Climate models
project a much more substantial warming than the 2
Global CO
In this framework, the future hydrological state needs to be assessed. The runoff production is the component of the hydrological cycle most representative to describe freshwater availability, as it expresses the amount of available water after the evapotranspiration and infiltration losses and before any stream formation process intervenes. Furthermore, ensembles of mean annual and seasonal runoff can provide information about the climate change impact on river flows (Döll and Schmied, 2012). Studies have shown that changes in runoff are not linearly correlated with changes in global mean temperature (Arnell and Gosling, 2013), nor are meteorological droughts with hydrological droughts (van Huijgevoort et al., 2013), concluding that for climate change impact assessments it is fundamental to use an impact model to translate the precipitation derived signal into runoff.
A substantial number of large-scale climate change impact studies that have
been performed recently examine the future hydrological state analysing
projections of runoff or river flow. Fung et al. (2011) compared the
projected future water availability under
Currently, global mean temperature has increased 0.85
Significant climate change induced alterations are projected for the flow
regime in Europe, with the most pronounced changes in magnitude projected for
the Mediterranean region and the northern part of the continent (Schneider et
al., 2013). Moreover, considering that southern Europe is identified as a
possible hotspot where the fraction of land under drought will increase
substantially (Prudhomme et al., 2014), along with global temperature rise
exceeding
GCM outputs, used as input in impact models to assess the effects of climate change, feature systematic errors and biases. To deal with these, several bias correction techniques have been developed to statistically adjust the GCM output against observations. This process adds another level of uncertainty in the chain of climate to impact modelling that has to be investigated and communicated to the impact research communities. Ehret et al. (2012) acknowledge the fact that inherent climate models' biases render them unsuitable for direct use in climate change impact assessments, but express scepticism towards adopting bias correction as a standard undisputed procedure. They argue that bias adjustment hides rather than reduces the uncertainty, as the narrowing of the uncertainty range is not supported by any physical explanation. Teutschbein and Seibert (2012) also accept the need for bias correction but raise awareness towards the increased uncertainty derived from adding this step to the modelling chain. Ehret et al. (2012) introduce the issue of how “correct” is the data set used as a baseline for the bias adjustment. Haerter et al. (2011) underline the fact that the statistical adjustments applied to GCM data with bias correction are bounded to the timescale selected for the adjustment and might have adverse effects on the statistics of another timescale. Haerter et al. (2011) also accentuate that one significant assumption is made when present-day-based bias correction methods are applied to climate scenario simulations: that of the bias stationarity throughout the future decades. Teng et al. (2015) argue that errors in bias corrected precipitation are inherited and augmented in modelled runoff.
The major tools for the investigation of large-scale hydrological changes due to climate change are global hydrological models (GHMs) and/or land surface models (LSMs). According to the classification proposed by Haddeland et al. (2011), the models that solve the water balance are considered as GHMs and the models that solve both the water and energy balance are categorised as LSMs. The LSM JULES (Joint UK Land Environment Simulator; Best et al., 2011) has been implemented for many recent climate change impact and model inter-comparison studies (Hagemann et al., 2013; Davie et al., 2013; Dankers et al., 2014; Prudhomme et al., 2014; Harding et al., 2014).
The scope of this work is to assess future water availability and identify
drought conditions in the European region under high-end scenarios of climate
change. Transient hydrological simulations for the period 1971 to 2100 were
performed by forcing the JULES model with five Euro-CORDEX (Coordinated
Downscaling Experiment over Europe) climate projections. Water availability
is described by the output of runoff production. In our analysis the model
results are mainly interpreted statistically, aiming to express the changes
found in the projected future periods with respect to the historical baseline
state rather than describing future regimes with absolute numbers. The
research objectives set by this study are the following.
To identify changes posed on the hydrological cycle (mean state and lower
extremes) at To analyse the effect of bias correction on projected hydrological
simulations. To achieve this, both raw and bias corrected Euro-CORDEX data
were used as input forcing in the impact model. To assess the effect of the observational data set used for bias correction. To identify climate change induced changes in drought climatology
at the basin scale.
Hydrological simulations were performed with the JULES land surface model
driven by Euro-CORDEX climate scenarios. To warm up the model, 10 spin-up
cycles from 1955 to 1960 were run. A daily time step was employed for all the
model runs. JULES was set up at the spatial resolution of the forcing
Euro-CORDEX data, which was 0.44
Brief descriptions of the climate data and the impact model are included in the following sections.
