Sediment yield mapping in a large river basin: the Upper Yangtze ...

Environmental Modelling & Software 18 (2003) 339–353
www.elsevier.com/locate/envsoft
Sediment yield mapping in a large river basin:
the Upper Yangtze, China
X.X. Lu a,∗ , P. Ashmore b , J. Wang b
a
Department of Geography, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260
b
Department of Geography, University of Western Ontario, London, Canada N6A 5C2
Abstract
A number of studies have mapped sediment yield at global or regional scales using sediment load measurements from rivers.
However, the suitability of the limited mapping methods has not been fully addressed, particularly for large river basins where the
sediment load data were obtained from a hierarchical river network. This study examines some of the issues related to the mapping
approaches using long-term sediment load data obtained in the Upper Yangtze basin, China. The sediment yield data are treated
as point values and interpolated using the kriging function in Arc/Info GIS. Barriers have been incorporated into the interpolation
procedure to confine the interpolation points to the same major flow systems. The incorporation of barriers causes sharp changes
of the interpolated values along the barrier lines, and significantly increases interpolation time. Scaling ratios relative to a standard
size of drainage area have been developed for major drainage basins to remove effects of drainage sizes on sediment yield. By
incorporating the scaling ratios the sediment load data from various sizes of drainage areas can be adjusted to what they would be
if the drainage areas were the same and a less biased sediment yield map can be obtained as compared to using the original dataset.
© 2003 Elsevier Science Ltd. All rights reserved.
Keywords: Sediment yield; Kriging; Interpolation; Scaling ratio; The Upper Yangtze; China
1. Introduction
Sediment yield is a ‘watershed wide’ measurement of
soil erosion, transport and deposition (Lane et al., 1997)
obtained by estimation of point loads at the outlet of the
basin. Sediment yield maps can be used to indicate the
regional variability of sediment sources within a drainage
basin and the temporal changes in the relative contributions
of parts of the catchment (Lu and Higgitt,
1998a,b). A number of authors have mapped sediment
yield using sediment load data at global (Fournier, 1960;
Strakhov, 1967; Milliman and Meade, 1983; Walling
and Webb, 1983; Jansson, 1988; Ludwig and Probst,
1998) or regional scales (Neil and Mazari, 1993; Stone
and Saunderson, 1996). However, the suitability and
comparability of mapping methods have not been fully
addressed. Problems arise for two main reasons. First,
sediment yield maps are areal representations of point
∗
Corresponding author. Tel.: +1-65-6874-6135; fax: +1-65-6777-
3091.
E-mail address: geoluxx@nus.edu.sg (X.X. Lu).
data that integrate the net upstream erosion effects—
which raises the question of to what area should the
point value be attributed? Second, the relationship
between upland erosion and downstream sediment yield
is complicated due to changes in sediment storage
(Trimble, 1981) and variation in sediment delivery ratios
(Walling, 1983) that often correlate with drainage basin
area. The significance of this effect of drainage area on
specific sediment yield lead Milliman and Syvitski
(1992) to conclude that it is not possible to accurately
map sediment yield.
Certainly, there is no generally agreed procedure for
mapping and interpreting sediment yield data from a series
of nested catchments. Lu and Higgitt (1998a) examined
three methods for sediment yield mapping in a large
river basin: polygon attribution, multiple regression
modeling and point interpolation procedure. The previous
methods used for sediment yield mapping at global
and regional scales are classified using the three categories
(Table 1). Each of the three methods has advantages
and limitations (Lu and Higgitt, 1998a). Polygon attribution
uses real values, but requires digitized boundaries
for automated mapping and gives uniform values over
1364-8152/03/$ - see front matter © 2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S1364-8152(02)00107-X

