SOIL EROSION AND SEDIMENT TRANSPORT IN THE
MEKONG BASIN
RIYADH AL-SOUFI
Saterinrinne 4(A1), Espoo, FIN-02600, Finland
This paper presents the results of applying the Soil and Water Assessment Tools (SWAT)
to analyze soil erosion and sediment transport in the Mekong Basin using a spatiotemporal modeling approach. Though the study was constrained by the limitation of data,
the results provided a quantitative portrait of the sediments patterns in the basin and the
places where soil erosion is found to be maximal.
INTRODUCTION
Sediments transport and erosion is a complex natural process that is strongly increased by
human activities such as deforestation, agriculture and urbanization. In particular,
Suspended sediments play a key role in controlling water quality and it can cause a major
reduction on stream capacity for handling flood waves.
The erosion in the Mekong Basin is mainly rainfall based runoff erosion subject to
the effects of land cover. Soil erosion patterns in the basin are heterogeneous; therefore,
patterns of soil erosion are difficult to model and predict particularly when data
availability becomes a second constraint. The fact that, the river basin lying across six
countries has caused the system analyses significantly a complex task. The objective of
this study is to determine soil erosion and sediments transport loading patterns using the
Modified Universal Soil Loss Equation within the Soil and Water Assessment Tool
(SWAT) model. SWAT model is being utilized to evaluate surface runoff from different
agricultural and hydrologic management practices.
The Basin covers an area of approximately 795,000 km2 (Figure 1). The Lower
Mekong Basin excludes Yunnan and Myanmar and thus the catchment’s area is estimated
around 615800 Km2. The basin consists of approximately 33 percent Forests. Compared
to other major rivers of the world, the Mekong ranks 12th with respect to length (4880
km), 21st with respect to catchment’s area and 8th with respect to average annual runoff
(475 x 109 m3 per year or 15000 m3/s). The Mekong river flow at China border forms
about 51% of the flow at Vientiane (Lao) and 16% of the flow at Kratie which is the
beginning of the lower flood plain (Al-Soufi and Richey [1]). The wet season lasts from
May to October where the average rainfall around 80-90% of the annual total. The Dry
season period starts from November and lasts until April. The minimum annual rainfall is
1000 mm/year (NE of Thailand) and the Maximum is 4000 mm/year (West of Vietnam).
The Mekong River itself deposits a considerable amount of fertile silt each year
during the flood season on lower forests and flood plain in Cambodia and Vietnam.
Published records have shown that in 1997, 84.6 million ton of soil were washed from the
Lancing-Jiang to the lower Mekong (Kelin & Chun [5]). Pantulu [6] pointed out in his
study that the annual sediment load of the Basin was estimated around 67 x 106 ton/year
at Chiang Saen, 109.106ton/year at Vientiane and 132.106ton/year at Khone Falls. Hården
and Sundborg [4] conducted a study in Lao and NE of Thailand on the suspended
sediment transport in the Mekong River network. They found that sediments vary very
regularly with water discharge. At Pakse, their published data indicated an increase in the
sediment load of about 50% between the 60s and 1992. This was attributed to the
sediment inflow from tributaries in Lao. The report of Hården and Sundborg [4]
presented a wide range of load values at Luang Prabang from a minimum of 63 million
ton in 1987 to 367 million ton in 1966. At Pakse, the minimum value presented was 81
million ton in 1967 to the maximum value of 330 million ton in 1978. The variation
might be attributed to the variation in river discharge particularly the year 1978 when the
flood was the highest ever recorded.
Figure 1. The Mekong Basin.
THE MODELLING APPROACH
The Soil and Water Assessment Tool (SWAT) model is developed by the USDA-ARS
(Arnold et al. 1995) as a basin scale model to simulate the impact of land management
practices on the eco-hydrologic system over long periods of time. It consists of several
modules that simulate varieties of agro-hydrologic processes. The model can be
considered as a pseudo physically based model.
SWAT model includes GIS interfaces, weather generator and water management
options. Runoff is simulated from daily rainfall series using the SCS curve number
method and the Green & Ampt infiltration method. Water is routed through the channel
network using the variable storage routing method or the well known Muskingum routing
method. Detailed descriptions of the two methods can be found in Chow et al. [3].