Projections from five Euro-CORDEX experiments under the Representative
Concentration Pathway RCP8.5 scenario were used as input to JULES. The
climate models were selected so as to cover the range of model sensitivity,
as expressed by the index of equilibrium climate sensitivity (ECS) which
ranges from 2.1 to 4.7 K for the CMIP5 ensemble (Andrews et al., 2012). ECS
is a useful metric of the response of a climate model, in terms of air
temperature change, to a doubling of the atmospheric CO
Historical and projected time slices comprise 30 years of simulations, for
which one time slice average is extracted. The historical or baseline time
slice covers the period from 1976 to 2005. The projected time slice varies
between the models. The definition for determining the projected time slice
here is to take the 30-year average of the slice centered on the year where
the
Using the SWL concept constitutes the results independent of the time that the warming occurs. Although by definition of the SWL, the models reach the same level of warming in their time slices, the different model sensitivity reflects on the evolution of temperature in the time slice, as more sensitive models are expected to have higher rates of changes in the period before and after a specific SWL is achieved compared to the less sensitive models. Moreover, considering models of different ECS is important to express the range of variables other than temperature forcing produced by the GCMs (e.g. radiation).
Euro-CORDEX climate scenarios used to force JULES.
The five scenarios along with information on the time slices extracted for our analysis and the corresponding exceeded warming levels and ECS indices are shown in Table 1. Two widely used observational data sets were used to adjust the biases of the RCM precipitation and temperature data. The first data set was a hybrid data set created by the Inter-Sectoral Impact Model Integration and Intercomparison Project ISI-MIP (Warszawski et al., 2014) that consists of the WFD (Weedon et al., 2010) and WFDEI.GPCC (Weedon et al., 2014) data sets. Additionally, the station data-based European Climate Assessment & Dataset (ECA & D) and the ENSEMBLES Observations gridded data set (E-OBS v10; Haylock et al., 2008) were also used for the bias adjustment of the aforementioned climate variables.
In the present study the multi-segment bias correction (MSBC) method is used to correct the precipitation and temperature data for their biases. A detailed description of the method can be found in Grillakis et al. (2013). This bias correction methodology has the ability to better transfer the observed precipitation statistics to the raw GCM data. The method utilises multiple discrete segments on the cumulative density function (CDF) to fit multiple theoretical distributions, as opposed to the commonly used single transfer function at the entire CDF space. Pragmatically, the method eliminates to a large extent the bias in mean precipitation, while significantly reducing the bias of the higher quantile of the precipitation CDF associated with extreme precipitation events.
JULES is a physically based land surface model that was established in 2006. It is comprised of two parts: the Met Office Surface Exchange Scheme (MOSES; Cox et al., 1998) and the Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID; Cox, 2001) component. MOSES is an energy and water balance model which is JULES' forerunner, and TRIFFID is a dynamic global vegetation model (Best et al., 2011; Clark et al., 2011; Cox, 2001). In our model application for this study we do not examine vegetation dynamics thus we are focusing on the MOSES component of JULES.
The meteorological forcing data required for running JULES are downward shortwave and longwave radiation, precipitation rate, air temperature, wind speed, air pressure and specific humidity (Best et al., 2011).
JULES has a modular structure, which makes it a flexible modelling platform, as there is the potential for replacing modules or introducing new modules within the model. The physics modules that comprise JULES include the following themes: surface exchange of energy fluxes, snow cover, surface hydrology, soil moisture and temperature, plant physiology, soil carbon and dynamic vegetation (Best et al., 2011), with the latter being disabled for this application.
In JULES, each grid box is represented by a number of surface types, each one represented by a tile. JULES recognises nine surface types (Best et al., 2011), of which five are vegetation surface types (broadleaf trees, needleleaf trees, C3 (temperate) grasses, C4 (tropical) grasses and shrubs) and four are non-vegetated surface types (urban, inland water, bare soil and ice). A full energy balance equation including constituents of radiation, sensible heat, latent heat, canopy heat and ground surface heat fluxes is calculated separately for each tile and the average energy balance for the grid box is found by weighting the values from each tile (Pryor et al., 2012).
