Eun Seok Kim^{1}
·Hanyong Lee^{1}
·Jae E Yang^{2}
·Kyoung Jae Lim^{3}
·Youn Shik Park^{1,*}
^{1}
Rural Construction Engineering, Kongju National University, Chungcheongnam-do 32439, Korea
^{2}
Department of Biological Environment, Kangwon National University, Gangwon-do 24341, Korea
^{3}
Department of Regional Infrastructure, Kangwon National University, Gangwon-do 24341, Korea
김은석^{1}
·이한용^{1
}·양재의^{2}
·임경재^{3}
·박윤식^{1,
*}
^{1}
공주대학교 지역건설공학과
^{2}
강원대학교 바이오자원환경학과
^{3}
강원대학교 지역건설공학과
The universal soil loss equation (USLE), a model for estimating the
potential soil loss, has been used not only in research areas but also in
establishing national policies in South Korea. Despite its wide applicability,
USLE cannot adequately address the effect of seasonal variances. To overcome
this limit, the ArcGIS-based Sediment Assessment Tool for Effective Erosion
(ArcSATEEC) has been developed as an alternative model. Although the
field-scale (< 100 m^{2})
application of this model produced reliable estimation results, it is still
challenging to validate accuracy of the model estimation because it only
estimates potential soil losses, not the actual sediment yield. Therefore, in
this study, a method for estimating actual soil loss based on the ArcSATEEC
model was suggested. The model was applied to eight watersheds in South Korea
to estimate sediment yields. Correction factor was introduced for each
watershed, and the estimated sediment yield was compared with that of the
estimated yield by LOAD ESTimator (LOADEST). Sediment yield estimation for all
watersheds exhibited reliable results, and the validity of the proposed
correction factor was confirmed, suggesting the correction factor needs to be
considered in estimating actual soil loss.
Keywords: ArcSATEEC, sediment delivery ratio, soil loss, USLE
As one of the most important environmental problems worldwide, soil loss
has received increasing attention. Soil particles on the ground surface move
due to the impact of rainfall and rainfall-runoff, which may lead to the loss
of soil resources and cause water pollution due to the nutrients caused by the
relocated soil particles. Recently due to reckless development projects, soil
loss occurs not only in agricultural lands (such as paddy and upland fields)
but also in undeveloped natural lands. Moreover, with rapid climate change, the
type of rainfall turns into localized heavy rainfall, making soil loss a more
serious problem.
For soil loss management, it is necessary to estimate the amount of
eroded soil first. Till date, the Universal Soil Loss Equation (USLE)
(Wischmeier and Smith, 1965; Wischmeier and Smith, 1978) has been developed and
used. In South Korea, the importance of soil loss management has been widely
recognized recently and the Ministry of Environment proposed the use of USLE
through the “A Bulletin on the Survey of the Erosion of Topsoil” (Ministry of
Environment, 2012). However, USLE only estimates the average annual potential soil
loss, thus has limitations in reflecting different rainfall patterns by season
or surface cover condition that varies depending on the growth of crops in
South Korea (Wischmeier and Smith, 1965; Wischmeier and Smith, 1978; Yu et al.,
2017). Because USE fails to consider seasonal variance. Thus, the Korean Soil
Loss Equation (KORSLE) was recently developed to reflect the monthly rainfall
conditions in South Korea together with surface cover condition based on crop
growth under climatic conditions(Sung et al., 2016; Kim et al., 2017; Kongju
National University, 2016). In addition, the ArcGIS-based Sediment Assessment
Tool for Effective Erosion (ArcSATEEC) model that can run KORSLE in the ArcGIS
software using geographic data was developed (Yu et al., 2017). The ArcSATEEC
model can estimate monthly potential soil loss through the digital elevation
model (DEM), landuse, soil map, and R factor map based on monthly rainfall
data. The field-scale application of this model exhibited reliable estimation
results in predicting the sediment yield that actually occurred when correction
factor using the volume of surface flow was applied in two fields of 76 m^{2}
and 91 m^{2} (Song et al.,
2019). However, this approach is limited to apply in watershed because flow
data measurement is for streamflow (i.e. sum of surface flow and baseflow), not
only for surface flow.
