Calibration and Validation of SWAT Model for Low Lying Watersheds: A Case Studyon the Kliene Nete Watershed, BelgiumNaraya...
sunshine hours per day (-) are used. Apart from this, land- ...
physical interpretation is based on the linear reservoir number of parameters that ...
parameter to adjust the same flow component was ESCO, 74 percentage for the calibration peri...
Corresponding Address: Email: Physics and Chemistry of the Earth 29, pp.737-743.Acknowledg...
of 5

Narayan Shrestha [Calibration and Validation of SWAT Model for Low Lying Watersheds: A Case Study on the Kliene Nete Watershed, Belgium]

This paper has been published in HYDRO NEPAL JOURNAL, ISSUE NO. 6, JANUARY, 2010.
Published on: Mar 3, 2016
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Transcripts - Narayan Shrestha [Calibration and Validation of SWAT Model for Low Lying Watersheds: A Case Study on the Kliene Nete Watershed, Belgium]

  • 1. Calibration and Validation of SWAT Model for Low Lying Watersheds: A Case Studyon the Kliene Nete Watershed, BelgiumNarayan K. Shrestha, P.C. Shakti, Pabitra Gurung Narayan K P.C. Shakti Pabitra Gurung ShresthaAbstract: Use of easily accessible; public domain modelling software called Soil and Water Assessment Tool (SWAT) and itstesting in watersheds has become essential to check developers’ claims of its applicability. The SWAT model performance onKliene Nete Watershed (Belgium) is examined. Given the watershed’s characteristic of a low lying; shallow ground water table,the test becomes an interesting task to perform. This paper presents calibration and validation of the watershed covering areaof 581km2. Flow separation is carried on using Water Engineering Time Series PROcessing tool (WETSPRO) and shows thataround 60% of the total flow is contributed by base flow. Altogether seven SWAT model parameters have been calibrated withheuristic approach for the time frame of 1994-1998. Validation of these calibrated parameters in another independent timeframe (1999-2002) is carried out. The parameter CH_k2 (Channel Effective Hydraulic Conductivity) is found to be the mostsensitive. Nash Sutcliff Efficiency (NSE) values for the calibration and validation periods are found to be 74 and 67 percent-age, respectively. These ‘goodness-of-fit’ statistics, supported by graphical representations, show that the SWAT model cansimulate such watershed with reasonable accuracy.Key words: SWAT, WETSPRO, Kliene Nete Watershed (Belgium), NSEIntroduction of sensitive parameters was achieved by sensitivity analysis, Models are used in decision making applications to select which is new in the SWAT 2005 version (Griensven 2005).an optimal courses of action, and are often constructed to Although SWAT 2005 offers an ‘auto-calibration’ option,enable reasoning within an idealized logical framework about an heuristic approach has been applied to calibrate. Thethe processes. Watershed models are essential for studying auto-calibration algorithm is based on maximizing certainhydrologic processes and their responses to both natural and objective functions; hence, it will return parameter valuesanthropogenic factors, but due to model limitations in the accordingly. Despite being fast and less subjective, automatedrepresentation of complex natural processes and conditions, calibration has major limitations by the assumption mademodels usually must be calibrated prior to application to for the objective function and the existence of local minimaclosely match reality (Bastidas et al 2002). Stream-flow, that are closely related to the number of model parameterswhich is known as integrated process of atmospheric and (Willems 2000). Hence, automated calibration shouldtopographic processes, is of prime importance to water be used with caution. On the other hand, the heuristicresources planning (Kahya and Dracup 1993). This becomes approach, which is time-consuming, makes the use ofan essential task for low lying catchments that are more modelers’ knowledge and experience and, therefore, cansusceptible to flooding and inundation; hence, subsequent prove to be useful.water resources planning and management on suchcatchment becomes a top priority. Materials and Method Use of process based, easily accessible, public domain The watershedmodelling software like the Soil and Water Assessment Tool The Flanders region of Belgium is subdivided into 11(SWAT) is an easy option for hydrologists while considering catchments. Among them, Kleine Nete is sub-catchment