Journal of Hydrology 533 (2016) 141–151

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Journal of Hydrology

journal homepage: www.elsevier .com/ locate / jhydrol

Coupling SWAT and ANN models for enhanced daily streamflowprediction

http://dx.doi.org/10.1016/j.jhydrol.2015.11.0500022-1694/� 2015 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Tel.: +1 (334) 844 4671; fax: +1 (334) 844 1084.E-mail addresses: nnoori@uga.edu, navideh.noori@gmail.com (N. Noori),

latif@auburn.edu (L. Kalin).

Navideh Noori a, Latif Kalin b,⇑aOdum School of Ecology, University of Georgia, 140 E Green St., Athens, GA 30602, USAb School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL 36849-5418, USA

a r t i c l e i n f o s u m m a r y

Article history:Received 2 July 2015Received in revised form 23 November 2015Accepted 28 November 2015Available online 15 December 2015This manuscript was handled byKonstantine P. Georgakakos, Editor-in-Chief,with the assistance of AlessioDomeneghetti, Associate Editor

Keywords:StreamflowForecastUnmonitored watershedANNSWAT

To improve daily flow prediction in unmonitored watersheds a hybrid model was developed by combin-ing a quasi-distributed watershed model and artificial neural network (ANN). Daily streamflow data from29 nearby watersheds in and around the city of Atlanta, Southeastern United States, with leave-one-site-out jackknifing technique were used to build the flow predictive models during warm and cool seasons.Daily streamflow was first simulated with the Soil and Water Assessment Tool (SWAT) and then theSWAT simulated baseflow and stormflow were used as inputs to ANN. Out of the total 29 test watersheds,62% and 83% of them had Nash–Sutcliffe values above 0.50 during the cool and warm seasons, respec-tively (considered good or better). As the percent forest cover or the size of test watershed increased,the performances of the models gradually decreased during both warm and cool seasons. This indicatesthat the developed models work better in urbanized watersheds. In addition, SWAT and SWATCalibration Uncertainty Procedure (SWAT-CUP) program were run separately for each station to comparethe flow prediction accuracy of the hybrid approach to SWAT. Only 31% of the sites during the calibrationand 34% of validation runs had ENASH values P0.50. This study showed that coupling ANN with semi-distributed models can lead to improved daily streamflow predictions in ungauged watersheds.

� 2015 Elsevier B.V. All rights reserved.

1. Introduction

Streamflow prediction is needed for operation and optimizationof water resources, and is important for flood control and waterresource management (Makwana and Tiwari, 2014; Santos andSilva, 2013). Accuracy and skill of flow prediction models can havedirect influence on water resources management decisions. Vari-ous statistical and conceptual streamflow prediction models havebeen developed to help urban planners, administrators and policymakers in better and informed decision making. Statistical tech-niques, including regression based models, are overly simplisticand are constrained to a functional form between variables priorto the analysis. Conceptual hydrological models can better simu-late streamflow in a watershed because they take into account var-ious processes of the hydrological cycle via mathematicalformulation. The Soil and Water Assessment Tool (SWAT) is a con-ceptual, semi-distributed model (Arnold et al., 1998), and has beenwidely used for predicting streamflow under varying land use/cover (LULC) and climate conditions (Dixon and Earls, 2012;

Shi et al., 2013; Noori et al., 2014; Fan and Shibata, 2015; Glavanet al., 2015; Huang et al., 1996; Krysanova and Srinivasan, 2015).However, this model requires a large amount of spatial and tempo-ral data and model parameters that are sometime hard to predict(Makwana and Tiwari, 2014). The performance of this modeldepends upon the quality of input data and model parameters. Fur-ther, the large number of parameters, broad range of values andtheir complex interactions make the calibration and validationprocesses time consuming and complicated (Tokar and Markus,2000; Rezaeianzadeh et al., 2013).

