Sensitivity of the SWAT model to the soi

Ecological Modelling 187 (2005) 27–39

Sensitivity of the SWAT model to the soil and land use data
parametrisation: a case study in the Thyle catchment, Belgium
A.A. Romanowicz a, ∗ , M. Vanclooster a , M. Rounsevell b , I. La Junesse b
a

b

Department of Environmental Sciences and Land Use Planning, Universit´e catholique de Louvain,
Croix du Sud 2, BP2, B-1348 Louvain-la-Neuve, Belgium
Department of Geography, Universit´e catholique de Louvain, Place Pasteur 3, B-1348 Louvain-la-Neuve, Belgium
Available online 19 February 2005

Abstract
The sensitivity of the distributed hydrological SWAT model to the pre-processing of soil and land use data was tested for
modelling rainfall-runoff processes in the Thyle catchment in Belgium. To analyse this sensitivity, 32 different soil and land
use parameterisation scheme were generated and evaluated. The soil input data sources were a generalised soil association
map at a scale of 1:500,000, a detailed soil map at a scale of 1:25,000 and the soil profile analytical database AARDEWERK.
These soil data were combined with a detailed and a generalised land use map. The results suggest that the SWAT model is
extremely sensitive to the quality of the soil and land use data and the adopted pre-processing procedures of the geographically

distributed data. The resolution and fragmentation of the original map objects are significantly affected by the internal aggregation
procedures of the SWAT model. The catchment size threshold value (CSTV) is thereby a key parameter controlling the internal
aggregation procedure in the model. It is shown that a parabolic function characterises the relationship between the CSTV and
the hydrological modelling performance of the uncalibrated model, suggesting that optimal uncalibrated modelling results are
not obtained when the CSTV is minimised. The hydrological response of the SWAT model to the calculated soil properties is
significant. Therefore preference should be given to the calculation of the derived hydrologic soil properties prior to averaging
of the profile data. Finally some general guidelines are suggested for parameterising soil and land use in the SWAT model
application.
© 2005 Elsevier B.V. All rights reserved.
Keywords: Integrated hydrological modelling; Spatially distributed hydrological models; SWAT; Soil parametrisation; Aggregation

1. Introduction

∗ Corresponding author. Tel.: +32 10 47 36 90;
fax: +32 10 47 38 33.
E-mail address: agaromanowicz@yahoo.co.uk
(A.A. Romanowicz).

0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolmodel.2005.01.025

Spatially distributed hydrological models are useful
tools to support the design and evaluation of water
management plans. A blueprint of this type of model
was already presented in the late sixties by Freeze
and Harlan (1969) and the current state of the art was
recently reviewed by Beven and Feyen (2002). The

28

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

spatially distributed nature of these models allows a
multi-objective evaluation of the impact of spatially
variable forcing terms and catchment properties on the
hydrological responses. This property makes spatially
distributed hydrological models attractive for the
evaluation of land and water management options.
Unfortunately, the use of spatially distributed modelling technology in water management decisionmaking suffers from a series of drawbacks (Abbot and
Refsgaard, 1996). First, the data demands for these

models is considerable. In many practical cases, data
requirements cannot directly be met since the data are
simply not available or they do not comply with standard quality targets. Secondly, distributed modelling is
exhaustive in terms of computer power and data processing: advanced geographical information systems
(GIS) and computer technology is needed for the efficient processing of data. Finally, there is a lack of
scientific understanding of the robustness, sensitivity
and validation of these models in relation to different
parametrisation schemes (Abbot and Refsgaard, 1996).
In particular, questions are raised about the appropriate resolution of the spatially distributed input data. As
it has already been proven in numerous studies (e.g.
Becker and Braun, 1999), the resolution and the aggregation of the GIS data have a considerable impact on
the modelling of the rainfall runoff processes. Therefore the parametrisation of the soil, the vegetation and
the climate should be analysed in detail for different
distributed hydrological modelling types and different
hydrological catchments, and this prior to any modelling calibration (Refsgaard et al., 1996).
To contribute to this last challenge, this paper
presents an analysis of the sensitivity of the distributed
hydrological model soil and water assessment tool
(SWAT, Arnold et al., 1993) to the soil and land

use parametrisation for simulating rainfall-runoff processes in a small agricultural catchment in the central
part of Belgium. The SWAT model was considered in
this study since it is an integrated hydrological model
that simulates both the qualitative as well as quantitative terms of hydrological balances (Fig. 1). Integrated
hydrological models are nowadays needed to support
the implementation of integrated water management
plans and to comply with the current requirements of
the European Water Directive. In addition, the SWAT
model is interfaced with the ARC-VIEWTM software
which allows easy pre-and post processing of the spatially distributed input data, driving the rainfall-runoff
process. Finally, the SWAT model is a spatially distributed hydrological model, which means that the impact of spatially variable input parameter changes such
as land use change can easily be modelled. The model
is well documented, transparent and the source code is
directly downloadable from the web page at no cost.
The software is supported by the knowledge of its developer and users by means of an e-mailing list server
and a discussion forum on the web page. It is therefore
a hydrological model which respects the principles of
good modelling practice (STOWA, 1999). Finally, the
validation status of this code is known, and continuous
to be developed (e.g. Conan et al., 2002).