In order to establish countermeasures against soil loss, but soil loss
estimation needs to be performed at the watershed scale, not at the
field-scale. In such watershed-scale prediction, the estimated sediment yield
need be compared with the measured sediment yield to determine its reliability. It
is difficult, however, to compare the ArcSATEEC model with the
measured value because ArcSATEEC predicts potential soil loss (Kongju National University, 2016) and because monthly sediment yield
data is not provided in general. Therefore, in the study, monthly sediment
yields were estimated by the LOAD ESTimator (LOADEST; Runkel et al., 2004)
model using measured flow rate and suspended solids concentration data, and the
estimated sediment yields were used to convert the estimated potential soil
loss by ArcSATEEC.
2.1. Study sites
and data collection
To evaluate the watershed-scale applicability of the ArcSATEEC model, the
measured sediment yield data are necessary. In the study, locations at which
the flow rate data and suspended solid (SS) data can be collected were selected
so that the measured sediment yield could be defined (Table 1). As for the flow
rate and SS data, the data from the Water Environment Information System of the
National Institute of Environmental Research (http://water.nier.go.kr/) were
used. Based on the period of available flow rate and SS data, the application
period of the ArcSATEEC model was determined (Table 1).
Based on the digital topographic map and the landuse map in 2007,
provided by the Ministry of Land, Infrastruc- ture and Transport and the
Ministry of Environment, the watershed area ranged from 29.61 km^{2}
(Watershed 1) to 609.73 km^{2}
(Watershed 8). In terms of landuse, the forest area covered the highest
proportion in all watersheds in the range from 61.74% (Watershed 5) to 87.19%
(Watershed 6) (Table 2), followed by Agriculture and Rice. Orchard, pasture,
and water.
2.2. Description
of the LOADEST model for monthly sediment yield computation
To determine the estimation accuracy or correction/test results of a
hydrologic model, a comparison with the measured data is required. In many
cases, flow rate and water quality data can be measured at specific points in
time, while the daily or monthly net loads (e.g., kg and ton) from the flow
rate and pollutants are not measured. Therefore, it is necessary to process the
measured flow rate and SS concentration data to define the measured monthly
sediment yield (kg or ton).
The LOADEST model, the best fit regression model, can estimate pollutant
loads based on the flow rate by defining the correlation between the flow rate
and water quality concentration. It has nine regression equations to estimate pollutant
loads, and can automatically determine a regression equation
with high estimation accuracy according to the flow rate and water quality
data. Moreover, its applicability has been verified from several
water-quality-related studies. Oh et al. (2014) predicted weather in 18 areas
in the southeastern United States using the K-nearest neighbor resampling
technique and calculated the daily flow rate, and then they applied the results
to the LOADEST model to estimate total daily nitrogen (T-N). Sun et al. (2013)
identified the long-term trend in the pollutant loads of the nitrogen and
phosphorus released from the upstream areas of the Yangtze River and Three
Gorges Dam in China using the LOADEST model. Jha and Jha (2013) evaluated the
applicability of the LOADEST model for the total pho- sphorus and nitrogen observed
in the Neuse River in North Carolina, USA, the estimation results for the total
phosphorus and nitrogen exhibited high accuracy. The model was used to estimate
monthly sediment loads in the La Sueur River watershed of 2,850 km^{2}
(Folle et al., 2007), in the Galveston and Matagorda watersheds (Onami et al.,
2012). Moreover, the estimated monthly sediment yield by the LOADEST was used
calibrate SWAT model(Wang et al., 2016) Major purpose of the model is to
interpolate pollutant load data from intermittently measured flow and water
quality concentration data, the model can be used to estimate continuous daily,
monthly, and yearly pollutant loads. However, the model does not consider
watershed characteristics such as landuses, soils, topography, and climates,
therefore it is not available to simulate landuse or climate changes.
