Introduction
Hydrological drought is a slowly developing natural phenomenon than can occur
anywhere, independently of the hydro-climatic regime . It
is expressed as a deficiency in river discharge compared to the expected
normal and is mainly caused by lower-than-average precipitation and soil
moisture or strong increases in evapotranspiration. In addition to natural
causes, human water use and reservoirs can significantly alter the drought
signal in many places . Droughts are rare events and
can propagate from meteorological to soil moisture to hydrological
droughts, finally resulting in socio-economic drought (Van Loon, 2015).
Hydrological droughts affect the environment and cause damage to society and
the economy. showed reduced potential for
thermoelectric power and hydropower generation under hydrological drought
worldwide. In Europe, the 2003 drought and heat wave resulted in a change of nearly
-6.6 % in hydropower power generation and -4.7 % in thermoelectric. The total loss
of the 2003 severe drought event was estimated to be EUR 8.7 billion in
central and southern Europe . More recently, the 2015 drought
event in central Europe also
caused significant socio-economic and environmental problems. Economic losses
due to droughts almost doubled between the 1976–1990 and 1991–2006 periods to
approximately EUR 6.2 billion per year. Social and environmental costs are
often not considered . A collection of hydrological drought
impacts for Europe can be found in the European drought impact inventory
sorted by impact categories, e.g. freshwater aquaculture
and fisheries, energy and industry, waterborne transportation, public water
supply, and freshwater ecosystems. Furthermore, water quality is directly
influenced by hydrological drought, e.g. in lowering the availability of the
diluting medium water resulting in increasing pollutant concentrations.
Climate change is expected to alter the hydrological cycle throughout Europe.
Temperature projections show significant warming for all emission scenarios
over Europe. Southern Europe is a warming hotspot with the greatest projected warming
in summer, whereas northern Europe shows the greatest projected warming in winter time .
projected mean annual precipitation to decrease under
RCP4.5 mainly in the Iberian Peninsula and Greece through the end of the
century. It is expected that large areas, from the UK to France and Italy to
the Balkans, will experience almost no annual precipitation changes,
whereas central Europe and northern Europe face precipitation increases.
Under RCP8.5, the signal intensifies with an increase in large parts of
central and northern Europe of up to approximately 25 % and a decrease
in southern Europe. Meteorological droughts are projected to occur more
frequently in the Mediterranean and to become less frequent in Scandinavia,
with an intensification of the signal with increased warming levels
.
In the Paris Agreement of 2015, the Conference of the Parties of the United
Nations Framework Convention on Climate Change emphasized “holding the
increase in the global average temperature to well below 2 ∘C above
pre-industrial levels and pursuing efforts to limit the temperature increase
to 1.5 ∘C above pre-industrial levels” and invited
the Intergovernmental Panel on Climate Change (IPCC) to prepare a special
report on the impacts of global warming of 1.5 ∘C in 2018. Notably,
based on the estimated emissions over the past decades, it remains unclear if
a limitation of global warming to 2 or 3 ∘C can be
achieved . Most climate impact studies in the past have
focused on future time periods, e.g. changes through 2071–2100 under different
emission scenarios or representative concentration pathways (RCPs).
argue that these studies are hardly usable for
determining differences between warming levels, partly because of the large
internal range of warming within the RCPs. reported
the likely range of global warming for 2081–2100 relative to 1986–2005 for
the CMIP5 models with 0.3 to 1.7 K under RCP2.6, 1.4 to 3.1 K under
RCP6.0, and 2.6 to 4.8 K under RCP8.5.
In recent literature, several
studies have investigated climate impacts on low flows and hydrological
droughts in Europe, focusing on differences between historical and future
time periods e.g.. Recent assessment studies have changed their focus more
towards analysing warming levels, covering the 2 ∘C goal
, comparing impacts between different levels of warming in
selected river basins , or focusing on runoff rather than
streamflow . This study investigates projected changes in
low streamflow, defined as Q90, representing daily streamflow exceeding 90 %
of the time, which has the potential to impact hydrological drought.
Hydrological drought is associated with shortfalls of surface or subsurface
water availability which can occur in low streamflow, groundwater, or
reservoir levels. Changes in low flows analysed in this study can, but not
always, result in drought. Exceptions include riverine-based
transport, where streamflow values below a threshold level are defined
as hydrological drought.
Whilst the climate and hydrological models in available studies and the formulation of low-flow indices vary
significantly, similar patterns could be found. Decreasing river low flows are projected for southern
Europe and increasing low flows for northern Europe. Nevertheless, there are
limited studies on changes in low-flow conditions across Europe
using an ensemble of general circulation model (GCM) and hydrological model (HM) simulations at high spatial resolution and for
different warming levels. We fill this gap by analysing the changes in low-flow conditions based on a large ensemble of hydrological simulations
conducted at high spatial resolution (5 km) over Europe for different
warming levels.
Specifically, we provide a comprehensive impact and uncertainty assessment
for hydrological low flows across Europe under global warming of 1.5, 2,
and 3 K. The study is based on a multi-member ensemble of high-resolution
simulations (5 km × 5 km) from the EDgE project
(http://edge.climate.copernicus.eu; End-to-end Demonstrator for improved
decision making in the water sector in Europe) which has been enlarged to 45
ensemble simulations consisting of three HMs driven by
five GCMs under three RCPs. A consistent set-up
is achieved using identical meteorological input and land surface data to
establish the three HMs. To investigate the usability of the simulation
results, information on the robustness and uncertainty of projected changes
as well as GCMs and HMs contributions to the overall uncertainty are
discussed. The research questions aim to close a knowledge gap with respect
to impacts of different levels of climate warming as follows:
What is the magnitude and robustness of change in low flows in Europe under global warming of 1.5, 2, and 3 K?
