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Austrian research cooperation
Austrian Climate Research Programme ACRP 3nd Call
Funded by Climate and Energy Fund
ACRP ‐ Austrian Climate Research Program
Power through Resilience of Energy Systems: Energy Crises,
Trends and Climate Change (PRESENCE)
Contributions to Work packages
4 – Hydrology and hydropower
5 – Availability of cooling water for thermal power plants and the industry
by
Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW)
University of Natural Resources and Life Sciences, Vienna (BOKU)
Project management: Em.O.Univ.Prof. Dipl.Ing. Dr. Hans Peter Nachtnebel
Authors: DI Philipp Stanzel, DI Mathew Herrnegger
Draft March 2013
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Table of Content
1. Introduction ............................................................................................................................ 4
2. Methods .................................................................................................................................. 6
2.1. Water balance simulations ...................................................................................................... 6
2.2. Changes in runoff due to precipitation and temperature changes (Runoff elasticity) ........... 8
2.3. Analyses of river runoff time series from scenario simulations .............................................. 8
2.3.1. Analysis of low flow periods ......................................................................................................... 9
2.3.2. Long‐term persistence and periodicity ....................................................................................... 11
2.4. Impact of climate change on alpine reservoirs ..................................................................... 12
2.5. Water temperature ............................................................................................................... 14
2.6. Groundwater recharge .......................................................................................................... 14
2.7. Trends in runoff in Europe .................................................................................................... 15
3. Input data .............................................................................................................................. 16
3.1. Corrections of RCM data ....................................................................................................... 16
3.2. Climate change signals .......................................................................................................... 16
4. Results ................................................................................................................................... 19
4.1. Performance of the water balance model ............................................................................ 19
4.1.1. Simulations with observed input data ........................................................................................ 19
4.1.2. Simulations with corrected climate model control runs............................................................. 21
4.2. Runoff elasticity ..................................................................................................................... 23
4.3. Spatial patterns of change in local runoff ............................................................................. 26
4.4. Changes in river runoff .......................................................................................................... 30
4.4.1. Mean runoff ................................................................................................................................ 30
4.4.2. Analysis for run‐of‐river power plants ........................................................................................ 31
4.4.3. Runoff seasonality ...................................................................................................................... 34
4.4.4. Low flow runoff under climate change conditions ..................................................................... 37
4.4.5. Low flow runoff for shorter periods than a month: Mean annual 7‐day minimum flow ........... 40
4.4.6. Time of occurrence of low flow periods ..................................................................................... 42
4.4.7. Low flow duration under climate change conditions ................................................................. 43
4.4.8. Long‐term persistence and periodicity in simulated runoff time series ..................................... 45
4.5. Alpine reservoirs .................................................................................................................... 52
4.5.1. Gepatsch ..................................................................................................................................... 52
4.5.2. Sellrain‐Silz .................................................................................................................................. 59
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4.5.3. Kaprun‐Uttendorf ....................................................................................................................... 60
4.6. Water temperature ............................................................................................................... 61
4.7. Groundwater recharge .......................................................................................................... 62
4.8. Trends in runoff and hydropower production in Europe ...................................................... 65
4.8.1. Mean runoff and hydropower potential ..................................................................................... 65
4.8.2. Seasonal changes ........................................................................................................................ 67
4.8.3. Low flow ...................................................................................................................................... 70
5. Conclusions ........................................................................................................................... 73
6. References ............................................................................................................................. 75
7. List of Figures ......................................................................................................................... 78
8. List of Acronyms .................................................................................................................... 84
9. Annex – Comments on the delivered runoff simulation time series ........................................ 85
9.1. Delivered runoff simulation time series ................................................................................ 85
9.2. Estimation of the Swiss and German Inn and the German Danube ...................................... 88
9.3. Estimation of a typical hydrological regime shift for German hydropower production ....... 89
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1. Introduction
The core objective of the overall project “PRESENCE – Power through Resilience of Energy Systems:
Energy Crises, Trends and Climate Change” is to provide measures and pathways how to increase the
resilience of energy systems in the view of climate change, possible trends and energy crises as well
as the transformation of our energy system into a low‐ and zero carbon future for the Austrian case.
In order to follow this basic idea of the project, the following sub‐objectives of “PRESENCE” have
been defined:
‐ Identify and quantify the impact of climate change on energy systems. This includes a
detailed description and modeling of energy systems in a highly disaggregated way. This will
imply modeling the climate sensitivity of (1) hydro power, (2) electricity generation, storage
and transmission, (3) heating and cooling of buildings and (4) selected aspects of cooling
water availability for thermal power plants and industrial energy related processes.
‐ Identify and quantify the possible impact of other exogenous trends, developments and
possible shocks on energy systems.
‐ Further elaborate the methodological concept of resilience for the case of energy systems.
‐ Further develop methodological concepts for assessing the impact of extreme events on
energy systems.
‐ Identify steps and concepts for increasing the resilience of energy systems and quantifying
the impact of transition paths and measures on resilience indicators.
‐ Investigate economic aspects of adaptation measures and discuss strengths and limitations
of economic concepts for assessing cost and benefits of adaptation measures.
This report summarizes the contributions of the IWHW group to Workpackage (WP) 4 – Hydrology
and hydropower – and WP 5 – Availability of cooling water for thermal power plants and the
industry. The main objectives of these investigations are:
‐ Assess the expected impact of climate change on the hydrology of Austria.
‐ Analyse electricity generation of hydropower in different climate scenarios.
‐ Investigate cooling water availability under climate change conditions.
The main tool to achieve these objectives is a detailed spatio‐temporal hydrological model for
Austria. After calibration for a period in the 20th century, the model is run with climate change
scenario input data for the 21st century. Climate scenarios provided by the latest generation of
climate models are prepared and corrected in WP 1 – Climate models and scenarios. Trends in the
resulting hydrological scenarios are analysed, with special focus on impacts on hydropower
production and cooling water availability. The simulated time series of runoff will also be used as
input in subsequent detailed modeling of hydropower production in an energy model based on an
inventory of all major hydropower stations (by the Energy Economics group, EEG). In addition to the
detailed investigation of expected changes in hydrology and hydropower production in Austria,
external factors related to hydropower production in Europe are investigated and summarized.
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Of the analyses of climate change impact presented here, the following points have special relevance
for hydropower production (WP4):
‐ Continuous monthly runoff time series of the 21st century for the main Austrian rivers
‐ Mean runoff and seasonality
‐ Low flow runoff, time of occurrence and duration of low flow periods
‐ Specific analysis for run‐of‐river power plants
‐ Specific analysis for alpine reservoirs
‐ Trends in runoff and hydropower production in Europe
For the investigation of cooling water availability under climate change conditions (WP5), the
following issues discussed here are of special relevance:
‐ Mean runoff and seasonality
‐ Low flow runoff, time of occurrence and duration of low flow periods
‐ Water temperature
‐ Groundwater recharge
‐ Trends in runoff in Europe
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2. Methods
2.1. Water balance simulations
For the water balance simulations, the continuous conceptual hydrological model COSERO is applied.
COSERO was developed at IWHW (Nachtnebel et al 1993, Fuchs 1998, Kling 2002, Eder et al. 2005,
Kling und Nachtnebel 2009, among others), its structure is similar to the HBV model (Bergström
1992). All main hydrological processes are represented: interception, snow accumulation and snow
melt, evaporation, storage in the soil, runoff separation into fast and slow components. Fig. 1 shows
a schematic illustration of the main modules of the COSERO model. These processes are simulated
for each model zone. River runoff and basin runoff are calculated as sums of the respective model
zones.
Fig. 1: Schematic description of the water balance model
In this application, the model zones are defined by a 1x1km grid and basin boundaries. 188
catchments are represented explicitly and are grouped into 16 river basins (Fig. 2). Simulation results
are calculated for monthly time steps. The model is based on the model applied for water balance
simulations (e.g. Kling et al. 2005) for the Hydrological Atlas of Austria (BMLFUW 2005). It is
calibrated in a regional calibration procedure for the period of 1961‐1990 (cf. Kling 2006). Runoff
simulations can be evaluated for 141 basins.
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Fig. 2: 188 Austrian catchments represented in the water balance model, grouped into 16 river basins
The impact of climate change on the terrestrial water balance is investigated using precipitation and
temperature data resulting from climate model simulations as input. Water balance modeling results
of future periods under climate change conditions can then be compared with the reference
simulations for 1961‐1990.
For the application with climate model data, the model is adapted regarding the simulation of
evaporation, snow melt and glaciers.
Evaporation is calculated with the method of Thornthwaite (Thornthwaite and Mathers 1957). This
method only requires temperature as input. Therefore, the climate signal in the temperature data is
considered in the calculations of evaporation. Other methods, like the method of Budyko (1974) that
is originally implemented in the water balance model, need other input data which is not readily
available from climate models or is more complicated to correct than temperature data. In the
Thornthwaite method, effects of temperature and radiation are combined in an empirical approach.
The use of the Thornthwaite method with climate change scenario temperatures implies a linear
relationship, with potential evaporation rising as much as temperature. However, radiation, which is
then implicitly assumed to increase in the same way as temperature, is not expected to generally
increase. This might lead to a small, but systematic overestimation of potential evaporation.
In the calculation of snow melt, the variability of daily temperature around the monthly mean is fixed
with a value calculated from data of the calibration period 1961‐1990. In applications for this period,
time series of observed daily temperature are used, which are not available for monthly scenario
data.
A simple glacier model is applied in combination with the water balance model for the scenario
simulations. Glaciers are not considered in earlier applications, and are also not included in the
simulations of the reference period of 1961‐1990. The glacier melt simulated by the glacier model is
therefore the additional contribution by glaciers that so far did not contribute to runoff in
1961‐1990. Model zones which are mostly snow covered in summer in 1961‐1990 are therefore
considered as glacierized (see Fig. 8 in chapter 2.4). Glacier melt is calculated with a temperature
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index method. Lateral mass movement is not considered. Ice thickness in one model zone is
distributed according to a log normal distribution. Ice volumes are estimated based on information
by Kuhn et al. (1999) and Span et al. (2005).
2.2. Changes in runoff due to precipitation and temperature changes
(Runoff elasticity)
A second approach of estimating changes in runoff due to changes in driving meteorological variables
without applying continuous water balance simulations is tested. For long term mean values, direct
relationships between runoff change and changes in precipitation and temperature can be
established. This analysis, which is usually referred to as “elasticity of runoff”, can be based on
observed data or on previous water balance simulations. Examples for the use of observations are
the investigation of precipitation elasticity of runoff in the US by Sankarasubramani et al. (2001), the
climate elasticity estimation including precipitation and temperature for US and Chinese basins by Fu
et al. (2007) and the formulation of Gardner (2009) that is based on precipitation, temperature and
potential evaporation. Based on modeling results, Chiew (2006) assessed precipitation elasticity of
runoff for Australia.
In the analysis presented here, changes in precipitation and temperature are considered and the
change in runoff is deduced from water balance modeling results. Runoff from 188 catchments in
Austria is included. The simulations are run with input data based on the three emission scenarios
A1B, A2 and B1 of the REMO‐UBA model.
Absolute and relative changes of 30‐year mean of annual precipitation (ΔP), temperature (ΔT) and
annual runoff (ΔQ) of three future periods (2021‐2050, 2036‐2065, 2071‐2069) relative to the control
period of 1961‐1990 are used to establish empirical relationships of runoff elasticity in Austria.
Multiple linear regression models are tested, based on these variables and also including mean
catchment elevation as additional independent variable.
As PRESENCE climate model data was used again for continuous water balance simulations leading to
detailed results, including time series of runoff as well as long term means for various periods In the
21st century, an estimation of runoff change based on runoff elasticity is not applied with the new
climate model data.
2.3. Analyses of river runoff time series from scenario simulations
The availability of streamflow water for hydropower production and for cooling purposes is
investigated for mean flow conditions, for higher runoff and for periods of low flow. The assessment
is based on runoff simulations in monthly time steps, using climate scenarios of three RCMs as input:
for REMO‐UBA, data with emission scenarios A1B and A2 is applied, for Aladin‐Arpege and RegCM3
only A1B scenarios are available (see chapter 3). Climate change impacts projected by REMO‐UBA are
analysed for the three 30 year periods of 2011‐2040 (around 2025), 2036‐2065 (around 2050) and
2061‐2090 (around 2075), relative to the simulations of the baseline period 1961‐1990. Results with
the other two models are examined for the period of 2061‐2090, which shows the effects of climate
change and therefore also the differences between the models most clearly. The ability of the water
balance model to accurately represent river runoff is shown in comparing observations and
simulations for the period of 1961‐1990. Three rivers are selected for detailed analysis: the Enns, the
Mur and the Drau. At these rivers, several run‐of‐river hydropower plants are located and there is
relevant discharge of cooling water from power plants and industry. These three large Austrian rivers
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have alpine headwater basins and therefore exhibit generally similar hydrological characteristics.
Therefore, some results are compared with simulations for the Ager river, a smaller river basin
entirely located in the northern foothills of the Alps. The location of the basins is shown in Fig. 3.
Fig. 3: Basins selected for the analysis of river runoff
Mean flow conditions are indicated by the mean discharge of each 30 year period (Qm). Long‐term
mean discharge is closely related to long‐term mean hydropower production in run‐of‐river power
plants.
Another important value for run‐of‐river power plants is the design flow. Design flow values differ
between rivers and power plants, according to the hydrological regime and purpose and design of
the power plant. Design values are mostly between mean runoff and rather high discharge values,
such as the runoff exceeded in only 10% of a year (Q10, see eg. Giesecke and Mosonyi, 2009).
Therefore, in addition to mean flow, Q10 Is analyzed for Enns, Mur and Drau. Q10 is determined
from duration curves in the same way as Q90 or Q95 (see below and Fig. 4).
The seasonality of river runoff is of high relevance for both, hydropower production and cooling
water availability. Changes in runoff seasonality are considered analyzing mean monthly discharges.
Continuous time series of river runoff of all 188 catchments for the periods of 1961‐1990 and
2051‐2080 of the Aladin‐Arpege and RegCM3 models are handed over to EEG as input data for
energy system modeling. To include inflow time series for the Danube hydropower plants, the Swiss
and German contributions to Danube runoff are estimated. The methods are described in the
comments handed over together with the data (see Annex).
2.3.1. Analysis of low flow periods
For low flow runoff, several indicators are applied: the mean lowest monthly runoff in a year (Qmin),
runoff exceeded on 95% of the days (Q95) and runoff exceeded on 90% of the days (Q90). The
derivation of Q95 and Q90 from duration curves is shown in Fig. 4. The figure also shows that there
are slight differences between the values calculated with daily runoff values and with monthly
values, especially for very high and very low runoff. Where daily observations are available, a transfer
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function between the two duration curves is calculated (according to Smakhtin, 2000). This function
is then also applied to monthly duration curves of future periods. Where no observations are
available, Q95 and Q90 values are based on duration curves from monthly runoff.
Fig. 4: Flow duration curves of the Mur river (from daily observations, red, and monthly means, blue); values of Q90 and Q95 are indicated by vertical lines
To include another indicator widely used in low flow studies (e.g. Stahl et al. 2010, Randall 2010) into
the analysis, the mean annual 7‐day minimum flow (MAM7), the relation to the mean lowest annual
monthly runoff (Qmin) is investigated from observational data. The indicator MAM7 relates to a
shorter low flow period of 7 days, which cannot directly be assessed with monthly runoff time series.
The time of occurrence of low flow periods is also analysed based on the month of the lowest annual
runoff. Relative frequencies of occurrence of the lowest runoff in each calendar month are calculated
for each investigated 30 year period.
The duration of low flow periods is assessed for the Enns and Drau rivers comparing run lengths
below a certain threshold. As threshold the Q90 calculated directly from the monthly values of each
considered 30‐year period, i.e. the runoff exceeded in 90% of the months is selected. As an example,
the left part of Fig. 5 shows the time series of monthly Q minus Q90 for the period of 1961‐90 of the
Drau gauge Lavamünd. For values below 0, the duration in months – the run length of the respective
low flow period – is counted. For each period, the frequencies of run lengths from one to seven
months (seven months being the highest simulated duration) are calculated. The right part of Fig. 5
shows the resulting frequencies for the time series in the left graph.
