Figure 2: Physical characteristics of the analyzed watershed:
a) morphology, b) land use classification (CLC, 2018), and c) soil types
(DGADR, 2013).
Climate
dataset
The time-series for calibration and validation purposes, including daily
precipitation and max/min temperatures, were obtained from the dataset
“Iberia01” (Herrera et al., 2012, 2016), which provides a dense
network of stations over the Iberian Peninsula. All the data were
extracted in seven grid points located inside or in the proximity of the
analyzed basin. The high reliability of the spatial pattern of the
reported variables is discussed by Herrera et al. (2019). These climate
data were utilized for calibration and validation procedures as they
overlap the simulation’s period (1985-1989) of available data for
streamflow and sediment transport.
As a reliable representation of precipitation’s intensity and
distribution is one of the predominant factors affecting the simulation
results of hydrological processes, the choice of a representative
climate dataset for the future scenarios is crucial to obtain accurate
and reliable SE estimates. The datasets contemplated are based on
currently available Regional Climate Models (RCMs), forced by different
Global Climate Models (GCMs), which were used in the Fifth Assessment
Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).
The datasets were made available by the World Climate Research Program’s
CORDEX initiative (www.euro-cordex.net). In this study, the selected
climate model, here called KNMI (i.e., RACMO22E driven
by ICHEC-EC-EARTH), is in agreement with Soares et al. (2017), who
assessed the performance of EURO-CORDEX historical (HIST) simulations to
represent temporal and spatial patterns of precipitation over Portugal.
Data were extracted from the RCM within 7 grid points, covering the
period from 2020 to 2040.
2.3 Modelling Framework
SWAT model has the main purpose of simulating and predicting the effects
of land management practices and CC on the hydrological cycle using
different timescale input/output (daily, monthly, and yearly). The main
step of the model consists in the creation of the hydrologic response
units (HRUs), referring to all those portions of a territory
characterized by unique land use, morphological, and soil attributes
combination (Neitsch et al., 2000). All the model’s outputs such as
runoff, evapotranspiration, aquifer recharge, sediment, and nutrient
loadings from each HRU are obtained using the input of climate, soil
properties, topography, vegetation, and land management practices and
further summarized to obtain the sub-basins loading. The outputs of
runoff and sediment yield are calculated using a modified version of the
curve number method (Brakensiek et al., 1984; USDA, 2004) or the
Green–Ampt infiltration method, and the Modified Universal Soil Loss
Equation (MUSLE) (Williams, 1995), respectively.
The SWAT model for the Guadiana river sub-basin was realized using
ArcSWAT interface on ArcGIS 10.2 environment. The regime of SE for the
whole watershed was evaluated with sequential steps.
First, all the physical characteristics of morphology, land cover, and
soil properties were evaluated and used as main inputs for the setup of
the SWAT model and to define the HRUs spatial distribution in the whole
basin. The watershed automatic delineation tool divided the area of
37,233 km2 in 99 sub-basins further discretized in
3000 HRUs. The HRUs were obtained intersecting five slope classes
(<5, 5-10, 10-15, 15-20, >20), seven land cover
categories (Fig. 2b), and six soil groups (Fig. 2c). The CLC
classification was reclassified to match with the vegetation cover types
present in the SWAT default database. Specifically, URMD was used to
describe artificial settlements, OATS and OLIV for rainfed crops and
olive plantations respectively, FRSE for rainfed forest, PINE for
coniferous and mixed forest, while the “montado” system was
represented using a combination of OAK and PAST (30% and 70%,
respectively) using the SWAT code WPAS. Some parameters characterizing
the typical Mediterranean vegetation were updated according to Nùnes et
al. (2008). Concerning the soils’ properties, all the information about
Ks, AWC, texture, soil organic carbon (SOC), BD, and soil albedo
utilized for the simulation are resumed in Table S1.
The meteorological data are the other necessary input for a proper SWAT
evaluation. For this model, the meteorological data from “Iberia01”
were used to run the model using daily data of precipitation and
temperature to estimate runoff and actual evapotranspiration (AET) via
the Hargreaves formula (Aschonitis et al., 2017). After the set-up
procedure, the SWAT simulation, performed monthly, was divided into
three blocks: i) a warm-up period of four years (1980-1984), ii) a
calibration procedure from 01/1985 to 06/1987, and iii) a validation
phase for the period 07/1987-12/1989 on four hydrometric stations for
streamflow. For SE only two hydrometric stations were available (Monte
da Ponte and Oeiras) with scattered daily data for the period 1984-1989.
In particular, 38 daily data of SE were available, which were evenly
split to perform calibration and validation analysis. Finally, the SWAT
model was forced to simulate the HIST (1980-2000) and future period
(2020-2040) using the chosen RCM (KNMI) under two different emission
pathways (RCP 4.5 and 8.5).
The robustness of the methodology was investigated through an extensive
calibration/validation procedure. A preliminary “trial and error
calibration” was applied along with an “auto-calibration and
validation”. In the trial and error calibration, the fitted values of
some parameters, such as groundwater ”revap” coefficient (GW_REVAP),
deep aquifer percolation fraction (RCHGDP), soil evaporation factor
(ESCO), plant uptake compensation (EPCO), and deep aquifer percolation
fraction (RCHGR_DP) were manually adjusted considering the results
obtained from Nùnes et al. (2017) which performed a SWAT application in
a nearby watershed. The other parameters responsible for streamflow and
sediment load processes utilized for the auto-calibration were
identified through an extensive literature review (Chen et al., 2019;
Khelifa et al., 2017; Serpa et al., 2015), and are reported in Table 2.
The standalone software SWAT-CUP via the Sequential Uncertainty Fitting
version 2 (SUFI-2) algorithm (Abbaspour, 2015) was used for the auto
calibration/validation. During the calibration phase, the SUFI-2
algorithm tries a different combination of the chosen parameters within
their fixed range of variation (generally ± 25% of the initial value)
and calculates the effect on the various fitting between the observed
and simulated variables.
Three well known statistical indices were utilized to evaluate the
simulation’s results according to the performance values suggested by
Moriasi et al. (2007) (Table S3): i) the coefficient of determination
(R2), ii) the Nash-Sutcliffe efficiency (NSE), and
iii) the percent of bias (PBIAS); while the P-factor and R-factor values
were investigated to account for model fit and uncertainties (Abbaspour
et al., 2004).
A total of three thousand calibration runs, divided in six interactions
of five hundred runs each, were performed until a satisfactory
calibration was obtained. The values of SE in t/ha/year were obtained
for the whole watershed. Furthermore, the values of SE for each HRU were
spatialized and classified to obtain three SE susceptibility maps as to
highlight the main changes over time and to identify those areas where
SE will increase/decrease in the future. Finally, the results were
analyzed to identify the mean yearly SE rate for each land cover
category identified in the watershed.