An alternative, data-based approach for streamflow forecastingis artificial neural networks (ANN). Such models have the ability tolearn about the nonlinear relationships between the variables andextract the relation between the inputs and outputs of a processwithout the need of a detailed understanding of its physical char-acteristics. The application of ANN in various hydrological predic-tions has been extensively evaluated and published in recentyears (Ha and Stenstrom, 2003; Sahoo et al., 2006; Singh et al.,2009; Kalin et al., 2010; Palani et al., 2011; Gazzaz et al., 2012;Isik et al., 2012; Amiri et al., 2012; Rezaeianzadeh et al., 2013,2014). The major concern of using ANN is selecting the best com-bination of the input variables for a parsimonious model and if anevent is beyond the training data range, the predictive model

142 N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151

would perform poorly with high uncertainty (Jajarmizadeh et al.,2015).

Regardless of the type of model, streamflow prediction inungauged watersheds, where no measured data is available, is achallenging task (Breuer et al., 2009; Isik et al., 2012). Direct appli-cation of ANN is not possible to ungauged watersheds becauseANNs require observed streamflow data to train the model. Water-shed models such as SWAT can be applied to ungauged water-sheds, however with no observed data their utility is anunknown. On the other hand, if there are watersheds withobserved streamflow data near the study watershed or in thatregion, then watershed models or empirical methods can be usedthrough regionalization approaches. Regionalization methods aimat estimating model parameter values at ungauged watersheds ina definable region of consistent hydrological response. Regression,physical similarity and proximity are the three commonly appliedregionalization techniques in rainfall–runoff modeling (Wang andKalin, 2011). Generally, information is transferred from neighbor-ing watersheds, called donor watersheds, to a target watershed.Depending on the type of problem and the region, different region-alization technique could work better.

Few works have compared the performance of SWAT and ANNin simulating streamflow (Srivastava et al., 2006; Demirel et al.,2009; Talebizadeh et al., 2010; Kim et al., 2012). To the best ofauthors’ knowledge, no study has combined the two models forstreamflow prediction. Coupling ANN and watershed models canhelp overcome the limitations of each model and result in a stron-ger model for streamflow prediction in ungauged watersheds. Thispaper, therefore, tests the utility of coupling ANN and SWAT forimproved daily streamflow forecasting in ungauged watershedsin 29 watersheds near the city of Atlanta, USA.

2. Material and methods

2.1. Soil Water Assessment Tool (SWAT)

SWAT is a quasi-distributed watershed model simulating themovement of water, sediment, nutrient, crop growth, nutrientcycling, etc. in a watershed. It is a conceptual hydrologic model,operating at daily and sub-daily time steps (Arnold et al., 2012).SWAT has widely been used for assessing water resources andnonpoint source pollution problems. Input information for eachsub-watershed includes weather, soil properties, topography, andvegetation. The sub-watersheds are divided into hydrologicresponse units (HRUs) which are lumped land areas with uniqueland use, soil type and slope combinations.

SWAT considers watershed hydrology in two parts. The firstpart is composed of the watershed land areas that controls thewater transported to the channels together with sediment, nutri-ents and pesticide in each sub-watershed. The second part includesthe movement of water through the stream network to the water-shed outlet (Neitsch et al., 2011). The climatic variables required bySWAT include precipitation, temperature. Depending on the poten-tial evapotranspiration calculation method used, wind speed, solarradiation and relative humidity may be required too. These datacan be input to the model as observed data or generated duringthe simulation. A data set of daily potential evapotranspiration(PET) values can be supplied to the model if a different PET methodthan those incorporated into SWAT is preferred. In this study, PETwas calculated externally through the Hamon method (Hamon,1961) and provided to the SWAT model as input. This methodwhich has been shown to work well in the southeastern UnitedStates (Lu et al., 2005), calculates PET based on mean air tempera-ture and hours of daylight for a given day. SWAT modelers are rec-ommended to use a warm-up period during the simulation process

to stabilize the model or calculate values that become initial valuesfor the period of interest. In this study, one year was used as awarm-up period in ArcSWAT version 2012.10.15.