340 X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
Table 1
A summary of the previous sediment yield mapping methods
Authors Number of Mapping area Mapping methods Notes
stations
Fournier (1960) 96 Global Polygon attribution
Strakhov (1967) 60 Global Polygon attribution
Milliman and Meade (1983) 65(?) Global Polygon attribution
Walling and Webb (1983) 1500 Global Polygon attribution Only the data from 1000–10,000 km 2 used
Jansson (1988) 2000 Global Polygon attribution 300–10,000 km 2 in drainage area, and 2 years
measurement included
Neil and Mazari (1993) 14 136 km 2 , Australia Multiple regression Investigated 14 reservoirs, and regression equation
used for mapping a 136 km 2 catchment
Stone and Saunderson (1996) 97 Laurentian Great Point interpolation Only 37 stations used as a basis for interpolation
Lakes area of
using multi-quadric interpolation
Canada
Ludwig and Probst (1998) 62 Global Comb. of multiple Modeled points in a 0.5×0.5° longitude/latitude grid
regression and corrected by 62 observations
interpolation
Church et al. (1999) 151 Canada (excluding Point interpolation A common scaling used. Maps presented for the
the Shield) standard scales of 1, 10×10, 100×100 km 2
interpolated using kriging
the sub-catchment unit. Multiple regression modeling is
attractive, because it avoids the need for digitized boundaries
and there is scope to extend the approach to physically
based modeling. However, the quality of the map
output is dependent on the accuracy of the model. The
third approach, point interpolation, can be undertaken
more efficiently because there is less work involved than
other approaches. The three methods can also be used as
a supplementary way. For example, Ludwig and Probst
(1998) investigated empirical relationships between sediment
yield data obtained from 60 major world river basins
and a large number of parameters extracted from
available hydroclimatic, biological, and geomorphological
datasets at a global scale, and subsequently combined
the results of multiple regressions with the interpolation
of the 60 points to produce a global sediment
yield map with a 2.5°×2.5° longitude/latitude grid resolution.
Comparison between this map and other global
maps (Walling and Webb, 1983; Jansson, 1988) shows
great differences, indicating that there is need for
further improvements.
One significant problem of mapping sediment yields
is the necessity of having a consistent drainage area.
Church et al. (1999) attempted to solve this problem by
empirical adjustment of specific sediment yield to a common
basin area. This was done by developing regional
scaling relations for the variation of suspended sediment
load with drainage basin area. Mapping was then done
by spatial interpolation (kriging) of the adjusted point
data. The issue of sediment storage and delivery is particularly
obvious for large river basins such as the Upper
Yangtze, where the sub-catchments represented by point
data are highly hierarchical. This study attempts to
address mapping suitability and scaling issues arising
from the use of sediment load data for mapping purposes
for a single, large river system using the sediment load
data for the Upper Yangtze basin as an example. The
aims are to (1) examine the suitability of methods for
mapping sediment yield for large river basins where
sediment load is measured from a series of overlapping
catchments; (2) improve the interpolation results by confining
the interpolation procedure within similar
hydrological units, i.e. drainage basins; and (3) attempt
to remove effects of drainage size on sediment yield by
developing a scaling ratio to normalize to a standard
drainage area. On the basis of the maps produced, it is
possible to discuss the major sources of sediment in the
area. An understanding of the links between soil erosion
and sediment yield and sediment transmission to the
fluvial system is an important component of environmental
management in the basin in the context of the
Three Gorges project.
2. Sediment load and sediment yield
Sediment load (SL, t yr 1 ) from a watershed (or catchment
or drainage basin) is the total quantity of sediment
moving out of the watershed in a given time interval,
while sediment yield (SY, t km 2 yr 1 ) is the total quantity
of sediment from a watershed relative to the watershed
area. SL should include bed load as well as suspended
load, but usually only suspended load is
measured due to the difficulty of measuring bed load.
Bed load is often assumed to be of minor importance
even though in extreme cases it can reach 60% (Lane
and Borland, 1951). The calculation for SY is not
straightforward for a large river basin consisting of a