In JULES the default soil configuration consists of four soil layers of thicknesses 0.1, 0.25, 0.65 and 2.0 m. This configuration however can be altered by the user. The fluxes of soil moisture between each soil layer are described by Darcy's law and a form of Richards' equation (Richards, 1931) governs the soil hydrology. Runoff production is governed by two processes: infiltration excess surface runoff and drainage through the bottom of the soil column, a process calculated as a Darcian flux assuming zero gradient of matric potential (Best et al., 2011). There is also the option of representing soil moisture heterogeneity. In that case total surface runoff also includes saturation excess runoff. The model allows for two approaches to introduce sub-grid-scale heterogeneity into the soil moisture: (1) use of TOPMODEL (Beven and Kirkby, 1979), where heterogeneity is taken into account throughout the soil column, or (2) use of PDM (Moore, 1985), which represents heterogeneity in the top soil layer only (Best et al., 2011). Calculation of potential evaporation follows the Penman–Monteith approach (Penman, 1948). Water held at the plant canopy evaporates at the potential rate while restrictions of canopy resistance and soil moisture are applied for the simulation of evaporation from soil and plant transpiration from potential evaporation.
JULES simulates fluxes in the vertical direction only. For hydrological applications this means that the model calculates runoff production in each grid box which needs to be routed to estimate streamflow. The standard version of the JULES model until very recently (February 2015) did not account for a routing mechanism. To overcome this model limitation, we use a conceptual lumped routing approach based on triangular filtering in order to delay runoff response. This is applied after discriminating the grid boxes that contribute to runoff production of a specific basin from the gridded model output. Determination of grid boxes upstream of the gauging station location is implemented using the TRIP river routing scheme (Oki and Sud, 1998).
JULES has been used in many recent studies as a tool for evaluating the exchange of water, energy and carbon fluxes between the land surface and the atmosphere. Van den Hoof et al. (2013) assessed JULES' performance in simulating evaporative flux (and its partitions) and carbon flux in temperate Europe. Marthews et al. (2012) implemented JULES in tropical forests of the Andes–Amazon to simulate all components of carbon balance and study possible flux variations between sites of different altitudes. Zulkafli et al. (2013) implemented JULES in a humid tropical mountain basin of the Peruvian Andes–Amazon. MacKellar et al. (2013) evaluated JULES, implemented in a region of southern Africa, concerning its ability to simulate the catchment streamflow. In the study of Bakopoulou et al. (2012), the sensitivity of the JULES outputs to the soil parameters of the model at a point scale was estimated. Dadson et al. (2010) sought to quantify the feedback between wetland inundation and heat and moisture fluxes in the Niger inland delta by adding an overbank flow parameterization into JULES. Burke et al. (2013) used JULES to simulate retrospectively the pan-Arctic changes in permafrost and Dankers et al. (2011) assessed JULES' performance in simulating the distribution of surface permafrost in large-scale catchments. In a study by Jiménez et al. (2013) soil moisture modelled with JULES is evaluated against satellite soil moisture observations.
Other studies give insight into the hydrological performance of JULES specifically. Blyth et al. (2011) extensively evaluated the JULES model for its ability to capture observed fluxes of water and carbon. Concerning discharge, their findings suggest that for the European region seasonality is captured well by the model. For temperate regions (like most of central Europe) the model exhibited a tendency towards underestimating river flows due to overestimation of evapotranspiration. Prudhomme et al. (2011) assessed JULES' ability in simulating past hydrological events over Europe. In general terms the model was found to capture the timing of major drought events and periods with no large-scale droughts present were also well reproduced. The model showed a positive drought duration bias, more profoundly present in north-western Spain and eastern Germany–Czech Republic. Prudhomme et al. (2011) argue that this feature is related to overestimation of evaporation by the model. For regions where droughts tend to last longer, JULES exhibited a better ability to reproduce the drought events' characteristics. Gudmundsson et al. (2012a) compared nine large-scale hydrological models, and their ensemble mean, based on their skill in simulating the interannual variability of observed runoff percentiles in Europe. According to the overall performance (accounting for all examined percentiles and evaluation metrics), JULES was ranked third best out of the 10 models, after the multi-model ensemble mean and the GWAVA model. For low and moderately low flows, expressed as the 5th and 25th percentiles respectively, JULES is also in the top three models regarding the representation of interannual variability in runoff. In the study of Gudmundsson et al. (2012b), where an ensemble of hydrological models is evaluated for their ability to capture seasonal runoff climatology in three different hydroclimatic regime classes in Europe, JULES exhibits a good performance, comparable to that of the best performing multi-model ensemble mean. In other studies employing multi-model ensembles, focusing on the whole European region (Gudmundsson and Seneviratne, 2015) or a single basin in Europe (Harding et al., 2014; Weedon et al., 2015), JULES' simulations also correspond to these of the other models.
