**5. Model implementation**

and 800 mm year-1 and is well distributed over the year. High intensity rainfall events occur mainly in spring and summer: such thunderstorms may reach peak rainfall intensities of ca. 70 mm h-1 while total rainfall amounts may amount to 40 mm, exceeding rarely 60 mm.

The hydrological database was collected during a recording period of about 2 years (May 1997-February 1999). The rainfall and flow/sediment measurement station was located at the outlet of the watershed. The rainfall events were recorded by a tipping-bucket rain gauge (logging interval equal to 1 minute with 0.5-mm tips). Water depths were continuously measured with a time interval of 2 minutes and an accuracy of 2 mm by a San Dimas flume equipped with a flowmeter, using a submerged probe level sensor. Water discharge was then calculated by a constant relationship between water depth and discharge. The suspend‐ ed sediment concentration, measured by an automated water sampler which a flow-propor‐

Seventeen runoff events, corresponding to rainfall depths in the range 5.5-57.5 mm, were ade‐ quately sampled (Table 1). The sampled events concerned generally low runoff volumes (15 with runoff depths lower than 2 mm), but the most intense event (13-14 September 1998) pro‐ duced a runoff volume of 9.5 mm. Event-based sediment yields were in the range 2 to 604 kg ha-1 (Table 1). Ten other events were not taken into account because of inadequate sampling.

**Rainfall Runoff**

**depth duration**

**volume**

19/05/1997 8.0 0.4 0.22 2.8 0.103 8.2 70.1 21/05/1997 6.5 8.4 0.13 2.0 0.056 2.7 23.3 11/07/1997 13.0 0.6 1.97 15.2 0.862 40.9 349.7 14/07/1997 5.5 0.6 0.37 6.7 0.181 4.4 37.6 17-18/07/1997 21.5 8.4 0.35 1.6 0.050 3.6 30.8 25/12/1997 6.5 1.0 0.09 1.4 0.043 0.2 2.1 05/01/1998 8.0 4.2 0.23 2.9 0.051 0.5 4.5 28/04/1998 11.0 1.4 0.14 1.3 0.037 0.2 1.8 05/06/1998\* 10.5 3.3 0.002 0.02 0.003 - - 06/06/1998\* 29.5 32.8 13.08 44.3 1.827 - - 11/06/1998\* 16.5 21.4 3.68 22.3 0.389 - - 22/08/1998\* 36.5 47.2 0.93 2.5 0.046 - - 26/08/1998 5.5 8.4 0.39 7.1 0.064 1.9 16.2 08-09/09/1998 24.5 1.5 0.45 1.8 0.067 1.3 11.1 13-14/09/1998 57.5 19.1 8.86 15.4 1.017 66.1 565.2

**Runoff coefficient**

**(mm) (h) (mm) (%) (m3 s-1) (Mg) (kg ha-1)**

runoff), was determined by oven-drying every sample at

**Peak**

**flow Sediment yield**

tional sampling rate (every 30 m3

**Event**

105 C for 24 hours.

10 Research on Soil Erosion Soil Erosion

The watershed discretization into homogeneous drainage areas ("cells") and the hydro‐ graphic network segmentation into channels ("reaches") were performed for both water‐ sheds using the GIS interface incorporated into AnnAGNPS.

The geometry and the density of the drainage network were modeled by setting the Critical Source Area (CSA) to 1.25 ha and the Minimum Source Channel Length (MSCL) to 100 m for the Cannata watershed, which allowed a suitable representation of the same watershed in a previous study (Licciardello et al., 2006). Such values were decreased to 0.5 ha and 50 m re‐ spectively for the Ganspoel watershed, because of its higher land use heterogeneity (Near‐ ing et al., 2005). The Cannata watershed resulted in 78 cells and 32 reaches (Figure 4a), while the Ganspoel watershed in 155 cells and 65 reaches (Figure 4b).

