**2. Development of SATEEC GIS system**

The SATEEC system was developed in 2003 (Lim et al., 2003) and has been upgraded with various enhanced modules incorporated into the system (Lim et al., 2005; Park et al., 2010). It has been applied to various watersheds with diverse purposes (Yoo et al., 2007; Park et al.,

SATEEC GIS System for Spatiotemporal Analysis of Soil Erosion and Sediment Yield 255

As indicated above, the SDR could be identical if the area-based SDR is applied to two different watersheds with the same area. Thus, the SATEEC was modified to integrate slope-based SDR module using equation (4). Park et al. (2007) reported that SDRs for 19 small watersheds having identical watershed area were different due to various average channel slope in each watershed, and sediment yields were different by not only USLE factors also estimated SDR for the watersheds. The SDRs by area-based SDR module were calculated as 0.762 for the same size watershed (0.0219 square kilometer). However, the SDRs by slope-based SDR module were from 0.553 to 0.999 with varying slope from 0.73 % to 3.17 %. It indicates that the SDR is one of watershed-specific conditions, thus, better ways to estimate SDR needs to be developed based on various characteristics of watershed and measured data, not just based on single parameter such as only area or only slope. The slope-based SDR module has similar limitation as area-based SDR module does (Figure 1).

> Watershed 2 Area = A Slope = 0.5 S

> Watershed 4 Area = 0.5 A Slope = S

SDR���� = 0.472 × (0.5 A)��.��� SDR����� = 0.627 × S�.���

The research indicated the SDR is not always the most influential and fundamental factor in sediment yield estimations. However it is also one of very important factors to estimate

SDR���� = 0.472 × A��.��� SDR����� = 0.627 × (0.5 S)�.���

Watershed 1 Area = A Slope = S

Watershed 3 Area = A Slope = S

Fig. 1. Comparison of SDR for watersheds

SDR���� = 0.472 × A��.��� SDR����� = 0.627 × S�.���

SDR���� = 0.472 × A��.��� SDR����� = 0.627 × S�.���

2007; Park et al., 2008). In the current SATEEC 2.1 system, the soil erosion is estimated using USLE with time-variant R and C modules. The sediment delivery ratio (SDR) is estimated using area-based SDR module, channel slope-based SDR module, and Genetic Algorithmbased SDR module in the SATEEC system. In addition, several miscellaneous modules are available for various soil erosion and sediment yield applications.

#### **2.1 SATEEC Version 1.0 ~ 1.8**

#### **2.1.1 USLE-based SATEEC for soil erosion and area-based sediment delivery ratio modules**

The system requiring only USLE inputs was developed with the philosophy of "very limited dataset for reasonable soil erosion estimation accuracy with commonly available GIS interface" and "easy-to-use", To keep the philosophy and provide user with watershed assessment capabilities, the SATEEC version 1.x system utilized are area-based and slopebased SDR methods. The SDR in general is defined as a ratio of the soil which reaches the watershed outlet to soil detached from source area (Yin et al., 2005). The SDR is affected by various watershed characteristics such as watershed area, geomorphologic properties, precipitation pattern, total runoff and peak flow volume, land use properties, physical properties of soil, etc. These characteristics affect it not only with *spatial properties* also *temporal properties*. However, it is not feasible to reflect all of these in estimating SDR due to limited data sets. Empirical regression models to estimate SDR for watersheds were proposed by many researchers; one of them is to estimate SDR using watershed area as shown below (USDA, 1972, Boyce, 1975, USDA, 1972).

$$\text{SDR} = 0.4720 \times \text{A}^{-0.125} \text{ (Vanoni, 1975)} \tag{1}$$

$$\text{SDR} = 0.5656 \times \text{A}^{-0.11} \text{ (Boyce, 1975)} \tag{2}$$

$$\text{SDR} = 0.3750 \times \text{A}^{-0.23\text{R}} \text{ (USDA, 1972)} \tag{3}$$

Where, SDR is sediment delivery ratio, and A is watershed area (km2).

These regression models with only watershed area as an input are useful for a watershed with very limited watershed characteristics data to estimate SDR. These area-based SDR is the first approach in SATEEC system to estimate sediment yield at the watershed outlet.

Yoo et al. (2007) reported that soil reconditioning in agricultural field could affect sediment yield at the watershed significantly. Thus area-based SDR was used to estimate sediment yield and it was found that soil loss could increase by 138.0 % per unit hectare while sediment yield could increase by 59.4 % per unit hectare with various activities in the watershed. It indicates that estimated soil loss and sediment yield may not show identical trend.

