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Hydrological Sciences Journal

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Simulating sub-daily hydrological process withSWAT: a review

Tássia Mattos Brighenti, Nadia Bernardi Bonumá, Raghavan Srinivasan &Pedro Luiz Borges Chaffe

To cite this article: Tássia Mattos Brighenti, Nadia Bernardi Bonumá, Raghavan Srinivasan &Pedro Luiz Borges Chaffe (2019) Simulating sub-daily hydrological process with SWAT: a review,Hydrological Sciences Journal, 64:12, 1415-1423, DOI: 10.1080/02626667.2019.1642477

To link to this article: https://doi.org/10.1080/02626667.2019.1642477

Accepted author version posted online: 12Jul 2019.Published online: 13 Aug 2019.

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Simulating sub-daily hydrological process with SWAT: a reviewTássia Mattos Brighenti a, Nadia Bernardi Bonumáb, Raghavan Srinivasan c and Pedro Luiz Borges Chaffe b

aGraduate Program in Environmental Engineering, Federal University of Santa Catarina, Florianopolis, Brazil; bDepartment of Sanitary andEnvironmental Engineering, Federal University of Santa Catarina, Florianopolis, Brazil; cSpatial Sciences Laboratory, Texas AandM University,College Station, Texas, USA

ABSTRACTThe Soil and Water Assessment Tool (SWAT) is a watershed-scale hydrologic model that integrateswater quantity and quality modules. Despite the large amount of knowledge on the SWAT model,specific understanding of sub-daily applications remains limited. In this review, we identify the short-comings and possible ways forward in simulating sub-daily processes with the model. A literaturereview was conducted, along with a participatory method based on a questionnaire. We reviewed 28scientific articles and categorized them into: (i) model development, (ii) streamflow methods compar-ison, (iii) water quality, and (iv) other applications. We found that using sub-daily data improveshydrograph peak simulation, while for medium flows use of daily data was better. From all the reviewedstudies, a 1-hour time step was the most suitable time scale for the sub-daily model application. Theparticipatory questionnaire confirmed the hypothesis that the main challenge for using the sub-dailyroutine was the lack of high-resolution data.

ARTICLE HISTORYReceived 14 January 2019Accepted 20 June 2019

EDITORA. Castellarin

ASSOCIATE EDITORA. Petroselli

KEYWORDShydrological modeling;SWAT; sub-daily simulation;time step

Introduction

The Soil andWater Assessment Tool (SWAT;Arnold et al. 1998)is a physically based, semi-distributed hydrological model forriver basin-scale application. SWAT is currently one of the mostapplied hydrological models in the world (Van Griensven et al.2012, Krysanova and Srinivasan 2015). This popularitymay havebeen enhanced by the code being freely available and readilyapplicable through GIS platforms (Van Griensven et al. 2012).The SWAT model has five main official versions, SWAT2000,SWAT2005, SWAT2009, SWAT2012, and SWAT+. A detaileddescription of the model history can be found in Arnold et al.(2012); SWAT versions of 2005, 2009, and SWAT+ werereviewed by Gassman et al. (2007), Tuppad et al. (2011), andBieger et al. (2017), respectively.

The basic components of the model include weather, hydrol-ogy, soil temperature, plant growth, erosion/sedimentation,nutrients, pesticides and land management. The SWAT modelis spatially discretized into sub-basins which, in turn, are sub-divided into hydrological response units (HRUs); each HRU iscomposed on unique information about slope, soil type and landuse. The HRU is the fundamental unit where the water balanceis defined. The processes are calculated in each sub-basin andthen routed through the channels (Arnold et al. 2012). In addi-tion, other models/extensions with many similar componentshave been developed based on SWAT, such as SWAT-MODFLOW (Bailey et al. 2016), SWAT-VSA (Easton et al.2008), SWIM (Krysanova et al. 1998), SWAT-LAG(Idhayachandhiran et al. 2019), and ESWAT (Van Griensvenand Bauwens 2001). The ESWAT (Extended version of Soil andWater Assessment Tool) implements the Water QualitySimulation Model (QUAL2E) and an auto-calibration module

in the original model (Van Griensven and Bauwens 2001). Laterthe RWQM1 was also integrated in the water quality module(Vandenberghe et al. 2006).

