HYDROLOGICAL PROCESSES
Hydrol. Process. 24, 714– 725 (2010)
Published online 10 November 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/hyp.7509
Simulating the integrated effects of topography and soil
properties on runoff generation in hilly forested catchments,
South China
Xi Chen,1 * Qinbo Cheng,1 Yongqin David Chen,2,3 Keith Smettem4 and Chong-Yu Xu5,6
1
4
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
2 Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
3 Centre of Strategic Environmental Assessment for China, The Chinese University of Hong Kong, Hong Kong, China
Centre for Ecohydrology, School of Environmental Systems Engineering, The University of Western Australia, Nedlands 6009, Australia
5 Department of Geosciences, University of Oslo, PO Box 1047, Blindern, NO-0316 Oslo, Norway
6 Department of Earth Sciences, Uppsala University, 75236 Uppsala, Sweden
Abstract:
Estimation of runoff components associated with catchment topography and soil properties is critical for planning water
resources utilization and evaluating hydrological changes due to artificially induced land surface manipulation. In this study,
the modified TOPMODEL by Scanlon et al. (2000) was applied to simulate runoff-generating processes and to separate runoff
components in two hilly forested catchments within the Dongjiang Basin of Southeast China. The modified TOPMODEL was
improved by integrating an evapotranspiration package with the model algorithms. Influences of catchment topography and
soil properties on runoff generation were analysed on the basis of explicit expression of catchment field capacity distribution
derived from the topographic index and catchment average field capacity. Study results demonstrate that the model is capable
of simulating hydrological processes and separate hydrological components in both hourly and daily time steps. Total runoff
generation primarily depends on the effective storage capacity of unsaturated zone. A 50% decrease of the effective storage
capacity from 0Ð22 to 0Ð11 m over the soil zone leads to a 6Ð6% increase in total runoff. Topography plays a dominant role
in formation of runoff components. When the catchment mean slope increases by 87%, subsurface storm flow could increase
by 50% whilst overland flow decreases by 7Ð5% and baseflow by 6Ð7%. Vertical changes of soil permeability influence runoff
components as well. Decrease of the lower layer hydraulic transmissivity may result in 2–3% increase of overland flow and
subsurface storm flow and 5% decrease of baseflow. Copyright  2009 John Wiley & Sons, Ltd.
KEY WORDS
TOPMODEL; topographic index; hydrological components; root zone storage capacity
Received 31 July 2008; Accepted 15 September 2009
INTRODUCTION
Watershed outflow in humid tropical regions is usually
classified as infiltration excess overland flow, saturation
excess overland flow, unsaturated subsurface flow and
saturated subsurface flow (Thomas, 1994). The formation
of these flow components and their pathways depends
on soil prosperities, e.g. soil texture and permeability,
and topography. In forested catchments of temperate
regions, substantial subsurface flow can be generated
(Whipkey, 1965; Hewlett and Hibbert, 1967; Weyman,
1973) because of the high infiltration capacities of the
forest surface soils perched above less permeable soil
layers or a slowly moving wetting front (Hammermeister
et al., 1982). Topography is regarded as a driving force
that subsurface storm flow occurs quickly enough to contribute to peak stream discharge and a greater percentage
of precipitation is converted to subsurface flow in the
lower hillslopes (Beven and Kirkby, 1979; O’Loughlin,
1986; Scanlon et al., 2000).
* Correspondence to: Xi Chen, State Key Laboratory of HydrologyWater Resources and Hydraulic Engineering, Hohai University, Nanjing
210098, China. E-mail: xichen@hhu.edu.cn
Copyright  2009 John Wiley & Sons, Ltd.
Distributed hydrological models possess the flexibility to deal with different conditions in a wide range of
geographical regions. They can be used to estimate the
hydrological components of runoff if the model parameters are appropriately calibrated using a set of measured
streamflow data and then validated using another set of
flow data. Among these models, TOPMODEL (Beven
and Kirkby, 1979) has provided hydrologists with a powerful tool to analytically simulate the hillslope response
of site-specific topography. It operates at basin scale by
making use of the statistics of topography, rather than
all the topographic details (Beven and Kirkby, 1979).
Moreover, many efforts have been made to improve the
TOPMODEL structure to better capture streamflow discharges or/and to increase model capability for describing
meaningfully the hydrological processes under different
climatic and catchment conditions. Among them, Scanlon et al. (2000) recognized that transient and perched
stormflow played a substantial role in hilly forested catchments and so modified TOPMODEL by augmenting the
single subsurface flow component defined by one continuous water table into two subsurface components. The
modified TOPMODEL has been successfully used for the
SIMULATING EFFECTS OF TOPOGRAPHY AND SOIL PROPERTIES ON RUNOFF
short term hydrological simulation in forested catchments
(Scanlon et al., 2000).
For distributed hydrological modelling, site-specific
information of soil properties and topography is usually
needed for unsaturated and saturated accounting. In
general, site-specific soil spatial information is the least
known of the land surface attributes (Nielsen and Bouma,
1985). But the distribution of soil properties and the
rooting depth can be estimated by approximating the soil
variations with more easily observable variables, such
as terrain and vegetation variables (Moore et al., 1993).
For catchments located at the steeper upland and the
gentler lowland, a steeper catchment usually possesses
shallower soil than a gentler watershed (Shaman et al.,
2002). The thick soil in the gentler areas is able to hold
more water than the shallower soil in the steeper areas.
