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The influence of parameter distribution uncertainty on hydrological and sediment modeling: a case study of SWAT model applied to the Daning watershed of the Three Gorges Reservoir Region, China

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Abstract

Parameter uncertainty involved in hydrological and sediment modeling often refers to the parameter dispersion and the sensitivity of the parameter. However, a limitation of the previous studies lies in that the assignment of range and specification of probability distribution for each parameter is usually difficult and subjective. Therefore, there is great uncertainty in the process of parameter calibration and model prediction. In this study, the impact of probability parameter distribution on hydrological and sediment modeling was evaluated using a semi-distributed model—the Soil and Water Assessment Tool (SWAT) and Monte Carlo method (MC)—in the Daning River watershed of the Three Gorges Reservoir Region (TGRA), China. The classic types of parameter distribution such as uniform, normal and logarithmic normal distribution were involved in this study. Based on results, parameter probability distribution showed a diverse degree of influence on the hydrological and sediment prediction, such as the sampling size, the width of 95% confidence interval (CI), the ranking of the parameter related to uncertainty, as well as the sensitivity of the parameter on model output. It can be further inferred that model parameters presented greater uncertainty in certain regions of the primitive parameter range and parameter samples densely obtained from these regions would lead to a wider 95 CI, resulting in a more doubtful prediction. This study suggested the value of the optimized value obtained by the parameter calibration process could may also be of vital importance in selecting the probability distribution function (PDF). Such cases, where parameter value corresponds to the watershed characteristic, can be used to provide a more credible distribution for both hydrological and sediment modeling.

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References

  • Abaci O, Papanicolaou AN (2009) Long-term effects of management practices on water-driven soil erosion in an intense agricultural sub-watershed: monitoring and modelling. Hydro Process 23:2818–2837

    Article  Google Scholar 

  • Abbott MB, Bathhurst JC, Cunge JA (1986) An introduction to the European hydrological system-systeme hydrologique Europpen, “SHE”, 2: structure of a physically-based distributed modeling system. J Hydrol 87:61–77

    Article  Google Scholar 

  • Arabi M, Govindaraju RS, Hantush MM (2007) A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices. J Hydrol 333:459–471

    Article  Google Scholar 

  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development. J Am Water Resour Assoc 34:73–89

    Article  CAS  Google Scholar 

  • Beck MB (1987) Water quality modeling: a review of the analysis of uncertainty. Water Resour Res 23:1393–1442

    Article  CAS  Google Scholar 

  • Bekele EG, Nicklow JW (2007) Multi-objective automatic calibration of SWAT using NSGA-II. J Hydrol 341:165–176

    Article  Google Scholar 

  • Benaman J, Shoemaker CA (2004) Methodology for analyzing ranges of uncertain model parameters and their impact on total maximum daily load process. J Environ Eng-ASCE 130:648–656

    Article  CAS  Google Scholar 

  • Bobba AG, Bourbonniere RA, Singh VP, Bengtsson L (1996) Numerical simulation model of fatty acids in lake sediments. Water Air Soil Poll 89:77–90

    Article  CAS  Google Scholar 

  • Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydro Process 24:1133–1148

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-streamflow models. Water Resour Res 28:1015–1031

    Article  Google Scholar 

  • Eckhardt K, Breue L, Frede HG (2003) Parameter uncertainty and the significance of simulated land use change effects. J Hydrol 273:164–176

    Article  Google Scholar 

  • Francos A, Elorza FJ, Bouraou F, Bidoglio G, Galbiat L (2003) Sensitivity analysis of distributed environmental simulation models: Understanding the model behaviour in hydrological studies at the catchment scale. Reliab Eng Syst Safe 79:205–218

    Article  Google Scholar 

  • Freissinet C, Vaucli M, Erlich M (1999) Comparison of first-order analysis and fuzzy set approach for the evaluation of imprecision in a pesticide groundwater pollution screening model. J Contam Hydro 37:21–43

    Article  CAS  Google Scholar 

  • Gassman PW, Reyes M, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future directions. Trans ASAE 50:1212–1250

    Google Scholar 

  • Geza M, Poeter EP, McCray JE (2009) Quantifying predictive uncertainty for a mountain-watershed model. J Hydrol 376:170–181

    Article  Google Scholar 

  • Ghaffari G, Keesstra S, Ghodousi J, Ahmad H (2010) SWAT-simulated hydrological impact of land-use change in the Zanjanrood Basin, Northwest Iran. Hydro Process 24:892–903

    Article  Google Scholar 

  • Huisman J, Breuer AL, Frede HG (2004) Sensitivity of simulated hydrological fluxes towards changes in soil properties in response to land use change. Phys Chem Earth 29:749–758

