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Land use can offset climate change induced increases in erosion in Mediterranean
watersheds
Xavier Rodriguez-Lloverasa *; Wouter Buytaertb and Gerardo Benitoa.
aGeology Department, National Museum of Natural Sciences, MNCN-CSIC, C/Serrano 115 dupl.,
28006, Madrid, Spain. *[email protected]
bDepartment of Civil and Environmental Engineering, Faculty of Engineering, Imperial College,
Skempton Building, South Kensington Campus, Exhibition Road, SW7 2AZ, London, UK.
ABSTRACT:
The aim of this paper is to assess the impacts of projected climate change on a Mediterranean
catchment, and to analyze the effects of a suite of representative land use practices as an
adaptation tool to reduce climate change-driven erosion and hydrologic extremes. Relevant
climatic variables from the ERA-Interim global atmospheric reanalysis of the European Centre
for Medium-Range Weather Forecasts (ECMWF) were downscaled for the study area, and
perturbed with the anomalies of 23 global circulation models for three emission scenarios (B1,
A1B and A2). Both a projected daily rainfall time series for the period 2010 - 2100, and a single
precipitation event with a one-hundred year return period were used to assess the impact of
climate change. The downscaled data were fed into a distributed hydro-sedimentary model
(TETIS) with five land use configurations representative of future demographic tendencies,
geographical characteristics and land management policies (e.g. European Union CAP). The
projected changes showed a general decrease in runoff and sediment production by the end of
the century regardless of land use configuration. Sediment production showed a positive
relationship with an increase in agricultural land and a decrease in natural land under present
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day agricultural management. According to our simulations, some conservation practices in
agriculture can effectively reduce net erosion while maintaining agricultural production. As
such, they can play a critical role as an adaptation tool to reduce climate change impacts in the
21st century.
KEY WORDS: Downscaling; Climate Change Adaptation; Land Use; Erosion; TETIS.
1. INTRODUCTION
The Mediterranean is one of the most sensitive regions to climate change in Europe (IPCC,
2014). Climate change projections for the southern Iberian Peninsula suggest a decreasing
trend in annual average precipitation and an increase in heavy rainfalls by the end of the
century (Barranco et al., 2014; Garcia et al., 2007; Rodrigo, 2010).Changes in total and extreme
precipitation are projected to alter runoff production (Barranco et al., 2014), but the impact of
changes on erosion and sediment yields at catchment scale has received less scientific
attention (Bussi et al., 2014b).
Hydrological and environmental planning of Mediterranean watersheds requires an
understanding of the future runoff and sediment yield response to climate and land use
changes to enable adaptation to the potential impacts on freshwater resources and economic
activities (e.g. agriculture). Sediment yields in Mediterranean catchments are mainly produced
during high intensity precipitation events which may generate up to 40% of total erosion
(Baartman et al., 2013; Rodriguez-Lloveras et al., 2015). At the same time, soil loss may be
increased by inadequate land use and agricultural production techniques, deforestation,
overgrazing, forest fires and construction activities (Boellstorff and Benito, 2005; Garcia-Ruiz,
2010; Puigdefábregas and Mendizabal, 1998). In these fragile Mediterranean environmental
conditions, any soil loss higher than the estimated mean of 1.3 t ha-1 yr-1(Cerdan et al., 2010)
can lead to a stage of irreversibility within a time span of 50-100 years (EEA, 1999; Gobin et al.,
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2004). Climate change may contribute to increased soil erosion as a consequence of the higher
frequency of heavy rainfalls projected by climatic models (Döll et al., 2015; Kundzewicz et al.,
2014). However, the net sediment production will result from the combined impact of climate
(dryness, heavy rainfall, total rainfall) and land use conditions (agriculture, forest, shrubs), and
their spatial and temporal variability. Future water and sediment yield projections need to
consider different socioeconomic pathway scenarios for both climate change and land
management at watershed scale.
Simulating climate change impacts on hydrology and sediment production requires the
generation of time series of projected climatic variables. General Circulation Models (GCMs)
produce projected climatic variables under different socioeconomic and technological
development scenarios. Several studies have used climate change projections from GCMs and
regional climate models to model long-term changes in sediment transport in Mediterranean
watersheds (Bussi et al., 2014a; Nerantzaki et al., 2015; Nunes et al., 2013). Most of these
models provide projected climate variables at a coarse spatial resolution, which reduces
precipitation intensities and disregards local patterns of variability. This is one of the most
critical characteristics of precipitation in the Mediterranean region (Gonzalez-Hidalgo et al.,
2009; Xoplaki et al., 2004), and it is therefore important to simulate it in detail. For this reason,
downscaling methods have been used to obtain representative climatic variables at smaller
scales, that take local relief and elevation characteristics into account(Christensen et al.,
2007). Among these, statistical downscaling uses statistical modelling techniques to
extrapolate and interpolate results generated by dynamic models (Benestad et al., 2008).
These statistical downscaling techniques have been widely used for hydrological projections
(Barranco et al., 2014; Hertig and Jacobeit, 2008; Segui et al., 2010), but their application to
the analysis of watershed erosion and sediment yields is still limited (Michael et al.,
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2005).Downscaling methods have the capacity to preserve the observed statistical structure
(mean and dispersion) of the projected climate parameters at local scale, and to include heavy
rainfalls not well reproduced by GCM projections.
Climate change impacts on soil erosion and degradation can be reduced by adequate land use
practices (Boellstorff and Benito, 2005), thus effectively offsetting climate-driven increases in
erosion in Mediterranean areas. When exploring the best land use practices for climate change
adaptation, it is necessary to project representative land use configurations, considering non-
natural variables such as demographic and socioeconomic factors, which cannot be easily
predicted (Aguirre Segura et al., 1997; Arnold et al., 1998; Barriendos, 1997).
The purpose of this paper is to analyse the effects of different climate change projection
scenarios on runoff and sediment production in a Mediterranean catchment, and to investigate
how these effects can be mitigated by different land use configurations and agricultural
techniques. The dataset of daily climate projections was obtained from the ECMWF ERA-
Interim project combined with the 4th IPCC General Circulation Models (GCMs). Land use
scenarios were established considering geography, demographic trends, traditional agricultural
use and techniques and plant phenology. Changes in hydrology and sediment yields were
calculated by routing the projected meteorological time series (2010-2100) through the TETIS
distributed hydro-sedimentary model simulated under different future land use and land cover
scenarios.
2. MATERIAL AND METHODS
2.1. STUDY AREA
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The study area comprised a section of the upper catchment of the Guadalentin river (SE Spain,
Andalusia) covering a surface area of 429 Km2 drained by the Rambla Mayor and Caramel
Rivers (Fig. 1).The elevation ranges between 2045 and 687 m above the main sea level
(m.a.m.s.l.; Fig. 1). The climate of the study area is Mediterranean (average annual
temperature ~13 ºC) with semiarid characteristics in the lower part of the catchment, and
mountainous features in the high reliefs. The average annual precipitation, calculated from 41
years of hourly precipitation records at 13 meteorological stations (Fig. 1B), ranges from 460
mm at the highest meteorological station in the catchment (1186 m.a.m.s.l.) to 320 mm at
Valdeinfierno dam (697 m.a.m.s.l.). Rainfall occurs mainly in spring and autumn, whereas
summers are characterized by dry conditions. As reported in Rodriguez-Lloveras et al.
(2015),soils within the study area are poorly developed, in agreement with its semiarid
Mediterranean characteristics. In the northern part of the catchment, soils are highly degraded
with a dominant occurrence of Calcaric Regosols, Cambisols and Calcisols. In the southern part,
Leptosols are dominant in the uplands and Regosols in the lowlands; the latter are especially
predominant in the eastern part, where the majority of agricultural land is located. The soil
organic matter content is usually moderately high, in general between 2 and 10%, with
maximum values of 17%. The soils are deeper in the lowlands (depth: 50 – 100 cm) and
shallower in the uplands (20 – 30 cm). The soil texture is mostly clay loam, loam and silt loam,
with some sandier patches located in the central part.
Present day land use combines natural vegetated areas (49%) with areas of human uses
(51%), which include agriculture (50 %, mainly cereal farming) and urban occupation (1 %).
Areas of natural vegetation (trees and shrubs) are located in the higher mountainous areas and
slopes, whereas human activities are concentrated in flat areas and valley bottoms (Fig. 1C).