The elevation GIS layer was arranged by digitizing contour lines every 2 m on a 5-m resolution DEM; land use and soil input data were derived from 25-m resolution GIS maps. The morpho‐ logic parameters (i.e., cell slope length and steepness) as well as the dominant land uses and soil types were directly associated with each drainage area by means of the GIS interface.

Meteorological and pluviometric input data were properly arranged by the AnnGNPS weather subroutines. For the Cannata watershed daily values of maximum and minimum air temperatures, relative humidity, solar radiation and wind velocity were measured at the meteorological station within the watershed. Daily rainfall input data were derived from re‐ cords provided by the three working rain gauges in the different periods and input to each drainage area by applying the Thiessen polygon method, except when only the rainfall re‐ corded at a single station was available (Figure 2). For the Ganspoel watershed, as no mete‐ orological information (except for rainfalls) was provided in the database, air temperature, relative humidity and wind velocity data were collected at the nearest meteorological sta‐ tion (Bruxelles, 50 54'N, 4 30'E, about 13 km far from the watershed outlet). Solar radiation was evaluated by the Hargreaves' formula. For both watersheds daily values of dew point temperature were calculated on the basis of air temperature and humidity.

ilar soils (Takken et al., 1999; Nearing et al., 2005). Given that, as above mentioned, soil physical parameters were much more related to land use than to soil texture (Van Oost et al., 2005), six different values of Ksat (one for each soil land use surveyed into the water‐

> **Land use**

**Cannata Ganspoel**

**after calibration**

All I Ia All II

**use default Value**

**Land**

urban zones

Forested, meadow and fallow zones

All

32 15 16

81[B]; 84[D]

http://dx.doi.org/10.5772/50427

13

71[B]; 78[D]

0.15\*

0.04\*

**Value**

Prediction of Surface Runoff and Soil Erosion at Watershed Scale: Analysis of the AnnAGNPS Model

Cropland 81[C]; 84[D] 75[C]; 78[D] Cropland +

**model**

Pasture 79[C]; 84[D] 72[C]; 78[D]

HYDROLOGICAL SUBMODEL

EROSIVE SUBMODEL

Pasture 0.13\* 0.1\*

Cropland 0.125\* 0.1\*

Pasture 0.13\* 0.1\*

Cropland 0.125\* 0.1\*

(\*) According to the indications in the AGNPS user manual (Young et al., 1994) integrated with those provided by the user manual of the EUROSEM model (Morgan et al., 1998).

**Table 2.** Input parameters subject to calibration process of the AnnAGNPS model in the experimental watersheds.

For both waterheds vegetation cover and soil random roughness data were collected during

Management information (crop types and rotation as well as agricultural operations) was en‐ tered in the plant/management files and modelled using the RUSLE database guidelines and database. For the crop cultivations it was necessary to modify some default parameter values such as crop planting and harvest dates as well as type and dates of agricultural operations.

The C factor was directly calculated by the model as an annual value for non-cropland and as a series of twenty-four 15-day values per year for cropland (based on prior land use, sur‐

Pasture +cropland

shed) were input to the model.

**Parameter**

Initial curve number (CN)

Synthetic 24-h rainfall distribution type

Sheet flow Manning's roughness coefficient (m-1/3 s)

Concentrated flow Manning's roughness coefficient (m-1/3 s)

Surface long-term random roughness coefficient (mm)

the whole monitoring period.

(1) The hydrologic groups are reported in brackets

**Figure 4.** Layouts of the Cannata (left) and Ganspoel (right) watershed discretisation by the AnnAGNPS model.

To allow the model to adjust the initial soil water storage terms, the first two years were ap‐ pended to the beginning of the precipitation and meteorological dataset. The initial values of CN, unique throughout the whole simulation period, were initially derived from the standard procedure set by the USDA Soil Conservation Service (Table 2).