ofwatershed modelling. Here, a case study is conducted Nete Catchment and is located northeast of Brussels; it hasfor a 581km2 Kliene Nete watershed in Belgium. The an area of about 581km2 at Grobbendonk. Kleine Nete Rivermain objectives of the study are: (a) to see the simulation has its source near Retie in the Belgian province of Antwerp.ability of SWAT in the case of a low-lying, low ground In Grobbendonk, the river is joined by the water of the Aa.water tablecatchment, and (b) to see the most sensitive It flows in a southwest direction past the towns of Herentalsparameters for such a catchment. It is difficult to measure and Nijlen before joining the Grote Nete at Lier. See Map 1.the most sensitive parameters of such a model, to which The elevation of the watershed varies from 7.62 m toa physical meaning is often assigned, as well as a spatial 92.76 m, with a mean elevation being 23.17 m; hence, it is arepresentativeness, and of which the value normally is relatively flat catchment. Sand is the predominent soil typeobtained in the calibration process (Refsgaard and Storm in the catchment. It covers almost 94% of the watershed,1996, Heuvelmans et al 2004). For this case, the analysis followed by land dunes covering around 5%, and clayHYDRO NEPAL ISSUE NO. 6 JANUARY, 2010 47
  • 2. sunshine hours per day (-) are used. Apart from this, land- use data, soil data, digital elevation module (50m x 50m resolution), and river network are used for model build-up. For calibration and validation, daily river series observed at flow gauging station 52 (Grobbendonk) are used. Setting the SWAT model As AVSWATX is designed for use in the USA, to adapt it for use in Belgium some default files such as ‘crop.dat’, ‘crop. dbf’, ‘fert.dat’, ‘fert.dbf’, ‘urban.dat, ‘urban.dbf’, ‘usersoil. dbf’ and ‘userwgn.dbf’ are modified. For the model built up, the Nete watershed is digitized using the SWAT extension Map 1 Belgium, Nate Basin and Kliene Nete Watershed in Arc View 3.2, with the projection type set as Lambertaround 1%. The watershed has almost 55% as agricultural conformal conic and spheroid type set as Internationalarea (covered by pasture, corn, etc.), 25% as mixed forest, 1909. The threshold of 1000 ha and the digitized streamsand the rest as high density residential area. HRU’s Landuse Soil Type % covered 1 Berm Sand 14 The modeling tool 2 Berm Land dune 01 SWAT is a continuous time model that operates on 3 Pasture Sand 20a daily time step at catchment scale, a physically based 4 Corn Sand 39semi-distributed hydrological model developed by the U.S. 5 Forest Sand 24Department of Agriculture in order to quantify the impact 6 Forest Land dune 02of land management practices on water quantity, sediment Table 1. Different HRU’s and Percentage of Coverage for Kleine Nete Basinand water quality in large complex watersheds with varyingsoils, land use and management conditions over a long period shape file are used for the digitizing process.of time (Arnold et al 1998, Neitsch et al 2001). The SWAT Subsequently, land use and soil type maps are incorporated into the model. (See Maps 2 and 3.) After overlaying these data threshold values of 5% for land data and 3% for soil data are chosen. This is to limit the number of HRU’s that would result for the given combination of land use and soil data. The Penman-Monteith method is used for calculation of evapo-transpiration and the Muskingum method is used for flow routing. The resulted HRU’s are shown on Table 1. Result and Discussion Flow filtering A time series of total rainfall-runoff discharges can be split into its subflows (such as the overland flow, the Map 2. Soil Types subsurface flow or interflow, and the groundwater flowis a process-based model that assesses long-term impacts or baseflow) using a numerical digital filter technique. Itsof management practices including empirical relationships.The model has been widely used but also further developedin Europe (Griensven et al 2002). It simulates at thehydrologic response units (HRU) level. HRUs are lumpedland areas within the sub-basin with unique combinationsof soil-type, land-use and management. It is limited toworking with a minimum time step of one day and at leasttwo sub-basins. The data Hydro-meteorological data such as daily meantemperature (°C), daily rainfall (mm), daily mean relativehumidity (%), daily mean wind speed (m/s), number of Map 3. Land Use48 HYDRO NEPAL ISSUE NO. 6 JANUARY, 2010