#### **2.1.2 Slope-based Sediment Delivery Ratio modules**

Area-based SDR module provides convenience to estimate SDR with limited data collection for a given watershed, The SDR in the watershed is affected by various geomorphologic properties such as average channel slope than watershed area. Thus, slope-based SDR module was incorporated into the SATEEC system to supplement limitation of area-based SDR module.

$$\text{SDR} = 0.627 \times \text{S}^{0.403} \text{ (Willians \& Berndt, 1977)} \tag{4}$$

Where, S is slope of watershed.

2007; Park et al., 2008). In the current SATEEC 2.1 system, the soil erosion is estimated using USLE with time-variant R and C modules. The sediment delivery ratio (SDR) is estimated using area-based SDR module, channel slope-based SDR module, and Genetic Algorithmbased SDR module in the SATEEC system. In addition, several miscellaneous modules are

**2.1.1 USLE-based SATEEC for soil erosion and area-based sediment delivery ratio** 

The system requiring only USLE inputs was developed with the philosophy of "very limited dataset for reasonable soil erosion estimation accuracy with commonly available GIS interface" and "easy-to-use", To keep the philosophy and provide user with watershed assessment capabilities, the SATEEC version 1.x system utilized are area-based and slopebased SDR methods. The SDR in general is defined as a ratio of the soil which reaches the watershed outlet to soil detached from source area (Yin et al., 2005). The SDR is affected by various watershed characteristics such as watershed area, geomorphologic properties, precipitation pattern, total runoff and peak flow volume, land use properties, physical properties of soil, etc. These characteristics affect it not only with *spatial properties* also *temporal properties*. However, it is not feasible to reflect all of these in estimating SDR due to limited data sets. Empirical regression models to estimate SDR for watersheds were proposed by many researchers; one of them is to estimate SDR using watershed area as

These regression models with only watershed area as an input are useful for a watershed with very limited watershed characteristics data to estimate SDR. These area-based SDR is the first approach in SATEEC system to estimate sediment yield at the watershed outlet. Yoo et al. (2007) reported that soil reconditioning in agricultural field could affect sediment yield at the watershed significantly. Thus area-based SDR was used to estimate sediment yield and it was found that soil loss could increase by 138.0 % per unit hectare while sediment yield could increase by 59.4 % per unit hectare with various activities in the watershed. It indicates

Area-based SDR module provides convenience to estimate SDR with limited data collection for a given watershed, The SDR in the watershed is affected by various geomorphologic properties such as average channel slope than watershed area. Thus, slope-based SDR module was incorporated into the SATEEC system to supplement limitation of area-based

SDR = 0.4720 × A��.��� (Vanoni, 1975) (1)

SDR = 0.5656 × A��.�� (Boyce, 1975) (2)

SDR = 0.3750 × A��.���� (USDA, 1972) (3)

SDR = 0.627 × S�.��� (Williams & Berndt, 1977) (4)

available for various soil erosion and sediment yield applications.

shown below (USDA, 1972, Boyce, 1975, USDA, 1972).

Where, SDR is sediment delivery ratio, and A is watershed area (km2).

that estimated soil loss and sediment yield may not show identical trend.

**2.1.2 Slope-based Sediment Delivery Ratio modules** 

**2.1 SATEEC Version 1.0 ~ 1.8** 

**modules** 

SDR module.

Where, S is slope of watershed.

As indicated above, the SDR could be identical if the area-based SDR is applied to two different watersheds with the same area. Thus, the SATEEC was modified to integrate slope-based SDR module using equation (4). Park et al. (2007) reported that SDRs for 19 small watersheds having identical watershed area were different due to various average channel slope in each watershed, and sediment yields were different by not only USLE factors also estimated SDR for the watersheds. The SDRs by area-based SDR module were calculated as 0.762 for the same size watershed (0.0219 square kilometer). However, the SDRs by slope-based SDR module were from 0.553 to 0.999 with varying slope from 0.73 % to 3.17 %. It indicates that the SDR is one of watershed-specific conditions, thus, better ways to estimate SDR needs to be developed based on various characteristics of watershed and measured data, not just based on single parameter such as only area or only slope. The slope-based SDR module has similar limitation as area-based SDR module does (Figure 1).