2.2. CLIMATE CHANGE DATA AND SCENARIOS
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In this study, the meteorological statistical downscaling model GLIMCLIM (Chandler and
Wheater, 2002; Yan et al., 2002; Yan et al., 2006; Yang et al., 2005)was applied to daily data
from 13 meteorological stations within the study area region(Fig. 1B).The GLIMCLIM
downscaling model is a two-step model: the first step involves fitting a gamma distribution to
the meteorological time series, while in the second step, generalised linear models are used to
find a relation between the statistical moments of the distribution of the daily meteorological
variables and synoptic climate variables in and nearby the study region. This predictive
statistical model can be used to generate synthetic time series with statistical properties that
are similar to the original observed time series. As GCMs are more skilful at projecting synoptic
variables, projected changes in these variables can be used to generate stochastic time series
for future periods. For more information about the GLIMCLIM procedure, see Yan et al., 2006
and references therein.
In this study, synoptic data for the period 1979 – 2012 were acquired at monthly scale from
the ERA-Interim reanalysis model(Dee et al., 2011) developed by the European Centre for
Medium-Range Weather Forecasts (ECMWF). A total of 124 synoptic variables for a period of
33 years (1979-2012) from 4 ERA-Interim cells that cover southern Spain were initially
considered. A total of 25 synoptic variables were retained for the generalised linear model.
Climate projections of three socioeconomic scenarios considered representative of lower,
medium and high greenhouse gas emissions (B1, A1B and A2 respectively;(Nakicenovic et al.,
2000)were selected. The precipitation and temperature anomalies of 23 GCMs (Table 1) for
these three scenarios (B1, A1B and A2) were included in the IPCC Fourth Assessment Report
(AR4; (IPCC, 2007). The reference period used to calculate the climatic anomalies
corresponded to the post-industrial reference period (1961-1990), considered in the 20th
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Century Climate in Coupled Models (20C3M). The anomalies were calculated for three 30-year
periods (2010-2040, 2040-2070, 270-2100;(IPCC, 2007). The effects of climate change on
hydrology and sediment production were calculated by combining the 30-year anomalies with
the downscaled meteorology and applying this projected meteorological data to a distributed
hydrological model.
2.3. HYDROLOGY AND SEDIMENT PRODUCTION
The TETIS distributed hydrological and sediment model was used to reproduce the hydro-
sedimentary response to climatic and land use scenario data. TETIS is a conceptually-based
spatially distributed model which has the capacity to simulate catchment hydrology and
sediment transport budgets at event scale over long-term simulations as well as for future
climate scenarios (Bussi et al., 2014a; Bussi et al., 2014b; Bussi et al., 2013; Frances et al.,
2007), and it simulates all the main components of the land phase of the hydrological
cycle(Frances et al., 2007). It is composed of a hydrological and a sediment transport sub-
model. The hydrological sub-model is based on a cell-tank structure, where terrain is divided
into cells (or pixels), each of which is conceptualised as a system of tanks which represent a
hydrological process (snowmelt, canopy interception, soil static storage, soil gravitational
storage and aquifer storage). The first two tanks (canopy interception and soil static storage)
are filled by precipitation and are only emptied by evapotranspiration. The remaining tank
depends on water flow, which is divided into overland flow and infiltration, depending on the
soil infiltration capacity. The water infiltrated into the soil is separated between interflow and
aquifer flow depending on the soil and aquifer properties. The total flow outlet which enters
the drainage network is calculated from the sum of overland flow, interflow and base flow
(Frances et al., 2007; Rodriguez-Lloveras et al., 2015; Velez et al., 2009). The total flow is
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routed downstream using geomorphological kinematic wave methodology, based on the
drainage network hydraulic geometry (Rodriguez-Lloveras et al., 2015).
The sediment sub-model depends on the balance between flow sediment transport capacity
and sediment availability (Bussi et al., 2014b). The sediment transport capacity of the overland
flow is calculated by means of the modified Kilinc-Richardson equation (Julien, 2010), with the
overland flow as input, and its transport capacity is determined using the Engelund and
Hansen (1967) equation. The sediment available is classified into three categories according to
its textural size (sand, silt and clay). Together, this parameter and flow transport capacity
determine the amount of sediment transported downstream, as well as particle settling
velocity, which is used to separate the transported material into suspended and deposited
sediment(Bussi et al., 2014a; Bussi et al., 2013). Implementation, calibration and validation of
the TETIS model for the study catchment are explained in more detail in Rodriguez-Lloveras et
al. (2015).
All the 23 GCM projected rainfall and temperature datasets under B1, A1B and A2 scenarios
were entered into the rainfall-runoff analysis. Results of rainfall-runoff simulations using this
climatic time series provided runoff and sediment production outputs at daily resolution
throughout the 21st century. The resulting GCMs with minimum, medium and maximum
runoffs and sediment yields were selected to characterise hydrology and sediment transport in
the study area.
A major drawback of the hydro-sedimentary model outputs based on climate change scenarios
implemented in the catchment is the misrepresentation of large peak flows. The absence of
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extreme flow discharges is not consistent with the present hydrological behaviour of the
catchment (Rodriguez-Lloveras et al., 2015), or the historical records (last 300 years) and
palaeoflood records (last 1000 years) of extreme floods (Benito et al., 2010). Given the lack of
rainfall extremes as a GCM limitation, the occurrence of such heavy rainfall and its effects on
future hydro-sedimentary projections was tested. Thus, a statistical analysis of annual
maximum daily rainfall was performed considering the annual maximum daily rainfall over the
period 1971 to 2012 (Fig. 2). A square-root exponential type distribution function SQRT-ET Max
(Etoh et al., 1987), frequently used in Spanish Mediterranean areas to determine maximum
precipitation events (CEDEX, 1999), was fitted using the maximum likelihood method.
The one-hundred year annual maximum daily rainfall matched the maximum daily rainfall
recorded in 1973 (Capel Molina, 1974), the heaviest rainfall on record. Similar extreme peak
flows have been reported over the last 300 years based on sedimentary palaeoflood evidence
(Benito et al., 2010). In order to determine the effects of extreme events on hydrology and
sediment transport under different climate change scenarios, one extreme rainfall event
associated with a one-hundred year flood was included in the projected GCMs. For each GCM
dataset, a total of three series were simulated, considering the occurrence of the extreme
rainfall in different time steps of 30 years (2010-2040, 240-2070 or 2070-2100). The extreme
rainfall was included randomly in autumn, since more than 60% of peak flows in the
instrumental record have occurred in this season.
2.4. LAND USE CONFIGURATIONS
Land use practices have a strong influence on soil erosion and sediment production,
particularly in Mediterranean regions (Boellstorff and Benito, 2005; Garcia-Ruiz, 2010). In the
study area, land use changes since the 1950s have led to large variations in water and
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sediment production (Rodriguez-Lloveras et al., 2015). As part of the projected hydro-
sedimentary modelling, five land use configurations were constructed representing the most
likely landscape and vegetation assemblage changes within the projected 30-year simulation
intervals. Land use types were classified into seven categories: Dense Forest, Forest, Scrubland,
Green Fallow, Bare Soil, Agricultural and Urban. In each category, the local vegetation species
and their natural evolution between simulation periods were considered (Table 2). The
projected land use configurations based on the characteristics shown in Table 2 (Fig. 3)were
determined considering local vegetation growth rates, most recent changes in the Common
Agricultural Policy (CAP) of the European Union (EEA, 2013; European-Comision, 2012b;
European-Comision, 2013), statistical projections of population growth for the region
(Junta_de_Andalucía, 2012), protected areas (Agencia-de-Medio-Ambiente, 2005), and local
slopes in the basin due to their relation with agriculture costs.
The natural evolution following abandonment of agricultural lands over the modelled 30-year
intervals was as follows: (1) bare soil; (2) scrubland; (3) forest; and (4) dense forest. This 30-
year scale of evolution was applied to the intervals [10] 2010-2040; [40] 2040-2070; and
[70]2070-2100,for each land use configuration (U1-U5; Fig. 4).Although this 30-year land use
evolution is an approximation, it is compatible with observed natural vegetation changes in the
study area. These different land use configurations combined with the different climate change
scenarios implemented in the basin offer a wide range of plausible hydrological and sediment
yield dynamics in the catchment in the 21st century.
3. RESULTS
3.1. Hydro-sedimentary response to climate change
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The hydrological simulations driven by the projected climatic variables (Table 3) were
compared to a reference simulation run with the synthetic dataset without GCM scenario
anomalies. Initially, the projected hydro-sedimentary results were obtained using current land
use and vegetation conditions.