Hence, in this study, the monthly sediment yield was calculated based on
the LOADEST model using the measured flow rate, and SS data and the results
were used as the reliably estimated monthly sediment yield to correct and
verify the monthly sediment yield estimated by ArcSATEEC.
2.3. Estimation
of the monthly potential soil loss using ArcSATEEC
USLE estimates the potential soil loss (ton/ha/year) per unit area over a
long time period. The USLE requires the following five factors: the rainfall
erosivity factor (R factor, MJ·mm/ha·yr·hr), the soil erodibility factor (K
factor, Mg·hr/MJ·mm), the slope length and slope steepness factor (LS factor,
dimensionless), the crop and cover management factor (C factor,
dimensionless), and the conservation practice factor (P factor,
dimensionless). The R factor reflects the conditions of rainfall which leads to
soil loss, while K, LS, C, and P indicate the degree of loss according to the
soil attributes, the slope or slope length on soil loss, the condition of the
ground surface that covers the soil and the furrow direction in agricultural
lands, respectively.
where A is potential soil loss (ton/ha/year), R is
the rainfall erosivity factor, K is the soil eridibility factor, LS is the
slope length and slope steepness factor, C is the crop and cover management
factor, and P is the conservation practice factor.
In areas where the precipitation or crop growth conditions show monthly
variance, such as South Korea, the impact of monthly variations needs to be
reflected when estimating potential soil loss (Park et al., 2010). They can be
reflected by the R and C factors, which are related to rainfall and crop growth
in USLE, respectively.
The R factor is not calculated simply using rainfall, but is defined by
calculating the kinetic energy generated when raindrops fall on the ground
surface. As for the calculation of kinetic energy, Whischmeier and Smith (1978)
presented the method of calculating the R factor for rainfall event (Equations
2-5). For rainfall event classification, if the interval between events is less
than six hours, it is defined as one single rainfall event and the minimum
rainfall for the occurrence of soil loss is 12.7 mm. In
addition, although the rainfall is less than 12.7 mm but is
more than 6.24 mm within 15 min, the
model assumes that soil loss occurred.
where I is the rainfall intensity (mm/hr), e is the
kinetic energy per unit time (Kinetic energy, MJ/ha·mm), P is the rainfall per
unit time (mm), E is the kinetic energy per rainfall event (MJ/ha), I_{30max}
is the maximum 30-min rainfall intensity (mm/hr), and R is the R factor (MJ·mm/ha·hr).
To calculate the R factor using equations 2 - 5, rainfall data that can
express the maximum 30-min rainfall intensity is necessary. Therefore, it is difficult
to calculate the R factor for a long period of more than several years and for
a number of points. Hence, Risal et al. (2016) and Kongju National University
(2016) proposed equations that can calculate the monthly R factor for a total
of 75 locations of the Korea Meteorological Administration (KMA) using the sum
of monthly rainfall, and regression equations with different indices and
coefficients depending on the area. In this study, the R factor was calculated
by applying the daily rainfall data to the R factor regression equation for 47
different KMA locations from which monthly rainfall data were collected.
As the surface cover condition by crops may vary depending on the crop
type and its growth condition, Kongju National University (2016) constructed a
database on the monthly C factor by investigating the crop cultivation schedule
for the Geumgang, Nakdonggang, Seomjingang/Yeongsangang, and Hangang
watersheds, and using the SWAT model, and the database was stored in ArcSATEEC.
In this study, this database was used to reflect the monthly surface cover
condition for agricultural lands. These monthly R and C factors are distinct
features which provide the opportunity to estimate monthly potential soil
losses in ArcSATEEC (Equation 6).
where A_{i} is potential soil loss for
month i, R_{i} is the rainfall erosivity factor for month i, and C_{i}
is the crop and cover management factor for month i.
