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

doi:10.1007/s11707-018-0695-y

Front. Earth Sci.

2018, Vol. 12

Issue (4) : 661-671 https://doi.org/10.1007/s11707-018-0695-y

RESEARCH ARTICLE

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

Ammar RAFIEI EMAM¹(

), Martin KAPPAS¹, Steven FASSNACHT², Nguyen Hoang Khanh LINH³

¹. Department of Cartography, GIS and Remote Sensing, University of Goettingen, 37077 Goettingen, Germany
². Colorado State University, Fort Collins, CO 80523-1476, USA
³. Faculty of Land Resources and Agricultural Environment (FLRAE), Hue University of Agriculture and Forestry (HUAF), Hue University, Hue City 0084, Vietnam

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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

Corresponding Author(s): Ammar RAFIEI EMAM

Online First Date: 31 January 2018 Issue Date: 20 November 2018

Cite this article:

Ammar RAFIEI EMAM,Martin KAPPAS,Steven FASSNACHT, et al. Uncertainty analysis of hydrological modeling in a tropical area using different algorithms[J]. Front. Earth Sci., 2018, 12(4): 661-671.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0695-y
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/661

Fig.1 Location map of the study area, including climate and hydrometric stations. The altitude in watershed changes from 65 m above sea level from the north to 1294 m above sea level in the south of the watershed.

Tab.1 Model parameters and their initial and final ranges for surface runoff simulation

Fig.2 Behavior of parameters against the objective function (i.e., NSE) showing the sensitivity of parameters. The x-axis shows the value of parameters, or relative changes, and the y-axis shows the objective function value. The higher the value of the objective function, the better the value for parameters. (a) Shows the best fitting values for curve number (CN2) against NSE in relative change, meaning that the initial value should decrease more than 50% to achieve the ideal objective function value. (b) Shows the best fitting values for threshold water level in shallow aquifer for base flow (GWQMN), meaning the higher the GWQMN value, the better the value for objective function. (c) Shows the behavior of soil bulk density (SOL_BD) against NSE in relative changes.

Fig.3 Sensitivity of parameters by using P-Value and t-Stat in different algorithms. (a) SUFI2; (b) GLUE; (c) ParaSol; (d) PSO. P-Value is the probability of observing a test statistic and t-Stat refers to T-test which measures the size of the difference relative to the variation in the data set. T-test trying to find evidence of a significant difference between population means.

Fig.4 Monthly runoff simulation by using (a) SUFI2; (b) GLUE; (c) ParaSol; (d) PSO methods.

Fig.5 Comparing of different uncertainty analysis algorithms, the figure indicated the high R² for PSO compare to the others.

Fig.6 The average monthly simulated discharge, and the uncertainty band (95PPU) in the validation period over 2009?2010 based on (a) SUFI-2 and (b) PSO.

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 https://doi.org/10.2136/vzj2004.1340
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 https://doi.org/10.1016/j.jhydrol.2006.09.014
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 https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
5	Beven K, Binley A (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrol Processes, 6(3): 279–298 https://doi.org/10.1002/hyp.3360060305
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 https://doi.org/10.1016/j.jhydrol.2007.12.026
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 https://doi.org/10.1016/j.advwatres.2007.12.003
8	Boughton W C (2004). The Australian water balance model. Environ Model Softw, 19(10): 943–956 https://doi.org/10.1016/j.envsoft.2003.10.007
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 https://doi.org/10.1016/j.jhydrol.2006.02.013
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 https://doi.org/10.1016/j.envsoft.2005.07.003
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 https://doi.org/10.1061/(ASCE)0733-9437(1985)111:2(113)
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 https://doi.org/10.1016/j.jhydrol.2009.12.028
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 https://doi.org/10.1016/j.gecco.2015.10.007
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 https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
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 https://doi.org/10.3390/hydrology4010016
25	Refsgaard J C (1997). Parameterization, calibration, and validation of distributed hydrological models. J Hydrol (Amst), 198(1–4): 69–97 https://doi.org/10.1016/S0022-1694(96)03329-X
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 https://doi.org/10.1111/j.1752-1688.2001.tb03630.x
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 https://doi.org/10.5194/hess-16-121-2012
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 https://doi.org/10.2166/wst.2006.007
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 https://doi.org/10.1623/hysj.53.5.1090
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 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 https://doi.org/10.1016/j.ecoleng.2014.05.014
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 https://doi.org/10.1016/j.jhydrol.2008.05.012
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 https://doi.org/10.1016/j.jhydrol.2007.04.006
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 https://doi.org/10.1002/hyp.10380
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 https://doi.org/10.1002/hyp.7152

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