For the assessment of the impact of the
Average runoff production is a good and widely used indicator of mean hydrological state of a region. The 10th percentile runoff is considered as a representative indicator of the low flow regime (Prudhomme et al., 2011). Consistent low flows (relative to the mean state) are connected with the formation of hydrological drought conditions. Thus the assessment of the changes in low flows could reveal trends towards more intense or/and often extreme lows in the future hydrological cycle. The impact of high-end climate scenarios on average and 10th percentile runoff is presented both as gridded results at the pan-European scale and aggregated at the basin scale for five major European river basins.
European study domain, tested basins as defined by the model's
0.5
The two hydrological indicators were deduced from monthly runoff data. For
the analysis of the gridded results at pan-European scale with the SWL
time-slice approach, each indicator was computed from the monthly values of
all years in the time slice. For the analysis of basin aggregated runoff
regimes, the two hydrologic indicators were calculated per year, for all the
years of the simulation. This resulted in time series of basin aggregated
average and 10th percentile runoff production, spanning from 1971 to 2100.
The trend of the annual time series was investigated employing a linear
regression analysis to estimate the sign and the average rate of the trend.
The significance of the trend was tested at the 95 % confidence interval
via a Student
The Europe study domain along with information on the catchments tested and their corresponding gauging stations are shown in Fig. 1.
Another aspect of our low flow analysis is to assess changes in drought
climatology, i.e. the number of days per year that particular lows in flow
occur. This is here done at the basin scale, following the threshold level
method to identify days of discharge deficiencies. The threshold level method
is a widely used tool for drought identification applications (Fleig et al.,
2006; Vrochidou et al., 2013). According to this method, drought conditions
are characterised as the periods during which discharge falls below a
pre-defined threshold level. In our application, the threshold is varying
daily and is established as in Prudhomme et al. (2011): for each Julian
day
Average runoff production from raw Euro-CORDEX data for all
dynamical downscaled GCMs and their ensemble mean. Runoff production averaged
over the baseline period (1976–2005) (left column panels), absolute change
in runoff in the
Figure 2 shows the average runoff production estimated by JULES forced with
the five participating dynamical downscaled GCMs, for each model separately
and for the ensemble mean. Measures of model agreement (coefficient of
variation between the ensemble members and model agreement on a wetter change
in the projected time slice) are also shown in Fig. 2. The change in runoff
in the
For the projected time slice, all models agree in a general pattern of increased runoff production in northern Europe and a small part in central Europe and decreased runoff production in Spain, Greece and parts of Italy. Especially for the negative trends shown in southern Europe it is important that, though small in absolute terms, they increase in magnitude when expressed as a percentage, meaning that small negative changes can pose severe stress in regions where water availability is already an issue.