The projected daily maximum flow (Qmax) showed only minor differences among the 23 GCMs
applied (2-3%). However, the accumulated total runoff volume (Vol. Acum) and yearly
averaged runoff volume (Yr. Avg.) based on GCM projections presented greater differences
between GCM projections (24-26%).Runoff volumes increased slightly with higher emission
scenarios(B1<A1B<A2), whereas the daily maximum flow did not follow this tendency
(A1B<B1<A2). In addition, the difference between maximum and average runoff volumes also
increased with adverse emission scenarios (B1<A1B<A2), indicating a slight trend towards an
increase in rainfall magnitude in high emission scenarios. In relation tithe instrumental period
(1971-2012¸ Rodriguez-Lloveras et al., 2015), the projected runoff volumes showed a tendency
to diminish overtime for all GCM models, whereas the projected maximum daily discharge
under the worst climate scenario was similar to the maximum daily peaks in the instrumental
record during dry periods (1995-2001, 35 m3/s; 2001-2009, 13 m3/s). However, these results
should be viewed with caution since the projected climatic variables depend on the reference
data from ERA-Interim reanalysis, which may misrepresent the complex orography of the
upper Guadalentin catchment.
Daily maximum sediment production showed higher variability among GCM simulations than
daily runoff flows (Qmax; 16-26%;Table 4). Depending on the GCM, sediment yields varied
between 23 and 27%, within a similar range tithe total runoff volume. This result indicates a
higher sensitivity of sediment flow peaks to climate variations than runoff peaks. In addition,
GCM simulations with the highest and lowest daily maximum sediment flows in each scenario
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coincided with those with maximum and minimum accumulated sediment volume,
respectively (which did not happen in the hydrology simulations). This result suggests a
stronger dependence of the total sediment transport budget on maximum daily sediment
flows.
The simulated sediment production was not consistently arranged according to increasing
emission scenarios but varied depending on GCMs. Nevertheless, for all GCMs, scenario A2
showed the highest sediment yield values, although sediment production for all the GCMs was
lower than during the instrumental period.
The runoff and sediment yields modelled from GCM simulation datasets only showed
moderate-low daily peak flow (< 30 m3 s-1) and sediment peaks (<5 m3 s-1). However,
instrumental (Rodriguez-Lloveras et al., 2015) and palaeoflood data (Benito et al., 2010) over
the last 500 years have shown the occurrence of high magnitude peak flows (> 50 m3 s-1 in daily
mean discharge). The lack of such hydrological extremes in the synthetic and projected data
series reflects the limited capacity of GCMs to simulate reliable extreme rainfalls in the study
area.
The potential effects of extreme rainfalls on projected datasets (e.g. one-hundred year rainfall)
were tested by including a representative 100-year precipitation event in the simulated GCMs.
The results of including one extreme discharge event in the simulations were consistent in
both the hydrological and sedimentary responses. The highest and lowest values of sediment
and water volumes corresponded to the same GCMs, with the exception of those using the
scenario A1B (Table 5). The hydrology and sediment values obtained were similar for scenarios
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B1 and A2, but different to the hydro-sedimentary behaviour observed in GCM-based
simulations for scenario A1B. This may be indicative of a higher climatic uncertainty for this
latter scenario in the study area (Fig. 5). Overall, these results indicate that maximum water
discharge and erosion tend to increase with the highest emission scenarios, since the highest
hydrological and sedimentary values were obtained for scenario A2(Table 5 and Fig. 5).
3.2. LAND USE CONFIGURATIONS
The hydrological and sedimentary response to climate change scenarios (B1, A1B and A2),for
representative GCMs(those with minimum, maximum and average accumulated volumes and
flows)and with extreme discharge intervals (Table 5, Fig. 5), was combined with five different
land use scenarios simulated in three 30-year periods. In the first 30-year interval, the
accumulated runoff curves tended to be arranged in relation to extreme rainfall occurrence
and to a lesser extent, following land use configurations (Fig.6). This sorting was less clear for
the subsequent 30-year intervals, as a random increase was observed in accumulated runoff
volume overtime. This result indicates that land use changes may affect the hydrological
response of the catchment in the short to medium term, but that they are not a critical factor
for runoff production in the long term. The projection curves of accumulated sediment yields
showed a strong dependence on land use configuration, with a lower influence of flow peak
magnitude. Land use configuration U5 (widespread abandonment of agricultural land)
presented the lowest volume of sediment production, whereas configuration U1 (increased
agricultural land cover) presented the highest (Table 6). Regarding the remaining uses, U3
(slight abandonment of crop land) presented high sediment production, while U2 (agriculture
with green fallow) and U4 (progressive abandonment of crop land) presented moderate-low
sediment production. Overall, land use scenarios yielded a much wider range of results in
sediment production than in runoff generation.
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An analysis of the average yearly hydrological output of the basin did not present systematic
increases or decreases due to critical climate or land use scenarios, with the exceptions of the
total maximum U1-A2 and minimum U2-B1 (Fig.7). Runoff production showed a tendency to
increase with more adverse emission scenarios. Nevertheless, yearly averaged hydrological
outputs did not present substantial differences between climate variations and land use
scenarios. In contrast, yearly averaged erosion presented a clear gradation of results related to
land use configuration and management techniques (green fallow in U2). High erosion in U1
(lack of abandoned land) contrasted with lower erosion in U3, U4 and U5 (10%, 20% and 30%
of abandoned crop land in each 30-year model period). This result is a consequence of a
combination of the agricultural crop cycle (cereals collected during summer) and the rainfall
regime (largest storms in autumn). In fact, extreme rainfalls frequently occur during autumn
when agricultural land is fallow or has recently been tilled and therefore behaves as a bare soil.
After the first 30-year period, abandoned land in U3, U4 and U5 became scrubland, which
presented higher resistance to erosion, whereas the agricultural land in U1 continued to
extend. Consequently, U1 yielded the highest values for runoff volume and erosion.
In the study catchment, runoff and sediment production was sensitive to climate change, but
was even more sensitive to land use changes, mainly in the case of sediment yields. This high
sensitivity confirms that land use management is a powerful tool to tackle erosion in
agricultural areas both in the short and medium term. Among the described land use
configurations, those with the lowest erosion (U4 and U5) implied extensive abandonment of
agricultural lands. In addition, more sustainable agricultural practices, such as green fallow
implemented in U2, maintained current agricultural production with a considerable reduction
in erosion even under increasing global warming conditions.
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4. DISCUSSION
An analysis has been conducted of the impact of climate change, extreme rainfall and land use
changes on runoff and sediment production in a Mediterranean catchment. A comparison of
the hydro-sedimentary modelling results over the instrumental period(Rodriguez-Lloveras et
al., 2015) with those from the projected datasets indicates that climate change scenarios
reduce the number and increase the magnitude of medium and extreme rainfall events. These
results coincide with local and regional studies of Mediterranean areas (Benito et al., 2015;
Christensen et al., 2007; Gibelin and Deque, 2003). The extreme precipitation events under
climate change predictions presented a reduction in frequency compared with the
instrumental record. Hydrological modelling based on climate projection datasets indicates a
reduction in runoff compared with the present behaviour of the catchment over the period
1976-2012(Rodriguez-Lloveras et al., 2015), in agreement with other hydrological projections
in Spain (Barranco et al., 2014). Sediment production is also affected by the reduction in
precipitation and runoff (Clarke and Rendell, 2010). As reported byRodriguez-Lloveras et al.
(2015), erosion in the study area is very sensitive to extreme events. In the study catchment,
yearly erosion rates considering climate change scenarios were half those of the erosion
estimated during the instrumental period, but extremely concentrated in short intervals of
intense precipitation. The decreasing trend of projected sediment yields agrees with other
studies based on GCMs and regional climate model projections in Mediterranean
watersheds(Nerantzaki et al., 2015).However, precipitation reduction in climate scenario
simulations may be conditioned by the limited capacity of GCMs to simulate extreme events
(Munoz-Diaz and Rodrigo, 2006).
