Hydrological modelling of a small catchment using SWAT2000 ensuring correct flow partitioning for contaminant modelling

Hydrological modelling of a small catchmentusing SWAT-2000 – Ensuring correct flow partitioning for contaminant modelling a Blackland Research and Extension Centre, Texas A&M University,

Hydrological modelling of a small catchment

using SWAT-2000 – Ensuring correct flow

partitioning for contaminant modelling

a

Blackland Research and Extension Centre, Texas A&M University, 720, East Blackland Road, Temple, TX 76502, USA b

Institute of Water and Environment, Cranfield University, Silsoe, MK45 4DT Bedfordshire, UK

c

Department of Earth Sciences, University of Durham, DH1 3LE County Durham, UK

d

Safety and Environmental Assurance Centre, Unilever Colworth Laboratory, Sharnbrook, MK44 1LQ Bedfordshire, UK

Received 14 November 2005; received in revised form 23 September 2006; accepted 28 September 2006

KEYWORDS

SWAT;

Hydrological modelling;

Colworth;

Small catchment;

Flow partitioning;

Curve number;

Crop growth

the outlet of the 142 ha Colworth catchment (Bedfordshire, UK) This catchment has been monitored since October 1999 The soil type consists of clay loam soil over stony calcar-eous clay and a rotation of wheat, oil seed rape, grass, beans and peas is grown Much of the catchment is tile drained Acceptable performance in hydrological modelling, along with correct simulation of the processes driving the water balance were essential first requirements for predicting contaminant transport Initial results from SWAT-2000 identi-fied some necessary modifications in the model source code for correct simulation of pro-cesses driving water balance After modification of the code, hydrological simulation, crop growth and evapotranspiration (ET) patterns were realistic when compared with empirical data Acceptable model performance (based on a number of error measures) was obtained in final model runs, with reasonable runoff partitioning into overland flow, tile drainage and base flow

ª 2006 Elsevier B.V All rights reserved

Introduction

Diffuse-source pollution of the aquatic environment has

re-ceived increased attention in recent years The impacts of

diffuse-source pollutants, such as pesticides, on stream ecol-ogy are of considerable interest in the context of new legis-lation in Europe, particularly, the Water Framework Directive (WFD:Chave, 2001) The control of such pollutants

at source (e.g via efficient land management practices) is often seen as the optimal solution to potential problems However, conducting field experiments to better-understand diffuse-source pollution and design appropriate management

0022-1694/$ - see front matter ª2006 Elsevier B.V All rights reserved

doi:10.1016/j.jhydrol.2006.09.030

* Corresponding author Tel.: +1 254 774 6122; fax: +1 254 774

6001.

E-mail address: kannan@brc.tamus.edu (N Kannan).

a v a i l a b l e a t w w w s c i e n c e d i r e c t c o m

j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / j h y d r o l

solutions can be prohibitively expensive There is, therefore,

a need for modelling tools to predict pesticide loss under

varying land use, management and climate

Historically, management decisions to control

diffuse-source pollution have often not fully considered the

interac-tions between climate, soil and hydrology (Thorsen et al.,

1996) Contaminant transfer via runoff is a complex

func-tion of rainfall timing, antecedent hydrology, slope and soil

characteristics and of the properties of the contaminant

un-der consiun-deration (Wauchope and Leonard, 1980)

There-fore, models designed to address this kind of problem

require a robust description of the hydrological processes

responsible for transport and of any partitioning and

trans-formation processes operating

As part of the CEFIC (European Chemical Industries

Coun-cil) LRI-funded (Long-range Research Initiative) TERRACE

project (TErrestrial Runoff modelling for Risk Assessment

of Chemical Exposure: White et al., 2001), a number of

models were reviewed in terms of their potential for

pre-dicting diffuse-source transfers The criteria considered

were:

1 Capability for application to large-scale catchments

(>100 km2)

2 Capability for interface with a Geographic Information

System (GIS)

3 A physically reasonable representation of hydrological

and contaminant transport processes

4 Input data requirements that allow the model to be

applied in a wide variety of European situations

5 A model that could be made available as part of a freely

accessible package

6 A model validated for pesticides, preferably in a

Euro-pean setting

Three suitable models were identified for further

explo-ration: the physically based event model ANSWERS-2000

(Beasley, 1991; Bouraoui and Dillaha, 1996, 2000); the

empirically based SWATCATCH model (Brown and Hollis,

1996; Hollis and Brown, 1996; Holman et al., 2001), and

SWAT (Arnold et al., 1993; Neitsch et al., 2001a) Of these,

SWAT was considered to best-achieve the above criteria It

represents a trade off between physical complexity and

in-put data requirements that is believed to be achievable

across Europe As land use and management are known to

be key controls over diffuse source pollution, the flexibility

offered by the SWAT modelling approach gives maximum

potential for defining sustainable and low environmental

im-pact farming practices As a first step, SWAT was applied to

predict pesticide transfers from land to surface water for a

small catchment in Bedfordshire, UK (Kannan et al., 2006a)