Fig. 5: Analysis of run length below Q90 for 1961‐90 for the Drau river (gauge Lavamünd)
20
200
2000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m³/s]
Probability of exceedence
Duration Curve (Mur)
Qobs monthly
Qobs daily
Q90
Q95
‐50
0
50
100
150
200
250
300
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
Monthly Q ‐Q90 (m³/s)
Year
0
5
10
15
1 2 3 4 5 6 7
Frequency
Run Length below Q90 (Months)
Drau Qsim (30y: 1961‐90)
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For comparison of different periods and scenarios a measure of the skewness of the run length
distribution is calculated according to Peel et al. 2004:
∑,
with ai being the frequency of run length bi, j the longest observed run length and N the number of
observed months.
2.3.2. Long‐term persistence and periodicity
Time series of runoff with 21st century scenario input data appear to show longer and more
pronounced cyclic fluctuations than observed time series of the 20th century when evaluated visually.
A systematic investigation of this effect is carried out exemplarily for two large Austrian rivers, the
Enns in the central northern Alps and the Drau, covering the south of Austria.
Mean annual runoff time series for the most downstream gauge are aggregated from observations,
simulations with observed input data and scenario simulations. Three 48‐year periods are compared:
1952‐1999, 2002‐2049 and 2043‐2090. Linear trends in each 48‐year time series are removed before
the analysis. The comparison of runoff observations and simulations with observed input data is
conducted for an upstream gauge for the Drau, where such a long observation is available, and for a
shorter period of 35 years for the Enns.
Long‐term persistence and fluctuations in the data is analysed with the run length below median (cf.
Peel et al. 2004, see also previous section on duration of low flow periods. For the calculation of this
value, the time series is divided into years above and below the long term median (as shown
exemplarily for the Enns in Fig. 6, left). The length of consecutive years – the run length – below the
median is counted. The resulting distribution of different run lengths is plotted, as in Fig. 6 right. The
5‐year run, for example, refers to the period of 1982 to 1986. As for monthly runoff below Q90, a
skewness measure g is calculated (according to Peel et al. 2004) for the run length distribution. Here,
N denominates the number of years.
Fig. 6: Example of the distribution of run length below the median (right) for a 35 year time series of the Enns (left).
Long‐term periodicity in the runoff time series was also assessed analyzing autocorrelation patterns
for different lags (cf. Pekárová et al. 2003). Autocorrelation plots are generated for lags of up to N/2.
Values of autocorrelation coefficients and their sequences are analysed visually.
‐80
‐60
‐40
‐20
0
20
40
60
80
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Mean
annual Q
‐Median Q
(m³/s)
Year
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Enns Qobs (35y:1965‐1999)
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For the REMO‐UBA model, results with emission scenarios A1B and A2 were compared. For this
model, no control run is available, so results from scenario simulations are compared with results
from hydrological simulations using observations as input data. These analyses are carried out for
both, Enns and Drau river in order to assess regional differences.
For the models Aladin‐Arpege and RegCM3, hydrological simulation with control run data as input
can be used. For these models, scenario simulations are compared with control run simulations, in
order to assess, if climate model data generally leads to different persistence and cyclicity behavior
in the hydrological time series or if trends are due to climate change.
2.4. Impact of climate change on alpine reservoirs
For the catchment areas of three selected alpine reservoirs changes in the long term means of
relevant water balance components are analysed. The development of runoff and precipitation in
general, but also portions of snow and rain precipitation, snow accumulation and snow melt and the
contributions of snow melt and glacier melt to runoff are considered. The analyses are mostly based
on water balance simulations driven by REMO‐UBA A1B input data, with some comparison to results
with different emission scenarios (A2, B1) and different models (Aladin‐Arpege, RegCM3). Only local
runoff and precipitation in the alpine catchment areas is included.
The location of the selected catchment areas is shown in Fig. 7, top left. The other maps in Fig. 7
show the catchment areas and the respective 1x1km elements of the water balance model.
The Gepatsch reservoir is located in the Kaunertal in Tyrol, damming the Fraggenbach or Fragge.
Water is also diverted from the adjacent catchments of the Pitze, of some smaller Inn tributaries and
some downstream Fragge tributaries (Fig. 7, bottom left). The areas in the water balance model that
are considered to be part of these catchments sum up to 286 km², with a glacierized area of 51 km²
(in 2000). The mean elevation is 2636 m a.s.l.
Also in Tyrol, the catchment areas of the Sellrain‐Silz area are dammed mainly in the Finstertal
reservoir and contribute to hydropower production of the Kühtai power plant. Water of several
tributaries of the Ötztaler Ache and the Inn is diverted to the reservoir (Fig. 7, bottom right). Of the
catchment areas of 138 km² in the water balance model, 18 km² are glacierized (in 2000). The mean
elevation in the catchments is 2530 m a.s.l.
Information about the catchment areas of the hydropower plant systems of Gepatsch/Kaunertal and
Sellrain‐Silz was provided by TIWAG.
The catchment areas of the Uttendorf and Kaprun reservoirs and hydropower plants are located in
Salzburg and Carinthia. Water from headwater basins of tributaries of the Salzach and Drau are
diverted to several alpine reservoirs (Fig. 7, top right). The catchment areas considered in this
analysis sum up to 192 km², with 36 km² of glaciers, and a mean elevation of 2571 m a.s.l.
For the Gepatsch reservoir, a detailed investigation of the development of the glaciers in the
catchment areas is conducted. In this context, the sensitivity of simulated glacier melt to the
selection of day‐degree‐factors (DDFs) for ice is assessed. In the original simulations with the simple
glacier melt model, the same day‐degree‐factors were assigned for snow and ice. Their values are
based on values determined by Kling (2006) for snow. Ice DDfs might, however, be higher than snow
DDFs due to the darker surface (and therefore reduced albedo) of glaciers. Based on publications by
Maniak (2010) , Nolin et al. (2010) and Kayastha et al. (2003) it is concluded, that values for ice DDFs
are in the range of 1 to maximum 2 times snow DDFs (most of the values in the cited literature are
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around 1.5). For the sensitivity analysis, the maximum of ice DDFs of 2 times the snow DDFs is
applied.
Fig. 7: Locations and and detailed maps of the three selected alpine reservoir catchment areas (background map: Pirker, 2005)
Glacierized areas in the water balance model are determined from results of the simulations of 1961‐
1990 (as explained in more detail in Nachtnebel et al. 2010). The areas with a mean August snow
cover of over 90% are regarded as glaciers for the scenario simulations. This leads to simulations that
are consistent with the reference and calibration simulations of the water balance models, in which
glacier runoff was not considered at all. With this definition, the areas defined as glaciers for the
scenario simulations were mostly snow covered in the reference period and did not (or almost not)
contribute to runoff with ice melt. If they become snow free in the scenario runs, they produce
additional ice melt runoff, which is considered now in the runoff scenario simulations. Due to this
methodology, the ice covered areas are slightly smaller than glacier areas in recent inventories (Kuhn
and Lambrecht 2005). Mostly, smaller glaciers of lower elevations are disregarded. The generally
good agreement of the areas determined as described and the inventory of Kuhn and Lambrecht
(2005) in the Hydrological Atlas of Austria is shown in Fig. 8 for Western Tyrol and the Gepatsch
reservoir catchments.
Gepatsch reservoir / Kaunertal Finstertal reservoir / Sellrain Silz
Kaprun Uttendorf
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Fig. 8: Glacier areas in the water balance (WB) model and in the inventory of Kuhn and Lambrecht (2005, in the Hydrological Atlas of Austria HAO)
2.5. Water temperature
The development of water temperature can be assessed by a model according to Hostetler (1991, in
Nobilis and Webb 1994). Only air temperature and discharge are needed as input data:
Mean water temperature Tw of month m is calculated with:
1,54,3210, 365
2cos
365
2sin
mwmmamw TBQBTB
dB
dBBT
where d ist he number of days from the beginning of the year to the middle of the respective month,
Ta is mean monthly air temperature, Qm is mean monthly runoff, and Tw,m‐1 is water temperature of
the preceding month.
Parameters B0 to B5 are estimated from observations of water temperature, minimizing the mean
squared error of model results for the observation period. Very high correlations between model
results and observations are obtained, from 0.977 for the Ager river to 0.99 for the Mur river.
2.6. Groundwater recharge
In south‐east Austria, substantial amounts of water for industrial use are abstracted from
groundwater. Therefore, changes in groundwater recharge are estimated from the water balance
simulations. The model variable examined is the inflow from the interflow reservoir into the base
flow reservoir, which can be regarded as groundwater recharge. However, no explicit groundwater
15
model is applied, and groundwater flow over borders of subbasins in the water balance model is not
considered. The results therefore refer only to local groundwater recharge. Groundwater bodies in
connection with larger rivers, as in the downstream Mur basin, are influenced by both, surface and
groundwater inflow from upstream. Further analysis of effects of changes in groundwater availability
for water abstraction in such regions should combine information about changes in local
groundwater recharge and in river runoff.
Mean values of simulated groundwater recharge are analysed for months, the whole year, and the
period of March to June, when generally the highest amounts of groundwater recharge occur in
Austria. Changes between 2061‐90 and 1961‐90 are analysed for four basins: the most downstream
reach of the Mur, Raab, Rabnitz and a part of the Leitha basin (Fig. 9).
Fig. 9: Basins selected for the analysis of groundwater recharge
2.7. Trends in runoff in Europe
For an analysis of possible changes in river runoff due to climate change for the whole of Europe a
hydrological model covering the entire area is needed. The task of setting up, calibrating and running
such a model goes beyond the scope of the PRESENCE project. Therefore, literature on possible
climate‐induced trends in European streamflow is reviewed. As analyses of single basins or countries
usually differ from each other in the applied methodologies (hydrological and climate models,
emission scenarios, trend analyses), mainly studies covering the whole of Europe are considered.
Some publications on large European basins were included.
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3. Input data
For PRESENCE, data of three regional climate models (RCMs) published in the ENSEMBLES project
(van der Linden and Mitchell 2009) are selected, downloaded, corrected and prepared for the use in
impact assessment by the Institute of Meteorology (BOKUMET). For the hydrological application, the
variables temperature and precipitation are used. The three selected models are RegCM3 by ICTP
(driving General Circulation Model – GCM – ECHAM5), Aladin‐Arpege by CNRM (the RCM Aladin
driven by the GCM Arpege) and REMO by MPI (driving GCM ECHAM5). In the new water balance
simulations in the framework of PREENCE, only the two models RegCM3 and Aladin‐Arpege are
applied. REMO results from the 25km ENSEMBLES version of this model are supposed to be rather
similar to the results from the 10km REMO‐UBA version, that has been successfully applied in the
preceding KlimAdapt project. Therefore, the results with REMO‐UBA are used together with new
simulation results with RegCM3 and Aladin‐Arpege for further and more detailed analysis of climate
change impacts on hydrology.
3.1. Corrections of RCM data
The selected ENSEMBLES models are corrected with a quantile mapping approach (cf. Déqué 2007)
using large scale 25x25km observation fields for temperature (E‐OBS, Haylock et al. 2008) and
precipitation (Frei and Schär 1998) as reference observations. The reference period for this
correction is 1971‐1999 for precipitation and 1971‐2000 for temperature.
It is known that local and regional distributions of the variables temperature and precipitation in the
large scale observation data set do not correspond well with local observations and that therefore
RCM data corrected based only on these data are not suitable for hydrological applications (Senoner
et al. 2011). Therefore, a 1x1km climatology for 1961‐1990 of temperature and precipitation that has
been developed at the IWHW especially for water balance modeling in Austria (Fürst et al. 2007), is
used as second reference in a further correction step. After preliminary application by the
meteorologists, the values for the catchment area of the Erlauf river (subbasin 09_15 in the water
balance model) were replaced by the values in the very similar climatology of Kling et al. 2005. While
the quantile mapping correction is conducted with daily data, the second correction is applied for
monthly data only. The differences between the long term monthly mean values of the large scale
observation data set (E‐OBS for temperature and Frei/Schär for precipitation) and the IWHW
observation data set is calculated for each 1x1km pixel. These mean monthly deviations are then
applied as correction factors to the RCM data that was corrected with the large scale observations.
Due to different reference periods (1971‐1999/2000 for the 25km data and 1961‐1990 for the IWHW
1km data), the resulting means of the reference period of the hydrological simulation, 1961‐1990,
deviate slightly from the original IWHW data (see chapter 4.1.2).
3.2. Climate change signals
The following graphs (Fig. 10 to Fig. 13) show climate change signals of mean annual temperature
and precipitation of the two PRESENCE climate models for the periods of 2051‐2080 and 2061‐2090,
both compared to 1961‐1990.
Generally, Aladin‐Arpege projects higher temperature increases (Fig. 10 and Fig. 11). The models
coincide projecting a more pronounced rise in temperature for alpine areas.
17
For precipitation (Fig. 12 and Fig. 13), Aladin‐Arpege projects small increases in the south and
decreases in the north. RegCM3 shows the opposite picture, with decreasing precipitation in the
south‐west and increases in the north‐east.
The comparison of the two periods shows that, as expected, the temperature change is more
pronounced in the later period (note the different scales for the different periods). Precipitation,
however, shows a slightly smaller change – i.e. higher precipitation – in the later period of
2061‐2090. This again demonstrates the rather linear trend in rising temperatures and the strong
effects of cyclic “weather” fluctuations in precipitation that can cover climate change effects.
Fig. 10: Climate change signals for temperature for 2051‐2080 in RegCM3 (left) and Aladin‐Arpege (right) data (Source: BOKUMET)
Fig. 11: Climate change signals for temperature for 2061‐2090 in RegCM3 (left) and Aladin‐Arpege (right) data (Source: BOKUMET)
18
Fig. 12: Climate change signals for precipitation for 2051‐2080 in RegCM3 (left) and Aladin‐Arpege (right) data (Source: BOKUMET)
Fig. 13: Climate change signals for precipitation for 2061‐2090 in RegCM3 (left) and Aladin‐Arpege (right) data (Source: BOKUMET)
19
4. Results
4.1. Performance of the water balance model
4.1.1. Simulations with observed input data
The following graphs (Fig. 14 to Fig. 16) show simulated (SIM) and observed (OBS) values for duration
curves (based on monthly runoff), mean monthly runoff and relative frequency of occurrence of the
lowest annual runoff in each calendar month for the rivers Enns, Mur and Drau. The left side always
shows results for one upstream gauge and the right side for one downstream gauge (in the cases of
Mur and Drau the last gauge on Austrian territory).
Fig. 14: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel) and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower panel) for one upstream gauge (Liezen) and one downstream gauge (Steyr/Ortskai) of the Enns river
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m
³/s]
Probability of Exceedance
Liezen
m_Qobs 1961‐1990m_Qsim 1961‐1990
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m
³/s]
Probability of exceedance
Steyr (Ortskai)
m_Qobs 1961‐1990m_Qsim 1961‐1990
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Liezen
QOBS 1961‐90
QSIM 1961‐90
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Steyr (Ortskai)
QOBS 1961‐90
QSIM 1961‐90
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Liezen
OBS
SIM
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Steyr (Ortskai)
OBS
SIM
20
The simulated duration curves show a very good agreement with duration curves from observed
discharge for all six gauges. The overall distribution of runoff is therefore simulated very well. In the
range of the lowest values some very small deviations appear.
The seasonal distribution of runoff is also simulated very well, but there are higher discrepancies
between simulations and observations. For Drau and Mur rivers, the simulations show slight
overestimations of simulated mean monthly runoff in spring and summer and a slight
underestimation in fall and winter. For the Enns, mean runoff in spring and winter is simulated
accurately, in summer, simulations are too low, and in fall slightly too high.