However, runoff generation depends on soil moisture
holding capacity or effective storage capacity (SC) of the
unsaturated zone. In the hilly lowland areas, groundwater
is shallow due to hillslope- (Anderson, 1982) or bedrock(Freer et al., 1997) induced convergence of subsurface
flow or return flow (Hewlett and Hibbert, 1967; Hewlett,
1974). In the shallow groundwater table areas, because
the rooting depth in the unsaturated zone for holding soil
moisture may be reduced due to the high groundwater
table occupation, the holding capacity or effective SC in
the unsaturated zone becomes small.
In a hilly area, the topographic index can represent
the influences of terrain on the spatial variations of soil
wetness. A larger topographic index is an indicator of
higher soil wetness and smaller soil moisture deficit,
which means easier runoff generation in response to rainfall input. Besides the soil wetness distribution driven
by basin terrain, the topographic index also indicates
the heterogeneity of the unsaturated zone SC across
the catchment as reflected by the spatial variations of
soil moisture holding capacity which is termed as field
capacity in the Xinanjiang model (Zhao et al., 1980).
Guo et al. (2000) demonstrated that the distribution of
normalized holding capacity or effective SC (f/F ¾
SRM/WMM) can be substituted by normalized topographic index (f/F ¾ IRDG). Here, f/F is a fraction
of watershed area less than SC; SRM is SC at a point,
which varies from zero to the maximum of the whole
watershed WMM; index of relative difficulty of runoff
generation
(IRDG) is derived from topographicindex
max[lna/ tan ˇ] lna/ tan ˇ
IRDG D max[lna/ tan ˇ] min[lna/ tan ˇ] . The
distribution of normalized SC together with catchment
average SC SRMAX can derive SC distribution (see Chen
et al., 2007 for details). Compared with distribution of
topographic index, this SC distribution can distinguish
influences of catchment characteristics of the terrain and
unsaturated zone soil on the runoff generation.
The modified model by Scanlon et al. (2000) is targeted for short term hydrology. The short term hydrological model, which deals with single or several rainfall
events, has developed in the way that effective rainfall and evapotranspiration are evaluated on some simple
Copyright  2009 John Wiley & Sons, Ltd.
715
assumptions. For a long period of hydrological simulation
in the forested watershed, however, vegetation canopy
and root distribution take a critical influence on evapotranspiration and thus soil moisture deficit and runoff
generation. TOPMODEL follows generally adopted practice in calculating actual evapotranspiration as a function
of potential evaporation and root zone moisture storage.
It does not explicitly describe how rainfall interception
and transpiration influence hydrological processes in the
forest catchments. Additionally, the long term hydrological behaviours are usually simulated in a daily time step
using daily data as the model’s input. Because of uneven
distributed rainfall and evapotranspiration during a day,
applicability of the modified model by Scanlon et al.
(2000) for the long term hydrology should be validated.
The main objective of this study is to examine how different runoff components simulated are affected by catchment topography and soil characteristics. For explicitly
expressing influences of unsaturated zone dynamics on
runoff generation in the long term simulation, we incorporate evapotranspiration package into the modified TOPMODEL and establish the spatial variation of effective
root zone SC based on topography index and watershed
average SC. The study is exemplified in two catchments
with different topographic conditions and soil depth and
properties. The capability of the model to simulate overland flow, subsurface storm flow and baseflow was first
assessed by calibration and validation on the basis of
observed streamflow in two hilly forest catchments of
Dongjiang River Basin, South China. A comparison of
the simulation results between the two catchments was
then made to reveal the influences of catchment topography and soil properties. A sensitivity analysis was performed in order to quantitatively evaluate the influences
of terrain slopes, soil moisture capacities and hydraulic
transmissivity on the runoff components.
DESCRIPTION OF STUDY SITE AND DATA
Dongjiang Basin has a drainage area of 35 340 km2
above Shilong hydrologic station (Figure 1). Subtropical monsoon climate dominates the region with average annual rainfall of 1750 mm and potential evaporation of 1297 mm. The region is characterized by
a highly seasonal rainfall and considerable streamflow
variability. Over 75% of the rainfall occurs during the
wet season (April–September). Dry-season streamflow
(October–March) accounts for only about 20% of the
annual streamflow. The basin has a complex geological structure and Precambrian, Silurian and Quaternary
geological formations are encountered at the surface
with granites, sandstones, shale, limestone and alluvium.
The stream networks have developed on deeply weathered granite and sandstones. Drainage densities of the
basin are high with an average value of 5Ð3 km/km2 .
The landscape is characterized by hills and plains, comprising 78Ð1 and 14Ð4% of the basin area, respectively.
Forest covers headwater areas and intensive cultivation
Hydrol. Process. 24, 714– 725 (2010)
716
X. CHEN ET AL.
Figure 1. Catchments (Lianping, Xingfeng and others) in the Dongjiang basin
Table I. Catchment characteristics
Catchment
Lianping
Xingfeng
Area
(km2 )
37Ð2
42Ð6
Elevation (m)
Stream Length,
Dmax (km)
14
10
Max.
Min.
