Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

Abstract

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and R-factor, coefficient of determination (R²), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor<0.56 and R²>0.91, NSE>0.89, and 0.18<PBIAS<0.32. Hence, we would suggest to use SUFI-2 initially to set the parameter ranges, and further use PSO for final analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.

Keywords

SWAT-CUP / GLUE / SUFI2 / ParaSol / PSO / Vietnam

References

[1] Abbaspour K C (2011). SWAT-CUP4: SWAT Calibration and Uncertainty Programs– A User Manual. Swiss Federal Institute of Aquatic Science and Technology, Switzerland

[2] Abbaspour K C, Johnson C A, van Genuchten M Th (2004). Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J, 3(4): 1340–1352

[3] Abbaspour K C, Yang J, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J, Srinivasan R (2007). Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol (Amst), 333(2–4): 413–430

[4] Arnold J G, Srinivasan P, Muttiah R S, Williams J R (1998). Large area hydrologic modeling and assessment, Part I: model development. J Am Water Resour Assoc, 34(1): 73–89

[5] Beven K, Binley A (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrol Processes, 6(3): 279–298

[6] Blasone R S, Madsen H, Rosbjerg D (2008a). Uncertainty assessment of integrated distributed hydrological models using GLUE with Markov chain Monte Carlo sampling. J Hydrol (Amst), 353(1–2): 18–32

[7] Blasone R S, Vrugt J A, Madsen H, Rosbjerg D, Robinson B A, Zyvoloski G A (2008b). Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Water Resour, 31(4): 630–648

[8] Boughton W C (2004). The Australian water balance model. Environ Model Softw, 19(10): 943–956

[9] Carpenter T M, Georgakakos K P (2006). Intercomparison of lumped versus distributed hydrologic model ensemble simulations on operational forecast scales. J Hydrol (Amst), 329(1–2): 174–185

[10] Croke B F W, Andrews F, Jakeman A J, Cuddy S M, Luddy A (2006). IHACRES Classic Plus: a redesign of the IHACRES rainfall-runoff model. Environ Model Softw, 21(3): 426–427

[11] Eberhart R C, Kennedy J A (1995). A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE Service Center, Piscataway, NJ, Nagoya, Japan

[12] 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 Resour Res, 32: 2161–2173

[13] Green W H, Ampt G A (1911). Studies on soil physics, 1. The flow of air and water through soils. J Agric Sci, 4: 11–24

[14] Hargreaves G L, Hargreaves G H, Riley J P (1985). Agricultural benefits for Senegal River Basin. J Irrig Drain Eng, 111(2): 113–124

[15] Jin X, Xu C Y, Zhang Q, Singh V P (2010). Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J Hydrol (Amst), 383(3–4): 147–155

[16] Khoi D N, Thom V T (2015). Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam. Glob Ecol Conserv, 4: 538–548

[17] Liu Y, Gupta H V (2007). Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour Res, 43(7): W07401

[18] McKay M D, Beckman R J, Conover W J (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21: 239–245

[19] Monteith J L (1965). Evaporation and environment. In the state and movement of water in living organisms. 19th Symposia of the society for experimental biology. London: Cambridge University Press

[20] Moriasi D N, Arnold J G, van Liew M W, Bringer R L, Harmel R D, Veith T L (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3): 885–900

[21] Neitsch S L, Arnold J G, Kiniry J R, Williams J R, King K W (2011). Soil and water assessment Tool. Theoretical documentation: Version 2000. TWRI TR-191. Texas Water Resources Institute, College Station, TX, USA

[22] Priestley C H B, Taylor R J (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev, 100(2): 81–92

[23] Rafiei Emam A, Kappas M, Hosseini S Z (2015). Assessing the impact of climate change on water resources, crop production and land degradation in a semi-arid river basin. Hydrol Res, 46(6): 854–870

[24] Rafiei Emam A, Kappas M, Linh N, Renchin T (2017). Hydrological modeling and runoff mitigation in an ungauged basin of central Vietnam using SWAT model. Hydrology, 4(1): 16

[25] Refsgaard J C (1997). Parameterization, calibration, and validation of distributed hydrological models. J Hydrol (Amst), 198(1–4): 69–97

[26] Refsgaard J C, Storm B (1995). MIKE SHE. In: Singh V P, ed. Computer Models of Watershed Hydrology. Colorado: Water Resources Publications, 809–846

[27] Santhi C J, 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. J Am Water Resour Assoc, 37(5): 1169–1188

[28] Setegn S G, Srinivasan R, Melesse A M, Dargahi B (2010). SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia. Hydrol Processes, 24: 357–367

[29] Shen Z Y, Chen L, Chen T (2012). Analysis of parameter uncertainty in hydrological and sediment modeling using GLUE method: a case study of SWAT model applied to Three Gorges Reservoir Region, China. Hydrol Earth Syst Sci, 16(1): 121–132

[30] Singh V P (1989). Hydrologic Systems Vol. II Watershed Modelling. Englewood Cliffs: Prentice-Hall, Inc.

[31] USDA (United States Department of Agriculture) (1986). Urban Hydrology for Small Watersheds, Technical Release 55(TR-55), 2nd ed. Natural Resources Conservation Service, Conservation Engineering Division, Washington, DC, USA

[32] van Griensven A, Meixner T (2006). Methods to quantify and identify the sources of uncertainty for river basin water quality models. Water Sci Technol, 53(1): 51–59

[33] van Griensven A, Meixner T, Srinivasan R, Grunwald S (2008). Fit-for-purpose analysis of uncertainty using split-sampling evaluations. Hydrol Sci J, 53(5): 1090–1103

[34] Vrugt J A, Gupta H V, Bouten W, Sorooshian S (2003). A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res, 39(8): https://doi.org/10.1029/2002WR001642

[35] 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. Ecol Eng, 76: 110–121

[36] Xu C Y (2002).  WASMOD—the water and snow balance modelling system. In:  Singh V P, Frevert D K, eds. Mathematical Models of Small Watershed Hydrology and Applications (Chapter 17). Chelsea: Water Resources Publications, LLC

[37] Yang J, Reichert P, Abbaspour K C, Xia J, Yang H (2008). Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J Hydrol (Amst), 358(1–2): 1–23

[38] Yang J, Reichert P, Abbaspour K C, Yang H (2007). Hydrological modelling of the Chaohe Basin in China: statistical model formulation and Bayesian inference. J Hydrol (Amst), 340(3–4): 167–182

[39] Zhang J, Li Q, Guo B, Gong H (2015). The comparative study of multi-site uncertainty evaluation method based on SWAT model. Hydrol Processes, 29(13): 2994–3009

[40] Zhang X, Srinivasan R, Zhao K, Liew M V (2009). Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol Processes, 23(3): 430–441

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature