Introduction
Several studies using simulations of future climate robustly indicate the
Mediterranean area as one of the regions of the world to be most severely
affected by global changes. This area has in fact been classified by Giorgi (2006)
as a primary hot spot most sensitive to climate change based on an
index that combines variations in precipitation and air temperature from a
multimodel ensemble of climate simulations. Specifically, the majority of
climate projections agree in the prediction of an increase in mean
temperature and a reduction in mean precipitation for the Mediterranean
region. For example, climate simulations under the A1B emission scenario
(Nakićeović et al., 2000; IPCC, 2007) predict a mean annual warming
from 2.2 to 5.1 ∘C. Christensen et al. (2008) found
that mean annual precipitation is expected to decrease between 4 and
27 %. Giorgi and Lionello (2008) provide a good synthesis of several
climate simulations conducted in the Mediterranean region that summarize
these main results.
Mediterranean watersheds are characterized by high spatial heterogeneity of
terrain and surface properties. These features lead to a hydrologic response
that is particularly sensitive to current climate variability, which is
characterized by a strong seasonality and large interannual fluctuations,
with alternations of dry and wet periods lasting several years. As a result,
these basins are prone to the occurrence of hydrologic extremes, including
drought periods (Hoerling et al., 2012) and floods and flash-floods (Delrieu
et al., 2005; Borga et al., 2007; Silvestro et al., 2012). Variations in
future climate are expected to further impact Mediterranean watersheds at
various spatial and temporal scales (Frei et al., 2006; Beniston et al.,
2007; Mariotti et al., 2008), as also demonstrated through observed data
(Mariotti, 2010; Hoerling et al., 2012). This, in turn, is expected to
affect important economic activities, especially those strongly dependent on
water resources such as agriculture and tourism. For example, a future
reduction in crop production is anticipated in southern Europe and
Mediterranean regions due to decreasing water availability and degradation
of soil and water quality (Olesen and Bindi, 2002; Falloon and Betts, 2010).
Given the high sensitivity of Mediterranean basins to climate variability
and its socioeconomic impacts, a multi-institutional research project, named
Climate-Induced Changes on the Hydrology of Mediterranean Basins (CLIMB),
was funded by the 7th Framework Program of the European Union (Ludwig
et al., 2010). The CLIMB project focused on seven study sites encompassing
different conditions. An approach based on simulations of various climate
and hydrologic models, analysis of environmental and economic data, field
campaigns and stakeholder engagement was adopted to (i) reduce the
uncertainty in the quantification of climate-induced changes on hydrological
responses, and (ii) develop projections and tools to support planning and
management of water resources and associated economic activities.
One of the CLIMB sites is the Rio Mannu basin (RMB, 472.5 km2) located
in an agricultural area in Sardinia, Italy. This basin has experienced
multiyear drought periods (the most recent during 1990–2000) that resulted
in water restrictions for the agricultural and tourist sectors and led to
substantial financial losses. Despite this, no extensive study has been
devoted to evaluating the hydrological vulnerability of this and other
Sardinian basins. In this paper, we provide a contribution to address this
issue by quantifying the hydrologic response of the RMB to different climate
change projections. For this aim, four bias-corrected climate forcings are
first set up for a reference and a future period, using the best-performing
combinations of global (GCM) and regional (RCM) climate models selected by
Deidda et al. (2013). These climate forcings are used as input for the
TIN-based Real-time Integrated Basin Simulator (tRIBS) hydrologic model,
which was calibrated and validated with reasonable accuracy as illustrated
in a previous study by Mascaro et al. (2013a). Since climate model outputs
are provided at coarse spatial (∼ 25 km) and temporal (daily)
scales while the hydrologic model requires hourly data, proper downscaling
tools are applied to increase their spatiotemporal resolution (up to 5 km, 1 h).
Hydrologic model outputs under the four climate scenarios, including
time series and spatial maps, are then post-processed to (i) evaluate the
impacts on water resources and hydrologic extremes, and (ii) investigate
possible changes on the dominant physical processes in the basin.
While the general approach adopted here has been used by other studies
(Abbaspour et al., 2009; Cayan et al., 2010; Liuzzo et al., 2010; Senatore
et al., 2011; Montenegro and Ragab, 2012; Sulis et al., 2011, 2012; Camici
et al., 2014; Tramblay et al., 2013), our methodology has novel
contributions. First, most studies carry out hydrologic simulations at the
daily scale. Here, a process-based model at subdaily (hourly) resolution is
used to simulate the hydrologic processes typical of Mediterranean basins
(Moussa et al., 2007), which are characterized by short response time and
nonlinear rainfall–runoff transformation resulting from different runoff
mechanisms (Piñol et al., 1997; Gallart et al., 2002; Beven, 2002). Second,
procedures are applied to downscale bias-corrected climate model outputs to
smaller spatial and temporal scales required for a reliable simulation of
the hydrological processes in a medium-sized basin. These downscaling
procedures are then distinct from the bias correction, which instead aims at
correcting the large discrepancy between climate model outputs and
observations of precipitation and temperature in the basin. Finally, the
uncertainty associated with different climate models is taken into account
by using four scenarios based on different combinations of GCMs and RCMs.
Location of the RMB within (a) Italy and (b) the island of
Sardinia. (c) DEM of the RMB in UTM (universal transverse Mercator) coordinates. In (b) and (c), crosses are
centroids of the 25 km grid of the RCMs, and the black square is the
104 km × 104 km coarse-scale domain for the precipitation downscaling scheme.
In (c), the circles are the centroids of the 5 km grid of the disaggregated
precipitation products, and the triangles are the rain gages used to perform
the local-scale bias correction.