X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
341
series of hierarchical sub-catchments. There are two
alternatives for calculating SY for such hierarchical subcatchments.
Jansson (1988), Lajczak and Jansson (1993),
and Ozturk (1996) calculated SY by deducting the sediment
load at the neighboring upstream station from the
load at the gauging station which is then divided by the
incremental catchment area (Fig. 1). This may give a
negative value, especially in downstream stretches of a
large river, if part of sediment is deposited in the channel
or on the floodplain. SY may be looked upon as net erosion,
because eroded soil and sediment may have been
deposited many times before it reaches the river, where
further deposition may occur on flood plains, in lakes or
in broad river sections upstream of the gauging stations
(Jansson, 1988). This budget method is effective for distinguishing
sediment generation or deposition for a certain
river section. The alternative is to express SY as the
total load divided by the total catchment area upstream
of the station. This means that problems of spatial averaging
increase in significance downstream and in hierarchical
catchments (or the nested sub-catchments),
which requires methods to reduce the impact of the spatial
averaging.
3. Data and methods
3.1. The Upper Yangzte
The Upper Yangtze in general refers to the area
upstream of Yichang in Hubei Province (Fig. 2a). Rising
from Qinghai-Tibet plateau and descending into the
Sichuan Basin, the Upper Yangtze basin includes a
diverse range of environments. The climate in the Upper
Yangtze is mainly controlled by elevation, due to the
effect of Qinghai-Tibet Plateau on atmospheric circulation
(Ruddiman et al., 1989). The plateau constrains
the penetration of the monsoon, resulting in a complex
pattern of precipitation within the basin, ranging from
arid in the extreme northwest to subtropical monsoon
climates in the south. With an area of over 1 million km 2 ,
the Upper Yangtze basin contains the following major
tributaries: the Jinsha, the Yalong, the Dadu-Min, the
Tuo, the Jialin and the Wu. The Upper Jinsha, the
Yalong, the Dadu- Min principally drain the mountainous
areas to the west of the basin, while the Tuo and
the Jialin flow through areas of high population density
and agricultural activity. The Wu, the only significant
right bank tributary, is largely agricultural but drains the
karst upland of Guizhou Province.
3.2. Hydrological data
Fig. 1. Schematic map showing hierarchical sub-catchments. SY:
sediment yield (t km 2 yr 1 ), SL: sediment load (t yr 1 ), and DA:
drainage area (km 2 ).
A hydrological measurement network in the Upper
Yangtze basin has been maintained since the 1950s.
There are over 255 stations with both water discharge
and sediment load measurements (Fig. 2b). The numbers
of stations by drainage area for the tributaries and the
main river are summarized in Table 2. The available data
cover the period 1957–1987. The data after 1987 are no
longer available in the public domain. The length of record
is varied: 187 stations have a length of record of 5
years or over and 56 stations 25 years or over. Seven
out of the 255 stations have no locations in latitude and
longitude and were excluded from the analysis. The
hydrological data set includes drainage area upstream,
monthly water discharge (m 3 s 1 ) and suspended sediment
load (kg s 1 ), maximum and minimum daily water
discharge and suspended sediment transport plus the
dates of their occurrences. Details about the data set have
been described elsewhere (Higgitt and Lu, 1996; Lu and
Higgitt, 1998a,b). There are some inconsistencies in the
original data set; it took considerable time to correct
those errors during the conversion from paper to digital
format. The drainage areas and locations for many stations
are not consistent throughout the time series. This
may reflect correction of previous measurement of drainage
area or a slight change of station location. The names
of some stations are also not consistent throughout the
measurement years. Limited data are available for bed
load transport in the Upper Yangtze basin. Dam engin-

342 X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
Fig. 2.
The Upper Yangtze basin: (a) major tributaries, and (b) hydrological stations and the interpolation barriers.
Table 2
Numbers of hydrological stations for main tributaries in the Upper Yangtze basin
Drainage area (km 2 ) Jinsha--Yalong Dadu-Min Jialin Wu Main Yangtze Total
100 1 0 0 0 0 1
100–1000 29 7 13 5 6 60
1000–10,000 29 22 42 15 12 120
10,000–100,000 11 19 14 6 1 51
100,000–1,000,000 14 2 1 0 5 22
1,000,000 0 0 0 0 1 1
Total 84 50 70 26 25 255

