-
* To whom all correspondence should be addressed. Email: [email protected]
Post-print version
Ramos MC; Martínez-Casasnovas JA. 2015. Soil water content, runoff
and soil loss prediction in a small ungauged agricultural basin in the
Mediterranean region using the Soil and Water Assessment Tool. Journal
of Agricultural Science 153: 481-496. DOI:
10.1017/S0021859614000422
Soil water content, runoff and soil loss prediction in a small un-gauged
agricultural basin in the Mediterranean region using the Soil and Water
Assessment Tool Short title: Runoff and soil losses in small basin with SWAT
M. C. RAMOS AND J.A. MARTÍNEZ-CASASNOVAS*
University of Lleida, Department of Environmental and Soil Sciences, Av Rovira
Roure, 191, E-25198 Lleida, Spain
(MS received 12 September 2013, revised 20 February 2014, accepted TBC April
2014)
SUMMARY
The aim of the present work was to evaluate the possibilities of using sub-basin data
for calibration of the Soil and Water Assessment Tool (SWAT) model in a small un-
gauged basin (46 ha) and its response. This small basin was located in the viticultural
Anoia-Penedès region (north-east Spain), which suffers severe soil erosion. The data
sources were: daily weather data from an observatory located close to the basin; a
detailed Soil Map of Catalonia; a 5-m resolution digital elevation model (DEM); a
crop/land use map derived from orthophotos taken in 2010 and an additional detailed
soil survey (40 points) within the basin, which included properties such as texture, soil
organic carbon, electrical conductivity, bulk density and water retention capacity at –
33 and –1500 kPa. A sensitivity analysis was performed to identify and rank the
sensitive parameters that affect the hydrological response and sediment yield to
changes of model input parameters. A one-year calibration and one-year validation
were carried out on the basis of soil moisture measured at 0.20-m intervals from
depths of 0.10 to 0.90 m in two selected sub-basins, and data related to estimations of
runoff and sediment concentrations in runoff collected in the same sub-basins. The
paper shows a methodological approach for calibrating SWAT in small un-gauged
basins using soil water content measurements and runoff samples collected within the
basin. The SWAT satisfactorily predicted the average soil water content, runoff and
soil loss for moderate intensity events recorded during the study periods. However, it
was not satisfactory for high intensity events which would require exploring the
possibilities of using sub-daily information as an input model parameter.
INTRODUCTION
In Mediterranean areas, factors such as climate, topography, soil characteristics, land
use change and intensive agricultural practices have made soil erosion the main cause
of land degradation (Cerdà 2009; García-Ruíz & López-Bermúdez 2009). The Anoia-
-
2
Penedès region, located in north-eastern Spain, provides a particularly good example
of the effects of intensive erosion processes in Mediterranean Spain (Ramos &
Martínez-Casasnovas 2010). In this region, the combination of frequent high-intensity
rainfall events, highly erodible soil parent materials (marls and unconsolidated
sandstones), extensive grapevine cropping, changes in land use and the abandonment
of traditional soil conservation measures have contributed to the acceleration of
erosion processes (Ramos & Martínez-Casasnovas 2007, 2010).
The need for clear and accurate estimations of soil erosion in agricultural areas
is crucial for an understanding of the underlying processes and the development of
prevention plans to reduce erosion (Casalí et al. 2008). The scale of study and the
assessment of land-climate interactions and their influences on soil erosion, water
quality and agriculture are also issues that have captured the interest of many
researchers, because their effects are seen off-site as well as on-site. On-site effects
include soil, organic matter and nutrient losses, diminished infiltration and water
availability, intra-field soil properties and crop variability, and the ultimate loss of soil
fertility and crop production. Sediments and nutrients are exported, leading to reduced
quality of water supplies and siltation of the drainage and irrigation systems (de Vente
& Poesen 2005).
Researchers have used different models to predict soil erosion and sediment
transport and to assess the impact of management practices. Models to predict soil
erosion include: a) spatially distributed models, involving empirical (WaTEM-
SEDEM - Van Rompaey et al. 2001; Haregeweyn et al. 2013; USPED - Leh et al.
2013) and physical approaches (PESERA - Kirkby et al. 2008; SWAT - Nearing et al.
2005; Shen et al. 2009; Tibebe & Bewket 2011); b) non-spatially distributed models,
including regression and factorial models such as LMRM (Marquez & Guevara-Pérez
2010; Verstraeten & Poesen 2001) R-USLE (Renard et al. 1997; Grimm et al. 2003),
and PSIAC (de Vente & Poesen 2005); and c) conceptual models, such as AGNPS
(Young et al. 1989) and MMF (Morgan 2001). Other physically based models, such
as EUROSEM (Morgan et al. 1998), WEPP (Flanagan et al. 2001; Shen et al. 2009),
CREHDYS (Laloy & Bielders 2009) and CREAMS (Knisel 1980) have also been
applied extensively to cover a range of scales and environmental conditions. The
selection of the model depends on the final objective, the data required to run and
calibrate the model and the implicit uncertainty in interpreting the results obtained.
The Soil and Water Assessment Tool (SWAT) has been used widely to predict
the impact of management practices on the yield of water, sediments and agricultural
chemicals from basins at different scales (Gikas et al. 2006; Lee et al. 2010;
Roebeling et al. 2014; Martínez-Casasnovas et al. 2013). The SWAT model provides
a powerful platform with which to analyse the influence of topography, soils, land
cover, land management and weather in a spatially distributed way and to use the
results to predict such parameters as runoff and sediment and nutrient losses.
Multiple applications of SWAT have been reported in the literature for
different purposes. At present, SWAT is increasingly being used to assist watershed
planning, with model applications becoming increasingly sophisticated in order to
target critical pollutant source areas and practices. However, to date, applications in
small basins have been limited (Bogena et al. 2003; Gevaert et al. 2008; Licciardello
et al. 2011) and few studies have focused on applications including detailed soil
information. According to Mukundan et al. (2010), the effect of spatial resolution on
soil data may not be relevant in large watersheds; however, it may be determinant/
pronounced in small ones and so it may be appropriate to formulate and simulate
land-use management strategies.
-
3
The aim of the present research was to analyse the suitability of using SWAT
in a small agricultural basin in the Mediterranean area, in which the local land and
climatic characteristics tend to favour erosion. Given the absence of a gauging station
in the study basin, soil water content data measured at different depths in the soil
profile and runoff samples collected in various sub-basins were used for model
calibration and validation. Another singular characteristic of the research was the
great detail of the soil information used in the study and the land use: grape vines
(Vitis vinifera), which were the main land use in the basin and there are very few
cases of SWAT application with grape vines as a target crop.
MATERIALS AND METHODS
Study area
The study area was located in the municipality of Piera, c. 40 km northwest of
Barcelona (1º 46ʹ E, 41º31ʹ N, 340 m a.s.l.) (Fig. 1). The main land use in the study
area was grape vine cultivation, which has a long tradition in this area and belongs to
the Penedès Designation of Origin . The study basin was selected according to the
following criteria: small size (0.46 km2), the existence of a detailed soil map, the
proximity to a meteorological station, and an area with non-irrigated vines as the main
crop in the basin (0.629), which suffers severe erosion problems. The study area
forms part of the Vallès – Penedès Tertiary Depression. The local soils developed on
alluvial deposits dating from the Pleistocene Epoch, which covered a substratum of
Miocene marls, sandstones and unconsolidated conglomerates. A high proportion of
coarse elements of metamorphic origin were also present. The most frequent soils in
the basin were classified as Typic Xerorthents, Fluventic Calcixerepts and Fluventic
Haploxerepts (Soil Survey Staff 2006); or Haplic Regosols, Cutanic Luvisols, Haplic
and Fluvic Cambisols (IUSS Working Group WRB 2006). The average slope in the
basin was c. 9 %. The basin drained into a gully system, which is characteristic of the
landscape of the region in which the basin was located (Martínez-Casasnovas et al.