Data and methods
The impacts on the hydrologic response due to changes in future climate were
quantified as follows. Outputs of different combinations of GCMs and RCMs
were processed to create four scenarios of hydrometeorological data in a
reference (REF) time slice from 1971 to 2000 and a future (FUT) period from
2041 to 2070. Changes in hydrologic response in terms of availability of
water resources and hydrologic extremes were quantified by comparing tRIBS
outputs in REF and FUT periods. Procedures to create the climate forcing for
the hydrologic simulations are discussed in Sect. 3.1, while the main
features of the tRIBS model are discussed in Sect. 3.2.
List of the GCMs used as drivers
of ENSEMBLES RCMs considered in this study
together with corresponding climatological center and model, and acronyms
adopted. The four GCM–RCM combinations used in this study are ECH–RCA,
ECH–REM, ECH–RMO and HCH–RCA.
Climatological center and model
Acronym
Global climate
Hadley Centre for Climate Prediction, Met Office, UK
HCH
models, GCMs
HadCM3 Model
Max Planck Institute for Meteorology, Germany
ECH
ECHAM5/MPI Model
Regional climate
Swedish Meteorological and Hydrological Institute (SMHI),
RCA
models, RCMs
Sweden RCA Model
Max Planck Institute for Meteorology, Hamburg, Germany
REM
REMO Model
Koninklijk Nederlands Meteorologisch Instituut (KNMI),
RMO
Netherlands RACMO2 Model
(a) Land cover and (b) soil texture maps used as input for
the tRIBS model. In (b), the boundaries of 20 sub-basins are also reported
along with the stream network.
Generation of the climate forcing
The procedure to create the high-resolution climate forcing in the REF and
FUT periods can be summarized in four steps: (i) selection of GCM–RCM
combinations; (ii) large-scale bias correction of climate model outputs;
(iii) disaggregation in space and time of precipitation (P) and local-scale
bias correction; and (iv) computation of hourly potential evapotranspiration
(ET0) from daily minimum (Tmin) and maximum (Tmax) temperatures,
as illustrated next.
Selection of GCM–RCM combinations
Deidda et al. (2013) evaluated the performance of 14 combinations
resulting from the coupling of six GCMs with six RCMs from the ENSEMBLES
project (http://ensembles-eu.metoffice.com) in some Mediterranean basins,
including the RMB. The analysis was restricted for the future period to the
A1B emissions scenario, because (i) this is commonly considered the most
realistic, and (ii) the ENSEMBLES climate models have the most complete
data set for this scenario. Model outputs at daily resolution in time and
0.22∘ (∼ 25 km) in space (see the grid in Fig. 1b)
were compared against historical data of daily P and daily mean, minimum and
maximum temperature (T) from the CRU (climate research unit) E-OBS data set (Haylock et al., 2008),
available on the same spatial grid. In the RMB, four combinations of two
GCMs and three RCMs were found by Deidda et al. (2013) to be the most
accurate: ECH–RCA, ECH–REM, ECH–RMO and HCH–RCA (see Table 1 for model
descriptions and acronyms). The selection of these GCM–RCM combinations,
hereafter simply referred as selected climate models (CMs), also obeys the
criterion of having at least two RCMs nested in the same GCM and two
different GCMs forcing the same RCM. The use of four climate scenarios
permits characterizing, to a certain extent, the uncertainties associated
with different climate models and possible model combinations.
Large-scale bias correction
Most climate models display some level of deficiencies in reproducing
climatological features and seasonality in large basins (Lucarini et al.,
2007, 2008; Hasson et al., 2013, 2014). In relatively small watersheds,
these deficiencies are exacerbated. To reduce these well-known discrepancies
and better reproduce the observed seasonal statistics, a large-scale bias
correction of P and T fields predicted by the considered CMs was applied using
the E-OBS data set. For this, the daily translation method was applied as it
has demonstrated skill in prior studies (Wood et al., 2004; Maurer and
Hildago, 2008; Sulis et al., 2012). The method is based on computing the
monthly cumulative distribution functions (CDFs) of observed (Fobs) and
simulated (Fsim) daily variables. For a given daily output variable of a
climate model, x, the unbiased value, x*, is obtained as x* = Fobs-1[Fsim(x)],
where Fobs-1 is the inverse of Fobs. To reproduce the seasonal cycles, Fobs and
Fsim functions were derived on a monthly basis, i.e., pooling together
all daily observations (or simulated records) for each month. The procedure
was applied to the daily P and the daily mean, minimum and maximum T. In this
effort, T was also corrected to account for the different elevations adopted
by CMs and E-OBS via a spatial and dynamic lapse rate.
Precipitation downscaling and local-scale bias correction
One source of uncertainty of climate models is related to the smoothing
effect induced by their coarse spatial (∼ 25 km) and temporal
(24 h) resolution (Wilby and Wigley, 1997; Maraun et al., 2010; Bardossy and
Pegram, 2011). This is especially true for P, which is characterized by high
intermittency and strong fluctuations in space and time, also affected by
local orographic effects. To reproduce this feature, we used the
precipitation downscaling technique based on a multifractal model
(Space-Time Rainfall, STRAIN) that is able to recreate the scale invariance
and multifractal properties of precipitation fields observed from coarse to
small spatiotemporal scales (Deidda, 1999, 2000). This is achieved by
means of a stochastic generator of multiplicative multifractal cascades,
whose parameters can be derived from the large-scale rainfall amount, R (mm h-1),
according to empirical calibration relations.
