A comparison of single- and multi-site calibration and validation: a case study of SWAT in the Miyun Reservoir watershed, China

Abstract

An essential task in evaluating global water resource and pollution problems is to obtain the optimum set of parameters in hydrological models through calibration and validation. For a large-scale watershed, single-site calibration and validation may ignore spatial heterogeneity and may not meet the needs of the entire watershed. The goal of this study is to apply a multi-site calibration and validation of the Soil and Water Assessment Tool (SWAT), using the observed flow data at three monitoring sites within the Baihe watershed of the Miyun Reservoir watershed, China. Our results indicate that the multi-site calibration parameter values are more reasonable than those obtained from single-site calibrations. These results are mainly due to significant differences in the topographic factors over the large-scale area, human activities and climate variability. The multi-site method involves the division of the large watershed into smaller watersheds, and applying the calibrated parameters of the multi-site calibration to the entire watershed. It was anticipated that this case study could provide experience of multi-site calibration in a large-scale basin, and provide a good foundation for the simulation of other pollutants in follow-up work in the Miyun Reservoir watershed and other similar large areas.

Keywords

calibration / soil and water assessment tool / Miyun Reservoir / multi-site

References

[1] AbbaspourK C (2011). SWAT-CUP4: SWAT Calibration and Uncertainty Programs–A User Manual.Department of Systems Analysis, Integrated Assessment and Modelling (SIAM), Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH, Switzerland

[2] AndertonS, LatronJ, GallartF (2002). Sensitivity analysis and multi-response, multi-criteria evaluation of a physical based distributed model.Hydrol Processes, 16(2): 333–353

[3] ArnoldJ G, SrinivasanR, MuttiahR S, WilliamsJ R (1998). Large area hydrologic modeling and assessment: part I. Model development.J Am Water Resour Assoc, 34(1): 73–89

[4] BaiJ, ShenZ, YanT (2016). Effectiveness of vegetative filter strips in abating fecal coliform based on modified soil and water assessment tool.Int J Environ Sci Technol, 13(7): 1723–1730

[5] BaoZ, FuG, WangG, JinJ, HeR, YanX, LiuC (2012). Hydrological projection for the Miyun Reservoir basin with the impact of climate change and human activity.Quat Int, 282: 96–103

[6] BekeleE G, NicklowJ W (2007). Multi-objective automatic calibration of SWAT using NSGA-II.J Hydrol (Amst), 341(3–4): 165–176

[7] CaoW, BowdenW B, DavieT, FenemorA (2006). Multi-variable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability.Hydrol Processes, 20(5): 1057–1073

[8] ChoK H, PachepskyY A, KimJ H, KimJ W, ParkM H (2012). The modified SWAT model for predicting fecal coliformsin the Wachusett Reservoir Watershed, USA.Water Res, 46(15): 4750–4760

[9] DuanQ, SorooshianS, GuptaV K (1992). Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resour Res, 28(4): 1015–1031

[10] FreyS K, ToppE, EdgeT, FallC, GannonV, JokinenC, MartiR, NeumannN, RueckerN, WilkesG, LapenD R (2013). Using SWAT, bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed.Water Res, 47(16): 6326–6337

[11] GongY W, ShenZ Y, HongQ, LiuR M, LiaoQ (2011). Parameter uncertainty analysis in watershed total phosphorus modeling using the GLUE approach.Agric Ecosyst Environ, 142(3–4): 246–255

[12] GongY W, ShenZ Y, LiuR M, HongQ, WuX (2012). A comparison of single- and multi-gauge based calibrations for hydrological modeling of the Upper Daninghe Watershed in China’s Three Gorges Reservoir Region.Hydrol Res, 43(6): 822–832

[13] LiZ J, LiX B (2008). Impacts of precipitation changes and human activities on annual runoff of Chaohe Basin during past 45 years.Sci Geogr Sin, 28(6): 809–813 (in Chinese)

[14] LiuR, ZhangP, WangX, ChenY, ShenZ (2013). Assessment of effects of best management practices on agricultural non-point source pollution in Xiangxihe watershed.Agric Water Manage, 117: 9–18