LULC data from 2006 National Land Cover Data (NLCD, http://www.mrlc.gov/nlcd2006.php), soil data from Soil Survey Geo-graphic Database (SURRGO, http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm), daily air temperature data from NationalClimatic Data Center (NCDC, http://www.ncdc.noaa.gov/) stationsin Atlanta area and daily precipitation data from North AmericanLand Data Assimilation Systems (NLDAS, http://ldas.gsfc.nasa.gov/index.php) were used as inputs to the SWAT model. In orderto reflect seasonal changes in vegetation and evapotranspirationand their impact on flow, and also consider the seasonal variationin contribution of baseflow and stormflow to total flow, the SWATsimulated flow was divided into cool and warm seasons and twoseparate ANN models with different structures were developed.Warm season was considered to be May through October and coolseason was considered to be November through April. Calibrationand validation were skipped and model parameters were set attheir default values for flow simulation.

2.2. Study area

29 US Geological Survey (USGS) monitoring stations in andaround the city of Atlanta, USA that have daily streamflow dataover the 2002–2010 period were considered in this study. Fig. 1shows the locations of selected USGS stations. Watershed areasvary from 3 to 552 km2 and are located within the Fulton, DeKalb,Cobb, and Gwinnett counties in the state of Georgia. The study areais within the Southeastern Piedmont physiographic province of theUSA. Streamflow in the Piedmont province is typically generatedby stormflow and from the release of shallow groundwater (Rose,2007). The city of Atlanta is one of the fastest growing metropoli-tan areas in the United States. The population of the metro area hasmore than tripled since the middle of the 20th century, from the1 million residents in 1950 to over 3 million by 2014, with noslowdown in sight (USGS, 2014). The rapid development with ahighly diverse LULC in this region makes it necessary to study itsimpact on hydrologic processes (Rose and Peters, 2001). Elevationin the study area ranges from about 157 to 513 m above mean sealevel. The dominant hydrologic soil group (HSG) is B which hasmoderate infiltration rates with silt loam to loam soil textures(Fig. 2). The climate is warm and humid with mean annual temper-ature of �16.5 �C and mean annual precipitation of �120 cm. Dom-inant LULC types are impervious surfaces, evergreen forest,deciduous forest, mixed forest, pasture and urban grass, based onNational Land Cover Database (NLCD, http://www.mrlc.gov/), in2006. Summary of different LULC classes of the watersheds drain-ing to the USGS stations shown in Fig. 2 are given in Table 1. Per-cent imperviousness is within the range of 13–52% and percentforest cover varies between 2% and 33%.

2.3. Streamflow prediction

2.3.1. Coupling SWAT with ANNTo improve streamflow predictions, a hybrid model based on

SWAT and ANN was developed. In this approach baseflow andstormflow (also called quick flow, i.e. surface runoff + interflow)components of streamflow are first simulated by the SWAT modelwith its default parameters (i.e. no calibration). The simulatedbaseflow and stormflow at day t then serve as inputs to the ANNmodel to predict streamflow on day t. This method reduces thetime needed to calibrate and validate SWAT and also time neededto select the optimal input combination to ANN. In a sense, SWATserves as a transfer function by combining climatic, topographic,soil and LULC data and producing two new outputs that serve as

Cobb

Fulton

DeKalb

Atlanta

Gwinnett

Fig. 1. Delineated watersheds for USGS stations in the Atlanta area. Black circles show the streamflow stations, blue triangles show climate stations. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version of this article.)

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input to the ANN model. This approach extends on the methodol-ogy introduced by Isik et al. (2012), who developed a hybrid modelbased on ANN and the Soil conservation Service (SCS) Curve Num-ber (CN) to predict the impact of LULC changes on daily stream-flow. The main difference between this study and their work isthe flow simulation process. In this study, instead of simulatingstreamflow using precipitation, potential evapotranspiration andcalculated surface runoff as inputs to ANN, SWAT is directly cou-pled with ANN in which SWAT generated stormflow and baseflowserve as the only inputs to ANN. The use of SWAT also helps in min-imizing the effects of initial conditions through the use of a warmup period.