2009).
In this area, deep ploughing (0.6–0.7 m) before the planting of vines is
common in order to favour root penetration. Land levelling is also a frequent practice
in order to create larger and more easily managed fields. This practice usually
involves the abandonment of traditional soil conservation measures and the
modification of soil profiles. Other studies conducted in this region have also reported
important changes in soil properties after levelling operations (Ramos & Martínez-
Casasnovas 2006), leading to the exposure of underlying marls, sandstones and
conglomerates. Another associated problem is an increase in soil erosion, as reported
by Martínez-Casasnovas et al. (2009), with a 26.5 % increase in average annual soil
loss associated with land transformation and the removal of traditional broad terraces.
The climate is Mediterranean, with average annual rainfall of 550 mm
(ranging between 380 mm and 900 mm) and frequent high-intensity events in spring
and autumn (>100 mm/h). The average annual rainfall erosivity (R factor = kinetic
energy × maximum intensity in 30-min period) based on 1-min interval data is c. 1200
MJ/ha mm/h/yr. However, in the decade 2000-2010, some of these values ranged
between 1350 and 3900 MJ/ha mm/h/yr (Ramos & Martínez-Casasnovas 2009).
SWAT input data and measurements
The SWAT model simulates the hydrological water balance of the basin on the basis
of hydrological response units (HRU), which are obtained from a combination of soil,
land use and slope degree characteristics. The model operates on a daily time step.
-
4
Flow and water quality variables are routed from the HRU to sub-basins and
subsequently to the watershed outlet. The SWAT model simulates hydrological
processes as a two-component system, comprised of surface hydrology and channel
hydrology, as described by Neitsch et al. (2011). It integrates various models: the Soil
Conservation Service curve number technique (USDA-SCS 1985) is used to estimate
runoff rates; the modified soil loss equation, MUSLE (Williams & Berndt 1977), is
used for erosion and sediment yield at the catchment scale; and the routing of channel
sediment is simulated through a modification of Bagnold’s sediment transport
equation (Bagnold 1977).
A detailed-scale soil map (1:25000) (DAR 2008) was used as input data for
the model. The soil information included in this map was detailed at the series level
(Fig. 2). Additional soil information on relevant soil properties was obtained with a
soil survey carried out in 2010. Forty sampling points located throughout the basin
were selected; the locations of the samples were based on differences in the multi-
spectral responses of soils which were seen in a false colour composite of a
WorldView-2 image acquired in July 2010. Soil samples from 0 to 0.90 m (0–0.20 m,
0.20–0.50 m, 0.50–0.70 m and 0.70–0.90 m) were taken at each point. Various soil
properties were analysed, such as soil particle distribution (Gee & Bauder 1986), bulk
density (Pla 1983), organic carbon (Allison 1965), electrical conductivity (Rhoades
1982) and water retention capacity at saturation, –33 kPa and –1500 kPa, using
Richard plates (Klute 1986). The coarse element fraction was evaluated in a 2-kg
aliquot fraction which was sieved through a 2 mm mesh. The infiltration capacity was
also analysed using rainfall simulation. The use of simulated rainfall to measure
infiltration rates increases the accuracy of the measurements in comparison with the
use of cylinder infiltrometers (Cerdà 1997). The simulation was done in three sub-
basins (SB1, SB2 and SB3), which corresponded to soils with different characteristics
and located up-, mid- and down-slope (Fig. 3). The simulated rainfall consisted of 2.5-
mm diameter drops of deionized water falling freely from drippers positioned 2.5 m
above the soil surface. Plots of 0.30 × 0.20 m were subjected to 70 mm/h of simulated
rainfall for 40 min. The runoff water was collected at 10-min intervals. Three
replications were carried out at each sample point. The steady infiltration rate was
reached in all cases after 30 min of rainfall. The intensity-frequency-duration used in
the current study had a return period (or recurrence interval) in the area of 7 years,
although during recent years the frequency of high intensity rainfall events has been
increasing. The eroded soil particles in suspension and the total runoff volume were
measured for each simulation. Water infiltration rates were calculated from the
difference between rainfall intensity and runoff rates. The rainfall intensity was
calibrated just before and just after each simulation. In each sub-basin, the rainfall
simulations were carried out in triplicate. The average value of the steady infiltration
rate was used for further calibration of the model.
The soil erodibility factor (KUSLE factor) was also computed for each soil unit,
as this is input data for calculating SWAT: the equation proposed by Wischmeier et
al. (1971) was used for this. The crop parameters were taken from the SWAT data
base and updated with existing information for the study area relating to biomass
phosphorus and nitrogen concentrations and crop fertilization and tillage operations
for each land use.
A 1-m resolution digital elevation model (DEM) of the study area was also
used for sub-basin delineation and slope degree calculation. The DEM was generated
from a low altitude photogrammetric aerial survey carried out in 2010. This permitted
the computation of the degree of slope at the level of each grid. The following slope
-
5
degree percentage intervals were considered 0–2, 2–5, 5–10, 10–15 and > 15 %.
These intervals were established in relation to the signs of erosion observed in the
field (no sign to slight, slight, moderate, severe and high erosion). They represented
0.05, 0.269, 0.508, 0.137 and 0.036 of the surface, respectively. These intervals of
slope degree together with soil type and land used were used in the definition of the
HRU. A land use map was created after orthorectification of the 2010 aerial photos at
a scale of 1:3000 and field work checking. The ArcSWAT 2009.93.5 program was
then run at a daily time scale.
Weather data were taken on a daily basis from the Els Hostalest de Pierola
observatory, which belongs to the Servei Meteorològic de Catalunya (1º 48ʹ E; 41º 31ʹ
N, 316 m a.s.l.) and was located 2.5 km away in east direction from the study basin.
Both daily data and average values for a 15-year series (1996–2011) of maximum and
minimum temperatures, precipitation, solar radiation, relative humidity and wind
velocity were used as inputs for the model. Precipitation was also recorded in the
basin at 1-min intervals in order to determine rainfall intensity which was used, in
combination with the steady infiltration rate, to estimate runoff rates.
The soil water content data for the profiles of the two sub-basins used for
SWAT calibration were acquired using soil moisture sensors Decagon capacitance
probes (Decagon, Pullman, WA, USA). These probes were installed at different
depths (0.10–0.30, 0.30–0.50, 0.50–0.70 and 0.70–0.90 m) in sub-basins SB1 and
SB2 (Fig. 3). Measurements were recorded every 4 h and then averaged to provide
daily means. The probes were calibrated by comparison with soil water contents
measured by gravimetry. Soil samples were taken at the same depths at which the
TDF probes were installed at ten dates during the year in order to have information for
a wide range of soil water contents. The correlation coefficients between gravimetric
and volumetric soil water content were calculated for each date and probe: they
ranged between 0.72 and 0.92. The correction factors, which ranged from 0.70 to
0.95, were averaged for each probe. With the corrected data obtained from the probes,
depth-weighted volumetric water was estimated and then soil water storage in the
profile was calculated.