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Land use practices are critical for mitigating soil erosion under climate change (Jiménez
Cisneros et al., 2014), especially during extreme events which produce accelerated erosion in
the area (Rodriguez-Lloveras et al., 2015). In our study catchment, five plausible land use
configurations were implemented in the hydro-sedimentary modelling, and demonstrated the
important effects of land use changes on hydrology and sediment production. Lower rates of
erosion and runoff were obtained using configurations with the highest extension of natural
vegetation areas (Boardman and Favis-Mortlock, 1998a; Boardman and Favis-Mortlock,
1998b). Sediment yields modelled with soil protection practices in agricultural areas, such as
green fallow, proved to be critical for soil conservation, corroborating the results of several
experimental studies (Boellstorff and Benito, 2005; Garcia-Ruiz and Lana-Renault, 2011). These
conservation practices are especially important in fragile or highly erosive environments, such
as the Mediterranean (Puigdefabregas et al., 1999). In such sensitive regions, appropriate
agricultural policies can encourage agricultural and conservation practices aimed at soil
protection, environmental sustainability and socioeconomic growth of these areas(Boellstorff
and Benito, 2005; Perez, 1990).
Hydro-sedimentary modelling considering land use configurations indicates that low erosion
rates coincide with areas with the highest rates of land abandonment. Abandoned lands
showed high erosion rates during the first 30-year modelled interval, with a progressive
reduction in the subsequent periods due to vegetation growth, which is consistent with
observed records in Mediterranean mountains (Garcia-Ruiz and Lana-Renault, 2011). Our
rainfall-runoff modelling confirms that land use configuration is the most decisive factor in
sediment production, even more so than climate change emission scenarios or peak flow
interval. This finding confirms the non-linear dependence of soil erosion on rainfall rate and its
strong dependence on land use and land cover, in agreement with the recent conclusions of
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the AR5 IPCC report (IPCC, 2014). Climate change provides opportunities for “low regret”
solutions that yield social and economic benefits and are adaptative to change. Soil
conservation policies can provide such adaptation pathways while at the same time improving
local environmental and socioeconomic sustainability (European-Comision, 2012a; European-
Comision, 2012b). In our Mediterranean study region, traditional agriculture combined with
conservation practices (e.g. U2) and natural vegetation cover clearly increases the capacity to
cope with projected climate risks without decreasing socioeconomic benefits in the region.
5. CONCLUSIONS
Climate change is expected to produce impacts on runoff and erosion rates in Mediterranean
catchments, but the net effects on sediment production will depend on land use and land
cover changes. In a torrential Mediterranean catchment in SE Spain, projected rainfall datasets
based on reanalysis data and statistical downscaling coupled with different GCMs indicate a
general tendency towards an increase in magnitude and a decrease in frequency of medium (>
40 mm, <60 mm) and high (>60 mm, < 90 mm)rainfall events in the 21st century. Extreme
events (>100 mm) showed a reduction in frequency under most of the scenarios considered.
Study area behaviour was highly dependent on extreme events; consequently, hydrology flows
and sediment production rates will tend to decrease in the 21st century.
In the study catchment, land use changes are the most critical factor affecting the hydrological
response, especially as regards erosion and sediment production. The land use configurations
that yielded the lowest sediment production were those with the largest extent of natural
vegetation. However, these configurations require a massive reduction in human activities. In
contrast, land use configuration U2, which considered agricultural lands under conservation
practices, maintained agricultural production with a substantial reduction in erosion in a global
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change environment, representing an excellent balance between economic sustainability and
natural vegetation cover. The strong influence of land uses on erosion, coupled with the fact
that land use changes allow faster local action than climate change mitigation, imply that land
use management should be viewed as an adaptive option to reduce climate change impacts.
ACKNOWLEDGEMENTS
This study was funded by the Spanish Ministry of Economy and Competitiveness through the
research projects CLARIES (ref. CGL2011-29176) and PALEOMED (CGL2014-58127-C3-1-
R).Meteorological data was provided by the Spanish Meteorological Agency (AEMET)
BIBLIOGRAPHY
Agencia-de-Medio-Ambiente, 2005. Plan de Ordenaciónde recursos naturales, Plan rector de uso y gestión, parque natural de sierra Maria - los Vélez. in: Consejería de medio ambiente, J.d.a. (Ed.). Agencia de medio ambiente, Sevilla, pp. 204 pp.
Aguirre Segura, A., Alcocer Martínez, F., Castro Nogueira, H., Cueto Romero, M., Domené Fernández, J., Guirado Romero, J., Guirado Romero, N., Mendoza Castellón, R., Soler Márquez, M., 1997. Análisis y problemática de gestión de los recursos naturales (florísticos y faunísticos) en el parque natural Sierra María-Los Vélez (Almería). Instituto de Estudios Almerienses.
Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT. JAWRA Journal of the American Water Resources Association, 34, 73-89.
Baartman, J.E.M., Temme, A.J.A.M., Veldkamp, T., Jetten, V.G., Schoorl, J.M., 2013. Exploring the role of rainfall variability and extreme events in long-term landscape development. Catena, 109, 25-38.
Barranco, L.M., Alvarez-Rodriguez, J., Olivera, F., Potenciano, A., Quintas, L., Estrada, F., 2014. Assessment of the Expected Runoff Change in Spain Using Climate Simulations. Journal of Hydrologic Engineering, 19, 1481-1490.
Barriendos, M., 1997. La reconstruccion paleoambiental a traves de documentacion historica. Las cuencas de Sobirans y Vallalta (Maresme, Barcelona) en 10s siglos XVI-XIX. Revista de Geografía, 30-31, 16.
Benestad, R.E., Hanssen-Bauer, I., Chen, D., 2008. Empirical-statistical downscaling. World Scientific Publishing Company Incorporated.
Benito, G., Macklin, M.G., Panin, A., Rossato, S., Fontana, A., Jones, A.F., Machado, M.J., Matlakhova, E., Mozzi, P., Zielhofer, C., 2015. Recurring flood distribution patterns related to short-term Holocene climatic variability. Scientific Reports, 5, 16398.
420
421
422
423
424
425
426
427
428
429
430
431432433434435436437438439440441442443444445446447448449450451452453454455
Benito, G., Rico, M., Sánchez-Moya, Y., Sopeña, A., Thorndycraft, V.R., Barriendos, M., 2010. The impact of late Holocene climatic variability and land use change on the flood hydrology of the Guadalentín River, southeast Spain. Global and Planetary Change, 70, 53-63.
Boardman, J., Favis-Mortlock, D., 1998a. Modelling soil erosion by water. in: Boardman, J., FavisMortlock, D. (Eds.), Modelling Soil Erosion by Water, pp. 3-6.
Boardman, J., Favis-Mortlock, D., 1998b. Modelling soil erosion by water: Some conclusions. in: Boardman, J., FavisMortlock, D. (Eds.), Modelling Soil Erosion by Water, pp. 515-517.
Boellstorff, D., Benito, G., 2005. Impacts of set-aside policy on the risk of soil erosion in central Spain. Agriculture, Ecosystems & Environment, 107, 231-243.
Bussi, G., Francés, F., Horel, E., López-Tarazón, J., Batalla, R., 2014a. Modelling the impact of climate change on sediment yield in a highly erodible Mediterranean catchment. Journal of Soils and Sediments, 14, 1921-1937.
Bussi, G., Francés, F., Montoya, J.J., Julien, P.Y., 2014b. Distributed sediment yield modelling: Importance of initial sediment conditions. Environmental Modelling & Software, 58, 58-70.
Bussi, G., Rodriguez-Lloveras, X., Frances, F., Benito, G., Sanchez-Moya, Y., Sopeña, A., 2013. Sediment yield model implementation based on check dam infill stratigraphy in a semiarid Mediterranean catchment. Hydrology and Earth System Sciences, 17, 3339-3354.
Cai, W., Collier, M.A., P.D., D., H.B., G., A.C., H., S.P., O.F., P.H., W., 2003. The response of climate variability and mean state to climate change: preliminary results from the CSIRO Mark 3 coupled model. CLIVAR Exchanges, 28, 8-11.
Capel Molina, J., 1974. Génesis de las inundaciones de octubre de 1973 en el sureste de la Península Ibérica. Cuadernos geográficos de la Universidad de Granada, 4, 149-166.
CEDEX, 1999. Máximas lluvias diarias en la España peninsular. in: Transportes, S.d.E.d.I.y., Carreteras, D.G.d. (Eds.). Ministerio de Fomento, Madrid, pp. 55 pp.
Cerdan, O., Govers, G., Le Bissonnais, Y., Van Oost, K., Poesen, J., Saby, N., Gobin, A., Vacca, A., Quinton, J., Auerswald, K., Klik, A., Kwaad, F.J.P.M., Raclot, D., Ionita, I., Rejman, J., Rousseva, S., Muxart, T., Roxo, M.J., Dostal, T., 2010. Rates and spatial variations of soil erosion in Europe: A study based on erosion plot data. Geomorphology, 122, 167-177.