Fig. 15: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel) and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower panel) for one upstream gauge (Gestüthof) and one downstream gauge (Mureck/Spielfeld) of the Mur river
1
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m
³/s]
Probability of exceedance
Gestüthof
m_Qobs 1961‐1990m_Qsim 1961‐1990
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m
³/s]
Probability of exceedance
Mureck/Spielfeld
m_Qobs 1974‐2000m_Qsim 1961‐1990
0
100
200
300
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Gestüthof
QOBS 1961‐90
QSIM 1961‐90
0
100
200
300
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Mureck/Spielfeld
QOBS 1961‐90
QSIM 1961‐90
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Gestüthof
OBS
SIM
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Mureck/Spielfeld
OBS
SIM
21
Larger differences between observed and simulated data are found for the time of occurrence of the
minimal annual runoff. This implies a larger uncertainty of projections of temporal shifts in the
occurrence of low flow periods. As will be seen in the respective section, the scenario simulations
show temporal shifts that are much more pronounced than the discrepancies between simulations
and observations.
Fig. 16: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel) and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower panel) for one upstream gauge (Oberdrauburg) and one downstream gauge (Lavamünd) of the Drau river
4.1.2. Simulations with corrected climate model control runs
Fig. 17 shows the differences for precipitation and potential evapotranspiration ETP0 (which is
calculated only from temperature) between Aladin‐Arpege and RegCM3 1961‐1990 reference runs
and the reference run with the original IWHW climate data (denominated KlimAdapt in Fig. 17). It is
obvious that differences are small, but for temperature and resulting ETP0, the corrected RCM data
are systematically lower than the IWHW data. The resulting differences in runoff amount to only +/‐
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m
³/s]
Probability of exceedance
Oberdrauburg
m_Qobs 1961‐1990m_Qsim 1961‐1990
20
200
2000
0.00 0.20 0.40 0.60 0.80 1.00Runoff [m
³/s]
Probability of Exceedance
Lavamünd
m_Qobs 1961‐1990m_Qsim 1961‐1990
0
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Oberdrauburg
QOBS 1961‐90
QSIM 1961‐90
0
100
200
300
400
500
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Lavamünd
QOBS 1961‐90
QSIM 1961‐90
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Oberdrauburg
OBS
SIM
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
9 10 11 12 1 2 3 4 5 6 7 8
Relative frequency (/)
Month
Lavamünd
OBS
SIM
22
4% (Aladin‐Arpege) and ‐3 to +5% (RegCM3) of annual runoff and are therefore regarded as
acceptable. Compared to observations in 140 basins with reliable measurements, simulated seasonal
runoff has high correlations (Fig. 18). The coefficients of determination are 0.976 for both input data
sets, which means that the model explains more than 97% of the spatial variance of seasonal runoff
in Austria (cf. Kling 2006). This value is only slightly below the value of 0.983 achieved in the
KlimAdapt simulations with the original IWHW input data.
Fig. 17: Scatterplots of precipitation (left) and potential evapotranspiration ETP0 (right) in the reference runs with Arpege (above) and RegCM3 (below) input data and the KlimAdapt reference run with IWHW input data
Fig. 18: Scatterplots of observed and simulated seasonal runoff in the reference period, with input data from Arpege (left) and RegCM3 (right)
500
1000
1500
2000
2500
500 1000 1500 2000 2500
Me
an
An
nua
l Pre
cip
itatio
n 1
96
1-9
0
PR
ES
EN
CE
AR
PE
GE
(mm
)
Mean Annual 1961-90 Precipitation KlimAdapt (mm)
200
300
400
500
600
700
200 300 400 500 600 700Me
an
An
nua
l ET
P0
19
61
-90
PR
ES
EN
CE
A
RP
EG
E (m
m)
Mean Annual ETP0 1961-90KlimAdapt (mm)
500
1000
1500
2000
2500
500 1000 1500 2000 2500
Me
an
An
nua
l Pre
cip
itatio
n 1
96
1-9
0
PR
ES
EN
CE
Re
gC
M3
(mm
)
Mean Annual 1961-90 Precipitation KlimAdapt (mm)
200
300
400
500
600
700
200 300 400 500 600 700Me
an
An
nua
l ET
P0
19
61
-90
PR
ES
EN
CE
R
eg
CM
3 (m
m)
Mean Annual ETP0 1961-90KlimAdapt (mm)
0
100
200
300
400
500
600
700
800
0 200 400 600 800
Me
an
se
aso
nal r
un
off
19
61
-90
P
RE
SE
NC
E A
RP
EG
E (m
m)
Mean seasonal runoff 1961-90 (observed, mm)
0
100
200
300
400
500
600
700
800
0 200 400 600 800
Me
an
se
aso
nal r
un
off
19
61
-90
P
RE
SE
NC
E R
eg
CM
3 (m
m)
Mean seasonal runoff 1961-90 (observed, mm)
23
4.2. Runoff elasticity
The analysis of relative runoff change and relative precipitation change generally shows a non‐linear
relation of these variables (Fig. 19). A clearly matching trend line can be drawn (in Fig. 17 a second‐
order polynomial), but there is substantial scatter. In Fig. 20 differences in temperature change are
depicted in different colors. The graph reveals that some, but not all of this scatter can be explained
by the different changes in temperature.
Fig. 19: Relative runoff change (ΔQ) in relation to relative precipitation change (ΔP)
Fig. 20: Relative runoff change (ΔQ) in relation to relative precipitation change (ΔP) for classes of temperature change (ΔT)
The same analysis for absolute values of runoff and precipitation change (Fig. 21) shows a linear
relationship and clearer shift of the linear correlations depending on temperature change. Therefore,
the regression formulations are based on absolute values. The lack of clarity in the relation of relative
values can be explained by the behavior of dry catchments in the east of Austria. In these areas with
generally low runoff, small differences in absolute runoff imply high relative changes. Also, water
‐20%
‐10%
0%
10%
20%
30%
‐5% 0% 5% 10% 15%
ΔQ (%
)
ΔP (%)
‐20%
‐10%
0%
10%
20%
30%
‐5% 0% 5% 10% 15%
ΔQ (%
)
ΔP (%)
3 < dT < +4.15
2 < dT < 3
1 < dT < 2
dT < 1.0
24
balance simulations have higher uncertainties in these catchments, which can lead to slightly
different results due to similar precipitation and temperature changes (which then mean high
relative differences).
Fig. 21: Absolute runoff change (ΔQ) in relation to absolute precipitation change (ΔP) for classes of temperature change (ΔT)
A multiple linear regression with ΔQ as dependent variable and ΔP and ΔT as explanatory variables is
carried out including all values (9 cases: 3 periods for each of the 3 scenarios) in all 188 Austrian
catchments. The regression yields a high coefficient of determination of 0.91 with the following
equation:
∆ 0.9893 ∙ ∆ 1.8400 ∙ ∆ 0.0456
Fig. 22 shows a scatter plot with the original results of runoff change ΔQ of the water balance model
and the results from this regression model based on the simulation data. There is a generally good
agreement, with larger deviations for lower runoff. Including mean catchment elevation as additional
explanatory variable leads to no significant improvement, with a coefficient of determination of 0.92.
Fig. 22: Comparison of results for absolute runoff change (ΔQ) with the water balance model and the regression model based on precipitation and temperature change
‐20
‐15
‐10
‐5
0
5
10
15
20
‐10 ‐5 0 5 10 15 20ΔQ (m
m)
ΔP (mm)
3 < dT < +4.15
2 < dT < 3
1 < dT < 2
dT < 1.0
‐25
‐20
‐15
‐10
‐5
‐
5
10
15
20
25
‐25 ‐20 ‐15 ‐10 ‐5 ‐ 5 10 15 20 25
∆Q (m
m) water balance model
ΔQ (mm) regression model
Austria (188 catchments)
25
As the highest deviations of the regression model for all Austrian catchments occur for eastern
lowland catchments, the procedure is rerun for alpine catchments and lowland catchments
separately. The total 188 catchments are grouped into 107 alpine catchments and 81 lowland
catchments, as shown in Fig. 23. The division is based on the main river basins in the water balance
model, which leads to the inclusion of some partially alpine catchments (as for example the Ybbs and
Traisen catchments) into the lowland part and vice versa (as for example the Ager catchment).
Alpine runoff elasticity can be explained with the following equation, yielding a very high coefficient
of determination of 0.97:
∆ 1.0264 ∙ ∆ 2.2641 ∙ ∆ 0.0377
Lowland runoff elasticity is explained with a lower coefficient of determination of 0.86 by:
∆ 0.8055 ∙ ∆ 1.0067 ∙ ∆ 0.4955
The scatter plots in Fig. 24 shows a better fit along the range of change values in both areas. The
remaining overestimated ΔQ values around ‐10mm of the regression model are all results for
catchments in the South of Austria. This suggests that a more detailed regional division could further
improve the performance of the regression model based on ΔP and ΔT.
Fig. 23: Division into alpine and lowland catchments
Fig. 24: Comparison of results for ΔQ with the water balance model and the regression models for alpine and lowland catchments
‐25
‐20
‐15
‐10
‐5
‐
5
10
15
20
25
‐25 ‐20 ‐15 ‐10 ‐5 ‐ 5 10 15 20 25
ΔQ (m
m) water balan
ce model
ΔQ (mm) regression model
Alpine (107 catchments)
‐25
‐20
‐15
‐10
‐5
‐
5
10
15
20
25
‐25 ‐20 ‐15 ‐10 ‐5 ‐ 5 10 15 20 25
ΔQ (m
m) water balan
ce model
ΔQ (mm) regression model
Lowland (81 catchments)
26
4.3. Spatial patterns of change in local runoff
The maps in Fig. 25 show the relative change in mean annual runoff of each of the 188 basins of the
water balance model for the period of 2051‐2080, relative to the mean of 1961‐1990, simulated with
Aladin‐Arpege and RegCM3 model data.
Fig. 25: Change in mean annual runoff 2051‐2080 relative to 1961‐1990, simulated with Aladin‐Arpege (top) and RegCM3 (bottom) input data
27
For almost the entire Austrian area, results from both models coincide in projecting decreasing mean
annual runoff. Spatial patterns, however, are distinct in the two models . While Aladin‐Arpege data
leads to stronger decreases in northern and especially north‐eastern Austria, RegCM3 produces the
most pronounced decrease in the south and south‐west. In some north‐eastern areas, a small
increase in mean runoff is expected according to RegCM3.
A comparison of the results with Aladin‐Arpege and RegCM3 with REMO‐UBA results with the A1B
emission scenario is possible for the period of 2061‐2090 (Fig. 26 and Fig. 27). There is a clear
resemblance between RegCM3 and REMO‐UBA results, which can be attributed to the same driving
GCM, ECHAM5, in both RCMs. Generally, RegCM3 is slightly “drier” than REMO‐UBA.
Fig. 26: Change in mean annual runoff 2061‐2090 relative to 1961‐1990, simulated with Aladin‐Arpege (top) and RegCM3 (bottom) input data
28
Comparing the maps of Aladin‐Arpege and RegCM3 in Fig. 25 and Fig. 26 reveals that the spatial
patterns of the mean change of each model is very similar for these two rather close periods, but the
general level of mean runoff is higher in the later period in both models. This means that
precipitation is considerably higher in the period of 2061‐2090 than in 2051‐2080, as temperature
and therefore evapotranspiration are also higher in the later period. This again underlines the
importance of keeping in mind that precipitation in climate model data – like observed precipitation
– shows cyclic fluctuations and that therefore the selection of the periods for comparison has a
relevant effect on the results.
Fig. 27: Change in mean annual runoff 2061‐2090 relative to 1961‐1990, simulated with REMO‐UBA (from Kranzl et al. 2010)
Spatial patterns in seasonal runoff resulting from Aladin‐Arpege and RegCM3 are more similar to
each other (Fig. 28 and Fig. 29). Also the results of the two periods 2051‐2080 and 2061‐2090 are
very similar (and the graphs for 2061‐2090 are not shown therefore). The clearest differences can be
found for autumn and winter, for which RegCM3 shows generally higher runoff than Aladin‐Arpege.
The seasonal runoff maps of REMO‐UBA (not reproduced here) closely resemble those of RegCM3,
with a slightly wetter autumn in the RegCM3 results.
29
Fig. 28: Change in mean seasonal runoff 2051‐2090 relative to 1961‐1990 with Aladin‐Arpege
Fig. 29: Change in mean seasonal runoff 2051‐2090 relative to 1961‐1990 with RegCM3
30
4.4. Changes in river runoff
4.4.1. Mean runoff
Results for mean flow conditions under climate change depend on the climate model and the
emission scenario. Fig. 30 shows the development of the simulated mean discharge MQsim for all
considered gauges along the three rivers Enns, Mur and Drau (from upstream, left, to
downstream,right) based on REMO‐UBA data. The mean value of the period 1961‐1990 is regarded
as reference value for each gauge and has the value 1.
Fig. 30: Development of simulated mean flow (MQsim) through 30‐year periods around 1975, 2025, 2050, and
2075 for Enns (top), Mur (middle) and Drau (bottom) with the REMO‐UBA scenarios A1B (left) and A2 (right)
The simulated changes are generally in the range of +/‐ 10%. In the second half of the 21st century
simulated discharges tend to be lower in the generally “drier” A1B scenario (left column in Fig. 30)
than in the “wetter” A2 scenario (right column in Fig. 30). Both scenarios, however, show a negative
0.8
0.9
1
1.1
1.2
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A1B
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
0.8
0.9
1
1.1
1.2
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A2
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
0.8
0.9
1
1.1
1.2
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur ‐ A1B
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
0.8
0.9
1
1.1
1.2
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur ‐ A2
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
0.8
0.9
1
1.1
1.2
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau ‐ A1B
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
0.8
0.9
1
1.1
1.2
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau ‐ A2
MQsim 1961‐90 MQsim 2011‐40 MQsim 2036‐65 MQsim 2061‐90
31
trend in mean runoff within the 21st century for almost all considered gauges. In the A2 scenario,
mean runoff around 2075 is still higher than around 1975, for Enns and Mur. For the entire Austrian
Drau basin (analysed at the last gauge in Lavamünd), mean runoff around 2075 is lower than around
1975 in both scenarios. In all three rivers, results for mean flow are very similar for all considered
gauges.
The comparison of results with the other models for the period 2061‐2090 (Fig. 31) includes the Ager
river. It shows that a decrease in mean annual runoff is simulated in all basins with all three climate
models. For the Enns, REMO‐UBA results are between the higher runoff values of RegCM3 and the
lower values of Aladin‐Arpege. For the Mur, RegCM3 and Aladin‐Arpege show very similar results,
with more pronounced decreases in mean flow than with REMO‐UBA. Also for the Drau, results of
the three models are close to each other, with Aladin‐Arpege data leading to slightly higher mean
runoff. For the Ager, RegCM3 shows only a very small decrease, Aladin‐Arpege leads to a high
reduction in mean runoff of almost 20%.
Fig. 31: Comparison of the ratio of simulated mean flow (MQsim) in the 30‐year periods around 2075 and 1975
with A1B scenarios of REMO‐UBA (black), RegCM3 (orange) and Aladin‐Arpege (violet) for Enns, Mur, Drau and
Ager
4.4.2. Analysis for run‐of‐river power plants
The maps in Fig. 32 show another graphic representation of the results for mean runoff for the
period of 2061‐1090 with REMO‐UBA data. The value for a river reach is assigned based on the value
simulated for its basin outlet gauge. Fig. 34 shows the results for changes in higher runoff (Q10) in
the upper range of possible design flow values. Q10 mostly shows a stronger decrease than MQ (or a
smaller increase). This can also be detected in duration curves (see Fig. 39) and is a negative effect
for run‐of‐river hydropower production.
0.8
0.9
1
1.1
1.2
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns : A1B ‐ 2061‐90
MQsim 1961‐90 MQsim REMO‐UBA MQsim RegCM3 MQsim Arpege
0.8
0.9
1
1.1
1.2
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur : A1B ‐ 2061‐90
MQsim 1961‐90 MQsim REMO‐UBA MQsim RegCM3 MQsim Arpege
0.8
0.9
1
1.1
1.2
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau : A1B ‐ 2061‐90
MQsim 1961‐90 MQsim REMO‐UBA MQsim RegCM3 MQsim Arpege
0.8
0.9
1
1.1
1.2
Ager (Schalchham)
Ager : A1B ‐ 2061‐90
MQsim 1961‐90 MQsim REMO‐UBA MQsim RegCM3 MQsim Arpege
32
Fig. 32: Mean runoff (MQ )of 2061‐2090 relative to 1961‐1990 for Enns, Mur and Drau, based on REMO‐UBA scenarios A1B (top) and A2 (bottom)
33
Fig. 33: Q10 of 2061‐2090 relative to 1961‐1990 for Enns, Mur and Drau, based on REMO‐UBA scenarios A1B (top) and A2 (bottom)
34
4.4.3. Runoff seasonality
For seasonal runoff, expected changes are rather independent of the climate model and scenario.