Mean
1262
508
247
149
593Ð0
267Ð6
dominates hills and plains lower in the basin. Vegetation species and coverage were estimated on the
basis of remote sensing images (data sources from
http://www.naturalresources.csdb.cn). Dongjiang Basin
is extensively covered by vegetation and the coverage is 53Ð9% on average. The vegetation species
include low shrub (36Ð9% in area in 1998), subtropical evergreen and deciduous broad-leaved mixed forest
(26Ð6%), evergreen coniferous forest (13Ð4%), subtropical evergreen broad-leaved forest (3Ð9%), subtropical
evergreen broad-leaved forest (2Ð3%), herb (0Ð4%) and
crops (16Ð5%). Normalized difference vegetation index
(NDVI) in the whole Dongjiang Basin is approximately
0Ð55, smaller in the downstream area of Pearl River
(Zhujiang) Delta region and larger in the upper watershed. The monthly NDVI in the whole watershed varies
from smallest of 0Ð43 in March to the largest 0Ð65 in
July.
Copyright  2009 John Wiley & Sons, Ltd.
Average Basin
Slope (%)
Mean annual
Rainfall (mm)
Forest Cover
(%)
16Ð6
9Ð86
1948Ð3
1871Ð6
95Ð0
91Ð4
The Dongjiang Basin has been experiencing rapid
economic growth over the past two decades and as a
result there has been an increasing competition for water.
Dongjiang is also a major water supply basin for Hong
Kong and other cities in the region. Understanding runoff
generation mechanism associated with basin characteristics is essential for managing water resources in the
coming decades as the demand for water is predicted to
grow significantly.
To evaluate the influences of topography and soil on
runoff response, we selected two catchments of similar
size in the Dongjiang Basin (Xingfeng and Lianping)
with different terrain and soil properities (Figure 1).
With elevation from 247 to 1262 m above mean sea
level, Lianping catchment is situated in the forested
headwater area and more than 90% land surface is
covered by forest (Table I). Located in central part of
the upper Dongjiang Basin, the ground surface elevation
Hydrol. Process. 24, 714–725 (2010)
SIMULATING EFFECTS OF TOPOGRAPHY AND SOIL PROPERTIES ON RUNOFF
of Xingfeng catchment varies from 149 to 508 m, and
its basin relief and slope are much smaller than those
in Lianping (Table I). Soil in the two catchments is
primarily red loam consisting of sandy loam and sandy
silt. In the whole Dongjiang Basin, the soil thickness is
1–2 m over the weathered rock which fracture is filled
with impervious silt (grain size <2 µm) (Liu et al., 2008).
Tree roots loosen the upper soil, and the upper layer of
10–30 cm thickness has rich pores and large infiltration
capacity, and the underlying soil is primarily red silt
with small infiltration capacity (Deng et al., 1999). After
heavy rainfall, a shallow intermittent groundwater system
develops on silt, and thus saturates part of the stream
zone and generates streamflow through saturation excess
overland flow and interflow processes. The soil depth
across the upland Lianping catchment is much shallower
than the lowland Xingfeng catchment where the soil
thickness can reach as large as 3 m. The streambed
deposits of sand and gravel facilitate strong interactions
between surface and groundwater systems in both study
catchments.
Observation data include daily rainfall from five stations in Xingfeng and four stations in Lianping, pan
evaporation for each catchment and streamflow discharge
at each catchment outlet during the period from 1982 to
1987. A heavy rainfall event with hourly observation data
during May 7–24 in Xingfeng catchment was selected
for validating simulation capacity of the modified model
in different time steps. A digital elevation model (DEM)
with a resolution of 25 m is used to calculate the topographic index by using the DTM9704 program (Beven,
1997a,b).
METHODS
The modified TOPMODEL
The original TOPMODEL has two subsurface components from unsaturated and saturated zones divided by
one continuous water table (Robson et al., 1992; Hornberger et al., 1994). Subsurface contribution to stream
discharge is determined by the position of this water table
relative to the upper unsaturated zone and the lower saturated zone. In hilly forested catchments, an additional
shallow saturated flow perched above low-conductivity
layers in the unsaturated zone disconnects from the main
water table (Scanlon et al., 2000) (Figure 2). Runoff
into the stream channel includes (1) saturated excess
overland flow plus channel precipitation, (2) unsaturated
flow or shallow storm flow from the perched saturated
layer, and (3) baseflow from the permanent groundwater
zone (Figure 2). Clapp et al. (1996) gave a generalized
presentation of the modified TOPMODEL and showed
how subsurface storm flow from the upper unsaturated
zone during precipitation and baseflow from groundwater storage contribute separately to stream discharge
(Figure 2).
Overland flow. Surface flow may be generated either
due to precipitation falling on the saturated area where
Copyright  2009 John Wiley & Sons, Ltd.
717
Figure 2. Schematic diagram of the modified TOPMODEL (adopted from
Scanlon et al., 2000)
saturation deficit (Ssfi for the upper layer and Sgwi for the
lower layer) is equal to zero or due to the unsaturated
zone deficit being satisfied. Both cases represent saturation excess mechanisms of runoff production. Areas of
larger values of lna/ tan ˇ, i.e. areas of convergence or
low slope angle, will saturate first and as the catchment
becomes wetter the area contributing surface flow will
increase. Calculated surface flow at any time step is simply the water in excess of any deficit in each lna/ tan ˇ
increment.
Subsurface flow. Vertical root zone drainage occurs
when unsaturated zone storage for free water SUZi for
the i-th increment of lna/ tan ˇ begins to be larger
than zero during a rainfall period. Vertical percolation
to the saturated subsurface zone below is calculated
as:
SUZi
Quz D
1
Ssfi td
where td is a time constant and Ssfi is the subsurface storm
flow saturation deficit at a specific site and is determined
(Ambroise et al., 1996) by




a/ tan ˇ
Ssfi D S maxsf 
S maxsf Ssf 
 
1 A a/ tan ˇdA
A
2
where S maxsf and Ssf are the maximum saturation deficit
of this zone and the mean catchment storage deficit over
the entire area A at a given time, respectively.