SWAT model calculates soil erosion caused by rainfall-runoff process using the
Modified Universal Soil Loss Equation (MUSLE) presented by Williams [7]. The model
is a modified form of the Universal Soil Loss Equation (USLE) developed by
Wischmeier and Smith [8]. The difference between the two approaches is that in MUSLE
rainfall energy factor is replaced with a runoff factor which represents energy used in
detaching and transporting sediment. It eliminates the need for the delivery ratios. It also
improves the determination of the sediment yield since runoff is a function of antecedent
moisture runoff factor. The sediment routing model consists of two components operating
simultaneously, namely, deposition and degradation.
SWAT model requires a digital elevation model (DEM) from which it determines the
drainage network and divides the basin into sub-basins defined by grid cells, spatially
related one to another, that each has geographic position in the watershed defined by
surface topography so that the entire area within a sub-basin flows to one common outlet.
Each sub-basin is subdivided into hydrologic response units (HRU) each possess unique
land-use and soil attributes. Detailed description of all modules and the theoretical
documentation can be found in Arnold et al. [2] and in the model manual.
The Modeled area and model adaptation
The study area constitutes Lao, North East of Thailand and the upper part of Cambodia
that is located in central part of the Mekong Basin. Its approximate height ranges from
about 1200 meter to 20 meter above mean sea level and it encompasses an area of
387000 km2. It extends north to Luang Prabang (Lao) and south to Phnom Penh
(Cambodia). Luang Prabang station at the Mekong has been selected as the inlet to the
simulated area for the reason that hydrologic data are reasonably available and relatively
reliable. The study area, which consists of a mosaic of different land uses, its soil is
predominantly Acrisols association of soils. These soils are mainly clay loam in the upper
stratum to clay in the lower stratum and contains little amount of organic matters. The
land-use is broadly classified into deciduous forest, mixed forest, evergreen forest and
agricultural land of two different categories. The identification of the extent of the study
area as well as its boundary and the resolution of the grid system has been done using
certain criterion. A full study has been conducted to derive soil physical parameters from
the soil survey map of the basin. It has been done first from deriving the sand, silt, clay,
rock and organic matters components of each soil unit and then calculating the
parameters using a computer program developed by the author. In determining those
parameters special attention was given to land use and land cover since the upper stratum
of the soil profile has a great effect on the rainfall-runoff process. Soil parameters database has been derived and loaded into SWAT model which can be regarded as the main
factor behind the accuracy achieved in the current model application.
One of the major obstacles was to identify combinations of parameters required to
operate the MUSLE module. In the absence of field investigations, careful examination
of the soil map, DEM and the land use were the only possibility to derive the parameters.
Research papers and text books that provide values of parameters were also reviewed in
this study. Information derived by SWAT from the DEM analyses was also helpful to
calculate MUSLE parameters.
Hydraulic parameters were determined based upon a variety of information sources
such as photos, geo-morphological characteristics of the Mekong River System at
different locations, the profile of the Main River and land cover in the basin.
RESULTS AND CONCLUSIONS
The hydrologic part of the model was first calibrated by using time series of 1990-1994
and validated by using the records from 1996 to 1999. The calibration was only necessary
to tune the hydraulic parameters required for operating the model. Soil parameters as
derived from the soil map were not adjusted during the calibration process.
Flow during the flood season was dealt with carefully. Special attention was given to
ensure the absence of negative outflows from channel reaches and to maintain numerical
stability during the whole simulation period. Time series of the suspended sediments
were re-constructed by utilizing the scattered field measurements of suspended sediments
carried out in 1961/80s and by applying the regression equation that was developed by
Hården and Sundborg [4].
Uncertainties in model simulation are expressed by the relative error (ER) defined
here as follows:
n
 n 
RE   O  P  /   O 
 i 1
  i 1 
(1)
where, n is the number of time steps, O is the Observed value and P is the predicted
value. When RE is multiplied by 100, it gives the percent error. In modeling of the
Mekong hydrologic system and in light of the data quality, the maximum allowable error
(RE) was set to a threshold value of 15%.