For the RMB, Mascaro et al. (2013a) calibrated the algorithm with rainfall
observations at 1 min resolution of 204 gages, collected in the period
1986–1996 in the coarse spatial domain of 104 × 104 km2 shown in
Fig. 1b. A total of 800 precipitation events were used to estimate the model
parameters and identify the calibration relations as a function of R for our
study area. As described in detail in Mascaro et al. (2013a), two tests were
conducted to validate the downscaling method. First, the model capability to
capture the small-scale rainfall distribution within the coarse-scale domain
was evaluated by visually comparing observed and synthetic empirical
cumulative distribution functions for each rainfall event. An example of
this comparison is provided in Mascaro et al. (2013a; Fig. 6), which shows
relatively good skill of the downscaling routine. A second validation was
carried out specifically on the study basin, by comparing observed and
simulated daily mean areal precipitation (MAP) from 1925 to 1935. In this
period, discharge data are available to calibrate and validate the
hydrologic model (see Sect. 3.2) and rainfall observations were only
collected at daily resolution. For each rainy day, the downscaling model was
applied from the coarse to the fine resolution generating an ensemble of
50 disaggregated fields. The observed daily MAP in the basin was calculated by
applying Thiessen polygons to the observations of 13 available gages, and
the simulated MAP was derived by aggregating the synthetic grids at daily
resolution and computing the spatial basin average. The root mean square
error (RMSE) and bias between the observed MAP and the ensemble average from
the downscaling model were then calculated. As reported in Mascaro et al. (2013a;
Table 7), the RMSE has little interannual variability (average value
of 4.38 mm), while the bias is negative (mean of -0.89 mm), indicating
that the downscaling procedure tends to slightly underestimate the observed
MAP (less than 10 %).
In this study, the downscaling routine was applied by (i) aggregating the
bias-corrected daily P outputs of the CMs in the coarse spatial domain to
compute R, (ii) using the RMB calibration relations to derive parameters
conditioned on R, and (iii) applying STRAIN to downscale R to 5 km and 1 h
resolution. The disaggregated fields were also corrected for orographic
effects using the elevation modulation function described by Badas et al. (2006).
In principle, the statistically based disaggregation technique
requires the generation of an ensemble of P downscaled fields, each
representing an equally probable realization of the coarse condition. For
example, Mascaro et al. (2013a) generated an ensemble of 50 P downscaled
members to calibrate and validate the tRIBS model. In this study, we only
created a single disaggregated realization for each selected CM for two main
reasons. First, climate models do not reproduce weather evolution in time
according to deterministic rules, but rather reproduce the statistical
peculiarity of the climatic features (Lucarini, 2008). In other words, a
one-to-one correspondence between an observation and a climate model
simulation does not exist for a certain day. Second, the multidecadal
length of the REF and FUT periods (30 years) is large enough to assure that
the use of a single disaggregated member is able to capture a large portion
of the small-scale rainfall variability occurring within each time slice.
After the disaggregation, a last procedure for local-scale bias correction
of P was applied to correct residual biases mainly due to the coarseness of
the rain gage network used for the E-OBS data set (Haylock et al., 2008),
which may fail to reproduce the local features of P fields. The procedure is
illustrated in Fig. 3. The climatological monthly average of the MAP in the RMB was first calculated using data observed by
13 gages within the catchment over the period 1951–2008. In parallel, the
same variable was computed for the disaggregated fields from all selected
CMs in the same period. The ratio between observed and simulated mean
monthly MAP was then used as a correction on the downscaled P fields to
eliminate the residual bias.
Computation of potential evapotranspiration
For each CM, we estimated the gridded ET0 at hourly resolution starting
from the bias-corrected daily Tmin and Tmax. For this purpose, the
T fields at ∼ 25 km resolution were first interpolated in the
same 5 km grid used for P as in Liston and Elder (2006), and then corrected
for elevation variations of the 5 km grid using a dynamic lapse rate. Then,
the downscaling technique proposed by Mascaro et al. (2013a) was applied to
derive the maps of hourly ET0 from Tmin and Tmax. The method
requires an estimate of the daily ET0 by applying the Hargreaves formula
with Tmin and Tmax and a linear correction to derive the value
returned by the Penman–Monteith equation. Next, dimensionless functions that
reproduce, for each month, the subdaily variability of ET0 are used to
derive the hourly ET0 from the daily estimate. The procedure was
calibrated and tested in the RMB using meteorological data (required to
apply the Penman–Monteith formula) observed in one station over 1995–2010.
To validate the method, we calculated the RMSE and bias between (i) the
hourly ET0 computed using the Penman–Monteith formula and (ii) the hourly
ET0 obtained with the disaggregation method starting from Tmin and
Tmin. Results show that, despite that the downscaling procedure slightly
underestimates the hourly ET0 (negative mean bias of -0.010 mm h-1),
performances are overall fairly good, as indicated by the low RMSE (mean of
0.030 mm h-1). More details are provided by Mascaro et al. (2013a).
Illustration of the local-scale bias correction. Black
line: climatological monthly average of the MAP
in the RMB observed by 13 rain gages over 1951–2008. Black dashed line: MAP
averaged across the four CMs during the same period before the bias
correction. Gray shades, continuous lines: MAP of the four CMs after removing
the bias.
The hydrologic model
tRIBS is a physically based, distributed hydrologic model that is able to
continuously simulate the coupled water and energy balance (Ivanov et al.,
2004a, b). Terrain is represented through triangulated irregular networks
(TINs) used to discretize the domain into Voronoi polygons. The use of TINs
allows for computational savings as compared to grid-based models due to the
multiresolution domain representation (Vivoni et al., 2004, 2005). This
feature is crucial for the feasibility of multidecadal hydrologic
simulations carried out in climate change studies. The spatially distributed
hydrologic response is reproduced by solving equations of the water and
energy fluxes in each Voronoi polygon. In tRIBS, several hydrologic
processes are represented, including canopy interception, infiltration and
soil moisture redistribution, lateral water movement in the unsaturated and
saturated zones, evaporation from bare soil and wet canopies, plant
transpiration, overland flow in the hillslopes, and routing in the stream
channel. The infiltration scheme allows for several configurations of soil
moisture in the unsaturated and saturated zones. As a result, runoff
generation is possible via four mechanisms: saturation excess, occurring
when the single domain element is fully saturated from below; infiltration
excess, occurring when the element is saturated from above by a
high-intensity rainfall; perched return flow, occurring as lateral flow on
the surface of a cell from a saturated layer in an upslope element; and
groundwater exfiltration, occurring as lateral redistribution in the
phreatic aquifer. As a result, this model has the capability to represent
the strongly nonlinear rainfall–runoff relation typical of Mediterranean
basins. The specific treatment of each process is described in detail by
Ivanov et al. (2004a).