The value of the P factor is determined by the environmental conditions
and management method of the cultivated land (Whischmeier and Smith, 1978),
such as contouring, contour strip cropping, and up and downhill culture. To
reflect these domestic conditions, Jung et al. (2004) proposed a method to
calculate the P factor based on three variables (conditions, such as crop type,
furrow direction, and mulch presence) for the cultivated lands in South Korea.
The application of this method, however, can be challenging for large
watersheds because investigating all cultivated lands is practically difficult
even though it can be applied to small watersheds where field surveys are
possible. Thus, Yu et al. (2018)
conducted a field survey on the crop cultivation method and slope of each area
for the representative locations of the four major river watersheds (Geumgang,
Nakdonggang, Seomjingang/Yeonsangang, and Hangang watersheds)(Table 3) so that
the P factor could be determined by the field slope in each watershed based on
the method proposed by Jung et al. (2004).
The K factor was determined using the precision soil map provided by the
Agricultural Science and Technology Institute of the Rural Development
Administration, and the soil environmental map service of the Korean Soil
Information System. The LS factor was estimated by the Equation 7
which is employed in ArcSATEEC, after creating DEM using the digital map
provided by the National Geographic Institute (Equation 7) (Whischmeier and
Smith, 1978).
where LS is the LS factor, λ is the slope
projection distance (m), β is the slope (radian), and m is a
slope-related variable.
The R, K, LS, C, and P factors used in this study are given in Table 4.
2.4. Correction
factor for potential soil loss estimation by ArcSATEEC
ArcSATEEC can reflect the monthly conditions of South Korea such as the R
and C factors, but uses the same factors as USLE for estimating potential soil
loss. In other words, the value estimated by ArcSATEEC does not represent the
actual sediment yield (amount of the eroded soil in
the watershed that reached the watershed outlet), thus it can be only compared
with the sediment yield by the LOADEST after applying a correction factor, such
as the sediment delivery ratio (SDR) (Wischmeier and Smith, 1965; Wischmeier
and Smith, 1978; Park et al., 2007; Kim et al., 2017). SDR is defined as the
ratio of the sediment yield that reached the watershed outlet to the total
amount of eroded soil in the watershed; it is affected by the surface runoff,
peak flow, watershed area, watershed slope, watershed geometry, rainfall type,
landuse conditions, soil attributes, crop type, and growth condition. Various
methods have been used to calculate SDR, including a method that considers
coefficients together with the slope or area of the watershed (USDA, 1972;
Vononi, 1975; Williams, 1977), a method that considers the curve number and
watershed area (Williams and Berndt, 1977), a method that considers the
rainfall-runoff volume (Song et al., 2019), and a method that calculates SDR
for each cell of GIS data (De Rosa et al., 2016).
This indicates application of SDR allows comparison of the potential soil
loss estimated using USLE or other models having an approach similar to that of
USLE with the sediment yield at the watershed outlet. Different values of the
correction factor can be defined differently depending on the target
watershed considering environmental condition. USLE has been widely used is
in soil loss estimation because it requires only five factors, and the
potential soil loss can be estimated only by multiplying these factors.
ArcSATEEC is an improved model of USLE by taking into account the seasonal
variance while maintaining the model simplicity. In the study, in order to
maintain the model simplicity when including the correction factor to convert
potential soil loss into sediment yield, a straightforward approach to
determine the factor was investigated.