The SWATmodel has been widely used and there are severalpapers that review its applications. Considerations about thegeneral model applications can be found in Arnold et al.(2012), Gassman et al. (2007, 2014), Krysanova and Arnold(2008), and Krysanova and White (2015). Bonumá et al.(2015) and de Bressiani et al. (2015) describe SWAT modelapplications in Brazil, including the challenges and prospects.VanGriensven et al. (2012) carried out a critical review ofmodelapplications in the Upper Nile Basin. Specific themes such asclimate changes and land use were discussed by Krysanova andSrinivasan (2015). Among the gaps reported in the reviews,there was the use of the Curve-Number method. The alternativein that case was to offer a second option to calculate the infiltra-tion (i.e. the Green-Ampt Mein-Larson), which in turn requiresa higher temporal data resolution.

The temporal structure of precipitation will affect themodeling of surface and groundwater of a river basin.Therefore, it would be preferred that precipitation patternsare well represented at the catchment scale in any study (VanGriensven and Bauwens 2003, Yang et al. 2016a; Bauwe et al.2017, Brighenti et al. 2019). The improvement of the modelwith the use of data of high temporal resolution has alreadybeen discussed (e.g. Ramos and Martinez-Cásasnovas 2015,Addis et al. 2016, Dutta and Sen 2017) and SWAT has bothlong-term continuous and short-term event-based simulationcapabilities (Jeong et al. 2010). However, the usage, advan-tages and limitations of sub-daily data in the SWAT modelhave not been fully explored.

CONTACT Tássia Mattos Brighenti tassiabrighenti@hotmail.com Department of Sanitary and Environmental Engineering, PO Box 476, 88040900,Florianopolis, Santa Catarina, Brazil

HYDROLOGICAL SCIENCES JOURNAL2019, VOL. 64, NO. 12, 1415–1423https://doi.org/10.1080/02626667.2019.1642477

© 2019 IAHS

Given the past SWAT model reviews, the listed gaps, andthe need for accurate simulations of the hydrological processat the watershed scale, the key objective of this review is toidentify shortcomings and possible ways forward in simulat-ing sub-daily processes with SWAT. We conducteda literature review along with a questionnaire based ona participatory method. We describe the main characteristicsof each paper and synthesize its most relevant conclusions. Inaddition, by incorporating in this review input informationreceived directly from model users, we expect to clarify whatis hindering the sub-daily application of SWAT and providea resource for model users to guide their applications.

Literature review and questionnaire application

A comprehensive literature search was carried out in order toidentify the scientific papers that use sub-daily precipitationdata as input in the SWAT model. No date restriction was set,and the articles search took place between May and Decemberof 2018. Finally, 28 articles were selected (Table 1). Alongwith the systematic review, we present the outcomes ofa participatory study – a questionnaire that explored the useof sub-daily data. We wanted a direct response from modelusers in order to test several hypotheses about the use of dailyand sub-daily data that arose from the literature review. Forthe target public decision, we selected Brazilian SWAT users.We sent out 29 e-mails to users with a comprehensivedescription of the questionnaire objectives. We took advan-tage of the fact that during the time when the article wasbeing written, there was the XXII Brazilian Symposium onWater Resources (XXII SBRH), and the symposium provided

the opportunity for personal interaction with the researchersselected and ensured that the questionnaire would beanswered in a timely fashion.

The questionnaire included general questions about themodel, as well as specific questions regarding the use of sub-daily data. The main questions were: How long have you beena SWAT model user? Where does the data you use comefrom? What are the basin sizes that you usually study? Howlong is the series of observed data that you use? What is thetime step of the observed data? What was the time step thatyou used with the SWAT model (i.e. sub-daily, daily,monthly, or annual)? At what time scale is the data usuallyanalyzed? Which processes do you usually analyze with themodel? Do you know about the sub-daily simulation module?Have you used sub-daily data for model simulation? Can youindicate a justification for not using sub-daily data for modelsimulations? How would you describe your experience withsub-daily data? What processes did you analyze?

Sub-daily model description

The largest difference between daily and sub-daily simulation inSWAT occurs in the infiltration routine. The Green-AmptMein-Larson (GAML) method is used for sub-daily rainfall,while the Curve Number (CN, USDA Soil ConservationService, 1972) is used for daily rainfall. The infiltration proce-dure is done in each HRU at every time interval (Δt ≥ 1 min).The GAML method calculates the infiltration as a function ofthe wetting front matric potential and effective hydraulic con-ductivity. Water that does not infiltrate becomes surface runoffin each time step and is added to the excess rainfall of the

Table 1. Summary of studies included in this review, with the model used, country, basin area, and the evaluated process (output variable).