  • 3. physical interpretation is based on the linear reservoir number of parameters that govern the model, and it ismodelling concept (Willems 2003). It is the essential performed using LH-OAT (LH (Latin-Hypercube) - OATtechnique to be implemented after the model built up so (One-factor-At-a-Time)) technique. The description ofthat evaluation of calibration processes in terms of different parameter used for stream-flow calibration and their relativeflow components such as baseflow, surface flow and total sensitivity resulted after sensitivity analysis is presented inwater yield can be made. Water Engineering Time Series Table 2.PROcessing Tool (WETSPRO) software is used for thisfiltering process. (WETSPRO was developed by Prof. P. Calibration and validationWillems, Hydraulics Laboratory, Katholieke Universiteit Calibration is the process of gathering the conceptualLeuven, Belgium.) WETSPRO is a time series processing parameters, and is done as a forerunner to testing of thetool that allows the users to conduct: model hypothesis. During calibration, parameters of  Sub-flow filtering. unmeasured variables are estimated using information  Peak flow selection and related hydrograph separation; that is available from the real system. The 11 years of for quick flow and slow flow periods; and related low observed series at flow gauging station 52 (Grobbendonk) is flow selection. divided into three time frames, namely: the ‘warming-up’,  Construction of the different model evaluation plots. ‘calibration’ and ‘validation’ periods from 1992-93, 1994-98 relative Parameter description and 1999-2002, respectively. The provision of the warming sensitivity Ch_k2 Channel Effective Hydraulic Conductivity 4.890 up period is to initialize unknown variables such as moisture surlag Surface Runoff Lag Time 2.510 content. An heuristic approach (i.e., manual calibration ch_n Manning Coefficient for Channel 0.692 based on experience) is used to decide which parameters Initial SCS Runoff Curve number for Wetting CN2 0.139 to adjust to obtain ‘good’ fit. During the validation period, Condition-2 SLSUBBSN Slope of Sub-basin 0.108 the model is run with the same model parameters obtained SLOPE Average Slope Steepness 0.074 from the calibration period to see how well the calibrated GWQMN Threshold Depth for shallow aquifer for flow 0.063 parameters work in another independent period. ALPHA_BF The Base Floe Alpha Factor 0.039 Generally the effects of the parameters on the system canmx Maximum Canopy Storage 0.033 include those impacting the surface response (CN2, SOL_AWC Soil Available Water Capacity 0.021 SOL_AWC, and ESCO), those impacting the subsurface sol_k Saturated Hydraulic Conductivity of Soil 0.017 response (GW_REVAP, REVAPMN, GWQMN, ALPHA_ rchrg_dp Deep Aquifer Percolation Factor 0.013 BF, GW_DELAY, RCHRG_DP, etc.), and those impacting GW_REVAP Ground Water “Revap” Coefficient 0.013 the shape of the hydrograph (Ch_k2, SURLAG, ALPHA_ sol_z Soil Depth from Surface to Bottom of layer 0.010 BF, etc.). For our case, viewing the pre-calibrated result GW_DELAY Ground Water Delay Time 0.009 on temporal level as well as global level and seasonal level, ESCO Soil Evaporation Compensation Factor 0.008 following parameters, were optimized as shown in Table sol_alb Moist Soil Albedo 0.005 BIOMIX Biological Mixing Efficiency 0.002 3. This calibration followed after making systematic use of epco Plant Uptake Compensation Factor 0.001 sensitivity analysis results and problem at hand. Threshold Depth of water in shallow aquifer for CN_2 was calibrated to adjust the surface flow, and it REVAPMN 0.000 “revap” was increased to 90 for HRU−BERM (urban) because of Table 2. Parameters Used for Flow Calibration and Their Relative Sensitivity its higher potential to contribute to surface runoff. Another File HRU Parameter Units state Type BErM BErM PASTURE CorN ForEsT ForEsT The WETSPRO tool makes use of acontinuous time series of any hydrological Initial 77 77 60 77 60 60 CN_2 - *.mgtvariable as input (Willems 2009). Analysis Final 90 90 80 80 60 60of observed flows on WETSPRO shows that Initial 0 0 0 0 0 0 GWQMN mm *.gwabout 60% of the flow is contributed by Final 350 450 350 350 350 450base flow. Initial 4 SURLAG day *.bsn Final 1 Initial 0.95 0.95 0.95 0.95 0.95 0.95 Sensitivity analysis ESCO - *.hru Final 0.95 0.95 0.4 0.3 0.4 0.4 Sensitivity analysis refers to the Initial 0.014 Ch_N2 - *.rteidentification of some few parameters that Final 0.02have important effects in the model. It is the Initial 0 Ch_k2 mm/h *.rteprior step to model calibration. It speeds up Final 0.45 Initial 0.048 0.048 0.048 0.048 0.048 0.048the optimization process by concentrating ALPHA_BF days *.gw Final 0.048 0.048 0.01 0.3 0.5 0.5on finding the optimum values for a limited Table 3: Optimized Parameters with Their Initial and Final ValuesHYDRO NEPAL ISSUE NO. 6 JANUARY, 2010 49