Is there a significant difference in projected changes of low flows between the three global warming levels?
How much do the GCMs and HMs contribute to the overall uncertainty for the particular warming levels?
Material and methods
The study presented here uses a consistent set of 45 high-resolution
hydrological simulations based on five GCMs under three RCPs driving three
HMs across Europe at a 5 km spatial resolution. The aim is to provide a
consistent framework using a compatible set of standardized forcings and
initial conditions for the impact models to investigate low-flow changes
under different levels of warming. This multi-model ensemble has recently
being used by to analyse projected changes in river floods and high flows in
Europe.
Climate and hydrologic models
Five CMIP5 general circulation models (GCMs; HadGEM2-ES, IPSL-CM5A-LR,
MIROC-ESM-CHEM, GFDL-ESM2, and NorESM1-M) provided temperature and
precipitation data to drive three hydrological models (HMs). Data for the
time period 1950 to 2099 at a daily time step is available under three
representative concentration pathways (RCPs; 2.6, 6.0, and 8.5) from the
ISI-MIP project data available under
10.5880/PIK.2016.001. A trend-preserving bias correction
is applied to GCM data by . GCM data at a horizontal
resolution of 0.5∘ is hardly applicable to describe land surface
processes on catchment scales in Europe. Therefore, this data has been
disaggregated to 5 km × 5 km using external drift kriging (EDK) and
the elevation as external drift within the EDgE project. This interpolation
technique accounts for altitude effects in temperature and precipitation and
is widely applied in hydrological simulations . EDK adds
sub-grid variability to the GCM fields, reflecting, for example, the altitude
dependency of temperature. Methods such as EDK generally perform better in
interpolating continuous meteorological variables compared to discontinuous
variables such as precipitation. It is worth noting that the long-term trends
are preserved using this interpolation technique. The variogram for EDK is
estimated using the original E-OBS station data.
This meteorological data set at a spatial resolution of 5 km × 5 km
is then used to force the three HMs: mHM, Noah-MP, and PCR-GLOBWB. Within the
EDgE project, the HMs have been consistently set-up using the same land
surface data sets (terrain, land cover, soil maps, and geological information).
Furthermore, a consistent external river-flow routing scheme has been applied
to outputs of all HMs based on the multiscale Routing Model (mRM) that has been
developed originally for mHM . Ultimately, the
differences in the hydrological simulations result from different process
representations and parameterizations of the surface and subsurface in the
HMs.
The HMs used in this study are grid-based distributed models grounded on
numerical approximations of dominant hydrologic processes. The mesoscale
Hydrological Model mHM; was originally developed in
central Europe and it uses the multiscale parameterization technique (MPR;
) that allows model applicability at
different spatial resolutions (1 km × 1 km to 50 km × 50 km)
and multiple locations without much calibration effort. The Noah-MP
model was originally developed as the land surface component of the 5th
generation mesoscale model MM5 to enable climate predictions with physically
based ensembles and represents both the terrestrial water and energy cycle
. The PCRaster global water balance model (PCR-GLOBWB) was
developed to represent the terrestrial water cycle with a special focus on
groundwater and modelling water resources under water stress
.
The three HMs used in this study are calibrated in nine near-natural European
focus basins located in Spain, Norway, and the UK, which are selected based on consultation with user groups within the EDgE project. Besides these locations, we
also include three more central European catchments (located in France and Germany)
to represent diversity in hydro-climatic regimes. All HMs parameters are
calibrated such that the model simulations represent a range of hydrologic
regimes, rather than tailored to any specific characteristics. This is done
in a consistent manner so that the model simulations can be used for a range
of indicators (including high, low, and average flows) within the EDgE
project, resulting in slightly lower performance for low flows. We note that
HMs could be calibrated to specific parts of the flow duration curve (FDC);
however, this is not done in this study to avoid too specific tuning of the
model simulations to those unique conditions and thereby losing valuable
information on the entire FDC. In the current simulations human water
management was not taken into account, since some models lack the ability to
include these processes. Human water management can, however, have a
significant impact on the low-flow conditions, due to abstraction of
additional water in drought conditions or changes in reservoir management. As
a result constraining the model to any specific low-flow characteristic can
result in a biased simulation. Also, for a similar reason we may expect
relatively lower model skill in matching observed low-flow characteristics.
The HMs are calibrated using observation-based E-OBS data
V12.0; and automatic calibration schemes are employed
for mHM and PCR-GLOBWB. Noah-MP has been calibrated
manually adjusting the parameter for evaporation surface resistance based on
the analysis by .
Scatter plot between observed low flow and GCM/HM simulated low flow (Q90) over 357
gauges across Europe. Simulated values correspond to the median of the annual estimates calculated
for the historical time period 1966–1995. The colours of the dots denote the five GCMs used to drive
the hydrologic models mHM (left column), Noah-MP (middle column), and PCR-GLOBWB (right column). The
location of the basins and the spatial pattern of the relative bias is shown on the lower right.
Temperature and precipitation data from GCMs with coarse resolution have
different statistical properties than interpolated observational data sets. To
investigate if the observation-based calibration of the HMs is applicable to
the disaggregated GCM data, model outputs are evaluated against 357 gauging
stations using the GCM forcing during the historic period 1966–1995 (Fig. ).
