Evaluation of alternative surface runoff accounting procedures using SWAT model
Keywords:
Soil and Water Assessment Tool (SWAT), curve number method, Bayesian model averaging, uncertainty analysis, hydrology, water qualityAbstract
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 field. From 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 method. The 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 methods. Results showed that SCSII and BMA associated approaches exhibit improved performance in both discharge and total NO3 predictions compared to SCSI. In 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 enhanced. Therefore, we recommend additional future SWAT calibration/validation research with an emphasis on the impact of SCSII on the prediction of other pollutants. Keywords: Soil and Water Assessment Tool (SWAT), curve number method, Bayesian model averaging, uncertainty analysis; hydrology, water quality DOI: 10.3965/j.ijabe.20150803.833 Online first on [2015-03-03] Citation: Yen H, White M J, Jeong J, Arabi M, Arnold J G. Evaluation of alternative surface runoff accounting procedures using SWAT model. Int J Agric & Biol Eng, 2015; 8(3): 54-68.References
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[34] Ahmadi M. A Multi Criteria Decision Support System for Watershed Management under Certain Conditions. Doctoral Dissertation, Colorado State University, 2012.
[35] Moriasi D N, Arnold J G, Liew M W V, Bingner R L, Harmel R D, Veith T L. Model validation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007; 50(3): 885–900.
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[48] Ajami N K, Duan Q, Sorooshian S. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 2007; 43(1): 1–19. doi:10.1029/2005WR 004745.
[49] Georgakakos K, Seo D, Gupta H, Schaake J, Butts M. Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 2004; 298(1-4): 222–241, doi: 10.1016/ j.jhydrol.2004.03.037.
[50] Burnash R J, Ferral R L, McGuire R A. A generalized streamflow simulation system conceptual modeling for digital computers. Report, U. S. Dep. of Commer. Natl. Weather Serv. and State of Calif. Dep. of Water Resour, 1973.
[51] Boyle D. Multicriteria calibration of hydrological models. Ph.D. dissertation, Univ. of Ariz., Tucson, 2001.
[52] Schaake J C, Koren V, Duan Q Y, Chen F. Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research, 1996; 101(D3): 7461–7475.
[53] NOAA (2012), Local weather observation station record, NOAA Satellite and Information Service and National Climatic Data Center, Available at: http://www.ncdc. noaa.gov/oa/ncdc. html. Accessed on [2013-05].
[54] Thyer M, Renard B, Kavetski D, Kuczera G, Franks S W, Srikanthan S. Critical validation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis. Water Resources Research, 2009; 45(3): 1–22. doi: 10.1029/2008 WR006825.
[2] Borah D K, Yagow G, Saleh A, Barnes P L, Rosenthal W, Krug E C, Hauck L M. Sediment and nutrient modeling for TMDL development and implementation. Transactions of the ASABE, 2006; 49(4): 967–986.
[3] Gassman P W, Reyes M R, Green C H, Arnold J G. The Soil and Water Assessment Tool: Historical development, applications, and future research directions. Transactions of The ASABE, 2007; 50(4): 1211–1250.
[4] Williams J R, Arnold J G, Kiniry J R, Gassman P W, Green C H. History of model development at Temple, Texas. Hydrological Sciences Journal, 2008; 53(5): 948–960.
[5] Yen H, Bailey R T, Arabi M, Ahmadi M, White M J, Arnold J G. The role of interior watershed processes in improving parameter estimation and performance of watershed models. Journal of Environmental Quality, Published online. doi:
10.2134/jeq2013.03.0110.
[6] USDA-NRCS, 2004. Chapter 10: Estimation of direct runoff from storm rainfall. In Part 630: Hydrology: NRCS National Engineering Handbook, USDA National Resources Conservation Service, Washington, DC. Available at: http://www.nrcs.usda.gov/wps/portal/nrcs/detailfull/mi/technical/?cid=stelprdb1043063. Accessed on [2008-06-16].
[7] Green W H, Ampt G A. Studies on soil physics, part I, the flow of air and water through soils. Journal of Agricultural Sciences, 1911; 4(1): 1–24.
[8] Ponce V M, Hawkins R H. Runoff curve number: Has it reached maturity? Journal of Hydrologic Engineering, 1996; 1: 11–19.
[9] Kannan N, Santhi C, Williams J R, Arnold J G. Development of a continuous soil moisture accounting procedure for curve number methodology and its behaviour with different evapotranspiration methods. Hydrological Processes, 2008; 22(13): 2114–2121, doi: 10.1002/ hyp.6811.
[10] Green C H, Tomer M D, Di Luzio M, Arnold J G. Hydrologic evaluation of the soil and water assessment tool for a large tile-drained watershed in Iowa. Transactions of the ASABE, 2006; 49(2): 413–422.