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343
eers for the Three Gorges Project estimated bed load as
0.05% of sediment load, but measurements at Yichang
suggest a bed load contribution of 1.5–2% (Higgitt and
Lu, 1996). This may be particularly significant in the
lower Jinsha, the upper Jialin and the Three Gorges area,
where landslides frequently occur. The underestimates
of bed load transport will result in a higher risk for the
potential sedimentation of the reservoir.
3.3. Kriging
The point data are interpolated using the kriging command
in Arc/Info GIS. Kriging, an advanced geostatistical
procedure that generates an estimated surface from
a scattered set of points, involves an interactive investigation
of the spatial behavior of the phenomenon, so that
the best estimation method for generating the output surface
can be made (ESRI, 1994). Kriging is based on the
regionalized variable theory that assumes that the spatial
variation in the phenomenon is statistically homogeneous
throughout the surface. The spatial variation of
a variable is quantified by a semi-variogram that can be
modeled by fitting a theoretical function to the sample
semi-variogram:
g∗(h) [2N(h)] 1[z(x)z(x h)] 2 (1)
where g∗(h) is the experimental variogram as a function
of distance h, z(h) and z(x + h) are measurements of a
given variable at locations x and x + h, separated by the
directional distance h, and N(h) is the number of pairs
of sampling units in the given distance class. The variogram
is derived using one of a number of suitable variogram
models. Arc/Info provides two types of surface estimators:
ordinary kriging and universal kriging. Ordinary
kriging, which assumes that the variation of the variable
is free of any structural component (drift), uses the
spherical, circular, exponential, Gaussian and linear
methods. Universal kriging, represented by the universal
1 (linear function) and universal 2 (quadratic equation)
methods, assumes that the spatial variation across the
surface has a structural component (drift), i.e. a systematic
change in a particular direction. The spatial variation
of a variable for universal kriging is the sum of three
components: a structural component (drift), a random but
spatially correlated component, and random noise representing
the residual error (ESRI, 1994).
The procedures outlined by ESRI (1994) are followed
to generate the best results. The universal 1 (linear
function) method is chosen because the sediment yields
show a clear increase from west to east (Fig. 2b). The
grid resolution has a substantial effect on the modeled
semi-variogram. The 10 km grid resolution was selected,
which gives a 100 km 2 of cell size and is close to the
size of the smallest catchment (Table 2). The grid resolution
is also close to the resolutions of most environmental
databases such as GTOPO30 and global land cover,
which were used in a sediment yield study by Lu and
Higgitt (1999) and Higgitt and Lu (1999). Under the
fixed grid resolution, the sample size and search radius
should be based on the distance (range) within which
the observed point values are spatially correlated. The
default value of sampling points for Arc/Info is 12. To
minimize the possibility of using trans-boundary points
during the interpolation procedure, the number of the
sampling points was reduced to 6. The reduction in the
sampling points permits local variations to be prominent.
We left the maximum search radius (the diagonal extent
of the input sample) as the default. If the number of
sampling points cannot be satisfied within the maximum
search radius, a smaller number of points will be used.
4. Results and discussion
The original sediment load dataset was first interpolated
using kriging. Subsequently, attempts were made
to improve the interpolation by (i) controlling the interpolation
processes within a similar hydrological unit, and
(ii) removing the drainage area effects on sediment yield
by developing a scaling ratio using the relationship
between sediment load and drainage area. Some of the
issues associated with these modifications are discussed
in the following sections.
4.1. Data interpolation
The original sediment load data were interpolated following
the procedures outlined above. The contour lines
indicate the four classes, 0–250, 250–500, 500–1000 and
1000 t km 2 yr 1 (Fig. 3a), which are the standard
categories of soil erosion classes used in China. The
mean value of the sediment yield throughout the basin
is 497 t km 2 yr 1 with a standard deviation of 519 t
km 2 yr 1 . The upper Jialin and the lower Jinsha tributaries
have the highest sediment yields (1000 t km 2
yr 1 ), and the upper Jinsha and Yalong tributaries the
lowest (250 t km –2 yr 1 ). The highest sediment yield
in the upper Jialin and the lower Jinsha is associated
with landslides, debris flows and a cover of loess
materials (Yu et al., 1991; Higgitt and Lu, 1999). In contrast,
the upper Jinsha-Yalong and the upper Dadu-Min
are tundra areas with lower population density and lower
sediment yield. Sediment yield is also higher in the
Sichuan basin (mainly the Jialin and the Tuo tributaries),
reflecting an area dominated by agricultural lands with
some of the highest population densities in China. Purple
soil, one of the most erodible soils (Lu and Shi, 1992),
is the dominant soil type in this area. When
implementing soil and water conservation measures,
attention should be paid to those areas with higher sediment
yield.