Fig. 34 again compares results with the different emission scenarios of REMO‐UBA, for the period
around 2075.
Fig. 34: Mean monthly runoff in 2061‐90 for the scenarios A1B and A2, compared to 1961‐90, for Enns, Mur and
Drau
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Enns
Steyr (Ortskai) 1961‐1990
Steyr (Ortskai) 2061‐90 A1B
Steyr (Ortskai) 2061‐90 A2
Liezen 1961‐1990
Liezen 2061‐90 A1B
Liezen 2061‐90 A2
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Mur
Mureck / Spielfeld 1961‐1990
Mureck / Spielfeld 2061‐90 A1B
Mureck / Spielfeld 2061‐90 A2
Gestüthof 1961‐1991
Gestüthof 2061‐90 A1B
Gestüthof 2061‐90 A2
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m
³/s)
Month
Drau
Lavamünd 1961‐1990
Lavamünd 2061‐90 A1B
Lavamünd 2061‐90 A2
Oberdrauburg 1961‐1990
Oberdrauburg 2061‐90 A1B
Oberdrauburg 2061‐90 A1B
35
The generally higher runoff in the A2 scenario is visible also in mean monthly flows, especially for the
Mur and Enns rivers. In both scenarios, however, a clear increase in winter and spring runoff is
simulated. Summer and fall runoff is projected to decrease, moderately in scenario A2, substantially
in scenario A1B. A one‐month shift of the peak in monthly runoff to earlier occurrence is simulated
more consistently for the upstream gauges, for the Drau river also for the downstream gauges. Peaks
in spring are caused by large snow melt contributions, which are expected to occur earlier and
decrease due to higher temperatures and less snow precipitation. The influence of these climate
change impacts is stronger with a higher relevance of snow processes for runoff generation in the
upstream reaches of the investigated alpine rivers.
These changes in runoff seasonality are also simulated by the other two models, RegCM3 and
Aladin‐Arpege. Simulated mean monthly runoff with all three models for 2061‐2090, compared to
1961‐1990, is shown for the same gauges in Fig. 35 (Enns), Fig. 36 (Mur) and Fig. 37 (Drau). For the
Enns, RegCM3 results are very close to REMO‐UBA simulations. For Mur and Drau, they are also very
similar, but RegCM3 shows a stronger decrease in summer. Scenario runoff simulated with
Aladin‐Arpege data generally exhibits less pronounced increases in winter than with the other two
models.
Fig. 35: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐Arpege
(bottom right), compared to 1961‐90, for two gauges of the river Enns
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m
³/s)
Month
Enns
Steyr (Ortskai) 1961‐1990
Steyr (Ortskai) 2061‐90 REMO‐UBA A1B
Liezen 1961‐1990
Liezen 2061‐90 REMO‐UBA A1B
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Enns
Steyr (Ortskai) 1961‐1990 RegCM3
Steyr (Ortskai) 2061‐90 RegCM3
Liezen 1961‐1990 RegCM3
Liezen 2061‐90 RegCM3
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Enns
Steyr (Ortskai) 1961‐1990 Arpege
Steyr (Ortskai) 2061‐90 Arpege
Liezen 1961‐1990 Arpege
Liezen 2061‐90 Arpege
36
Fig. 36: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐Arpege
(bottom right), compared to 1961‐90, for two gauges of the river Mur
Fig. 37: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐Arpege
(bottom right), compared to 1961‐90, for two gauges of the river Drau
The seasonal distribution of runoff exhibits a generally different behavior on the Ager (Fig. 38), with a
less pronounced seasonality and an earlier peak, in March/April, resulting from earlier snow melt.
With all three models decreasing summer runoff is simulated with scenario data. The decrease is
strongest with Aladin‐Arpege, which again projects no increases in winter runoff and leads to a lower
peak in the beginning of the year. In the other two models, winter discharge increases. RegCM3
simulations show a lower peak in March, but no changes in April. With REMO‐UBA, the March/April
peak is projected to increase.
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Mur
Mureck / Spielfeld 1961‐1990
Mureck / Spielfeld 2061‐90 REMO‐UBA A1B
Gestüthof 1961‐1991
Gestüthof 2061‐90 REMO‐UBA A1B
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Mur
Mureck / Spielfeld 1961‐1990 RegCM3
Mureck / Spielfeld 2061‐90 RegCM3
Gestüthof 1961‐1991 RegCM3
Gestüthof 2061‐60 RegCM3
0
50
100
150
200
250
300
1 2 3 4 5 6 7 8 9 10 11 12Runoff (m³/s)
Month
Mur
Mureck / Spielfeld 1961‐1990 Arpege
Mureck / Spielfeld 2061‐90 Arpege
Gestüthof 1961‐1991 Arpege
Gestüthof 2061‐60 Arpege
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Drau
Lavamünd 1961‐1990
Lavamünd 2061‐90 REMO‐UBA
Oberdrauburg 1961‐1990
Oberdrauburg 2061‐90 REMO‐UBA
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Drau
Lavamünd 1961‐1990
Lavamünd 2061‐90 RegCM3
Oberdrauburg 1961‐1990
Oberdrauburg 2061‐90 RegCM3
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Drau
Lavamünd 1961‐1990
Lavamünd 2061‐90 Arpege
Oberdrauburg 1961‐1990
Oberdrauburg 2061‐90 Arpege
37
Fig. 38: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐Arpege
(bottom right), compared to 1961‐90, for the river Ager
4.4.4. Low flow runoff under climate change conditions
The more balanced seasonal runoff in climate change scenarios generally leads to decreasing slopes
in the duration curves, as shown in the example graphs of REMO‐UBA simulations for the upstream
Liezen gauge of the Enns in Fig. 39.
Fig. 39: Development of simulated duration curves through 30‐year periods around 1975, 2025, 2050, and 2075
for the upstream gauge Liezen of the Enns river
This implies rising low flow values under climate change assumptions for the 21st century. Fig. 40
shows the development of the three low flow runoff indicators Qmin (mean lowest monthly runoff in
a year), Q95 (runoff exceeded on 95% of the days) Q90 (runoff exceeded on 90% of the days) for the
0
10
20
30
40
50
1 2 3 4 5 6 7 8 9 10 11 12Runoff (m³/s)
Month
Ager
REMO‐UBA 2061‐2090
REMO‐UBA 1961‐1990
0
10
20
30
40
50
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Ager
RegCM3 1961‐1990
RegCM3 2061‐2090
0
10
20
30
40
50
1 2 3 4 5 6 7 8 9 10 11 12
Runoff (m³/s)
Month
Ager
Aladin‐Arpege 1961‐1990
Aladin‐Arpege 2061‐2090
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m³/s]
Probability of exceedance
Enns A1B (Liezen)
d_Qsim 1961‐1990d_Qsim 2011‐40d_Qsim 2036‐65d_Qsim 2061‐90
10
100
1000
0.00 0.20 0.40 0.60 0.80 1.00
Runoff [m³/s]
Probability of exceedance
Enns A2 (Liezen)
d_Qsim 1961‐90d_Qsim 2011‐40d_Qsim 2036‐65d_Qsim 2061‐90
38
four considered gauges of the Enns river and REMO‐UBA simulations. Qmin has the highest value of
these indicators with a reference values for 1961‐90 at Steyr (Ortskai) of 89m³/s, while Q90 has a
value of 80m³/s and Q95 of 77m³/s. The tendencies of the three indicators are analogue for all
gauges, time periods and scenarios. The only exception can be found in the last two periods at
Steyr/Ortskai, where the scenario A2 shows a clear increase of runoff and A1B shows only a
moderate increase for Qmin and Q90 and stable values for Q95. Generally, the changes (increases)
are higher in the upstream gauges. This again can be attributed to the stronger influence of snow
processes on the runoff regime, which are altered by increasing temperature. The higher
precipitation in the A2 scenario leads to generally higher values of low flow runoff.
Fig. 40: Development of simulated values of Qmin (upper row),Q90 (middle row) and Q95 (bottom row) through
30‐year periods around 1975, 2025, 2050, and 2075 for the Enns river
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A1B
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A2
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A1B
Q90(d) 1961‐1990 Q90(d) 2011‐40 Q90(d) 2036‐65 Q90(d) 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A2
Q90(d) 1961‐90 Q90(d) 2011‐40 Q90(d) 2036‐65 Q90(d) 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A1B
Q95(d) 1961‐1990 Q95(d) 2011‐40 Q95(d) 2036‐65 Q95(d) 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns ‐ A2
Q95(d) 1961‐90 Q95(d) 2011‐40 Q95(d) 2036‐65 Q95(d) 2061‐90
39
Due to the comparable tendencies in all three indicators, only Qmin is shown for the comparison of
A1B and A2 scenarios of REMO‐UBA for the rivers Mur (Fig. 42) and Drau (Fig. 41). For both rivers we
again see increasing low flow runoff for all gauges and both scenarios, in almost all time periods, but
consistently for the second half of the 21st century. In the A2 scenario the increase is slightly higher
than in the A1B scenario, again. For the Mur, like for the Enns, the changes are more pronounced in
the more alpine upper reaches. For the Drau, there are almost no differences between the
projections for the different gauges.
Fig. 41: Development of simulated values of Qmin through 30‐year periods around 1975, 2025, 2050, and 2075
for the Mur river
Fig. 42: Development of simulated values of Qmin through 30‐year periods around 1975, 2025, 2050, and 2075
for the Drau river
The comparison between results for Qmin with different climate models for 2061‐2090 (Fig. 43)
reveals larger discrepancies. In this analysis, the Ager basin is included again.
For the Enns (Fig. 43, top left), RegCM3 shows even larger increases in low flow runoff than
REMO‐UBA. With Aladin‐Arpege, low flow runoff increase only marginally, and for the most
downstream gauge even a slight decrease is expected. In scenarios with all three models more
pronounced changes are simulated for the more headwater reaches.
Lower low flow runoff with Aladin‐Arpege than with the other two models is also simulated in the
other analysed basins. This can be connected with the less pronounced increase in winter runoff in
the basins with alpine influence with this model (as shown in Fig. 35, Fig. 36, Fig. 37), as low flow
0.8
0.9
1
1.1
1.2
1.3
1.4
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur ‐ A1B
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur ‐ A2
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau ‐ A1B
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
0.8
0.9
1
1.1
1.2
1.3
1.4
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau ‐ A2
Qmin,sim 1961‐90 Qmin,sim 2011‐40 Qmin,sim 2036‐65 Qmin,sim 2061‐90
40
mainly occurs in winter in these rivers. But also for the Ager, where low flow occurs mainly in
autumn, Aladin‐Arpege results in the lowest low flow runoff of all models.
For the Mur (Fig. 43, top right), this leads to low flow scenario simulations with Aladin‐Arpege below
the reference values for 1961‐1990 for all but the most upstream gauge. With RegCM3, increasing
low flow runoff is projected, but less pronounced than with REMO‐UBA. The gradient of simulated
change along the river length is visible in results with REMO‐UBA and Aladin‐Arpege, but not with
RegCM3.
For the Drau (Fig. 43, bottom left), results for changes in low flow with RegCM3 and Aladin‐Arpege
are very similar and are markedly smaller than with REMO‐UBA. Aladin‐Arpege leads to practically no
changes, except for the most downstream gauge a slight increase is expected. RegCM3 generally
simulates small increases in Qmin. Here, results are similar for all gauges along the river length in all
models.
For the Ager (Fig. 43, bottom right), all models simulate a decrease in low flow runoff. REMO‐UBA
and Aladin‐Arpege both result in strong reductions of around 30%, while RegCM3 shows a moderate
decrease of less than 10%.
Fig. 43: Comparison of the ratio of simulated low flow (Qmin) in the 30‐year periods around 2075 and 1975 with
A1B scenarios of REMO‐UBA (black), RegCM3 (orange) and Aladin‐Arpege (violet) for Enns, Mur, Drau and Ager
4.4.5. Low flow runoff for shorter periods than a month: Mean annual 7‐day minimum
flow
For the rivers Mur, Drau and Enns a very close linear relation between the average lowest monthly
runoff of a year(Qmin) and mean annual 7‐day minimum flow (MAM7) is shown (Fig. 44). Each point
0.8
0.9
1
1.1
1.2
1.3
1.4
Schladming Liezen (Röthelbrücke)
KW Schönau / SB 8_6
Steyr (Ortskai)
Enns : A1B ‐ 2061‐90
Qmin,sim 1961‐90 Qmin,sim REMO‐UBA Qmin,sim RegCM3 Qmin,sim Arpege
0.8
0.9
1
1.1
1.2
1.3
1.4
St.Michael i. Lg. (Mur)
Gestüthof St.Georgen ob
Judenburg
Leoben Bruck an der Mur
unter Mürz
Friesach Mureck / Spielfeld
Mur : A1B ‐ 2061‐90
Qmin,sim 1961‐90 Qmin,sim REMO‐UBA Qmin,sim RegCM3 Qmin,sim Arpege
0.8
0.9
1
1.1
1.2
1.3
1.4
Rabland Oberdrauburg Sachsenburg (Brücke)
Villach Lavamünd
Drau : A1B ‐ 2061‐90
Qmin,sim 1961‐90 Qmin,sim REMO‐UBA Qmin,sim RegCM3 Qmin,sim Arpege
0.6
0.7
0.8
0.9
1
1.1
1.2
Ager (Schalchham)
Ager : A1B ‐ 2061‐90
Qmin,sim 1961‐90 Qmin,sim REMO‐UBA Qmin,sim RegCM3 Qmin,sim Arpege
41
in the plots in Fig. 44 represents a gauge. For the Drau and the Enns, the upstream gauges with lower
runoff show Qmin values slightly closer to MAM7 than the most downstream gauge. At the Mur no
such differences are recognizable. For Enns and Drau rivers, tests show that polynomial expressions
fit even better, but they are not shown, as linear relations already exhibit a very good fit and are
applicable to all three rivers. The ratio Qmin/ MAM7 exhibits values around 1.2 for all three rivers.
With these factors (also shown in Fig. 44), MAM7 values can be deducted from Qmin. The calculated
relations are valid for the analysed rivers and long term means of Qmin and MAM7. As the factors
show similar values, the use of values in their range for other larger Austrian rivers with alpine
influence seems justifiable.
Fig. 45 shows scatter plots of each year’s Qmin and the respective 7‐day minimum flow for gauges
with long observed time series of the three rivers. The scatter around the trend line is larger than for
the long term mean values of different gauges along the river (in Fig. 44), especially for Steyr/Ortskai
at the Enns. However, there are still strong linear relations in all investigated time series. The
calculation of individual years’ 7‐day low flow from Qmin is therefore also possible. For the analysed
Mur and Drau gauges, the ratio is slightly below the values for long term values for the entire river,
which can be attributed to the fact that the gauges are upstream gauges, with long term means also
below the trend line for the entire river.
Fig. 44: Relation between long term mean annual 7‐day minimum flow (MAM7) and mean lowest annual monthly runoff (Qmin) for different gauges along the rivers Enns (left), Mur (middle) and Drau (right)
Fig. 45: Relation between annual 7‐day minimum flow ( AM7 ) and lowest annual monthly runoff (Qmin) for time series of selected gauges of the rivers Enns (left), Mur (middle) and Drau (right)
y = 1.2253xR² = 0.9989
0
20
40
60
80
0 20 40 60 80
Qmin (m
³/s)
MAM7 (m³/s)
Enns
y = 1.2284xR² = 0.9978
0
20
40
60
80
0 20 40 60 80
Qmin (m
³/s)
MAM7 (m³/s)
Mur
y = 1.1727xR² = 0.9929
0
20
40
60
0 20 40 60
Qmin (m
³/s)
MAM7 (m³/s)
Drau
y = 1.2346xR² = 0.7279
40
60
80
100
120
40 60 80 100 120
Qmin (m
³/s)
AM7 (m³/s)
Enns (Steyr/Ortskai)
y = 1.1732xR² = 0.904
10
20
30
40
50
10 20 30 40 50
Qmin (m
³/s)
AM7 (m³/s)
Mur (Leoben)
y = 1.0661xR² = 0.9519
10
20
30
10 20 30
Qmin (m
³/s)
AM7 (m³/s)
Drau (Sachsenburg)
42
4.4.6. Time of occurrence of low flow periods
Temporal patterns of the occurrence of low flow periods are shown only for REMO‐UBA simulations.