The hydrological modelling component of the work is

dis-cussed in this paper

Outlook

The ultimate objective of the work described here is the

simulation of pesticide transport from land to surface

water This requires an accurate estimation of chemical

transfer via both surface and subsurface flow Leaching of

pesticide through the soil profile depends on infiltration

and percolation rates, which, thus, need to be well de-scribed In addition to matching predicted and observed stream flow it is, therefore, essential to partition runoff correctly into different hydrological pathways This, in turn requires a robust simulation of the processes driving water balance such as crop growth and evapotranspiration (ET)

Study area and data availability The study catchment (Fig 1) is located near Sharnbrook, Bedfordshire, UK (in an area bounded by National Grid Ref-erences SP 495000, SP 263000 and SP 499000, SP 263000) The total catchment area is 141.5 ha The predominant soil series is Hanslope, consisting of clay loam soil over stony, calcareous clay (1:25 000 outline soil map R112 TL14; http://www.silsoe.cranfield.ac.uk/nsri/services/cf/gate-way/pdf/bibliography.pdf, last accessed on September 25, 2005) Most of the catchment is covered by arable fields in which a rotation of wheat, oil seed rape, grass, beans and peas is grown Many of the fields in the catchment have extensive drainage systems, mostly installed during the 1960s using clay tile drains with gravel backfill at an approx-imate spacing of 40 m Secondary drainage treatments in-clude mole drainage and sub-soiling All field drains eventually discharge into the main stream, which runs through the centre of the study area The remainder of the catchment consists of woodland, grass and some con-crete areas

Soil horizon data with key properties such as land use group, depth of horizon, percentage of sand, silt, clay, or-ganic carbon, bulk density, saturated hydraulic conductivity and water content at different tension values for each hori-zon were obtained from the National Soil Resources Insti-tute (http://www.silsoe.cranfield.ac.uk/nsri/services/cf/

gateway/pdf/bibliography.pdf, last accessed on September

25, 2005) The Hanslope soil association provides some of the most extensive cereal growing land in Eastern England The soils are developed in chalky till on low plateaux Although, the soils of this particular soil association have slowly permeable subsurface horizons, they are seldom waterlogged The soil type in the study area has prolonged opportunities for spring cultivation, even in wet years 30-Minute interval rainfall data for the catchment were collected from September 1999 to December 2002 Daily maximum and minimum temperature values are also re-corded for the catchment During the simulation period, the highest and lowest temperatures recorded were 30.6C and 8.9 C, respectively Solar radiation and wind speed data were downloaded from the British Atmospheric Data Centre (BADC) web site for the nearest weather station

to the study area (Bedford) The average annual wind speed during the simulation period was 4.54 m s1and the maxi-mum value recorded was 13.04 m s1 More details on wind speed estimation and the measurement device used can be found in

http://badc.nerc.ac.uk/data/surface/ukmo_gui-de.html#5.5 (last accessed on August 30, 2006) Relative humidity values were computed from dew point tempera-ture (from BADC) and daily maximum and minimum temper-ature (after Oke, 1987) The average relative humidity during the simulation period was 83% A detailed list of man-agement operations (e.g tillage, sowing and harvesting,

fertilizer and pesticide application rates) carried out in the

catchment (with dates) was available

An automatic flow recording system was installed by

Agricultural Development and Advisory Service (ADAS) at

the catchment outlet to measure stream flow The system

continuously records flow using a Wessex flume equipped

with an ultrasonic probe to record water depth and velocity

in the flume The ultrasonic probe was linked to an

elec-tronic data capture system based on a Campbell Scientific

CR10 data logger The data were transferred for processing

on a daily basis by means of a mobile phone link

Model description – SWAT

SWAT (Soil and Water Assessment Tool) is a conceptual

model developed to quantify the impact of land

manage-ment practices in large, complex catchmanage-ments (Arnold

et al., 1993; Neitsch et al., 2001a) It operates with a daily

time step although sub-daily rainfall can also be used (with

the Green and Ampt infiltration method) SWAT

incorpo-rates simulation of weather, crop growth,

evapotranspira-tion, surface runoff, percolaevapotranspira-tion, return flow, erosion, nutrient transport, pesticide fate and transport, irrigation, groundwater flow, channel transmission losses, pond and reservoir storage, channel routing, field drainage, plant water use and other supporting processes Tile drainage is simulated when the soil water content exceeds field capac-ity in a soil layer Estimation of tile drainage is a function of the depth of drains, time required for the tile drains to bring the soil layer to field capacity and a drainage lag parameter SWAT divides sub-catchments into hydrological response units (HRUs), which are unique combinations of soil and land cover Flow is not routed between HRUs but routing is used for flow in the channel network A large number (hundreds