The research indicated the SDR is not always the most influential and fundamental factor in sediment yield estimations. However it is also one of very important factors to estimate

SATEEC GIS System for Spatiotemporal Analysis of Soil Erosion and Sediment Yield 257

activities for each crop so that temporal land use change of the year in the given watershed can be considered. These Time-Variant R and C modules are available in SATEEC 2.0 system (Park et al., 2010). The application of SATEEC with these modules described in

Additional and significant enhancement in the SATEEC is an integration of SDR module considering various watershed characteristics. One of benefits to use the SATEEC is to estimate sediment yield with only USLE factors and SDR; however, the SDRs in SATEEC ver. 1.x had limitation because it only estimated SDR based on either area or average slope. As indicated by Park et al. (2007), the SDR needs to be estimated with various watershed characteristics not with single factor because of complicated sediment yield mechanism. To derive SDR for a given watershed with given data for soil loss simulation, a SDR module was developed with optimization algorithm to determine coefficient and exponents of basic formula with watershed area, average slope, Curve Number as fundamental parameters for a given watershed. The modified SDR module in SATEEC ver. 2.0 estimates the SDR equation using three watershed parameters and four coefficient and exponents, that are watershed area, average slope, and curve number to explain sediment transport processes to

Where, A-D are coefficient and exponents, Area is watershed area (km2), Slope is average

The Genetic-Algorithm developed by Holland (1975) was utilized to derive these coefficient and exponents to derive the SDR (Genetic-Algorithm-based Sediment Delivery Ratio: GA-SDR) in the SATEEC system. It is available in the SATEEC 2.0 system. The genetic algorithm has been applied to many scientific studies to solve complex problems, based on the

Figure 2 shows how the GA-SDR module was integrated to SATEEC ver. 2.0; the module requires only the data for soil loss estimation in SATEEC. The basic formula of the module requires watershed area, watershed average slope, and CN, they are derived by Digital Elevation Model (DEM), land use, and soil map data, then the coefficient and exponents are

SATEEC ver. 2.0 was applied to a watershed which has 1,361 square kilometers with the Time-Variant modules and GA-SDR modules, it showed reasonable results represented with 0.721 and 0.720 of determination coefficient (R2) and Nash-Sutcliff efficiency index (NSE) in

The latest version of SATEEC through various modifications is the SATEEC ver. 2.1 that allows user to estimate daily soil loss and sediment yield with enhanced R5 module. The SATEEC ver. 2.0 provides time-variant estimation of soil loss and sediment yield with the Time-Variant C and R modules and GA-SDR module, daily assessment of soil loss and sediment yield at the watershed outlet is in need due to the particularity of precipitation affecting to soil erosion in a single day or a few days, such as typhoon. But deriving USLE R

slope of watershed (%), and CN is average curve number of watershed.

determined by the algorithm, compared measured data.

principle of 'survival of the fittest' and setting up a population of individuals.

calibration, 0.906 and 0.881 of R2 and NSE in validation (Park et al., 2010).

**2.2.3 Daily USLE R modules for daily soil erosion estimation** 

SDR = A × Area� × Slope� × CN� (8)

following section showed reasonable results, when compared with measured data.

**2.2.2 Sediment yield estimation using genetic-algorithm-based SDR module** 

the watershed outlet.

sediment yield which is site-specific/watershed-specific. For instance with Figure 1, watershed areas for Watersheds 1 and 3 with different slope condition, the SDR based on area are identical, but the SDRs based on slope are different. As shown in this comparison, the influence of slope is ignored if SDR is calculated with only their area. In the other way, if the average channel for Watersheds 3 and 4 are the same, while the area of Watershed 3 is greater than that of Watershed 4, the slope-based SDR values are the same while the areabased SDRs are different. From these examples, area-based and slope-based SDR need to be enhanced by incorporating more watershed characteristics affecting generation and transportation of soil erosion and sediment to the watershed outlet.