Scenario simulations with input data of the other models generally show the same trends. For each
calendar month it is counted how frequently this month exhibits the lowest annual runoff. For the
reference period of 1961‐90, this analysis shows that low flow mostly occurs in the months of
December, January and February, for all three rivers along their entire Austrian reaches. The relative
frequencies of this period are depicted in blue in the upper part of the graphs in Fig. 46 to Fig. 47. Fig.
46 shows results for the period of 2061‐90 for a more upstream and the most downstream gauge of
the Enns river, for both considered scenarios. A shift of the occurrence of low flow from winter
months to fall can be seen clearly. Results for the two scenarios are very similar. The temporal shift,
which leads to a more even distribution of low flow periods over the year, is also very similar
upstream and downstream, but slightly more pronounced at the downstream Enns gauge.
Fig. 46: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for the 30‐
year periods around 1975 (blue, upper part of graphs) and 2075 (lower part) for two gauges of the Enns river
Also for the Mur and Drau rivers, the projected changes are very similar along the entire Austrian
reaches, but most pronounced at the most downstream gauges, which are therefore exemplarily
shown in Fig. 47. Again, a clear shift from almost exclusively winter low flow to fall and winter low
flow is simulated for both climate change scenarios.
At the Ager, low flow periods occur throughout the year in the 20th century, but most frequently in
autumn. Also here, a shift to earlier months is simulated in the scenario simulations with REMO‐UBA,
leading to a distribution with almost no low flow periods in winter and spring and a marked increase
of low flow periods in summer.
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Enns A1B (Liezen) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Enns A1B (Steyr/Ortskai) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Enns A2 (Liezen) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Enns A2 (Steyr/Ortskai) 2061 ‐ 90 1961 ‐ 90
43
Fig. 47: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for the 30‐
year periods around 1975 and 2075 for the most downstream gauges of the Mur and Drau rivers
Fig. 48: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for the 30‐
year periods around 1975 and 2075 for the Ager rivers
4.4.7. Low flow duration under climate change conditions
The run lengths of low flow periods, defined as months with discharge below Q90 of the respective
30‐year period, is assessed for the Enns and Drau rivers. The skewness measures g of the run length
distributions (Fig. 49) exhibit increases in the scenario simulations of both basins with REMO‐UBA,
because higher values of run length of 5 to 7 months occur (the highest value in the reference period
is 4 months). These high values occur only one to two times in 30 years. Within the 21st century,
there is a decreasing tendency of g for both REMO‐UBA scenarios for the Enns. For the Drau, the
REMO‐UBA A1B and the RegCM3 scenarios also exhibit this decreasing trend within the 21st century,
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Mur A1B (Mureck/Spielfeld) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Mur A2 (Mureck/Spielfeld) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Drau A1B (Lavamünd) 2061‐90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Drau A2 (Lavamünd) 2061 ‐ 90 1961 ‐ 90
0.0
0.5
1.0
1.50.0
0.5
1.0
1.5
9 10 11 12 1 2 3 4 5 6 7 8
Relative
frequency (/)
Relative
frequency (/)
Month
Ager A1B 2061 ‐ 90 1961‐1990
44
but REMO‐UBA A2 and Aladin‐Arpege show an increasing development, with very high values for
2061‐2090 for Aladin‐Arpege which result from one 9 month run below Q90. RegCM3 scenario
results are generally below the values of the reference period in the 20th century.
For RegCM3 and Aladin‐Arpege, the 1961‐1990 simulations for the Drau result from climate model
control run data. They can be compared to the results for 1961‐1990 in the REMO‐UBA graph, which
are calculated with observed input data. Both results with model control run input, but especially
RegCM3 results, show higher g measures than the simulations based on observations. For RegCM3,
the difference from simulations with observations as input is in the range of the change between
control run and scenario run. The simulations based on climate model data do not reproduce well
the persistence behavior of monthly runoff below Q90 (in contrast to the persistence of annual
runoff below the median, which is reproduced satisfactorily).
Fig. 49: Skewness measure g of the frequency distributions of run length below Q90 in the 30‐year periods
around 1975 and 2075 for the most downstream gauges of the Enns(top) and Drau(bottom) rivers, simulated
with REMO‐UBA A1B and A2 scenarios (left) and Aladin‐Arpege and RegCM3 scenario (right, both A1B)
Generally, the changes in low flow persistence projected by the different models and scenarios are
rather small – with the exception of Aladin‐Arpege – and even differ in the sign of the expected
change. Simulations with climate model control runs show deficiencies in reproducing monthly
persistence behavior. Also, as the low flow periods leading to low monthly runoff in reality have
shorter durations than a month, the analysis on the base of monthly runoff can only give hints of
changes in low flow duration. In total, no clear conclusion can be drawn concerning the duration of
low flow periods under climate change conditions.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1961‐90 2011‐2040 2036‐2065 2061‐2090
Skewness g of Run Len
ght below Q
90
(months)
Enns (Steyr/Ortskai)
A1B
A2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1961‐90 2011‐2040 2036‐2065 2061‐2090
Skewness of Run Len
ght below Q
90
(months)
Drau (Lavamünd)
A1B
A2
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
1961‐90 2011‐2040 2036‐2065 2061‐2090
Skewness of Run Len
ght below Q
90
(months)
Drau (Lavamünd)
Aladin‐Arpege
RegCM3
45
4.4.8. Long‐term persistence and periodicity in simulated runoff time series
Observed run lengths and simulations based on observed input data
Fig. 50 shows observed time series of annual runoff (in blue) and water balance model simulations
driven by observed meteorological data (in red). The visual comparison of both the absolute
numbers (above) and the differences to the median of the respective time series (below) shows a
very good agreement. In the graphs that show the difference of each man annual Q to the median of
the entire series, the division into runs above and below the median is clearly visible. The comparison
of the resulting distributions of run length below the median (Fig. 51) shows some differences. The
skewness measure g amounts to 5.8 for the observation and 7.1 for the simulation.
Fig. 50: Time series of annual runoff of the Enns at Steyr/Ortskai, observed (blue) and simulated with observed input data (red), above, and shown as difference to the median Q of the respective time series (below)
Fig. 51: Distribution of run lengths for the Enns, resulting from the time series in Fig. 50
0
50
100
150
200
250
300
1965 1970 1975 1980 1985 1990 1995
mean
annual Q
(m³/s)
Year
Qobs
Qsim
‐80
‐60
‐40
‐20
0
20
40
60
80
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Mean
annual Q
‐Median Q
(m³/s)
Year
‐80
‐60
‐40
‐20
0
20
40
60
80
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
Mean
annual Q
‐Median Q
(m³/s)
Year
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Enns Qobs (35y:1965‐1999)
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Enns Qsim (35y:1965‐1999)
46
Differences between run length distributions of observed and simulated time series can also be
found in the results for the Drau (Fig. 52, for the upstream gauge Sachsenburg, as no long
observation series is available for Lavamünd, the most downstream gauge in Austria). The skewness
measure g is 3.7 for the observed time series and 5.5 for the simulated time series.
Fig. 52: Distribution of run lengths for the Drau (at Sachsenburg)
Scenario run length
Time series of mean annual runoff below and above the median of the respective period and the
resulting run length distributions are shown for the periods of 2002‐2049 and 2043‐2090 in Fig. 53
for the Enns. Fig. 54 shows only the run length distributions for the Drau (where results for the
simulations for the 20th century are included, as they were not shown above.)
The A1B Enns time series for 2043‐2090 shows a behavior remarkably different from the 20th century
time series driven by observations (Fig. 53, top right). This is reflected in the occurrence of a very
high run of seven years (Fig. 53, top right) and in a resulting increase in g to 11.1 (Fig. 55, left). The
earlier scenario period is similar to the reference run in the 20th century. The Drau at Lavamünd
already exhibits more higher run lengths in the simulation driven by observed input data (Fig. 54,
top). Here we see an increase in the maximum run length already for 2002‐2049 and very
pronounced in 2043‐2090, where a very long run below the median of 10 years occurs (Fig.
54,bottom). Corresponding to this, the skewness measure g increases drastically to 26.6 (Fig. 55,
left). In Fig. 55, also the results for the A2 emission scenario are shown. In these simulations, there is
generally no large change in the distribution of ruin lengths. For the Enns, we see a slight decrease of
g for 2002‐2049, and the value of 7.5 for 2043‐2090 is very close to the value of 6.9 of the reference
simulations for the 20th century. For the Drau, g decreases in both scenario periods, from 9.8 in the
reference to 4.4 for 2043‐2090.
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10 12
Frequency
Run Length below the Median (Years)
Drau (Sachsenburg) Qobs (48y:1952‐1999)
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10 12
Frequency
Run Length below the Median (Years)
Drau (Sachsenburg) Qsim (48y:1952‐1999)
47
Fig. 53: Difference between mean annual runoff and the median for the respective period for 2002‐2043 (left) and 2049‐2090 (right), above; resulting distributions of run lengths, below; REMO‐UBA A1B for the Enns at Styr/Ortskai
Fig. 54: Distribution of run lengths in simulated runoff for the periods of 1952‐1999, (top left, blue) and 2002‐2049 (bottom left) and 2043‐2090 (bottom right); REMO‐UBA A1B for the Drau at Lavamünd
‐80
‐60
‐40
‐20
0
20
40
60
80
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
Mean
annual Q
‐Median Q
(m³/s)
Year
‐80
‐60
‐40
‐20
0
20
40
60
80
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
Mean
annual Q
‐Median Q
(m³/s)
Year
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Enns Qsim (48y: 2002‐49)
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Enns Qsim (48y: 2043‐90)
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Drau (Lavamünd) Qsim (48y: 1952‐99)
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Drau (Lavamünd) Qsim (48y: 2002‐49)
0
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7 8 9 10
Frequency
Run Length below the Median (Years)
Drau (Lavamünd) Qsim (48y: 2043‐90)
48
Fig. 55: Skewness measure g for the distributions of run lengths in simulated runoff for the periods of 1952‐1999 (blue) and 2002‐2049 and 2043‐2090 (REMO‐UBA, black: A1B, red: A2), for the Enns (left) and the Drau (right)
In the analysis of water balance simulations driven by Aladin‐Arpege and RegCM3 climate data,
results with control run data can be included. Fig. 56 shows the two control run simulations for the
Drau (depicted as the difference between mean annual Q and the median of the entire period). It is
clearly visible, that the sequences are different in the two simulations, resulting from different and
random “weather” in each model (only the long term means are expected to be the same or very
similar). The amplitudes are markedly higher in the simulations driven by Aladin‐Arpege data (Fig. 56,
left) than in the RegCM3 results (Fig. 56, right). The resulting values of g of the run length
distributions (Fig. 57), however, are close to each other (11.1 for Aladin‐Arpege and 9.8 for RegCM3)
and also very close to the simulations driven by observed climate data (9.8). This means that the
water balance simulations driven by climate model control run data generally show a long‐term
persistence behavior in accordance with observations and simulations with observed climate data.
Fig. 56: Mean annual runoff time series (mean annual Q ‐ median Q of the entire period) for the simulations with control run climate model data for Aladin‐Arpege (left) and RegCM3 (right).
The distributions of run lengths in the scenario simulations of the two models, as described by the
value of g (Fig. 57), are also rather close to each other. For the period of 2002‐2049, they both show
a decline of g, which is in contrast to the slight increase in the REMO‐UBA A1B simulations. For the
more distant period of 2043‐2090, with a stronger climate change signal, they both show an increase
0
5
10
15
20
25
30
1952‐1999 2002‐2049 2043‐2090
Skewness of Run Lenght below M
edian
Enns (Steyr/Ortskai)
A1B
A2
0
5
10
15
20
25
30
1952‐1999 2002‐2049 2043‐2090
Skewness of Run Lenght below M
edian
Drau (Lavamünd)
A1B
A2
‐100
‐80
‐60
‐40
‐20
0
20
40
60
80
100
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
Mean
annual Q
‐Median Q
(m³/s)
Year
‐100
‐80
‐60
‐40
‐20
0
20
40
60
80
100
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
Mean
annual Q
‐Median Q
(m³/s)
Year
49
in g, as in the REMO‐UBA model, but markedly less pronounced. This generally consistent behavior of
all three models with the A1B scenario indicates that the change towards longer periods of
persistence in annual runoff might be attributed to the effects of climate change. Also the fact that
the simulations with control run data do not exhibit higher run lengths than those driven by
observations suggests that the different run length distribution in the distant future scenario
simulations are not just climate model artefacts. However, the change simulated with these models
is much smaller than that projected with REMO‐UBA, for which control run results are not available.
Fig. 57: Skewness measure g for the distributions of run lengths in simulated runoff for the periods of 1952‐1999, 2002‐2049 and 2043‐2090 (Aladin‐Arpege: violet, RegCM3: orange, both A1B)
Autocorrelation of mean annual runoff
The following graphs show the autocorrelation plots for the analysed 48‐year time series of mean
annual runoff. Autocorrelation coefficients for increasing lags of up to 24 years are shown. The
sequence of autocorrelation coefficients in runoff time series simulated with observed
meteorological input shows considerable differences between the Enns (Fig. 58, top) and the Drau
(Fig. 59, top). The Enns data has the strongest negative autocorrelation for 3 and 6 years and a peak
of positive autocorrelation for 8 years. A cyclic alternation of positive and negative autocorrelation
periods is visible. The Drau shows no such longer periods of alternating autocorrelation. High positive
coefficients are detected for 2, 5, 7 and 12 years, less pronounced negative peaks in autocorrelation
for 9 and 11 years.
The results for the A1B scenario simulations (Fig. 58 and Fig. 59, middle row) exhibit markedly
different patterns of autocorrelation. For the period of 2002‐2049, higher peaks of especially
negative autocorrelation are reached for both rivers. For the Enns, these highest values occur for lags
of 7‐9 years (Fig. 58, middle left). For the Drau, they occur for lags of 4 and 10‐11 years. The
autocorrelation plot for 2002‐2049 for the Drau (Fig. 59, middle left) also shows very distinct cyclic
fluctuations of positive and negative autocorrelation. For the period of 2043‐2090, the Drau (Fig. 59,
middle right) results do not exhibit such a remarkable periodicity, but very long periods of positive
autocorrelation (1‐7 years) and negative autocorrelation (8‐15, with just one exception at a lag of 10
years). Like the drastic change in the run length distribution presented above, this shows a significant
change in long term persistence characteristics of the runoff time series, as the 20th century
simulations exhibit very short periods of autocorrelation coefficients with the same sign. This change
0
5
10
15
20
25
30
1952‐1999 2002‐2049 2043‐2090
Skewness of Run Lenght below M
edian
(Years)
Drau (Lavamünd)
Aladin‐Arpege
RegCM3
50
is even more obvious in the A1B autocorrelation plot for 2043‐2090 for the Enns (Fig. 58, middle
right), where positive autocorrelations are found for lags of 1‐7 years (with very high coefficients for
1‐3 years and one exception with low negative autocorrelation for 5 years) and a series of 14
negative coefficients for lags of 11‐24 years.
The results for the simulations with A2 data do not show any clear trend for the Enns (Fig. 58, bottom
row), which corresponds with the almost unchanged values of g of the run length distributions (Fig.
55, left). For the Drau (Fig. 59, bottom row), there is rather a decrease in autocorrelation coefficients,
especially for the period of 2043‐2090. This also matches the decrease of g of run length distributions
(Fig. 55, right).