[11] Amatya D M, Jha M K. Evaluating the SWAT model for a low-gradient forested watershed in coastal South Carolina. Transactions of the ASABE, 2011; 54(6): 2151–2163.
[12] Gassman P W. A simulation assessment of the Boone River watershed: Baseline calibration/validation results and issues, and future research needs. Doctoral Dissertation, 2008, Iowa State University, Ames, Iowa.
[13] Setegn S G, Srinivasan R, Melesse A M, Dargahi B. SWAT model application and prediction uncertainty analysis in the Lake Tana Basin, Ethiopia. Hydrological Processes, 2010; 24: 357–367, doi: 10.1002/hyp.7457.
[14] Yen H. Confronting Input, Parameter, Structural, and Measurement Uncertainty in Multi-site Multiple Responses Watershed Modeling using Bayesian Inferences. Doctoral Dissertation, Colorado State University, 2012.
[15] Yen H, Wang X, Fontane D G, Harmel R D, Arabi M. A framework for propagation of uncertainty contributed by input data, parameterization, model structure, and calibration/validation data in watershed modeling. Environmental Modelling and Software, 2014; 54: 211–221. doi: 10.1016/j.envsoft.2014.01.004
[16] Jajarmizadeh M, Harun S, Gharaman B, Mokhtari M H. Modeling daily stream flow using plant evapotranspiration method. International Journal of Water Resources and Environmental Engineering, 2012: 4(6): 218–226. doi: 10.5897/IJWREE12.019.
[17] Srinivasan R X, Zhang J A. Swat ungagged: Hydrological budget and crop yield predictions in the upper Mississippi River Basin. Transactions of the ASABE, 2010; 53(5): 1533–1546.
[18] Hoeting J A, Madigan D, Raftery A E, Volinsky C T. Bayesian model averaging : A tutorial. Statistical Science, 1999; 14(4): 382–417.
[19] Newman J E. The Natural Heritage of Indiana: Our Changing Climate, Edited by M. T. Jackson, 1997.
[20] USGS NED. United States Geological Survey National Elevation Database, 1 arc Second Digital Elevation Model, 2010.
[21] USDA NASS. United States Department of Agriculture - National Agricultural Statistics Service, Cropland Data Layer, 2003.
[22] USDA NRCS. United States Department of Agriculture - Soil Data Mart, 2010.
[23] United States National Climatic Data Center (2013), http://www.ncdc.noaa.gov/; Accessed on [2013-12-03].
[24] Arnold J. G, Allen P M, Volk M, Williams J R, Bosch D D. Assessment of different representations of spatial variability on SWAT model performance. Transactions of the ASABE, 2010; 53(5): 1433–1443.
[25] Daggupati P, Douglas-Mankin K R, Sheshukov A Y, Barnes P L, Devlin D L. Field-level targeting using SWAT: Mapping output from HRUs to fields and assessing limitations of GIS input data. Transactions of the ASABE, 2011; 54(2): 501–514.
[26] Douglas-Mankin K R, Srinivasan R, Arnold J G. Soil and Water Assessment Tool (SWAT) model: current developments and applications. Transactions of the ASABE, 2010; 53(5): 1423–1431.
[27] Hoque Y M, Hantush M M, Govindaraju R S. On the scaling behavior of reliability-resilience-vulnerability indices in agricultural watersheds. Ecological Indicators, 2014; 40: 136–146.
[28] Osorio J, Jeong J, Arnold J G, Beiger B. Influence of potential evapotranspiration on the water balance of sugarcane fields in Maui, Hawaii-special issue: evapotranspiration. J. Water Res. 2014; 6(9): 852–868.
[29] Yen H, Ahmadi M White M J, Wang X, Arnold J G. C-SWAT: The Soil and Water Assessment Tool with Consolidated Input Files in Alleviating Computational Burden of Recursive Simulations. Computers & Geosciences. 2014; 72: 221–232. doi: 10.1016/j.cageo.2014.07.017.
[30] Arnold J G, Srinivasan R, Muttiah R S, Williams J R. Large area hydrologic modeling and assessment. I. Model development. Journal of the American Water Resources Association, 998; 34(1): 73–89.
[31] Rallison R E, Miller N. Past, present, and future SCS runoff procedure. In Rainfall Runoff Relationship. ed. V. P. Singh.
Littleton, Colo.: Water Resources Publication, 1981; 353–364.
[32] Neitsch S L, Arnold J G, Kiniry J R, Williams J R. Soil and Water Assessment Tool: Theoretical Documentation Version 2009, Texas Water Resources Institute TR-406, College Station, Texas, 2011.
