1
ELIMINATING UNCERTAINTY ASSOCIATED WITH
CLASSIFYING SOIL TYPES IN DISTRIBUTED HYDROLOGIC
MODELING
HAPUARACHCHIGE P. HAPUARACHCHI, ANTHONY S. KIEM, HIROSHI ISHIDAIRA,
JUN MAGOME and KUNIYOSHI TAKEUCHI
Takeuchi-Ishidaira Lab., Interdisciplinary Graduate School of Medicine and
Engineering, Yamanashi University, Takeda 4-3-11, Kofu, Yamanashi 400-8511, JAPAN
This paper presents a method to estimate the parameter values associated with soil
characteristics in a distributed hydrologic model. The new approach accounts for all soil
types in a catchment, regardless of the number of soil types present, eliminating the need
for subjective soil reclassification methods and more accurately approximating the true
physical characteristics of the catchment. The hydrologic model parameter values
corresponding to soil type are calculated as a function of the amount (%) of basic soil
types (sand, silt and clay) within each sub unit (e.g., grid, hydrological response unit or
sub catchment) based on three coefficients (which represent the actual parameter value
for sand, silt and clay respectively). The three coefficients can be determined based on
either field measurements or by calibration (or in the case of ungauged catchments by
transferring coefficient values previously determined in other similar catchments). The
current study uses the new method in the BTOPMC model (block-wise use of
TOPMODEL with Muskingum-Cunge flow routing method) to estimate lateral
transmissivity (T0). The results indicate that the performance of distributed hydrologic
models can be improved using the new method and that reclassifying or grouping soil
types so that the effects of soil on hydrologic processes can be modeled is a significant
and unnecessary source of uncertainty that can be eliminated.
INTRODUCTION
The impact of soil type on the basic hydrological behaviors of a catchment is an
important factor in distributed hydrologic modeling. However in any catchment,
numerous different types of soil can be present making reclassification of soil type
difficult and some times unrealistic. In a distributed hydrologic model, it is difficult to
incorporate different parameter values for each soil type due to the large amounts of data
and calculations required to estimate such a large number of parameters. Therefore in
many applications, only the dominating soil types present in the catchment are identified
and the minor types are incorporated into these dominating classes considering their
similar properties. Thus a great deal of subjectivity and uncertainty is inevitable in these
reclassification methods since it is hard to determine exactly which soil types have
similar properties (e.g., is sandy clay more similar to clay or sand and what criteria are
used to decide).
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This paper presents a method to estimate the parameter values associated with soil
characteristics in a distributed hydrologic model. In the new approach, hydrologic model
parameter values corresponding to soil type are calculated as a function of the amount
(%) of basic soil types (sand, silt and clay) within each sub unit (e.g., grid, hydrological
response unit or sub catchment) based on three coefficients (which represent the actual
parameter value for sand, silt and clay respectively) that can be determined based on
either field measurements or by calibration. Consequently, the actual soil texture of each
sub-unit is uniquely considered and this is achieved through the estimation of only three
coefficients – note that the number of coefficients to be determined is always three
regardless of the heterogeneity of soil present in any study catchment. In addition, this
new approach can effectively be adopted to reduce uncertainty in model predictions for
both gauged and ungauged catchments.
BTOPMC MODEL
The BTOPMC (Block-wise use of TOPMODEL with Muskingum-Cunge flow routing
method) is a grid based semi distributed hydrological model developed at the University
of Yamanashi (Japan) for hydrological simulations in large river basins (Takeuchi et al.,
[6]; Ao et al., [1]). To facilitate the application of the model in large river basins, the total
basin area is automatically subdivided into natural sub-basins by the Pfafstetter
numbering system (Verdin and Verdin, [8]). The three dimensional physiographic
heterogeneity of the basin is considered in the model mutually in terms of topography,
soil types, geology, vegetation cover, and rooting depth. Basically the soil column is
divided into three layers as root zone, unsaturated zone and the saturated zone. The non
uniformity of the root zone depth over the catchment is taken into account using the
distribution and the type of vegetation. In the model, runoff generation is based on the
TOPMODEL (Beven and Kirkby, [2]; Beven and Binley, [3]; Quinn et al., [5]) concepts
and flow routing is carried out by the Muskingum-Cunge method. The model parameters
(Table 1) are calibrated either manually or automatically using the SCE-UA algorithm
(Duan et al., [4]). The inputs of the model are land cover map, digital elevation model
(DEM), soil map, precipitation, and evaporation data. It is possible to obtain the
hydrologic characteristics (e.g., depth to the water table, actual evaporation, overland
flow, base flow etc.) of any location (grid cell) of the catchment as the model output.