Measured and predicted values of discharge are plotted in Figure 2 (left side). The
same figure (right side), visualizes how far away the prediction from the measurements
is. All points falling on the 450 line would indicate perfect prediction capability. When
data points in Figure 2 were regressed, a fitting line (solid) was obtained with a
coefficient of determination ranging from 0.99 to 0.98. At Vientiane, the model is
positively biased as it tends to overestimate the discharges (middle), whereas, at Pakse,
the results are equally biased. In all cases, SWAT has shown incapability in simulating
the peaks due to what is believed inaccurate discharge measurements during the flood
10000
0
90 91 92 93 94 95 96 97 98 99 00
Years
0
20000
0
9000
18000
Measured Q - m3/sec
Simulated - Measured
Discharge - m3/sec
Station: Vientiane
40000
5000
4000
3000
2000
1000
0
-1000
90 91 92 93 94 95 96 97 98 99 00
Years
4000
Measured Q - m3/sec
20000
Measured Q - m3/sec
Simulated - Measured
Discharge - m3/sec
seasons caused in some cases by the backwater flow effects. Accurate simulation of the
base flow was achieved indicating that the soil parameters were accurately derived.
2000
0
-2000
-4000
-6000
0
16500
33000
Measured Q - m3/sec
18000
9000
2
r : 0.99
0
0
9000
18000
Simulated Q - m3/sec
30000
20000
10000
2
r : 0.98
0
0
15000
30000
Simulated Q - m3/sec
Station: Pakse
Figure 2. Comparison between measured (crosses) and simulated river discharges at key
stations along the river Mekong (left). Bias of the variance is shown in the middle. The
deviation of the simulation from the optimum solution is shown on the right.
Table 1. Relative Error (RE) in SWAT output.
Vientiane
Measured Monthly Averages during the 90s
Discharge m3/sec
Suspended Sediments mg/l
max
min
RE%
max
min
RE%
13986
756
13.5
1658
22
26
Mukdahan
26217
1343
14
515
57
21
Pakse
32941
1449
13
672
19
44
Location
Table 1 lists values of the maximum and minimum observed monthly average
discharge during the 90s. Relative Error (RE) of the hydrologic simulation obtained for
the three benchmark stations where all below the threshold level of 15%. Errors can be
minimized further by reducing the base cell size of the DEM theme and by reducing the
“Threshold area” required to define the beginning of a stream. The smaller the specified
“Threshold area”, the more detailed the drainage network delineated by SWAT interface
which certainly results in a longer computing time. The results show that SWAT model
has successfully simulated the hydrologic behavior of the Mekong basin. SWAT model
uses the SCS Curve Number method which assumes runoff volume is independent of
1400
1050
700
350
0
1700
400
Measured - mg/l
Simulated - Observed
Sediments - mg/l
rainfall intensity, in other word; it is based on long duration storms which coincides with the
conditions prevail in the Mekong basin.
Figure 3 shows the results of simulating sediment concentration in river water.
Results are not as accurate as the case with hydrologic simulation. At Pakse, the model
negatively biased for high concentrations and positively biased for low concentrations.
The ER of the monthly averages of the predicted values ranges from 21% to 44%. The
simulation accuracy at Vientiane and Mukdahan is slightly above the threshold level of
15%, whereas at Pakse, the RE is rather high. The accuracy of the model to closely
predict the actual value of sediment concentration is undermined by the fact that the
regression lumped coefficients presented by Hården and Sundborg [4], were determined
under different agro-hydrologic conditions and need to be modified consequently. Hården
and Sundborg [4], indicated an increase of sediment inflow at Pakse since the 60s, and,
pointed out that no indications of changes in sediment loads with time in the upper parts
of the Basin from the beginning of the 60s until the 90s. This may explain the high
simulation errors obtained at Pakse compared to that at Vientiane and Mukdahan.
200
0
-200
2
r : 0.96
RMSR: 66
0
-400
0
90 91 92 93 94 95 96 97 98 99 00
Years
850
850
1700
0
Measured - mg/l
850
1700
Simulated - mg/l
400
0
90 91 92 93 94 95 96 97 98 99 00
Years
Station: Pakse
200
100
0
-100
-200
-300
-400
Measured - mg/l
Simulated - Observed
Sediments - mg/l
Station: Vientiane.
800
700
350
2
r : 0.86
RMSR: 23
0
0
350
700
Measured - mg/l
0
350
700
Simulated - mg/l
Figure 3. Comparison between measured (crosses) and simulated suspended sediments in
Mekong River at two key stations. Bias of the variance is shown in the middle. The
deviation of the simulation from the optimum solution is shown on the right.