ANN is a black box type lumped model that has the ability toidentify a relationship from given patterns which makes it possibleto solve nonlinear relationships. ANNs provide a novel solution tothe problem of relating input and output variables in complex sys-tems (Dawson and Wilby, 2001). They are composed of a networkof interconnected nodes that are organized according to a particu-lar arrangement. ANNs can be categorized based on the direction ofinformation flow and processing. In a feed-forward network, theconnections between nodes are from an input layer, through oneor more hidden layers, to an output layer (Dawson and Wilby,2001). The nodes in one layer are connected to those in the next,but not to those in the same layer. A weight is assigned to each linkto represent the relative connection strength of two nodes at bothends in predicting the input–output relationship (Govindaraju andRamachandra Rao, 2000). There can be several hidden layers, witheach layer having one or more nodes. The most common methodused to find the number of hidden layers and nodes is a trial-and-error approach (Kalin et al., 2010). In this study, the numberof hidden neurons changed from 3 to 10, and number of hiddenlayer changed from 1 to 2. The transfer functions used in this studyto translate input signals to output signals were log-sigmoid andhyperbolic tangent sigmoid. MATLAB version 7.10.0 (2010) wasused for the development, training and testing of the ANN models.The main goal of training is to minimize the value of the error func-tion by modifying the weights that link its neurons. In this studymean square error (MSE) was used as the error function, which isgiven by

MSE ¼ 1n

Xn

i¼1

ðSi � OiÞ2

where Si and Oi are the predicted and observed daily streamflowvalues, respectively.

Separate ANN models were developed for flow prediction dur-ing cool and warm seasons. Multiple runs were conducted for eachmodel using the leave-one-site-out jackknifing technique (Seficket al., 2015). Jackknifing is a resampling technique to estimatethe precision of sample statistics by using subsets of available data.The jackknife estimator of a parameter is obtained by systemati-cally leaving out one observation at a time from a dataset. In thisstudy, out of n stations, one station was excluded for testing pur-poses and the ANN model was trained with the remaining(n � 1) stations. This step was repeated until all stations had beenremoved once. Fig. 3 shows a flowchart describing this methodol-ogy. Model performances were assessed with the Nash–Sutcliffeefficiency (ENASH) (Nash and Sutcliffe, 1970), and bias ratio (RBIAS)(Salas et al., 2000). Moriasi et al. (2007) proposed some guidelinesfor the evaluation of model simulation related to streamflow, sed-iment and nutrients at monthly time step. Considering that ourtime scale is smaller (daily), the adjusted ratings in evaluatingthe ANN model performance developed by Kalin et al. (2010) wereadapted in this study:

Very good: ENASH P 0.70; jRBIASj 6 0.25.Good: 0.50 6 ENASH < 0.70; 0.25 < jRBIASj 6 0.50.Satisfactory: 0.30 6 ENASH < 0.50; 0.50 < jRBIASj 6 0.70.Unsatisfactory: ENASH < 0.30; jRBIASj > 0.70.

2.3.2. SWAT-CUPTo compare the flow prediction accuracy of the hybrid approach

to SWAT, calibration of the SWAT model was performed usingSWAT Calibration Uncertainty Procedure (SWAT-CUP) program.SWAT-CUP is an auto-calibration tool that allows for sensitivityanalysis, calibration, validation, and uncertainty analysis of theSWAT model (Abbaspour, 2011). The sequential uncertaintydomain parameter fitting algorithm (SUFI-2) was conducted toestimate SWAT parameters related to streamflow. This algorithm

(a) Topographic map (b) Hydrologic soil group map

(c) LULC 2006

Fig. 2. Topographic (in meter), soil and LULC maps of watersheds around Atlanta, Georgia, USA.