Clarke, M.L., Rendell, H.M., 2010. Climate-driven decrease in erosion in extant Mediterranean badlands. Earth Surface Processes and Landforms, 35, 1281-1288.
Collins, W.D., Bitz, C.M., Blackmon, M.L., Bonan, G.B., Bretherton, C.S., Carton, J.A., Chang, P., Doney, S.C., Hack, J.J., Henderson, T.B., Kiehl, J.T., Large, W.G., McKenna, D.S., Santer, B.D., Smith, R.D., 2006. The Community Climate System Model version 3 (CCSM3). Journal of Climate, 19, 2122-2143.
CSMD, 2005. An introduction to The Fist-generation Operational Climate Model at National Climate Center, Advances in Climate System Modeling. in: Division, C.S.M. (Ed.). National Climate Center, Beijing, pp. 14.
Chandler, R.E., Wheater, H.S., 2002. Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland. Water Resources Research, 38, 1192.
Christensen, J.H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R.K., Kwon, W.-T., Laprise, R., Rueda, V.M., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr, A., Whetton, P., 2007. Regional Climate Projections. in: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.) (Ed.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. pp. 847-940.
Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L.,
456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507
Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.N., Vitart, F., 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553-597.
Delworth, T.L., Broccoli, A.J., Rosati, A., Stouffer, R.J., Balaji, V., Beesley, J.A., Cooke, W.F., Dixon, K.W., Dunne, J., Dunne, K.A., Durachta, J.W., Findell, K.L., Ginoux, P., Gnanadesikan, A., Gordon, C.T., Griffies, S.M., Gudgel, R., Harrison, M.J., Held, I.M., Hemler, R.S., Horowitz, L.W., Klein, S.A., Knutson, T.R., Kushner, P.J., Langenhorst, A.R., Lee, H.C., Lin, S.J., Lu, J., Malyshev, S.L., Milly, P.C.D., Ramaswamy, V., Russell, J., Schwarzkopf, M.D., Shevliakova, E., Sirutis, J.J., Spelman, M.J., Stern, W.F., Winton, M., Wittenberg, A.T., Wyman, B., Zeng, F., Zhang, R., 2006. GFDL's CM2 global coupled climate models. Part I: Formulation and simulation characteristics. Journal of Climate, 19, 643-674.
Deque, M., Dreveton, C., Braun, A., Cariolle, D., 1994. THE ARPEGE/IFS ATMOSPHERE MODEL - A CONTRIBUTION TO THE FRENCH COMMUNITY CLIMATE MODELING. Climate Dynamics, 10, 249-266.
Diansky, N.A., Bagno, A.V., Zalesny, V.B., 2002. Sigma model of global ocean circulation and its sensitivity to variations in wind stress. Izvestiya Atmospheric and Oceanic Physics, 38, 477-494.
Diansky, N.A., Volodin, E.M., 2002. Simulation of present-day climate with a coupled atmosphere-ocean general circulation model. Izvestiya Atmospheric and Oceanic Physics, 38, 732-747.
Döll, P., Jiménez-Cisneros, B., Oki, T., Arnell, N.W., Benito, G., Cogley, J.G., Jiang, T., Kundzewicz, Z.W., Mwakalila, S., Nishijima, A., 2015. Integrating risks of climate change into water management. Hydrological Sciences Journal, 60, 4-13.
EEA, 1999. Environment in the European Union at the turn of the century., European Environment Agency, Copenhagen.
EEA, 2013. Towards a green economy in Europe - EU environmental policy targets and objectives 2010-2050 European Environment Agency, Copenhagen.
Etoh, T., Murota, A., Nakanishi, M., 1987. SQRT-Exponential Type Distribution of Maximum. in: Singh, V. (Ed.), Hydrologic Frequency Modeling. Springer Netherlands, pp. 253-264.
European-Comision, 2012a. The Common Agricultural Policy – A story to be continued. in: Comision, E. (Ed.). Publications Office of the European Union, Luxemburg, pp. 20 pp.
European-Comision, 2012b. The Common Agricultural Policy, A partnership between Europe and Farmers. in: Comision, E. (Ed.). Publications Office of the European Union, Luxemburg, pp. 16 pp.
European-Comision, 2013. Political agreement on new direction for common agricultural policy. European Comission, Brussels, pp. 3 pp.
Flato, G.M., Boer, G.J., 2001. Warming asymmetry in climate change simulations. Geophysical research letters, 28, 195-198.
Frances, F., Velez, J.I., Velez, J.J., 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332, 226-240.
Furevik, T., Bentsen, M., Drange, H., Kindem, I.K.T., Kvamstø, N.G., Sorteberg, A., 2003. Description and evaluation of the bergen climate model: ARPEGE coupled with MICOM. Climate Dynamics, 21, 27–51.
Garcia-Ruiz, J.M., 2010. The effects of land uses on soil erosion in Spain: A review. Catena, 81, 1-11.
Garcia-Ruiz, J.M., Lana-Renault, N., 2011. Hydrological and erosive consequences of farmland abandonment in Europe, with special reference to the Mediterranean region - A review. Agriculture Ecosystems & Environment, 140, 317-338.
508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559
Garcia, J.A., Gallego, M.C., Serrano, A., Vaquero, J.M., 2007. Trends in block-seasonal extreme rainfall over the Iberian Peninsula in the second half of the twentieth century. Journal of Climate, 20, 113-130.
Gibelin, A.L., Deque, M., 2003. Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Climate Dynamics, 20, 327-339.
Gnanadesikan, A., Dixon, K.W., Griffies, S.M., Balaji, V., Barreiro, M., Beesley, J.A., Cooke, W.F., Delworth, T.L., Gerdes, R., Harrison, M.J., Held, I.M., Hurlin, W.J., Lee, H.C., Liang, Z., Nong, G., Pacanowski, R.C., Rosati, A., Russell, J., Samuels, B.L., Song, Q., Spelman, M.J., Stouffer, R.J., Sweeney, C.O., Vecchi, G., Winton, M., Wittenberg, A.T., Zeng, F., Zhang, R., Dunne, J.P., 2006. GFDL's CM2 global coupled climate models. Part II: The baseline ocean simulation. Journal of Climate, 19, 675-697.
Gobin, A., Jones, R., Kirkby, M., Campling, P., Govers, G., Kosmas, C., Gentile, A.R., 2004. Indicators for pan-European assessment and monitoring of soil erosion by water. Environmental Science & Policy, 7, 25-38.
Gonzalez-Hidalgo, J.C., Lopez-Bustins, J.A., Stepanek, P., Martin-Vide, J., de Luisa, M., 2009. Monthly precipitation trends on the Mediterranean fringe of the Iberian Peninsula during the second-half of the twentieth century (1951-2000). International journal of climatology, 29, 1415-1429.
Gordon, H.B., Rotstayn, L.D., McGregor, J.L., Dix, M.R., Kowalczyk, E.A., O’Farrell, S.P., Waterman, L.J., Hirst, A.C., Wilson, S.G., Collier, M.A., Watterson, I.G., Elliott, T.I., 2002. The CSIRO Mk3 climate system model, CSIRO atmospheric research technical paper.
Hertig, E., Jacobeit, J., 2008. Assessments of Mediterranean precipitation changes for the 21st century using statistical downscaling techniques. International journal of climatology, 28, 1025-1045.
Hourdin, F., Musat, I., Bony, S., Braconnot, P., Codron, F., Dufresne, J.L., Fairhead, L., Filiberti, M.A., Friedlingstein, P., Grandpeix, J.Y., Krinner, G., Levan, P., Li, Z.X., Lott, F., 2006. The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Climate Dynamics, 27, 787-813.
IPCC, 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Cambridge, United Kingdom and New York, NY, USA.
IPCC, 2014. Climate Change 2014: Impacts, Adaptation and Vulnerability: The Working Group II (WGII) contribution to the Fifth Assessment Report, IPCC.
Jiménez Cisneros, B.E., Oki, T., Arnell, N.W., Benito, G., Cogley, J.G., Döll, P., Jiang, T., Mwakalila, S.S., 2014. Freshwater resources. in: Field, C.B. et al. (Eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229-269.