Table 1. Parameters of the BTOPMC model
Description
Block average Manning's coefficient
Decay factor of lateral transmissivity
Average saturation deficit
Lateral transmissivity under saturated conditions
Maximum root zone capacity
Parameter
n0
m
S0
T0
Srmax
Units
m
m
m2/h
m
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METHODOLOGY
In the BTOPMC model, the lateral transmissivity (T0) of soil is used to calculate the base
flow and it is one of the model parameters that has to be calibrated. Traditionally, it is
assumed that the soil properties in a sub-catchment are homogeneous and a fixed T0 value
is assigned to every grid cell in the sub-catchment. However it is clear that this
assumption is not realistic, especially when considering a large catchment area. In order
to incorporate the actual soil properties of each grid cell in the calculation of base flow,
the T0 value was assigned to each grid cell based on the dominant soil type present. It was
observed that the model performance could be improved significantly using this method.
However, the uncertainty associated with soil classification is inevitable. For example, if
the grid cell contains nearly equal percentages of sand, silt and clay, it is hard to
determine the dominating soil type. Therefore, in order to incorporate the effect of actual
soil properties in the T0 value regardless of the number of soil types present in the
catchment, the following method is proposed.
The value of T0 for each grid cell is calculated using the following equation.
T0  U clay  T0
Clay
 U sand  T0
sand
 U silt  T0
silt
(1)
Where Ucaly, Usand, and Usilt are the percentages of clay, sand, and silt present in each grid.
It is assumed that the soil texture in a grid cell is homogeneous and the sum of Ucaly,
Usand, and Usilt is 1. T0 clay, T0 sand, T0 silt are parameters which represent the other soil
textural properties (particle size, pore size etc.) present in the catchment. In other words,
T0 clay represents the T0 value of a particular soil column which only consists of clay.
Usually these three parameter values are calibrated in the model. The basic advantage of
the new approach is the number of coefficients to be determined is always three
regardless of the heterogeneity of soil present in any study catchment and the actual soil
texture of each sub-unit (e.g., grid, hydrological response unit) is uniquely considered.
DATA AND APPLICATION
In the present study the BTOPMC model is applied to the Mekong River basin (Figure 1)
for simulating the river flow at two discharge gauging stations, Pakse and Mukdahan.
The Mekong River is the longest river in southeastern Asia. It starts from the Qinghai
province in China and crosses Myanmar (Burma), Thailand, Laos, Cambodia and
Vietnam and flows to the South China Sea passing 4200 km. The Mekong Basin
population is about 60 Million and many of them directly or indirectly depend on the
river. The annual average precipitation of the basin is about 1570 mm and the upstream
drainage area of Pakse and Mukdahan are 545000, and 391000 km2 respectively
(UNESCO, [7]).
In this study, the new method (Eq. 1) is used to calculate the T0 value in the
BTOPMC model and the result is compared with the normal application where the T0
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value is based on the dominating soil type in each grid. The hydrological data from 1972
to 1977 is used for calibrating the model (daily time step) and from 1978 to 1985 for
validating the model. The model is calibrated using the observed discharge at Pakse and
validated at both Pakse and Mukdahan discharge gauging stations. The precipitation
measured at 64 gauging stations over the catchment is used (from the Mekong River
Commission and the China Meteorological Administration and Chinese Academy of
Sciences). The monthly average potential evaporation is calculated using the PenmanMonteith method for each grid (Zhou et al., [9]). The digital elevation model for the basin
is generated from the USGS 30 arc second GTOPO30 data set.
100°0'0"E
110°0'0"E
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Discharge Gauging Stations
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Precipitation Gauging Stations
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Mekong Basin
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Mukdahan
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Pakse
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10°0'0"N
10°0'0"N
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Decimal Degrees
100°0'0"E
110°0'0"E
Figure 1. Location of the Mekong River basin and the discharge and precipitation
gauging stations
The IGBP (International Geosphere Biosphere Program) land cover and the FAO (Food
and Agriculture Organization) soil map are used in this application. The land cover which
is associated with parameter Srmax, is reclassified into four classes as deep rooted (forest),
shallow rooted (grass/shrub), shallow rooted and irrigated (crop), and impervious to
reduce the number of parameters to be calibrated. There are altogether 23 soil textural
classes present in the catchment.