Model calibration and validation
A sensitivity analysis was conducted using the SWAT sensitivity tool. It identified
and ranked the sensitive parameters that affected the response of the model and the
rate of change of its output with respect to changes in inputs. This analysis combines
the Latin Hypercube and One-factor-At-a-Time sampling. During the sensitivity
analysis, SWAT was run (p+1) × 10 times, where p was the number of parameters
being evaluated and 10 the number of loops. Different soil characteristics such as the
saturated hydraulic conductivity, the soil depth and the soil available water capacity
(AWC) were included in the analysis. Additional parameters such as the runoff curve
number (CN2), the degree of slope, the surface runoff lag coefficient (SURLAG), the
evaporation compensation factor (ESCO), the maximum potential leaf area index
(BLAI) and the amount of water removed by transpiration from plants (Plant_ET), as
well as parameters related to the groundwater such as the groundwater revap
coefficient (accounts for water movement into overlaying unsaturated layers as
function of water demand for evapotranspiration, GW_revap), the threshold depth of
water in the shallow aquifer required to return flow (GW_Qmin), the groundwater
delay time (delay time or drainage time for aquifer recharge in days, GW_delay) and
the Base flow alpha factor (it represents the ratio of base flow at the present time to
the flow one day earlier and ranges between 0 and 1, Alpha_Bf), were included in the
-
6
analysis of the hydrological response. For sediment production, the USLE-C
(Universal Soil Loss Equation Cover factor) and USLE-P (soil conservation practices
factor in the USLE equation) and the SPCON (sediment transport coefficient) and
SPEXP (exponent in the sediment transport equation ranging from 1 to 2) parameters
were included. Additional explanations about the parameters and coefficients that
were used can be found in Neitsch et al. (2011).
The calibration was carried out by adjusting the selected parameters manually,
one at a time, until the statistical calibration criteria were met. Calibration was carried
out for the period 1 May 2010 to 30 April 2011, which included events with different
characteristics (depth and intensity), as well as long dry periods and periods of high
intensity rainfall. The model was individually calibrated for sub-basins SB1 and SB2,
trying to fit the parameters in order to obtain the best results. The control parameters
were: crop evapotranspiration, soil water content, runoff rates and soil loss due to
runoff. Evapotranspiration (ETo) was estimated in the SWAT using the Hargreaves
equation (Hargreaves et al. 1985) The evapotranspiration estimated by the model for
the two sub-basins (planted with vines) was compared with the values calculated
using the ETo obtained from the Els Hostalets de Pierola meteorological station and
the crop coefficients proposed by Allen et al. (1998). The ESCO (soil evaporation
compensation coefficient), EPCO (plant uptake compensation factor), and Plant_ET
coefficients (Neitsch et al. 2011).were adjusted to find the best fit between simulated
and estimated evapotranspiration and soil moisture. Runoff rates, which were
calculated taking into account steady infiltration rates and sealing and precedent soil
moisture, were compared with the surface runoff simulated by the model. The
comparison was carried out for the same sub-basins in which soil water probes were
installed. Daily rainfall events with precipitation > 9 mm, which some authors have
identified as erosive rainfall (Mannaerts & Gabriels 2000), were also considered in a
detailed rainfall analysis. Runoff samples were collected after the main rainfall events
recorded during the calibration period using Gerlach troughs. The collectors were 0.5
m wide, with covered tops to prevent the entry of precipitation water. The Gerlach
troughs were connected to underground collectors, hidden in the soil to collect runoff.
After each rainfall event, total runoff was taken from the collectors using a vacuum
pump, after homogenizing. After measuring the volume, these samples were analysed,
in aliquots, for sediment concentration in runoff; the aliquots were dried at 105 ºC
during 24 h and then weighed. The results obtained were then used in conjunction
with runoff water volumes to calculate soil losses for each runoff sampling point and
compared with simulated soil losses. For each event the simulated runoff and soil loss
integrated over time were compared with the estimated values.
Validation was carried out for the period 1 May 2011 to 15 May 2012. Field
measurements for soil water content, rainfall and runoff were also recorded for that
period. Model performance for both calibration and validation periods was defined
based on three statistical methods: the Nash–Sutcliffe efficiency (NSE; Nash &
Sutcliffe 1970), the percent bias (PBIAS, %; Gupta et al. 1999) and the ratio of the
root mean square error to standard deviation (RSR) (Eqns 1, 2 and 3).
n
i m
n
i sm
YY
YYNSE
1
2
2
11 (1)
n
i m
n
i sm
Y
YYPBIAS
1
1100
(2)
-
7
2
1
2
1
n
i m
n
i sm
YY
YYRSR (3)
where Ym is the measured value and Ys is the simulated value with SWAT, and Y is the mean of the measured values of each of the parameters analysed.
RESULTS
Table 1 presents a summary of the statistics of the soil properties of the study basin.
Most soils had a loamy or a sandy-loam texture, with the average proportion of coarse
elements ranging from 0.098 to 0.284 in the top horizon. The organic matter content
was relatively low, ranging from 9 to 23 g/kg. The available water capacity ranged
from 7.7 to 12.2 mm and the steady infiltration rate ranged from 8.0 to 29.5 mm/h.
Some soils in the basin were very erodible, with KUSLE factors ranging from 0.033 to
0.055 (t ha h)/(ha MJ mm). Soil depth ranged from 0.80 to 1.10 m, and none of the
sampled soils showed any signs of (pseudo) gley phenomena, which indicated a good
circulation of drainage water within the soil profile.
Precipitation events and soil moisture dynamics
During the calibration period (1 May 2010 to 30 April 2011) rainfall events with
different characteristics were recorded, with levels of daily precipitation ranging from
< 1 mm to 97.7 mm (Fig. 4). Twenty-three events recorded > 9 mm of precipitation,
which represented 0.833 of the total rainfall in the period. Precipitation was
distributed throughout the year, but the main rainfall events were recorded in May,
September and October 2010 and March 2011. Four events with precipitation > 99 %
percentile were recorded. Total depths of those events were 69.7, 85.9, 97.7 and 69.6
mm, with 30-min rainfall intensities that were > 50 mm/h and 10-min intensities of up
to 120 mm/h.
During the model validation period (1 May 2011 to 15 May 2012), 24 erosive
events of different characteristics were recorded; these were mainly distributed in
spring and autumn (Fig. 4). Precipitation ranged from 9.2 to 87.9 mm, including three
extreme events of 69.6, 87.9 and 46.5 mm and with 30-min rainfall intensities of up to
37 mm/h. The precipitation recorded in these erosive events represented 0.852 of total
rainfall and was mainly concentrated in October–November 2011 and March–April
2012, with a long dry period in between.
Figure 4 shows the variations in one of the control sub-basins (SB1) at four
different soil depths. The soil water response after each rainfall event depended on the
antecedent soil water content and on rainfall intensity. It was observed that soil water
content changed in a different way in each soil layer. The greatest variations were
observed in the surface layers, which could be explained by evaporation processes,
particularly during dry periods. After high-intensity rainfall events, soil water
increased in the surface layer but not in the deeper layers. Another notable aspect was
that the highest soil water content was found in the layer between 0.50 and 0.70 m
than in deeper layers, which could be due to the higher water retention capacity of the
soil in that layer. This was found in most soils in the basin.
SWAT Model application in the study basin
Land use and hydrological response units
Thirty-four sub-basins were identified within the study basin. Detailed spatial
information about soil units, slope degree and land use allowed the definition of 1180
-
8
HRU in the sub-basins. The extensions of these HRU ranged from < 0.01 to 1.39 ha,
with 0.96 being < 0.3 ha. Vines occupied 0.629 of the area: other crops present in the
basin were: olive trees (0.048), alfalfa (0.085), winter barley (0.094), winter pasture
(0.015) and scrub (0.036). Urban areas and (paved and un-paved) roads and tracks
represented c. 0.093 of the total surface area (Fig. 3).