An evaluation is made of the prepared available soil
and land use data for the SWAT model and how the
internal aggregation procedures affect the simulation
of the rainfall-runoff processes. The SWAT model is a
physically based model and includes therefore a physical description of the soil water balance, which means
that the soil parameterisation is one of the main parameters during the modelling process. Previous studies
(e.g. Muttiah and Wurbs, 2002) have shown that the
SWAT model is sensitive to its soil parameterisation,

Fig. 1. The modules included in the SWAT model.

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

in particular to available water capacity. Yet, in these
studies, the sensitivity of the soil parameterisation was
investigated for catchments in the United States and the
conclusions were conditional to the considered land
use and surface topography parameterisation. In the
case of other catchments where different land use and
topographical pre-processing is considered, this may

give different results (Chess Project, 2001). Specific
attention is also paid to the spatial resolution of soil
data and the interaction of the soil data parameterisation with the modelling of the land use and surface
topography. In practice, SWAT simulates the rainfallrunoff process at the sub-catchment level by deriving
hydrological response units (HRUs). Up-scaling of the
spatially distributed soil and land use maps takes place
during the creation of the HRUs and the result of this
up-scaling will depend therefore on the initial resolution considered for these data and will affect the simulated rainfall-runoff process.

2. Materials and methods
2.1. Model description
The SWAT model (Arnold et al., 1993) was developed to predict the impact of land management
practices on water, sediment and agricultural chemical
yields in large complex catchments with different soil,
land use and management conditions over long periods
of time. It is a physically based, continuous model. In
this study, only the hydrological and climate component of the SWAT modelling system was considered.
Use was made of the AV-SWAT version of the model
(an integrated version within ArcViewTM ), which has
been fully documented by Neitsch et al. (2001) and is

available at http://www.brc.tamus.edu/swat/ (access in
November 2001). Only the parts of the model that are
relevant to this case study are presented in the below
description.
In AV-SWAT, the pre-processing of the data is done
by applying some of the ArcViewTM GIS functions.
This involves the creation of the river network, the
catchment area, and the sub-catchments. The latter step
is crucial, since it creates the boundaries for the further
simulation. The definition of the sub-catchment size is
based on the threshold value (CSTV) which is defined
by the user. This value is also the basis for the definition

29

of the hydrological response units (HRUs), which also
involves aggregation of the input data (FitzHugh and
Mackay, 2000). The threshold value for the catchment
size is selected by the model user from a series of predefined relative CSTVs defined by the developer (the
absolute value depends on the total catchment size).

Possible relative threshold values are (i) the smallest
size, implying a low aggregation level of input data,
(ii) the suggested value size by the model developer,
implying that the modelling depends more on the computational time and the size of the catchment, (iii) an
intermediate value, implying an intermediate aggregation level; and (iv) the maximum value, implying a
high aggregation level. Since hydrological attributes
are defined at the sub-catchment level, precaution is
needed when defining the CSTV. After the definition of
the CSTV, SWAT disaggregates heteorogenous catchments into sub-catchments and homogeneous HRUs.
Hydrological attributes are assigned at the HRU level
by considering either multiple or dominant attributes
of the underlying spatially distributed input attributes.
In this case study, only the dominant input attribute
option was considered. The HRU is also the reference
unit for which the hydrological balance is calculated.
Total catchment balance terms are obtained by aggregating the results of each HRU calculation. The use
of the HRU concept allows consideration of spatially
distributed catchment properties in a computationally
efficient way and has been used in different hydrological models (e.g. Becker and Braun, 1999).
2.2. The catchment area

The study area is the Thyle catchment (subcatchment of the Dyle river) situated in the central part
of Belgium (Brabant Walloon), southeast of Brussels.
The Thyle catchment was selected, because of data
availability and extensive knowledge of its hydrological regime (Persoons, 1999). Land use in the Thyle
catchment is dominated by agriculture (66.37% of the
total area). Forest represents 27.18% of the area, urban
5.6%, industry 0.8% and open water 0.05%. The total surface area of the Thyle catchment equals 59 km2 .
Though the smallest simulation time step in the SWAT
model is daily, the results of this analysis is going to
be used in application to a larger catchment, where a
good understanding of the parameterisation procedure
is needed.