  • 4. parameter to adjust the same flow component was ESCO, 74 percentage for the calibration period and 67 percentagewhich accounts for the easiness with which water from lower for the validation period. Figure 1 shows the observed andlayers is available for evaporation. Lower value accounts simulated hydrograph both for the calibration period andfor higher evapotranspiration. The value of ESCO was the validation period. Apart from the flow comparisondecreased to 0.3 for corn fields because of its high potential in temporal level, hydrological processes have also beenfor evapotranspiration and less canopy cover. For adjusting accessed to HRU Level as shown in Figure 2. Only HRU-subsurface flow (baseflow), GWQMN was adjusted. The 1 (BERM+Sand) is presented as the representative one.value of GWQMN was increased to 350 mm and 450 mm for As can be read from the figure, the hydrological processessandy and land dune, respectively, because of lower water have followed the trend of precipitation. Precipitation onholding capacity of sand compared with land dune. The 1998 (calibration period) and 2001 (validation period) wasdefault value of 0.014 for Ch_N2 is, of course, unrealistic; highest and the response on water yield was clearly highhence, it was increased to 0.02, a typical value for channel on those years. Most importantly, the soil water contenthaving predominant sandy soil. For low lying catchments showed no marked difference with trend of precipitationunder study, where ground water depth is near or above the which reflects good performance of the model because theriver bottom, parameter Ch_k2 adjusts the water exchange soil water content should be almost constant whatever thefrom ground water to river and was found to be very trend of precipitation is.sensitive to adjust the shape of hydrograph, especially forlow flows. ALPHA_BF was also used to smoothen the shape Conclusionsof hydrograph, especially for recession period, and it was By tuning seven parameters, the calibration and validationincreased to 0.5 for HRU−FOREST because of higher root of the SWAT model for a low lying catchment (Kliene Nete)depth distribution of forest; hence, higher water holding was carried out. The parameter CH_k2, which allowscapacity. Optimized parameters are listed on Table 3. interaction between the ground water flow and river flow, After tuning the above stated parameters (Table 3), the was found to be the most sensitive for such catchment. TheNash-Sutcliff Efficiency (NSE), a widely used ‘goodness-of- NSE value was found to be 74 percentage for calibrationfit’ statistics indicator to access the goodness of model to and 67 percentage for the validation period. The slightsimulate the flows (Nash and Sutcliffe 1970), was found to be underperformance of the model in validation period may be due to the fact that there is significant change in precipitation during the calibration period (852.7 mm/year) and the validation period (1002.5 mm/yr). Performance of the model is also known to be affected by a significant change in trend of annual average precipitation (Pipat et al 2005). Owing to the general trend of the NSE value for acceptance of rainfall-runoff model, this calibration can be adjudged as good because of having NSE around 70 percentage, which is quite acceptable for water engineering problem assessment and application. The fairly matching of hydrograph as well as hydrological processes on each HRU’s also supports it. The calibrated parameter values can also be used for further Figure 1. Observed and Simulated Discharge Series for Calibration and Validation stream-flow simulations in this catchment. Periods -- Narayan Kumar Shrestha, PhD researcher, Department of Hydrology & Hydraulic Engineering, Vrije Universiteit Brussels, Belgium Corresponding address: PC Shakti, Master in Meteorology, Tribhuvan University Nepal, and Master in Water Resources Engineering, Katholieke Universiteit Leuven, Belgium Corresponding Address: Pabitra Gurung, Master in Water Resources Engineering, Katholieke Universiteit Leuven, Belgium. Mr Gurung is a Consulting Engineer in Prarup Development Pvt. Ltd. Lalitpur, Nepal. Figure 2: Hydrological Processes at HRU1 (BERM+SAND)50 HYDRO NEPAL ISSUE NO. 6 JANUARY, 2010