    Article  Google Scholar 

  • Isaac RA (1997) Estimation of nutrient loadings and their impacts on dissolved oxygen demonstrated at Mt. Hope Bay Environ Inter 23:151–165

    CAS  Google Scholar 

  • Jin X, Xu CY, Zhang Q, Singh VP (2010) Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model. J Hydrol 383:147–155

    Article  Google Scholar 

  • Kannan N, White SM, Worrall F, Whelan MJ (2007) Sensitivity analysis and identification of the best evapotranspiration and streamflow options for hydrological modelling in SWAT-2000. J Hydrol 332:456–466

    Article  Google Scholar 

  • Kao JJ, Hong HJ (1996) NPS model parameter uncertainty analysis for an off-stream reservoir. J Am Water Resour Assoc 32:1067–1079

    Article  CAS  Google Scholar 

  • Kim NW, Chung IM, Won YS, Arnold JG (2008) Development and application of the integrated SWAT-MODFLOW model. J Hydrol 356:1–16

    Article  Google Scholar 

  • Kingston DG, Taylor RG (2010) Sources of uncertainty in climate change impacts on river discharge and groundwater in a headwater catchment of the Upper Nile Basin, Uganda. Hydrol Earth Syst Sci 14:1297–1308

    Article  Google Scholar 

  • Lei JH, Schilling W (1994) Parameter uncertainty propagation analysis for urban rainfall modeling. Water Sci Technol 29:145–154

    Google Scholar 

  • Lenhart T, Eckhardt K, Fohrer N, Frede HG (2002) Comparison of two different approaches of sensitivity analysis. Phys Chem Earth 27:645–654

    Article  Google Scholar 

  • Levy JK, Hall JW (2005) Advances in flood risk management under uncertainty. Stoch Environ Res Risk A 19:375–377

    Article  Google Scholar 

  • Li ZL, Xu ZG, Shao QX, Yang J (2009) Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin. Hydro Process 23:2744–2753

    Article  Google Scholar 

  • Li ZL, Shao QX, Xu ZX, Cai XT (2010) Analysis of parameter uncertainty in semi-distributed hydrological models using bootstrap method: A case study of SWAT model applied to Yingluoxia watershed in northwest China. J Hydrol 385:76–83

    Article  Google Scholar 

  • Liao YL, Wang JF, Guo YQ, Zheng XY (2010) Risk assessment of human neural tube defects using a Bayesian belief network. Stoch Environ Res Risk A 24:93–100

    Article  Google Scholar 

  • Lu HW, Huang GH, He L (2009) An inexact programming method for agricultural irrigation systems under parameter uncertainty. Stoch Environ Res Risk A 23:759–768

    Article  Google Scholar 

  • Luo B, Zhou D (2009) Planning hydroelectric resources with recourse-based multistage interval-stochastic programming. Stoch Environ Res Risk A 23:65–73

    Article  Google Scholar 

  • Melching CS, Yoon CG (1996) Key sources of uncertainty in QUAL2E model of Passaic River. J Water Resour 122:105–113

    Google Scholar 

  • Migliaccio KW, Chaubey I (2008) Spatial distributions and stochastic parameter influences on SWAT flow and sediment predictions. J Hydro Eng 13:258–269

    Article  Google Scholar 

  • Miller SA, Landis AE, Theis TL (2006) Use of Monte Carlo analysis to characterize nitrogen fluxes in agroecosystems. Environ Sci Technol 40:2324–2332

    Article  CAS  Google Scholar 

  • Muleta MK, Nicklow JW (2005) Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. J Hydrol 306:127–145

    Article  Google Scholar 

  • Murdoch EG, Whelan MJ (2005) Incorporating uncertainty into predictions of diffuse-source phosphorus transfers (using readily available data). Water Sci Technol 51:339–346

    CAS  Google Scholar 

  • Nandakumar N, Mein RG (1997) Uncertainty in rainfall-streamflow model simulations and the implications for predicting the hydrologic effects of land-use change. J Hydrol 192:211–232

    Article  Google Scholar 

  • Parajuli PB, Douglas-Mankin KR, Barnes PL, Rossi CG (2009) Fecal bacteria source characterization and sensitivity analysis of SWAT 2005. Trans ASAE 52:1847–1858

    Google Scholar 

  • Parson SC (1995) The impact of input parameter uncertainty on decision making with the agricultural non-point source pollution model. Dissertation, Pennsylvania State University

  • Ruark M, Niemann J, Greimann B, Arabi M (2011) Method for assessing impacts of parameter uncertainty in sediment transport modeling. J Hydraul Eng 137:623–636