The stations and time period are selected to ensure
the largest possible complete data set over 30 years. Their median basin area
is 1680 km2. The analysis focuses on matching the median of the 30-year
annual percentile for low flows (Q90). The indicator for low flows is used
herein for the impact assessment studies as detailed described in Sect. 2.3.
The evaluation results show overall an overestimation of observed Q90 by all
HMs and GCMs (Fig. , lower left). This overestimation
in the ensemble average is mainly the result of the overestimation by the HMs
PCR-GLOBWB and Noah-MP simulations, while the mHM runs show only a slight
overestimation and result in closest correspondence to the observed values.
Nevertheless, it cannot be concluded that mHM performs best as it neglects human activities in many basins (abstraction as well as,
for example,
ensuring minimum ecological flow). Well-calibrated HMs do not necessarily
mean that future simulated discharge under a changed climate can be
reproduced satisfactorily . Furthermore, the selection of HMs
may have a larger effect than the calibration of parameters in hydrological
climate impact studies . The spatial pattern of the
relative bias for the multi-model ensemble average is shown in Fig. (lower right). It is important to assert that this
spatial pattern differs significantly between the HMs while the climate
change signal for low-flow projections in this study (see Sect. 3) is
remarkably similar across all three HMs.
Determination of 1.5, 2, and 3 K time periods for different GCM/RCP combinations. A time-sampling
approach was used comparing 30-year running means to the period 1971–2000 with an assumed warming of 0.46 K to pre-industrial conditions.
Warming
RCP
GFDL-ESM2M
HadGEM2-ES
IPSL-CM5A-LR
MIROC-ESM-CHEM
NorESM1-M
level
1.5 K
2.6
–
2007–2036
2008–2037
2006–2035
2047–2076
6.0
2040–2069
2011–2040
2009–2038
2012–2041
2031–2060
8.5
2021–2050
2004–2033
2006–2035
2006–2035
2016–2045
2 K
2.6
–
2029–2058
2060–2089
2023–2052
–
6.0
2060–2089
2026–2055
2028–2057
2028–2057
2054–2083
8.5
2038–2067
2016–2045
2018–2047
2017–2046
2031–2060
3 K
2.6
–
–
–
–
–
6.0
–
2056–2085
2066–2095
2055–2084
–
8.5
2067–2096
2035–2064
2038–2067
2037–2066
2057–2086
Determination of 1.5, 2, and 3 K time periods
The five CMIP5 GCMs used in this study have different sensitivities to
climate forcing. The development of annual global temperature varies
significantly over time between the models and RCPs. Therefore, the time
period with a mean global warming of 1.5, 2, and 3 K with respect to
pre-industrial conditions also varies between the GCM simulations. Here, a
time-sampling method is used to determine the time period for different
levels of global warming . This approach has been used to
investigate climate impacts over Europe for global warming of 2 K
and for global differential impacts
between warming of 1.5 and 2 K . Thirty-year
running mean global temperatures are compared to those of the 1971–2000
period in the GCM simulations. This period corresponds to global
warming of 0.46 K (average value from three estimations with a spread between
0.437 and 0.477 K) with respect to pre-industrial conditions
. The first 30-year period with global warming crossing
one of the three warming levels (1.5, 2, 3 K) is then determined for each of
the 15 GCM/RCP combinations. The identified 30-year time period for the
corresponding GCM/RCP combination is shown in Table . It is
worth noting that for some of the combinations, we could not identify any
30-year period for a selected warming level. For example, none of the GCM
simulations crossed the 3 K warming level under the RCP2.6 over the entire
simulation period up to 2099.
Available methods for identifying regional
climate responses to global warming targets have advantages and disadvantages
. Limitations in the time-sampling method occur in the
direct comparison between different warming levels because the number of
ensemble members varies. Available simulations are reduced from 14 under 1.5 K
warming to 13 under 2 K to 8 simulations under global warming of 3 K.
Furthermore, the annual temperature within future 30-year periods may be
pathway dependent, e.g. a rapid or slower warming. This may influence the
results in climate impact simulations. Nevertheless, the time-sampling method
is advantageous, creating a large ensemble of simulations, which is essential
to determine differences between warming levels .
Low-flow indicator used, uncertainty metrics, and spatial aggregation of results
The impact of climate change is quantified for low flows. Commonly, the 70th
to 90th percentile of exceedance is used to define hydrological droughts for
rivers with perennial type streamflow . Within the framework
of the EDgE project, the co-production with stakeholders from the water
sector in Norway, Spain, and the UK resulted in Q90 (daily flows exceeded 90 %
of the time) as low-flow index. The Q90 is estimated for each calendar year
over a given 30-year period, and the median of Q90 is subsequently calculated
from the respective 30 samples as a final indicator. We recognize that the
use of a calendar year may influence our results in snow-influenced
catchments where the low-flow period may span over 2 consecutive years. To
assess possible consequences, we compared the annual results against
simulations for the winter half-year and found only minor changes in overall
results, especially in snow-dominated regions. Further seasonal assessment is
not performed in this study. We use the period 1971–2000 as a reference for
the estimation of climate impacts, and the relative changes in Q90 is
estimated with respect to this reference period for different warming levels.