344 X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
Fig. 3. Interpolated SY maps: (a) without barrier control, (b) with barrier control, (c) scaled to 100 km 2 , (d) scaled to 1000 km 2 , and (e) scaled to 10,000 km 2 .

X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
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Fig. 3. Continued

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Fig. 3. Continued

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Fig. 3. Continued

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Fig. 3. Continued

X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
349
4.2. Data interpolation with controls
The above interpolation uses adjoining points regardless
of whether they are in the same flow system. In
order to confine the interpolation processes within relatively
uniform environmental conditions, a barrier option
is incorporated into the interpolation procedures. The
basin can be divided into the six hydrological units: the
upper Jinsha-Yalong, the lower Jinsha, the Dadu-Min,
the Jialin, the Wu and Main River (Fig. 2), based upon
the major tributaries and environmental variables such
as elevation, land cover and population density (Lu and
Higgitt, 1999). Main River here refers to the rest of the
area which does not fall in any of the tributaries and
mainly includes the Three Gorges area. Each of the six
units has unique characteristics in terms of explaining
sediment yield variability (Lu and Higgitt, 1999). The
division using the watershed boundaries as major interpolation
units is also relevant for watershed-based natural
resource management and soil erosion control. The alternative
is to put in barriers related to particular physiographic
boundaries that cut across a basin (e.g. a geological
boundary), but allow interpolation across watershed
boundaries provided they have similar physiography.
This may be better than use of the drainage basins in
terms of the controls on erosion rate, but it is difficult
to obtain such boundaries without further data.
The original data are interpolated with the incorporation
of the drainage divides (Fig. 2b) as barriers using
the kriging barrier function in Arc/Info, and shaded
using the same sediment yield classes introduced above
(Fig. 3b). The mean sediment yield for the entire basin
is 493 t km 2 yr 1 with a standard deviation of 559 t
km 2 yr 1 , which is very close to the interpolation without
any barriers. Using major basin boundaries does
allow step changes in yield that cannot occur with the
polygon attribution method (one of the mapping methods
listed in Table 1). In other words using major drainage
basin boundaries resembles polygon attribution but
allows for variation within polygons (i.e. the drainage
basins). If the barriers are set for each point, then the
barriers become the boundary of each watershed and the
point interpolation becomes polygon attribution, as
described by Lu and Higgitt (1998a). However, the introduction
of barriers causes sharp changes along the barrier
lines (Fig. 3b). The effects of the barriers on interpolation
values are examined using the three lines across
the basin as outlined in Fig. 2b. The lines of A–A1 and
B–B1 cross the whole basin approximately from west to
east and north to south, while the line of C–C1 is the
line used as a barrier. The values interpolated without
or with barriers are very similar, except for the areas
near the barrier lines (Fig. 4). The incorporation of barriers
into the kriging interpolation considerably increases
the computer processing time, and reduces the number
of interpolation points which is problematic for the units
with fewer data points such as the main river unit. The
effects of the barriers on the sediment yield map will
also depend on the numbers of the sampling points, e.g.
the larger the number of sampling points the stronger
the barrier effect will be.
4.3. Sediment yield scaling
The above interpolation procedures treat the data as
point values located at the sub-catchment outlets (the
gauging stations), which does not allow for a weighting
of the value according to the size of sub-catchment area,
upon which sediment yield is dependent. The drainage
areas of the sub-catchments from which the sediment
measurement data were obtained in the Upper Yangtze
range from less than 100 to over 1,000,000 km 2 (Table
2). SY from basins of widely varying sizes cannot be
used directly for SY comparison because of the likely
relation between SY and drainage basin area (Lu and
Higgitt, 1998a; Church et al., 1999). The classic way to
examine sediment yield for different drainage areas is to
plot sediment yield against drainage area. Recent discussion
has questioned the validity of regression
between variables that have catchment area as a common
term (Waythomas and Williams, 1988; De Boer and
Crosby, 1996). Milliman and Syvitski (1992) plotted
sediment load and drainage area to examine global sediment
yield. A plot of SL and DA and their residuals for
the Upper Yangtze basin are shown in Fig. 5. The best
fit equation is:
SL 849.15DA 0.9215 (n 248, r 2 0.7735) (2)
Despite the significant scatters in Fig. 5b, Eq. (2) indicates
that over 77% of the variance in sediment load
(SL) can be explained by the size of drainage area (DA).
The exponent (hereafter referred to as b) for Eq. (2) indicates
the extent to which SL increases in the downstream
direction (Higgitt and Lu, 1999). When b 1, SL is
increasing more slowly than basin area, which has been
reported in many agricultural environments as a consequence
of sediment storage (Walling, 1983). When b
1, SL increases more rapidly than basin area. This
situation is generally found in mountainous regions as a
consequence of channel processes and/or reworking of
Quaternary sediments (Ashmore and Day, 1988; Church
et al., 1989; Dedkov and Moszherin, 1992). The
exponent for the whole Upper Yangtze is higher than
those reported by Milliman and Syvitski (1992), indicating
that the proportion of eroded material transferred
downstream is somewhat higher than the global average
(Table 3).
The close relationship between SL and DA is not surprising
due to the fact that larger areas have the potential
to generate more sediment. Eq. (2) divided by DA yields
a relationship between SY and DA as:
SY 849.15DA 0.0785 (3)