Reference Model Country of study Climatic zone Basin area (km2) Process

King et al. (1999) SWAT USA Subtropical 21.30 streamflowVan Griensven and Bauwens (2001) ESWAT Belgium Temperate 1384.00 streamflow/nutrientVandenberghe et al. (2001) ESWAT Belgium Temperate 1384.00 nutrientVandenberghe et al. (2002) ESWAT Belgium Temperate 1384.00 nutrientVan Griensven and Bauwens (2003) ESWAT Belgium Temperate 1384.00 nutrientDi Luzio and Arnold (2004) SWAT USA Subtropical 1233.00 streamflowVan Griensven and Bauwens (2005) ESWAT USA and Belgium Subtropical/Temperate 2600.00

1384.00nutrient

Vandenberghe et al. (2006) ESWAT Belgium Temperate 1384.00 nutrientKannan et al. (2007) SWAT United Kingdom Temperate 1.42 streamflowDebele et al. (2009) ESWAT USA Subtropical 504.80

1663.00rainfall/nutrient

Jeong et al. (2010) SWAT USA Subtropical 1.90 streamflowJeong et al. (2011) SWAT USA Subtropical 1.90 sedimentHan et al. (2012) SWAT USA Temperate 193.09 streamflowMaharjan et al. (2013) SWAT South Korea Subtropical 0.01 streamflowFicklin and Zhang (2013) SWAT USA Temperate 14 983.00 streamflowAli and Bruen (2014) SWAT

(NCM model)Ireland Temperate 88.00 streamflow/nutrient

Kannan et al. (2014) SWAT USA Subtropical 1.90 retention irrigation systemCheng et al. (2016) SWAT(WB) China Temperate 86.70 streamflowBauwe et al. (2016) SWAT Germany Temperate 1.88 streamflowYang et al. (2016a) SWAT China Transition* 5803.00 streamflowYang et al. (2016b) SWAT China Transition* 5803.00 nutrientGolmohammadi et al. (2017) SWAT Canada Temperate 10.50 streamflowBauwe et al. (2017) SWAT Germany Temperate 1.81 streamflowBoithias et al. (2017) SWAT France Temperate 810.00 streamflowCampbell et al. (2018) SWAT USA Temperate 520.87 streamflowKaffas et al. (2018) SWAT Greece Subtropical 840.00 streamflow/sedimentYang et al. (2018) SWAT China Transition* 5803.00 nutrientYu et al. (2018) SWAT-EVENT China Subtropical 30 630.00 streamflow

*Transition zone between the northern subtropical and temperate climate.

1416 T. M. BRIGHENTI ET AL.

previous time step. The values estimated at the end of thesimulated day are kept in temporary arrays for continuoussimulation past midnight. There are three types of iterationloops in time, a sub-daily, a daily, and an annual one. There isone iteration loop for spatial discretization (HRUs). Surfacerunoff, channel flow and impoundment storage are routed atthe sub-daily time interval, but baseflow and evapotranspirationare calculated at a daily scale and are equally distributed for eachtime step (Jeong et al. 2010).

Among the model sub-daily applications, the ESWAT (VanGriensven and Bauwens 2001) incorporates a new water qualitymodule. Water, nutrients, pesticides and river quality processescan be simulated at an hourly time step. Simulation of erosionprocesses is performed at a user-defined fraction of an hour (VanGriensven and Bauwens 2001). The extension includes a newinfiltration module, a new river routing module, and a riverwater quality module (Van Griensven and Bauwens 2003).ESWAT also has a multi-objective auto-calibration module thatcan be applied for multi-site and multi-variable calibration.Rainfall excess is calculated by a potential infiltration rate thatvaries linearly from the maximum infiltration rate to the satura-tion infiltration rate, as a function of soil water content (VanGriensven and Bauwens 2005).

Results and discussions

We found 17 studies that used SWAT and seven that useESWAT (Table 1). The basin areas range from 0.01 to 14983 km2 and the processes that were analyzed include stream-flow, suspended sediments, and nutrient budget. All studycatchments are located in the Northern Hemisphere (Fig. 1);mainly in the USA, Belgium, and China. Daily temporal-scalestudies are more evenly distributed across the globe (Fig. 1);however, those daily applications are not discussed further inthis paper.

There was a significant increase in the use of sub-daily rou-tines in the last three years (2016, 2017, and 2018). Figure 2(a)shows the journals in which the articles were published, in orderof journal impact factor, and howmany articles there are in eachjournal. After 2009 the application of ESWAT was no longerreported (Fig. 2(b)), this can be associated with most of themodifications made in ESWAT were subsequently incorporatedin the SWAT2012 version.