When the monthly measured values for the sediment yield at the watershed
outlet are compared with the monthly estimated values by a hydrologic model,
the estimated values can be accepted when the difference between simulated and
estimated values is no bigger than 45%, and the coefficient of determination (R^{2})
is no smaller than 0.65 according to Duda et al. (2012). They can be accepted
when the Nash-Sutcliffe efficiency (NSE) is 0.50 or higher according to Skaggs
et al. (2012), and when the absolute error (%) is less than 50% according to
Herr and Chen (2012). Wang et al. (2012) mentioned that the estimated values
can be accepted when NSE is 0.50 or higher, R^{2} is 0.60 or higher,
and percent bias (PBIAS) is within ±15%. Moriasi et al. (2015) stated that the estimated
values are acceptable when NSE is 0.45 or higher, R^{2} is 0.40 or
higher, and PBIAS is within ±20%. Song et al. (2019) determined the reliability
of the estimated values considering the significance by the t-test when NSE was
0.4 or higher and R^{2} was 0.5 or higher. In this study, based on
these criteria, it was determined that the sediment yield could be accepted
when both NSE and R^{2} were 0.45 or higher, and the significance by
the t-test was satisfied. The p-value was examined for the significance probability
of 95% (signifi- cance level α=0.05), and the
estimated value was determined to be reliable when the p-value was higher than
the significance level (when the test statistic was within the range of the
threshold).
Table 3 USLE P definition with watersheds and slopes in ArcSATEEC (Yu et al., 2018) |
3.1. Comparison
of monthly soil erosion by LOADEST and ArcSATEEC
The monthly sediment yield by the LOADEST model was calculated using the
daily measured flow rate and SS data (Table 5). The mean monthly sediment yield
was lowest (52.0 ton/month) in Watershed 3 and
highest (1,065.5 ton/month) in Watershed 6. In
addition, the mean monthly sediment yield was hardly proportional to the
watershed area. For example, the watershed areas of Watersheds 1
and 2 were 29.61 and 160.39 km^{2},
respectively, indicating that the area of Watershed 2 was
approximately 5.4 times larger. However, the mean monthly sediment yields were
71.6 and 71.1 ton/month, respectively, exhibiting no
significant difference. When comparison between Watersheds 5 and 8 showed that
were the mean monthly sediment yield was higher for Watershed 5 (246.2 ton/month)
than Watershed 8 (133.6 ton/month)
although compared, the watershed areas were 295.13 and 609.73 km^{2},
indicating that area for Watershed 8 (609.73 km^{2})
was approximately 2.1 times larger than Watershed 5 (295.13 km^{2}). It
was also difficult to find correlations between the minimum and maximum monthly
sediment yields and the watershed area.
The mean monthly potential soil loss by ArcSATEEC showed a tendency to
generally increase as the watershed area increased. For the minimum and maximum
values of the monthly potential soil loss, however, correlations between them
and the watershed area were difficult to find as with the minimum and maximum
values of the monthly sediment yield by LOADEST. For example, while the
watershed area increased from Watershed 5 to Watershed 8, the minimum value of
the monthly potential soil loss rather exhibited a tendency to decrease but the
maximum value increased or decreased repeatedly. In other words, it was also
difficult to find a correlation between the monthly potential soil loss by
ArcSATEEC and the watershed area.
When the monthly sediment yield by LOADEST was compared with the monthly
potential soil loss by ArcSATEEC, the correlation coefficient (R) exceeded
+0.70 for all the watersheds as it ranged from +0.74 (Watershed 1) to +0.86
(Watersheds 2 and 4) (Table 5). When the correlation coefficient between two
samples is +0.70 or higher, a strong positive linear relationship is generally
considered between the two samples (Ratner, 2009). Therefore, it was assumed
that the monthly potential soil loss by ArcSATEEC could reflect the tendency of
the sediment yield at the watershed outlet.
However, the monthly potential soil losses estimated by ArcSATEEC were
significantly higher than the monthly sediment yield by LOADEST, from 135 times
(Watershed 1) and up to 2,197 times (Watershed 8). This is because ArcSATEEC
has the same approach as USLE when estimating the potential soil loss, and,
thus, it has the limitations of USLE. In other words, the potential soil loss
estimated by ArcSATEEC displayed very large values, compared to the sediment
yields by LOADEST. Therefore, it was confirmed that the application of the
correction factor is necessary to compare the potential soil loss by ArcSATEEC
with the sediment yield at the watershed outlet.