350 X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
Fig. 4. Profiles showing barrier effects on interpolation (lines: without barriers, dots: with barriers). See positions of A–A1, B–B1 and C–C1 in
Figure 2b.
Fig. 5.
SL and DA relationship and residuals for the entire Upper Yangtze basin.
An exponent of 0.0785 (close to 0) indicates almost
no effect of area on SY, so there is no need to adjust
SY for differences in the entire Upper Yangtze basin.
However, the SL–DA relation differs between the six
regions (Table 3), so there is a need to make such adjustment
for most of the regions. The exponents are contrary
to expectation. The predominantly agricultural areas (i.e.
the Jialin and the Wu drainage basins) have values of
b 1, and the units located in the west part of the basin,
notably the Jinsha-Yalong and the Dadu-Min basins,
have b 1. The results may in part be an artifact due
to the geographic spacing of gauging stations (Higgitt
and Lu, 1999). In the Jialin and the Wu basins agricultural
land is mainly located in the lower parts of the
basins that have more sediment generation than sediment
deposition, whereas in the western mountainous regions
there is a dramatic reduction in the relief in the lower
parts of the basins, which provides more opportunity for
sediment deposition. The difference in the exponent b
between the six regions indicates that there is no spatially
consistent scaling, as compared with the Canadian
case in which the scaling relation for SY–DA was almost
the same in all regions so that a single adjustment could
be made for all the data (Church et al., 1999).
On the basis of Eq. (3), a scaling ratio (SR) relative
to a standard drainage area (SDA) can be developed as:
SR SDA 0.0785 DA 0.0785 (4)
where SR = SY/SSY, with SSY being the sediment
yield for the standard drainage area. Assuming a standard
drainage area of 100, 1000 and 10,000 km 2 , Eq. (4)
results in a constant a = SDA 0.0785 of 1.44, 1.72 and 2.06,
respectively. Using the same approach as developing Eq.
(3) and (4), scaling ratios relative to certain standard
drainage areas can be developed. Fig. 6 shows the scaling
ratios relative to the three standard drainage areas
for each of the six hydrological units. There are two distinctive
groups: (1) the Jialin and the Wu, and (2) the
Jinsha-Yalong, the lower Jinsha, the Dadu-Min, and
Main River. The scaling ratio of 1 indicates an upward
shift, and 1 a downward shift.
With the scaling ratios available, the original sediment
yield data were scaled to the three standard areas. The
scaled data were then interpolated and shaded using the
same procedures outlined above (without barriers). One
of the marked features is the pattern of the sediment
yield changing with the standard drainage sizes in the
southwest corner of the basin (Fig. 3c–e). Fig. 3e has a