Of all the papers, only 11 carried out all the analysis that isusually expected in a modeling study, i.e. sensitivity analysis,model calibration and model validation (Table 2). Five studiesperformed only calibration and three performed sensitivityanalysis and calibration. Four of the studies did not applysensitivity analysis, calibration or validation (i.e. King et al.1999, Han et al. 2012, Ficklin and Zhang 2013, Kannan et al.2014). Since these studies were aimed at tool developmentand method comparison, the lack of calibration or validationwas not necessarily a methodological problem as the authorsargue that calibration could mask the results obtained by theoriginal model formulation. In ESWAT applications, the mostused algorithm for parameter optimization was the ShuffledComplex Evolution (SCE), which is a tool available on theplatform itself. For current SWAT applications, theSequential Uncertainty Fitting (SUFI2) is the tool most usedfor sensitivity analysis and model calibration.

The most sensitive parameters in SWAT were found to beCN2 (Curve Number for soil moisture condition II),GWQMN (threshold depth of water in the shallow aquiferrequired for return flow to occur), ALPHA_BF (baseflowalpha factor), GW_DELAY (groundwater delay), ESCO (soilevaporation compensation factor), SURLAG (surface runofflag coefficient), SOL_K (saturated hydraulic conductivity ofthe soil layer), and GW_REVAP (groundwater “revap” coeffi-cient). The CN2 parameter was also sensitive for the GAMLmethod, since it controls the effective hydraulic conductivityin the model.

The evaluation statistics provide important guidance forjudging model performance. Although we recognize the limita-tions of using a fixed range of values of a specific objectivefunction for judging performance, we base our analysis on thestandardized guidelines and ranges of values proposed byMoriasi et al. (2007). Moriasi et al. (2007) proposed thatmodel simulation can be judged as satisfactory if the Nash-Sutcliffe efficiency criterion (NS) > 0.50 and the ratio of theroot mean square (RSR) < 0.70, and if the percent bias (Pbias) is± 25% for streamflow, ± 55% for sediment, and ± 70% for nitrateand phosphorus for measured data of typical uncertainty. Themost commonly analyzed evaluation metric is the NS, followedby Pbias, determination coefficient (r2), RSR, persistence modelefficiency (PME), root mean square criterion (RMS), meansquare error (MSE), and volumetric efficiency (VE).

Figure 1. SWAT sub-daily and daily applications around the world. Shading indicates the number of studies per country that use sub-daily data, and hatched linesindicate countries with locations studied by Gassman et al. (2007, 2014).

HYDROLOGICAL SCIENCES JOURNAL 1417

The NS values for simulation were mostly satisfactory in thestudies using both daily and sub-daily scales (Fig. 3(a) and (b)).However, when analyzing the quartile range, sub-daily datasetgenerally provide more satisfactory results. Figure 3(a) indicatesthat the model efficiency (NS) is not related to the basin area,even though King et al. (1999) suggested that when the drainagearea increases, streamflow peaks tend to smooth out and the useof the GAML approach becomes ineffective.

The results of the studies selected for this review werecategorized into: model development, streamflow comparison,

water quality, and other applications (Fig. 4) by analyzing themain approach of each article; most of the studies came undereither model development or other applications.

Model development

The ESWATmodel was first developed by combining QUAL2Eand an auto-calibration module for the simulation of water andnutrients at an hourly time step with an erosion module thatcould be run at a fraction of an hour. The modification showed

Table 2. Overview of papers describing the time step of rain as input (column 2). SA: sensitivity analysis; Cal.: calibration; Val.: validation; SCE: shuffled complexevolution; SST: split-sample test; n/a: not applicable; n/d: not described.

Reference Rainfall (min) SA Cal. Val. Algorithm Cal. method Objective function Time step calibration

King et al. (1999) 15 no no no n/a n/a 1 n/aVan Griensven and Bauwens (2001) 60 no yes no SCE n/a 4 hourlyVandenberghe et al. (2001) 60 yes yes no SCE n/a 1 hourlyVandenberghe et al. (2002) 60 no yes no SCE Method proposition 1(17)* hourlyVan Griensven and Bauwens (2003) 10 yes yes no SCE-UA n/a 1 (13)* dailyDi Luzio and Arnold (2004) 60 no yes no SCE Event by event 2 hourlyVan Griensven and Bauwens (2005) 60 no yes no SCE-UA SST 1 (10)* dailyVandenberghe et al. (2006) 60 yes yes no SCE-UA n/a 2 weeklyKannan et al. (2007) 30 yes yes yes Autorun SST 4 dailyDebele et al. (2009) 60 no yes no n/a n/a 2 n/aJeong et al. (2010) 15/60 yes yes yes n/d SST 4 sub-daily/dailyJeong et al. (2011) 15 yes yes yes n/d SST 3 sub-daily/dailyHan et al. (2012) 10 no no no n/a n/a n/a n/aMaharjan et al. (2013) 15/120/