[15] MaH, YangD, TanS K, GaoB, HuQ (2010). Impact of climate variability and human activity on streamflow decrease in Miyun Reservoir catchment.J Hydrol (Amst), 389(3–4): 317–324

[16] MéndezM, ArayaJ A, SánchezL D (2013). Automated parameter optimization of a water distribution system.J Hydroinform, 15(1): 71–85

[17] MoriasiD N, ArnoldJ G, Van LiewM W, BingnerR L, HarmelR D, VeithT L (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Trans ASAE, 50(3): 885–900

[18] NashJ, SutcliffeJ (1970). River flow forecasting through conceptual models part I—A discussion of principles.J Hydrol, 10: 282–290

[19] ParajuliP B, MankinK R, BarnesL P (2009). Source specific fecal bacteria modeling using soil and water assessment tool model.Bioresour Technol, 100(2): 953–963

[20] ParajuliP B, MankinK R, BarnesP L (2008). Applicability of targeting vegetative filter strips to abate fecal bacteria and sediment yield using SWAT.Agric Water Manage, 95(10): 1189–1200

[21] RasolomananaS D, LessardP, VanrolleghemP A (2012). Single-objective vs. multi-objective autocalibration in modelling total suspended solids and phosphorus in a small agricultural watershed with SWAT.Water Sci Technol, 65(4): 643–652

[22] ShenZ, ChenL, ChenT (2013). 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.Stochcastic Environmental Research and Risk Assessment, 27(1): 235–251

[23] ShenZ Y, ChenL, ChenT (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(1): 121–132

[24] WangG, XiaJ, ChenJ (2009). Quantification of effects of climate variations and human activities on runoff by a monthly water balance model: a case study of the Chaobaihe basin in northern China.Water Resour Res, 45(7): 206–216

[25] WangG S, XiaJ, WanD H, YeZ A (2006). A Distributed monthly water balance model for identifying hydrological response to climate changes and human activities.J Nat Res, 21(1): 86–91 (in Chinese)

[26] WangS, ZhangZ, SunG, StraussP, GuoJ, TangY, YaoA (2012). Multi-site calibration, validation, and sensitivity analysis of the MIKE SHE Model for a large watershed in northern China.Hydrol Earth Syst Sci, 16(12): 4621–4632

[27] WangX Y, QinF L, OuY, XueY F (2008). SWAT-based simulation on non- point source pollution in the northern watershed of Miyun Reservoir.J Agro-Environ Sci, 27(3): 1098–1105 (in Chinese)

[28] XuZ X, PangJ P, LiuC M, LiJ Y (2009). Assessment of runoff and sediment yield in the Miyun Reservoir catchment by using SWAT model.Hydrol Processes, 23(25): 3619–3630

[29] YangJ, ReichertP, AbbaspourK C, XiaJ, YangH (2008). Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China.J Hydrol (Amst), 358(1–2): 1–23

[30] ZhangX, BeesonP, LinkR, ManowitzD, IzaurraldeR C, SadeghiA, ThomsonA M, SahajpalR, SrinivasanR, ArnoldJ G (2013). Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python.Environ Model Softw, 46: 208–218

[31] ZhangX, SrinivasanR, BoschD (2009). Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging.J Hydrol (Amst), 374(3–4): 307–317

[32] ZhangX, SrinivasanR, Van LiewM (2008). Multi-site calibration of the SWAT model for hydrologic modeling.Trans ASABE, 51(6): 2039–2049

[33] ZhaoY, YuX, ZhengJ, WuQ (2012). Quantitative effects of climate variations and land-use changes on annual streamflow in Chaobai river basin.Transactions of the Chinese Society of Agricultural Engineering, 28(22): 252–260 (in Chinese)

Acknowledgements

The research was funded by National Natural Science Foundation of China (Grant No. 51579011), National Science Foundation for Innovative Research Group (No. 51421065) and State Key Program of National Natural Science of China (Grant No. 41530635).

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2017 Higher Education Press and Springer-Verlag Berlin Heidelberg