In the simulations with control run data of the models Aladin‐Arpege (Fig. 60, top) and RegCM3 (Fig.
61, top) we can see distinct patterns of autocorrelation coefficients for increasing lags, but a
generally similar periodicity in changing signs. Aladin‐Arpege shows one distinct negative peak at a
lag of 3 years, RegCM3 a value of comparable magnitude at a lag of 5 years.
Fig. 58: Autocorrelation plots for the Enns; simulations for 1952‐1999 driven by observations (top), driven by REMO‐UBA A1B data for 2002‐2049 (middle left) and for 2043‐2090 (middle right), and driven by REMO‐UBA A2 data for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right)
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
1952‐1990
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
51
Fig. 59: Autocorrelation plots for the Drau; simulations for 1952‐1999 driven by observations (top), driven by REMO‐UBA A1B data for 2002‐2049 (middle left) and for 2043‐2090 (middle right), and driven by REMO‐UBA A2 data for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right)
Fig. 60: Autocorrelation plots for the Drau with Aladin‐Arpege; simulations for 1952‐1999 (top), for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right)
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
1952‐1990
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
1952‐1990
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
52
Fig. 61: Autocorrelation plots for the Drau with RegCM3; simulations for 1952‐1999 (top), for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right)
The simulations for the period of 2002‐2049 (Fig. 60 and Fig. 61, bottom left) show autocorrelation
coefficients lower than in the control run simulations for both models, which corresponds with
decreasing g of the run length distributions (Fig. 57). The increasing values of the coefficients and the
more pronounced periodicity in the period of 2043‐2090 is better visible in the Aladin‐Arpege
simulations (Fig. 60, bottom right) than in those of RegCM3 (Fig. 61, bottom right). This also
corresponds with the more pronounced increase in the g value of run length distributions for
Aladin‐Arpege (Fig. 57).
4.5. Alpine reservoirs
For all three considered reservoir systems the development of runoff and precipitation, liquid and
solid precipitation portions, snow accumulation and snow melt and the runoff contributions of snow
melt and glacier melt are described. These results are based mainly on REMO‐UBA A1B climate
change scenarios. For the catchment areas of the Gepatsch reservoir, a more detailed analysis of
glacier development is conducted, comparing results with different ice melt DDFs and with different
emission scenarios and different climate models. Therefore, the results for Gepatsch are displayed
first and described in more detail. The main results for Sellrain‐Silz and Kaprun‐Uttendorf are then
presented briefly.
4.5.1. Gepatsch
Fig. 62 shows the expected seasonal changes in precipitation (liquid and solid) and runoff. While for
precipitation only minor changes are projected – slight increase in winter and decreases in summer –
the runoff pattern changes drastically. In 2011‐40, there is almost no change yet, the slightly higher
runoff in summer can mainly be attributed to glacier melt. In 2036‐65, the trend to a decrease of the
summer peak and an increase of spring runoff is already visible. In 2061‐90, summer runoff is much
lower than in the reference period, and has mainly shifted to spring months.
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
1952‐1990
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2002‐2049‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
0.3
0.4
0.5
1 6 11 16 21
k (autocorrelation)
Lag (Years)
2043‐2090
53
Fig. 62: Development of precipitation and runoff in the 21st century, compared to 1961‐1990 (Gepatsch)
The decrease in summer runoff occurs despite an increase of the rainfall portion of precipitation (Fig.
63) and an increase in glacier melt (shown below in Fig. 65). It can mainly be attributed to earlier
snow melt (Fig. 64).
Fig. 63: Seasonal course of the snowfall portion of total precipitation, for 1961‐90 (left) and 2061‐90 (right)
The seasonality of snow processes is expected to change significantly in the Gepatsch catchment
areas. Fig. 64 shows mean monthly values of snow water equivalent, snow cover, snow melt and
accumulated snowfall for the two periods of 1961‐90 and 2061‐90 (note the x‐axis, which in contrast
to the other graphs in this report follows the hydrological year from August to July to improve
readability). Snow cover and the amount of snow water equivalent in the catchment is markedly
lower in 2061‐90. While in the reference period some snow remains in the area over the summer
months, there is practically no snow from July to September at the end of the century. Snow melt
starts in April and has a peak in May in the future scenario, one to two months earlier than at
present. Snow melt in August and September, which remains high in 1961‐90, drops to very low
values in 2061‐90.
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
Runoff 2061‐90
Precipitation 2061‐90
Runoff 2036‐65
Precipitation 2036‐65
Runoff 2011‐40
Precipitation 2011‐40
Runoff 1961‐90
Precipitation 1961‐90
0
100
200
300
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
1961‐1990 Precipitation
Snowfall
0
100
200
300
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2061‐2090 Precipitation
Snowfall
54
Fig. 64: Seasonal course of snow water equivalent, accumulated snowfall, snow melt and snow cover for 1961‐90 and 2061‐90 (Gepatsch)
Part of the reduced summer snow melt runoff is compensated by increases in glacier melt runoff. Fig.
65 shows the development of seasonal snow melt and glacier melt portions of total runoff. The
graphs of the left column correspond to the simulation results shown above, with ice melt DDFs
according to snow melt DDFs in the areas. Again, the seasonal shift and general decrease of snow
melt in the course of the 21st century is clearly visible. Glacier melt contributions start in July and are
low in 2011‐40. In 2036‐65 they are expected to increase significantly and start already in June. The
glacier melt pattern for 2061‐90 remains similar, but snow melt contributions have decreased to
values below ice melt in summer.
With higher ice melt DDFs, simulated glacier contributions to runoff are already slightly higher in
2011‐40 and markedly higher in 2036‐65. Due to faster melting, the peak of glacier melt occurs
earlier (see also Fig. 67), which is reflected in slightly lower ice melt runoff in 2061‐90. It has to be
noted that doubled ice melt DDFs are a rather extreme assumption – values in the range of 1.5 might
be more probable. The impact of this parameter can clearly be seen in the development of glacier
melt. The seasonal characteristics and the interaction with snow processes, however, are
comparable in both simulations, and the effect on seasonal total runoff is small.
For annual values, the slightly higher values of glacier melt runoff and thus total runoff are better
identifiable (Fig. 66). Also the still increasing ice melt in 2061‐90 with ice DDFs according to snow
DDFs, contrasted by the already decreasing ice melt with higher DDFs is visible in Fig. 66. But also in
the first simulation, glacier melt is decreasing towards the end of the 21st century – which is just not
reflected in the 30‐year mean due to the peak occurring around the beginning of this period.
0.1
0.4
0.7
1.0
0
500
1000
8 9 10 11 12 1 2 3 4 5 6 7
Month
snow cover (/)
mm1961‐90 SWE
2061‐90 SWE
1961‐90 Sum of Snowfall
2061‐90 Sum of Snowfall
1961‐90 Snow melt
2061‐90 Snow melt
1961‐90 Snow cover
2061‐90 Snow cover
55
Fig. 65: Seasonal course of total runoff and snow melt and glacier melt portions, for the glacier simulation with ice DDF values like snow DDF values (left) and the simulation with doubled ice DDF values (right)
Fig. 66: Annual sums of total runoff and snow melt and glacier melt contributions, for the glacier simulation with ice DDF values like snow DDF values (left) and the simulation with doubled ice DDF values (right)
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2011‐40Total runoff
Snow melt
Glacier melt
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2011‐40DDFice=2*DDFsnow
Total runoff
Snow melt
Glacier melt
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2036‐65Total runoff
Snow melt
Glacier melt
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2036‐65DDFice=2*DDFsnow
Total runoff
Snow melt
Glacier melt
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2061‐90Total runoff
Snow melt
Glacier melt
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2061‐90DDFice=2*DDFsnow
Total runoff
Snow melt
Glacier melt
0
200
400
600
800
1000
1200
1400
1600
2011‐40 2036‐65 2061‐90 2011‐40 2036‐65 2061‐90
DDFice = DDFsnow DDFice = 2*DDFsnow
(mm) Total runoff Snow melt runoff Ice melt runoff
56
The start of this decrease is visible in Fig. 67, where relative contributions of ice melt to total runoff
from different scenarios are compared for the catchment of the river Pitze (around 50% of which
discharges into the Gepatsch reservoir). The very similar course of the A1B scenario, which
corresponds to the results shown above, and the A2 scenario result from similar temperature
increases projected by these two scenarios. In the B1 scenario, temperature rises slower and less,
leading to significantly slower melting of glaciers and a delayed increase in ice melt. The A1B
simulation with doubled ice DDF values on the other hand result in faster melting of glaciers and an
earlier increase and also an earlier peak and decrease of ice melt portions of total runoff. Although
very high ice DDFs are used, the difference to the A1B scenario with lower ice DDFs is in the range or
even below the difference between A1B and B1 scenarios. This indicates that the uncertainty in
glacier melt simulations arising from uncertain ice melt factors is rather below the uncertainty from
temperature change projections.
Fig. 67: Floating 30‐year means of ice melt contributions to runoff in the Pitze catchment for different climate change scenarios
This conclusion is supported by the comparison of results with different models, as shown in Fig. 68.
All depicted simulations are based on low ice DDFs (equal to snow DDFs). Results with RegCM3 are
between REMO‐UBAs A2 and B1 simulations. Aladin‐Arpege results show high ice melt already in the
beginning of the 21st century, almost comparable to the simulations with doubled ice DDFs shown in
Fig. 67. The reason for this differing behavior is a stronger increase in summer temperature in the
Aladin‐Arpege projections.
0
0.05
0.1
0.15
0.2
2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 2068 2072
Glacier melt portion of annual runoff (/)
Years (Center of 30‐year mean)
A2, DDFice=DDFsnow
B1, DDFice=DDFsnow
A1B, DDFice=DDFsnow
A1B, DDFice=2*DDFsnow
57
Fig. 68: Floating 30‐year means of ice melt contributions to runoff in the Pitze catchment simulated with climate data of different models and different emission scenarios
The drastic loss of glacier volumes corresponding to the shown increases in ice melt runoff is shown
in Fig. 69, again for the A1B scenario and both ice melt DDF assumptions. This graph, like the
analyses before, refers to 30‐year means and sums over the entire Gepatsch catchment. Fig. 70
shows the spatial distribution of simulated ice water equivalents and refers to snapshots for specific
years. Therefore, also the very last simulated year, 2090, can be shown. This reveals that, based on
very high ice DDF values, the melting of all glaciers in the Gepatsch area is almost completed in 2090
(Fig. 70, bottom right). Assuming low ice DDFs, substantial areas with glaciers remain in 2090, but
with very low volumes (Fig. 70, bottom left).
Fig. 69: Development of simulated ice water equivalent in the cachment areas of the Gepatsch reservoir in the 21st century, with ice DDF values like snow DDF values (left) and doubled ice DDF values (right)
0
0.05
0.1
0.15
0.2
2016 2020 2024 2028 2032 2036 2040 2044 2048 2052 2056 2060 2064 2068 2072
Glacier melt portion of annual runoff (/)
Years (Center of 30‐year mean)
Pitze
REMO‐UBA A2
REMO‐UBA B1
REMO‐UBA A1B
ARPEGE A1B
RegCM3 A1B
0
500
1000
1500
2000
2500
3000
DDFice = DDFsnow DDFice = 2*DDFsnow
Ice water equivalent (M
io. m³)
Start value 2000 Mean 2011‐40 Mean 2036‐65 Mean 2061‐90
58
Fig. 70: Simulated development of glaciers in western Tyrol, with low ice DDFs (left) and high ice DDFs (right, for 2011 only the results with low ice DDFs are displayed, as differences are negligible)
59
4.5.2. Sellrain‐Silz
In the catchment areas of the Sellrain‐Silz hydropower system, the decrease in summer runoff is
especially pronounced (Fig. 71). This strong decrease is caused by a significant decrease in summer
precipitation that is more pronounced than in the other two analysed areas. Additionally, the
Sellrain‐Silz catchments have smaller glacierized portions. This means that the decrease in snow melt
runoff in summer months, which is shown in Fig. 72, is less compensated by ice melt runoff. The
general pattern of lower summer and higher spring runoff, with an earlier peak (Fig. 71), is
comparable to the development in the other reservoir catchment areas. Also, overall decrease of
accumulated snow water equivalent and seasonal changes in snow cover and snow melt (Fig. 72) are
similar to the changes in the other investigated areas.
Fig. 71: Development of precipitation and runoff in the 21st century, compared to 1961‐1990 (Sellrain‐Silz)
Fig. 72: Seasonal course of snow water equivalent, accumulated snowfall, snow melt and snow cover for 1961‐90 and 2061‐90 (Sellrain‐Silz)
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
Runoff 2061‐90
Precipitation 2061‐90
Runoff 2036‐65
Precipitation 2036‐65
Runoff 2011‐40
Precipitation 2011‐40
Runoff 1961‐90
Precipitation 1961‐90
0.1
0.4
0.7
1.0
0
500
1000
1500
8 9 10 11 12 1 2 3 4 5 6 7
Monat
snow cover (/)mm
1961‐90 SWE
2061‐90 SWE
1961‐90 Sum of Snowfall
2061‐90 Sum of Snowfall
1961‐90 Snow melt
2061‐90 Snow melt
1961‐90 Snow cover
2061‐90 Snow cover
60
4.5.3. Kaprun‐Uttendorf
Seasonal runoff changes in the catchment areas of the Kaprun‐Uttendorf hydropower systems are
less pronounced than in the other two analysed areas (Fig. 73). Here, changes in seasonal
precipitation patterns are very small (Fig. 73), and substantial ice melt runoff compensates lower
snow melt runoff in the summer months in the future scenario (Fig. 74). But again the overall pattern
of hydrological change induced by the changing climatic drivers is very similar to those found in the
other two areas.
Fig. 73: Development of precipitation and runoff in the 21st century, compared to 1961‐1990 (Kaprun‐Uttendorf)
Fig. 74: Seasonal course of total runoff and snow melt and glacier melt portions (Kaprun‐Uttendorf)
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
Runoff 2061‐90
Precipitation 2061‐90
Runoff 2036‐65
Precipitation 2036‐65
Runoff 2011‐40
Precipitation 2011‐40
Runoff 1961‐90
Precipitation 1961‐90
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2011‐40Total runoff
Snow melt
Glacier melt
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2036‐65Total runoff
Snow melt
Glacier melt
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12
mm
Month
2061‐90Total runoff
Snow melt
Glacier melt
61
4.6. Water temperature
Water temperature shows a high correlation with air temperature. Therefore, clear trends in water
temperature are calculated for the scenarios of the 21st century. For the REMO‐UBA A1B scenarios, a
rise in water temperature of 2°C for the Ager and 3.5°C for the Mur is expected (Fig. 75).
Increasing water temperature has different effects for the use of cooling water at different rivers.
Legislative water temperature thresholds are defined individually for each case, and differ according
to the locally relevant guidelines (based mainly on ecological requirements). Generally, the threat of
surpassing legislative water temperature thresholds strongly depends on the actual water
temperature in the respective river reach and the imposed threshold. If the water temperature
currently is usually well below the threshold, a rise by some degrees will not lead to problems for
cooling water use. Possible negative effects for cooling water use also depend on the time of
occurrence of low flow periods. During these periods with low runoff, the introduction of cooling
water into rivers has more impact. Smaller rivers with low flow periods in warmer months, such as
the Ager river, are therefore more vulnerable to rising water temperature than larger rivers with
alpine influence. In such river, as the Mur, Enns or Drau low flow periods mainly occur in winter,
when water temperature is very low. Runoff in these periods is rather expected to increase with
climate change. Also the expected shift in the time of occurrence to autumn cannot be expected to
have relevant negative influence regarding water temperature in these rivers. For rivers like the Ager,
however, the shift of low flow periods to summer months and the expected decrease of low flow
runoff can aggravate the situation.