360/720yes yes yes n/d SST/Event by event 2 hourly

Ficklin and Zhang (2013) 60 no no no n/a n/a 2 n/aAli and Bruen (2014) 60 yes yes yes SCE SST 2 hourly/dailyKannan et al. (2014) 15 no no no n/a n/a n/a n/aCheng et al. (2016) n/d yes yes yes DREAM SST 2 dailyBauwe et al. (2016) 15 yes yes yes SUFI2 SST 3 daily/monthlyYang et al. (2016a) 15/120/

360/720yes yes yes SUFI2 SST 2 hourly/daily

Yang et al. (2016b) 60 no yes yes SUFI2 SST 2 hourly/daily/monthly

Golmohammadi et al. (2017) 60 yes yes yes n/d n/d 3 dailyBauwe et al. (2017) 5/15/30/60 yes yes yes SUFI2 SST 3 dailyBoithias et al. (2017) 60 yes yes yes SUFI2 SST 3 hourly/dailyCampbell et al. (2018) 5 yes yes yes SUFI2 SST 1 5 min.Kaffas et al. (2018) 30 no yes yes n/d multi-site n/d hourlyYang et al. (2018) 60 no yes yes SUFI2 SST 2 hourly/daily/

monthlyYu et al. (2018) 120 yes yes yes SCE SST/Event by event 1 hourly/daily

*The quantity of objective functions used; the three cases in parentheses indicate multi-variable techniques, meaning that for each output evaluated, one objectivefunction was used in calibration. While for the final result a global optimization criterion method was applied (e.g Van Griensven and Bauwens 2003), all of theobjective functions used in calibration were translated to the Nash-Sutcliffe Efficiency (NS) in the final model evaluation. The time step used in calibration is thesame time step to calculate the objective functions.

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Figure 2. (a) Quantity of studies per journals sorted by impact factor; (b) Year and model used in each article. Key: 1: Transactions of the ASABE; 2: Lake and ReservoirManagement; 3: Water Science and Technology; 4: Journal of Hydrologic Engineering of ASCE; 5: Frontiers of Environmental Science and Engineering; 6: Journal of theAmerican Water Resources Association; 7: Environmental Technology; 8: Water; 9: Hydrological Science Journal; 10: Water Resources Management; 11: HydrologicalProcesses; 12: Stochastic Environmental Research and Risk Assessment; 13: Catena; 14: Journal of Hydrology; 15: Hydrology and Earth System Sciences; 16: WaterResources Research; 17: Science of the Total Environment; 18: Water Resources.

1418 T. M. BRIGHENTI ET AL.

a good fit for several water quality variables and also for thestreamflow (Van Griensven and Bauwens 2001). A secondmod-ification consists of a new time convolution and evapotranspira-tion module (Van Griensven and Bauwens 2005), and a thirdvariation incorporated hourly evapotranspiration and an over-land flow routine module (Debele et al. 2009).

Anauto-calibrationmodule using ShuffledComplexEvolutionwas incorporated into ESWAT (Van Griensven and Bauwens2001), and a methodology was developed to disaggregate dailyrainfall into hourly rainfall (Debele et al. 2009). Confirmation ofthe improvementwasmadebyanapplicationof themodel to somebasins. Although water quality data were sparsely available, thenewmodel could simulate it better than previous versions (Debeleet al. 2009), with satisfactory simulations in both calibration andvalidation (Van Griensven and Bauwens 2005).

Modifications related to surface runoff (Jeong et al. 2010),sediment yield and erosion (Jeong et al. 2011), and irrigationsystems (Kannan et al. 2014) were also made to the originalSWAT model without necessarily introducing a new extension(e.g. the changes that gave rise to ESWAT, SWAT-MODFLOW,etc.). The newer versions of SWAT can be used with sub-hourlyrainfall data, making possible the application of the GAMLmethod. Jeong et al. (2010) found that the SWAT model withthe GAML performed better with the CNmethod in themodifiednewer versions. Sensitivity analysis has shown that channel flowparameters are more sensitive in sub-daily simulations with a 15-min time step, while baseflow parameters are more important indaily simulation. When the analysis was made using the flow

duration curve resulting from model simulation, the results forhigh flows were better for sub-daily than daily time steps (Jeonget al. 2010). For low flows, no improvement was reported. Thiswas expected, since low flows are most influenced by baseflow. Inrelation to sediment yield, a model running with sub-daily dataperformed aswell as or better than onewith daily data (Jeong et al.2011).