3.2. Correction
of monthly potential soil loss by ArcSATEEC
Although rainfalls are closely related to the sediment yield as soil loss
mainly occurs during rainfall. It might be necessary to exclude the R factor in
the course of defining the correction factor to avoid redundancy because it was
already used for calculating the potential soil loss. DEM has information on
the altitude or slope of the watershed; thus, elements for the slope and slope
length can be extracted and used. Such elements, however, have already been
reflected to the LS factor. In addition, soil attributes are the elements that
have been reflected by the K factor. In other words, there is a need to replace
or exclude one of USLE factors when extra factor is applied in the process. For
instance, the R factor was replaced to the terms of runoff volume and peak
runoff rate in the Modified Universal Soil Loss Equation (MUSLE)(Williams,
1975), since rainfall and runoff are similar components in soil loss
estimation.
As no correlation was found between the watershed area and sediment yield
for the target watersheds of this study (Table 5), using the watershed area for
the definition of the correction factor is also considered to be difficult.
Consequently, although the correction factor is necessary to compare the
potential soil loss estimated using USLE or a similar approach with the
measured value, evaluation of the correction factor is limited because it needs
to maintain simplicity.
As the sediment yield showed a tendency of being linearly proportional to
the potential soil loss for the target watersheds of this study, it was assumed
that reasonable sediment yield can be estimated if the size of the estimated
value is adjusted for each watershed. Therefore, the estimated sediment yield
for month i was defined as the product of the potential soil loss_{i} for
month i, and the correction factor (CF), an invariable number (Equation 8).
Estimated sediment yield_{i} = CF × Potential soil loss_{i} (Eq.
8)
To estimate CF, the period in which the monthly potential soil loss of
each watershed was estimated, was divided into calibration and validation
periods. The value of CF was determined by comparing the sediment yield by the
LOADEST during the calibration period with the estimated sediment yield by
ArcSATEEC. During the validation period, the sediment yield by the LOADEST and
estimated sediment yields were compared by applying the determined CF.
The determined CF for each watershed ranged from 0.456 × 10^{-}^{3} (Watershed
8) to 7.435 × 10^{-}^{3} (Watershed
1). NSE and R^{2} for the sediment yield by the LOADEST and estimated
sediment yields were 0.45 or higher, and the p-value also exceeded the
significance level for all the watersheds, indicating that the model was well
corrected by CF (Table 6). In addition, NSE and R^{2} for the test
period were 0.45 or higher and the p-values were above the significance level
for all the watersheds. Therefore, it was concluded that the test of the model
was successful. Several distinct features were found in comparison of monthly
sediment yields by LOADEST and ArcSATEEC. The first feature was that, the
potential soil loss estimated by ArcSATEEC tended to be linearly proportional
to the sediment yield that actually occurred, indicating that the ArcSATEEC
model could be sufficiently corrected and tested using the correction factor in
the form of an invariable number rather than a complicated correction factor
with additional variables. The second feature was that the potential sol loss
estimated by ArcSATEEC which has similar approach to USLE were significantly
overestimated, compared to the sediment yields by LOADEST, although the
tendencies of monthly potential soil loss to sediment yields were similar (Fig.
1(a) - (h)). CFs are proportions (or percentages) to represent the sediment
yields reached watershed outlet, the CFs as percentages ranged from 0.046%
(Watershed 8) to 0.744% (Watershed 1), The result are similar to the
proportions of other USLE application for watershed. Park et al. (2014) reported that 1.12% of potential soil loss reached watershed
outlet, and it was from 0.08% to 1.67% in the study of Santos et al. (2017). Therefore there is a need to correct or adjust the potential soil
loss estimated by USLE and similar approaches so that they can be compared to
the actual sediment yield at watershed outlet. The third feature was that
watershed area does not influence sediment transportation. Comparing the CFs for
Watershed 1 and 2, CFs were significantly decreased from 7.435 × 10^{-}^{3} to 0.860 × 10^{-}^{3} while the
areas were increased from 26.61 km^{2}
to 160.39 km^{2}. However, CFs were increased when
watershed areas were increased in the comparison of Watershed 4 and 5.