Model equations are parameterized through lookup tables and related spatial
maps of soil texture and land cover. Precipitation can be provided as point
time series or spatial grids. This last alternative is used in this study to
force the model with gridded downscaled fields, as described in
Sect. 3.1.3. Computing actual evapotranspiration (ETa) and its components
requires estimating ET0. This can be performed by applying the
Penman–Monteith equation with meteorological data or by forcing the model
with ET0 computed offline, either in point or grid format. Again, this
last alternative is used in this study to provide downscaled ET0 as
described in Sect. 3.1.4. ETa is then estimated as a fraction of
ET0 based on the available soil moisture using a piecewise-linear
equation (Mahfouf and Noilhan, 1991; Ivanov et al., 2004a). Model outputs
include time series of discharge at any location in the stream network and
spatial maps of hydrologic state variables and fluxes (e.g., evapotranspiration,
soil water content at different depths, ground water
table position) at specified times or integrated over defined periods.
Mean annual values of MAP, T and Q in the RMB in REF and FUT
periods with relative changes for each CM. The mean and standard deviation
(SD) are also reported.
Climate
Mean annual
Mean annual
Mean annual
model
MAP
T
Q
combination
REF
FUT
ΔMAP
REF
FUT
ΔT
REF
FUT
ΔQ
(mm)
(mm)
(%)
∘C
∘C
∘C
(mm)
(mm)
(%)
ECH–RCA
570.93
502.81
-11.93
16.85
18.72
1.87
107.39
71.90
-33.05
ECH–REM
559.71
519.18
-7.24
16.77
18.68
1.91
86.74
71.87
-17.14
ECH–RMO
542.80
487.87
-10.12
16.83
18.72
1.89
91.30
67.87
-25.66
HCH–RCA
575.06
453.19
-21.19
16.52
19.59
3.08
107.96
53.71
-50.24
Mean
562.13
490.76
-12.70
16.74
18.93
2.18
98.35
66.34
-32.55
SD
14.42
28.12
6.03
0.15
0.44
0.60
10.93
8.63
14.07
The model has been previously used in the areas of hydrometeorology (Mascaro
et al., 2010; Moreno et al., 2013), climate change (Liuzzo et al., 2010) and
ecohydrology (Mahmood and Vivoni, 2014). Recently, Mascaro et al. (2013a)
calibrated and validated tRIBS in the RMB against streamflow data. A TIN
with 171 078 nodes was derived from a 10 m digital elevation model (DEM),
retaining 3.6 % of the DEM nodes and resulting in a vertical accuracy of 3 m.
Vegetation parameters, involved in the processes of rainfall interception
and estimation of ETa, have been derived for the land cover classes
in Fig. 2a, based on values published in literature for similar land cover
classes. The model was calibrated for 1 year (1930) and validated for 2
years (1931–1932), where daily discharge data collected by the Italian
Hydrologic Survey were judged to have the highest quality. To identify
robust model parameters while using a relatively short record of
observations, we selected a wet year (total annual runoff of 183 mm) with
several flood events for calibration and 2 dry years (annual runoff of 76 and
71 mm) with a few floods for validation. Since in the period 1930–1932
hydrometeorological data include rainfall data at daily resolution and daily
Tmin and Tmax, the downscaling procedures previously illustrated
were used to create the high-resolution forcings. Despite the presence of
several uncertainty sources, Mascaro et al. (2013a) showed adequate performances
in the RMB for the tRIBS model, which is used here with the same parameterization.
Results and discussion
In this section, we first analyze the monthly variability of the
basin-averaged P and T fields with the goal of highlighting the main
climatological differences between the REF and FUT periods. Subsequently, we
present results of the hydrologic simulations forced with the disaggregated
P and ET0. Specifically, the changes on stream discharge (Q) are evaluated,
focusing on both water resources availability and hydrologic extremes.
Finally, variations in evapotranspiration (ETa), soil water content
(SWC), and ground water level are explored.
Changes in climate forcing
Figure 4 reports different features of mean monthly variability of
basin-averaged P grids for the four CMs in the REF and FUT periods: MAP (Fig. 4a, b), number of rainy days (N; Fig. 4c, d),
and mean precipitation intensity in rainy days (I; Fig. 4e, f). In the left
panels, the bars represent the mean ± standard deviation across the
four CMs of the 30-year monthly average of each variable. Note that the
months are ordered according to the water year. For each CM, the relative
monthly changes Δα (%) from REF to FUT, computed by the
following Eq. (1) for a generic variable α, are plotted in the right panels:
Δα=αFUT-αREFαREF⋅100,
where αFUT and αREF are the 30-year monthly mean
of α in FUT and REF, respectively. Equation (1) is used in this paper for
all variables, except for T for which the changes are calculated through the
simple difference between FUT and REF.
(a) Mean monthly MAP in the RMB in REF (black) and
FUT (gray). Bars are mean ± standard deviation across the CMs.
(b) Relative change between FUT and REF periods in mean monthly MAP
(ΔMAP). (c) and (d) same as (a) and (b),
but for the mean monthly N. (e) and (f) same as (a) and (b),
but for the mean monthly I.