Concerning the ensemble mean, smoothing of the projected changes due to
averaging has revealed clear patterns of change, which however have to be
interpreted considering the full spread of the GCM-forced outcomes and the
agreement between them in order to avoid misguided conclusions. Less extreme
values are encountered in the ensemble mean of projected changes in runoff,
compared to the change projected by each ensemble member individually (Fig. 2). Especially for
percent change a clear trend of runoff increase is revealed in northern
Europe and decrease in southern Europe, with a mixed pattern for central
Europe. Four or five out of the five ensemble members agree on the wetter
response in the northern regions and the drier response in the southern part
of Europe. The smaller cv value (cv
Figure 3 has the same features as Fig. 2 but concerns the 10th percentile
runoff production instead of the average. The 10th percentile limit is used
to describe low flows that are related to the creation of hydrological
drought conditions. For 10th percentile runoff, model agreement in the
baseline period is notably reduced compared to agreement for average runoff,
with the coefficient of variation for most regions exceeding 0.5, while it
exceeds the unity for a large part of Europe. For the
10th percentile of runoff production from raw Euro-CORDEX data for
all dynamical downscaled GCMs and their ensemble mean. 10th percentile runoff
production derived on an annual basis and averaged over the baseline period
(1976–2005), absolute change in 10th percentile runoff in the
Regarding the ensemble mean changes, percent change in 10th percentile runoff
(Fig. 3) shows more significant reductions (up to 100 %) compared to
average runoff (for which changes range between
The ensemble mean of average runoff derived from the five participating downscaled GCMs whose temperature and precipitation were bias adjusted according to the WFDEI data set is presented in Fig. 4. Bias adjustment of the forcing data resulted in a drier ensemble mean runoff for the baseline period for 70.40 % of the pan-European land surface, in comparison to the 26.01 % of the land area that had a wetter response after bias adjustment. The remaining 3.59 % of the European area had changes that were classified as insignificant (see Supplement for details). Projected changes from bias adjusted data exhibit very similar patterns and magnitudes with the raw data derived changes. For some regions in central Europe, where a small negative change is reported by the raw data run, a sign change of the projected difference is documented after bias correction. Lastly, bias correction has a strong positive effect on model agreement as it can be documented from the low values of the coefficient of determination all over Europe, with the exception of the Scandinavian Peninsula, where model disagreement appears increased after bias correction.
In Fig. 5, the effect of bias correction on the representation of the 10th percentile runoff is shown. Some hotspots of pronounced negative changes in western Europe have been eliminated and replaced with milder projected absolute changes. There are areas where sign change is observed (central and central–western Europe); however, it is difficult to interpret this result and correlate it with bias correction as these are also the areas where models show the lowest agreement (coefficient of variation exceeding 1 and agreement towards wetter change 40–60 %). Although the coefficient of variation for the baseline period is considerably reduced compared to the raw data runs, there are still areas of high model uncertainty in the representation of lower flows.
In Fig. 6, annual time series of basin averaged runoff production (average and 10th percentile) for five European basins are shown. These cover the whole length of historical and projected years simulated (1971–2100) in an attempt to identify general trends in average and low runoff, calculating 10-year moving averages from the ensemble mean. Results in Fig. 6 include both raw and bias adjusted output; thus, an assessment of the effect of the bias correction on the basin-scale hydrology can be made. A common observation for all the basins is that runoff decreases considerably for bias adjusted input forcing.
Ensemble mean of average runoff production from Euro-CORDEX data
bias adjusted against the WFDEI data set. Top row panels: runoff production
averaged over the baseline period (1976–2005) (top row panels), absolute
(middle row panels) and percent change (bottom row panels) in ensemble mean
runoff in the
Ensemble mean of 10th percentile runoff production from Euro-CORDEX
data bias adjusted against the WFDEI data set. Top row panels:
10th percentile runoff production derived on an annual basis averaged over
the baseline period (1976–2005) (top row panels), absolute (middle row
panels) and percent change (bottom row panels) in ensemble mean runoff in the
Annual time series of basin averaged runoff production (average and 10th percentile of annual runoff) for raw and bias adjusted Euro-CORDEX data. For both average and 10th percentile time series, the ensemble range, mean and 10-year moving average is shown.
For the Danube and Guadiana, statistically important negative trends are
identified for average runoff (
Basin-scale average annual runoff production for raw and bias adjusted
Euro-CORDEX data as well as the
Figure 7 shows the results of the drought threshold level method analysis for
the five study basins, for raw and bias corrected output. For each year, the
number of days under the historical drought threshold has been counted. This
allows a comparison of the tendency towards the formation of drought
conditions between the historical period and the projected period. As this is
a statistically oriented interpretation of our data, we can see that the
differences between raw and bias corrected time series are very small,
especially compared to the difference in the magnitude of their absolute
values. For the Danube, Rhine and Guadiana, strong rising trends (all
statistically significant) were identified in the time series of ensemble
mean of days under threshold per year. Before bias correction these
were 0.43, 0.37 and 0.52 day yr
Figure 8 shows the basin average runoff production
for raw and bias corrected Euro-CORDEX data with respect to the
corresponding SWL in degrees Celsius. This analysis considers the runoff
values corresponding to the
Comparing the annual average runoff production for raw and bias corrected input forcing, it is clear that bias corrected output exhibits a considerably reduced range, which translates into increased model agreement for the basins of the Danube, Rhine, Elbe and Guadiana. In Kemijoki basin the bias adjusted output has a greater range than the raw output. Concerning the range of the low flows, an increase in model agreement for the bias corrected forcing is observed for all basins.