Table 3 shows the values or range of the RUSLE parameters set utilised by the erosive sub‐ model. The average annual rainfall factor (R), its cumulative percentages for 24 series of 15 day periods in a year and the soil erodibility factor (K) were determined according to guidelines by Wischmeier and Smith (1978), the latter on the basis of a field survey of soil hydrological characteristics (Indelicato, 1997; Steegen et al., 2001; Van Oost et al., 2005).

In the Cannata watershed, for each of the five soil textures, a uniform soil profile was modeled up to 1500 mm by averaging the required physical characteristics from the field samples. Soil wilting point and field capacity were derived from the experimental dataset. The whole Ganspoel watershed was modelled assuming a unique soil type (silt loam) up to a depth of 1000 mm. Values of soil wilting point and field capacity, not available from the Ganspoel dataset, were estimated by a pedo-transfer function (Saxton et al., 1986). The values of the soil saturated hydraulic conductivity (Ksat, in the range 0.001-205 mm h-1) was derived from the LISEM Limburg database, as these data were collected on very sim‐ ilar soils (Takken et al., 1999; Nearing et al., 2005). Given that, as above mentioned, soil physical parameters were much more related to land use than to soil texture (Van Oost et al., 2005), six different values of Ksat (one for each soil land use surveyed into the water‐ shed) were input to the model.


(1) The hydrologic groups are reported in brackets

Meteorological and pluviometric input data were properly arranged by the AnnGNPS weather subroutines. For the Cannata watershed daily values of maximum and minimum air temperatures, relative humidity, solar radiation and wind velocity were measured at the meteorological station within the watershed. Daily rainfall input data were derived from re‐ cords provided by the three working rain gauges in the different periods and input to each drainage area by applying the Thiessen polygon method, except when only the rainfall re‐ corded at a single station was available (Figure 2). For the Ganspoel watershed, as no mete‐ orological information (except for rainfalls) was provided in the database, air temperature, relative humidity and wind velocity data were collected at the nearest meteorological sta‐ tion (Bruxelles, 50 54'N, 4 30'E, about 13 km far from the watershed outlet). Solar radiation was evaluated by the Hargreaves' formula. For both watersheds daily values of dew point

temperature were calculated on the basis of air temperature and humidity.

12 Research on Soil Erosion Soil Erosion

**Figure 4.** Layouts of the Cannata (left) and Ganspoel (right) watershed discretisation by the AnnAGNPS model.

standard procedure set by the USDA Soil Conservation Service (Table 2).

To allow the model to adjust the initial soil water storage terms, the first two years were ap‐ pended to the beginning of the precipitation and meteorological dataset. The initial values of CN, unique throughout the whole simulation period, were initially derived from the

Table 3 shows the values or range of the RUSLE parameters set utilised by the erosive sub‐ model. The average annual rainfall factor (R), its cumulative percentages for 24 series of 15 day periods in a year and the soil erodibility factor (K) were determined according to guidelines by Wischmeier and Smith (1978), the latter on the basis of a field survey of soil hydrological characteristics (Indelicato, 1997; Steegen et al., 2001; Van Oost et al., 2005).

In the Cannata watershed, for each of the five soil textures, a uniform soil profile was modeled up to 1500 mm by averaging the required physical characteristics from the field samples. Soil wilting point and field capacity were derived from the experimental dataset. The whole Ganspoel watershed was modelled assuming a unique soil type (silt loam) up to a depth of 1000 mm. Values of soil wilting point and field capacity, not available from the Ganspoel dataset, were estimated by a pedo-transfer function (Saxton et al., 1986). The values of the soil saturated hydraulic conductivity (Ksat, in the range 0.001-205 mm h-1) was derived from the LISEM Limburg database, as these data were collected on very sim‐ (\*) According to the indications in the AGNPS user manual (Young et al., 1994) integrated with those provided by the user manual of the EUROSEM model (Morgan et al., 1998).

**Table 2.** Input parameters subject to calibration process of the AnnAGNPS model in the experimental watersheds.

For both waterheds vegetation cover and soil random roughness data were collected during the whole monitoring period.