Figure 4a shows that MAP is expected to decrease in
FUT in all months, except in winter (December–February) where mean values
are similar. Negative ΔMAP values are predicted by all combinations in
September, November, March, April, and May, while in the other months the
sign and magnitude of ΔMAP vary among the four combinations, even
significantly (e.g., October and December), suggesting higher uncertainty in
climate predictions (Fig. 4b). The mean annual MAP in REF and FUT periods
and the relative changes are reported in Table 2 for each combination: we
can observe that the four CMs predict a decrease in annual precipitation
from -7 % (ECH–REM) to -21 % (HCH–RCA). These results are consistent
with a number of studies that analyzed climate projections in the
Mediterranean region under the A1B scenario (e.g., IPCC, 2007; Giorgi and
Lionello, 2008; Senatore et al., 2011).
Similarly to MAP, N is expected to decrease in FUT
over the year except for winter, where no significant variations are
expected (Fig. 4c). Changes in N are similar for the four CMs,
indicating lower model uncertainty in predicting rainfall occurrence (Fig. 4d).
The projections for the mean precipitation intensity (I) are instead
characterized by high variability over the year and across the combinations.
Figure 4e shows that higher I is predicted in FUT during the months with larger
total precipitation (from October to December), and most of the summer (June
and July). The rainfall intensity in FUT will be lower from January to May
and in August and September. Figure 4f shows that sign and magnitude of
ΔI are different in each month, highlighting a large uncertainty
across the CMs. Since rainfall intensity is a crucial variable influencing
runoff, this underlines the importance of using multiple combinations of
GCMs and RCMs to account for climate model uncertainty in simulating
hydrologic responses.
The mean monthly T in REF and FUT periods is reported in Fig. 5a, while the
relative changes (ΔT) are shown in Fig. 5b. As found in previous
works (e.g., Giorgi and Lionello, 2008), the uncertainty in the prediction
of future T is considerably reduced as compared to P. All scenarios show a
future increase of T for all months with a low standard deviation among the
combinations. Higher ΔT are expected in summer, with an average
yearly variation from 1.87 ∘C (ECH–RCA) to 3.08 ∘C
(HCH–RCA); see Table 2 for more details. As for P, the HCH–RCA combination
predicts the largest variations in T. Overall, the monthly changes in P and
T predicted by the CMs are very similar to the forcing used in another
Mediterranean climate change study carried out by Senatore et al. (2011) in
a watershed in southern Italy.
Same as Fig. 4, but for the mean monthly T.
Same as Fig. 4, but for the mean monthly Q at the RMB outlet.
Changes in stream discharge and runoff mechanisms
The hourly gridded P and ET0 from the four selected CMs were used to force
the tRIBS model. A spin-up interval of 2 years was adopted before each
30-year run, totaling 256 years of simulation. This computational effort was
carried out using the parallelized version of tRIBS (Vivoni et al., 2011),
which took 880 h of CPU time over 64 processors. Model outputs including
time series at distributed locations and spatial maps of hydrologic fluxes
and state variables were postprocessed to quantify the changes from REF to
FUT periods. Figure 6 presents results for the mean monthly Q at the RMB
outlet, according to Eq. (1). Despite no significant variation in MAP is
anticipated during winter, Q is predicted to diminish in FUT for all months
(Fig. 6a) and by all scenarios (Fig. 6b). A slightly positive ΔQ is
only found in December and June in one of the combinations. Note that the
decrease of Q in months with little variation in P can be mostly ascribed to
the diminution of the runoff portion due to groundwater exfiltration
occurring throughout the year, as better illustrated below. Table 2 shows
the mean annual changes, which range from -17 % (ECH–REM) to -50 %
(HCH–RCA). Note that the different percentages observed for each CM are
related to the decrease in P.
The change in mean annual Q was further analyzed using the streamflow time
series for the 20 sub-basins shown in Fig. 2b (sub-basin 20 refers to the
entire RMB). The terrain, soil texture and land cover characteristics of the
sub-basins are summarized in Table 3. The relation between ΔQ and the
contributing area (Ac) is shown in Fig. 7a, in terms of mean and
standard deviation across the CMs. Results indicate the presence of two
groups of sub-basins. The first includes five subwatersheds labeled as 1–4
and 9, with a slightly positive mean ΔQ (∼ +8 %) and
higher standard deviation that suggests larger uncertainty due to the
different climate forcings. These sub-basins are located in the northwestern
portion of the RMB and are characterized by a relatively low slope (mean of
∼ 8 %), dominance of clay loam–clay soil texture
(> 77 %) and agricultural land use (> 71 %). The
second group includes all the other sub-basins and displays a significant
drop of Q (average of about -28 %) and lower variability across the CMs.
(a) Relation between the change in annual runoff, ΔQ,
and sub-basin contributing area, Ac. (b) Relation between the mean
level of the groundwater table, Nwt, in the FUT period and Ac. Bars
represent mean ± standard deviation across the CMs. The number of each
sub-basin as reported in Fig. 2b and Table 3 is also indicated.
Terrain, soil texture and land cover characteristics of
the RMB sub-basins shown in Fig. 2b, including: contributing area
(Ac), slope, and length of the main channel (L); percentages of sandy
loam–sandy clay loam (SL–SCL), clay loam–clay (CL–C), sandy loam–loam (SL–L);
and percentages of agriculture (A), sparse vegetation (SV), and olives (O).