The non-parametric Wilcoxon rank-sum test is applied to account for the
robustness of the results. The null hypothesis of equal means between the
climate periods per GCM/HM simulation is tested at 5 % significance, which
has been applied in among others. Based on the ensemble
of Wilcoxon rank-sum tests, the robustness is estimated following the IPCC
AR4 procedure presented in . Robustness is computed as the
percentage of projections showing a significant change. Important thresholds
are less than 33 % for unlikely changes and greater than 66 % for likely changes,
representing the percentage of ensemble simulations showing a significant
change. Significance here does not account for the sign or magnitude of
change.
The signal-to-noise ratio (SNR) is commonly used to quantify the uncertainty
in studies of hydrological extremes . Here, the SNR is computed as the median divided by the
inter-quantile range (i.e. the difference between the 25th and 75th
percentile). It has been shown in recent literature that both GCMs and
HMs contribute to the uncertainty in projected changes
. In this study, the
sequential sampling approach of , following
, is applied. In this approach, the uncertainty due to GCM
is estimated by first fixing an HM and then calculating the range (max–min) of
Q90 changes corresponding to five GCM outputs. We repeated the previous step for
all other remaining HMs. Finally, we estimated the average of ensemble ranges
that would then represent the uncertainty due to GCMs. Likewise, the same
steps could be repeated by fixing the GCM and calculating the range
statistics over the HMs to represent the uncertainty component due to HMs. We
use the bootstrap technique to account for different GCM and HM sample sizes, and perform the sequential uncertainty assessment with three GCM and
HM outputs over the 1000 realizations.
To account for regional differences
in climate impacts, the results of our analyses are displayed over Europe and
additionally aggregated for five different regions (Fig. ).
These macro-scale regions were used in the latest IPCC WGII report for
Europe and were originally identified based on the
environmental stratification presented in , using a
principal component analysis accounting for 20 different environmental
variables. Furthermore, the low-flow impact assessment carried out here is
limited to river basins with upstream areas greater than 1000 km2. Smaller
(and headwater) basins are not considered here as to limit the delineation
errors of river network in the runoff routing scheme (see, e.g.,
Fig. for the resulting river network).
Relative changes (%) in streamflow Q90 between the past (1971–2000) and different warming levels
averaged over IPCC AR4 Europe regions shown in Fig. .
Warming
Absolute
Alpine
Atlantic
Continental
Northern
Mediterranean
level
warming
1.5 K
1.04 K
22.2
-7.3
-4.1
8.4
-12.0
2 K
1.54 K
29.6
-10.0
-4.5
15.9
-16.3
3 K
2.54 K
44.8
-21.6
-19.1
24.1
-35.1
European macro-regions used in the IPCC AR5 based on an environmental
stratification after . Graphics created by the authors, based on GIS data provided by
Marc J. Metzger, University of Edinburgh. The data are remapped to the 5 km grid used in this study.
Results and discussion
Changes in low flows under different levels of warming compared to 1971–2000
The change signal in low flows gets stronger with increased levels of warming
in most parts of Europe (Fig. , left row). An
amplification in decreasing low flows can be identified in the Iberian
Peninsula, the south-western part of France, and south-east Europe, including
Greece and the Balkans. Conversely, large parts of the Alps and
Scandinavia face an intensification of increasing low-flow signal with higher
levels of warming. The region from Germany to Poland to the Baltics
shows generally very small changes, and the sign of change in low
flows alters with increased warming. Under global warming of 1.5 K, the
mean change in streamflow Q90 over Europe is approximately zero
(Fig. , upper left), but with large spatial
differences between the IPCC AR5 Europe regions and with different directions
of change. The regional low-flow statistics are based on the average of all
the grid cells per region. Approximately half of the rivers in Europe show
decreases in low flows under 1.5 K warming, with an hotspot in the Iberian
Peninsula region and the strongest decrease in the Mediterranean (-12 % over
the whole area) and the “Atlantic” region (-7 %; Table ). Conversely, increases in low flow are expected in the Alpine (+22 %) and
Northern areas (+8 %). This occurs mainly due to changes in snow accumulation
and melt, and consequently results in higher winter low flows. The
“Continental” area shows the smallest changes overall with both positive and
negative values, but less than 10 % even under global warming of 2 K.
Change in multi-model ensemble mean low flow (%) under different warming levels compared to
the 1971–2000 baseline (a, c, e) and robustness (b, d, f). The latter is expressed by the percentage of
simulations based on a Wilcoxon rank-sum test with 5 % significance level. An agreement of more than
66 % in the ensemble is classified as ”likely” change. The values given in the upper left of the
subplots are the continental average along the river network for all grid cells with a contributing area greater than 1000 km2.
More
regions in Europe show significant changes in low flow with an increased
level of warming (Fig. , left row). Robustness is
expressed as the percentage of simulations passing the Wilcoxon rank-sum test
at 5 %. Under global warming of 1.5 K, approximately 57 % of the ensemble
simulations show significant changes. Highest values are found in
snow-dominated regions (e.g. Alpine and Northern region). Under global warming
of 2 K, the percentage of ensemble simulations with significant changes
increases to approximately 70 %, being distributed equally over Europe, and
this number increases to 80 % for global warming of 3 K. Under global
warming of 3 K, the agreement among the ensemble simulations increases to
overall 80 %. The strongest regional change is found in the Mediterranean,
with likely changes across 31 % of the river basins under global warming of
1.5 K, 64 % under 2 K, and 90 % under 3 K respectively. The significance is
highest in regions with strong (positive and negative) change signals.