During a storm, perched water develops positive pressure over a resistive layer. Transmissivity of the upper
layer surface Tsfi is linear with saturated deficit if the
hydraulic conductivity is a constant (Ambroise et al.,
1996).
Ssfi
Tsfi D Tsf0 1 3
S maxsf
where Tsf0 is transmissivity of upper layer surface.
Hydrol. Process. 24, 714– 725 (2010)
718
X. CHEN ET AL.
Discharge from this zone is expressed as:
Ssf
Qsf D Q0sf 1 S maxsf
4
where Q0sf is a storm flow zone recession parameter and
influences the storm flow recession slope.
Ssf is reduced by inflow from the overlying unsaturated
zone, Quz , and is increased by outflow to the stream,
Qsf , and vertical drainage to the groundwater zone, Qv .
The change in average storm flow zone deficit over a
simulation time step is expressed as:
Ssf
D Quz C Qsf C Qv
t
5
Baseflow. Vertical drainage that depletes the water in
the subsurface storm flow zone and replenishes the water
stored in the groundwater zone (Scanlon et al., 2000) is
expressed as:
Qv D
N
min cS maxsf Ssfi , Sgwi Ai
6
iD1
where c is a simple transfer coefficient, N is the number
of topographic index bins, and Ai is the fractional
catchment area corresponding to each bin. Thus, Qv
is calculated with the condition that recharge for each
topographic index bin i is limited to the corresponding
local groundwater saturation deficit Sgwi . There is no
time delay between variations in subsurface storm flow
drainage and groundwater discharge because the vadose
zone thickness tapers to zero at the base of the slope.
The transmissivity of lower layer T0i is exponentially
decreased with saturated deficit (Ambroise et al., 1996).
Sgwi
T0i D T0 exp
7
m
where T0 is transmissivity of lower layer surface and m
is a scaling parameter related to soil properties (Beven
and Wood, 1983).
Following Beven and Wood (1983), the local groundwater storage deficit Sgwi for any value of lna/ tan ˇ is
related to average catchment storage deficit, Sgw , by
Sgwi D Sgw C m[ lna/ tan ˇgwi ]
8
where  is the areal average of lna/ tan ˇ.
The average groundwater storage deficit Sgw changes
over each simulation time step with inputs from vertical
recharge Qv and outputs to stream discharge Qgw :
Sgw
D Qv C Qgw
t
9
TOPMODEL computes the recession of hydrograph by
relating groundwater discharge Qgw to average groundwater deficit Sgw :
Sgwi
Qgw D Q0 e m
Copyright  2009 John Wiley & Sons, Ltd.
10
where Q0 D T0 e^ is a model parameter related to
soil hydraulic properties and topography according to the
TOPMODEL concept (Beven and Wood, 1983; Wolock,
1993).
Equations (9) and (10) demonstrate that baseflow Qgw
is large during the rainfall period when groundwater
aquifer gains more recharge and keeps a high groundwater table. When recharge to the groundwater aquifer is
zero (Qv D 0), a linear relationship between 1/Q and time
on the recession hydrograph should therefore be obtained.
After both surface and subsurface water flow into
the stream channel, they are routed through the channel
system to the basin outlet. The number of time steps for
routing should first be determined based on a specified
maximum channel flow distance, Dmax and a constant
channel wave velocity parameter, RV.
Evapotranspiration. Effective rainfall and evapotranspiration are important in a long term hydrological model.
Our modified model includes an evapotranspiration package which deals with canopy and evapotranspiration processes in detail. The total evapotranspiration (ET) is the
sum of (1) direct evaporation from the top shallow soil
layer, Edir ; (2) transpiration via canopy and roots, ET ;
and (3) evaporation of precipitation intercepted by the
canopy, Ec .
A simple linear method is used to calculate Edir
(Mahfouf and Noilhan, 1991):
Edir D 1 f ˇEp
11
where f is the green vegetation fraction (cover) which
is estimated by a scaled NDVI (Zeng et al., 2000), ˇ D
SRZ/SRMAX, SRZ and SRMAX are root zone storage
and SC, respectively, Ep is the potential evaporation calculated using a Penman-based energy balance approach
that includes a stability-dependent aerodynamic resistance (Mahrt and Ek, 1984). ET is calculated by
Wc n
12
E T D f E p Bc 1 S
where Bc is a function of canopy resistance, and Wc
is intercepted canopy water content estimated from the
budget for intercepted canopy water, and S is the maximum canopy capacity and n D 0Ð5. Finally, the third
component of the total evapotranspiration (ET), Ec , can
be estimated by
Wc n
13
Ec D f Ep
S
The budget for intercepted canopy water is
∂Wc
D f P Dp E c
∂t
14
where P is total precipitation. If Wc exceeds S, the excess
precipitation or drip, Dp , reaches the ground. For additional details concerning each term in Equations (11)–
(14), the reader is referred to Chen and Dudhia (2001).