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RESULTS AND CONCLUSIONS
This paper presents a method to estimate the parameter values associated with soil
characteristics (T0) in a distributed hydrological model (BTOPMC). The new approach is
applied to the Mekong River basin and the results are compared with the normal
application. The parameter sets resulting from the two different approaches (Table 2) are
similar except for the T0 value. However the parameter set obtained in the new approach
seems to be more physically based than the normal application (e.g., normally the T0
value for silt should be higher than that for clay). The model performance in both
applications (Table 3) is acceptable both in calibration and validation stages (for Pakse).
However the model performance with the new approach is slightly better than the normal
application. The hydrographs (Figures 2 & 3) show a good agreement between the
observed and calculated discharges in both applications except for few extreme events.
The model performance (validation) at the Mukdahan station from 1978 to 1985 is better
than the period 1972 to 1977 (Table 3). However the parameter set calibrated using the
Pakse observed discharge data is valid for the Mukdahan station. This implies that the
calibrated parameter set is strongly physically based.
In the normal application, the T0 value of each grid is assumed as the T0 value of the
dominating soil type present in the grid, which seems to be unrealistic especially when
nearly equal percentages of soil types (sand, silt, and clay) are present. In fact the
reclassifying or grouping soil types is unrealistic and an unnecessary source of
uncertainty. In the new approach, we only have to ever estimate three parameters to
represent all possible soil textural combinations regardless of how many soil types are
present in the basin to be modeled. This also makes it particularly appealing for the
International Association of Hydrological Sciences (IAHS) Prediction in Ungauged
Basins (PUB) project since all we need to know to estimate T0 is the percentages of sand,
silt, and clay in each grid which is readily available - even though T0 may change slightly
from region to region this can be determined by future applications in numerous different
basins. It is clear that the new approach used to calculate the T0 value in the BTOPMC
enhances the model performance and eliminates the uncertainty associated with soil
classification, as well as improving the physical soundness of the model. However,
further investigations are necessary to determine whether using non-linear functions to
calculate T0 is more suitable than the linear function (Eq. 1) used in this study.
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0.0
100000
10.0
20.0
30.0
60000
Preci
Qobs
Qs im
40.0
40000
Precipitation (mm)
Discharge (m3/s)
80000
50.0
20000
60.0
01/01/1984
01/01/1983
01/01/1982
01/01/1981
01/01/1980
01/01/1979
01/01/1978
01/01/1977
01/01/1976
01/01/1975
01/01/1974
01/01/1973
70.0
01/01/1972
0
Date
Calibration
Figure 2. Observed and simulated hydrographs of the modified application (at Pakse)
0.0
100000
10.0
20.0
30.0
60000
Preci
Qobs
Qs im
40.0
40000
50.0
20000
60.0
Calibration
Date
Figure 3. Observed and simulated hydrographs of the normal application (at Pakse)
01/01/1984
01/01/1983
01/01/1982
01/01/1981
01/01/1980
01/01/1979
01/01/1978
01/01/1977
01/01/1976
01/01/1975
01/01/1974
01/01/1973
70.0
01/01/1972
0
Precipitation (mm)
Discharge (m3/s)
80000
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Table 2. Calibrated parameter sets
Impervious
Application
Srmax
Modified application
0.0001
Normal application
0.0001
Forest
Srmax
0.750
0.750
Grass
Srmax
0.738
0.738
Table 3. The performance of the BTOPMC model
Calibration
Pakse
Nash
Vol%
Qo/P%
Modified application 82.70
101.60
38.40
Normal application
82.30
103.70
38.40
Mukdahan
Modified application 69.50
115.00
39.40
Normal application
68.10
117.40
39.40
Crop
Srmax
0.745
0.745
Clay
T0
50.0
45.0
Sand
T0
300.0
200.0
Silt
T0
150.0
25.0
Qs/P%
39.00
39.80
Ea/P%
62.80
62.80
Nash
81.70
81.40
Vol%
102.00
102.55
45.30
46.20
53.60
53.60
73.40
72.80
110.20
111.20
n0
0.2
0.2
m
0.030
0.030
S0
0.001
0.001
Validation
Qo/P% Qs/P%
39.90
40.55
39.90
40.75
43.00
43.00
47.40
47.90
Ea/P%
61.35
61.35
51.60
51.60
Note: Nash is the Nash-Sutcliffe coefficient, Qo and Qs are the observed and simulated discharges respectively (m3/s), Vol%=Qs/Qo%, P
is the precipitation (mm) and Ea is the actual evapotranspiration (mm)
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REFERENCES
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(2003).
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