Johns, T.C., Durman, C.F., Banks, H.T., Roberts, M.J., McLaren, A.J., Ridley, J.K., Senior, C.A., Williams, K.D., Jones, A., Rickard, G.J., Cusack, S., Ingram, W.J., Crucifix, M., Sexton, D.M.H., Joshi, M.M., Dong, B.W., Spencer, H., Hill, R.S.R., Gregory, J.M., Keen, A.B., Pardaens, A.K., Lowe, J.A., Bodas-Salcedo, A., Stark, S., Searl, Y., 2006. The new Hadley Centre Climate Model (HadGEM1): Evaluation of coupled simulations. Journal of Climate, 19, 1327-1353.
Johns, T.C., Gregory, J.M., Ingram, W.J., Johnson, C.E., Jones, A., Lowe, J.A., Mitchell, J.F.B., Roberts, D.L., Sexton, D.M.H., Stevenson, D.S., Tett, S.F.B., Woodage, M.J., 2003. Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics, 20, 583-612.
Jungclaus, J.H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.J., Latif, M., Marotzke, J., Mikolajewicz, U., Roeckner, E., 2006. Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. Journal of Climate, 19, 3952-3972.
560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611
Junta_de_Andalucía, 2012. Población proyectada por Zonas de Trabajo Social según sexo, Andalucía, 2009-2035. Instituto de Estadística y cartografía de andalucíaConsejerería de economía innovación ciencia y empleo, Sevilla, pp. La Proyección de la población de Andalucía por ambitos subregionales 2009 -2035 ofrece datos desagregados por sexo y edad en diversas zonificaciones usadas por la Administración Pública andaluza. .
K-1_model_developers, 2004. K-1 coupled model (MIROC) description, K-1 technical report, Center for Climate System Research, University of Tokyo, Tokyo.
Kim, S.J., Flato, G.M., Boer, G.J., 2003. A coupled climate model simulation of the last glacial maximum, Part 2: approach to equilibrium. Climate Dynamics, 20, 635-661.
Kim, S.J., Flato, G.M., Boer, G.J., McFarlane, N.A., 2002. A coupled climate model simulation of the Last Glacial Maximum, part 1: transient multi-decadal response. Climate Dynamics, 19, 515-537.
Kundzewicz, Z.W., Kanae, S., Seneviratne, S.I., Handmer, J., Nicholls, N., Peduzzi, P., Mechler, R., Bouwer, L.M., Arnell, N., Mach, K., Muir-Wood, R., Brakenridge, G.R., Kron, W., Benito, G., Honda, Y., Takahashi, K., Sherstyukov, B., 2014. Flood risk and climate change: global and regional perspectives. Hydrological Sciences Journal, 59, 1-28.
Legutke, S., Voss, R., Klimarechenzentrum, D., Betreuungsgruppe, M., 1999. The Hamburg atmosphere-ocean coupled circulation model ECHO-G. 0940-9327 Deutsches Klimarechenzentrum, Hamburg, Germany.
Martin, G.M., Ringer, M.A., Pope, V.D., Jones, A., Dearden, C., Hinton, T.J., 2006. The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part I: Model description and global climatology. Journal of Climate, 19, 1274-1301.
Michael, A., Schmidt, J., Enke, W., Deutschländer, T., Malitz, G., 2005. Impact of expected increase in precipitation intensities on soil loss—results of comparative model simulations. CATENA, 61, 155-164.
Munoz-Diaz, D., Rodrigo, F.S., 2006. Seasonal rainfall variations in Spain (1912-2000) and their links to atmospheric circulation. Atmospheric Research, 81, 94-110.
Nakicenovic, N., Alcamo, J., Davis, G., Vries, B.d., Fenhann, J., Gaffin, S., Gregory, K., Griibler, A., Jung, T.Y., Kram, T., Rovere, E.L.L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., Rooijen, S.v., Victor, N., Dadi, Z., 2000. Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, IPCC, Cambridge U.K.
Nerantzaki, S.D., Giannakis, G.V., Efstathiou, D., Nikolaidis, N.P., Sibetheros, I.Α., Karatzas, G.P., Zacharias, I., 2015. Modeling suspended sediment transport and assessing the impacts of climate change in a karstic Mediterranean watershed. Science of The Total Environment, 538, 288-297.
Nunes, J.P., Seixas, J., Keizer, J.J., 2013. Modeling the response of within-storm runoff and erosion dynamics to climate change in two Mediterranean watersheds: A multi-model, multi-scale approach to scenario design and analysis. CATENA, 102, 27-39.
Perez, M.R., 1990. Development of Mediterranean agriculture: An ecological approach. Landscape and Urban Planning, 18, 211-219.
Pope, V.D., Gallani, M.L., Rowntree, P.R., Stratton, R.A., 2000. The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Climate Dynamics, 16, 123-146.
Puigdefábregas, J., Mendizabal, T., 1998. Perspectives on desertification: Western Mediterranean. Journal of Arid Environments, 39, 209-224.
Puigdefabregas, J., Sole, A., Gutierrez, L., del Barrio, G., Boer, M., 1999. Scales and processes of water and sediment redistribution in drylands: results from the Rambla Honda field site in Southeast Spain. Earth-Science Reviews, 48, 39-70.
612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662
Ringer, M.A., Martin, G.M., Greeves, C.Z., Hinton, T.J., James, P.M., Pope, V.D., Scaife, A.A., Stratton, R.A., Inness, P.M., Slingo, J.M., Yang, G.Y., 2006. The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model (HadGEM1). Part II: Aspects of variability and regional climate. Journal of Climate, 19, 1302-1326.
Rodrigo, F.S., 2010. Changes in the probability of extreme daily precipitation observed from 1951 to 2002 in the Iberian Peninsula. International journal of climatology, 30, 1512-1525.
Rodriguez-Lloveras, X., Bussi, G., Frances, F., Rodriguez-Caballero, E., Soler, A., Calle, M., Benito, G., 2015. Patterns of runoff and sediment production in response to land-use changes in an ungauged Mediterranean catchment. Journal of Hydrology, 531, 1054-1066.
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U., Tompkins, A., 2003. The atmospheric general circulation model ECHAM 5. PART I: Model description, Hamburg.
Russell, G.L., Miller, J.R., Rind, D., 1995. A coupled atmosphere-ocean model for transient climate change studies. Atmosphere-Ocean, 33, 683-730.
Schmidt, G.A., Ruedy, R., Hansen, J.E., Aleinov, I., Bell, N., Bauer, M., Bauer, S., Cairns, B., Canuto, V., Cheng, Y., Del Genio, A., Faluvegi, G., Friend, A.D., Hall, T.M., Hu, Y.Y., Kelley, M., Kiang, N.Y., Koch, D., Lacis, A.A., Lerner, J., Lo, K.K., Miller, R.L., Nazarenko, L., Oinas, V., Perlwitz, J., Rind, D., Romanou, A., Russell, G.L., Sato, M., Shindell, D.T., Stone, P.H., Sun, S., Tausnev, N., Thresher, D., Yao, M.S., 2006. Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data. Journal of Climate, 19, 153-192.
Segui, P.Q., Ribes, A., Martin, E., Habets, F., Boe, J., 2010. Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins. Journal of Hydrology, 383, 111-124.
Stouffer, R.J., Broccoli, A.J., Delworth, T.L., Dixon, K.W., Gudgel, R., Held, I., Hemler, R., Knutson, T., Lee, H.C., Schwarzkopf, M.D., Soden, B., Spelman, M.J., Winton, M., Zeng, F., 2006. GFDL's CM2 global coupled climate models. Part IV: Idealized climate response. Journal of Climate, 19, 723-740.
Velez, J.J., Puricelli, M., Unzu, F.L., Frances, F., 2009. Parameter extrapolation to ungauged basins with a hydrological distributed model in a regional framework. Hydrology and Earth System Sciences, 13, 229-246.
Washington, W.M., Weatherly, J.W., Meehl, G.A., Semtner, A.J., Bettge, T.W., Craig, A.P., Strand, W.G., Arblaster, J., Wayland, V.B., James, R., Zhang, Y., 2000. Parallel climate model (PCM) control and transient simulations. Climate Dynamics, 16, 755-774.
Wittenberg, A.T., Rosati, A., Lau, N.C., Ploshay, J.J., 2006. GFDL's CM2 global coupled climate models. Part III: Tropical pacific climate and ENSO. Journal of Climate, 19, 698-722.
Xoplaki, E., Gonzalez-Rouco, J.F., Luterbacher, J., Wanner, H., 2004. Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends. Climate Dynamics, 23, 63-78.