Hydrol. Process. 24, 714–725 (2010)
719
SIMULATING EFFECTS OF TOPOGRAPHY AND SOIL PROPERTIES ON RUNOFF
Table II. Model parameters after calibration and validation
Catchment
m (m)
td
(m h)
RV
(m/h)
S maxsf
(m)
SRMAX
(m)
C
(m2 / h)
T0sf
(m2 /h)
T0
(m2 / h)
Lianping
Xingfeng
0Ð04
0Ð16
0Ð5
0Ð35
1650
1650
0Ð100
0Ð125
0Ð12
0Ð22
0Ð8
0Ð7
3Ð1
2Ð0
1
1
Table III. Model calibration and validation results
Catchment
Lianping
Xingfeng
Period
1982–1985
1986–1987
Mean
1982–1985
1986–1987
Mean
Rainfall
(mm/day)
5Ð572
4Ð864
5Ð218
5Ð452
4Ð490
4Ð971
Evapotranspiration
(mm/day)
Ec
ET C Edir
Observed
Simulated
0Ð959
0Ð769
0Ð864
0Ð946
0Ð759
0Ð853
0Ð768
1Ð096
0Ð932
1Ð300
1Ð265
1Ð283
3Ð768
3Ð004
3Ð386
3Ð187
1Ð994
2Ð591
3Ð921
3Ð012
3Ð467
2Ð889
2Ð112
2Ð501
MODEL SIMULATION RESULTS AND ANALYSIS
Model calibration and validation
The model was calibrated for stream discharge from
1982 to 1985 using the trial and error method and
validated from 1986 to 1987, generating the model
parameters presented in Table II. The calibration and
validation results at a daily time step for both catchments
were summarized in Table III.
The parameters in Table II and their differences
between the two catchments reflect the hydrological characteristics influenced by topography and soil properties.
Thick soil usually deposited in gentle slope catchment has
large value of available water capacity SRMAX in the
root zone and large maximum saturation deficit S maxsf
in the storm flow zone. Calibration results demonstrate
that SRMAX in Xingfeng is approximately two times of
that in Lianping, which is a clear evidence of the relative difficulty in total runoff generation in Xingfeng.
Larger maximum saturation deficit S maxsf , along with
smaller values of C and T0sf in Xingfeng are indicators
of the difficulty of subsurface storm flow generation. The
much larger scaling parameter m in Xingfeng implies that
baseflow recession in Xingfeng is much slower than in
Lianping and thus Xingfeng is capable to contribute more
baseflow than Lianping.
As shown in Table III, for Xingfeng catchment,
Nash–Sutcliff efficiency coefficient (NSC) is 0Ð78 and
0Ð74, and root mean squared error (RMSE) is 0Ð38
and 0Ð32 mm/d in the calibration and validation periods,
respectively. For Lianping catchments, NSC is 0Ð85 and
0Ð84 and RMSE is 0Ð49 and 0Ð50 mm/d in the calibration and validation periods, respectively. Simulated and
observed stream discharges are shown in Figures 3 and 4.
The agreement between simulated and observed streamflow demonstrates the reliability of the modified model
for streamflow simulation.
Copyright  2009 John Wiley & Sons, Ltd.
Runoff (mm/day)
NSC
RMSE
(mm/day)
0Ð85
0Ð83
0Ð84
0Ð78
0Ð74
0Ð76
0Ð49
0Ð50
0Ð50
0Ð38
0Ð32
0Ð35
Simulated evapotraspiration and runoff components
Simulated total evapotranspiration ET (D Ec C ET C
Edir ) during the whole period of 1982–1987 is 1Ð796
and 2Ð135 mm/d for Lianping and Xingfeng catchments, respectively (Table III). In both catchments, substantial amount of ET is from precipitation intercepted by the canopy Ec , e.g. about 48 and 42%
of the total ET for Lianping and Xingfeng catchments, respectively. The total of direct evaporation from
the top shallow soil layer Edir and transpiration via
canopy and roots ET is 52 and 58% of the total ET,
respectively.
The three simulated components of overland flow, subsurface storm flow, and baseflow in 1983 were shown
in Figure 5 for Lianping and Xingfeng. Proportions
of contribution of the three components to the total
runoff during 1982–1987 were presented in Table IV.
In Xingfeng, contributions of baseflow, subsurface storm
flow and overland flow are 71Ð62, 11Ð07 and 17Ð31%
of the total flow, respectively, and in Lianping, 52Ð58,
33Ð49 and 13Ð93%, respectively. Contribution of subsurface storm flow to the total streamflow in Lianping
is much larger than that in Xinfeng. On the contrary,
groundwater flow in Lianping is significantly less important. This implies that subsurface storm flow is easily
generated in the steeper slope catchment where more
perched water flows out and less remains in the relatively impermeable layer or percolates downward into
aquifer. As a result, the amounts of baseflow and overland
flow will decrease as more water runs off as subsurface storm flow. The flow duration curve analysis also
showed that in comparison with 1-day stream flow at
50% of the time (Q50), the low flow ratios of Q75/Q50
and Q95/Q50 in Lianping are 0Ð5 and 0Ð32, respectively, smaller than the corresponding values of 0Ð64 and
0Ð37 in Xingfeng (Zhang et al., 2009). The smaller low
flow ratio in Lianping indicates quick baseflow recession
Hydrol. Process. 24, 714– 725 (2010)
720
X. CHEN ET AL.
(a)
Discharg, m3/s
20
Simulated
Observed
Calibration
10
0
1982
1983
1984
1985
Discharge, m3/s
(b) 20
Validation
Simulated
Observed
10
0
1986
1987
Figure 3. Daily simulated and observed streamflow discharge of Lianping catchment for 1982– 1985 (a) and for 1986– 1987 (b)
(a)
25
Simulated
Observed
Discharge, m3/s
20
Calibration
15
10
5
0
1982
Discharge, m3/s
(b)
1983
1984
1985
20
Simulated
Observed
Validation
10
0
1986
1987
Figure 4. Daily simulated and observed streamflow discharge of Xingfeng catchment for 1982– 1985 (a) and for 1986– 1987 (b)
and small groundwater contribution in comparison with
Xingfeng.