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is a semi-automated inverse modeling procedure for combinedcalibration-uncertainty analysis. It accounts for all sources ofuncertainties, such as uncertainty in measured data and drivingvariables (Abbaspour et al., 2004).

SWAT and SWAT-CUP were run for each station separately forthe whole data period. Performance of the SUFI-2 technique wasevaluated using ENASH. For validation, model parameters wereobtained from a nearby watershed through regionalization basedon spatial proximity (Wang and Kalin, 2011). This means that thebest parameters of SWAT-CUP from the neighboring station withENASH P 0.5 were transferred to the target station for validationpurposes.

3. Results and discussion

For each set of runs for warm and cool seasons, observed flowfrom the USGS gages and SWAT simulated flow data of 28 sites

were used for training the ANN model. The station left out wasused for testing the developed model. Note that each set of runscorresponds to a different ANN model where one of the 29 siteswas tested. However, the model parameters of these models arelikely not that different from each other. This is because for anytwo runs there are always 27 common watersheds. Fig. 4 showsthe ENASH and RBIAS values for each test watershed for warm andcool seasons for daily flow (Q) and square root of daily flow (Sqrt(Q)). Out of the total 29 runs, two of them had ENASH values belowzero during cool season simulations. Those two are not shown onthe figures for clarity. Performance ratings for each set of run forwarm and cool seasons for daily flow (Q) also are given in Fig. 5.Overall, 62% of the runs for cool season and 83% of the runs forwarm season had ‘‘good” to ‘‘very good” performance ratings.The average ENASH and absolute RBIAS values for the warm seasonruns were 0.59 and 14% respectively and for the cool season runswere 0.55 and 9% respectively. For the square root of predicted

Table 1Watershed characteristics of selected USGS sites.

Station ID IM* (%) DF (%) EF (%) MF (%) PA (%) UG (%) Area (km2) Data period Number of data Streamflow (cm/km2) � 100**

2203603 46 4 5 0 0 16 6 2007–2010 1342 1.632203655 36 7 8 1 0 18 58 2002–2010 2949 1.712203700 34 7 4 1 2 18 27 2003–2010 2815 1.722205522 28 5 9 0 1 18 19 2003–2010 2822 1.822207120 23 10 10 0 1 16 420 2002–2010 3270 1.892207185 16 12 17 0 4 13 26 2002–2010 3270 1.772207220 21 12 12 0 2 15 552 2002–2010 2942 1.902207385 16 8 17 0 6 13 45 2002–2010 3270 1.722207400 16 6 13 1 11 13 21 2002–2010 3270 1.782208150 18 15 11 1 6 14 80 2002–2010 3270 1.762217274 14 20 5 0 7 11 3 2002–2010 3270 2.042218565 17 17 6 1 2 13 15 2002–2010 3270 1.872334480 13 24 9 0 2 11 24 2002–2010 3270 2.052334578 13 19 6 0 4 11 13 2002–2010 3270 1.692334885 18 21 8 1 5 14 122 2002–2010 3270 1.872335350 37 7 5 0 1 18 23 2002–2010 3270 2.122335870 20 8 13 1 1 15 80 2002–2010 3270 1.842336030 52 2 0 0 0 13 4 2002–2010 3238 2.152336120 30 6 9 0 0 18 90 2003–2010 2779 1.872336240 24 8 9 1 0 17 71 2003–2010 2788 1.902336300 31 6 8 1 0 18 225 2002–2010 3270 1.682336313 51 8 2 0 0 14 7 2005–2010 1908 1.332336360 26 8 10 1 0 17 69 2003–2010 2765 1.922336410 22 12 12 1 0 16 98 2002–2010 3005 1.682336517 45 2 2 0 0 16 20 2003–2007 1454 1.582336526 34 7 6 0 1 18 35 2002–2010 2935 1.662336644 21 7 18 0 0 15 10 2002–2010 2970 11.272336658 18 11 11 1 0 14 17 2003–2007 1463 1.182336728 18 16 16 1 0 14 88 2003–2007 1448 0.35

* IM = Imperviousness, DF: Deciduous Forest, EF: Evergreen Forest, MF: Mixed Forest, PA: Pasture, UG: Urban Grass.** Daily mean flow per unit area.