30

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

The study focused on the modelling of the rainfallrunoff processes at the outlet of a catchment and its
sensitivity to different discretisation procedures. The
gauging station of the catchment outlet is situated at

Suzeri and daily measurements are available from the
early 1970s. The outflow data are collected by R´egion
Wallonne (DGRNE-SCENN). Data were used for the
year 1999.
2.3. General input data
It is well known that the quality of the DEM will
have a strong influence on the final output of the hydrological model (Defourny et al., 1999). We used therefore the finest resolution DEM available for the study
area. The DEM map used was created by the local government authorities and was used in previous studies for
the Dyle catchment (Persoons, 1999). The weather data
for 1999 were obtained from the Belgian Royal Meteorological Institute (KMI-IRM). These data include the
daily precipitation rate, the daily maximum/minimum
temperature, the mean monthly values of wind speed,
and solar radiation. Data from five precipitation stations with daily measurements were used. Three stations were used to characterize the minimum and maximum temperatures. All the data were validated by the
standard procedures used by the IRM. The selected
stations are located either within the Thyle catchment
itself or within the upper catchment of the Dyle river.
The 1999 land use map was created using the SIGEC
data set, combined with LandsatTM satellite images
and the IGN topographical map 1/50,000. The SIGEC
data set includes information about the crop distribution over the catchment area and is based on the claims

of the farmers for the EU subsidies. A standard classification has resulted in 50 types of land use within
the study area, which includes, inter alia, all types of
agriculture crops. The SWAT data set consist of much
less land use types. Additionally to that those of the
land use types which were representing the area in less
then 1% would not be taken into consideration while
modelling with SWAT. Therefore, the detailed SIGEC
land use data classes were generalised to 23 land use
types, later called detailed land use map. Those of the
agriculture practice which were represented in small
percentage where summarised as generic agriculture.
To evaluate the impact of the aggregation of the land
use on the hydrological modelling performance, a gen-

eralised 5 classes land use map was defined from the
23 classes land use map. Where all agricultural crops
are summarised as ‘agriculture’, two forests types are
represented by one mixed forest, and one class also
represents two urban classes of detail land use map. In
the subsequent discussion reference will be made to the
generalised land use map of 5 classes.
Two different soil maps were used: (i) a soil association map, which is available at a scale of 1:500,000
(Comit´e National de G´eographie, 1970) referred to as
the generalised soil map; and (ii) a detailed soil map
(maps: 129◦ E, 130◦ W, 142◦ E, and 143◦ W), which is
available at a scale of 1:25,000 (IRSIA, 1972). The association map has 39 different soil associations for Belgium (Van Orshoven and Vandenbroucke, 1993a). The
study catchment is characterised by three associations
and 60 detailed soil map units. To parameterise the soil
map units of the two different soil maps, use was made
of the Belgian analytical soil database AARDEWERK
(Van Orshoven, 1993b). This analytical database was
matched with the different soil mapping units of the
two soil maps. For the generalised map, all the soil
profiles that were located within one of the three associations were extracted and stored in a file considering
a pure class matching procedure. For the detailed soil
map, however, soil profiles could not be matched to all
the 60 mapping units. Therefore, the detailed soil map
was aggregated to result in a new six class soil map
and then the available profiles where connected to the
new obtained soil units. The aggregation was done by
combining soils to there higher level, in terms of the
soil taxonomic groupings for Belgian soils. For each
of the new mapping units, soil profiles were extracted,
resulting in 87 profiles and 716 horizon descriptions.
2.3.1. Soil parameterisation
In SWAT, one single value of a soil property is
needed for each considered soil map unit. Since the
principal objective of this study was to analyse the sensitivity of the SWAT model to soil parameterisation
and since multiple soil profiles were matched to the
different mapping units of the association and detailed
soil map, different calculation procedures were considered. The basic soil properties (percentage of sand,
clay, and silt; the texture class, the percentage of carbon
and the horizon thickness) of the selected profiles were
obtained directly from the analytical database. For derived soil properties (hydraulic conductivity, bulk den-

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

sity, available water capacity, soil organic matter) use
was made of the pedotransfer rules described below.
The organic matter (MO) content was calculated
from the equation (Van Orshoven, 1993b):
MO = percentage of carbon × 1.7924

(1)

The bulk density (BD) was calculated using the pedotransfer function of Adams (1973), later on developed
by Van Orshoven (1993b)



1
BD = 100 ×
(MO/ρMO ) + (100 − MO)/ρMM
(2)
where MO is the percentage of organic matter; ρMO
the density of organic matter = 0.224 g/cm3 ; ρMM the
density of mineral matter according to the texture
class (class Z,S-sand: 1.55 g/cm3 ; class P,A,E-sandyloamy: 1.41 g/cm3 ; class L-loamy:1.3 g/cm3 ; class Uclay: 1.35 g/cm3 ).
In order to calculate the hydraulic conductivity
(Ksat ) and the available water capacity (AWC), pedotransfer functions from the HYPRESS database were
used (Woesten et al., 1998).
Different averaging procedures were used to derive
soil attributes at the mapping unit level. The ensemble of basic soil data per mapping unit was averaged to
yield a single mean value of the basic properties (for the
same soil type) for the considered mapping unit prior to
the calculation of the derived property. Alternatively,
the derived properties were first calculated for each
single profile after which a single value for a mapping