Nevertheless, there are exceptions; for example, under 2 K warming the signal for
the Mediterranean might be stronger, but it is less robust than that for the
Atlantic.
The results presented here confirm those found in earlier studies for low-flow and hydrological drought projections across Europe.
, for example, gave an overview on projected changes in
average 7-day minimum flows through the end of the century under the SRES A1B
scenario. A single HM was selected for the analysis in that study, which was
then driven by 12 regional climate model (RCM) precipitation and temperature
data set. The analysis showed that streamflow droughts become more severe and
persistent in southern Europe, while droughts decrease in northern and
north-eastern parts of Europe. found similar patterns
over Europe using five GCMs and a single HM, with a clear influence of
decreasing snow accumulation in northern Europe and an increase in drought
impacts in the Mediterranean. Recently, investigated
changes in hydrologic droughts under global warming of 1, 2, and 3 K over
large river catchments (greater than 50 000 km2) including two European
basins – the central European Rhine and Mediterranean Tagus River. They used
Q95 as a low-flow indicator, based on the same five GCMs applied in our study
with an ensemble of global and catchment hydrological models.
Nevertheless, the results from both studies are comparable under global
warming of 2 and 3 K, with projected decrease in low flows in the Rhine and
Tagus rivers. Low flow (Q90) in this study under global warming of 2 K is
almost unchanged in the Rhine, and up to -11 % under 3 K. The more pronounced
low flow decrease is found in the Tagus River showing -16 % under 2 K and
-33 % under global warming of 3 K. The GCMs used in
are also identical to those used in this study. However, the HMs E-HYPE
and VIC were used to simulate the
changes in Q90 for RCP2.6 and RCP8.5 for the 2050s and 2080s. Overall, the
spatial pattern of changes in flow indicator fits to our results quite well;
likewise, the amplification of the signal over time through the end of the
century was also found in both studies. The strongest reductions in low flows
are exhibited in southern Europe and are related to decreasing annual
precipitation. The spatial pattern under global warming of 2 K compares
well with those reported by for low flows with a 10-year
return period. Notably, the underlying model ensemble consists of 11
bias-corrected RCMs and two hydrologic models, which are different from those
used in this study. They found a 15 % reduction in low flows for the
Mediterranean, which is very similar to the 16 % reduction found in this
study. Although the results on the climate-induced change in low flows
presented herein are generally comparable to other studies, we provide new
spatially explicit information on low flows under different levels of warming
over Europe.
Our study shows contrasting results for the Mediterranean region compared to
under different levels of warming. At global warming
of 3 K, large decreases of up to -35 % and high robustness (very likely) are
observed here, whereas no projected changes in absolute grid-specific runoff
values with little robustness was reported by . These
differences can be explained through methodological choices of low-flow
indices used between the two studies. The relative changes in the routed
river low flow quantified here is more informative for water resources
assessments compared to the absolute changes of grid-specific runoff. This
holds especially true in drier regions, which are characterized by very small
Q90 runoff values. From a practitioner point of view, our study highlights
the need for adaptation to climate-induced low flows in these regions, which
would not be concluded based on the metrics reported in .
Relationship between the median changes in the annual total precipitation and simulated river
low flows (Q90) under global warming of 1.5 K (a), 2 K (b), and 3 K (c).
Unlike other results
shown in this study, only river grid cells from basins greater than 10 000 km2 are shown for clarity
in the figure. Results are similar to those including river grid cells with contributing areas greater
than 1000 km2. Linear regression lines are shown for positive values (blue dashed), negative values
(red dashed), and all data points (black dashed). The Alpine region with overall smaller catchment sizes is not
included, but shows similar behaviour to the basins in the Northern region. All changes are expressed
as multi-model ensemble mean changes (GCM/HM combinations for low flows and GCMs for annual precipitation).
Changes in river low flows can be explained to a
large extent by the median change in annual precipitation over all levels of
global warming (Fig. ). To investigate the influence
of precipitation on low flows, we compare the relative change of Q90
discharge to the changes in the annual total precipitation over the 30 years
for different levels of warming. The Mediterranean region shows the strongest
decrease in precipitation and low flows among all warming levels. The
correlation coefficient between changes in annual precipitation and Q90
increases from 0.45 under 1 K to 0.62 under 3 K of warming. Notably,
the increased spread in the median changes of annual total precipitation and
simulated river low flows under global warming of 3 K contribute to higher
correlation compared to other warming levels. Furthermore, we observe
relatively stronger correspondence between changes in annual total
precipitation and low-flow indicator in river basins characterized by
projected decrease in low flows. The r2 value rises from 0.61 to 0.77 with
an increase in global warming from 1.5 to 3 K compared to an
increase of 0.45 to 0.65 for the same warming levels in river basins showing
projected increase in low flows. Overall, the Continental and Atlantic
regions show the smallest changes in precipitation and low flows. In the
Northern region, the projected increases in changes of both variables are
highest. In this region, the relationship between precipitation and low flows
is the weakest as exemplified by the low r2 values for the positive
precipitation changes. This can be explained due to the increasing influence
of snow processes: accumulation and snow melt. This also holds true
for catchments greater than 1000 km2 in Alpine regions (not displayed
here).