X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
351
Table 3
The relationships between SL and drainage area (DA) for the global
data set (after Milliman and Syvitski, 1992) and the Upper Yangtze
basin
River systems
SL=aDA b
a b R 2 Number of
stations
3000 m 280 0.46 0.80 21
1000–3000 m
N/S America, Africa, 170 0.52 0.70 41
Alpine Europe
South Asia and Oceania 65 0.56 0.74 90
Non-alpine Europe and 50 0.73 0.78 10
High Arctic
500–1000 m 12 0.42 0.82 55
100–500 m 8 0.66 0.81 43
100 m 1 0.64 0.81 15
Upper Yangtze 849 0.92 0.77 248
Jinsha-Yalong 5606 0.65 0.65 33
Lower Jinsha 639 1.01 0.76 47
Dadu-Min 1569 0.84 0.76 44
Jialin 222 1.13 0.84 75
Wu 98 1.14 0.80 25
Main Yangtze 611 0.96 0.97 24
Table 4
Sediment yield relative to each standard drainage area calculated based
on SY–DA relationships for the six units
Major tributary Standard drainage areas (km 2 )
100 1000 10,000 100,000
Jinsha-Yalong 1102 489 216 96
Lower-Jinsha 465 475 486 497
Dadu-Min 747 516 356 245
Jialin 397 531 710 950
Wu 183 250 342 468
Main Yangtze 606 564 524 487
very similar pattern to Fig. 3a, but it removes the effect
of drainage size on sediment yield. The effects of the
scaling ratios are clearly shown in Table 4 in which sediment
yield relative to each of the standard drainage areas
is calculated for the six units. The upper Jinsha-Yalong
has a very high sediment yield for the smaller standard
drainage area, but rapidly dropped down when drainage
area increases. An appropriate scaling ratio for each of
the six units can be determined according to the majority
of drainage basin sizes (Table 2). The majority of drainage
areas of the entire Upper Yangtze basin are within
the range of 1000–10,000 km 2 , an appropriate standardized
size therefore should fall in this range.
5. Summary and conclusion
Mapping sediment yield using sediment load measurements
is not straightforward for a single large river basin
where the data set includes hierarchical relationships.
After reviewing the available sediment yield mapping
methods, this study interpolates sediment load measurements
from hydrological stations using kriging. The
characteristics of the interpolated surface can be controlled
by limiting the number of points used for calculating
each interpolated value, or by defining the radius
within which all points are used in the interpolation. The
interpolation uses the nearest points regardless of
whether they are from the same hydrological system;
therefore, an attempt has been made to confine the points
used for interpolation by incorporating major hydrological
units as barriers. The incorporation of barriers causes
sharp changes of the interpolated values near the barriers,
and significantly increases interpolation time. The
control of the interpolation might be necessary if the distribution
of land surfaces undergoing high rates of soil
erosion does not coincide with tributary boundaries.
Kriging interpolates point values, which does not allow
a weighting of the value according to the size of the subcatchment
area. This study attempts to standardize the
sediment yield data by developing a scaling ratio relative
to the standard sizes of drainage areas for each major
hydrological unit. An appropriate standard size for scaling
is critical to obtain a less biased map.
Sediment yield mapping through point interpolation
procedures treats the data as a point value located at the
sub-catchment outlet (the gauging station). This does not
Fig. 6.
Scaling ratios as affected by drainage areas.

352 X.X. Lu et al. / Environmental Modelling & Software 18 (2003) 339–353
incorporate any locational information about the subcatchment
position other than the location of the outlet
into the interpolation procedure. Stichling (1973) plotted
sediment concentration in the center of gravity of the
catchment when drawing a map of Canada. At present
there appear to be no suitable methods for interpolating
values which have hierarchical relationships. It is hoped
that this paper has raised attention to the lack of a uniform
approach to sediment yield mapping at present. The
maps which have been generated have some utility for
macro-scale management of problems imposed by high
sediment transport, particularly in the identification of
sediment sources attributable to soil erosion. The
increasing availability of high resolution environmental
databases suggests that it is possible to employ physically-based
models for sediment yield mapping at both
global and regional scales.
Acknowledgements
The research started while the first author was in
receipt of a Natural Sciences and Engineering Research
Council of Canada (NSERC) Postdoctoral Fellowship,
and subsequently completed at National University of
Singapore (NUS). We acknowledge financial support
from NUS to X.X. Lu, an NSERC grant to P. Ashmore,
and an Academic Development Fund to J. Wang. We
are grateful to Dr I. Creed, Department of Geography,
University of Western Ontario, for accessing Arc/Info
GIS facilities, and Mrs Lee Li Kheng, Department of
Geography, NUS, for preparing some of the diagrams.
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