Fig. 75: Trends in water temperature resulting from water REMO‐UBA A1B climate model data, for the Mur (at Mureck/Spielfeld) and the Ager (at Schalchham)
‐10
‐5
0
5
10
15
20
25
T [°C]
Mur ‐ Temperaturentwicklung A1B Szenario
Lufttemperatur Wassertemperatur Mur Trend der Wassertemperatur
‐10
‐5
0
5
10
15
20
25
T [°C]
Ager ‐ Temperaturentwicklung A1B Szenario
Lufttemperatur Wassertemperatur Ager Trend der Wassertemperatur
Water temperature Mur (REMO‐UBA A1B)
Water temperature Ager (REMO‐UBA A1B)
Water temperatureAir temperature Trend in water temperature
Water temperatureAir temperature Trend in water temperature
62
4.7. Groundwater recharge
The spatial distribution of relative changes in annual groundwater recharge (Fig. 76, for the REMO‐
UBA A1B simulations) resembles the changes in runoff (see Fig. 27). For the regions analysed in more
detail, REMO‐UBA shows distinct trends, with groundwater recharge decreasing in the south and
increasing in the east. As in simulations of runoff (see Fig. 25 and Fig. 26), a similar spatial pattern
results from RegCM3 data, while Aladin‐Arpege shows decreases in runoff and groundwater recharge
all over Austria.
Fig. 76: Change in simulated mean annual groundwater recharge 2061‐2090 relative to 1961‐1990, results with REMO‐UBA A1B input data
In the seasonal patterns of simulated groundwater recharge (Fig. 77 to Fig. 80), differences between
the regions and the models are visible. In the downstream Mur area (Fig. 77) and the Raab basin (Fig.
78) groundwater recharge declines after a peak in March and April, but there are still relevant
summer contributions. In the basins of Rabnitz (Fig. 79) and Leitha (Fig. 80), groundwater recharge is
extremely low in July, August and September. In the model projections for 2061‐2090, Aladin‐Arpege
for all basins shows a decrease of groundwater recharge in spring and summer and very small
increases in autumn and winter. This leads to changes relative to the values of 1961‐1990
between ‐7% and ‐13% for the entire year and between ‐23% and ‐16% for the period of March to
June (MAMJ). With RegCM3 data, similar changes are projected for Mur and Raab basins, with a
slightly stronger decrease in spring and summer. In the Rabnitz and Leitha basins, RegCM3 shows
almost no changes, with slight decreases in spring and slight increases in winter recharge. The
resulting changes for the year are positive, while the changes for March to June are negative.
REMO‐UBA results show almost no changes in spring and winter recharges for Mur and Raab. For
Rabnitz and Leitha increasing peaks in spring are projected, and a slight increase in winter. For
summer and autumn, REMO‐UBA generally expects the most pronounced decrease of groundwater
recharge. In the Mur and Raab regions, where summer recharge plays a relevant role, this leads to
decreases in annual values that are higher than the expected decrease for March to June. For the
63
regions further to the east, REMO‐UBA projects considerable increases of 14% (Rabnitz) and 19%
(Leitha) in annual groundwater recharge.
Fig. 77: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative changes of the three models for the entire year and the period of March to June( MAMJ, bottom left); results for the downstream Mur basin
Fig. 78: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative changes of the three models for the entire year and the period of March to June( MAMJ, bottom left); results for the Raab basin
0
10
20
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Mur
Aladin‐Arpege 1961‐90
Aladin‐Arpege 2061‐90
0
10
20
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Mur
RegCM3 1961‐90
RegCM3 2061‐90
0
10
20
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Mur
REMO‐UBA 1961‐90
REMO‐UBA 2061‐90
0.6
0.8
1.0
1.2
Aladin‐Arpege 2061‐90 RegCM3 2061‐90 REMO‐UBA 2061‐90
Simulated groundwater recharge: Mur
Year 2061‐90 relative to 1961‐90MAMJ 2061‐90 relative to 1961‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Raab
Aladin‐Arpege 1961‐90
Aladin‐Arpege 2061‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Raab
RegCM3 1961‐90
RegCM3 2061‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Raab
REMO‐UBA 1961‐90
REMO‐UBA 2061‐90
0.6
0.8
1.0
1.2
Aladin‐Arpege 2061‐90 RegCM3 2061‐90 REMO‐UBA 2061‐90
Simulated groundwater recharge: Raab
Year 2061‐90 relative to 1961‐90MAMJ 2061‐90 relative to 1961‐90
64
Fig. 79: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative changes of the three models for the entire year and the period of March to June( MAMJ, bottom left); results for the Rabnitz basin
Fig. 80: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative changes of the three models for the entire year and the period of March to June( MAMJ, bottom left); results for the Leitha basin
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Rabnitz
Aladin‐Arpege 1961‐90
Aladin‐Arpege 2061‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Rabnitz
RegCM3 1961‐90
RegCM3 2061‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Rabnitz
REMO‐UBA 1961‐90
REMO‐UBA 2061‐90
0.6
0.8
1.0
1.2
Aladin‐Arpege 2061‐90 RegCM3 2061‐90 REMO‐UBA 2061‐90
Simulated groundwater recharge: Rabnitz
Year 2061‐90 relative to 1961‐90MAMJ 2061‐90 relative to 1961‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Leitha
Aladin‐Arpege 1961‐90
Aladin‐Arpege 2061‐90
0
10
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Leitha
RegCM3 1961‐90
RegCM3 2061‐90
0
10
20
30
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
mm
Simulated groundwater recharge: Leitha
REMO‐UBA 1961‐90
REMO‐UBA 2061‐90
0.6
0.8
1.0
1.2
1.4
Aladin‐Arpege 2061‐90 RegCM3 2061‐90 REMO‐UBA 2061‐90
Simulated groundwater recharge: Leitha
Year 2061‐90 relative to 1961‐90MAMJ 2061‐90 relative to 1961‐90
65
4.8. Trends in runoff and hydropower production in Europe
4.8.1. Mean runoff and hydropower potential
Based on an application of the WaterGAP model, runoff trends and their implications for hydropower
generation are analysed by Lehner et al. (2005). In a comparison of several large scale hydrological
and land surface models, the WaterGAP model recently achieved the best representation of seasonal
runoff trends (Stahl et al. 2011). The global hydrological model WaterGAP is driven by precipitation
and temperature input data from two GCMs, ECHAM4 and HADCM3. The hydrological model also
includes scenarios on human water use and management. The greenhouse gas emission scenario
applied is similar to the widely used IPCC A1B scenario. The analysed time periods are the decade of
the 2020s and the 2070s, and the reference period of 1961‐1990.
Resulting from an overall decrease in runoff, the gross hydropower potential is projected to decrease
by about 6% by the 2070s, with large regional variability (Lehner et al. 2005). The spatial pattern of
simulated runoff trends due to climate change projections is shown in Figure 1. For the 2020s, the
differences between the two climate models are immense (different signs in Spain and almost the
entire northern part of Europe). The large discrepancies can probably be explained by the larger
influence of the – random – model “weather” relative to the climate change signal. For the 2070s
with their stronger climate change signal, the patterns are similar, with a general decrease of runoff
south of the Alps and an increase north. With both models, Austria is located at the transition of
increasing and decreasing trends, with HADCM3 showing mostly a decrease and ECHAM4 no relevant
change. Calculated with HADCM3 results, the gross hydropower potential for Austria decreases by
15.9 to 17.5% by the 2070s (Table1, depending on the calculation method). In addition to the gross
hydropower potential, which is based only on discharge and elevation, the developed hydropower
potential was analysed by Lehner et al. (2005). For this value, the discharge at main reservoir and
run‐of‐river stations was considered, assuming a constant level of hydropower development. For the
developed hydropower potential, results for both models are presented countrywise (Table 1). For
Austria and the 2070s, a similar value of decrease results for the HADCM3 model (‐13.1%), while the
results with the ECHAM4 model show practically no change (+1.3%), which is consistent with the
projection of unchanged runoff in Fig. 1.
The results for Austria and the 2070s can be compared to the results of the KlimAdapt project (Kranzl
et al. 2010, Stanzel and Nachtnebel 2010) for the period of 2061‐2090 and the A1B scenario. In
KlimAdapt, hydropower production was deducted from the weighted gross hydropower potential,
with weights according to actual production. Therefore it corresponds rather to the developed
hydropower potential of Lehner et al. (2005). The projected decrease of 15% between 2025 and 2075
in KlimAdapt is very close to their results of ‐13% with the HADCM3 model. Gross hydropower
potential was calculated to decrease by 10% in KlimAdapt, which is lower than the ‐16 to ‐17% of
Lehner et al. (2005). The differences can be attributed to different spatial patterns in input variables,
especially precipitation. Also different topographic information on the larger scale (0.5°, as opposed
to 1km in KlimAdapt) can contribute to differences in resulting gross hydropower potential. The
value for the 1961‐90 gross hydropower potential in Lehner et al. (2005) of 150 or 160 TWh/a is
similar to that calculated by Pöyry (2008) and slightly higher than that estimated in KlimAdapt, where
south‐eastern Austria was excluded. As especially in the Austrian south‐east runoff is expected to
decrease (in both, KlimAdapts REMO‐UBA model and the HADCM3 model, see Fig. 1.), also this
different coverage of the area of Austria might add to the differences in the projections of gross
hydropower potential.
66
Corresponding to the depicted pattern of projected discharge changes, hydropower potential is
expected to rise in northern and north‐eastern Europe and to fall in southern and central Europe.
The largest increases are projected for Scandinavia, the Baltic countries and Russia. The highest
decreases are expected in Spain, the Balkan countries, Turkey and Ukraine.
Fig. 81: Relative change of mean runoff (compared to 1961‐90) for the 2020s (top) and the 2070s (bottom), and the ECHAM4 (left) and HadCM3 (right) models. Source: Lehner et al. 2005
67
Table 1: Hydropower potential in Europe and projected future change. Source: Lehner et al. 2005
4.8.2. Seasonal changes
In addition to changes in the mean runoff, seasonal changes in runoff regimes are anticipated to
change with changing climatic conditions.
Arnell (1999) applied a hydrological model with the same scale as WaterGAP (0.5°), driven by
scenarios for the 2050s of four different climate models. In his analysis of possible seasonal changes
he concludes, that the main changes are to be expected in snow‐dominated basins. Fig. 2 shows his
results for the change in the month of maximum runoff. In large parts of Europe, where maximum
flows correspond with snow melt, the annual runoff peak shifts to earlier months due to earlier
snow‐melt. A more extreme change is simulated mainly for areas around the Baltic Sea, where winter
precipitation changes from snow to rain and the runoff regime shifts from snow dominated to a
more maritime regime with winter maxima.
68
Fig. 82: Change in the month of maximum runoff by 2050, compared to 1961‐90. Source: Arnell 1999
Graham et al. (2007) do not simulate such drastic shifts of the annual runoff peak for the Baltic Sea
drainage basin (Fig. 3), but still expect a shift of two months because of earlier snow‐melt towards
the end of the 21st century. Applying several RCMs from the PRUDENCE project, they estimate a
decrease of 22% in summer flows and an increase of 54% in winter flows on average for the Baltic
Sea area. For the Rhine basin, Graham et al. 2007 predict a similar change, with decreases in summer
runoff of 42% and increases in winter runoff of 14% on average. For the Alpine parts, peaks due to
snow melt are expected to occur one month earlier and with a reduced magnitude of around 20%.
69
Fig. 83: Change in seasonal runoff to the Baltic Sea, 2071‐2100 compared to 1961‐90, in 2 B2 scenarios (left) and 3 A2 scenarios (right). Source: Graham et al. 2007
Middelkoop et al. (2001) come to analog results in their hydrological modeling study of the Rhine
basin, using UKHI and CCC climate model scenarios for 2050 (like Arnell, 1999, see above): “Due to
climate change the river Rhine is expected to shift from a combined rainfall‐snowmelt regime to a
more rainfall dominated regime. This coincides with a seasonal change in the discharge regime:
winter discharge will increase, and summer discharges decrease.” For middle‐mountain areas they
expect the least seasonal changes in runoff.
For the Elbe basin, with a major influence from middle‐mountain areas, this is also shown by
Hattermann et al. (2008). While they do not expect major seasonal changes in their application with
an ECHAM4 A1 scenario, they also come to the conclusion that by 2050 there will be less water
available in summer and that the high flow period due to snow melt will occur earlier in the year.
For the Danube basin, which has a stronger Alpine influence, a more pronounced change in runoff
seasonality, as in the Rhine basin, is projected by Kling et al. (2012). Their simulation results for
2071‐2100 with all 23 RCMs from the ENSEMBLES project show a considerable decrease of summer
runoff, a moderate increase of winter runoff and the occurrence of the annual runoff peak one to
two months earlier. These results correspond with those for the major Austrian Danube tributaries in
KlimAdapt (Stanzel and Nachtnebel 2010), who furthermore show that the seasonal changes in
runoff are more pronounced with a higher relevance of snow processes.
For the Ebro basin, Zambrano‐Bigiarini et al. (2011) use two PRUDENCE RCMs and expect a reduction
of mean runoff throughout the year for 2071‐2100, with especially strong decreases in winter.
The outlined projected changes are already observable in runoff measurements, as shown by Stahl et
al. (2010), who analysed trends in a large dataset of 1962‐2004 streamflow data from basins with no
direct human influence. They found predominant positive trends in winter runoff, with exceptions of
decreasing flow in Spain, Southern France, South‐east Austria, Czech Republic and Slovakia. For Italy
and South‐east Europe, no observations were included in the analysis. For the months from April to
August, negative trends were detected almost everywhere in Europe, with some exceptions in
Scandinavia and the UK.
70
4.8.3. Low flow
For the PRESENCE project, possible trends in low flow are of relevance for future cooling water
availability for industry and thermal power production. In this context, especially summer low flow is
important, when high air and water temperatures coincide with reduced water availability.
Therefore, the more snow‐dominated regions with lowest flow in winter, the Alpine region and
northern and north‐eastern Europe, are less vulnerable to cooling water shortages than the western
and central European regions with lowest flow in summer. The month of minimal stream flow in
several regions of Europe is shown in the left part of Fig. 4 from Stahl et al. (2011).
In addition to their lower sensitivity to cooling water deficiencies, areas with lowest flow in winter
months exhibit an increase of runoff in these months in the period of 1962‐2004, as shown in the
right part of Fig. 4. Areas with summer low flow on the other hand mainly show negative trends of
runoff in the respective months. Stahl et al. (2011) also analysed trends in 7‐day low flow in summer
(May to November) for all depicted runoff gauges and found that their negative trends are mostly
stronger than the month of regime minimum in summer low flow areas. In the Alpine region,
summer low flow has mostly increased, whereas it has decreased in many Scandinavian areas. In
central Europe, the occurrence of 7‐day low flow in summer has shifted to an earlier date.
Fig. 84: Month of minimal runoff (left) and trend of runoff in the respective month (right), from 1962‐2004 runoff observations. Source: Stahl et al. 2010
These already observed trends persist in model based future projections of climate change impacts
on low flow. Maps of changes in simulated Q90 (the flow exceeded in 90% of the time) of Arnell
(1999) for the 2050s (Fig. 5) show a pattern very similar to that detected by Stahl et al. in the second
half of the 20st century. Varying only in the magnitude of change, simulations with all four climate
models result in decreasing runoff Q90 for the summer low flow regions in most of continental
Europe. For the winter low flow regions of the Alps, Scandinavia and north‐eastern Europe increases
in Q90 are expected.
71
Fig. 85: Change in the flow exceeded 90% of the time (Q90) by 2050, compared to 1961‐90. Source: Arnell 1999
Also Lehner et al. (2006) come to corresponding results in the analysis of runoff extreme of their
WaterGAP application with climate model input data. Fig. 6 shows the return period of the actual
100‐year low flow events in the simulations for the 2070s. Again, for most of Europe, except the
snow‐dominated Alpine and northern European regions, an aggravation of the low flow water
availability is expected, as today’s drought runoff level is projected to be reached more frequently.
Discrepancies of Lehner et al.’s projections and those of Arnell (1999) for Germany and parts of
eastern Europe might be attributed to water use scenarios assumed by Lehner et al. (2006), as they
expect a decrease in drought intensity only from changed water use in Germany, and a substantial
increase in eastern Europe.