Under global warming of 3 K, we identified a larger spread between total
annual precipitation and low flows. In the Northern area, this can be
explained due to higher temperatures which could then lead to less snow
accumulation and increased winter low flows. In contrast, higher temperatures
combined with lower-than-average annual precipitation in the Mediterranean
result in higher evapotranspiration and decreased low flows. Our results
agree with other studies reporting on the general relationship between
precipitation and low-flow changes e.g., even though our study shows a different relationship between precipitation and low
flow. In the following section, the differences between policy-relevant levels of warming are examined.
Differences in low flows between different future levels of warming
One of the objectives of this study is to analyse differences in the change
signal and the sensitivity of the low-flow changes to different levels of
global warming. This provides additional information compared to the results
presented above. These results, in combination, are important for the
discussion on mitigation targets and for adaptation planning in accordance
with the Paris Agreement . With increased levels of global
warming from 1.5 to 2, 2 to 3, and 1.5 to 3 K, an amplification of the
change signal in low flow is expected over a large part of Europe
(Fig. a, c, and e). This holds especially
true in regions with relatively big positive and negative changes in low
flows. The overall robustness of the low-flow changes in Europe increases
with increasing temperature differences between the global warming levels
(Fig. b, d, and f).
Relative changes averaged over regions (%) in multi-model ensemble mean low-flow indicator (Q90)
between different levels of global change.
Warming level
Absolute
Alpine
Atlantic
Continental
Northern
Mediterranean
warming
1.5 K → 2 K
0.5 K
8.6
-1.1
-0.3
10.7
-6.6
2 K → 3 K
1.0 K
17.0
-9.0
-12.3
13.5
-16.0
1.5 K → 3 K
1.5 K
23.9
-12.9
-12.2
22.6
-24.0
Relative change (%) in multi-model ensemble median Q90 between different levels of warming (a, c, e)
and robustness of the signal between those (b, d, f). The latter is expressed by the percentage
of simulations based on a Wilcoxon rank-sum test with 5 % significance level. The values given in the
upper left of the subplots are the continental average along the river network for all grid cells with
a contributing area greater than 1000 km2.
The changes in streamflow Q90 between 1.5 and 2 K warming are generally
small, with few rivers exhibiting changes larger than 10 % in magnitude. The
pattern is similar to the one shown in Fig. , which
highlights that the sign of change is conserved in areas with relatively
large changes (more than ±10 %), even under the small warming of only
0.5 K. These results, however, are not robust. None of the rivers show likely
changes, meaning that less than 66 % of the ensemble simulations are
significant at the river grid cell level. Moreover, most parts of Europe show
changes marked as unlikely, with total agreement of only 15 % over Europe
and all simulations. The regional changes in low flows between the two
warming levels are also small (see Table ). The Atlantic and
Continental areas show an almost unchanged situation. The Northern region
exhibits the largest increase in low flows averaged over the considered stratified region of 11 %, and the Mediterranean shows a -7 %
decrease.
The robustness results presented in Fig. b,
d, f alone do not allow for determining warming level thresholds of change
in low-flow indicator. Therefore, we included the robustness of the change
between the warming levels in this section. Combining the information in
Fig. b, d with Fig. b,
we see robust changes between the past time period and a 2 K warmer world.
The information of non-significant differences between 1.5 and 2 K warming
allows for the conclusion that the majority of change already happens before
reaching global warming of 1.5 K. Limiting climate change to global warming of 1.5 K in comparison to 2 K has only a limited effect on low flows. These
results point out that an even lower mitigation goal would be needed for
regions where substantial negative impacts occur.
Low flow changes between 2 and 3 K warming (Fig. c and d) are more pronounced with large parts of the central Alps and
Scandinavia showing an increase of more than 10 % in low flows. Conversely, most regions on the Iberian Peninsula, France, Italy, the Balkans, and Greece face a decrease of more than 10 % low flow. The strongest
increase is projected for the Alpine region (+17 %) and the strongest
decrease for the Mediterranean (-16 %). Overall, half of the simulations show
robust changes over Europe with large regional differences. Likely changes
are found in the south-west of Europe, northern Norway, and the Balkans.
It is worth emphasizing that the differences between global warming of 2
and 3 K in low flows are substantial. These changes are on top of those
projected between 1971 and 2000 and a 2 K warming, where already 70 % of the
simulations show significant changes (Fig. d). As a
result, the increase in low flows in the Alpine and Northern regions could, in combination with increased future annual precipitation in the GCMs
(see Fig. ), lead to higher hydropower potential.
Conversely, a further decrease in available water (in low flows as well
as annual precipitation) in the Mediterranean may pose additional water
stress in that area. Although human influences such as reservoir management
and human water demand are not considered in this study, different regional
adaptation options should be considered depending on whether the world warms
2 or 3 K. This also holds true for the more pronounced warming between 1.5
and 3 K (Fig. e and f), where the regional
changes in low flows as well as the robustness amplify compared to 2 and 3 K
warming. These results also highlight the non-linear sensitivity of changes
in low flows to different levels of global warming. For example with
long-lasting infrastructure or long planning horizons, adaptation strategies
should be put in place now whether or not the 3 K level is reached.
Overall, the robustness in the change signal rises with increased temperature
differences between the warming levels. Based on the results of the
multi-model assessment conducted here, significant differences in low flows
between the policy-relevant 1.5 and 2 K warming could not be identified.
Few differences between these two warming levels have been observed
because of the high variability among the GCM/HM simulations. The multi-model
variability is further analysed in detail in the following section.
Uncertainty contributions from GCMs and HMs
To provide a comprehensive picture of uncertainties, the SNR is investigated in addition to the robustness of the change
signal based on the Wilcoxon rank-sum test presented in Sect. 3.1 and 3.2.