or thousands) of HRUs can be continuously simulated using SWAT

Model setup The Digital Elevation Model (DEM) of the catchment was pre-pared using contour data from the 1:25,000 scale topographic map of the study area Detailed land use information,

obtained from ADAS, was used to prepare the land use map of

the catchment The soil map was prepared based on the

information obtained fromNational Soil Resources Institute

(NSRI) The Arc View-SWAT interface (AVSWAT-2000 version

1.0) was used to delineate the catchment boundary and the

burning-in option was used to derive the drainage network

A visual inspection of the derived drainage network and

net-work delineated on the paper map showed good agreement

The multiple HRU option available in the AVSWAT interface

was used with the objective of representing each field as a

separate HRU As a result, the study area was discretised into

three sub-basins and 18 hydrological response units (HRUs)

Methods

Model performance evaluation criteria

Model performance was evaluated using a range of different

error measures: Percent BIAS (PBIAS), Persistence Model

Efficiency (PME), Nash and Sutcliffe Efficiency (NSE), and

Daily Root Mean Square (DRMS) error criteria (Table 1)

The power of these model performance measures decreases

from PBIAS to DRMS in the above-mentioned order (Gupta

et al., 1999)

Initial hydrological modelling

Data from the period September 1, 1999 to June 29, 2001

were used as the simulation period for calibration and

vali-dation Because of their simplicity and limited data

require-ments, the NRCS-curve number method for rainfall-runoff

modelling and the Hargreaves method for estimation of

evapotranspiration were used for initial model runs In

accordance with the hydrological behaviour of the study

area, tile drainage and surface runoff (together) are

consid-ered as the quick response component and base flow as the

slow response component of runoff Base flow is separated

from the observed flow using an automated digital filter

technique (Nathan and McMahon, 1990) proposed byArnold

et al (1995) The filter has three passes and pass 3 gave

acceptable base flow values for the hydrograph (Kannan,

2003) Calibration of stream flow was carried out in

accor-dance with SWAT user manual and other published

litera-ture from SWAT users (e.g Santhi et al., 2001; Lenhart

et al., 2002; Moriasi et al., in press) ESCO (soil evaporation compensation factor), AWC (available water capacity), GWQMN (a threshold minimum depth of water in the shallow aquifer for base flow to occur), GWREVAP (groundwater re-evaporation coefficient), REVAPMN (minimum depth of water in shallow aquifer for re-evaporation to occur), Ksat (saturated hydraulic conductivity of the first soil layer) and curve number (CN) parameters were manually adjusted (one at a time) for calibration The performance evaluation

of daily hydrological modelling for the combined calibration and validation periods is discussed here From the perspec-tive of PME (65.85%) and NSE (67.87%), the model perfor-mance is acceptable with regard to the target values of the model performance evaluation criteria considered (

Ta-ble 1) In addition, the DRMS estimation criterion (0.78 mm) is low which also indicates good model perfor-mance In the case of PBIAS, the value obtained (11.95) is above the optimum value of zero indicating under estima-tion of stream flow in general In summary, according to the performance evaluation criteria, the overall model per-formance is good, indicating the suitability of SWAT for hydrological modelling of this catchment

Problems identified – implementation of necessary remedial measures

The overall predicted water balance generated by the initial calibrated SWAT run (expressed as percentage of rainfall) is

as follows: surface runoff (9.38%); throughflow (0%); base-flow (23.53%); tile drainage (21.05%); evapotranspiration (47.98%) Separate measurements of surface runoff, base flow and tile drainage are not available for our study area Results of field-scale investigations to ascertain the relative proportion of surface runoff and base flow contributing to stream flow conducted at another site (Boxworth, UK) with similar characteristics to Colworth (Pers Commun., John Hollis, 2002) were used as a qualitative check for the SWAT predictions reported here From the breakdown of water balance components, it can be seen that the SWAT-pre-dicted surface runoff is relatively high In addition, the per-centage of rainfall lost through evapotranspiration is well below the normally expected values (Smith, 1976) This was identified as being principally due to problems associ-ated with modelling crop growth In fact, the predicted

values

Values obtained

larger/smaller than the observed

<0: over-estimation Persistence Model

Efficiency (PME)