Sensitivity analysis
The sensitivity analysis results showed the most sensitive parameters that affected
both the hydrological response and the sediment production: they were ranked on a
scale from 0 to 33. The ones with rank > 10 demonstrated variations in the SWAT
output and were considered as sensitive parameters. Among them, soil characteristics
such as the CN2, the saturated hydraulic conductivity, the soil depth and the AWC
were the most sensitive parameters with regard to hydrological processes. The model
was also sensitive to other parameters such as SURLAG, CN2, slope, ESCO, BLAI,
Plant_ET, GW_revap, GW_Qmin, GW_delay and Alpha_Bf. For sediment
production all parameters included in the analysis except the channel resistance to
erosion were sensitive. Despite the fact that some parameters, such as AWC, saturated
hydraulic conductivity and soil depth or slope, were highlighted as sensitive
parameters, the final values adopted were not modified since they were obtained from
the specific field survey carried out for the present research. For those susceptible to
change, a detailed sensitivity analysis was performed in order to know not only the
influence on runoff but on other components of the water balance. The adjusted
parameters and their final values are shown in Table 2. The initial soil water
conditions were also updated with measured data.
Model calibration
Soil water data measured in the field and soil water data simulated for SB1 and SB2
during the calibration period are shown in Fig. 5. The calibration statistics for soil
water content are shown in Table 3: the calibration sample for soil water content
included 327 days. There were differences between the basins, with the fit between
simulated and measured data for SB2 being better than that for SB1. For the
calibration period, the RSR statistics for the soil water content in the profile were
0.488 and 0.670, respectively for SB1 and SB2. The PBIAS were –1.752 and 2.684 %
and the NSE was 0.687 for both sub-basins. The statistics for runoff and soil loss
calibration were based on 15 and 14 samples, respectively (Table 3). Simulated daily
runoff rates and estimates obtained, taking into account steady infiltration rates based
on the rainfall simulation and the antecedent soil water for both sub-basins, are shown
in Fig. 6. The fit for runoff rates was slightly better for SB1 than for SB2 according to
RSR, but not according to the other two statistics (PBIAS and NSE).
Model validation
The statistics obtained for the validation period are also shown in Table 3. For soil
water, the validation period included 245 days. The RSR were 0.444 and 0.742, the
PBIAS were 0.328 and 2.249%, and the NSE were 0.862 and 0.852, respectively, for
the SB1 and SB2 sub-basins. For the runoff and soil loss rates, the validation statistics
referred to 14 days. For the runoff rates, RSR had values of 0.528 and 0.384, PBIAS
of –13.823 and –8.964 %, and NSE of 0.817 and 0.881, respectively, for the two sub-
basins. Similarly, for soil loss, the validation results showed better fits for RSR and
NSE than for PBIAS in SB2 and the opposite in SB1, with RSR values of 0.714 and
0.281, PBIAS values of 8.627 and 23.120 %, and NSE values of 0.714 and 0.910,
-
9
respectively, for the two sub-basins. The runoff rates and soil losses predicted by the
model were on average in agreement with the soil losses estimated by combining
runoff rates and sediment concentrations in runoff. The greatest differences occurred
with extreme rainfall events of high intensity and a short duration, which do not tend
to be very well detected in a daily scale analysis. Among the two analysed sub-basins
the greater discrepancies between simulated and estimated were found in the area
where erosion was highest. During the analysed period, the average runoff rate in the
basin was about 0.22, but with higher values for some specific sub-basins in which
vines were cultivated and due to the combination of slope degree, soil characteristics
and management practices (with bare soil throughout most of the year).
DISCUSSION
Calibration and validation
Following similar criteria to those provided by Moriasi et al. (2007), the first
calibration analysis for runoff and sediment could be considered satisfactory,
particularly considering that the analysis was carried out using daily data. The NSE
was of the same order as those observed by Narasimhan et al. (2005), and the RSR
were similar to those observed by Li et al. (2010) for soil moisture analysis. The use
of soil water content for model calibration and validation was useful because this is a
parameter that can be measured at different points in the basin. This also means that it
is possible to have additional information about water infiltration and soil response
(infiltration and redistribution within the soil profile). In addition, antecedent soil
moisture has been identified as one of the main factors that conditions runoff rates
(Castillo et al. 2003; Zhang et al. 2011). This could be important for understanding
when to apply runoff and soil erosion models in order to assess soil conditions and the
effects of rainstorms, including soil erosion.
Although the soil loss results could be considered satisfactory for the control
sub-basins, the agreement between simulated and measured soil loss was better for
SB2 than for SB1, according to RSR and NSE, but was poorer according to PBIAS.
By analysing the ratios between soil erosion and sediment yield (soil losses in the
text) estimated by SWAT for these sub-basins, it was possible to observe that the
poorer fits were associated with high intensity precipitation events of a short duration
(Fig. 7). This was the case of some of the events recorded in June (14, 16, 18, and 21)
and September (8, 15 and 21) 2010 and in August (13) and November (10, 11, 17. 18
and 19) 2011. In addition, for the events in which the fit was good, sedimentation was
very low or null. However, for higher intensity events for which higher erosion rates
were expected, sedimentation was quite important, accounting for between 0.20 and
0.30 of soil losses. This might explain the differences between the amount of soil
mobilized at some specific points and that modelled in the sub-basins.
For the validation period, there was not a clear better fit between simulated
and measured soil loss in one of the sub-basins and few differences were observed
when they were compared with the values observed for calibration. Only PBIAS
improved, slightly. The greatest discrepancies were found for short duration and high
intensity events.
From the current analysis, it can be concluded that the model gives a
satisfactory fit for the hydrological components of the balance. The methodological
approach for calibrating SWAT in small un-gauged basins and the use of detailed soil
data allowed suitable runoff rates and reasonably good soil loss predictions to be
obtained for most situations, but less satisfactory results were seen when extreme
events or high intensity, short duration, rainfall events occurred. This agrees with the
-
10
poorer performance of other runoff and sediment yield predictions found in
Mediterranean areas in predicting extreme peak flows (Licciardello et al. 2007). The
characteristics of the rainfall events in the study area, which in many cases would
have been of short duration and high intensity, would require exploring the
possibilities of using sub-daily information as an input model parameter. However,
despite this fact, the model could be a good tool to predict the hydrological processes.
In addition, the model allowed the comparison of soil losses for years with different
characteristics.
The agreement between results of the SWAT application in the present case
study and with other works in the same and other areas with Mediterranean conditions
suggests validity of the calibration method proposed for small un-gauged basins. This
methodological approach differs from that used in other works, in which the SWAT
model has been mostly applied to large basins (Rossi et al. 2009; Arnold et al. 2010;
Parajuli 2011), for which water flow data in gauge stations is available.
The erosion rates obtained during the two calibration and validation periods
were comparable to those estimated at plot scale for vines with similar management
regimes in the same area (Ramos & Martínez-Casasnovas 2009). These values were
always highest in the most disturbed areas of the new vineyards (Ramos & Martínez-
Casasnovas 2007). Vineyards have been reported as one of the most erosion-prone
types of cultivated land in Europe. At plot scale in the Mediterranean area, Cerdan et
al. (2010) reported a mean value of 8.64 t/ha/yr as an average of different plots, with
high variation (S.D.=27.4). The measured and simulated results obtained in the
current analysis were comparable to these values and also similar to the maximum
values reported by various authors in relation to arable land in other European
countries, with values ranging from 10 to 20 t/ha/yr (Verheijen et al. 2009) and
simulated erosion rates for vineyards in other areas with Mediterranean climates
(Potter & Hiatt 2009). Thus, Bienes et al. (2012) indicated soil losses up to 20 t/ha/yr
in vineyards with traditional management. Similarly, for bare-soil vineyards, Maetens
et al. (2012) found soil losses of 10–20 t/ha/yr. Other studies have cited higher
erosion rates: up to 60 t/ha/yr in Sicilian vineyards without soil cover (Novara et al.