31

unit was obtained by averaging the set of derived properties. The chain of calculations can be summarised by
the following logical sequence:
1. PM
on
the
map → CDP → CMDP → SWAT,
2. PM
on
the
map → CMBP → CDP → SWAT,
3. PM
on
the
map → CDP − CMDP → SWAT,
4. PM
on
the
map → CMBP → CDP → SWAT

detailed
detailed
generic
generic

with PM, profile matching; CDP, calculate derived
properties; CMDP, calculate mean derived properties;
CMBP, calculate mean basic properties.
2.3.2. Modelling
Four values for the CSTV were considered in the
scenario-analysis: sub-catchment size corresponding
to the maximum area, i.e. one sub-catchment; subcatchment size suggested by the model developer, i.e.
based on the total size of the considered catchment, resulting in 27 sub-catchments of size below 100 ha; a
CSTV of 60 ha, resulting in 47 sub-catchments; and finally a CSTV of 20 ha resulting in 145 sub-catchments.
Lower threshold values were not possible due to computational limitations.
The combination of the two soil maps, with two land
use maps, two soil analytical data averaging schemes,
and four CSTVs resulted in a total of 32 different
schemes for modelling. To characterize the soil type

Table 1
Map indicators used to compare the basic input map and the generic derived map
Symbol

Definition of map indicator

Calculation procedure

G1
G2
G3
G4
G5
NP
PD
Area-Min
PR
PRD
RPR

Number of cells of class x that after aggregation correspond to the original map
Number of cells assumed to be a different class
Number of original cells not used for simulation
Total cells per class x generated by the SWAT model
Original number of cells per class x
Number of patches (extent of subdivision or fragmentation of the patches)
Number of patches on a per area unit basis (value in hectares)
Patch area distribution the smaller the size the more fragmented the map is, value in hectares
Number of patches within the landscape
Number of different patch types presented within the landscape (value in hectares)
Number of different patch types presented within the landscape boundary divided by the maximum potential number of patch types in the original data (value in percentage)—used only for
the comparison
Aggregation Index—it is a measure of the map aggregation

ArcView
ArcView
ArcView
ArcView
ArcView
Fragstat
Fragstat

AI

Fragstat
Fragstat
Fragstat

Fragstat

32

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

Table 2
The hydrological evaluation indices
Definition


(Oi −Pi )2
CNS = 1 − 
2
¯
(Oi −O)

AE =

n

Pi −Oi
n

i=1

RMSE =

RMS =

CRM =





n

(Pi −Oi )2
n

i=1

n

(Pi −Oi )2
n

i=1

n

O−

in

i=1

n

i=1





i=1

Oi

Comments

Nash–Sutcliffe (Nash and Sutcliffe, 1970)

The optimal statistical value occurs when the
value does reach 1

Average error (Jannsen and Heuberger, 1995)

The optimal statistical value is close to 0

× 100 Root mean square error (Jannsen and Heuberger, 1995) In %; the optimal statistical value is close to 0

× 100

Pi

Reference

Root mean square (Loague and Green, 1991)

In %; the optimal statistical value is close to 0

Coefficient of residual mass (Loague and Green, 1991)

This indicator identifies when the model over estimates (negative values) or underestimates (positive values) the values

¯ mean observed value; Pi , simulated value.
Oi , observed value; O,

Fig. 2. Land use defined by SWAT CSTV values using the generalised land use input.

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

and land use within each HRU, the dominant soil and
land use was considered (here dominant means the type
which covers the area in majority). All other SWAT parameters were given their default values, and no calibration was performed.
2.4. Model evaluation
2.4.1. Evaluation of the pre-processing
The modeller does not have any influence on how
the HRU is attributed. The attribution are internally
defined by overlaying the input soil map and land use
map with a sub-catchment map. That creates a transformation of the input soil and land use information to
the model. A visual comparison between the generic
maps, i.e. the maps considered by SWAT as input to
the hydrological modelling component, and the basic
input maps should give some indication as to the loss
of information in the internal aggregation procedure
used by SWAT. A visual comparison however does not

33

allow to elucidate the differences in a synthetic way.
Therefore the visual comparison was completed with
the calculation of five map indicators in ArcViewTM
and seven map indicators using FRAGSTATS
(http://www.umass.edu/landeco/research/fragstats/
fragstats.html, access in August 2002) (McGarigal et
al., 2002). The latter tool is a public domain software
program designed to compute a wide variety of landscape metrics the emphasis on the spatial distribution
of map patterns. The map indicators are defined in
Table 1.
2.4.2. Evaluation of the hydrological modelling
Preliminary results with the SWAT model showed
that the baseflow component in the catchment is not
appropriately modelled. This was also observed by
Kannan et al. (2002). To solve this problem, the
authors of the model recommend to couple SWAT
with a groundwater flow model as illustrated e.g. by
Sophocleous and Perkins, 2000. The use of this cou-