#### **2.2 SATEEC version 2.0 ~ 2.1**

#### **2.2.1 Time-variant SATEEC R and C modules for monthly and yearly soil erosion and sediment**

One of highly beneficial modification in SATEEC ver. 2.0 is time-variant soil erosion simulation with temporal USLE factors to reflect surface condition of land and precipitation, represented by USLE C and R factors respectively. Soil loss or sediment yield represented with a single value using long-term precipitation data is not sufficient for various soil erosion studies to develop site-specific Best Management Practices. Soils erosion at the watershed is affected by not only total volume of precipitation but also precipitation intensity or patterns. The SATEEC ver. 2.0 was enhanced to reflect precipitation pattern for soil erosion estimation monthly and annually. Time-variant R module integrated into SATEEC ver. 2.1 derives monthly or yearly USLE R factors using daily precipitation data, using regression models suggested by Jung et al (1983).

$$\text{USLE monthly R factor:} \newline R = 0.0378 \times \text{X}^{1.4190} \tag{5}$$

$$\text{USLE yearly R factor:} \,\text{R} = 0.0115 \times \text{Y}^{1.494\%} \tag{6}$$

Where, X is monthly rainfall amount (mm) and Y is yearly rainfall amount (mm).

With this Time-Variant R module in the SATEEC ver. 2.0, the SATEEC could be used for temporal analysis of soil erosion with USLE input data and readily available rainfall data. In addition to the Time-Variant R module, the Time-Variant C module was developed to reflect crop growth and various management practices such as planting, growth, withering, and kill/harvest at the agricultural fields as well as forest. USLE factor representing land use condition is USLE C factor; they vary depending on land use.

$$\mathbb{C}\_{\text{USLE}} = \exp\{ [\ln(0.8) - \ln(\mathbb{C}\_{\text{USLE,min}})] \times \exp\{-0.00115 \times \text{rsd}\_{\text{surf}}\} + \ln\{\mathbb{C}\_{\text{USLE,min}}\} \} \tag{7}$$

Where CUSLE,mn is the minimum value for the cover and management for the land cover and rsdsurf is the amount of residue on the soil surface (kg/ha).

SWAT model estimates daily USLE C values for each crop (equation (7)). Instead of adding SWAT USLE C module for dynamic simulation of crop growth in the SATEEC system, the Time-Variant C module was developed in the SATEEC, allowing user to use daily-based USLE C DB containing 30 representative crops and adjust planting date of each crop for watersheds. The USLE C factor values in SATEEC ver. 2.0 represent crop growth well. For instance, the USLE C factor for potato shows the range from 0.370 to 0.659, from 0.644 to 0.784 for watermelon, from 0.491 to 0.689 for cucumber, and from 0.250 to 0.628 for tomato. Simple interface was developed to allow users to adjust a certain schedule of agricultural

sediment yield which is site-specific/watershed-specific. For instance with Figure 1, watershed areas for Watersheds 1 and 3 with different slope condition, the SDR based on area are identical, but the SDRs based on slope are different. As shown in this comparison, the influence of slope is ignored if SDR is calculated with only their area. In the other way, if the average channel for Watersheds 3 and 4 are the same, while the area of Watershed 3 is greater than that of Watershed 4, the slope-based SDR values are the same while the areabased SDRs are different. From these examples, area-based and slope-based SDR need to be enhanced by incorporating more watershed characteristics affecting generation and

**2.2.1 Time-variant SATEEC R and C modules for monthly and yearly soil erosion and** 

One of highly beneficial modification in SATEEC ver. 2.0 is time-variant soil erosion simulation with temporal USLE factors to reflect surface condition of land and precipitation, represented by USLE C and R factors respectively. Soil loss or sediment yield represented with a single value using long-term precipitation data is not sufficient for various soil erosion studies to develop site-specific Best Management Practices. Soils erosion at the watershed is affected by not only total volume of precipitation but also precipitation intensity or patterns. The SATEEC ver. 2.0 was enhanced to reflect precipitation pattern for soil erosion estimation monthly and annually. Time-variant R module integrated into SATEEC ver. 2.1 derives monthly or yearly USLE R factors using daily precipitation data,

Where, X is monthly rainfall amount (mm) and Y is yearly rainfall amount (mm).

use condition is USLE C factor; they vary depending on land use.

rsdsurf is the amount of residue on the soil surface (kg/ha).