The results in Figure 4 show that the east and north of Lao has the highest rates of
sediment yield, ranges from a monthly average of 0.32 ton/h to 0.1 ton/h. There subcatchments are mountainous area with steep slopes classified as mainly forestmixed/deciduous with some scattered evergreen forests and agricultural/grass land. The
suspended sediments are high during the first heavy rains of the season up to 1000mg/l
and gradually decrease to the minimum of less than 10mg/l in the dry season. At
Mukdahan and Pakse, that load values are lower indicating a dilution by less turbid water
from the tributaries. In NE-of Thailand where many irrigation schemes were developed,
the majority of the predicted average monthly sediment yield was in the range of
0.003ton/ha to 0.02ton/h. SWAT model also estimated the average annual loading of the
suspended sediments during the 90s at the following key locations along the Mekong
River:
1. Vientiane: 105.6185 million ton/yr.
2. Mukdahan: 74.0704 million ton/yr.
3. Mun-Chi/Mekong Junction: 0.96 million ton/yr.
4. Pakse: 78.7818 million ton/yr.
5. Kratie: 84.5505 million ton/yr.
The patterns of sediment loading are approximately similar to those figures presented
by Kelin & Chun [5] and slightly less than that presented by Pantulu [6]. However, Field
investigations are necessary to determine the parameters that operate MUSLE equation in
order to achieve an accurate simulation of sediment transport in the basin.
In light of the testing results, SWAT model can be useful in a wide range of studies
particularly for monitoring the deterioration in basin health and in calculating the amount of
sediments and surface applied chemicals that are washed out annually from the agricultural
fields. Admittedly, there is still a lot of work needed to improve both the customization and
the algorithm scheme of the model.
Luangprabang
Vientiane
Mukdahan
Mun-Chi
River network
Pakse
Stung Trung
Kratie
Phnom Penh
Figure 4. (a) The Modeled part of the Mekong Basin
4 Sub-basin #10
4 Sub-basin #4
Monthly Average = 0.27 tons/ha
Monthly Average = 0.32 tons/ha
2
2
0
0
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #22
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #35
Monthly Average = 0.17 tons/ha
Monthly Average = 0.17 tons/ha
1
1
0
0
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #2
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #9
Monthly Average = 0.15 tons/ha
Monthly Average = 0.16 tons/ha
1
1
0
0
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #5
90 91 92 93 94 95 96 97 98 99 00
2 Sub-basin #1
Monthly Average = 0.13 tons/ha
Monthly Average = 0.13 tons/ha
1
1
0
0
90 91 92 93 94 95 96 97 98 99 00
1.0 Sub-basin #24
90 91 92 93 94 95 96 97 98 99 00 01
1.0 Sub-basin #17
Annual Average = 0.11 tons/ha
0.5
Monthly Average = 0.10 tons/ha
0.5
0.0
0.0
90 91 92 93 94 95 96 97 98 99 00
90 91 92 93 94 95 96 97 98 99 00
Figure 4. (b) The ten highest yields of the suspended sediments during the 90s as
calculated by SWAT model.
REFERENCES
[1] Al-Soufi, R. and Richey, J., “Analyses of the Mekong Flow”, Proc. 1st International
Conference on Hydrology and Water Resources in Asia Pacific, Kyoto, (2003) pp
769-774.
[2] Arnold, J.G., Williams, J.R. and. Maidment D.R., “Continuous-time water and
sediment-routing model for large basins”, Journal of Hydrology, Vol. 121, No. 2,
(1995), pp171-183.
[3] Chow, V. T., Maidment, D.R. and Mays L.W., “Applied Hydrology”, McGraw-Hill,
New York 1988.
[4] Hården, P.O. and Sundborg Å.,”The Lower Mekong Basin Suspended Sediment
Transport and Sedimentation Problems”, Computer Processing and Editorial Work:
AB Hydroconsult, Uppsala, Sweden. (1992), 71p.
[5] Kelin, C. and Chun L., “The Wetlands of Mekong Basin in China”, Proc. Workshop
Wetlands, Awareness, Local People and the Ramsar Convention in the Mekong River
Basin, Phnom Penh (1999) pp19-22.
[6] Pantulu, V.R., “The Mekong River System" in Davies B.R. and Walker, K.F. (eds),
“The Ecology of River Systems”, Dr. W. Junk Publishers, Dodrecht, The
Netherlands, (1986), pp. 695-719.
[7] Williams, J.R., “Sediment routing for agricultural watersheds”, Water Resour. Bull.
Vol. 11, No. 5, (1975), pp: 965-974.
[8] Wischmeier, W.H. and Smith D.D., “Predicting rainfall-erosion losses from
cropland east of the Rocky Mountains”, Agriculture Handbook 282, USDA-ARS
(1965).