Table 3
Fragstats calculation for different map inputs and different pre-processing schemes
Index

NP

PD

Area-Min

Land use—generic
Original map
145 sub-basins
47 sub-basins
27 sub-basins
1 sub-basin

2150
33
14
8
1

36.65
0.56
0.23
0.14
0.02

2.78
177.75
418.98
733.21
5865.72

6
4
4
4
1

0.1023
0.0682
0.0682
0.0682
0.017

–
66.67
66.67
66.67
16.67

80.18
97.46
98.48
98.92
99.73

Land use—detail
Original map
145 sub-basins
47 sub-basins
27 sub-basins
1 sub-basin

5141
48
15
14
1

87.64
0.82
0.26
0.24
0.02

1.14
122.2
391
418.98
5865.72

23
12
7
6
1

0.39
0.20
0.12
0.10
0.017

–
52.17
30.43
26.09
4.35

68.8
96.85
98.4
98.83
99.73

28
15
10
7
1

0.48
0.26
0.17
0.12
0.02

209.49
391
586.57
837.96
5865.72

3
3
3
3
1

0.05
0.05
0.5
0.5
0.017

–
100
100
100
33.33

97.60
97.77
98.47
98.87
99.73

1159
29
8
8
1

18.84
0.49
0.14
0.14
0.02

5.31
202.27
733.2
733.2
5865.72

6
6
3
3
1

0.1
0.1
0.05
0.05
0.017

–
50
50
50
16.67

82.21
97.3
98.79
98.79
99.73

Soil map—generic
Original map
145 sub-basins
47 sub-basins
27 sub-basins
1 sub-basin
Soil map—detail
Original map
145 sub-basins
47 sub-basins
27 sub-basins
1 sub-basin

PR

PRD

RPR

AI

NP, number of patches; PD, number of patches on a per area unit basis; Area-Min, patch area distribution the smaller the size the more fragmented
the map is, value in hectares; PR, number of patches within the landscape; PRD, number of different patch types presented within the landscape;
RPD, number of different patch types presented within the landscape boundary; AI, aggregation index.

34

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

pled version, however, was not possible in this study,
since the code is not publicly available. An alternative
approach is to use the SWAT model to simulate only the
surface and interflow component of the hydrological
cycle. This can be implemented by separating the base
flow component from the surface flow component in
the observed hydrograph data by means of automated
separation techniques (Arnold and Allen, 1999) and
comparing modelled surface flow with estimated surface flow. In this paper, the hydrograph recession curve
displacement method was used as presented by Arnold
and Allen (1999) and Arnold et al. (1995). This is an automated digital filter program based on the Rorabaugh
hydrograph recession curve displacement method using daily stream flow. The method was later extended
and tested in the work of Nathan and McMahon (1990)
and Rutledge and Daniel (1994) and is called the automated master recession curve method. The equation
of the filter yields:
qt = βqt−1 +

(1 + β)
(Qt − Qt−1 )
2

(3)

where qt is a filtered surface runoff at the time step t;
Qt the original stream flow; and β the filter parameter
provided by the SWAT model developer. The base flow
(B) is calculated with the equation:
Bt = Qt − qt

(4)

The modelled peak flow for the different modelling
schemes were compared with the peak flow measured
at the Suzeri outlet of the Thyle catchment for the
year 1999. Graphical comparison of simulated versus measured outflow was combined with statistical
modelling performance indicators to assess the modelling performance. The modelling performance was
calculated by using a set of different modelling evaluation indicators. The definition of the different indicators is given in Table 2. It has to be added that because the initial conditions of the modelling have to
be stabilised therefore the model have been ran for 2
years but only the second year is taken into consideration.

Fig. 3. Evaluation of the number of grid indicators (Table 1) for the pasture land use class (PAST) and a certain soil class (soil class 3) for
different threshold values for the sub-catchment size, G1–G3 refer to the definitions given in Table 1.

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

3. Results and discussion
3.1. Evaluation of the generic land use and soil
map
The land use maps generated by the SWAT model
using the generic land use map as an input to the
hydrological simulation are given in Fig. 2. The impact
of the CSTV on the generic land use map is marked.
The geometry of the patches of land use classes
changes significantly, as do the total area and the shape
of the land use class patches. In addition, some land
use classes disappear in the aggregation procedure.
The change of shape of the land use class patches
is of concern for hydrological modelling. Compared
with the generic land use map, land use class patches
are much more dispersed throughout the catchment

35

and that affects significantly the fast response of the
catchment to intense rainfall.
A quantitative comparison of the original and
generic land use maps using the performance indicators
of Table 1 is given in Table 3 and Fig. 3a. Because the
model with different classes as well as four sizes of the
sub-catchment generated four maps, only the results
for a single land use class are presented (the pasture
class).
As might be expected, the aggregation procedure
used to derive the generic maps resulted in a considerable loss of information. The change in the distribution
of the land use class patches and the disappearance
of some of the land use classes in the generic map
is illustrated by the decrease of parameters (NP, PD,
PR and RPR) when the number of sub-catchments decreases. As can be seen, the number of patches in the

