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

The calibration of hydrologic models is a worldwide challenge due to the uncertainty involved in the large number of parameters. The difficulty even increases in a region with high seasonal variation of precipitation, where the results exhibit high heteroscedasticity and autocorrelation. In this study, the Generalized Likelihood Uncertainty Estimation (GLUE) method was combined with the Soil and Water Assessment Tool (SWAT) to quantify the parameter uncertainty of the stream flow and sediment simulation in the Daning River Watershed of the Three Gorges Reservoir Region (TGRA), China. Based on this study, only a few parameters affected the final simulation output significantly. The results showed that sediment simulation presented greater uncertainty than stream flow, and uncertainty was even greater in high precipitation conditions (from May to September) than during the dry season. The main uncertainty sources of stream flow came from the catchment process while a channel process impacts the sediment simulation greatly. It should be noted that identifiable parameters such as CANMX, ALPHA_BNK, SOL_K could be obtained with an optimal parameter range using calibration method. However, equifinality was also observed in hydrologic modeling in TGRA. This study demonstrated that care must be taken when calibrating the SWAT model with non-identifiable parameters because these may lead to equifinality of the parameter values. It was anticipated this study would provide useful information for hydrology modeling related to policy development in the Three Gorges Reservoir Region (TGRA) and other similar areas.

[1]  Richard G. Taylor,et al.  Sources of uncertainty in climate change impacts on river discharge and groundwater in a headwater catchment of the Upper Nile Basin, Uganda , 2010 .

[2]  Indrajeet Chaubey,et al.  Application of a pseudo simulator to evaluate the sensitivity of parameters in complex watershed models , 2011, Environ. Model. Softw..

[3]  Neil McIntyre,et al.  Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .

[4]  Robert A. Vertessy,et al.  Predicting water yield from a mountain ash forest catchment using a terrain analysis based catchment model , 1993 .

[5]  Yan Wang,et al.  Reconstruction of sediment flux from the Changjiang (Yangtze River) to the sea since the 1860s , 2008 .

[6]  R. Taylor,et al.  Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China , 2010 .

[7]  Elias Dimitriou,et al.  Integrated water management scenarios for wetland protection: application in Trichonis Lake , 2005, Environ. Model. Softw..

[8]  A. Rousseau,et al.  GIBSI: an integrated modelling system for watershed management - sample applications and current developments , 2007 .

[9]  Jeffrey G. Arnold,et al.  Automatic calibration of a distributed catchment model , 2001 .

[10]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[11]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

[12]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[13]  Soroosh Sorooshian,et al.  Model Calibration in Watershed Hydrology , 2009 .

[14]  George Burba,et al.  Seasonal and interannual variability in evapotranspiration of native tallgrass prairie and cultivated wheat ecosystems , 2005 .

[15]  S. L. Yang,et al.  Temporal variation in the sediment load of the Yangtze river and the influences of human activities , 2002 .

[16]  K. Beven,et al.  Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach , 1996 .

[17]  C. Chang,et al.  Uncertainty in watershed response predictions induced by spatial variability of precipitation , 2007, Environmental monitoring and assessment.

[18]  G. V. Johnson,et al.  Non-point source water quality management under input information uncertainty , 1999 .

[19]  R. Srinivasan,et al.  Fit-for-purpose analysis of uncertainty using split-sampling evaluations , 2008 .

[20]  W. Bouten,et al.  Towards reduced uncertainty in catchment nitrogen modelling: quantifying the effect of field observation uncertainty on model calibration , 2004 .

[21]  Adel Shirmohammadi,et al.  Uncertainty Analysis of Hydrologic and Water Quality Predictions for a Small Watershed Using SWAT2000 , 2003 .

[22]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[23]  Fred Worrall,et al.  Sensitivity analysis and identification of the best evapotranspiration and runoff options for hydrological modelling in SWAT-2000 , 2007 .

[24]  Qian Hong,et al.  Parameter uncertainty analysis in watershed total phosphorus modeling using the GLUE methodology , 2011 .