Sub-basin
Ac
Slope
L
Main soil texture classes
Main land cover classes
(km2)
(%)
(km)
SL–SCL
CL–C
SL–L
A
SV
O
1
28.00
10.43
14.60
9.35
88.33
0.00
87.01
7.21
0.84
2
14.82
9.03
7.15
5.05
89.98
0.00
71.81
3.48
17.34
3
50.17
8.96
16.55
7.44
89.02
0.00
82.38
5.31
5.71
4
10.78
5.56
8.09
17.40
77.35
0.00
90.83
0.00
4.44
5
68.10
13.79
18.36
18.72
60.89
15.98
67.74
10.46
6.77
6
42.67
22.93
16.51
3.37
26.98
69.05
31.33
39.13
5.82
7
113.51
16.98
20.06
12.79
49.09
34.89
54.20
20.70
6.69
8
20.95
16.59
13.55
0.00
58.55
31.52
30.34
25.43
16.77
9
70.16
7.70
19.55
8.09
88.09
0.00
84.90
4.12
5.31
10
135.01
16.89
21.07
10.85
50.38
34.38
50.68
21.43
8.16
11
11.54
7.46
8.11
23.14
65.28
0.00
74.95
7.07
4.02
12
221.99
13.71
27.40
11.46
60.65
21.49
60.65
16.19
7.67
13
244.99
13.14
30.55
13.30
60.05
19.60
61.96
15.40
7.26
14
58.18
19.05
22.43
21.42
3.28
42.32
25.05
47.24
8.86
15
41.99
33.82
13.43
0.81
0.00
93.06
4.70
67.23
0.00
16
23.96
34.58
10.76
5.57
0.09
94.18
2.44
74.56
4.35
17
315.75
13.77
34.77
15.83
48.48
23.39
55.95
20.67
7.41
18
436.41
16.67
25.45
19.25
35.63
34.06
45.39
28.16
8.54
19
28.59
6.35
15.09
27.73
58.31
0.77
76.55
2.35
4.53
20 outlet
472.50
17.30
38.75
19.61
36.67
31.91
47.43
26.38
8.21
(a) Partitioning of Q at the RMB outlet in the REF period
among the four runoff generation mechanisms: infiltration excess
(QIE), saturation excess (QSE), perched return flow (QPR), and
groundwater exfiltration (QGE) runoff components. (b) ΔQ for the
runoff mechanisms.
To investigate the physical reasons underlying the changes in Q, we inspected
the variation in the dominant runoff mechanisms. The partitioning of Q at the
RMB outlet into infiltration and saturation excess (QIE and QSE),
groundwater exfiltration (QGE) and perched return flow (QPR) runoff
is shown for each CM forcing in Fig. 8a for the REF period. The four
combinations indicate the dominance of QGE, followed by QSE,
QIE and QPR. Figure 8b presents the change in the amount of total Q
produced for each mechanism. All CMs predict a decrease in QSE,
QGE, and QPR, which are the components controlled by water availability
in the soil, while QIE is expected to grow for all combinations except
for ECH–RCA. This last runoff type occurs when the rainfall rate exceeds the
infiltration capacity, suggesting that a variation of QIE in FUT may be
due to a change in rainfall intensities during extreme events. To analyze
this hypothesis, we derived the mean of the annual maxima of hourly P over
the 30-year records in FUT and REF periods for each CM. Next, we computed
the variation between these two average P maxima from REF to FUT and we found
a perfect correlation with the changes in QIE.
Modifications in runoff generation mechanisms within the basin were
evaluated by focusing on the sub-basins. We first point out that the mean
annual change in P is expected to be fairly constant in all sub-basins (not
shown), suggesting that spatial differences may be mostly ascribed to
surface and subsurface properties. In sub-basins 1–4 and 9, located in the
northwest part of the RMB, QSE, QGE, and QPR decrease considerably
more than in the rest of the watershed (mean changes of -75, -70 and
-50 %), while QIE slightly grows (mean change of +10 %). For this
set of sub-basins, we can conclude that (i) the small increase in Q is due
to a growth in QIE, (ii) higher occurrence of QIE is due to more
impermeable soils that make these sub-basins more sensitive to changes in
rainfall intensity, and (iii) higher occurrence of QIE and the reduced
buffer effect due to a deeper groundwater table (mean values shown in
Fig. 7b for the FUT case) make their runoff response more uncertain for the CMs.
For the other set of sub-basins (i) total Q decreases due to a general
reduction of all components, and (ii) the uncertainty in runoff response is
relatively lower, especially for increasing Ac.
Changes in hydrologic extremes
Changes in hydrologic extremes are investigated in terms of (i) low flow
persistence, which can be assumed as a proxy of drought periods, and
(ii) occurrence of high flows. To analyze the impacts on the first type of
extremes, we computed flow duration curves (FDCs) for Q at the outlet. Figure 9
clearly shows a downward shift in the FDCs over most exceedances, consistent
with the predicted reduction of total Q in the FUT period. To identify the
low flow conditions, we first calculated a threshold discharge, QLF, as
the streamflow corresponding to the 70 % percentage of exceedance for the
REF period (circle in Fig. 9). Low flow conditions were then defined as the
periods during which Q < QLF. Figure 10a shows that the monthly mean
number of low flow days is expected to increase in FUT for about 5 days for
each month, implying more frequent dry conditions. The annual average of the
maximum consecutive length of low flow days is reported in Fig. 10b. In
current conditions, all combinations robustly simulate a value of about
50 days occurring during the summer months. In the future, the length is
expected to increase from 19 to 52 days on average, depending on the CM,
thus extending the low flow conditions to spring and/or fall. This result
confirms and further details previous findings on future drought in the
Mediterranean region (e.g., Beniston et al., 2007).
FDCs computed from the discharge at the RMB outlet.
Continuous (dashed) lines are used for REF (FUT). Circle shows the threshold
discharge, QLF, used to identify low flow conditions.
(a) Mean monthly number of low flow days (LFDs) in REF
(black) and FUT (gray). Bars are mean ± standard deviation across the
CMs. (b) Mean annual maximum consecutive length of LFDs in REF (black) and
FUT (gray) periods.
Concerning the second type of extremes, we used the time series of Q at the
outlet and 19 internal sub-basins. For the REF and FUT periods, (i) the
index flood was obtained for each sub-basin by averaging the corresponding
30 yearly Q maxima, and (ii) the ratio between the index flood and the
corresponding Ac was computed. This ratio, labeled as μc, was
found to remain fairly constant as a function of Ac and, thus, was used
to remove the effect of their size. We then computed the changes
of Δμc from REF to FUT and explored their relation with terrain
attributes and soil texture. Results of this analysis are summarized in Fig. 11
where Δμc is plotted against the mean sub-basin slope
for each CM. Predictions under three combinations (ECH–REM, ECH–RMO and
HCH–RCA) indicate that the magnitude of the mean annual Q maxima will
increase in the FUT period as the basin slope decreases and when soils are
dominated by clay and loam (Fig. 11b–d). For the ECH–RCA case, a
negative Δμc was instead systematically detected for all
sub-basins, without any clear link to soil type and basin slope (Fig. 11a).