Table 4
Statistical evaluations of the 32 parameterisation schemes
Soil map

Land use map Detail soil map
20 ha (145)a

Residual mean square (RMS)
CMBP → CDP Detail
Generic
CDP → CMBP Detail
Generic

0.0037
0.0057
0.0034
0.003

Generic soil map

60 ha (47) 100 ha (27) 2600 ha (1) 20 ha (145) 60 ha (47) 100 ha (27) 2600 ha (1)
0.0033
0.0055
0.0028
0.003

0.0035
0.006
0.0033
0.0034

0.0043
0.0043
0.0043
0.0009

0.006
0.008
0.004
0.006

0.053
0.008
0.004
0.006

0.006
0.009
0.005
0.007

0.012
0.015
0.011
0.014

Root mean square error (RMSE)
CMBP → CDP Detail
Generic
CDP → CMBP Detail
Generic

7.04
10.793
6.46
5.898

6.268
10.4
5.326
5.701

6.744
11.091
6.255
6.475

8.279
18.009
8.185
8.152

10.74
14.953
8.005
11.695

10.068
15.223
7.683
12.064

10.96
16.564
8.506
13.626

21.823
27.92
20.157
27.262

Average error (AE)
CMBP → CDP Detail
Generic
CDP → CMBP Detail
Generic

−0.0122
−0.011
−0.004
−0.007

−0.0148
−0.013
−0.0048
−0.0054

−0.019
−0.017
−0.0037
−0.004

−0.0337
−0.026
0.0037
0.0036

0.003
0.004
−0.002
−0.0013

0.0046
0.006
−0.0008
0.0008

0.008
−0.008
0.0025
0.003

0.035
0.037
0.032
0.034

0.23
0.2087
0.085
0.126

0.28
0.251
0.0908
0.1022

0.36
0.33
0.07
0.079

0.64
0.486
−0.07
−0.068

−0.063
−0.080
0.046
0.025

−0.087
−0.114
0.015
−0.015

−0.146
−0.159
−0.047
−0.063

−0.66
−0.711
−0.597
−0.647

0.386
−0.0189
0.478
0.442

0.339
−0.086
0.387
0.366

0.189
−0.764
0.198
0.202

−0.052
−0.465
0.216
−0.1455

0.0138
−0.491
0.247
−0.182

−0.07
−0.622
0.167
−0.33

−1.137
−1.735
−0.974
−1.67

Coefficient of residual mass (CRM)
CMBP → CDP Detail
Generic
CDP → CMBP Detail
Generic
Nash–Sutcliffe coefficient (CNS)
CMBP → CDP Detail
Generic
CDP → CMBP Detail
Generic

0.31
−0.057
0.367
0.422

CDP, calculate derived properties; CMBP, calculate mean basic properties.
a Threshold value for the sub-catchment area (number of sub-basins).

36

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

generic map decreases rapidly and some of the classes
in certain combinations were not used, despite covering
the area substantially in absolute terms (Fig. 3b). This
means that, when patches of a certain class are widely
dispersed, the model may not consider this class. It
should also be noted that the degree to which the original map is fragmented is very high. The comparison
index (RPR) confirms that most of the information is

not used by the model. From the aggregation index
(AI) it can be concluded that the data are aggregated to
a very high level.
When analysing the number of grid indicators in
terms of CSTVs a similar pattern can be observed as
in the example in Fig. 3a. The number of grids in the
generic map classified as the truth-value on the original
map increases with an increase in the number of sub-

Fig. 4. An example simulation hydrograph of the detail land use map with two different soil maps and two different soil calculation schemes.

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

37

Fig. 5. The Nash–Sutcliffe coefficient for different map inputs combination and different amount of sub-catchments (the CDP → CMBP
calculation).

catchments. For the pasture land use class the number
of grids aggregated as pasture decreases. This can be
explained by the spatial dispersion of pasture patches
in the original map. Assigning the dominant land use
to a sub-catchment unit will result in the disappearance
of dispersed land use classes when aggregation takes
place.
Similar conclusions can be drawn from the analysis
of the properties of the generic soil map in relation to
the original soil map.
Hence, it is evident that the internal aggregation produced within SWAT, driven by the CSTV, will have significant effects on the generic land use and soil maps.
Even if the original detailed land use and soil maps
were used, considerable information would have been
lost in the internal aggregation, if high CSTVs were
used. Users of the model should therefore be aware
of the impact of this threshold value on land use and
soil maps and evaluate the consequences of this for
the performance of the hydrological model. Reducing the CSTV to represent correctly the fragmentation of the original map on the derived map is not
possible from a computational point of view. Therefore, we propose an external aggregation of the soil
and land use map that is compatible with the CSTV
imposed by the SWAT model. By doing so, the user
will have greater influence on what is happening during
the pre-processing, and this will ensure that the maps
are aggregated for the HRUs values in a more realistic
way.