The last two developments have two different focuses,a hybrid of SWAT and HSPF (Hydrological SimulationProgram Fortran) to predict flow and total phosphorus (Aliand Bruen 2014) and a method to perform event-based floodsimulation (SWAT-EVENT) (Yu et al. 2018). The hybrid wasreferred to as the New Combined Model (NCM) and wasevaluated to ensure it was capable of predicting flow and totalphosphorus load at the same level or better than SWAT andHSPF when they are used separately. The authors consideredthe amount of data was insufficient and recommendeda higher temporal resolution of phosphorus data (Ali andBruen 2014). In the SWAT-EVENT model, simulation offlood events was improved by separating the entire loopstructure of the SWAT2005 sub-daily model in order to per-form the simulation of individual floods rather than to simu-late them in a continuous way.

Streamflow comparison

In SWAT model applications, the CN method is used moreoften than the GAML approach. For Han et al. (2012), this is

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Figure 4. Number of papers according to the analyzed categories.

HYDROLOGICAL SCIENCES JOURNAL 1419

associated with the difficulty of obtaining sub-daily precipita-tion data, as well as some considerations in the literature thatquestion whether GAML is better than CN. The use of theSWAT model with sub-daily rainfall data was first reportedby King et al. (1999), who compared the two infiltrationmodules available on the model platform, GAML and CN –for surface runoff calculation. The GAML method performedbetter than CN on the daily output analysis, but the perfor-mance was worse on the monthly scale, indicating limitationsin representing seasonal variations.

Jeong et al. (2010) found an overall improvement withGAML; however, the efficiency of using sub-daily data sig-nificantly depended on the basin size and the part of thehydrograph studied. Three pointed to better results for CN(Kannan et al. 2007, Cheng et al. 2016; Bauwe et al. 2016),found that the GAML method performed better (Yang et al.2016a), and three studies showed that the improvementdepends on the output that is analyzed (e.g. time scale:daily, monthly or annually; hydrograph division: high, med-ium or low flows) (King et al. 1999, Han et al. 2012, Ficklinand Zhang 2013).

A model framework with the different equations availablein the SWAT model, was made in order to find the bestcombination of them; the equations are related to the calcula-tion of surface runoff, evapotranspiration and tile drain. Thecombination Hargreaves (evapotranspiration)/CN (runoff)had better results than Penman-Monteith/CN, for example(Kannan et al. 2007). For the combination of Hooghoudt andKirkham tile drain equations, the results showed that the CNmethod was slightly better than the GAML and the two tiledrain equations are a valid choice (Bauwe et al. 2016). Chenget al. (2016) compared: (1) two variations of the CN method –one that considers soil water storage (CN-Soil) and anotherthat considers evapotranspiration (CN-ET); (2) the GAMLmethod; and (3) a water balance model with variable sourcearea (WB-VSA). They concluded that the WB-VSA modelmore accurately reflects the spatial variations of the flowgeneration, topography and soil properties.

In the study where GAML improved the results, Yanget al. (2016a) used daily and sub-daily data of 28 rainfallstations. The results showed improvement in the simulationsusing hourly data, mainly due to improvement in the hydro-graph peaks. It was also found that the temporal resolution ofrainfall inputs have a significant impact on daily streamfloweven when large basins are simulated.

In some cases there is no conclusion on which method isbetter, i.e. King et al. (1999), Han et al. (2012) and Ficklin andZhang (2013). Han et al. (2012) used sub-daily data ina simple one-day test to predict the potential for improve-ment in GAML surface runoff relative to CN and found thatboth methods have limitations in reflecting the soil moistureupdates in the runoff routine. The improvement dependsdirectly on the portion of the hydrograph – the GAML hadbetter results for peak flows and the CN for medium flows(Ficklin and Zhang 2013). When the time scale was consid-ered, GAML had better results for daily and annual scales andCN did better with monthly ones (King et al. 1999).