Therefore, watershed area can not be used to determine or explain the
proportions of sediment yield reached watershed outlet.
As it stated above, comparing the sediment yields by ArcSATEEC to the
ones by LOADEST, the tendencies were similar (Fig. 1(a) - (h)), however
ArcSATEEC missed the peak points of monthly sediment yields at the time steps
indicated by arrows in the figure. This is because the sediment yields by
LOADEST was estimated by flow rate, while ArcSATEEC estimated potential soil
loss using precipitation. Therefore there is a possibility of providing
differences between the estimated sediment yields by ArcSATEEC and the actual
sediment yields at the time step of precipitation and flow have different
behaviors. In other words, if watershed condition (e.g.
irrigation and drainage, reservoirs, water uses by the inhabitant, etc.)
influences the rainfall-runoff process, the differences might be observed.
Fig. 1 Comparison of estimated sediment yields. |
Table 5 Comparison of monthly sediment yield (ton) by LOADEST and monthly potential soil loss (ton) by ArcSATEEC |
Table 6 Calibration and validation results of sediment yields (ton) by ArcSATEEC |
The USLE has been widely used for potential soil loss estimation because
the model requires only five factors (i.e. rainfall erosivity factor, soil
erodibility factor, slope and slope steepness factor, crop and cover management
factor, and conservation practice factor), and it is capable of calculating the
potential soil loss by multiplying these five factors. However, the model only
estimates the annual average values of soil loss, thus has limitations in reflecting
the seasonal variances of soil losses in South Korea (Wischmeier and Smith,
1965; Wischmeier and Smith, 1978; Yu et al., 2017). ArcSATEEC was developed to
overcome these limitations and calculate the monthly potential soil
loss. ArcSATEEC can reflect seasonal variance while maintaining the model
simplicity of USLE, but it cannot be compared with the measured value because
it estimates the potential soil loss as with USLE rather than the sediment
yield at the watershed outlet.
Therefore the application of the SDR is required to compare the potential
soil loss with the measured sediment yield. SDR is a type of correction factor
that may vary depending on the watershed. Various approaches have been used to
define SDR, from the methods that used the area, slope, and curve number of the
watershed to those that considered the rainfall-runoff volume or used
sophisticated equations. When such SDR calculation methods are applied to USLE
and similar models, it is important to maintain the model simplicity as well as
to avoid elements that overlap with the factors required to calculate the
potential soil loss.
In this study, ArcSATEEC was applied to eight watersheds in South Korea
and examined the definition of the correction factor through a comparison with
the sediment yield by the LOADEST. The monthly potential soil loss by ArcSATEEC
exhibited a significant difference from the sediment yield by the LOADEST, but
they tended to be linearly proportional to each other. Thus, it was confirmed
that a size adjustment of the estimated value alone could exhibit reliable
estimation accuracy when compared with the sediment by the LOADEST. Therefore,
the possibility of estimating the sediment yield by applying an invariable
number without using the watershed area, or elements that overlap with other
factors that were used to calculate the potential soil loss, were examined. As
both the correction and test processes of sediment yield estimation for the
eight watersheds exhibited reliable results, the proposed correction factor
definition method was assessed to be valid.
Based on the results of this study, it is verified that the estimated
value by USLE and similar methods can overestimate the amount of the actually
eroded soil because the potential soil losses significantly differs from the
actual sediment yield. It was also found that the potential soil loss can be
sufficiently corrected to be the estimated sediment yield using an invariable
number without a complicated process if the potential soil loss has a tendency
to be proportional to the sediment yield by the LOADEST.
This subject is supported by Korea Ministry of Environ- ment as “The
SS (Surface Soil conservation and manage- ment) projects; 2019002820001”.
2020; 25(3): 52-64
Published on Sep 30, 2020
1.
introduction
2.
material and methods
3. results
and discussion
4.
conclusions
Rural Construction Engineering, Kongju National University, Chungcheongnam-do 32439, Korea