3.2. Evaluation of the hydrological modelling
The impact of the different aggregation and soil
parameterisation procedures on the predicted rainfallrunoff is illustrated in Table 4 and Fig. 4. It must be
emphasised that the model was not calibrated and so,
the performance indices are not high. However given
the uncertainty due to the input data (e.g. rainfall data,
discharge measurements) the results with the uncalibrated SWAT model were satisfactory.
In most cases, the simulation model underestimates
the observed values, which is in agreement with the
study of Chanasyk et al. (2002). The indices in Table 4
suggest that with an increase in the sub-catchment number the statistical performance of the model increases
to a certain point. After reaching this point increasing
the number of sub-basins will create a poor representation of the catchment leading to decreases in model
performance. The use of the pedotransfer function (ptf)
prior to averaging reflects more precisely each of the
soil properties in mathematical terms, and as hypothesized this procedure gave better results then the use
of ptf after averaging the basic properties. Thus, the
soil component of the SWAT model is very sensitive
and proper preparation of available data for the case
study is advisable. The difference created by the two
soil property calculations is represented by an example
in Fig. 4.
When analysing the impact of the catchment threshold value on modelling performance, it is observed that

38

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39

the number of sub-catchments is not linearly proportional to the modelling performance. Fig. 5 illustrates
that the optimal CNS index is obtained with 47 subcatchments. This means there is an optimal value for
CSTV beyond which the model improvement will decrease. However, the value of 60% of the CSTV ‘suggested by the developer’ is probably the optimal value
for the catchment size when using a similar set up.
The major reason for observing a decrease in model
performance could be because of the input maps (soil
and land use) which were used with the ArcView interface. A decrease in the number of sub-basins will
create an artificial soil and land use combination and
so it is advisable to use the multiple HRU distribution
with a considerably larger CSTV than a smaller value
for CSTV and a dominant HRU distribution.

The work presented here also raises concerns that
are relevant to a broader community of people (scientists and water authorities). The implementation of
the Water Framework Directive in Europe requires the
application of different hydrological models with GIS
technology. As this work has shown a good understanding is needed of not only hydrological models but also
of the GIS procedures for data input. This implies the
need for a methodology of model application which
will be understandable to all users. Thus special attention needs to be given to the ways in which GIS data
are created, manipulated and transformed by the applied model. Special attention should be paid to the
pre-processing of data for which the model user has
no, or very little, influence.

Acknowledgements
4. Conclusion
This study has shown that the SWAT model is very
sensitive to the internal and external pre-processing
of the soil and land use data. Consequently, the input data resolution and classification should be transformed prior to the hydrological modelling itself by the
internal aggregation procedure. Care should be taken,
therefore, to see how the model pre-processes the soil
and land use input data and to which degree some of
the input transformations affect the model outputs. The
threshold value for the sub-catchment size is a critical
parameter in the SWAT model, driving the internal aggregation and therefore its impacts on modelling performance.
The simulated hydrographs at the outlet of a rural
catchment in the central part of Belgium were strongly
influenced by the soil parameterisation scheme. With
GIS technology, soil map information can easily be
included within distributed hydrological models. Yet,
the parameterisation procedures from readily available
data should be evaluated carefully. The modelling performance analysis suggested that derived soil properties should be calculated directly on the basic soil
data, prior to averaging. In terms of using the AVSWAT
model, the user should analyse the input maps and prepare them prior to using the model. This will provide a
more reliable overlay of the land use and soil map for
the application of the hydrological model.

This study is supported by the Fifth Framework
Programme of the European Communities, the Directorate General Research and the Energy, Environment and Sustainable Development Programme. The
authors would like to acknowledge the developers of
the AvSwat interface.

References
Abbot, B.M., Refsgaard, J.Ch., 1996. Distributed Hydrological Modelling. Kluwer Academic Publishers, 321 pp.
Adams, W.A., 1973. The effect of organic matter on the bulk and
true densities of some uncultivated podsolic soils. Soil Sci. 24,
10–17.
Arnold, J.G., Engel, B.A., Srinivasan, R., 1993. Continuous-time,
grid cell watershed model. In: Proceedings of the Conference,
Spokane, WA, June 18–19, pp. 267–278.
Arnold, J.G., Allen, P.M., Muttiah, R., Bernhardt, G., 1995. Automated base flow separation and recession analysis techniques.
Groundwater 33 (6), 1010–1018.
Arnold, J.G., Allen, P.M., 1999. Automated methods for estimating
baseflow and ground water recharge from streamflow records. J.
Am. Water Resour. Assoc. 35 (2), 411–424.
Becker, A., Braun, P., 1999. Disaggregation, aggregation and spatial
scaling in hydrological modelling. J. Hydrol. 217, 239–252.
Beven, K., Feyen, J., 2002. The Future of Distributed Modelling,
Hydrological Processes, vol. 16, Willy, No. 2, p. 1.
Chanasyk, D.S., Mapfumo, E., Willms, W., 2002. Quantification and
simulation of surface runoff from fescue grassland watersheds.
Agric. Water Manage. 1782, 1–17.