2011), 35 t/ha/yr in the Mid Aisne (France) (Wicherek 1991), and 8–36 t/ha/yr in the
Languedoc region (France) (Paroissien et al. 2010).
The measured and simulated soil losses exceed the soil loss tolerance rates
established for Europe (0.3 to 1.4 t/ha/yr) which depend on driving factors (Verheijen
et al. 2009), but are also higher than the threshold values accepted for arable lands,
which range from 9 t/ha/yr (Singh et al. 1992) to 11.2 t/ha/yr (Mannering 1981). The
following ranks of soil loss have been defined for soils: slight (0–5 t/ha/yr), moderate
(5–10 t/ha/yr), high (10–20 t/ha/yr), very high (20– 40 t/ha/yr), severe (40–80 t/ha/yr)
and very severe (> 80 t/ha/yr). According to this classification, the level of soil
erosion in the study basin of the current work would be classified as high to very high.
Spatial soil loss distribution in the basin
Due to the management practices used in the vines of the study basin, which include
bare soil and frequent tillage throughout the crop cycle, high rates of erosion were
expected. Within the basin, however, differences on parameters such as water runoff
and sediment yield were found between sub-basins due to the combined influence of
soil properties and slope degree. Figure 8 shows the spatially distributed simulated
soil losses produced in the basin for both calibration and validation periods. The
highest erosion rates were recorded near the outlet, where the slope degree is highest
and the infiltration capacity of the soils is lowest. Furthermore, due to levelling
-
11
operations undertaken before the vineyard plantation, the area was severely disturbed,
with a great amount of unconsolidated material having been left on the surface.
In both periods (calibration and validation), but particularly during the first
one, highly erosive rainfall events were recorded. Accordingly, high erosion rates
were observed. During the calibration period, four events produced 0.87 of the total
erosion. In this case, the differences between the simulated and estimated erosion
rates were c. 23% in the SB1 sub-basin and up to 40 % in the SB2 sub-basin. During
the validation period, there was only one extreme event of similar characteristics (86.9
mm) to those recorded in the previous period and four additional events that produced
erosion rates of greater than 0.2 t/ha. For that extreme event, the differences between
the measured and estimated erosion rates were 12.1 and –16 %, respectively, for the
two areas. For the rest of the events, the differences were 17 and 30 %, respectively,
for sub-basins SB1 and SB2. The results showed that the model simulations presented
higher levels of variability, and with less agreement, in the zones located near the
outlet, where the model produced higher erosion rates.
The SWAT allowed identification of the areas that suffer greater erosion. This
is important in order to establish conservation measures in specific areas of the basin
which could reduce soil erosion. This would not only result in a reduction in soil
losses, but would also increase the amount of water available for agricultural needs.
This work is part of research project AGL2009-08353 funded by the Spanish Ministry
of Science and Innovation. We would like to thank Dr. R. Srinivasan for his help in
setting up the model and helping to adapt its parameters to achieve a better fit for the
study case. We would also like to thank the Castell d’Age winery for its support and
for allowing us to carry out our field experiments on its property.
REFERENCES
ALLEN, R. G., PEREIRA, L. S., RAES, D. & SMITH, M. (1998). Crop Evapotranspiration.
Guidelines for Computing Crop Water Requirements. FAO Irrigation and
Drainage Paper, no. 56. Rome: FAO.
ALLISON, L. E. (1965). Organic carbon. In Methods of Soil Analysis. Part 2. (Eds C.
A. Black, D. D. Evans, L. E. Ensminger, J. L. White, F. E. Clark & R. C.
Dinauer), pp. 1367-1378. Madison, WI, USA: American Society of America.
ARNOLD, J. G., ALLEN, P. M., VOLK, M., WILLIAMS, J. R. & BOSCH, D. D. (2010).
Assessment of different representations of spatial variability on SWAT model
performance. Transactions of the ASABE 53, 1433-1443.
BAGNOLD, R. A. (1977). Bed load transport by natural rivers. Water Resources
Research 13, 303-312.
BIENES, R., MARQUÉS, M. J. & RUIZ-COLMENERO, M. (2012). Herbaceous crops,
vineyards and olive groves. The traditional land management and its impact on
water erosion. Cuadernos de Investigación Geográfica 38, 49-74.
BOGENA, H., DIEKKRÜGER, B., KLINGEL, R., JANTOS, K. & THEIN, J. (2003). Analysing
and modelling the solute and sediment transport in the catchment of the
Wahnbach River. Physics & Chemistry of the Earth 28, 227-237.
CA A , J., GASTESI, R., A LVAREZ-MOZOS, J., DE SANTISTEBAN, L. M., DEL VALLE DE
LERSUNDI, J., G , R., LA A A A, A., G , M., AGIRRE, U., CAMPO, M.
A., L , J. J. & D A , M. (2008). Runoff, erosion, and water quality of
agricultural basins in central Navarre (Spain). Agricultural Water Management
95, 1111-1128.
-
12
CASTILLO, V. M., GÓMEZ-PLAZA, A. & MA -MENA, M. (2003). The role of
antecedent soil water content in the runoff response of semiarid catchments: a
simulation approach. Journal of Hydrology 284, 114–130.
CERDÀ, A. (1997). Seasonal changes of the infiltration rates in a Mediterranean
scrubland on limestone. Journal of Hydrology 198, 209-225.
CERDÀ, A. (2009). Erosión y Degradación del Suelo Agrícola en España. Valencia,
Spain: Publ. Universidad de Valencia.
CERDAN, O., GOVERS, G., LE BISSONNAIS, Y., VAN OOST, K., POESEN, J., SABY,
N., GOBIN, A., VACCA, A., QUINTON, J., AUERSWALD, K., KLIK, A., KWAAD, F. J.
P. M, RACLOT, D., IONITA, I., REJMAN, J., ROUSSEVA, S., MUXART, T., ROXO, M.
J. & DOSTAL, T. (2010). Rates and spatial variations of soil erosion in Europe: A
study based on erosion plot data. Geomorphology 122, 167-177
DAR (2008). Mapa de Sòls (1:25.000) de l’Àmbit Geogràfic de la Denominació
d’Origen Penedès. Vilafranca del Penedès-Lleida, Spain: Departament
d’Agricultura, Alimentació i Acció ural, eneralitat de Catalunya.
DE VENTE, J. & POESEN, J. (2005). Predicting soil erosion and sediment yield at the
basin scale: Scale issues and semi-quantitative models. Earth-Science
Reviews 71, 95-125.
FLANAGAN, D. C., ASCOUGH II, J. C., NEARING, M. A. & LAFLEN, J. M. (2001). The
Water Erosion Prediction Project (WEPP) model. In Landscape Erosion and
Evolution Modeling (Eds R. S. Harmon & W. W. Doe III), pp. 145-199. New
York: Kluwer Academic / Plenum Publishers.
GARCÍA-RUÍZ, J. M. & LÓPEZ-BERMÚDEZ, F. (2009). La Erosión del Suelo en España.
Zaragoza, Spain: Sociedad española de Geomorfología.
GEE, G. W. & BAUDER, J. W. (1986). Particle-size analysis. In Methods of Soil
Analysis. Part 1, 2nd
Ed (Eds A. Klute, G. S. Campbell, R. D. Jackson, M. M.