[25]  J. Feyen,et al.  GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application , 2009 .

[26]  Thorsten Wagener,et al.  Numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox , 2007, Environ. Model. Softw..

[27]  Xixi Lu,et al.  Sediment delivery to the Three Gorges: 2: Local response , 2001 .

[28]  Qian Hong,et al.  Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China. , 2008, The Science of the total environment.

[29]  Indrajeet Chaubey,et al.  Spatial Distributions and Stochastic Parameter Influences on SWAT Flow and Sediment Predictions , 2008 .

[30]  Qian Hong,et al.  Parameter uncertainty analysis of non-point source pollution from different land use types. , 2010, The Science of the total environment.

[31]  Jing Yang,et al.  Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China , 2008 .

[32]  K. Beven,et al.  Model Calibration and Uncertainty Estimation , 2006 .

[33]  Tao Chen,et al.  Sensitivity of a large-scale hydrologic model to quality of input data obtained at different scales; distributed versus stochastic non-distributed modelling , 2002 .

[34]  M. B. Beck,et al.  Water quality modeling: A review of the analysis of uncertainty , 1987 .

[35]  Keith Beven,et al.  Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology , 2001 .

[36]  K. Abbaspour,et al.  Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT , 2007 .

[37]  J. Michael INCORPORATING UNCERTAINTY INTO PREDICTIONS OF DIFFUSE-SOURCE PHOSPHORUS TRANSFERS (USING READILY AVAILABLE DATA) , 2003 .

[38]  Xuesong Zhang,et al.  Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging , 2009 .

[39]  J. Latron,et al.  Assessing the sources of uncertainty associated with the calculation of rainfall kinetic energy and erosivity – application to the Upper Llobregat Basin, NE Spain , 2010 .

[40]  Jehng-Jung Kao,et al.  NPS model parameter uncertainty analysis for an off-stream reservoir , 1996 .

[41]  Shelie A. Miller,et al.  Use of Monte Carlo analysis to characterize nitrogen fluxes in agroecosystems. , 2006, Environmental science & technology.

[42]  Hans-Georg Frede,et al.  Comparison of two different approaches of sensitivity analysis , 2002 .

[43]  Misgana K. Muleta,et al.  Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model , 2005 .

[44]  J C Refsgaard,et al.  Model uncertainty--parameter uncertainty versus conceptual models. , 2005, Water science and technology : a journal of the International Association on Water Pollution Research.

[45]  Ian Cluckie,et al.  Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction , 2006 .

[46]  K. Eckhardt,et al.  Parameter uncertainty and the significance of simulated land use change effects , 2003 .

[47]  L. Molina,et al.  Assessing the sources of uncertainty associated with the calculation of rainfall kinetic energy and erosivity – application to the Upper Llobregat Basin , NE Spain , 2011 .

[48]  S. Sorooshian,et al.  A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters , 2002 .

[49]  B. Bates,et al.  A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling , 2001 .

[50]  Pierre Y. Julien,et al.  Peak Flow Forecasting with Radar Precipitation and the Distributed Model CASC2D , 2005 .

[51]  T. Cochrane,et al.  EFFECT OF DEM RESOLUTIONS IN THE RUNOFF AND SOIL LOSS PREDICTIONS OF THE WEPP WATERSHED MODEL , 2005 .

[52]  N. Nandakumar,et al.  Uncertainty in rainfall—runoff model simulations and the implications for predicting the hydrologic effects of land-use change , 1997 .

[53]  Jeffrey G. Arnold,et al.  The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions , 2007 .

[54]  K. Lindenschmidt,et al.  Structural uncertainty in a river water quality modelling system , 2007 .

[55]  Charles S. Melching,et al.  Key sources of uncertainty in QUAL2E model of passaic river , 1996 .

[56]  Mazdak Arabi,et al.  A probabilistic approach for analysis of uncertainty in the evaluation of watershed management practices , 2007 .