[33] Williams J, Kannan N, Wang X, Santhi C, Arnold J. Evolution of the SCS runoff curve number method and its application to continuous runoff simulation. Journal of Hydrologic Engineering, 2012; 17(11): 1221–1229.
[34] Ahmadi M. A Multi Criteria Decision Support System for Watershed Management under Certain Conditions. Doctoral Dissertation, Colorado State University, 2012.
[35] Moriasi D N, Arnold J G, Liew M W V, Bingner R L, Harmel R D, Veith T L. Model validation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 2007; 50(3): 885–900.
[36] Stedinger J R, Vogel R M, Lee S U, Batchelder R. Appraisal of the generalized likelihood uncertainty estimation (GLUE) method. Water Resources Research, 2008; 44: 1–17. doi: 10.1029/2008WR006822.
[37] Vrugt J A, Braak C J F, Gupta V K, Robinson B A. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment, 2009; 23(7): 1011–1026. doi: 10.1007/s00477-008-0274-y.
[38] Tolson B A, Shoemaker C A. Dynamically dimensioned search algorithm for computationally efficient watershed model calibration. Water Resources Research, 2007; 43(1): 1–16, doi: 10.1029/2005WR004723.
[39] Yen H, Jeong J, Tseng W-H, Kim M-K, Records R M, Arabi M. Computational procedure in evaluating sampling techniques for parameter estimation and uncertainty analysis in watershed modeling. Journal of Hydrologic Engineering. 2014; doi: 10.1061/(ASCE)HE.1943- 5584.0001095.
[40] Seo M-J, Yen H, Jeong J. Transferability of Input Parameters between SWAT 2009 and SWAT 2012. Journal of Environmental Quality. 2014; 43(3): 869–880. doi: 10.2134/jeq2013.11.0450
[41] Beck M B, Fath B D, Parker A K, Osidele O O, Cowie G M, Rasmussen T C, et al. Developing a concept of adaptive community learning: Case study of a rapidly urbanizing watershed. Integrated Assessment, 2002; 3(4): 299–307, doi: 10. 1076/iaij.3.4.299.13583.
[42] Kass R E, Raftery A E. Bayes factors. Journal of the American Statistical Association, 1995; 90: 773–795.
[43] Leamer E E. Specification Search. Wiley, 1978; pp 370.
[44] Raftery A E, Gneiting T, Balabdaoui F, Polakowski M. Using Bayesian Model Averaging to Calibrate Forecast Ensembles. American Meteorological Society, 2005; 133: 1155–1174.
[45] Wöhling T, Vrugt J A. Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models. Water Resources Research, 2008; 44(12): 1–18. doi: 10.1029/2008WR 007154.
[46] Raftery A E, Balabdaoui F, Gneiting T, Polakowski M. Using Bayesian model averaging to calibrate forecast ensembles, Tech. Rep. 440, Dep. of Stat., Univ. of Wash., Seattle, 2003.
[47] Duan Q, Ajami N K, Gao X, Sorooshian S. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Advances in Water Resources, 2007; 30(5): 1371–1386. doi:10.1016/j.advwatres.2006.11.014.
[48] Ajami N K, Duan Q, Sorooshian S. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 2007; 43(1): 1–19. doi:10.1029/2005WR 004745.
[49] Georgakakos K, Seo D, Gupta H, Schaake J, Butts M. Towards the characterization of streamflow simulation uncertainty through multimodel ensembles. Journal of Hydrology, 2004; 298(1-4): 222–241, doi: 10.1016/ j.jhydrol.2004.03.037.
[50] Burnash R J, Ferral R L, McGuire R A. A generalized streamflow simulation system conceptual modeling for digital computers. Report, U. S. Dep. of Commer. Natl. Weather Serv. and State of Calif. Dep. of Water Resour, 1973.
[51] Boyle D. Multicriteria calibration of hydrological models. Ph.D. dissertation, Univ. of Ariz., Tucson, 2001.
[52] Schaake J C, Koren V, Duan Q Y, Chen F. Simple water balance model for estimating runoff at different spatial and temporal scales. Journal of Geophysical Research, 1996; 101(D3): 7461–7475.
[53] NOAA (2012), Local weather observation station record, NOAA Satellite and Information Service and National Climatic Data Center, Available at: http://www.ncdc. noaa.gov/oa/ncdc. html. Accessed on [2013-05].
[54] Thyer M, Renard B, Kavetski D, Kuczera G, Franks S W, Srikanthan S. Critical validation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis. Water Resources Research, 2009; 45(3): 1–22. doi: 10.1029/2008 WR006825.
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2015-02-28
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Yen, H., White, M. J., Jeong, J., Arabi, M., & Arnold, J. G. (2015). Evaluation of alternative surface runoff accounting procedures using SWAT model. International Journal of Agricultural and Biological Engineering, 8(3), 64–68. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/833
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