Water quality

There is agreement among most of the reviewed papers thata daily time step is not suitable for the modeling of river waterquality processes that change on a sub-daily time step(Vandenberghe et al. 2001, 2002, 2006, Yang et al. 2016b).Pollutant/sediment transport and transformation processes arehighly dependent on the temporal resolutions of precipitation(Yang et al. 2016b, Brighenti et al. 2019). The analyzed waterquality processes included dissolved oxygen (Vandenbergheet al. 2001, 2002), algae concentration (Vandenberghe et al.2001, 2006), ammonia, nitrate, phosphate (Vandenberghe et al.2002), and nitrogen (Yang et al. 2016b). Model parameter sen-sitivity was shown to change depending on the equations used tosimulate water quality. While the most sensitive parameters ina simulation with QUAL2E were related to algae processes,those when using RWQM1 were related to sediment processes(Vandenberghe et al. 2006). Vandenberghe et al. (2002) alsoshowed that the hourly time step is the most appropriate to beused in a modeling exercise for water quality sampling.

Yang et al. (2016b) found that when the model is run withhourly rainfall data as input, the representation of the sub-surface processes, where nitrogen removal mostly takes place,improves considerably. This samemodel calibrationwas used byYang et al. (2018) to evaluate the impacts of climate change andwater pollution control measures on the total nitrogen (TN)loads. After the future rainfall disaggregation into an hourlytime step, their results showed that climate change under thetested emissions scenarios were much more likely to increaseboth the average and extreme rainfall. Furthermore, Yang et al.(2018) reinforce the importance of using sub-daily data insimulating the nitrogen pollution process.

Other applications

Several different sub-daily applications were explored, includ-ing the implementation of global optimization methods (VanGriensven and Bauwens 2003), modeling of rainfall-runoffevents (Di Luzio and Arnold 2004, Maharjan et al. 2013,Campbell et al. 2018), the influence of sub-daily precipitationtime steps (Bauwe et al. 2017, Boithias et al. 2017), time-varying streamflow-contributing area estimation(Golmohammadi et al. 2017), and modeling the hydromor-phological process (Kaffas et al. 2018). Three works focusedtheir efforts on finding a better way to calibrate the model(Van Griensven and Bauwens 2003, Di Luzio and Arnold2004, Maharjan et al. 2013). The global optimization methodproposed by Van Griensven and Bauwens (2003) was used tooptimize water quantity and quality at the same time. Theresults show a good fit for the water quality and the waterflow variables.

Rainfall data of high spatial and temporal resolution pro-vides further potential advances for distributed models (DiLuzio and Arnold 2004, Bauwe et al. 2017). The simulationenhancement of storm water dynamics and associated pollu-tant phases appears to be a promising direction of develop-ment. Starting from this premise, Di Luzio and Arnold (2004)

1420 T. M. BRIGHENTI ET AL.

calibrated the SWAT model for extreme rainfall events, withdata obtained from the Next Generation Weather Radar(NEXRAD). Their results show a significant improvement inusing the SWAT model with sub-daily data. When forcedwith NEXRAD, the model was able to reproduce the runoffvolumes for storm events of various sizes and duration.

Storm water dynamics such as flash floods were analyzedby Boithias et al. (2017). Their study compared SWAT ina continuous mode with the MARINE model, an event-basedfully distributed model. SWAT and MARINE performedequally satisfactorily for floods, with SWAT being slightlybetter at simulating the total streamflow for some events.There was no benefit in decreasing the size of the minimumdrainage area (e.g. from 15 km2 to 1 km2) when delineatingsub-basins in SWAT (Boithias et al. 2017). Campbell et al.(2018) used a 5-min rainfall and outflow to assess howchanges in land use might affect flood events; they state thatthe use of sub-daily data is the safest way to achieve the studygoals. The scenarios with greater urban land use saw anincrease in the mean volume of water, but no full agreementwas found in relation to an increase in flood peaks. Applinga rainfall disaggregation model, Kaffas et al. (2018) used an30-min rainfall to compare the simulation of hydromorpho-logical process between SWAT and the CompositeMathematical Model (CMM). In an attempt to fill the gapin the low availability of sub-daily monitoring data, since theyassume that the processes must be simulated on a sub-dailytime scale and for a long period of time in order to have goodmodel parameterization. In the end the results indicates thatboth models seem to follow a similar pattern.

Maharjan et al. (2013) simulated 18 precipitation events,nine for calibration and nine for validation and evaluated thedifferent temporal resolutions using rainfall gauge data (1 h,2 h, 6 h, and 12 h); they concluded that the model resultsdegrade with decreasing temporal resolution. Bauwe et al.(2017) also tested time-varying precipitation, but in theircase the temporal resolutions of precipitation were all sub-hourly to one hour (i.e. 5 min, 15 min, 30 min, and 60 min).The measured hydrograph was well simulated for all timesteps, and the authors concluded (different from Maharjanet al. 2013) that there is no improvement by increasing pre-cipitation resolution; there is no need to use a precipitationtime step of less than 60 min, but results might depended oncatchment characteristics.