Relative magnitude of residual variance (noise)

to the variance of errors obtained by the use

of a simple persistence model

to the variance of flows (information)

Daily Root Mean Square (DRMS)

estimation criterion

pattern of crop growth did not match expectations based on

typical crop development (shown inTable 2) Crop growth

modelling in SWAT is carried out using the concept of

accu-mulated plant heat units (Neitsch et al., 2001b) Following

the approach presented in the SWAT theoretical

documen-tation (Neitsch et al., 2001b), heat units were calculated

for every HRU outside the model and then used as input

In addition, published crop growth parameters (such as

max-imum leaf area index, canopy height, root depth) applicable

for the study were used in the crop database These changes

resulted in a much improved predicted crop growth pattern

(Fig 5) (more details can be found in ‘‘Simulation of

pro-cesses driving the water balance’’ section of this article)

It should be noted that the SWAT 2000 version of the model

was used for this study The modifications discussed above

and other changes have now been incorporated in new

up-grades of the SWAT code by the model development team

Sensitivity analysis

The modified model was re-calibrated against stream flow

following the methodology in the SWAT user manual Before

calibration, a sensitivity analysis was performed involving

the parameters ESCO, AWC, GWQMN, GWREVAP, REVAPMN

and CN (Table 3) In addition, saturated hydraulic

conduc-tivity of the first soil layer (Ksat) was also considered

Parameters were varied one at a time in an efficient way

using an automated model run setup (Kannan et al.,

2006b) Based on the sensitivity analysis, predicted stream

flow was found to be relatively insensitive to the

parame-ters GWREVAP and REVAPMN and they were, therefore, ex-cluded from the calibration procedure (Kannan et al.,

2006b)

Calibration and validation of stream flow

Simulation was carried out from September 1, 1999 to May

31, 2002 and a standard split sample calibration-validation procedure was used (Klemesˇ, 1986) The period from Sep-tember 1, 1999 to October 23, 1999 served as a warm up period for the model (allowing state variables to assume realistic initial values for the calibration period) Data from October 24, 1999 to December 31, 2000 were used for cali-bration (Fig 2) and the remaining data for validation (Figs 3

and 4) Three values were considered for each parameter (low, medium and high) Parameter values were varied one at a time covering all different possible combinations

of parameters forming 3n simulations, where n is the num-ber of parameters (Table 3)

Results and discussion For the study area, it has been established that the combi-nation of CN method with Hargreaves ET estimation method gave better results than the other combinations (Kannan

et al., 2006b) Further discussion here is, therefore, re-stricted to results generated with the CN method, Harg-reaves ET combination Evaluation criteria for model performance are listed for both calibration and validation

Growth stage

date

Green area index

Total dry weight (t/ha)

Source The Wheat Growth Guide – To improve husbandry decisions, Home Grown Cereals Authority, London

a

GS is growth stage customarily denoted by a decimal number.

b

Canopy size is commonly assessed in terms of GAI, the ratio between the total green area of all tissues, one side only, and the equivalent area of ground.

modelling

Note Optimised values are highlighted in bold font.

inTable 4 From the table it is evident that all evaluation

criteria show acceptable values (based on Table 1) during

both calibration and validation periods

The water balance for the different periods is shown in

Table 5 Overall, the percentage of rainfall appearing as

surface runoff is more reasonable than in the initial model

run described above (Pers Commun., John Hollis, 2002)

Predicted surface runoff is higher in the calibration period than the validation period This is partly because the cali-bration period (October 1999 to December 2000) includes more winter months, but also because the calibration

peri-od had higher rainfall (mean rainfall 1.91 mm day1 cf 1.75 mm day1 during the validation period) and included several storms in the winter of 2000 For the same reasons,

0 3 6 9 12 15

12 24 36 48 60

-1 )

Precipitation Predicted Observed

0 3 6 9 12 15

12 24 36 48

60 Precipitation (mm day

-1 )

Precipitation Predicted Observed

0 1 2 3 4

-1 )

predicted evapotranspiration is higher during validation

than during calibration Analysis of base flow shows a close

agreement between prediction (27% of rainfall) and

obser-vation (24% of rainfall) However, quick response processes

(overland flow, throughflow and tile drainage) are not

par-ticularly well predicted by SWAT (32% observed versus 20%

predicted)