Yan, Z., Bate, S., Chandler, R.E., Isham, V., Wheater, H., 2002. An Analysis of Daily Maximum Wind Speed in Northwestern Europe Using Generalized Linear Models. Journal of Climate, 15, 2073-2088.
Yan, Z., Bate, S., Chandler, R.E., Isham, V., Wheater, H., 2006. Changes in extreme wind speeds in NW Europe simulated by generalized linear models. Theoretical and Applied Climatology, 83, 121-137.
Yang, C., Chandler, R.E., Isham, V.S., Wheater, H.S., 2005. Spatial-temporal rainfall simulation using generalized linear models. Water Resources Research, 41, W11415.
Yu, Y., Yu, R., Zhang, X., Liu, H., 2002. A flexible coupled ocean-atmosphere general circulation model. Advances in Atmospheric Sciences, 19, 169-190.
663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714
Yu, Y., Zhang, X., Guo, Y., 2004. Global coupled ocean-atmosphere general circulation models in LASG/IAP. Advances in Atmospheric Sciences, 21, 444-455.
Yukimoto, S., Noda, A., 2002. Improvements of the Meteorological Research Institute Global Ocean-atmosphere Coupled GCM (MRI-CGCM2) and its climate sensitivity. National Institute for Environmental Studies, Tsukuba, Japan, 37-44.
Yukimoto, S., Noda, A., Kitoh, A., Hosaka, M., Yoshimura, H., Uchiyama, T., Shibata, K., Arakawa, O., Kusunoki, S., 2006. Present-day climate and climate sensitivity in the Meteorological Research Institute coupled GCM version 2.3 (MRI-CGCM2.3). Journal of the Meteorological Society of Japan, 84, 333-363.
Yukimoto, S., Noda, A., Kitoh, A., Sugi, M., Kitamura, Y., Hosaka, M., Shibata, K., Maeda, S., Uchiyama, T., 2001. The new Meteorological Research Institute coupled GCM(MRI-CGCM 2)- Model climate and variability. Papers in Meteorology and Geophysics, 51, 47-88.
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728
729
Tables
Table 1. Global circulation climatic models (GCMs) considered in the AR4 IPCC report.
Alias Model Name Organization Reference
BCCM1 BCC-CM1 China Meteorological Administration (CSMD, 2005)
BCM2 BCM Version 2 BCCR, Univ. of Bergen (Furevik et al., 2003)
CGHR CGCM3 high resolution CCCma
(Flato and Boer, 2001; Kim et al., 2003; Kim et al.,
2002)
CGMR CGCM3 medium resolution CCCma
(Flato and Boer, 2001; Kim et al., 2003; Kim et al.,
2002)
CNCM3 CNRM-CM3 Meteo France (Deque et al., 1994; Gibelin and Deque, 2003)
CSMK3 CSIRO Mark 3.0 CSIRO (Cai et al., 2003; Gordon et al., 2002)
ECHOG ECHO-G = ECHAM4 + HOPE-G
Meteorological Institute of the University of Bonn (Legutke et al., 1999)
FGOALS FGOALS1.0_g LASG (Yu et al., 2002; Yu et al., 2004)
GFCM20 CM2.0 - AOGCM NOAA
(Delworth et al., 2006; Gnanadesikan et al., 2006;
Stouffer et al., 2006; Wittenberg et al., 2006)
(GFCM21 CM2.1 - AOGCM NOAA
(Delworth et al., 2006; Gnanadesikan et al., 2006;
Stouffer et al., 2006; Wittenberg et al., 2006)
GIAOM GISS AOM 4x3 GISS, NASA (Russell et al., 1995)GIEH GISS ModelE-H GISS, NASA (Schmidt et al., 2006)GIER GISS ModelE-R GISS, NASA (Schmidt et al., 2006)
HADCM3 HadCM3 Met Office (Johns et al., 2003; Pope et al., 2000)
HADGEM HadGEM1 Met Office(Johns et al., 2006; Martin et al., 2006; Ringer et al.,
2006)
INCM3 INMCM3.0 Russian Academy of Science
(Diansky et al., 2002; Diansky and Volodin, 2002)
IPCM4 IPSL-CM4 IPSL (Hourdin et al., 2006)
MIHR MIROC3.2 CCSR/NIES/FRCGC (K-1_model_developers, 2004)
MIMR MIROC3.2 CCSR/NIES/FRCGC (K-1_model_developers, 2004)
MPEH5 ECHAM5/MPI-OM Max Planck Institute for Meteorology
(Jungclaus et al., 2006; Roeckner et al., 2003)
MRCGCM MRI-CGCM2.3.2 Japan Meteorological Agency
(Yukimoto and Noda, 2002; Yukimoto et al., 2006; Yukimoto et al., 2001)
NCCCSMCommunity Climate
System Model, version 3.0 (CCSM3)
NCAR (Collins et al., 2006)
NCPCM Parallel Climate Model (PCM)
NCAR, NSF, NASA, and NOAA (Washington et al., 2000)
730
731
732
733
734
Table 2. Characteristics of the lanscape evolution of the land use configurations considering the different land use categories and the 30 years time intervals defined (P1: 2010-2040; P2: 2040-2070; P3: 2070-2100).
Land useConfig. Population Agricultural
land Scrubland Forest Comments
Uses U1 Increase
+5% each 30 years;
managed after CAP
rules
-5% each 30 year for
agriculture
Forest become dense forest in
P2 &P3
Agriculture on slopes <3%,
<5%, and <6% each 30-years
Uses U2 Stable
Stable with conservation
practices (Green Fallow)
Scrubland become forest in later period
Forest become dense forest in
later period
CAP rules with green fallow (EEA, 2013)
Uses U3 Decrease - 10% each 30-yr period
Abandoned land become scrubland and forest in later
period
Stable
Abandon crop land with slope
>16%, 12% and 10% in
P1, P2 and P3
Uses U4 Decrease - 20% each 30-yr period
Abandoned land become scrubland and forest in later
period
Stable
Abandon crop land with slope >12%, 8% and 6% in P1, P2
and P3
Uses U5 Decrease - 30% each 30-yr period
Abandoned land become scrubland and forest in later
period
Stable
Abandon crop land with slope >10%, 6% and 3% in P1, P2
and P3
735736737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
Table 3. Results of the hydrologic simulation for the different climate scenarios and GCMs
Scenario B1 Scenario A1B Scenario A2
GCMQ
max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Avg
(Hm3)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Avg(H
m3)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Avg(H
m3)
BCCM1 28.29 65.61 0.66BCM2 28.34 81.12 0.81 28.27 79.33 0.79CGHR 27.92 77.14 0.77 27.95 75.56 0.76CGMR 28.16 90.24 0.90CNCM3 27.78 75.53 0.76 27.82 75.47 0.76CSMK3 27.90 72.34 0.72 27.88 71.18 0.71 27.90 70.37 0.70ECHOG 27.82 72.15 0.72 27.80 72.06 0.72FGOALS 28.01 72.37 0.72 27.98 71.44 0.71GFCM20 27.95 75.54 0.76 27.84 73.50 0.74 27.83 73.55 0.74(GFCM21 27.95 75.54 0.76 27.84 73.50 0.74 27.92 73.41 0.73GIAOM 28.04 68.03 0.68 28.07 67.52 0.68GIEH 27.65 72.55 0.73GIER 28.34 88.91 0.89 28.19 92.19 0.92
HADCM3 28.07 84.80 0.85 28.14 75.55 0.76 28.06 75.39 0.75HADGEM 28.31 76.35 0.76 28.51 76.90 0.77
INCM3 27.95 72.90 0.73 27.98 71.62 0.72 27.94 71.81 0.72IPCM4 27.82 71.92 0.72 27.80 70.46 0.71 27.83 70.10 0.70MIHR 28.10 71.64 0.72 28.02 70.01 0.70MIMR 28.19 74.00 0.74 28.10 72.32 0.72 28.09 72.75 0.73
