بررسی عدم قطعیت پارامترهای مدل هیدرولوژیکی SWAT با استفاده از الگوریتم تکامل تفاضلی تطبیقی متروپولیس (DREAM-ZS)

1.Abbaspour, K.C. 2011. User Manual for SWAT-CUP: SWAT Calibration and Uncertainty Analysis Programs. Eawag: Swiss Fed. Inst. of Aquat. Sci. and Technol. Duebendorf, Switzerland. 103p.

2.Afshar, A.A., Hassanzadeh, Y., Pourreza-Bilondi, M., and Ahmadi, A. 2018. Analyzing long-term spatial variability of blue and green water footprints in a semi-arid mountainous basin with MIROC-ESM model (case study: Kashafrood River Basin, Iran). J. Theor Appl. Climatol. 134: 3-4. 885-899.

3.Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Williams, J.R. 1998. Large area hydrologic modeling and assessment part I: model development 1. J. Am. Water Resour. Assoc. 34: 1. 73-89.

4.Athira, P., Nanda, C., and Sudheer, K.P. 2018. A computationally efficient method for uncertainty analysis of SWAT model simulations. J. Stoch Environ. Res. Risk Assess. 32: 6. 1479-1492.

5.Box, G.E., and Tiao, G.C. 2011. Bayesian inference in statistical analysis. John Wiley and Sons. 40.

6.Gelman, A., and Rubin, D.B. 1992. Inference from iterative simulation using multiple sequences. J. Stat Sci.7: 4. 457-472.

7.Han, F., and Zheng, Y. 2018. Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach. J. Adv. Water Resour. 116: 77-94.

8.Jafarzadeh, M.S., Rouhani, H., Salmani, H., and Fathabadi, A. 2016. Reducing uncertainty in a semi distributed hydrological modeling within the GLUE framework. J. Water Soil Cons. 23: 1. 83-100. (In Persian)

9.Kabir, A., and Bahremand, A.R. 2013. Study uncertainty of parameters of rainfall-runoff model (wetspa) by Mont Carlo method. J. Water Soil Cons.20: 5. 81-97. (In Persian)

10.Kumar, N., Singh, S.K., Srivastava, P.K., and Narsimlu, B. 2017. SWAT Model calibration and uncertainty analysis for streamflow prediction of the Tons River Basin, India, using Sequential Uncertainty Fitting (SUFI-2) algorithm. J. Model Earth Syst Environ. 3: 1. 30.

11.Laloy, E., Fasbender, D., and Bielders, C.L. 2010. Parameter optimization and uncertainty analysis for plot-scale continuous modeling of runoff using a formal Bayesian approach. J. Hydrol. 380: 1-2. 82-93.

12.Laloy, E., and Vrugt, J.A. 2012. High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. J. Water Resour. Res. 48: 1.

13.Leta, O.T., Nossent, J., Velez, C., Shrestha, N.K., van Griensven, A., and Bauwens, W. 2015. Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium). J. Environ. Modell. Software. 68. 129-146.

14.Li, B., Liang, Z., He, Y., Hu, L., Zhao, W., and Acharya, K. 2017. Comparison of parameter uncertainty analysis techniques for a TOPMODEL application. J. Stochastic Environ. Res. Risk Assess. 31: 5. 1045-1059.

15.Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., and Veith, T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. J. Trans. ASABE. 50: 3. 885-900.

16.Neitsch, S.L., Arnold, J.G., Kiniry, J.R., and Williams, J.R. 2011. Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute, USA. 647p.

17.Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol. 10: 3. 282-290.

18.Nourali, M., Ghahraman, B., Pourreza-Bilondi, M., and Davary, K. 2016. Effect of formal and informal likelihood functions on uncertainty assessment in a single event rainfall-runoff model. J. Hydrol. 540. 549-564.

19.Pourreza-Bilondi, M., Samadi, S.Z., Akhoond-Ali, A.M., and Ghahraman, B. 2016. Reliability of semiarid flash flood modeling using Bayesian framework. J. Hydrol. Eng. 22: 4. 05016039.

20.Pourreza-Bilondi, M., Akhoond-Ali, A.M., Gharaman B., and Telvari, A.R. 2015. Uncertainty analysis of a single event distributed rainfall-runoff model by using two different Markov Chain Monte Carlo methods. J. Water Soil Cons. 21: 5. 1-26. (In Persian)

21.Schoups, G., and Vrugt, J.A. 2010. A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors. J. Water Resour. Res. 46: 10.

22.USDA-SCS. 1986. US Department of Agriculture-soil Conservation Service (USDASCS): Urban Hydrology for Small Watersheds. USDA, Washington, DC. USA.

23.Vrugt, J.A., Ter Braak, C.J., Clark, M.P., Hyman, J.M., and Robinson, B.A. 2008. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. J. Water Resour. Res. 44: 12. 1-15.

24.Vrugt, J.A., Ter Braak, C.J.F., Diks, C.G.H., Robinson, B.A., Hyman, J.M., and Higdon, D. 2009a. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. J. Int. J. Nonlinear Sci. Numer. Simul. 10: 3. 273-290.

25.Vrugt, J.A., Ter Braak, C.J., Gupta, H.V., and Robinson, B.A. 2009b. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?. J. Stochastic Environ. Res. Risk Assess. 23: 7. 1011-1026.

26.Zheng, Y., and Han, F. 2016.Markov Chain Monte Carlo (MCMC) uncertainty analysis for watershed water quality modeling and management. J. Stochastic Environ. Res. Risk Assess. 30: 1. 293-308.