With this Time-Variant R module in the SATEEC ver. 2.0, the SATEEC could be used for temporal analysis of soil erosion with USLE input data and readily available rainfall data. In addition to the Time-Variant R module, the Time-Variant C module was developed to reflect crop growth and various management practices such as planting, growth, withering, and kill/harvest at the agricultural fields as well as forest. USLE factor representing land

C���� = e�� (�ln(0.8) − ln�C��������� × e���−0.00115 × rsd����� � ln�C��������) (7)

Where CUSLE,mn is the minimum value for the cover and management for the land cover and

SWAT model estimates daily USLE C values for each crop (equation (7)). Instead of adding SWAT USLE C module for dynamic simulation of crop growth in the SATEEC system, the Time-Variant C module was developed in the SATEEC, allowing user to use daily-based USLE C DB containing 30 representative crops and adjust planting date of each crop for watersheds. The USLE C factor values in SATEEC ver. 2.0 represent crop growth well. For instance, the USLE C factor for potato shows the range from 0.370 to 0.659, from 0.644 to 0.784 for watermelon, from 0.491 to 0.689 for cucumber, and from 0.250 to 0.628 for tomato. Simple interface was developed to allow users to adjust a certain schedule of agricultural

USLE monthly R factor: R = 0.0378 × X�.���� (5)

USLE yearly R factor: R = 0.0115 × Y�.���� (6)

transportation of soil erosion and sediment to the watershed outlet.

using regression models suggested by Jung et al (1983).

**2.2 SATEEC version 2.0 ~ 2.1** 

**sediment** 

activities for each crop so that temporal land use change of the year in the given watershed can be considered. These Time-Variant R and C modules are available in SATEEC 2.0 system (Park et al., 2010). The application of SATEEC with these modules described in following section showed reasonable results, when compared with measured data.

#### **2.2.2 Sediment yield estimation using genetic-algorithm-based SDR module**

Additional and significant enhancement in the SATEEC is an integration of SDR module considering various watershed characteristics. One of benefits to use the SATEEC is to estimate sediment yield with only USLE factors and SDR; however, the SDRs in SATEEC ver. 1.x had limitation because it only estimated SDR based on either area or average slope. As indicated by Park et al. (2007), the SDR needs to be estimated with various watershed characteristics not with single factor because of complicated sediment yield mechanism. To derive SDR for a given watershed with given data for soil loss simulation, a SDR module was developed with optimization algorithm to determine coefficient and exponents of basic formula with watershed area, average slope, Curve Number as fundamental parameters for a given watershed. The modified SDR module in SATEEC ver. 2.0 estimates the SDR equation using three watershed parameters and four coefficient and exponents, that are watershed area, average slope, and curve number to explain sediment transport processes to the watershed outlet.

$$\text{SDR} = \text{A} \times \text{Area}^{\text{B}} \times \text{Slope}^{\text{C}} \times \text{CN}^{\text{D}} \tag{8}$$

Where, A-D are coefficient and exponents, Area is watershed area (km2), Slope is average slope of watershed (%), and CN is average curve number of watershed.

The Genetic-Algorithm developed by Holland (1975) was utilized to derive these coefficient and exponents to derive the SDR (Genetic-Algorithm-based Sediment Delivery Ratio: GA-SDR) in the SATEEC system. It is available in the SATEEC 2.0 system. The genetic algorithm has been applied to many scientific studies to solve complex problems, based on the principle of 'survival of the fittest' and setting up a population of individuals.

Figure 2 shows how the GA-SDR module was integrated to SATEEC ver. 2.0; the module requires only the data for soil loss estimation in SATEEC. The basic formula of the module requires watershed area, watershed average slope, and CN, they are derived by Digital Elevation Model (DEM), land use, and soil map data, then the coefficient and exponents are determined by the algorithm, compared measured data.

SATEEC ver. 2.0 was applied to a watershed which has 1,361 square kilometers with the Time-Variant modules and GA-SDR modules, it showed reasonable results represented with 0.721 and 0.720 of determination coefficient (R2) and Nash-Sutcliff efficiency index (NSE) in calibration, 0.906 and 0.881 of R2 and NSE in validation (Park et al., 2010).

#### **2.2.3 Daily USLE R modules for daily soil erosion estimation**

The latest version of SATEEC through various modifications is the SATEEC ver. 2.1 that allows user to estimate daily soil loss and sediment yield with enhanced R5 module. The SATEEC ver. 2.0 provides time-variant estimation of soil loss and sediment yield with the Time-Variant C and R modules and GA-SDR module, daily assessment of soil loss and sediment yield at the watershed outlet is in need due to the particularity of precipitation affecting to soil erosion in a single day or a few days, such as typhoon. But deriving USLE R

SATEEC GIS System for Spatiotemporal Analysis of Soil Erosion and Sediment Yield 259

human-made roads should be reflected in estimating USLE L factor. Thus a simple module to consider this effect was developed and integrated to the SATEEC system. By considering detailed characteristics such as segmentation of slope length in watershed, the SATEEC model can provide more realistic effects of field slope segmentation on soil

erosion.

