Evaluation of alternative surface runoff accounting procedures using SWAT model

作     者：Haw Yen Michael J.White Jaehak Jeong Mazdak Arabi Jeffrey G.Arnold 

作者机构：Department of Civil and Environmental EngineeringColorado State UniversityFort Coll insColorado 80523USA Blackland Research&Extension CenterTexas A&M Agrilife ResearchTempleTexas 76502USA GrasslandSoil&Water Research LaboratoryUSDA-ARSTempleTexas 76502USA 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2015年第8卷第3期

页      面：54-68页

核心收录：

学科分类：08[工学] 081501[工学-水文学及水资源] 0815[工学-水利工程] 

基　　金：This study was supported in part by the US DA-National Institute of Food and Agriculture grants 2007-51130-03876,2009-51130-06038 the Research Program for Agricultural Science&Technology Development(Project No.PJ008566) National Academy of Agricultural Science,Rural Development Administration,Republic of Korea,and the USDA-NRCS Conservation Effects Assessment Project(CEAP)-Wildlife and Cropland components 

主　　题：Soil and Water Assessment Tool(SWAT),curve number method,Bayesian model averaging,uncertainty analysis hydrology,water quality 

摘      要：For surface runoff estimation in the Soil and Water Assessment Tool(SWAT)model,the curve number(CN)procedure is commonly adopted to calculate surface runoff by dynamically updating CN values based on antecedent soil moisture condition(SCSI)in *** SWAT2005 and onward,an alternative approach has become available to apply the CN method by relating the runoff potential to daily evapotranspiration(SCSII).While improved runoff prediction with SCSII has been reported in several case studies,few investigations have been made on its influence to water quality output or on the model uncertainty associated with the SCSII *** objectives of the research were:(1)to quantify the improvements in hydrologic and water quality predictions obtained through different surface runoff estimation techniques;and(2)to examine how model uncertainty is affected by combining different surface runoff estimation techniques within SWAT using Bayesian model averaging(BMA).Applications of BMA provide an alternative approach to investigate the nature of structural uncertainty associated with both CN *** showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to *** addition,the application of BMA has a positive effect on finding well performed solutions in the multi-dimensional parameter space,but the predictive uncertainty is not evidently reduced or ***,we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants.