Parameter estimation and uncertainty analysis of SWAT model in upper reaches of the Heihe river basin

Heihe river basin, the second largest inland river basin in China, has attracted more attention in China due to the ever increasing water resources and eco-environmental problems. In this article, SWAT (Soil and Water Assessment Tool; http://www.brc.tamus.edu/swat/) model was applied to upper reaches of the basin for better understanding of the hydrological process over the watershed. Parameter uncertainty and its contribution on model simulation are the main foci. In model calibration, the aggregate parameters instead of the original parameters in SWAT model were used to reduce the computing effort. The Bayesian approach was employed for parameter estimation and uncertainty analysis because its posterior distribution provides not only parameter estimation but also uncertainty analysis without normality assumption. The results indicated that: (1) SWAT model performs satisfactorily in this watershed as a whole, although some low and high flows were under- or overestimated, particularly in dry (e.g. 1991) and wet (e.g. 1996) years; (2) all calibrated parameters were not normally distributed (essentially positively or negatively skewed) and the parameter uncertainties were relatively small; and (3) the contributions of parameter uncertainty on model simulation uncertainty were relatively small. Copyright © 2009 John Wiley & Sons, Ltd.

[1]  John Doherty,et al.  Parameter estimation and uncertainty analysis for a watershed model , 2007, Environ. Model. Softw..

[2]  P. Reichert,et al.  Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference , 2007 .

[3]  Henrik Madsen,et al.  An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation , 2004 .

[4]  Kaoru Takara,et al.  Hydrological model performance comparison through uncertainty recognition and quantification , 2007 .

[5]  Xibin Ji,et al.  The impact of the development of water resources on environment in arid inland river basins of Hexi region, Northwestern China , 2006 .

[6]  Li Shuo,et al.  Effect of climatic change on snowmelt runoffs in mountainous regions of inland rivers in Northwestern China , 2006 .

[7]  F. Veroustraete,et al.  Change in area of Ebinur Lake during the 1998–2005 period , 2007 .

[8]  Yeou-Koung Tung,et al.  Storm resampling for uncertainty analysis of a multiple-storm unit hydrograph , 1997 .

[9]  Ilona Bärlund,et al.  Assessing SWAT model performance in the evaluation of management actions for the implementation of the Water Framework Directive in a Finnish catchment , 2007, Environ. Model. Softw..

[10]  C. Johnston,et al.  Hydrologic response to climatic variability in a Great Lakes Watershed: A case study with the SWAT model , 2007 .

[11]  V. V. Srinivas,et al.  Hybrid matched-block bootstrap for stochastic simulation of multiseason streamflows , 2006 .

[12]  Laura Toran,et al.  Subsurface stormflow modeling with sensitivity analysis using a Latin-hypercube sampling technique , 1996 .

[13]  Qingyun Duan,et al.  An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction , 2006 .

[14]  Shanzhong Qi,et al.  Land-use change and its environmental impact in the Heihe River Basin, arid northwestern China , 2006 .

[15]  Jun Xia,et al.  Development of distributed time-variant gain model for nonlinear hydrological systems , 2005 .

[16]  K. Loague,et al.  Statistical and graphical methods for evaluating solute transport models: Overview and application , 1991 .

[17]  Pao Shan Yu,et al.  Comparison of uncertainty analysis methods for a distributed rainfall–runoff model , 2001 .

[18]  Genxu Wang,et al.  Effects of land‐use changes on hydrological processes in the middle basin of the Heihe River, northwest China , 2007 .

[19]  Kolbjørn Engeland,et al.  Assessing uncertainties in a conceptual water balance model using Bayesian methodology / Estimation bayésienne des incertitudes au sein d’une modélisation conceptuelle de bilan hydrologique , 2005 .

[20]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

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

[22]  Peter Reichert,et al.  Bayesian uncertainty analysis in distributed hydrologic modeling: A case study in the Thur River basin (Switzerland) , 2007 .

[23]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[24]  G. Kuczera.  Improved parameter inference in catchment models: 1. Evaluating parameter uncertainty , 1983 .

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

[26]  Wang Zhong.  The Theory of SWAT Model and its Application in Heihe Basin , 2003 .

[27]  Amin Elshorbagy,et al.  Toward improving the reliability of hydrologic prediction: Model structure uncertainty and its quantification using ensemble‐based genetic programming framework , 2008 .

[28]  Junliang Zhang.  Barriers to water markets in the Heihe River basin in northwest China , 2007 .

[29]  Zhulu Lin,et al.  Automatic Calibration and Predictive Uncertainty Analysis of a Semidistributed Watershed Model , 2006 .