Comparison of daily and hourly simulated results
TOPMODEL uses two simple linear storage reservoirs
for computation of the subsurface and groundwater zone
dynamics [Equations (4) and (5), and Equations (9) and
(10), respectively]. Hydrological dynamics of the two
zones can be integrated as following:
dSsf
Ssf
D f t, Ssf 15
D Quz C Qv C Qsf0 1 dt
Smax
Copyright  2009 John Wiley & Sons, Ltd.
Sgw
dSgw
16
D Qv C Q0 e m D f t, Sgw dt
For the model parameters in Table II, Equations (15)
and (16) are mathematically convergent and stable in both
daily and hourly time steps because they meet Lipschitz
condition: jf x, y1 f x, y2 j Ljy1 y2 j, where L
is a constant. Therefore TOPMODEL can be applied for
short term (an hour) and long term (a day) streamflow
simulation in the study catchments.
The influences of time steps and model inputs on
simulated results of runoff and hydrological components
Hydrol. Process. 24, 714–725 (2010)
721
SIMULATING EFFECTS OF TOPOGRAPHY AND SOIL PROPERTIES ON RUNOFF
15
Lianping
Discharge, m3/s
Overland flow
Subsurface storm flow
Base flow
10
5
0
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Nov
Dec
20
Discharge, m3/s
Xingfeng
Overland flow
15
Subsurface storm flow
Base flow
10
5
0
Jan
Feb
Mar
Apr
May
Jun
July
Aug
Sep
Oct
Nov
Dec
Figure 5. Simulated overland flow, subsurface storm flow and baseflow in 1983
Catchment
Lianping
Xingfeng
Period
Overland
Flow (%)
Subsurface
Storm Flow
(%)
Baseflow
(%)
1982–1985
1986–1987
Mean
1982–1985
1986–1987
Mean
14Ð02
13Ð86
13Ð93
17Ð38
16Ð75
17Ð31
33Ð86
33Ð11
33Ð49
11Ð86
10Ð27
11Ð07
52Ð12
53Ð03
52Ð58
70Ð76
72Ð98
71Ð62
were analysed as following. Firstly, the model was
executed in an hourly step during a heavy rainfall
period of 7–24 May 1987, using parameters from daily
calibration (Table II) except that c in Equation (6) is
changed as c/24 for hourly computation. The model
inputs are hourly observed rainfall and hourly even
distributed rainfall within a day (Phour D Pday /24). The
hourly simulated results are shown in Figure 6. Secondly,
the model was executed in a daily step during the
heavy rainfall period. The daily simulated hydrological
components were compared with the daily accumulated
results from the hourly simulation (Figure 7). Lastly,
model was executed for a long period of 1982–1985 in
time steps of an hour and a day, using daily and hourly
even distributed rainfall and potential evapotranspiration
as the model inputs, respectively (Table V).
The simulated results demonstrate: (1) model simulation using daily data or hourly even distributed data
reflects average state of hydrological processes within a
day, and can not capture hydrological dynamics of the
hourly flood events. For a large rainfall event uneven
distributed within a day, the even distributed treatment
Copyright  2009 John Wiley & Sons, Ltd.
Discharge (m3/s)
Table IV. Model simulated flow components
80
70
60
50
40
30
20
10
0
Observation (hourly)
Simulation (hourly observed data)
Simulation (hourly even distributed data)
0
48
96
144
192
240
Time (hour)
288
336
384
Figure 6. Hourly simulated and observed streamflow discharge of
Xingfeng catchment during May 7–24
decreases rainfall amount during the short term heavy
rainfall period and increases rainfall amount during the
rest small rainfall period. Peak discharges of hourly storm
flow resulting from an hourly heavy rainfall are severely
under predicted using hourly even distributed data. For
example, with hourly observed inputs, the hourly simulated storm flood discharge at the time step of 216th hour
is 62Ð9 m3 / s, three times larger than the simulated discharge of 17Ð51 m3 / s with hourly even distributed inputs
(Figure 6). However, based on hourly even distributed
inputs, the simulated discharges during the rest small
rainfall period are significantly increased. (2) Therefore,
the total runoff for different time steps and model inputs
is very close, e.g. during the heavy rainfall period of
7–24 May 1987, 5Ð198, 5Ð117 and 5Ð115 m3 / s for the
daily step using daily observed rainfall as model inputs,
and for the hourly step using hourly observed and hourly
even distributed data, respectively. (3) Moreover, for different time steps and model inputs, daily processes and
the total amount of simulated hydrological components
Hydrol. Process. 24, 714– 725 (2010)
722
Discharge (m3/s)
(a)
X. CHEN ET AL.
20
Overland flow
Daily observed data
Hourly observed data
Hourly even distributed data
15
10
5
0
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Time (hour)
Subsurface storm flow
(b)
Daily observed data
Hourly observed data
Hourly even distributed data
Discharge (m3/s)
4
3
2
1
0
0 24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Time (hour)
Discharge (m3/s)
(c)
Base flow
4
Daily observed data
Hourly observed data
Hourly even distributed data
3
2
1
0
0
24 48 72 96 120 144 168 192 216 240 264 288 312 336 360 384
Time (hour)
Figure 7. Daily hydrographs of simulated overland flow (a), subsurface
storm flow (b) and baseflow (c) with model inputs of daily and hourly
observed data, and hourly even distributed data during May 7–24
of the overland flow, subsurface storm flow and baseflow
during the heavy rainfall event and the 4-year period are
very close as well (Figure 7 and Table V).