Fig. 3. Model development flowchart.

N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151 145

Q

Q

(a) Warm Season

(b) Cool Season

Sqrt (Q)

Sqrt (Q)

Fig. 4. ENASH and RBIAS values and performance ratings for each set of run conducted for predicting flow during (a) warm and (b) cool seasons with the coupled model. Graphson the right show the model performances for square root of flow and the graphs on the left show the model performances for flow. (Performance rating: VG: Very Good,G/VG: Good/Very Good, S: Satisfactory, UnS: Unsatisfactory.)

(a) Warm Season (b) Cool Season

Fig. 5. Performance ratings for each set of run conducted for predicting flow during (a) warm and (b) cool seasons by the coupled model.

146 N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151

N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151 147

versus observed daily flow, 62% of the runs for cool season and 72%of the runs for warm seasons had the ENASH values of 0.50 andabove. This shows that the developed models work well fordifferent range of flow values especially for normal and high flows.Considering the defined objective error function for the models,MSE, it is not expected that the developed models work well forthe low-flow condition. For low-flow analysis the ANN can betrained by setting the objective function as MSE of lnQ.

Comparison of the performance statistics from the SWATmodel, which were calibrated and validated using SWAT-CUP,with the SWAT–ANN coupled approach shows that the latterapproach has improved the prediction accuracy. Except forstation #2203603 where SWAT-CUP run could not be conducteddue to errors encountered, the calibration and validation resultsfor the 28 stations are given in Fig. 6. During the calibration,31% of the sites had ENASH values P0.50 with the performancerating of ‘‘good” to ‘‘very good”. Only the sites with ENASH P 0.50were allowed to serve as donor watersheds during the validationstage. During the validation stage of each watershed, the closeststation having ENASH P 0.50 was selected as its donor watershed.Then, the calibrated parameter ranges from the SWAT-CUPmodel were transferred from the donor watershed to the targetwatershed and SWAT-CUP was run. As shown in Fig. 6(b), 34%of validation runs had ‘‘good” to ‘‘very good” performance ratings.This reveals that the coupled model is more transferable thanSWAT model in this region and its prediction accuracy is alsohigher.

The log-scaled flow duration curves of observed versus pre-dicted streamflow with the coupled model are given in Fig. 7 foreach site. Also, Fig. 8 shows plots of coupled model performance(ENASH) versus % imperviousness, % forest cover and watershedarea. During the warm seasons, the test watersheds of models withhigher ENASH and lower RBIAS values had the percent impervious-ness of 13–52% and percent forest cover of 2–33%. Their areas ran-ged from 4 to 90 km2. For the cool seasons, the size of testwatersheds of models with better performances, ranged from 4to 69 km2 with 13% to 52% imperviousness and 2% to 25% forestcover. Developed models for the warm seasons performed betterthan those for the cool seasons (Fig 6). Since most of selected USGSwatersheds are urbanized, the ANN predictive models worked bet-ter for developed watersheds. In general, as the percent forest

(a) Calibration

Fig. 6. ENASH and RBIAS values for each set of run conducted for predicting flo

cover or the size of test watershed increased, the modelperformances gradually decreased during both cool and warm sea-sons and for Q and Sqrt (Q). Also, the test watershed with higherpercent imperviousness had a better model performance for theflow prediction during the cool seasons. This indicates that thedeveloped models work better in urbanized watersheds in thisregion with the size <200 km2 (Fig. 7). As the percent forest coverin test watersheds goes above 20%, the model performance ratingdrops. Also, models developed for test watersheds having 30% orhigher imperviousness predicted flow more accurately, especiallyduring the cool season. The models with highest prediction accu-racy for cool and warm seasons had the ENASH values of 0.82 and0.79 and RBIAS values of 6% and �8%, respectively. The test water-sheds relative to these models had the percent forest cover of 2–4%and percent imperviousness of 45–52% and their size ranged from4 to 20 km2. This reveals that the developed models can be appliedin this region for daily streamflow prediction in highly urbanizedwatersheds that have similar characteristics as the USGSwatersheds.

