* To whom all correspondence should be addressed. Email: j.martinez@macs.udl.cat

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.

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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.