Furthermore, the uncertainty contribution of the GCMs and HMs for different
levels of warming is also investigated.
The upper row (a–c) shows the SNR (ensemble median divided by the
inter-quartile range) for the change in low flows (Q90) between the 1980s and 1.5 K (a), 2 K (b), and
3 K (c) warming. The relative uncertainty contribution of GCMs and HMs is shown in the lower
row (d–f) for the three warming levels. Low values of GCM/HM indicate large HM uncertainty; values larger
than 1 indicate a higher contribution of the GCMs to the total uncertainty.
Under global warming of 1.5 and 2 K, large parts of Europe exhibit
substantial uncertainty, expressed as the SNR (Fig. a and b). It is estimated as the ensemble median divided by the
ensemble inter-quartile range . Using the inter-quartile
range partly accounts for outliers in the ensemble simulations. The SNR is
small for changes in low flows under global warming of 1.5 K and increases with
further warming. These results are similar to the increasing changes and
robustness of the simulations with the increased warming level as also
previously discussed in Fig. . Under global
warming of 1.5 K, the spatial patterns of SNR and robustness in the different methods coincide
(Fig. b compared to
Fig. a). Nevertheless, a direct comparison of the
uncertainty patterns under higher levels of warming between SNR and
robustness leads to different conclusions in some regions. As an example,
large parts of Germany show a robust change under 2 and 3 K warming
(Fig. d and f), whereas the SNR is less
than 0.8 over the same regions, indicating high uncertainty. This occurs
because the Wilcoxon rank-sum test is performed for each ensemble member
separately, and the result is independent of the sign of change and absolute
value. Conversely, the SNR shows the uncertainty among the ensemble
members and depends on the variability between those ensemble simulations.
Additionally, thresholds selected for rejecting results or marking them as
uncertain have greater influence on the presented results in both methods.
This highlights that the uncertainty information conveyed strongly depends on
the metrics selected to represent them. In other words, the robustness
indicates that most ensemble members project significant changes in Germany,
but there is disagreement among them as indicated by a low SNR.
Dimensionless uncertainty contribution of GCMs and HMs averaged over
the stratified European regions described in Sect. 2.3.
European regions
Warming
Alpine
Atlantic
Continental
Northern
Mediterranean
level
GCM uncertainty
1.5 K
27.3
25.8
35.2
31.2
31.4
2 K
32.1
31.6
44.7
43.7
38.5
3 K
52.1
32.9
48.3
63.4
31.3
HM uncertainty
1.5 K
26.7
19.8
21.1
31.9
25.0
2 K
33.4
24.0
25.3
39.6
30.2
3 K
55.6
31.4
31.1
55.1
34.7
The SNR results presented here are in line with the findings for the Rhine and Tagus rivers in .
Comparison to other studies like or is in this case difficult because those
studies used different metrics to describe uncertainty and, consequently, the patterns in those studies vary significantly from the patterns shown here.
GCM and HM contributions to total uncertainty separated with the sequential
sampling method are shown in
Fig. d–f, and the spatially aggregated results
over the IPCC Europe regions in Table . The uncertainty rises
with higher levels of warming for both sources of uncertainty for two
reasons. The GCM uncertainty increases because a 30-year period reaching
3 K warming often has a strong temperature period (with higher-than-average
annual temperature) within this 30-year period. Conversely, GCM runs
under the RCP2.6 often stabilize around global warming of 1.5 K. This
pathway dependency of GCM runs influences the variability of the results, with
expectedly higher variability in the case of 3 K warming . The HM
uncertainty increases with global warming because certain regions might cross
thresholds. For example, parts of France might move from an energy-limited to
a water-limited regime. The contribution of the GCMs to the overall
uncertainty across Europe is approximately 21 % higher under global warming
of 1.5 K, 25 % higher under 2 K, and only 10 % higher under global
warming of 3 K in comparison to the HM contribution. This decrease in GCM/HM
contribution can be mostly attributed to the Mediterranean and Atlantic
regions (France in particular). In these dry regions, the different
representations of evaporation using temperature-based potential
evapotranspiration used in mHM and PCR-GLOBWB will lead to a different
evaporative response compared to explicitly solving the full energy balance
of the land surface as in Noah-MP. Furthermore, HMs contribution to the total
uncertainty is regionally higher than average in the Alpine and Northern
regions, where snow accumulation and melt play an important role
(Fig. d–f). Snow processes are treated
differently between the HMs, which explains the relatively high uncertainties
in the Northern and Alpine area. Both mHM and PCR-GLOBWB use a temperature-based conceptual degree-day method for snow processes, whereas the Noah-MP
model employs an energy balance scheme to resolve the snow accumulation and
melt processes. In the Atlantic and Continental regions, GCM uncertainty is
higher under all levels of warming. One reason is that the lower quantiles of
summer precipitation in CMIP5 simulations are generally underestimated and
have a large spread in central Europe . In the Rhine River
basin, the spread in summer precipitation across the five GCMs used in this
study is highest compared to other seasons . Remarkably,
within the summer season the spread was higher under RCP8.5 compared to
RCP2.6. Furthermore, HMs generally show nearly similar skill in humid areas,
where most of the models have been developed and calibrated
. The Northern area shows nearly identical contributions in
GCMs and HMs. In the Mediterranean, the uncertainty due to the HMs rises with
increased warming. Reasons for such behaviour could be the increased
importance of the soil moisture and resulting actual evapotranspiration and infiltration treatment, which differ substantially between the HMs.
