Comparison of two Calibration-Uncertainty Methods for Soil and Water Assessment Tool in Stream Flow and Total Suspended Solids Modeling

Authors

1 Professor, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

2 Phd candidate, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

3 Associate Professor , Department of Environmental Engineering, , University of Tehran, Tehran, Iran

Abstract

Estimation of total suspended solids in upland watershed of reservoirs using simulation models is a vital key to manage reservoirs water quality. It is consequently essential that these models undergo calibration and uncertainty analysis before their application. In this study, Soil and Water Assessment Tool model was applied to estimate stream flow and total suspended solids for Sofichai Watershed upstream of the Alavian Reservoir located in East-Azarbayjan province. The Generalized Likelihood Uncertainty Equation (GLUE) and Sequential Uncertainty Fitting (SUFI-2) were used in this study to calibrate and analyze the uncertainty of SWAT model. The performance of the GLUE and SUFI-2 was evaluated using four objective functions namely: Nash–Sutcliffe Efficiency (NS), coefficient of determination (R2), RMSE-observations standard deviation ratio (RSR) and the adjusted R2 coefficient (bR2). Uncertainty statistics used were the P-factor and R-factor. SUFI-2 proved to be a very efficient optimization algorithm for Calibration and uncertainty analysis. The model calibrated with SUFI-2 can therefore be applied confidently for water resources management, for quantification of scenarios of climate and land use change, and for estimation of the Best Management Practices efficiencies in the watershed.

Keywords


Abbaspour KC, Johnson CA, van Genuchten MTH, 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal 3 (4), 1340–1352.
Abbaspour KC, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R, 2007. Spatially distributed modelling of hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology 333, 413–430.
Arabi M, Govindaraju RS, and Hantush MM, 2007. A probabilistic approach for analysis of uncertainty in evaluation of watershed management practices. Journal of Hydrology 333: 459-471.
Arnold JG, Srinivasan R, Muttiah RS and Williams JR, 1998. Large area hydrologic modeling and assessment–Part1: Model development. Journal of the American Water Resources Association 34:1 73–89.
Besalatpour AA, Hajabbasi MA, Ayoubi S and Jalalian A, 2014. A Determining the Suitable Algorithm to Calibrate SWAT Model for Daily-Runoff Simulation: A Case Study of Bazoft Watershed, Southwestern Iran. International Bulletin of Water Resources and Development, 4: 7 13-26.
Beven K, Binley A, 1992. The future of distributed models – model calibration and uncertainty prediction. Hydrological Processes 6:3 279–298.
Bicknell BR, Imhoff J, Kittle J, Jobes T, Donigian AS, 2000. Hydrological Simulation Program – Fortran User’s Manual. Release 12, US EPA.
Bilondi MP, Abbaspour KC and Ghahraman B. 2013. Application of Three Different Calibration-Uncertainty Analysis Methods in a Semi-Distributed Rainfall-Runoff Model Application. Middle-East Journal of Scientific Research, 15:9 1255-1263.
Blazkova S, Beven K, Tacheci P and Kulasova A, 2002. Testing the distributed water table predictions of TOPMODEL (allowing for uncertainty in model calibration): the death of TOPMODEL? Water Resources Research 38:11 1257.
Cameron D, Beven K, Naden P, 2000a. Flood frequency estimation by continuous simulation under climate change (with uncertainty). Hydrology and Earth System Sciences 4:3 393–405.
Cameron D, Beven K, Tawn J, Naden P, 2000b. Flood frequency estimation by continuous simulation (with likelihood based uncertainty estimation). Hydrology and Earth System Sciences 4:1 23–34.
Freer J, Beven K, Ambroise B, 1996. Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resources Research 32:7 2161–2173.
Hossain F, Anagnostou EN, Lee KH, 2004. A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model. Nonlinear Processes in Geophysics 11:4 427–440.
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL, 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3): 885–900.
Motovilov YG, Gottschalk L, Engeland K, Rodhe A, (1999). Validation of distributed hydrological model against spatial observations. Agricultural and Forest Meteorology 98: 257–277.
Rostamian R, Aazam J, Afyuni M, Mousavi, F, 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.
Shirmohammadi A, Chaubey I, Harmel RD, Bosch DD, Muñoz-Carpena R, Dharmasri C, Sexton A, Arabi M, Wolfe ML, Frankenberger J, Graff C and Sohrabi TM, 2006. Uncertainty in TMDL Models. Transactins of ASABE, 4:94 1033 – 1049.
Wu H, 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.