Fig. 3. Overview of the SATEEC ver. 2.1

Fig. 2. Overview of SATEEC GA-SDR module (Park et al., 2010)

factor is not deemed as simple process, although daily assessment of soil erosion is strongly suggested to a hydrology model. Another module named R5 module was integrated to SATEEC for the assessment, the module estimates daily USLE R factor with the process that distributes monthly USLE R factor to each day based on daily precipitation data values. Moreover, the module calculates 5-days antecedent rainfall values with observed daily precipitation data to consider soil moisture condition indirectly (Woo et al., 2010). Eventually, the process to estimate daily USLE R factor with this module can be represented by the equation (9).

$$\text{Daily USLE R factor} = \frac{5 \text{ Days} \text{Interest Precipitation}}{\text{Monthly 5 days Atocated Prediction}} \times \text{Monthly USLE R factor} \times 0.172 \text{ (9)}$$

Woo et al. (2010) applied the SATEEC ver. 2.1 with the module to identical watershed to the watershed used for the application of SATEEC ver. 2.0. The application of SATEEC 2.1 showed more reasonable result represented with 0.776 and 0.776 of R2 and NSE in calibration, 0.927 and 0.911 of R2 and NSE in validation. The SATEEC ver. 2.1 allows user to estimate daily, monthly, and yearly soil loss and sediment yield with DEM, land use, soil map, and measured data (Figure 3). SATEEC, operating in ArcView GIS platform, provides various options selectively (Figure 4).

#### **2.2.4 L modules for topography changes**

USLE LS factor is derived based on the flow accumulation map and flow direction map which are derived using DEM. DEM represents the elevation of each cell so that hydrologic model estimates direction of flow in a given watershed. However the DEM is not detail enough to represent the forest roads of agricultural canals which affects the flow and soil erosion estimation to some degrees. The segmentation of slope length by

factor is not deemed as simple process, although daily assessment of soil erosion is strongly suggested to a hydrology model. Another module named R5 module was integrated to SATEEC for the assessment, the module estimates daily USLE R factor with the process that distributes monthly USLE R factor to each day based on daily precipitation data values. Moreover, the module calculates 5-days antecedent rainfall values with observed daily precipitation data to consider soil moisture condition indirectly (Woo et al., 2010). Eventually, the process to estimate daily USLE R factor with this module can be represented

Woo et al. (2010) applied the SATEEC ver. 2.1 with the module to identical watershed to the watershed used for the application of SATEEC ver. 2.0. The application of SATEEC 2.1 showed more reasonable result represented with 0.776 and 0.776 of R2 and NSE in calibration, 0.927 and 0.911 of R2 and NSE in validation. The SATEEC ver. 2.1 allows user to estimate daily, monthly, and yearly soil loss and sediment yield with DEM, land use, soil map, and measured data (Figure 3). SATEEC, operating in ArcView GIS platform, provides

USLE LS factor is derived based on the flow accumulation map and flow direction map which are derived using DEM. DEM represents the elevation of each cell so that hydrologic model estimates direction of flow in a given watershed. However the DEM is not detail enough to represent the forest roads of agricultural canals which affects the flow and soil erosion estimation to some degrees. The segmentation of slope length by

������� � ���� ���������� ������������� × Monthly USLE R factor × 0.172 (9)

Fig. 2. Overview of SATEEC GA-SDR module (Park et al., 2010)

Daily USLE R factor = � ���� ���������� �������������

various options selectively (Figure 4).

**2.2.4 L modules for topography changes** 

by the equation (9).

human-made roads should be reflected in estimating USLE L factor. Thus a simple module to consider this effect was developed and integrated to the SATEEC system. By considering detailed characteristics such as segmentation of slope length in watershed, the SATEEC model can provide more realistic effects of field slope segmentation on soil erosion.