Observed and predicted hydrographs for the calibration

and validation periods are shown inFigs 2 and 3,

respec-tively It is clear that the timing of runoff events is well

pre-dicted by SWAT, as is the base flow

Overall hydrograph peaks are under-estimated by SWAT

Under-estimation of peak flow has also been reported by

other SWAT users (e.g Fohrer et al., 2001; Chanasyk

et al., 2003; Bosch et al., 2004; Chu and Shirmohammadi,

2004; Du et al., 2005) One possible cause may be the way

curve number is updated based on changes in soil moisture

(Van Liew and Garbrecht, 2003) In SWAT, the curve number

value is updated based on the available water content of the

entire soil profile However, it is probably more appropriate

to update the curve number values in accordance with soil

water content of the topmost soil layer, which would more

closely reflect the process of surface saturation during

hea-vy rainfall events This would also allow consideration of soil

sealing, crusting and smearing which change soil hydraulic

properties and reduce infiltration capacity

Simulation of processes driving the water balance

Simulated leaf growth, biomass development (Fig 5), soil

water content (above wilting point) (Fig 6) and

evapo-transpiration (Fig 7), are shown for winter wheat in one

of the HRUs in the Colworth catchment The simulated

maximum value of biomass occurs after the simulated

max-imum root depth Water uptake progressively increases

with root growth resulting in a depletion of soil water

be-low field capacity (Fig 6) As the crop begins to establish,

both transpiration and evaporation are significant (Fig 7)

in line with the pattern of root and leaf growth (Figs 5

and 6) Eventually, the transpiration component dominates

which is in accordance with the rate of root growth and

leaf development

At senescence, transpiration drops significantly, simu-lated leaf area index starts to decline (Fig 5) and soil water content starts to increase (Fig 6) The predicted inter-rela-tionship between the establishment of leaves, the develop-ment of biomass, the growth of roots, transpiration from the plant and evaporation from the soil looks sensible com-pared with monitored data sources (e.g.Hough, 1990;

Brad-ley et al., 2001)

Rainfall, runoff generation and

evapotranspiration

Surface runoff

Through flow

Base flow

Tile drainage

storage Daily rainfall, curve number and

Hargreaves ET

0 5 10 15 20

Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01

-1 )

0 1 2 3 4

Biomass growth Leaf area index

Senescence

0 50 100 150 200

Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01

-1 )

0.0

0.4

0.8

1.2

1.6

Depletion of available water Root development

Field capacity

in field 37

The average annual actual evapotranspiration predicted

by the Hargreaves equation in SWAT (371.83 mm) is also

reasonable compared with typical value for this area (e.g

Smith, 1976) In combination withFig 7, this suggests that

evapotranspiration is adequately modelled by SWAT

Simi-larly, the predicted maximum soil moisture deficit for the

whole catchment in summer (103.45 mm) is very close to

the typical value (103 mm) for this region (Smith, 1976)

For peas (field 45) and oilseed rape (field 37), return of soil

moisture deficit to field capacity is predicted to occur in the

first week of November and to increase at the beginning of

April Again, this pattern is very similar to the average

ob-served period for soils to be at field capacity, reported by

Smith (1976) These comparisons suggest that the processes

driving the water balance in this catchment are adequately

modelled by SWAT and that this model set up can be used

for modelling sediment and diffuse source pollutant transfer

from land to water

Conclusions

The inter-relationships between various hydrological

pro-cesses simulated in the SWAT model are explored in this

pa-per The importance of proper modelling of all water

balance components and the correct allocation of runoff

to different hydrological pathways for simulating

diffuse-source contaminant transport is highlighted Whilst initial

model application resulted in good simulation of observed

hydrographs, the modelling of internal catchment processes

was incorrect A range of available hard and soft data were

used to verify model performance within the catchment and

to correct errors in internal state variable prediction The

work, highlights the importance of examining the complete

range of processes simulated in complex process-based

models rather than simply relying on traditional calibration

and validation based on flow data at the catchment outlet

In summary, the following conclusions can be drawn

1 Proper modelling of water balance components such as

crop growth and evapotranspiration is crucial for correct

represention of flow pathways

2 The NRCS-curve number method appears to be suitable

for modelling stream flow under UK conditions, although

peaks are not well reproduced

3 The SWAT model (with the modifications introduced as a result of this work) can be reliably used to model stream flow

Acknowledgements The authors thank Unilever and the CEFIC Long-range Re-search Initiative (LRI) for funding and ADAS-UK and the Brit-ish Atmospheric Data Centre for providing data

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