Management information (crop types and rotation as well as agricultural operations) was en‐ tered in the plant/management files and modelled using the RUSLE database guidelines and database. For the crop cultivations it was necessary to modify some default parameter values such as crop planting and harvest dates as well as type and dates of agricultural operations.

The C factor was directly calculated by the model as an annual value for non-cropland and as a series of twenty-four 15-day values per year for cropland (based on prior land use, sur‐ face cover, surface roughness and soil moisture condition (AnnAGNPS, 2001; Bingner and Theurer, 2005). The practice factor (P) was always set to 1, due to the absence of significant protection measures in the watershed (Table 3).

et al., 2001; Shrestha et al., 2006); and the most important input parameter to which the runoff is sensitive (Yuan et al., 2001; Baginska et al., 2003), besides soil (field capacity, wilting point and saturated hydraulic conductivity) as well as climate parameters (precipi‐

Prediction of Surface Runoff and Soil Erosion at Watershed Scale: Analysis of the AnnAGNPS Model

In order to calibrate/validate the peak flows and the sediment yields, both 24-h rainfall dis‐ tributions typical of a Pacific maritime climate (types I and Ia) with wet winter and dry summers (USDA-NCRS, 1986) derived by the extended TR-55 method database were used. The sediment yields were evaluated at event scale by adjusting the surface long-term ran‐ dom roughness coefficient (which affects the RUSLE C-factor) as well as the sheet and con‐

For simulation of surface runoff, peak flow and sediment yield events, the AnnAGNPS model run with default input parameters (Table 3). No calibration/validation processes

In both the experimental watersheds surface runoff volumes and sediment yields were eval‐ uated at the event scale; in the Cannata watershed the analysis of surface runoff was extend‐

Model performance was assessed by qualitative and quantitative approaches. The qualita‐ tive procedure consisted of visually comparing observed and simulated values. For quanti‐ tative evaluation a range of both summary and difference measures were used (Table 4).

The summary measures utilized were the mean and standard deviation of both observed

misleading evaluation criterion, the Nash and Sutcliffe (1970) coefficient of efficiency (E) and its modified form (E1) were also used to assess model efficiency (Table 4). In particular, E is more sensitive to extreme values, while E1 is better suited to significant over- or underpre‐ diction by reducing the effect of squared terms (Krause et al, 2005). As suggested by the same authors, E and E1 were integrated with the Root Mean Square Error (RMSE), which de‐ scribes the difference between the observed values and the model predictions in the unit of the variable. Finally, the Coefficient of Residual Mass (CRM) was used to indicate a preva‐ lent model over- or underestimation of the observed values (Loague and Green, 1991).

and CRM (Table 4). According to common practice, simulation results are considered good for values of E greater than or equal to 0.75, satisfactory for values of E between 0.75 and

, is an insufficient and often

http://dx.doi.org/10.5772/50427

15

, E and E1 and 0 for RMSE

and simulated values. Given that coefficient of determination, r2

The values considered to be optimal for these criteria were 1 for r2

0.36 and unsatisfactory for values below 0.36 (Van Liew and Garbrecht, 2003).

tation, temperature and interception).

*5.1.2. Ganspoel watershed*

**6. Model evaluation**

ed to the monthly and annual scale.

were undertaken.

centrated flow Manning's roughness coefficients (Table 3).


[b] Before calibration

[c] After calibration and for validation

[d] Annual value (AnnAGNPS, 2001).

**Table 3.** Values or range of the RUSLE parameters set at the experimental watersheds for the evaluation of the AnnAGNPS model.

#### **5.1. Hydrological simulation**

After processing the input parameters of the hydrological and erosive sub-models (respec‐ tively requiring the determination of the initial Curve Numbers for the USDA SCS-CN mod‐ el and the calculation of the RUSLE model factors), daily values of surface runoff, peak flow and sediment yield were continuously simulated at the outlet of both watersheds by An‐ nAGNPS (version 3.2).