Mortland & D. R. Nielsen), pp. 383-412. Madison, WI, USA: American Society
of Agronomy.
GEVAERT, V., VAN GRIENSVEN, A., HOLVOET, K., SEUNTJENS, P. & VANROLLEGEHM,
P. A. (2008). SWAT developments and recommendations for modelling
agricultural pesticide mitigation measures in river basins. Hydrological Sciences
Journal 53, 1075-1089.
GIKAS, G. D., YIANNAKOPOULOU, T. & TSIHRINTZIS, V. A. (2006). Modeling of non-
point source pollution in a Mediterranean drainage basin. Environmental
Modeling and Assessment 11, 219-233.
GRIMM, M., JONES, R. J. A., RUSCO, E. & MONTANARELLA, L. (2003). Soil Erosion
Risk in Italy: a Revised USLE Approach. European Soil Bureau Research Report
No. 11, EUR 20677 EN. Luxembourg: Office for Official Publications of the
European Communities.
GUPTA, H. V., SOROOSHIAN, S. & YAPO, P. O. (1999). Status of automatic calibration
for hydrologic models: Comparison with multilevel expert calibration. Journal of
Hydrologic Engineering 4, 135-143.
HAREGEWEYN, N., POESEN, J., VERSTRAETEN, G., GOVERS, G., DE VENTE, J., NYSSEN,
J., DECKERS, J. & MOEYERSONS, J. (2013). Assessing the performance of a
spatially distributed soil erosion and sediment delivery model
(WATEM/SEDEM) in Northern Ethiopia. Land Degradation and Development
24, 188-204.
HARGREAVES, G. L., HARGREAVES, G. H. & RILEY, J. P. (1985). Agricultural
benefits for Senegal River Basin. Journal of Irrigation and Drainage Engineering
111, 113-124.
-
13
IUSS WORKING GROUP WRB (2006). World Reference Base for Soil Resources 2006.
World Soil Resources Reports No. 103. Rome: FAO.
KIRKBY, M. J., IRVINE, B. J., JONES, R. J. A., GOVERS, G. & THE PESERA TEAM (2008).
The PESERA coarse scale erosion model for Europe I.- Model rationale and
implementation. European Journal of Soil Science 59, 1293–1306.
KLUTE, A. (1986). Water retention: Laboratory methods. In Methods of Soil Analysis.
Part 1, 2nd
Ed (Eds A. Klute, G. S. Campbell, R. D. Jackson, M. M. Mortland &
D. R. Nielsen), pp. 635–662. Madison, WI, USA: American Society of
Agronomy.
KNISEL, W. G. (1980). CREAMS: A Fieldscale Model for Chemical, Runoff, and
Erosion from Agricultural Management Systems. Conservation Report No. 26.
Washington, D.C.: USDA, Science and Education Administration.
LALOY, E. & BIELDERS, C. L. (2009). Modelling intercrop management impact on
runoff and erosion in a continuous maize cropping system: Part I. Model
description, global sensitivity analysis and Bayesian estimation of parameter
identifiability. European Journal of Soil Science 60, 1005-1021.
LEE, M. S., PARK, G., PARK, M. J., PARK, J. Y., LEE, J. W. & KIM, S. J. (2010).
Evaluation of non-point source pollution reduction by applying Best Management
Practices using a SWAT model and QuickBird high resolution satellite imagery.
Journal of Environmental Science 22, 826–833.
LEH, M., BAJWA, S. & CHAUBEY, I. (2013). Impact of land use change on erosion risk:
an integrated remote sensing, geographic information system and modelling
methodology. Land Degradation & Development 24, 409-421.
LI, M. X., MA, Z. G. & DU, J. W. (2010). Regional soil moisture simulation for
Shaanxi Province using SWAT model validation and trend analysis. Science
China: Earth Sciences 53, 575-590.
LICCIARDELLO, F., ZEMA, D. A., ZIMBONE, S. M. & BINGNER, R. L. (2007). Runoff and
soil erosion evaluation by the AnnAGNPS model in a small Mediterranean
watershed. Transactions of the ASABE 50, 1585-1593.
LICCIARDELLO, F., ROSSI, C. G., SRINIVASAN, R., ZIMBONE, S. M. & BARBAGALLO, S.
(2011). Hydrologic evaluation of a mediterranean watershed using the SWAT
model with multiple PET estimation methods. Transactions of the ASABE 54,
1615-1625.
MAETENS, W., VANMAERCKE, M., POESEN J, JANKAUSKAS, B., JANKAUSKIEN, G. &
IONITA, I. (2012). Effects of land use on annual runoff and soil loss in Europe and
the Mediterranean: A meta-analysis of plot data. Progress in Physical Geography
36, 599-653.
MANNAERTS, C. M. & GABRIELS, D. (2000). Rainfall erosivity in Cape Verde. Soil &
Tillage Research 55, 207-212.
MANNERING, J. V. (1981). The use of soil loss tolerance as a strategy for soil
conservation. In Soil Conservation: Problems and Prospects (Ed. R. P. C.
Morgan), pp. 337–349. New York: John Wiley & Sons.
MARQUEZ, A. M. & GUEVARA-PÉREZ, E. (2010). Comparative analysis of erosion
modeling techniques in a basin of Venezuela. Journal of Urban and
Environmental Engineering 4, 81-104.
MARTÍNEZ-CASASNOVAS, J. A., RAMOS, M. C. & GARCÍA-HERNÁNDEZ, D. (2009).
Effects of land-use changes in vegetation cover and sidewall erosion in a gully
head of the Penedès region (northeast Spain). Earth Surface Processes and
Landforms 34, 1927-1937.
-
14
MARTÍNEZ-CASASNOVAS, J. A., RAMOS, M. C. & BENITES, G. (2013) Soil and water
assessment tool soil loss simulation at the sub-basin scale in the Alt Penedès-
Anoia vineyard region (NE Spain) in the 2000s. Land Degradation and
Development DOI: 10.1002/ldr.2240
MORGAN, R. P. C. (2001). A simple approach to soil loss prediction: a revised
Morgan–Morgan–Finney model. Catena 44, 305–322.
MORGAN, R. P. C., QUINTON, J. N., SMITH, R. E., GOVERS, G., POESEN, J. W. A.,
AUERSWALD, K., CHISCI, G., TORRI, D. & STYCZEN, M. E. (1998). The European
Soil Erosion Model (EUROSEM): a dynamic approach for predicting sediment
transport from fields and small catchments. Earth Surface Processes and
Landforms 23, 527–544.
MORIASI, D. N., ARNOLD, J. G., VAN LIEW, M. W., BINGNER, R. L., HARMEL, R. D. &
VEITH, T. L. (2007). Model evaluation guidelines for systematic quantification of
accuracy in watershed simulations. Transactions of the ASABE 50, 885-900.
MUKUNDAN, R., RADCLIFFE, D. E. & RISSE, L. M. (2010). Spatial resolution of soil
data and channel erosion effects on SWAT model prediction of flow and
sediment. Journal of Soil and Water Conservation 65, 92-104.
NARASIMHAN, B., SRINIVASAN, R., ARNOLD, J. G. & DI LUZIO, M. (2005). Estimation
of long-term soil moisture using a distributed parameter hydrologic model and
verification using remotely sensed data. Transactions of the ASAE 48, 1101-1113.
NASH, J. E. & SUTCLIFFE, J. V. (1970). River flow forecasting through conceptual
models: Part I. A discussion of principles. Journal of Hydrology 10, 282−290.