Fig. 3. Overview of the SATEEC ver. 2.1

SATEEC GIS System for Spatiotemporal Analysis of Soil Erosion and Sediment Yield 261

USLE is a field-scale model to estimate soil erosion by sheet and rill erosion, therefore soil erosion estimated by SATEEC represents sheet and rill erosion, excluding gully erosion which is also one of the soil erosion types occurring in a watershed. To estimate soil erosion containing all of the erosion stated above, nLS model (McCuen & Spiess, 1995) for gully head detection and Unit Stream Power-based Erosion/Deposition (USPED, Mitas, L. & Mitasova, 1998; Mitasova et al., 1996) model for gully erosion was integrated with the SATEEC system. The nLS model detects gully head location based on the estimated nLS values, it requires Manning's n coefficient, length of overland flow, and slope (equation (10))

Gully head = �����

Where, n is Manning's n coefficient, L is the length of overland flow, and S is slope (m/m). The L and S parameters for the model are derived using DEM by SATEEC, and the other

> 1 Water area 0.030 2 Urbanization 0.015 3 Eroded land 0.035 4 Marsh 0.050 5 Grassland 0.130 6 Forest 0.100 7 Paddy field 0.050 8 Cropland 0.035

The USPED model estimates soil erosion considering erosion and deposition based on tractive force (equation (11), Mitas, L. & Mitasova, 1998; Mitasova et al., 1996), most

Where, T is tractive force, R is USLE R factor, K is USLE K factor, C is USLE C factor, P is USLE P factor, A is area in square kilometer, and both m and b are coefficient for types of

The nLS and USPED model are used to estimate soil loss by gully erosion, nLS model defines the points which are gully head, USPED model estimates gully erosion, and then

The SATEEC system has been applied for various soil erosion studies because it is available in GIS interface and is freely downloadable from the SATEEC website (http://www.EnvSys.co.kr/~sateec). In this chapter, several SATEEC applications will be

gully erosion map is developed using output with the nLS and USPED models.

introduced to give various insights of using SATEEC system to the readers.

T=R×K×C×P×A� × (sin b)� (11)

Table 1. Manning's n coefficient for different land uses (Vieux et al., 2004)

parameters are available to be defined with USLE input parameters.

**3. Application of SATEEC GIS system** 

Class Land use Manning's n coefficient

√� (10)

**2.2.5 nLS and USPED in SATEEC** 

for gully head detection as described below.

parameter can be set with Table 1.

soil erosion.

Fig. 4. SATEEC 2.x GIS Interface

(A-G: segmentation of slope length in real fields) Fig. 5. Field Slope Length at Watershed (Foster et al., 1996)

#### **2.2.5 nLS and USPED in SATEEC**

260 Soil Erosion Studies

Fig. 4. SATEEC 2.x GIS Interface

(A-G: segmentation of slope length in real fields)

Fig. 5. Field Slope Length at Watershed (Foster et al., 1996)

USLE is a field-scale model to estimate soil erosion by sheet and rill erosion, therefore soil erosion estimated by SATEEC represents sheet and rill erosion, excluding gully erosion which is also one of the soil erosion types occurring in a watershed. To estimate soil erosion containing all of the erosion stated above, nLS model (McCuen & Spiess, 1995) for gully head detection and Unit Stream Power-based Erosion/Deposition (USPED, Mitas, L. & Mitasova, 1998; Mitasova et al., 1996) model for gully erosion was integrated with the SATEEC system. The nLS model detects gully head location based on the estimated nLS values, it requires Manning's n coefficient, length of overland flow, and slope (equation (10)) for gully head detection as described below.

$$\text{Gully head} = \frac{\text{3.3} \times \text{n} \times \text{L}}{\sqrt{\text{S}}} \tag{10}$$

Where, n is Manning's n coefficient, L is the length of overland flow, and S is slope (m/m). The L and S parameters for the model are derived using DEM by SATEEC, and the other parameter can be set with Table 1.


Table 1. Manning's n coefficient for different land uses (Vieux et al., 2004)

The USPED model estimates soil erosion considering erosion and deposition based on tractive force (equation (11), Mitas, L. & Mitasova, 1998; Mitasova et al., 1996), most parameters are available to be defined with USLE input parameters.

$$\mathbf{T} = \mathbf{R} \times \mathbf{K} \times \mathbf{C} \times \mathbf{P} \times \mathbf{A}^{\mathbf{m}} \times (\sin \mathbf{b})^{\mathbf{n}} \tag{11}$$

Where, T is tractive force, R is USLE R factor, K is USLE K factor, C is USLE C factor, P is USLE P factor, A is area in square kilometer, and both m and b are coefficient for types of soil erosion.

The nLS and USPED model are used to estimate soil loss by gully erosion, nLS model defines the points which are gully head, USPED model estimates gully erosion, and then gully erosion map is developed using output with the nLS and USPED models.
