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

doi:10.1007/s11707-017-0656-x

Front. Earth Sci.

2017, Vol. 11

Issue (3) : 592-600 https://doi.org/10.1007/s11707-017-0656-x

RESEARCH ARTICLE

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

Jianwen BAI, Zhenyao SHEN(

), Tiezhu YAN

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China

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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

Corresponding Author(s): Zhenyao SHEN

Just Accepted Date: 19 April 2017 Online First Date: 19 May 2017 Issue Date: 12 July 2017

Cite this article:

Jianwen BAI,Zhenyao SHEN,Tiezhu YAN. A comparison of single- and multi-site calibration and validation: a case study of SWAT in the Miyun Reservoir watershed, China[J]. Front. Earth Sci., 2017, 11(3): 592-600.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0656-x
https://academic.hep.com.cn/fesci/EN/Y2017/V11/I3/592

Fig.1 Location of the study watershed and the hydrological sites.

Tab.1 The type and sources of available data in the Miyun watershed

Tab.2 Basin characteristics of Baihe Watershed

Tab.3 Selected parameters of the three sites for calibration

Tab.4 The E_NS and R² values of different sites in the calibration and validation periods

Fig.2 Observed and simulated flow of Sandaoying from 2006 to 2010.

Fig.3 Observed and simulated flow of Xiabao from 2006 to 2010.

Fig.4 Observed and simulated flow of Zhangjiafen from 2006 to 2010.

Fig.5 Observed and simulated flow of single- and multi-sites.

Tab.5 The parameter values showing the greatest differences for the three sites in SWAT

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 https://doi.org/10.1002/hyp.336
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 https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
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 https://doi.org/10.1007/s13762-016-1011-6
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 https://doi.org/10.1016/j.quaint.2012.07.012
6	BekeleE G, NicklowJ W (2007). Multi-objective automatic calibration of SWAT using NSGA-II.J Hydrol (Amst), 341(3–4): 165–176 https://doi.org/10.1016/j.jhydrol.2007.05.014
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 https://doi.org/10.1002/hyp.5933
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 https://doi.org/10.1016/j.watres.2012.05.057
9	DuanQ, SorooshianS, GuptaV K (1992). Effective and efficient global optimization for conceptual rainfall-runoff models.Water Resour Res, 28(4): 1015–1031 https://doi.org/10.1029/91WR02985
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 https://doi.org/10.1016/j.watres.2013.08.010
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 https://doi.org/10.1016/j.agee.2011.05.015
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 https://doi.org/10.2166/nh.2012.021
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 https://doi.org/10.1016/j.agwat.2012.10.018
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 https://doi.org/10.1016/j.jhydrol.2010.06.010
16	MéndezM, ArayaJ A, SánchezL D (2013). Automated parameter optimization of a water distribution system.J Hydroinform, 15(1): 71–85 https://doi.org/10.2166/hydro.2012.028
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 https://doi.org/10.13031/2013.23153
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 https://doi.org/10.1016/j.biortech.2008.06.045
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 https://doi.org/10.1016/j.agwat.2008.05.006
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 https://doi.org/10.2166/wst.2012.895
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 https://doi.org/10.1007/s00477-012-0579-8
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 https://doi.org/10.5194/hess-16-121-2012
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 https://doi.org/10.5194/hess-16-4621-2012
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 https://doi.org/10.1002/hyp.7475
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 https://doi.org/10.1016/j.jhydrol.2008.05.012
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 https://doi.org/10.1016/j.envsoft.2013.03.013
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 https://doi.org/10.1016/j.jhydrol.2009.06.023
32	ZhangX, SrinivasanR, Van LiewM (2008). Multi-site calibration of the SWAT model for hydrologic modeling.Trans ASABE, 51(6): 2039–2049 https://doi.org/10.13031/2013.25407
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)

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