    Article  Google Scholar 

  • Setegn SG, Srinivasan R, Melesse AM, Dargahi B (2010) SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia. Hydrol Process 24:357–367

    Google Scholar 

  • Shen ZY, Hong Q, Yu H (2008) Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China. Sci Total Environ 405:195–205

    Article  CAS  Google Scholar 

  • Shen ZY, Hong Q, Yu H (2010) Parameter uncertainty analysis of non-point source pollution from different land use types. Sci Total Environ 408:1971–1978

    Article  CAS  Google Scholar 

  • Shen ZY, 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 16:121–132

    Article  Google Scholar 

  • Sohrabi TM, Shirmohammadi A, Chu TW, Montas H, Nejadhashem AP (2003) Uncertainty analysis of hydrologic and water quality predictions for a small watershed using SWAT2000. Environ Forensics 4:229–238

    Article  CAS  Google Scholar 

  • Sudheer KP, Lakshmi G, Chaubey I (2011) Application of a pseudo simulator to evaluate the sensitivity of parameters in complex watershed models. Environ Model Softw 26:135–143

    Article  Google Scholar 

  • Sun FJ, Chen Q, Tong S, Zeng S (2008) Managing the performance risk of conventional waterworks in compliance with the natural organic matter regulation. Water Res 42:229–237

    Article  CAS  Google Scholar 

  • Vachaud G, Chen T (2002) Sensitivity of a large-scale hydrologic model to quality of input data obtained at different scales; distributed versus stochastic non-distributed modeling. J Hydrol 264:101–112

    Article  Google Scholar 

  • Van GA, Meixner T (2007) A global and efficient multi-objective auto-calibration and uncertainty estimation method for water quality catchment models. J Hydro 9:277–291

    Article  Google Scholar 

  • Van GA, Breuer L, Di LM (2006) Environmental and ecological hydroinformatics to support the implementation of the European water framework directive for river basin management. J Hydro 8:239–252

    Article  Google Scholar 

  • Van GA, Meixner T, Srinivasan R, Grunwals S (2008) Fit-for-purpose analysis of uncertainty using split-sampling evaluations. Hydro Sci J 53:1090–1103

    Article  Google Scholar 

  • Vrugt JA, Gupta HV, Bouten W, Sorooshian S (2003) A shuffled complex evolution metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters. Water Resour Res 39:1201–1216

    Article  Google Scholar 

  • Wang S, Huang GH (2011) Interactive two-stage stochastic fuzzy programming for water resources management. J Environ Manage 92:1986–1995

    Article  CAS  Google Scholar 

  • Warwick JJ, Wilson JS (1990) Estimating uncertainty of storm water streamflow computations. J Water Res Plan 116:187–204

    Article  Google Scholar 

  • Wu J (2004) Water-quality-based BMP approach and uncertainty analysis for integrated watershed management. Dissertation, University of Virginia

  • Yang J, Reichert P, Abbaspour KC (2007) Hydrological modelling of the chaohe basin in china: Statistical model formulation and Bayesian inference. J Hydrol 340:167–182

    Article  Google Scholar 

  • Yang J, Reichert P, Abbaspour KC, Xia J, Yang H (2008) Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J Hydrol 358:1–23

    Article  Google Scholar 

  • Yoon CG (1994) Uncertainty analysis in stream water quality modeling: Reliability and data collection for variance reduction. Dissertation, Rutgers, The State University of New Jersey

  • Zhang HX (2001) The critical flow-storm approach and uncertainty analysis for the TMDL develop process. Dissertation, University of Virginia

  • Zhang H, Huang GH, Wang DL, Zhang XD (2010) Uncertainty assessment of climate change impacts on the hydrology of small prairie wetlands. J Hydrol 396:94–103

    Article  Google Scholar 

  • Zhang XD, Huang GH, Nie XH (2011) A possibilistic stochastic water management model for agricultural nonpoint source pollution. J Water Resour Plan Manage 137:101–112

    Article  Google Scholar 

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Acknowledgments

The study was supported by National Science Foundation for Distinguished Young Scholars (No. 51025933), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT0809) and the Nonprofit Environment Protection Specific Project (No. 200709024). The authors wish to express their gratitude to the editor of Stochastic Environmental Research and Risk Assessment, as well as to anonymous reviewers who helped to improve this paper though their thorough review.

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Zhenyao, S., Lei, C. & Tao, C. The influence of parameter distribution uncertainty on hydrological and sediment modeling: a case study of SWAT model applied to the Daning watershed of the Three Gorges Reservoir Region, China. Stoch Environ Res Risk Assess 27, 235–251 (2013). https://doi.org/10.1007/s00477-012-0579-8

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