For example, mHM uses separate storage for actual evapotranspiration and
different runoff components (fast and slow interflow and baseflow
components), whereas actual evapotranspiration and runoff depend on the same
storage in Noah-MP leading to a higher inter-variable dependency. This
suggests that differences in soil and runoff representations within a model
can have a significant effect on the simulation of future low flows, and can
have a significant impact on the trend signal, as also had been previously
noted by .
The procedure to differentiate between GCM and HM uncertainty was previously
presented in . They used six HMs forced with
bias-corrected outputs from five GCMs under two RCPs set up in seven large
river basins worldwide for the period 1971–2099. Similar to the findings of
this study, they also reported that uncertainty for a runoff index increases
with time, which corresponds to increased warming. Furthermore, the GCMs
generally dominate the HMs uncertainty in low flows. Nevertheless, they also
agree on the fact that the uncertainty contribution of the HMs depends on the
hydro-climatic regime. Similarly, used the ANOVA method to
distinguish between different sources of uncertainty, including RCP
uncertainty, which is not separately investigated here. For low flows, they
came up with a 70 % contribution of RCPs to drought impacts, with RCP
uncertainty rising through the end of the 21st century. This may be explained
due to the widening temperature range in the RCPs over time, which is not
comparable to our approach of using a time-sampling approach to identify
different warming levels .
Overall, the regions
showing higher uncertainty contribution from GCMs exhibited comparably lower
SNR, indicating a significant variability in the GCM projections that are
propagated through the HMs to the low-flow signal. Furthermore, the
contribution of the GCMs to the total uncertainty is higher than the
contribution of HMs over Europe. Nevertheless, the influence of HMs cannot be
neglected and outperforms the uncertainties in GCMs in some regions and
depending on the warming level. Our results therefore strongly suggest the
use of multiple hydrologic models for climate change impact assessment
studies for future low-flow projections, and that the use of single
hydrologic models may provide misleading results.
Summary and conclusions
Climate change is projected to alter
low flows expressed as the Q90 indicator in Europe under global warming of
1.5, 2, and 3 K. The magnitude of changes and robustness in the 45-member multi-model ensemble is amplified with increased levels of warming.
Higher levels of warming therefore demand more distinctive adaptation
actions. The mountainous regions in Europe show the strongest low flow
increase from 22 % under 1.5 K to 45 % under global warming of 3 K. Continental
Europe faces slight decreases in low flows. Higher decreases are expected in
the Mediterranean (up to -35 % under 3 K warming) and the Atlantic. We
conclude that global warming of 3 K will impose higher water stress over a
large part of the Mediterranean, an area which already suffers from limited
water resources making adaptation necessary. Further limitations in water
availability may result in new managing challenges for water resource
managers and policy makers, including the management of competition for water
resources between sectors.
The projected changes in Q90 across Europe between the reference period
(1971–2000) and global warming of 1.5 K, as well as between global warming
of 1.5 and 2 K are generally small with low robustness and a small SNR. It is not possible to distinguish climate impacts between global warming of 1.5 and 2 K. Nevertheless, some hotspot regions show
changes greater than ± 10 % between all warming levels investigated in
this study. It would be misleading to conclude that mitigation of greenhouse
gases is not needed. It is revealed here that large parts of the change in
the climate-induced low-flow signal between the reference period and global
warming of 2 K will already happen before reaching global warming of
1.5 K, specifically in the Alpine, Northern, and
Mediterranean regions. Therefore, mitigating climate change even below the 1.5∘
goal would be necessary to reduce negative drought impacts
in hotspot regions like the Mediterranean.
The results shown here are independent of the uncertainty in emission
scenarios. On the other hand, the uncertainty of the determination in the
time periods for different warming levels is introduced. Generally, the
robustness in the simulations and SNR in the ensemble rise
with increased warming and with the magnitude of change. As a result, regions
with relatively large changes in low flows show relatively low uncertainty
in the results and have therefore the greatest need to adapt to changing
conditions. It is observed here that the selection of metrics to define
uncertainty strongly influences the result. Here, we use the combination of
robustness covering the significance in the change of every single ensemble
member together with SNR to show the variability and strength of the
signal for the overall ensemble. Uncertainties should be considered in
adaptation planning, e.g. in deciding to use climate impact simulations to
determine regional vulnerability quantitatively or qualitatively. We conclude
that the combination of different kinds of information, namely the change
signal, robustness, and SNR, should be used in the adaptation process.
These can be used to decide on the need for adaptation or if a quantitative
or qualitative approach should be chosen for the estimation of regional
vulnerability to climate change.
It is observed that the GCM contribution to
the overall uncertainty is higher than the HM contribution across Europe and
that the HM contribution to total uncertainty rises with increased warming.
This is related to the exhibited strong correspondence between the changes in
mean annual total precipitation and streamflow Q90, which is strongest in
lower warming levels and in the Atlantic and Continental regions.
Nevertheless, the HM contribution cannot be neglected and it
is higher than the GCM contribution in some regions, especially in the Alpine, Northern, and
Mediterranean regions, with rising global temperatures. The main reasons are the
rising importance of the hydrological process description of snow, soil moisture
and evapotranspiration, and infiltration. We conclude that climate change
studies focusing on river low flows should employ large multi-model ensembles
including multiple driving climate models and multiple impact models
to provide a comprehensive analysis of model uncertainty.