This behavior is again explained with changes in the rainfall intensities of
extreme events: for the first three CMs, the mean of the annual maxima of
hourly P is expected to increase in the future, while a reduction is
predicted for the latter CM. As previously discussed, this is reflected in
similar changes in QIE, which is the dominant runoff mechanism during
floods. It is worth noting that the highest positive Δμc
values in Fig. 11b–d are found for sub-basins 1–4 and 9, characterized by lower
slope and dominated by more impermeable soils (clay and loam), where a
relatively higher increase in QIE is expected.
Changes in evapotranspiration and soil water content
Figure 12a shows time series of the mean and standard deviation of monthly
average ET0 and ETa in the REF and FUT periods. As expected,
projections of higher T in the future leads to increasing ET0. In
contrast, a reduced ETa is simulated for most of the year, except for
January, May and November. This is mainly due to the reduction of soil water
content (SWC) in the root zone in the FUT period, which is related to the
decreases of P. This is clearly shown in Fig. 12b, where we can observe a
marked reduction throughout the year of SWC and a negative change of
ETa, despite a systematic positive variation of ET0. These findings
are mostly in accordance with Senatore et al. (2011), who found decreasing
ETa in winter and diminishing SWC across the year.
Relation between the change in the mean of the annual
maximum Q, Δμq, and the corresponding mean slope. Black
(gray) circles indicate sub-basins dominated by the clay loam–clay (sandy
loam–loam) class; a cross is used to indicate sub-basins 1–4 and 9. Each
panel refers to results obtained for each CM.
(a) Mean monthly ET0 (dashed lines) and ETa
(continuous lines) plotted as mean ± standard deviation of the four
CMs in REF (black) and FUT (gray); (b) mean across the CMs of the relative
changes of ET0, ETa, and SWC.
The feedbacks among changes in ETa and SWC, and their relation with
meteorological forcing (P and T, and consequently ET0) and basin
characteristics (soil texture and topography) were investigated using the
spatial model outputs. As an example, Figs. 13 and 14 show maps of
ΔP, ΔSWC, ΔET0 and ΔETa in winter
(December–February) and spring (March–May) seasons, which are characterized
by the smallest and largest ΔP and ΔET0 in the ECH–RCA
forcing. The behavior found in the other seasons is similar to the dynamics
in spring, while results derived for other climate model combinations are
not significantly different.
In winter, the basin-averaged changes in P are small (ΔP = -1.92 %),
limiting SWC decreases and leaving enough soil water for
evapotranspiration. A higher ET0 (ΔET0 = +3.30 %) allows
ETa to rise slightly (ΔETa = +0.14 %). The combined
effect of decreasing water input from P and higher ETa causes a
basin-averaged reduction of SWC of -3.66 %. The pattern of ΔSWC
(Fig. 13b) is mostly influenced by soil texture and, to a less extent, by
ΔP (Fig. 13a) and ΔET0 (Fig. 13c). Lower ΔSWC (from
-2.0 to +0.9 %) are found in the sandy loam–loam class where ΔP is
slightly negative to positive (indicated with L in Fig. 13b). In these
regions, soil water is available to be extracted at a higher rate
(ΔET0 varies from +3.1 to +4.0 %), thus causing ETa to grow
from +3 to +8 %. SWC is expected to decrease more significantly (from
-3 to -20 %) in areas of clay loam–clay and sandy loam–sandy
clay loam (labeled H in Fig. 13b), where P decreases by up to -7 % and
ET0 does not vary substantially (+2 %). Note that this area mostly
contains sub-basins 1–4 and 9, which experience the highest reductions of
QSE, QGE and QPR. As expected, the spatial pattern of
ΔETa is highly correlated with ΔSWC (correlation coefficient
of 0.80), with a minor dependence on ΔET0, although its signature is also apparent.
Changes between REF and FUT periods averaged over the
winter season (December–February) for (a) P, (b) SWC, (c) ET0, and
(d) ETa under the ECH–RCA combination. In (b), areas where the variables are
characterized by positive or lower negative changes are indicated with L,
while regions with higher negative changes are indicated with H.
Same as Fig. 13, but for the spring season.
In spring, P is predicted in FUT to be noticeably lower (basin-averaged
ΔP = -28.37 %) and ET0 higher (ΔET0 = +5.51 %).
As a consequence, the decrease in SWC is more significant
(ΔSWC = -7.13 %) and the water available for evapotranspiration is
limited, causing ETa to diminish (ΔETa = -2.12 %), despite
the positive trend of ET0. In most of the basin, ΔSWC ranges from
-6 to -7 % (L areas in Fig. 14b), likely due to the relatively low
spatial variability of ΔP (Fig. 14a). Higher drops in SWC (up to
-20 %) occur in the areas dominated by sandy loam–sandy clay loam where
P decreases more (H areas in Fig. 14b). Topography also plays a role, as
reduced drops of SWC appear in areas of flow convergence close to streams.
ΔETa (Fig. 14d) is still well correlated to ΔSWC
(correlation coefficient of 0.75) and also affected by ΔET0
(Fig. 14c). ETa remains essentially constant in the areas labeled with L in
Fig. 14d, characterized by lower changes in SWC and relatively higher
ΔET0. ETa decreases instead significantly (up to -12 %; H areas) in
the regions where the drop of SWC is the largest and changes in ET0 are
modest. The effect of topography can be better appreciated in the map of
ΔETa: higher values (+10 %) are simulated in the areas close
to the stream network with higher availability of water.