In this study, by coupling ANN and SWAT a hybrid approachwas introduced for predicting daily streamflow in unmonitoredwatersheds. Combining ANN and SWAT could enrich the modelingenvironment by excluding the calibration and sensitivity analysisto adjust the SWAT model parameters and by narrowing downthe number of inputs to ANN. SWAT outflow was the only inputto ANN and in each run a watershed was left out for testing themodel. The information and knowledge obtained from each modelwere coupled together and facilitated addressing the problem andpredicting the streamflow. Since in this approach, SWAT was notcalibrated, the proposed method also can help in parameter trans-ferability. Suppose that SWAT was calibrated and validated in oneof the watersheds. To apply this calibrated model to an ungaugednearby watershed the developed model parameters need to betransferred to the target watershed having similar characteristics(Wang and Kalin, 2011). If the two watersheds characteristics dif-fer, then transferring the model parameters would not be feasible.In that sense the developed SWAT–ANN coupling could be consid-ered as a regionalization approach to predict flow in ungaugedwatersheds because it uses all the information from nearby water-sheds having differing characteristics and comes up with a regionalmodel.

(b) Validation

w using the SWAT model during the (a) calibration and (b) validation.

(a) Warm Season

Fig. 7. Log scaled flow duration curves of observed versus predicted daily streamflow during (a) warm season, (b) cool season with the coupled model.

148 N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151

(b) Cool Season

Fig. 7 (continued)

N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151 149

(a) Warm SeasonQ Sqrt (Q)

(b) Cool SeasonQ Sqrt (Q)

Fig. 8. ENASH values of coupled flow predictive models during (a) warm seasons and (b) cool seasons versus percent imperviousness, percent forest and area of the testwatersheds. Rights graphs show the model performances for square root of flow and left graphs show the model performances for flow.

150 N. Noori, L. Kalin / Journal of Hydrology 533 (2016) 141–151

4. Summary and conclusions

This study investigated the ability and efficiency of ANN modelcoupled with SWAT for daily streamflow prediction in ungaugedwatersheds. In this approach, ANN served, in a sense, as an opti-mization tool to improve the simulated streamflow by SWAT.Using the leave-one-site-out jackknifing technique, the forecastingskill of models was tested. SWAT simulated baseflow and storm-flow were used as inputs to the ANN model and the developedmodels were evaluated using the metrics ENASH and RBIAS. In theproposed method, there was no need to try different combinationsof LULC, precipitation and other hydrological parameters related toflow as inputs. Finding the best input combinations to ANN playsan important role in building the optimal networks which wasnot needed in this study. Also, since SWAT calibration and sensitiv-ity analysis were skipped, the application of the developed coupledmodel to an ungauged nearby watershed is eased in terms of

parameter transferability. Developed models predicted daily flowmore accurately during the warm seasons than during the cool sea-sons. Also, the performance ratings of models decreased with theincreased percent forest cover or the size of test watershed. There-fore, these models are more reliable to be applied to a developedwatershed for streamflow prediction purposes in the study region.Since streamflow sampling for a large area is time consuming andexpensive, the SWAT–ANN coupling provides a regionalizedapproach to estimate flow over the area of interest using the infor-mation from nearby watersheds.

Acknowledgements

This project is funded by USDA Forest Service, National Urban &Community Forestry Council, United States, and Center for Envi-ronmental Studies at the Urban-Rural Interface, School of Forestryand Wildlife Sciences, Auburn University, United States.

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