DISCUSSION
With its proven capability for distinguishing runoff components, the modified TOPMODEL can be used to evaluate effects of variations of topography and soil properties
on runoff generation and to separate subsurface storm
flow from groundwater flow in the two study catchments.
Distribution of effective SC and its influence on runoff
generation
In the two study catchments, topographical influences
on runoff generation are represented by the IRDG indices
in Figure 8b derived from the relative frequency of topographic indices in Figure 8a. As shown in the two figures,
Lianping catchment has larger areas with smaller values of lna/ tan ˇ than Xingfeng catchment (Figure 8a),
and correspondingly the IRDG of Lianping catchment is
larger than Xingfeng catchment (Figure 8b). Therefore,
regarding the topographic influence, runoff generation
in response to rainfall input in Lianping is more difficult than that in Xingfeng. In terms of the soil properties, however, runoff generation becomes easier in Lianping catchment with thinner soil deposit than Xingfeng
where the areal average SC, SRMAX, is larger and thus
more infiltrated water in soil layers will be lost through
evapotranspiration.
Figure 8c shows the spatial distribution of SC derived
from IRDG in Figure 8b and SRMAX in Table II. For
Lianping, SC varies from zero in the wet lowland and
nearby stream channels to the largest of 150 mm in the
hilly ridges; for Xingfeng, SC ranges from 0 to 280 mm.
Figure 8c indicates that, due to the integrated influences
of topography and soil deposit, Lianping catchment
can produce more runoff than Xingfeng catchment for
the same atmospheric forcing. Model results show that
the total recharge rate over the Lianping catchment
is 3Ð0 mm/d, approximately 50% larger than that in
Xingfeng catchment. A greater rate of recharge and more
rapid outflow in Lianping when compared to Xingfeng
indicates that the catchment with steeper topography is
more hydrologically responsive to atmospheric inputs.
Such steep catchments may be more prone to extremes
of flood and drought, leading to difficulties of water
resource utilization. This conclusion is consistent with
the statistical results that runoff coefficient (mean annual
runoff divided by mean annual rainfall) in Lianping is
larger than Xinfeng, 0Ð65 versus 0Ð52, on the basis of the
multi-year mean of rainfall and runoff during 1982–1987.
Sensitivity analyses of topography and SC on streamflow
components
Sensitivity analyses were performed to analyse the
influences of topographic index determined by slope gradient and effective SC on overland flow (OF), subsurface
storm flow (SSF) and groundwater flow (GF). To demonstrate the effects of topographic variations, we raised the
ground surface elevation Hgs of Xingfeng catchment to
be as high as 1Ð2–2Ð0 times of Hgs in 0Ð2 Hgs increment;
the corresponding hillslope angles increase by 1Ð19–1Ð87
times on the average. When all the altitudes are doubled,
Table V. Model simulated results of flow components for different time steps and model inputs
Period
Time Step and Model Input
A heavy rainfall during May 7–24
1-h observed data
1-h even distributed data
1-day observated data
1-h even distributed data
1-day observed data
1982–1985
Copyright  2009 John Wiley & Sons, Ltd.
Overland Flow
(%)
Subsurface Storm Flow
(%)
Baseflow
(%)
42Ð91
43Ð38
44Ð60
17Ð89
17Ð38
22Ð52
22Ð23
23Ð32
10Ð89
11Ð86
34Ð53
34Ð38
32Ð05
71Ð22
70Ð76
Hydrol. Process. 24, 714–725 (2010)
723
0.16
Lianping
Xingfeng
0.12
0.08
0.04
0
5
0
10
15
ln(a/tanβ) value
20
25
8
OF
SSF
GF
Total flow
6
4
2
0
0
10
(b) 1.00
Figure 10 shows the influences of the catchment average SC SRMAX on the amounts of overland flow, subsurface storm flow and groundwater flow, i.e. increase of
runoff as a result of SRMAX decrease. A 50% decrease
of SRMAX from 0Ð22 to 0Ð11 m leads to 6Ð6% increase
of the total runoff, and 7Ð0, 7Ð3 and 6Ð5% increases for
overland flow, subsurface storm flow and groundwater
flow, respectively.
Lianping
Xingfeng
0.40
0.00
0.0
0.2
0.4
0.6
0.8
Cumulative frequency (f/F)
1.0
(c) 300
SC (mm)
250
200
Sensitivity analyses of hydraulic transmissivity
on streamflow components
150
100
Lianping
Xingfeng
50
0
0.0
0.2
0.4
0.6
0.8
1.0
Cumulative frequency (f/F)
Figure 8. Integrated effects of variations of topography and unsaturated
zone soil on runoff: relative frequency distribution of lna/ tan ˇ values
(a), cumulative frequency of IRDG (b), cumulative frequency of effective
storage capacity (c)
the uneven increase of ground surface elevation would
cause 87% increase in hillslope angles and reduce the
basin average topographic index from 14Ð14 to 13Ð43.