Examining the changes in annual average runoff, a slight decreasing trend can
be identified for the Danube and a slight increasing trend for the Elbe,
while for the Rhine there is no clear trend present. In contrast, the
Guadiana and Kemijoki exhibit strong decreasing and increasing trends
respectively. The falling trend in the Guadiana is marginally intensified
between
According to the results in Fig. 8, the 10th percentile runoff in the Danube
and Rhine decreases as SWLs increase, while the opposite trend is observed
for the low flows in Kemijoki. For the Elbe the raw results show an intense
decreasing trend up to
Figure 9 illustrates the correlation between the
percent projected change in annual average and 10th percentile runoff
production from bias corrected and raw forcing, for the
Concerning the effect of bias adjustment, it can be observed that regardless
of the significant differences in magnitude between runoff from raw and bias
corrected data discussed before, the projected change in average flow by the
two forcings almost coincides for the
Comparing the difference on percent projected change in average annual runoff
from
Basin's annual average runoff production for raw and bias adjusted Euro-CORDEX data.
Number of days under drought threshold per year for raw and bias adjusted Euro-CORDEX data. Ensemble mean and 10-year moving average of the ensemble mean (top panel) and ensemble range (bottom panel).
Basin's 10th percentile of runoff production, derived on an annual basis, for raw and bias adjusted Euro-CORDEX data.
The aspect of the impact posed by the observational data set used for bias correction to the results of the hydrological simulations is introduced in this part of our analysis. Additional model runs performed with bias adjusted Euro-CORDEX precipitation and temperature, corrected against the E-OBS (instead of the WFDEI) data set, participate in a comprehensive comparison between all the outputs used in this study. The results are illustrated in Fig. 10. Three different sets of outputs are compared: one driven by raw downscaled and two driven by Euro-CORDEX data bias corrected against two different data sets. The comparison considers both the mean and range of the ensembles and results are presented as basin aggregates. The first part of the comparison concerns the long-term annual average for the period 1976 to 2005 (Fig. 10, top row panels) and apart from the model results includes values corresponding to observations, derived from GRDC discharge measurements. Observations can serve as a baseline for this comparison, allowing us to evaluate which configuration can better simulate “true” water budget numbers and the effect of bias correction with respect to this baseline.
For all basins the raw data result in overestimates of runoff production, which is though significantly reduced after bias correction. E-OBS corrected data however produce values lower than the observations (with the exception of the Guadiana), while the WFDEI-corrected data produce the best simulation in terms of approximating the observed values. From Figs. S1 and S2 of the Supplement (showing the effect of bias correction on the forcing variables of precipitation and temperature) it can be deduced that E-OBS corrected precipitation has lower values than precipitation adjusted against the WFDEI data set. This explains the lower runoff produced by the E-OBS bias adjusted data set, as it is reasonable for the differences in precipitation to reflect on the output of the hydrological model. As has already been revealed in previous stages of this analysis, the positive impact that bias adjustment has on the increase of model agreement is again clear. The only exception is Kemijoki basin due to its high latitude position (the coefficient of variation was increased after bias correction for the high latitude areas).
Changes in annual average runoff production at the
Variation of runoff production with respect to temperature change
(
From the application of the same analysis to 10th percentile runoff production (Fig. S6), it is deduced that for the low flows the E-OBS corrected data again produce lower values of runoff compared to WFDEI. In this case, however, even the raw forced output (which is wetter than the bias corrected) underestimates the observed 10th percentile runoff values. Regarding the percent projected changes, results from bias corrected data produce smaller values compared to the raw data, while E-OBS adjusted data result in decreased changes compared to output from WFDEI adjusted forcing.
