uncertainty in semidistributedhydrological models using bootstrap method: A case study of SWAT model applied to Yingluoxia watershed in northwest China. Journal of Hydrology. Vol. 385 p. 76–83.
  • LI Z., XU Z., LI Z. 2011. Performance of WASMOD and SWAT on hydrological simulation in Yingluoxia watershed in nourthwest of China. Hydrological Processes. Vol. 25. Iss. 13 p. 2001–2008.
  • LI Z., XU Z., SHAO Q., YANG J. 2009. Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin. Hydrological Processes. Vol. 23. Iss. 19 p. 2744–2753.
  • LIU Y., XU Z., NAN Z. 2012. Study on ecological compensation in upper stream of Heihe river basin based on SWAT model and minimum-data approach. Transactions of the Chinese Society of Agricultural Engineering. Vol. 28. No 10 p. 124–130.
  • LU Z., ZOU S., XIAO H., ZHENG C., YIN Z., WANG W. 2015. Comprehensive hydrologic calibration of SWAT and water balance analysis in mountainous watersheds in northwest China. Physics and Chemistry of the Earth. Vol. 79 p. 76–85.
  • LU Z., ZOU S., YIN Z., LONG A., XU B. 2012. A new suitable method for SWAT model calibration and its application in data-scarce basins. Journal of Lanzhou University (Natural Sciences). Vol. 48. Iss. 1 p. 1–7.
  • MORIASI D.N., ARNOLD J.G., VAN LIEW M.W., BINGER R.L., HARMEL R.D.,VEITH T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASAE. Vol. 50. Iss. 3 p. 885–900.
  • NEITSCH S.L., ARNOLD J.G., KINIRY J.R., WILLIAMS J.R. 2005. Soil and Water Assessment Tool. Theoretical Documentation. Version 2005. Berlin. Springer.
  • NEITSCH S.L., ARNOLD J.G., KINIRY J.R., WILLIAMS J.R. 2011. Soil and Water Assessment Tool. Theoretical documentation version 2009. TWRI Report TR-406. Texas. Texas Water Resources Institute, College Station pp. 618.
  • NIRAULA R., NORMAN L., MEIXNER T., CALLEGARY J. 2012. Multi-gauge calibration for modeling the semi-arid Santa Cruz watershed in Arizona–Mexico border area using SWAT. Air, Soil and Water Research. Vol. 5 p. 41–57.
  • NOOR H., VAFAKHAH M., TAHERIOUN M., MOGHADASI M. 2014. Hydrology modelling in Taleghan mountainous watershed using SWAT. Journal of Water and Land Development. No. 20 p. 11–18.
  • PHOMCH P., WIROJANGUD P., VANGPAISAL T., THAVEEVOUTHTI T. 2011. Suitability of SWAT model for simulating of monthly streamflow in Lam Sonthi Watershed. Journal of Industrial Technology. Vol. 7. Iss. 2 p. 49–56.
  • Project Report of Talar watershed 2000. Iran Ministry of Jijad and Agriculture, assistant of watershed management, Bureau of Studies and Assessment of Watersheds.
  • RAHMAN K., MARINGANTI C.H., BENISTON M., WIDMER F., ABBASPOUR K., LEHMAN A. 2013. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: The Upper Rhone River watershed case in Switzerland. Water Resources Management. Vol. 27 p. 323–339.
  • REFSGAARD J. 1997. Parameterization, calibration and validation of distributed hydrological models. Journal of Hydrology. Vol. 198 p. 69–97.
  • ROSTAMIAN R., JALEHA A., AFYUNIA M., MOUSAVIAN S.F., HEIDARPOUR M., JALALIAN A., ABBASPOUR K.C. 2010. Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal. Vol. 53. Iss. 5 p. 977–988.
  • SANTHI C., ARNOLD J.G., WILLIAMS J.R., DUGAS W.A., SRINIVASAN R., HAUCK L.M. 2001. Validation of the SWAT model on a large river basin with point and nonpoint sources. Journal of the American Water Resources Association. Vol. 37. Iss. 5 p. 1169–1188.
  • SETEGN S.G., SRINIVASAN R., MELESSE A.M. ,DARGAHI B. 2010. SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia. Journal of Hydrological Processes. Vol. 24 p. 357–367.
  • SINGH V., BANKAR N., SALUNKHE S.S., BERA A.K., SHARMA J.R. 2013. Hydrological stream flow modelling on Tungabhadra catchment: parameterization and uncertainty analysis using SWAT CUP. Current Science. Vol. 104. Iss. 9 p. 1187–1199.
  • SOROOSHIAN S., GUPTA V. K. 1995. Model calibration. In: Computer models of watershed hydrology. Ed. V.P. Singh. Littleton. Water Resources Publications p. 23–68.
  • TEDELAN H., MCCUTCHEON S.C., RASMUSSEN T.C., HAWKINS R.H., SWANK W.T., CAMPBELL J.L., ADAMS M.B., JAKSON C.R., TOLLNER E.W. 2013. Runoff curve numbers for 10 small forested watersheds in the mountains of the Eastern United States. Journal of Hydrologic Engineering. Vol. 17. Iss. 11 p. 1188–1198.
  • WANGPIMOOL W., PONGPUT K., SUKVIBOOL CH., SOMBATPANIT S., GASSMAN P.W. 2013. The effect of reforestation on stream flow in Upper Nan river basin using Soil and Water Assessment Tool (SWAT) model. International Soil and Water Conservation Research. Vol. 1. No. 2 p. 53–63.
  • WANG Z., LIU C., HUANG Y. 2003. The theory of SWAT model and its application in Heihe basin. Progress in Geography. Vol. 22. Iss. 1 p. 79–86.
  • WHITE K., CHAUBEY I. 2005. Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT model. Journal of the American Water Resources Association. Vol. 41. Iss. 5 p. 1077–1089.
  • WOJAS W., TYSZEWSKI S. 2013. Some examples comparing static and dynamic network approaches in water resources allocation models for the rivers of high instability of flows. Journal of Water and Land Development. No. 18 p. 21–27.
  • WOODWARD D.E., SCHEER C.C., HAWKINS R.H. 2006. Curve number update used for runoff calculation. Annals of Warsaw Agricultural University – SGGW, Land Reclamation. Vol. 37 p. 33–42.
  • XIE H., LIAN Y. 2013. Uncertainty-based evaluation and comparison of SWAT and HSPF applications to the Illinois River Basin. Journal of Hydrology. Vol. 481 p. 119–131.
  • XIE H., YANQING L. 2013. Uncertainty-based evaluation and comparison of SWAT and HSPF applicationsto the Illinois River Basin. Journal of Hydrology. Vol. 481 p. 119–131.
  • YAPO P., GUPTA H., SOROOSHIAN S. 1996. Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data. Journal of Hydrology. Vol. 181 p. 23–48.
  • ZHANG X., SRINIVASAN R., BOSCH D. 2009a. Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging. Journal of Hydrology. Vol. 374. Iss. 3–4 p. 307–317.
  • ZHANG X., SRINIVASAN R., VAN LIEW M. 2008. Multi-site calibration of the SWAT model for hydrologic modeling. Transactions of the ASAE. Vol. 51. Iss. 6 p. 2033–2049.
  • ZHANG X., SRINIVASAN R., VAN LIEW M. 2010. On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model. Hydrological Processes. Vol. 24. Iss. 3 p. 955–969.
  • ZHANG X., SRINIVASAN R., ZHAO K., VAN LIEW M. 2009b. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrological Processes. Vol. 23. Iss. 3 p. 430–441.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1d637c0a-b19a-47ce-8201-ba81069467c9
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