MPEH5 27.66 71.67 0.72 27.63 69.92 0.70 27.68 70.08 0.70MRCGCM 27.80 70.99 0.71 27.88 69.80 0.70 27.80 69.90 0.70NCCCSM 27.99 74.27 0.74 27.92 71.93 0.72 27.95 71.42 0.71NCPCM 28.00 77.92 0.78 27.89 77.76 0.78Average 27.98 74.63 0.75 27.94 73.73 0.74 27.89 73.30 0.73Max diff 2% 26% 26% 2% 25% 24% 3% 24% 24%
753
754
755
756
757
758
759
760
761
762
763
764
765
766
Table 4. Results of the sedimentary simulation for the different climate scenarios and GCMs
Scenario B1 Scenario A1B Scenario A2
GCM
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Ero-sion (t/Ha)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Ero-sion (t/Ha)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Ero-sion (t/Ha)
BCCM1 4.63 12.31 2.96BCM2 5.96 15.45 3.71 5.87 15.08 3.62CGHR 5.63 14.64 3.51 5.72 14.31 3.44CGMR 6.12 16.89 4.06CNCM3 5.78 14.46 3.47 5.89 14.45 3.47CSMK3 5.48 13.79 3.31 5.51 13.70 3.29 5.53 13.51 3.24ECHOG 5.92 13.86 3.33 5.80 13.82 3.32FGOALS 5.31 13.70 3.29 5.57 13.63 3.27GFCM20 5.69 14.40 3.46 5.77 14.11 3.39 5.83 14.18 3.40GFCM21 5.69 14.40 3.46 5.77 14.11 3.39 6.07 14.13 3.39GIAOM 4.95 12.78 3.07 5.12 12.84 3.08GIEH 5.63 13.84 3.32GIER 6.24 16.91 4.06 6.63 17.41 4.18
HADCM3 6.05 16.12 3.87 5.83 14.50 3.48 5.88 14.47 3.47HADGEM 5.84 14.59 3.50 5.77 14.70 3.53
INCM3 5.44 13.85 3.33 5.49 13.74 3.30 5.56 13.80 3.31IPCM4 5.47 13.63 3.27 5.59 13.46 3.23 5.44 13.40 3.22MIHR 5.40 13.55 3.25 5.41 13.33 3.20MIMR 5.66 14.05 3.37 5.68 13.84 3.32 5.71 13.93 3.34
MPEH5 5.44 13.60 3.27 5.41 13.36 3.21 5.54 13.40 3.22MRCGCM 5.28 13.41 3.22 5.43 13.34 3.20 5.34 13.35 3.20NCCCSM 5.49 14.11 3.39 5.61 13.77 3.31 5.38 13.63 3.27NCPCM 5.79 14.89 3.57 5.73 14.82 3.56average 5.50 14.16 3.40 5.66 14.08 3.38 5.74 14.20 3.41Max diff 26% 27% 27% 16% 24% 24% 19% 23% 23%
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
Table 5. Results of the hydrologic and sedimentary simulation for the different climate scenarios and representative GCMs including the centennial peak flow
Hydrology Sediment
Scenario GCM Peak
intervalQ max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Avg
(Hm3)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Ero-sion (t/Ha)
B1
BCCM12010-2040 165.13 84.51 0.85 52.92 16.26 3.902040-2070 165.31 89.04 0.89 53.15 17.23 4.142070-2100 164.94 91.47 0.92 52.93 17.69 4.25
GIER2010-2040 180.40 105.63 1.06 61.04 20.20 4.852040-2070 177.31 99.21 0.99 59.68 18.90 4.542070-2100 175.55 95.49 0.96 58.87 18.13 4.35
NCCCSM2010-2040 170.80 95.66 0.96 56.30 18.48 4.442040-2070 170.91 94.88 0.95 56.49 18.37 4.412070-2100 169.61 104.07 1.04 55.72 20.05 4.81
A1B
CGMR2010-2040 119.72 101.25 1.01 42.32 18.26 4.382040-2070 169.78 97.64 0.98 56.16 18.43 4.422070-2100 168.87 98.65 0.99 55.48 18.69 4.49
GFCM202010-2040 172.13 94.58 0.95 56.88 18.26 4.382040-2070 169.48 92.43 0.92 55.59 17.93 4.312070-2100 168.09 102.13 1.02 54.49 19.17 4.60
GIAOM2010-2040 167.94 100.15 1.00 53.77 19.19 4.612040-2070 166.02 89.53 0.90 52.76 17.21 4.132070-2100 165.24 90.37 0.90 52.27 17.36 4.17
HADGEM2010-2040 170.51 95.84 0.96 55.90 18.38 4.412040-2070 167.87 93.95 0.94 54.50 18.03 4.332070-2100 165.97 95.20 0.95 53.00 18.21 4.37
A2
GFCM202010-2040 171.96 93.98 0.94 56.85 18.23 4.382040-2070 172.77 94.10 0.94 57.49 18.37 4.412070-2100 167.91 91.30 0.91 54.47 17.70 4.25
GIER2010-2040 182.74 102.48 1.03 62.21 19.30 4.632040-2070 177.19 113.80 1.14 59.78 21.43 5.152070-2100 170.32 94.45 0.95 55.92 18.06 4.34
HADGEM2010-2040 170.43 93.94 0.94 55.91 17.99 4.322040-2070 168.56 95.11 0.95 54.78 18.27 4.392070-2100 168.56 94.33 0.94 54.76 18.14 4.36
MRCGCM2010-2040 167.65 92.16 0.92 54.36 17.82 4.282040-2070 166.53 94.71 0.95 53.77 18.23 4.382070-2100 163.40 89.33 0.89 51.67 17.13 4.11
782783
784
785
786
787
788
789
790
Table 6. Maximum and minimum results of the hydrologic and sedimentary simulations for the different climate scenarios and soil use configurations
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Avg (Hm3)
Q max (m3/s 24h)
Vol. Acum (Hm3)
Yr. Ero-sion (t/Ha)Scenario Uses Max/
min
B1
U1Max 172.86 104.71 1.05 49.81 16.14 3.88Min 159.33 81.08 0.81 42.75 12.98 3.12
U2Max 143.49 80.35 0.80 23.32 8.59 2.06Min 130.87 70.70 0.71 18.78 8.59 2.06
U3Max 170.77 99.24 0.99 39.82 11.04 2.65Min 155.57 78.47 0.79 25.47 8.63 2.07
U4Max 170.80 97.14 0.97 33.84 8.51 2.04Min 151.38 77.58 0.78 17.58 6.16 1.48
U5Max 170.82 95.64 0.96 30.38 7.25 1.74Min 148.01 76.93 0.77 13.64 4.97 1.19
A1B
U1Max 165.58 121.45 1.21 46.18 20.45 4.91Min 162.25 83.61 0.84 43.50 13.26 3.19
U2Max 135.53 102.17 1.02 20.72 11.01 2.64Min 130.12 73.01 0.73 17.97 7.58 1.82
U3Max 163.57 116.07 1.16 37.02 12.72 3.05Min 148.38 80.11 0.80 19.22 8.11 1.95
U4Max 163.60 113.96 1.14 31.48 9.33 2.24Min 140.93 78.87 0.79 9.62 5.68 1.36
U5Max 163.62 112.47 1.13 28.28 7.82 1.88Min 135.80 82.19 0.82 7.70 4.61 1.11
A2
U1Max 175.11 107.35 1.07 50.87 16.38 3.93Min 160.28 85.16 0.85 41.49 13.26 3.18
U2Max 146.94 80.84 0.81 24.63 8.71 2.09Min 128.24 71.60 0.72 19.11 7.47 1.79
U3Max 172.98 101.90 1.02 40.62 11.32 2.72Min 146.17 80.64 0.81 19.10 7.77 1.87
U4Max 173.01 100.01 1.00 34.51 8.73 2.10Min 152.84 78.73 0.79 11.61 5.41 1.30
U5Max 173.03 98.61 0.99 30.97 7.45 1.79Min 133.90 77.36 0.77 8.89 4.40 1.06
791792
793
794
Figures
Figure 1. (A) Location of the study area. (B) Topography of the basin and location of the meteorological stations. (C) Present day soil uses distribution and percentage.
795
796
797
798799
Figure 2. Distribution function SQRT-ET Max indicating the return period of maximum precipitations.
Figure 3. Land use configurations described in Table 2, implemented on the study area in the different time periods.
800
801802
803
804
805806
807
Figure 4. Percentage of soil uses in each configuration for each time period.
808
809
810
Figure 5. Accumulated results of the hydrologic and sedimentary volumes for the different climate scenarios and GCMs including the centennial peak flow.
811
812813
814
Figure 6. Accumulated results of the hydrologic and sedimentary volumes of the different climate scenarios and representative GCMs including the centennial peak flow considering different land use configurations.
815
816817818
819
Figure 7.Average yearly hydrologic output and average erosion as a function of percentage of agricultural use of soils and greenhouse gas emissions.
820
821822
823