This analysis reveals that, despite higher ET0, the RMB will experience
in the future a decrease in ETa in most areas and times of the year, due
to the lack of soil water caused by lower rainfall. The only season with a
different behavior is winter, where P is expected to decrease to a lesser
extent or slightly increase, thus limiting the reduction in SWC and leading in
certain areas to higher ETa. The patterns of SWC and ETa are mainly
controlled by soil texture and the interaction of P and ET0. Terrain plays
also a role when reductions of P are more significant.
Changes in groundwater
A last analysis was devoted to evaluate the impact of climate change on
groundwater. For this aim, we computed the difference between the basin-averaged groundwater level at the end of the 30-year simulation in FUT and
REF periods. For all sets of climate forcing, we found a drop of the water
table ranging from 1.0 to 4.6 m, constant across the year. The amount
of the drop simulated for each CM is linked to the corresponding diminution
in P input (lowest for ECH–REM and highest for HCH–RCA). In fact, a
decreasing rainfall input leads to a decrease of the soil water content in
the unsaturated zone and reduces the recharge to the aquifer. This result is
confirmed by the diminishing occurrence of QGE (Fig. 8b).
Conclusions
In this study, we quantified the impacts of climate change on water
resources and hydrologic extremes in an agricultural Mediterranean basin of
472.5 km2 located in Sardinia, Italy. For this aim, the process-based
tRIBS model was used to capture the large variety of the hydrologic
conditions occurring in Mediterranean areas. The forcings in reference
(1971–2000) and future (2041–2070) periods were provided by outputs from four
combinations of GCMs and RCMs, bias-corrected and downscaled in space and
time through statistical tools. The adoption of disaggregation tools was
crucial to create high-resolution (5 km, 1 h) inputs required to apply this
type of hydrologic model. Outputs of the hydrologic simulations were then
compared in the reference and future periods to quantify the changes in
several variables. The main results of this study are summarized below.
At annual scale, all CMs predict decreasing P (mean of -12.70 %) and
increasing T (mean +2.18 ∘C), leading to a significant diminution
of Q (-32.55 %) at the basin outlet. The changes in future climate will
mostly lead to a reduction of those runoff generation mechanisms that depend
on water available in the soil, namely QSE, QPR and QGE. A
higher degree of uncertainty across the climate model combinations was found
while predicting the variation in QIE, which depends on the combined
effect of rainfall intensities and soil hydraulic properties.
Changes in annual Q were also investigated at distributed locations, finding
two sets of sub-basins with different behavior. In the northwest region,
characterized by flatter terrain and clay-loam soils, the mean Q is expected
to increase somewhat in the future. Specifically, a small growth in
QIE is anticipated, while QSE, QPR and QGE will have the
largest reductions over the basin. Hydrologic responses in this area under
different CMs are affected by higher uncertainty, due to the higher
occurrence of the faster runoff component (QIE) and the lower
contribution of slower subsurface components (QPR and QGE) that tend
to attenuate the variability of the climate forcing. In contrast, for other
sub-basins in the RMB, Q is anticipated to diminish with relatively low
uncertainty across the four CMs, due to a decreasing contribution of all
runoff components.
At basin scale, the combined effect of lower P and higher T leads to
increasing ET0 and decreasing SWC throughout the year, and diminishing
ETa over all months except for winter. The spatiotemporal analysis of the
interactions between SWC and ETa reveals that (i) in most areas and times
of the year, negative changes of P lead to a reduction in ETa, because
there is not enough soil water to sustain the higher evaporative demand;
(ii) in winter, some areas experience a modest decrease or a slight rise of
P, leading to local growth in ETa; (iii) soil texture controls the amount
of the variations in SWC, with higher drops in the sandy loam–sandy clay
loam class; and (iv) topography also plays a role with positive changes in SWC
and ETa found in areas of flow convergence near the stream network.
To our knowledge, this is the first climate change study conducted in
Sardinia at the watershed scale. Results suggest that the basin's hydrologic
regime will be significantly impacted by variations in future climate. The
diminution in annual Q at the outlet implies that (i) the inflow at the
reservoir located in proximity of the outlet will be reduced, and (ii) more
frequent and longer low flow conditions, which are an indication of
hydrological drought, are expected. In addition, agricultural areas are
anticipated to experience the largest drop in SWC in the root zone (mean of
-6 %) among all land cover classes. This finding, in conjunction with the
decreasing P, may have important impacts on the crops (especially the rain-fed
areas) that are currently grown in the basin. As a result, the implications
of this study are useful to support the selection of adaptive strategies for
water and crop management and planning under climate change, as well as to
quantify the social and economic vulnerability of the region.
Finally, we point out that, as any climate change study, the methodology
adopted here is affected by uncertainties and limitations. The climate model
uncertainty was addressed by selecting the best-performing four combinations
of global and regional climate models in reproducing precipitation and
temperature in the study basin. Due to the lack of observations (a common
problem in this region), the hydrologic model was calibrated and validated
only with 3 years of data. To address this issue and identify robust
model parameters, we selected the calibration and validation periods
characterized by markedly different hydrologic conditions. A single set of
techniques, commonly used in climate change studies, was applied to correct
the bias of CM outputs. In addition, statistical downscaling tools were
adopted to simulate the small-scale variability of precipitation and
temperature, with calibration relations assumed stationary from current to
future climate. While a process-based model like tRIBS has the potential to
capture the basin response at high resolution and allows conducting
distributed analyses, current computational constraints have limited the
possibility to deal with other sources of uncertainty, including testing the
effect of different types of bias correction methods (e.g., Tramblay et al.,
2013). Finally, we plan to devote future work to address the uncertainty of
hydrologic modeling, by comparing outputs from different models applied in
the RMB by several research groups in the context of the CLIMB project.