  • 5. Corresponding Address: Email: Physics and Chemistry of the Earth 29, pp.737-743.Acknowledgements Kahya, E. and J.A. Dracup, 1993, US streamflow patternsWe would like to acknowledge Prof. W. Bauwens and in relation to the El Nino/southern oscillation, WaterAssistant Eliseo Ana Jr., Vrije Universiteit, Brussels for Resources Research 28(8), pp.2491-2503.providing data as well as for their guidance and suggestions. Nash, J. E. and J.V. Sutcliffe, 1970, River flow forecastingSpecial thanks go to VLIR-UOS for providing the scholarship through conceptual models, Part I: A discussion of principles,for the International Masters Program in Water Resources Journal of Hydrology 10(3), pp.282–290.Engineering (2007-09), and both Katholieke Universiteit Neitsch, S.L., JG. Arnold, J.R. Kiniry and J.R. Williams,Leuven, Belgium and Vrije Universiteit Brussel, Belgium, 2001, Soil and Water Assessment Tool, Theoreticalfor providing the platform. Documentation, Version 2000, Temple Texas: Blackland Research Center, Agricultural Research Service.References Pipat, R., S.K. Ramesh. J. Manoj, WG. Philip, A. KhalilArnold, J.G., R. Srinivasan, R.S. Muttiah and J.R. Williams, and S. Ali, 2005, Calibration and Validation of SWAT for the1998, Large area hydrologic modelling and assessment, Upper Maquoketa River Watershed, Working Paper 05-WPPart I: Model development, Journal of the American Water 396, Centers for Agricultural and Rural Development, IowaResources Association 34(1), pp.73-89. State University, Ames, Iowa. Bastidas, L.A., H.V. Gupta and S. Sorooshian, 2002, Refsgaard, J.C. and B. Storm, 1996, Construction,Emerging paradigms in the calibration of hydrologic models, calibration and validation of hydrological models, pp. 41-Mathematical Models of Large Watershed Hydrology, 1, 54 in M.B. Abbott and J.C. Refsgaard, eds., Distributedpp.25-56 (Water Resources Publications, LLC, Englewood, Hydrological Modelling, Dordrecht and Boston: KluwerCO, USA). Academic. Griensven, V.A., A. Francos and W. Bauwens, 2002, Willems, P., 2000, Guidance Document for CalibrationSensitivity analysis and autocalibration of an integral and Verification of Rainfall-Runoff Models (unpublisheddynamic model for river water quality, Water Science and manual; pp.4.3-4.31. Leuven, Belgium: KatholiekeTechnology 45(5), pp.321-328. Universiteit. Griensven,V.A., 2005, AVSWAT-X SWAT-2005 Advanced Willems, P., 2003, WETSPRO, Water Engineering TimeWorkshop, SWAT 2005 3rd International Conference, Series PROcessing Tool, Methodology and Users Manual,Zurich, Switzerland. Leuven, Belgium: Katholieke Universiteit. Heuvelmans, G., B. Muys and J. Feyen, 2004, Evaluation Willems, P., 2009, A time series tool to support the multi-of hydrological model parameter transferability for simulating criteria performance evaluation of rainfall-runoff models,the impact of land use on catchment hydrology, Journal of Environmental Modelling and Software 24, pp.311-321. w Call For Nomination for the Excellence Award 2010 There are many people and institutions who have rendered their services in the pursuit of excellence in the field of water, energy and/or environment. The HYDRO Nepal Excellence Award is established to recognize their efforts and honor them. HYDRO Nepal journal solicits nominations from among the experts, professionals or other individuals/ institutions involved in the pursuit of excellence in the field of water, energy and environment in Nepal for the annual HYDRO Nepal Excellence Award for the year 2010. Please send one page brief of the work and contributions of the nominated person or an institution for the year 2010 (and previous to that). Please include the nominee’s full name, address, contact phone number and email address, including your own phone and email contact details. The deadline for submission of nominations is May 20, 2010. Nominations will be evaluated by a three person Evaluation Committee consisting of prominent authorities in the sector. The Evaluation committee will decide the winner by June 30, 2010. The Decision of the Evaluation Committee will be final. The winner will be announced in the 7th issue of HYDRO Nepal in July 2010. Address your nominations to: HYDRO Nepal P. O. Box: 15142 KPC 609, Kamalpokhari (near Kumari Hall), Kathmandu, Nepal Email:; media4energy@gmail.comHYDRO NEPAL ISSUE NO. 6 JANUARY, 2010 51

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