Considering that baseflow is not considered by AnnAGNPS, the surface runoff separation from baseflow was performed by the traditional manual linear method applied to observed stream flow data. Based on studies by Arnold et al. (1995) as well as Arnold and Allen (1999), these results match reasonably well with those obtained through an automated digi‐ tal filter; the differences in the surface runoff component extracted by the two methods are up to 20% at yearly scale.

#### *5.1.1. Cannata watershed*

Both the hydrological and erosion components of AnnAGNPS were calibrated/validated separating the calibration and validation periods by the split-sample technique. The cali‐ bration/validation process was carried out by modifying the initial values of CN, which represent a key factor in obtaining accurate prediction of runoff and sediment yield (Yuan et al., 2001; Shrestha et al., 2006); and the most important input parameter to which the runoff is sensitive (Yuan et al., 2001; Baginska et al., 2003), besides soil (field capacity, wilting point and saturated hydraulic conductivity) as well as climate parameters (precipi‐ tation, temperature and interception).

In order to calibrate/validate the peak flows and the sediment yields, both 24-h rainfall dis‐ tributions typical of a Pacific maritime climate (types I and Ia) with wet winter and dry summers (USDA-NCRS, 1986) derived by the extended TR-55 method database were used. The sediment yields were evaluated at event scale by adjusting the surface long-term ran‐ dom roughness coefficient (which affects the RUSLE C-factor) as well as the sheet and con‐ centrated flow Manning's roughness coefficients (Table 3).

#### *5.1.2. Ganspoel watershed*

face cover, surface roughness and soil moisture condition (AnnAGNPS, 2001; Bingner and Theurer, 2005). The practice factor (P) was always set to 1, due to the absence of significant

> R (MJ mm ha-1 h-1 year-1) 1040 1496 (Mg ha-1 per R-factor unit) 0.39 to 0.53 0.06 LS (-) 1.72 to 4.94 0.10 to 2.29

> > Cropland[a] 0.0002 to 0.042[b];

[a] Series of twenty-four 15-day period values per year (AnnAGNPS, 2001)

**Table 3.** Values or range of the RUSLE parameters set at the experimental watersheds for the evaluation of the

After processing the input parameters of the hydrological and erosive sub-models (respec‐ tively requiring the determination of the initial Curve Numbers for the USDA SCS-CN mod‐ el and the calculation of the RUSLE model factors), daily values of surface runoff, peak flow and sediment yield were continuously simulated at the outlet of both watersheds by An‐

Considering that baseflow is not considered by AnnAGNPS, the surface runoff separation from baseflow was performed by the traditional manual linear method applied to observed stream flow data. Based on studies by Arnold et al. (1995) as well as Arnold and Allen (1999), these results match reasonably well with those obtained through an automated digi‐ tal filter; the differences in the surface runoff component extracted by the two methods are

Both the hydrological and erosion components of AnnAGNPS were calibrated/validated separating the calibration and validation periods by the split-sample technique. The cali‐ bration/validation process was carried out by modifying the initial values of CN, which represent a key factor in obtaining accurate prediction of runoff and sediment yield (Yuan

P (-) 1

Rangeland[d] 0.016[b]; 0.029[c] 0.0074

**Value or range Cannata Ganspoel**

0.0001 to 0.043[c] 0.00002 to 0.269

protection measures in the watershed (Table 3).

C (-)

**5.1. Hydrological simulation**

nAGNPS (version 3.2).

up to 20% at yearly scale.

*5.1.1. Cannata watershed*

AnnAGNPS model.

14 Research on Soil Erosion Soil Erosion

[b] Before calibration

[c] After calibration and for validation [d] Annual value (AnnAGNPS, 2001).

**RUSLE factor**

For simulation of surface runoff, peak flow and sediment yield events, the AnnAGNPS model run with default input parameters (Table 3). No calibration/validation processes were undertaken.