Questionnaire analysis

The participatory approach of sending out a questionnaireenabled us to learn directly from modelers about some of theissues in sub-daily model use that were identified in theliterature review, increasing the credibility of the conclusions.The response rate was 62% (18 model users). For generalaspects, most modelers had worked with the model overa period of 1–5 years, with a data series of 10 and 30 years,at a daily time step. The basin areas studied vary from lessthan 10 km2 to more than 1000 km2. The basin data aremainly obtained from their own field work, and national,state, or international agencies. The most analyzed processis streamflow, with daily and monthly time steps as the mostcommonly-used (Fig. 5).

In relation to the use of sub-daily data, it was interesting tofind that not all model users (35%) knew that the model canperform a sub-daily simulation. Among those who used thistype of data, the most common time step was 60 min andstreamflow was the only process that was analyzed, with theexperiences reported ranging from excellent to bad (Fig. 6(a)).All sub-daily simulations were made for basin areas less than100 km2. The hypothesis raised by the scientific review that thelack of data was the main problem for sub-daily simulationcould be validated by the questionnaire responses (Fig. 6(b)).

The questionnaire also left an open space for commentsfrom users. One user said that perhaps it is not necessary touse sub-daily data because the basin of interest was largerthan 1000 km2 and the analyzed output was at the annualscale. Two other users reported unfamiliarity with the sub-daily routine, explaining that SWAT products are very inter-esting to work with, but the literature on how to use parts ofthe model is still limited.

Conclusions

Despite the vast amount of literature published on SWAT appli-cations in recent decades, specific understanding of its sub-dailyapplication remains limited. By combining a literature review(28 papers) with a questionnaire for SWAT users, we identifiedsome shortcomings and possible ways forward in simulatingsub-daily processes. We did not find any consensus on whichSWAT application yields more robust results. However, someimportant conclusions were made:

(c)(b)(a)

0 2 4 6 8 10 12 14

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0 2 4 6 8 10 12 14

Sub-daily

Daily

Monthly

Yearly

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0 2 4 6 8 10 12 14

Sub-daily

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Monthly

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Figure 5. Answers to the questionnaire. (a) What is the time step of the series used? (b) With what time step time is the simulation made? and (c) With what timestep do you usually analyze the data?.

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(1) use of the GAML method improves the results, mainlywith improved hydrograph peaks (e.g. Jeong et al.2010, Ficklin and Zhang 2013, Yang et al. 2016a);

(2) use of CN gives good performance for medium flows(e.g. Kannan et al. 2007, Jeong et al. 2010);

(3) sub-daily data significantly improves the suspendedsediment and nutrients modeling (e.g. Van Griensvenand Bauwens 2005, Debele et al. 2009, Jeong et al.2011, Yang et al. 2016b);

(4) overall, exported flood volume was slightly bettersimulated with the sub-daily modules (e.g. Di Luzioand Arnold 2004, Boithias et al. 2017);

(5) no relationship was found between the watershed areaand the simulation efficiency; and

(6) the combined results from Maharjan et al. (2013) andBauwe et al. (2017) indicate that the best time scale forsub-daily SWAT is the 1-h interval.

The questionnaire confirmed a hypothesis raised duringthe literature review: that the absence of high-resolution long-term data, mainly for large basins, is the main problem forsub-daily simulation. In some cases, it was expressed that thelimited literature available on sub-daily SWAT applicationhinders the use of this tool. Despite the lack of data andlimited literature, many applications had satisfactory simula-tion results (NS ≥ 0.5). In order to improve future research,we need to emphasize the great importance of knowledge thatcomes from monitoring at high resolution. We need to lookat ways to improve modeling in areas with relatively low dataavailability (e.g. developing countries), as we believe thatencouraging long-term monitoring programs will contributeto better model representation of hydrological processes.

Acknowledgements

The authors would like to thank the National Council for Scientific andTechnologicalDevelopment for a PhDscholarship toTássiaMattosBrighenti.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by the Conselho Nacional de DesenvolvimentoCientífico e Tecnológico [203924/2018-4].

ORCID

Tássia Mattos Brighenti http://orcid.org/0000-0001-7430-6131Raghavan Srinivasan http://orcid.org/0000-0001-8375-6038Pedro Luiz Borges Chaffe http://orcid.org/0000-0002-9918-7586

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