A.A. Romanowicz et al. / Ecological Modelling 187 (2005) 27–39
Chess Project, 2001. Climate, hydrochemistry and economics of
surface-water systems. Summary of the Chess project, Report July 2001. Final report: http://www.nwl.ac.uk/ih/research/
iresearch.html.
Comit´e National de G´eographie, 1970. Carte p´edologique
(d’association des soil) de la Belgique ou 1:500 000.
Conan, C., Bouraoui, F., de Marsily, G., Bidoglio, G., 2002. A modelling of the water quality in the Coet-Dan catchment (France).
In: Proceedings from the Third International Conference on Water Resources and Environment Research, vol. II, Dresden, Germany, pp. 222–226.
Defourny, P., Hecquet, G., Philippart, T., 1999. In: Lowell, K., Jaton, A. (Eds.), Digital Terrain Modeling: Accuracy Assessment
and Hydrological Simulation Sensitivity, Published in Spatial
Accuracy Assessment: Land information Uncertainty in Natural
Resources. Ann Arbor Press, Chelsea, Michigan, p. 323.
FitzHugh, T.W., Mackay, D.S., 2000. Impacts of input parameter
spatial aggregation on an agricultural nonpoint source pollution
model. J. Hydrol. 236, 35–53.
Freeze, R.A., Harlan, R.L., 1969. Blueprint for a physically-based
digitally-simulated hydrological response model. J. Hydrol. 9,
237–258.
IRSIA, 1972. Institut pour l’encouragement de la Recherche Scientifique dans l’Industrie l’Agriculture, maped issued by Institut
G´eographique Militaire in 1972. Survey taken in 1958.
Jannsen, P.H.M., Heuberger, P.S.C., 1995. Calibration of processoriented models. Ecol. Modell. 83, 55–66.
Kannan, N., White, S.M., Worrall, F., Groves, S., 2002. Hydrological modelling of small catchments using SWAT. In: Proceedings
of the XXVII Conference on General Assembly, Nice, France,
April, 21–26. European Geophysical Society.
Loague, K., Green, R.E., 1991. Statistical and graphical methods for
evaluating solute transport models: overview and application. J.
Contam. Hydrol. 7, 51–73.
McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E., 2002.
FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Computer software program produced by the authors
at the University of Massachusetts, Amherst.

39

Muttiah, R.S., Wurbs, R.A., 2002. Scale-dependent soil and climate variability effects on watershed water balance of the SWAT
model. J. Hydrol. 256, 264–285.
Nash, J.E., Sutcliffe, J.E., 1970. River flow forecasting through conceptual models. Part I. A discussion of principles. J. Hydrol. 10
(3), 282–290.
Nathan, R.J., McMahon, T.A., 1990. Evaluation of automated techniques method for base flow and recession analyses. Water Resour. Res. 26 (7), 1465–1473.
Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R., 2001. Soil
and Water Assessment Tool. Theoretical Documentation and
User’s Manual. Version 2000. USDA-ARS, Temple, TX, USA,
506 pp.
Persoons, E., 1999.Bassin versant de la Dyle en R´egion
wallonne—Crues de projet (pour des p´eriodes de retour de 1/201/50-1/100 ans). Internal document.
Refsgaard, J.Ch., Storm, B., 1996. Construction, calibration and validation of hydrological models. In: Abbot, B.M., Refsgaard, J.Ch.
(Eds.), Distributed Hydrological Modelling. Kluwer Academic
Publishers, p. 321.
Rutledge, A.T., Daniel, C.C., 1994. Testing an automated method
to estimate ground-water recharge from streamflow records.
Groundwater 32 (2), 180–189.
Sophocleous, M., Perkins, S.P., 2000. Methodology and application
of combined watershed and ground-water models in Kansas. J.
Hydrol. 236, 185–201.
STOWA, 1999. STOWA Report 99-05. Good Modelling Practice
handbook, 122 pp.
Van Orshoven, J., Vandenbroucke, D., 1993a. Guide de l’utilisateur
de Aardewerk base de donn´ees de profils p´edologiques, November 1993 Rapport 18B, Katholieke Universiteit Leuven, 40 pp.
Van Orshoven, J., 1993b. Assessing hydrodynamic land qualities
from the soil survey data. Th`ese de doctorat. Katholiek Universiteit Leuven, 263 pp.
Woesten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1998. Using existing soil data to derive hydraulic parameters for simulation models
in environmental studies and in land use planning. Report 156,
Wageningen, The Netherlands, 106 pp.