In our analysis we investigated the effects of climate change on the European
hydrological resources, extracting time periods that correspond to an
increase of 4
Correlation between projected change in basin averaged runoff
production derived from WFDEI-bias adjusted and raw Euro-CORDEX data, for
both annual average (left panels) and 10th percentile (right panels) runoff
production. Correlation is examined at
In our study only one impact model (JULES) was used. Hagemann et al. (2013) argue that impact model induced uncertainty in future hydrological simulations is larger than that of the GCMS for some regions of the land surface and suggest using multi-impact model ensembles to deal with this issue. However useful conclusions can be drawn also from studies employing a single GHM/LSM. Examples of such single model climate change impact assessments performed recently are the studies of Schneider et al. (2013) and Laizé et al. (2013) with the WaterGAP GHM, the studies of Arnell and Gosling (2013), Gosling and Arnell (2013) and Arnell et al. (2013) with the GHM MacPDM and of Hanasaki et al. (2010) using the H08 LSM.
Comparison between the simulations of raw Euro-CORDEX data and bias
adjusted against two different data sets (WFDEI and E-OBS) for five study
basins. Bars show the ensemble means and error bars the minimum and maximum
ensemble member values. Top row panels: annual average runoff production for
the period 1976 to 2005. OBS values are derived from GRDC discharge
measurements converted to basin averages at the annual timescale. Middle row
panels: percent change in annual average runoff production at the
The findings of the study regarding the climate changed induced alterations
of the mean hydrological state in Europe show decreasing trends for southern
Europe, including the Mediterranean region, and strong increasing trends for
northern and north-eastern Europe. These follow the same patterns as
identified by previous studies. Schneider et al. (2013)
found that the most pronounced changes in the magnitude of European river
flows are projected for the Mediterranean region and the northern part of
the continent. Hagemann et al. (2013)
reported positive changes in projected runoff for the high latitudes and
negative changes for southern Europe. For central Europe the projected
changes are smaller (mostly in the range of
The projected relative changes found for 10th percentile runoff are far more pronounced than the changes in average, even for the regions where changes in average-state annual runoff were negligible. This finding implies that seasonality in runoff is likely to intensify under climate change and is in accordance with the results of Fung et al. (2011) and Van Vliet et al. (2013) who also reported pronounced seasonality in their projected simulations. This may translate to increased dry spells and thus elevated drought risks in the future. Under the light of these findings (mean-state runoff changing slightly and low-state changing significantly), more extreme hydrological droughts are expected in the future. It should be noted however that projections of low flow bear higher uncertainty compared to average-state, as indicated by the higher values of the coefficient of variation. Similar results of increased model spread expressed as cv for low flows compared to average state flows were found by Koirala et al. (2014).
Specifically for the Guadiana River, the close to zero values of
10th percentile runoff encountered even in the historical period indicate
that the river exhibits an intermittent flow regime. This is relevant for
this particular river, as it is located in a semi-arid region and
intermittent flows typically characterise its hydrological regime
(Collares-Pereira et al., 2000; Filipe et al., 2002; Pires et al., 1999).
Given the changes that are projected for the Iberian Peninsula at
Concerning the effects of a
Our analysis of drought climatology at the basin scale was based on the total number of days under a predefined daily varying drought threshold. We did not employ any buffering criterion for the days under threshold to be accounted for in the total sum (as discussed for example by Sung and Chung, 2014, and Tallaksen et al., 1997). The use of such a criterion would have decreased the calculated dry days. However, as the interpretation of the results of this study is mostly oriented towards identifying trends of change rather than absolute numbers describing the future regime, the lack of a buffering criterion is not supposed to notably affect the extracted conclusions. Wanders et al. (2015) employed a transient variable threshold for the assessment of the drought conditions under climate change, considering a gradual adaptation of the ecosystem on the altered hydrological regime. This is an interesting alternative, especially for climate change mitigation and adaptation studies. In our study we aimed to identify global warming induced changes in the future hydrological state without considering adaptation; thus, the same historically derived threshold was applied to the whole length of the simulated runoff time series.
From the analysis performed on drought climatology, an increased number of days per year under the historically defined drought threshold is found for the basins of the Danube, Rhine and Guadiana. Our results correspond with the findings of previous studies about drought regime under climate change. Giuntoli et al. (2015), investigating future high and low flow regimes at the global scale, using multiple impact models and climate scenarios, found increased number of low flow days in southern Europe. In the study of Wanders and Van Lanen (2015) the impact of climate change on the hydrological drought regime of different climate regions was assessed, using a conceptual hydrological model forced with three GCMs. The study findings describe a decrease in the frequency of drought events in the future, which however does not point towards drought alleviation. In contrast, it relates to increased drought event duration and deficit volume. These effects are more pronounced for the arid climates that already face problems of water availability.