NEARING, M. A., JETTEN, V. BAFFAUT, C., CERDAN, O., COUTURIER, A. HERNANDEZ,
M., LE BISSONNAIS, Y., NICHOLS, M. H., NUNES, J. P., RENSCHLER, C. S.,
SOUCHÈRE, V. & VAN OOST, K. (2005). Modeling response of soil erosion and
runoff to changes in precipitation and cover. Catena 61, 131-154.
NEITSCH, S. L., ARNOLD, J. G., KINIRY, J. R. & WILLIAMS, J. R. (2011). Soil and Water
Assessment Tool: Theoretical Documentation Version 2009. Texas Water
Resources Institute technical Report No. 406. College Station, Texas, USA:
Texas A&M University System. Available online from:
http://twri.tamu.edu/reports/2011/tr406.pdf (accessed March 2014).
NOVARA, A., GRISTINA, L., SALADINO, S. S., SANTORO, A. & CERDÀ, A. (2011). Soil
erosion assessment on tillage and alternative soil managements in a Sicilian
vineyard. Soil & Tillage Research 117,140-147.
PARAJULI, P. B. (2011). Effects of spatial heterogeneity on hydrologic responses at
watershed scale. Journal of Environmental Hydrology 19, 1-18
PAROISSIEN, J., LAGACHERIE, P. & LE BISSONNAIS, Y. (2010). A regional-scale study
of multi-decennial erosion of vineyard fields using vine-stock unearthing–burying
measurements. Catena 82,159-168.
PLA, I. (1983). Metodología para la Caracterización Física con Fines de Diagnóstico
de Problemas de Manejo y Conservación de Suelos en Condiciones Tropicales.
Alcace 32. Maracay, Venezuela: Revista de la Facultad de Agronomia UCV.
POTTER, C. & HIATT, S. (2009). Modelling river flows and sediment dynamics for the
Laguna de Santa Rosa watershed in Northern California. Journal of Soil & Water
Conservation 64, 383-393.
RAMOS, M. C. & MARTÍNEZ-CASASNOVAS, J. A. (2006). Impact of land levelling on
soil moisture and runoff variability in vineyards under different rainfall
distributions in a Mediterranean climate and its influence on crop productivity.
Journal of Hydrology 321, 131-146.
http://twri.tamu.edu/reports/2011/tr406.pdf
-
15
RAMOS, M. C. & MARTÍNEZ-CASASNOVAS, J. A. (2007). Soil loss and soil water
content affected by land levelling in Penedès vineyards, NE Spain. Catena 71,
210-217.
RAMOS, M. C. & MARTÍNEZ-CASASNOVAS, J. A. (2009). Impacts of annual
precipitation extremes on soil and nutrient losses in vineyards of NE Spain.
Hydrological Processes 23, 224–235.
RAMOS, M. C. & MARTÍNEZ-CASASNOVAS, J. A. (2010). Effects of field reorganisation
on the spatial variability of runoff and erosion rates in vineyards of northeastern
Spain. Land Degradation & Development 21, 1-12.
RENARD, K. G., FOSTER, G. R., WEESIES, G. A., MCCOOL, D. K. & YODER, D. C.
(coordinators) (1997). Predicting Soil Erosion by Water: A Guide to
Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE).
USDA Agriculture Handbook No 703. Washington, D.C.: USDA.
RHOADES, J. D. (1982). Soluble salts. In Methods of Soil Analysis: Part 2 (Eds A. L.
Page, R. H. Miller, D. R. Keeney, D. E. Baker, R. Ellis & J. D. Rhoades), pp.
167-178. Madison, WI: ASA and SSSA.
ROEBELING, P. C., ROCHA, J., NUNES, J. P., FIDELIS, T., AVES, H. & FONSECA, S.
(2014). Using the Soil and Water Assessment Tool to estimate dissolved
inorganic nitrogen water pollution abatement cost functions in Central Portugal.
Journal of Environmental Quality 43, 168-176.
ROSSI, C. G., SRINIVASAN, R., JIRAYOOT, K., LE DUC, T., SOUVANNABOUTH, P., BINH,
N. & GASSMAN, P. W. (2009). Hydrologic evaluation of the lower mekong river
basin with the soil and water assessment tool model. International Agricultural
Engineering Journal 18, 1-13.
SHEN, Z. Y., GONG, Y. W., LI Y. H., HONG, Q., XU, L. & LIU, R. M. (2009). A
comparison of WEPP and SWAT for modeling soil erosion of the Zhangjiachong
Watershed in the Three Gorges Reservoir Area. Agricultural Water Management
96, 1435–1442.
SINGH, G., BABU, R., NARAIN, P., BHUSHAN, L. S. & ABROL, I. P. (1992). Soil erosion
rates in India. Journal of Soil and Water Conservation 47, 97-99.
SOIL SURVEY STAFF (2006). Keys to Soil Taxonomy. Tenth edition. Washington, D.C.:
United States Department of Agriculture, Natural Resources Conservation
Service.
TIBEBE, D. & BEWKET, W. (2011). Surface runoff and soil erosion estimation using
the SWAT model in the Keleta Watershed, Ethiopia. Land Degradation &
Development 22, 551–564.
USDA-SCS (1985). National Engineering Handbook, Section 4 - Hydrology.
Washington, D.C.: USDA-SCS.
VAN ROMPAEY, A., VERSTRAETEN, G., VAN OOST, K., GOVERS, G. & POESEN, J.
(2001). Modelling mean annual sediment yield using a distributed approach.
Earth Surface Processes and Landforms 26, 1221-1236.
VERHEIJEN, F. G. A., JONES, R. J. A., RICKSON, R. J. & SMITH, C. J. (2009). Tolerable
versus actual soil erosion rates in Europe. Earth-Science Reviews 94, 23-38.
VERSTRAETEN, G. & POESEN, J. (2001). Factors controlling sediment yield for small
intensively cultivated catchments in a temperate humid climate. Geomorphology
40, 123-144.
WICHEREK, S. (1991). Viticulture and soil erosion in the north of Parisian Basin.
Example: the mid Aisne region. Zeitschrift fur Geomorphologie, Supplementband
83, 115-126.
-
16
WILLIAMS, J. R. & BERNDT, H. D. (1977). Sediment yield prediction based on
watershed hydrology. Transactions of the ASAE 20, 1100-1104.
WISCHMEIER, W. H., JOHNSON, C. B. & CROSS, B. V. (1971). A Soil erodibility
nomograph for farmland and construction sites. Journal of Soil & Water
Conservation 26, 189-193.
WRB (IUSS Working Group WRB) (2006). World Reference Base for Soil Resources
2006. World Soil Resources Reports No. 103. Rome: FAO.
YOUNG, R. A., ONSTAD, C. A., BOSCH, D. D. & ANDERSON, W. P. (1989). AGNPS: A
non-point-source pollution model for evaluating agricultural watersheds. Journal
of Soil & Water Conservation 44, 168-173.
ZHANG, Y., WEI, H. & NEARING, M. A. (2011). Effects of antecedent soil moisture on
runoff modeling in small semiarid watersheds of southeastern Arizona.
Hydrology & Earth System Sciences 15, 3171-3179.
-
17
Table 1. Mean values (m) and standard deviation (S.D.) of soil properties of the most representative soils in the study basin: root depth,
lower boundary depth of each horizon (SDH), texture fraction (clay, silt, sand- USDA), coarse elements, organic carbon (OC), electrical
conductivity (EC), bulk density (BD), available water capacity (AWC= water retention capacity at 33kPa – water retention capacity at -
1500kPa), steady infiltration rate (StIR); K-erodibility USLE factor (K-factor)
SOIL
serie
Root depth
(mm)
SDH
(mm)
(m)
Fine fraction (< 2mm) Coarse
element
fraction
of total
soil
(%)
(m±S.D.)