As shown in Figure 9, such changes of reducing topographic index or increasing hillslope angles would substantially increase subsurface storm flow by 50% and
slightly decrease overland flow and groundwater flow by
7Ð5 and 6Ð7%, respectively.
Subsurface storm flow (SSF)
Overland flow (OF)
Groundwater flow (GF)
50
0
−2
40
−4
30
20
−6
10
13.5
13.6
13.7
13.8
13.9
Topgraphic index
14
14.1
Change of OF and GF (%)
60
−8
14.2
Figure 9. Changes of OF, SSF and GF with the topographic
index
Copyright  2009 John Wiley & Sons, Ltd.
In this implementation of TOPMODEL from
Equation (7), the lower layer soil depth is indirectly
defined by m as m controls the depth of active hydraulic
conductivity. For the fixed upper layer hydraulic transmissivity T0sf in Table II, changes of hydrological components are computed as m ranges from 0Ð04 to 0Ð20
(Figure 11). For small m, the lower layer soil becomes
compact and hydraulic conductivity decreases fast as
the depth to groundwater increases. The overland flow
and subsurface stormflow is expected to be increased
and groundwater flow decreased because more infiltrated
water perches on the lower layer surface.
Sensitivity of m on hydrological components is conducted in Xingfeng catchment. When the depth to
groundwater reduces to 1 m below the lower layer surface, hydraulic transmissivity at the lower layer decreases
to 2Ð14 ð 105 and 0Ð067 m2 / h as m is 0Ð04 and 0Ð16,
respectively. Figure 11 shows that when m increases from
0Ð04 to 0Ð16, overland flow and subsurface flow are
Change of GW (%)
IRDG
0.60
0.20
Change of SSF (%)
50
Figure 10. Changes of OF, SSF, GF and total flow with SRMAX
0.80
0
13.4
20
30
40
Percent of SRMAX decrease (%)
71
20
70
18
69
Groundwater flow (GF)
Overland flow (OF)
Subsurface storm flow (SSF)
68
14
12
67
66
0.02
16
0.04
0.06
0.08
0.1
m
0.12
0.14
0.16
Change of OF and SSF (%)
0.2
Change of runoff components (%)
(a)
Relative frequency
SIMULATING EFFECTS OF TOPOGRAPHY AND SOIL PROPERTIES ON RUNOFF
10
0.18
Figure 11. Changes of OF, SSF and GF with m
Hydrol. Process. 24, 714– 725 (2010)
724
X. CHEN ET AL.
decreased by about 2 and 3% of the total flow, respectively, and meanwhile groundwater flow is increased
by 5%.
flood period. This study is very important for environmental protection and for water resources utilization in
the rapidly changing region of Dongjiang watershed.
CONCLUSIONS
ACKNOWLEDGEMENTS
Traditionally hydrograph analysis involves the decomposition of streamflow into three major components of
surface runoff, interflow and baseflow. Distinguishing
runoff components is often very difficult because they
are influenced by highly complicated and spatially variable conditions. This study demonstrates that hydrological models can offer an approach for investigating runoff
components and their influences by topography and soil
properties. The modified TOPMODEL is proven to be
mathematically convergent and stable for both daily and
hourly simulations and successfully applied in two catchments. The calibrated model suggests a certain separation
of runoff components for a fixed parameter set.
Simulation results demonstrate that in the forest catchment, evapotranspiration from precipitation intercepted
by the canopy is more than 40% of the total ET,
indicating that forest takes a significant regulation on
rainfall–runoff processes. Topography and soil properties are two dominantly physical drivers of flow and are
primary determinants of catchment response. To examine
the influence of individual factors, we found that topography is less influential on the total amount of runoff but
significantly alters constituent components. The steeper
slope catchment generates more subsurface storm flow
under the same climate forcing. When the hillslope
increases by 87%, subsurface storm flow could increase
by 50% whilst overland flow and baseflow decrease by
7Ð5 and 6Ð7%, respectively. This study demonstrates that
the distribution curve of catchment effective root zone
SC, which integrates the topographic index and the catchment average SC, is a better way to illustrate the combined influences of both topography and soil properties
on runoff generation. We showed that the hilly Lianping catchment, located in the upper headwater regions
with a steep slope and thin soil deposit, generates more
runoff and has larger proportion of quick flow than the
Xingfeng catchment which has a gentle slope and thick
soil deposits. A 50% decrease of average effective SC
from 0Ð22 to 0Ð11 m results in a 6Ð6% increase of the
total runoff, and 7Ð0, 7Ð3 and 6Ð5% increases for overland flow, subsurface storm flow and groundwater flow,
respectively.
Vertical variations of soil permeability influence runoff
components as well. Decrease of hydraulic transmissivity
as the lower layer soil becomes compact results in
more overland flow and subsurface storm flow, and less
baseflow. However, this influence on runoff components
is less significant than topographic variations.
Changes of runoff components are implicated as possible alternations of catchment pathway for water flow and
contaminant transport, and possible alternations of temporal distribution of streamflow during the drought and
The work described in this paper was supported by
the Key Project of Chinese Ministry of Education (No.
308012), the National Natural Science Foundation of
China (No. 50679025), and partially supported by Gledden Visiting Senior Fellowship, Australia. Thanks to the
editor and two anonymous reviewers for their constructive comments on the earlier manuscript, which lead to a
great improvement of the paper.
Copyright  2009 John Wiley & Sons, Ltd.
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