As proposed by Ehret et al. (2012), both raw and bias corrected data driven simulations are presented in our study, in order to comprehensively assess the effect of bias correction on our results. In four of the five study basins, raw data driven simulated runoff overestimates the corresponding observed values. After bias correction, the modelled results represent more accurately the past hydrological regime. Similar improvements in the bias corrected output have been reported by Hagemann et al. (2011), Muerth et al. (2013) and Harding et al. (2014).
For some regions, the sign of the projected change in runoff shifted after bias correction. This finding was also encountered in the study of Hagemann et al. (2011). Hagemann et al. (2011) underline that these changes in the climate signal reveal another uncertainty aspect of the GCM to GHM modelling procedure, that is inherent to the GCM but becomes apparent after the bias adjustment of the climate model output. Teng et al. (2015) argue that signal changes are produced by bias correction errors in higher percentiles' precipitation, thus adding another factor to the uncertainty of the runoff projections.
Although the absolute values of raw and bias corrected simulations differ significantly, this does not apply to the projected relative changes. Liu et al. (2014) also found that raw and bias corrected data resulted in similar estimations of relative changes for a series of variables, including ET and runoff. The study of Muerth et al. (2013) investigates the effect of bias adjustment on hydrological simulations and their climate change induced alterations. Concerning the relative changes between baseline and future time slices, it is reported that bias correction does not influence notably the hydrologic indicators, apart from the one describing flow seasonality.
Chen et al. (2011) identify three uncertainty components in bias correction applications: the uncertainty of the different GCMs, the variable emission scenarios and that of the decade used for bias adjustment. A comparison of the latter uncertainty source with the two former concluded that the choice of correction decade has the smallest contribution to total uncertainty. In this paper we address another uncertainty source: that of the data set used for correction. It was found that the WFDEI bias corrected simulation captured better the past hydrological regime compared to the E-OBS bias corrected configuration. The differences between the two simulations abate when results are expressed as percent change but their variations are still of the same magnitude as that between raw and bias corrected data. This implies that the selection of the observational data set used for bias correction is not a trivial step of the modelling procedure, and it should be treated as an extra factor that causes the uncertainty window of the projected hydrologic conditions to further open.
In this paper, the future mean- and low-hydrological states under
The concluding remarks of this study are summarised below.
Projections show an intensification of the water cycle at
Drought climatology is projected to change to more dry days per year for the Danube, Rhine and Guadiana basins. Thus these areas are projected to experience more usual and more intense drought events in the future.
For the areas where clear decreasing or increasing runoff trends are
identified in the projections, these changes are considerably intensified
when moving from the
Bias correction results in an improved representation of the historical hydrological conditions. However, raw and bias corrected simulations exhibit minor variations for results of statistical interpretation (in our study: percent change, number of days under drought threshold).
The data set used for bias correction can affect the quality of the projections in absolute terms to a great extent. The comparison performed here showed that the WFDEI-corrected data set produces simulations that capture better the past observed hydrologic state compared to the E-OBS-corrected data set, and should thus be preferred for bias correction applications over Europe. The selection of the “correct” data set is an added uncertainty to the climate impact modelling chain, with magnitude similar to that of the bias correction procedure itself.
The research leading to these results has received funding from the HELIX
project of the European Union's Seventh Framework Programme for research,
technological development and demonstration under grant agreement no. 603864.
We acknowledge the World Climate Research Programme's Working Group on
Regional Climate, and the Working Group on Coupled Modelling, former
coordinating body of CORDEX and responsible panel for CMIP5. We also thank
the climate modelling groups (listed in Table 1 of this paper) for producing
and making available their model output. We also acknowledge the Earth System
Grid Federation infrastructure, an international effort led by the
US Department of Energy's Program for Climate Model Diagnosis and
Intercomparison, the European Network for Earth System Modelling and other
partners in the Global Organisation for Earth System Science
Portals (GO-ESSP). Finally, we acknowledge the E-OBS data set from the
ENSEMBLES EU-FP6 project (