OC
(%)
(m±S.D.)
EC
(dS/m )
(m±S.D.)
BD
(kg/m3)
(m±S.D.)
AWC
(%)
(m±S.D.)
StIR
(mm/h)
(m±S.D.)
KUSLE factor (t ha h) /
(ha MJ mm)
(m±S.D.)
Clay
(%)
(m±S.D.)
Silt
(%)
(m±S.D.)
Sand
(%)
(m±S.D.)
S1 800 240 11±4 20±3 68±2 25±2 1.1±0.3 0.14±0.01 1754±320 10.8±0.3 27±2 0.043±0.008
Falguerar 620 14±3 37±5 48±2 25±2 0.2±0.1 0.19±0.01 1953±280 13.1±0.4 0.055
1380 28±4 39±4 33±2 5±1 0.1±0.1 0.16±0.01 1810±300 14.2±0.8 0.040
S2 1000 330 19±4 40±4 41±4 44±2 0.7±0.2 0.10±0.01 1638±160 13.7±0.5 8±1 0.037±0.007
Pierola 670 13±3 24±3 62±4 72±3 0.3±0.1 0.10±0.01 1725±231 13±1 0.030
1000 6±2 2.3±2 91±4 71±4 0.2±0.1 0.2±0.01 1920±285 12±1 0.020
S3 1000 330 20±5 30±3 50±3 25±3 1.4±0.2 0.1±0.01 1750±320 9±1 12.2±0.5 0.045±0.006
Marquet 670 13±3 24±3 62±3 72±4 0.3±0.1 0.1±0.01 1710±295 13±2 0.030
1000 6±2 2±1 91±4 71±5 0.2±0.1 0.2±0.01 1920±350 12±1.5 0.020
S4 1670 240 20±3 43±4 36±2 23±3 1.5±0.13 0.14±0.01 1350±220 8±1.1 8±2 0.045±0.070
Hostalets 540 14±2 38±3 48±3 50±3 0.6±0.2 0.18±0.01 1451±230 8±2 0.047
860 15±2 42±3 43±2 50±3 0.3±0.1 0.19±0.01 1530±290 2±0.5 0.043
S5 800 240 19±3 27±3 53±4 17±2 1.3±0.1 0.16±0.01 1900±310 8±0.5 10±1 0.038±0.005
Cabanyes 550 16±2 52±3 31±3 35±2 0.6±0.2 0.17±0.01 1498±250 13±0.8 0.041
800 18±2 32±4 49±3 35±3 0.1±0.1 0.19±0.01 1800±300 12±0.6 0.043
-
18
Table 2. Parameter values used for modelling runoff and soil loss
Parameter Description Units Min Max Final value
Alpha_Bf: Baseflow Alpha factor days 0 1 0.05
BLAI: Maximum potential leaf area index
Alfalfa 1 5 4
Olive trees 1.5
Grape vines 5
Winter pasture 4
Winter barley 4
CN2: runoff curve number for moisture
condition II 45 98 72-79 agric.
92-96 urban
EPCO: Plant evaporation compensation
factor 1 1 0.9
ESCO: Soil evaporation compensation
factor 0 1 0.9
EVLA: leaf area index at which no
evaporation occurs from water surface 1 5 3
W_ VA : roundwater ‘revap’
coefficient 0.02 0.2 0.15
GW_DELAY: Groundwater delay mm 14
GW_Qmin: Threshold depth of water in
shallow aquifer required for return flow to
occur
mm 0 5000 100
Plant_ET: amount of water removed by
transpiration from plants mm 0.5 2 1.5
REVAPMIN: Threshold depth of water in
the shallow aquifer required for “revap” to
occur
mm 10
SURLAG: the surface runoff lag
coefficient
0 10 4
-
19
Table 3. Statistics of the comparisons between simulated and measured data during
calibration and validation periods
RSR PBIAS
%
NSE RSR PBIAS
%
NSE
calibration validation
Soil Water
Soil water SB1
Soil water SB2
0.488
0.670
-1.752
2.684
0.687
0.687
0.444
0.742
0.329
2.249
0.862
0.852
Runoff
Runoff rates SB1
Runoff rates SB2
0.381
0.421
-16.333
-16.200
0.885
0.637
0.528
0.384
-13.823
-8.964
0.817
0.881
Soil loss
Soil loss SB1
Soil loss SB2
0.517
0.139
–15.791
–28.701
0.663
0.331
0.714
0.281
8.627
23.120
0.714
0.910
-
20
Fig. 1. Location of the study basin in the municipality of Piera (Barcelona province,
NE Spain).
Fig. 2. Main soil series and classification according to World Reference Base (WRB
2006).
Fig. 3. Land use and the sub-basins as defined within the study basin. SB1, SB2 and
SB3: sub-basins used for infiltration evaluations and model calibration and validation.
Fig. 4. Daily precipitation (P) and volumetric soil water content (m3/m
3) measured at
different depths in the study sub-basins SB1.
Fig. 5. Comparison between modelled and measured soil water in the soil profile in
two sub-basins (SB1 and SB2) and daily precipitation (P).
Fig. 6. Comparison between estimated and simulated runoff for SB1 and SB2 for the
calibration and validation periods.
Fig. 7. Comparison between estimated and simulated soil erosion for SB1 and SB2 for
the calibration and validation periods.
Fig. 8. Soil erosion rate simulated within the basin during the calibration and
validation periods.
-
21
Fig. 1.
-
22
Fig. 2.
-
23
Fig. 3.
-
24
Fig. 4.
-
25
Calibration period Validation period
SB1
0
50
100
150
200
250
300M
ay-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
So
il w
ate
r (m
m)
0
20
40
60
80
100
120
P (
mm
)
P SW-model SW-measured
SB2
0
50
100
150
200
250
300
Ma
y-1
0
Ju
l-1
0
Au
g-1
0
Oct-
10
De
c-1
0
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
So
il w
ate
r (m
m)
0
20
40
60
80
100
120
P (
mm
)
P SW-model SW-measured
SB1
0
50
100
150
200
250
300
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
So
il w
ate
r (m
m)
0
20
40
60
80
100
120
P (
mm
)
P SW-model SW-measured SB2
0
50
100
150
200
250
300
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
So
il w
ate
r (m
m)
0
20
40
60
80
100
120
P (
mm
)
P SW-model SW-measured
Fig. 5.
-
26
SB1
0
5
10
15
20
25
30
35
40
45
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
Ru
no
ff (
mm
)
Sim Runoff (mm) Est. Runoff (mm)
Calibration period Validation period
SB2
0
10
20
30
40
50
60
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
Ru
no
ff (
mm
)
Sim Runoff (mm) Est. Runoff (mm)
Fig. 6.
-
27
Calibration period Validation period
SB2
0
5
10
15
20
25
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
So
il lo
ss
(M
g/h
a)
Sim. Soil loss Est. Soil loss
SB1
0
2
4
6
8
10
12
14
16
Ma
y-1
0
Ju
l-1
0
Se
p-1
0
No
v-1
0
Ja
n-1
1
Ma
r-1
1
Ma
y-1
1
Ju
l-1
1
Se
p-1
1
No
v-1
1
Ja
n-1
2
Ma
r-1
2
Ma
y-1
2
day
So
il lo
ss
(M
g/h
a)
Sim. Soil loss Est. Soil loss
Fig. 7.
-
28
Fig. 8.