72
Fig. 86: Change in recurrence of today’s 100‐year droughts (1961‐90) by the 2070s. Source: Lehner et al. 2006
73
5. Conclusions
In this report, the impact of climate change on hydrology is investigated with a special focus on the
effects for hydropower production and cooling water availability. The assessment is based on water
balance simulations for Austria, using climate model scenarios as input data. Scenario data from
three different RCMs, REMO‐UBA, Aladin‐Arpege and RegCM3 are applied.
The relation between changes in long term mean annual runoff and changes in long term means of
precipitation and temperature, also termed runoff elasticity, is assessed for 188 Austrian catchments.
A multiple linear regression model can be fitted very well to simulation results of the water balance
model driven by three different scenarios of the REMO‐UBA model. Results can be improved by
grouping the catchments into alpine and lowland areas and fitting two regression models. For further
analysis, the results from water balance simulations with different climate model input are used
directly, and the regression model is not applied.
Spatial patterns of changes in runoff induced by climate change differ according to the applied
climate model. REMO‐UBA and RegCM3, which are driven by the same GCM, lead to hydrological
scenarios with decreasing runoff in the south and west of Austria and small increases in the north‐
east. Aladin‐Arpege scenarios result in decreasing runoff all over Austria, but more pronounced in
the south and west.
Simulated runoff time series are analysed for the rivers Enns, Mur and Drau. Several run‐of‐river
hydropower plants are located along these rivers and substantial discharge from thermal power
plants and industry is expected. Results from the river Ager are shown to investigate the situation for
smaller rivers with less alpine influence.
In simulations with the REMO‐UBA model, mean runoff differs according to the greenhouse gas
emission scenario. The A2 scenario, with higher precipitation, leads to higher runoff than the drier
A1B scenario. Both scenarios show a decreasing trend within the 21st century. Similar trends are
simulated by the other two models (both only with A1B scenarios). Stronger decreases are projected
by Aladin‐Arpege. The expected changes are very similar along the entire Austrian reaches of Enns,
Mur and Drau. The simulated decrease in mean runoff until the period of 2061‐2090 is around 10%,
which does not indicate general shortages of cooling water, but results in an overall reduction of
hydropower production.
Seasonal changes are simulated consistently between models, scenarios and basins, with runoff
increases in winter and spring and decreases in summer and fall. The summer runoff decrease is
more severe in the scenario A1B. Increases in winter runoff are less pronounced in the simulations
with Aladin‐Arpege. With earlier and less snow melt, the peak of seasonal runoff in Enns, Mur and
Drau is expected to be lower and occur one month earlier. This change is more pronounced in alpine
upstream catchments with stronger influence of snow processes. For the Ager, the projections for
changes in the seasonal peak in early spring differ between the models. The trend towards a less
pronounced runoff seasonality in all rivers with Alpine influence has positive effects for hydropower
production, as the divergence between production and demand diminishes.
In the rivers Enns, Mur and Drau low flow occurs mainly in winter. Increasing winter runoff therefore
leads to increasing low flow runoff. This increase in low flow discharge is projected consistently by
RegCM3 and both REMO‐UBA emission scenarios. Simulations with Aladin‐Arpege do not show
relevant changes in low flow runoff for these three rivers. For the Ager, all models expect significant
reductions in low flow runoff.
74
Low flow periods are expected to partly shift to earlier months, from winter to autumn for Enns, Mur
and Drau, from autumn to summer for the Ager. For the Ager and similar rivers, the more frequent
occurrence of low flow periods in summer together with increasing water temperature can have
negative effects for cooling water use. Concerning the duration of low flow periods, no clear
conclusions can be drawn from the conducted analyses.
Long‐term persistence and periodicity in runoff time series is more pronounced in simulations for the
second half of the 21st century with A1B emission scenarios than under reference conditions. This
effect is small in simulations driven by RegCM3 and Aladin‐Arpege, but considerable in REMO‐UBA
results. This weak coincidence and the fact that persistence in simulations with climate model control
run data and with observations is similar might indicate that longer periods of persistent runoff can
be expected under climate change conditions. With A2 data, however, no such change in persistence
behavior is detected.
Changes in groundwater recharge show spatial patterns similar to those of changes in runoff. For the
south of Austria, scenario simulations with all models consistently show decreasing groundwater
recharge. For the east, the sign of the expected change depends on the applied model, with
REMO‐UBA showing increases, Aladin‐Arpege decreases and RegCM3 no relevant changes.
Inflow into alpine reservoirs is assessed for three reservoir systems. While inflow in summer
decreases under climate change conditions, spring inflow increases. The peak in monthly runoff is
less pronounced and occurs one month earlier. The main cause for this is that snow melt starts
earlier, which leads to higher snow melt runoff in spring, and also ends earlier, which leads to less
snow melt contributions in summer. More ice melt runoff from glacierized areas in summer months
partly compensates the decreasing snow melt contributions. Towards the end of the 21st century, ice
melt runoff decreases again, due to the advanced loss of glacier ice volumes. The projected changes
in high alpine areas are triggered by temperature change to a high extent and are therefore subject
to less uncertainty than changes driven by precipitation changes. Especially glacier development,
however, is also highly sensitive to the (smaller) differences in temperature projections.
A literature review of possible changes in runoff and hydropower production in Europe shows
expected increases in northern and north‐eastern Europe, especially in Scandinavia, the Baltic
countries and Russia. Decreases are expected in southern and central Europe, notably in Spain, the
Balkan countries, Turkey and Ukraine. A more balanced seasonal course of runoff and power
production is generally expected, with streamflow increasing in winter and decreasing in summer. In
southern Europe, as e.g. in the Ebro basin, general decreases of streamflow throughout the year can
be expected. Seasonal changes are more pronounced in regions with stronger influence of snow
processes, as in the Alps or in northern Europe. In these areas, annual peaks of runoff are projected
to decrease and to occur one to two months earlier. Low flow runoff, which mainly occurs in winter
in snow dominated regions, is expected to rise. For regions with low flow in summer a decrease of
low flow runoff is expected.
In Austria as well as in an overall view of Europe, no serious negative effects of climate change on
hydro power generation can be expected, as the production is rather concentrated in areas with high
influence of snow processes. In these areas, possible decreases in annual runoff can be compensated
by favorable changes towards less pronounced runoff seasonality. Also cooling water availability is
not at risk in these regions. In lowland areas, however, low flow periods with less runoff are expected
to occur more frequently in summer. Together with increasing water temperature this can increase
the vulnerability to cooling water shortages.
75
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78
7. List of Figures
Fig. 1: Schematic description of the water balance model ..................................................................... 6
Fig. 2: 188 Austrian catchments represented in the water balance model, grouped into 16 river basins
................................................................................................................................................................. 7
Fig. 3: Basins selected for the analysis of river runoff ............................................................................. 9
Fig. 4: Flow duration curves of the Mur river (from daily observations, red, and monthly means, blue);
values of Q90 and Q95 are indicated by vertical lines .......................................................................... 10
Fig. 5: Analysis of run length below Q90 for 1961‐90 for the Drau river (gauge Lavamünd) ............... 10
Fig. 6: Example of the distribution of run length below the median (right) for a 35 year time series of
the Enns (left). ....................................................................................................................................... 11
Fig. 7: Locations and and detailed maps of the three selected alpine reservoir catchment areas
(background map: Pirker, 2005) ............................................................................................................ 13
Fig. 8: Glacier areas in the water balance (WB) model and in the inventory of Kuhn and Lambrecht
(2005, in the Hydrological Atlas of Austria HAO) .................................................................................. 14
Fig. 9: Basins selected for the analysis of groundwater recharge ......................................................... 15
Fig. 10: Climate change signals for temperature for 2051‐2080 in RegCM3 (left) and Aladin‐Arpege
(right) data (Source: BOKUMET) ............................................................................................................ 17
Fig. 11: Climate change signals for temperature for 2061‐2090 in RegCM3 (left) and Aladin‐Arpege
(right) data (Source: BOKUMET) ............................................................................................................ 17
Fig. 12: Climate change signals for precipitation for 2051‐2080 in RegCM3 (left) and Aladin‐Arpege
(right) data (Source: BOKUMET) ............................................................................................................ 18
Fig. 13: Climate change signals for precipitation for 2061‐2090 in RegCM3 (left) and Aladin‐Arpege
(right) data (Source: BOKUMET) ............................................................................................................ 18
Fig. 14: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel)
and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower
panel) for one upstream gauge (Liezen) and one downstream gauge (Steyr/Ortskai) of the Enns river
............................................................................................................................................................... 19
Fig. 15: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel)
and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower
panel) for one upstream gauge (Gestüthof) and one downstream gauge (Mureck/Spielfeld) of the
Mur river ................................................................................................................................................ 20
Fig. 16: Simulated and observed duration curves (upper panel), mean monthly runoff (middle panel)
and relative frequencies of occurrence of the lowest annual runoff in each calendar month (lower
panel) for one upstream gauge (Oberdrauburg) and one downstream gauge (Lavamünd) of the Drau
river ....................................................................................................................................................... 21
Fig. 17: Scatterplots of precipitation (left) and potential evapotranspiration ETP0 (right) in the
reference runs with Arpege (above) and RegCM3 (below) input data and the KlimAdapt reference run
with IWHW input data ........................................................................................................................... 22
79
Fig. 18: Scatterplots of observed and simulated seasonal runoff in the reference period, with input
data from Arpege (left) and RegCM3 (right) ......................................................................................... 22
Fig. 19: Relative runoff change (ΔQ) in relation to relative precipitation change (ΔP) ......................... 23
Fig. 20: Relative runoff change (ΔQ) in relation to relative precipitation change (ΔP) for classes of
temperature change (ΔT) ...................................................................................................................... 23
Fig. 21: Absolute runoff change (ΔQ) in relation to absolute precipitation change (ΔP) for classes of
temperature change (ΔT) ...................................................................................................................... 24
Fig. 22: Comparison of results for absolute runoff change (ΔQ) with the water balance model and the
regression model based on precipitation and temperature change .................................................... 24
Fig. 23: Division into alpine and lowland catchments ........................................................................... 25
Fig. 24: Comparison of results for ΔQ with the water balance model and the regression models for
alpine and lowland catchments ............................................................................................................ 25
Fig. 25: Change in mean annual runoff 2051‐2080 relative to 1961‐1990, simulated with Aladin‐
Arpege (top) and RegCM3 (bottom) input data .................................................................................... 26
Fig. 26: Change in mean annual runoff 2061‐2090 relative to 1961‐1990, simulated with Aladin‐
Arpege (top) and RegCM3 (bottom) input data .................................................................................... 27
Fig. 27: Change in mean annual runoff 2061‐2090 relative to 1961‐1990, simulated with REMO‐UBA
(from Kranzl et al. 2010) ........................................................................................................................ 28
Fig. 28: Change in mean seasonal runoff 2051‐2090 relative to 1961‐1990 with Aladin‐Arpege ........ 29
Fig. 29: Change in mean seasonal runoff 2051‐2090 relative to 1961‐1990 with RegCM3 .................. 29
Fig. 30: Development of simulated mean flow (MQsim) through 30‐year periods around 1975, 2025,
2050, and 2075 for Enns (top), Mur (middle) and Drau (bottom) with the REMO‐UBA scenarios A1B
(left) and A2 (right) ................................................................................................................................ 30
Fig. 31: Comparison of the ratio of simulated mean flow (MQsim) in the 30‐year periods around 2075
and 1975 with A1B scenarios of REMO‐UBA (black), RegCM3 (orange) and Aladin‐Arpege (violet) for
Enns, Mur, Drau and Ager ..................................................................................................................... 31
Fig. 32: Mean runoff (MQ )of 2061‐2090 relative to 1961‐1990 for Enns, Mur and Drau, based on
REMO‐UBA scenarios A1B (top) and A2 (bottom) ................................................................................ 32
Fig. 33: Q10 of 2061‐2090 relative to 1961‐1990 for Enns, Mur and Drau, based on REMO‐UBA
scenarios A1B (top) and A2 (bottom) .................................................................................................... 33
Fig. 34: Mean monthly runoff in 2061‐90 for the scenarios A1B and A2, compared to 1961‐90, for
Enns, Mur and Drau ............................................................................................................................... 34
Fig. 35: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐
Arpege (bottom right), compared to 1961‐90, for two gauges of the river Enns ................................. 35
Fig. 36: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐
Arpege (bottom right), compared to 1961‐90, for two gauges of the river Mur .................................. 36
Fig. 37: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐
Arpege (bottom right), compared to 1961‐90, for two gauges of the river Drau ................................. 36
80
Fig. 38: Mean monthly runoff in 2061‐90, with REMO‐UBA (top), RegCM3 (bottom left) and Aladin‐
Arpege (bottom right), compared to 1961‐90, for the river Ager ......................................................... 37
Fig. 39: Development of simulated duration curves through 30‐year periods around 1975, 2025, 2050,
and 2075 for the upstream gauge Liezen of the Enns river ................................................................... 37
Fig. 40: Development of simulated values of Qmin (upper row),Q90 (middle row) and Q95 (bottom
row) through 30‐year periods around 1975, 2025, 2050, and 2075 for the Enns river ......................... 38
Fig. 41: Development of simulated values of Qmin through 30‐year periods around 1975, 2025, 2050,
and 2075 for the Mur river .................................................................................................................... 39
Fig. 42: Development of simulated values of Qmin through 30‐year periods around 1975, 2025, 2050,
and 2075 for the Drau river ................................................................................................................... 39
Fig. 43: Comparison of the ratio of simulated low flow (Qmin) in the 30‐year periods around 2075 and
1975 with A1B scenarios of REMO‐UBA (black), RegCM3 (orange) and Aladin‐Arpege (violet) for Enns,
Mur, Drau and Ager ............................................................................................................................... 40
Fig. 44: Relation between long term mean annual 7‐day minimum flow (MAM7) and mean lowest
annual monthly runoff (Qmin) for different gauges along the rivers Enns (left), Mur (middle) and Drau
(right) ..................................................................................................................................................... 41
Fig. 45: Relation between annual 7‐day minimum flow ( AM7 ) and lowest annual monthly runoff
(Qmin) for time series of selected gauges of the rivers Enns (left), Mur (middle) and Drau (right) .... 41
Fig. 46: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for
the 30‐year periods around 1975 (blue, upper part of graphs) and 2075 (lower part) for two gauges of
the Enns river ......................................................................................................................................... 42
Fig. 47: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for
the 30‐year periods around 1975 and 2075 for the most downstream gauges of the Mur and Drau
rivers ...................................................................................................................................................... 43
Fig. 48: Relative frequencies of the occurrence of the lowest annual runoff in each calendar month, for
the 30‐year periods around 1975 and 2075 for the Ager rivers ............................................................ 43
Fig. 49: Skewness measure g of the frequency distributions of run length below Q90 in the 30‐year
periods around 1975 and 2075 for the most downstream gauges of the Enns(top) and Drau(bottom)
rivers, simulated with REMO‐UBA A1B and A2 scenarios (left) and Aladin‐Arpege and RegCM3
scenario (right, both A1B) ..................................................................................................................... 44
Fig. 50: Time series of annual runoff of the Enns at Steyr/Ortskai, observed (blue) and simulated with
observed input data (red), above, and shown as difference to the median Q of the respective time
series (below) ........................................................................................................................................ 45
Fig. 51: Distribution of run lengths for the Enns, resulting from the time series in Fig. 50 .................. 45
Fig. 52: Distribution of run lengths for the Drau (at Sachsenburg) ....................................................... 46
Fig. 53: Difference between mean annual runoff and the median for the respective period for 2002‐
2043 (left) and 2049‐2090 (right), above; resulting distributions of run lengths, below; REMO‐UBA
A1B for the Enns at Styr/Ortskai ........................................................................................................... 47
81
Fig. 54: Distribution of run lengths in simulated runoff for the periods of 1952‐1999, (top left, blue)
and 2002‐2049 (bottom left) and 2043‐2090 (bottom right); REMO‐UBA A1B for the Drau at
Lavamünd .............................................................................................................................................. 47
Fig. 55: Skewness measure g for the distributions of run lengths in simulated runoff for the periods of
1952‐1999 (blue) and 2002‐2049 and 2043‐2090 (REMO‐UBA, black: A1B, red: A2), for the Enns (left)
and the Drau (right) ............................................................................................................................... 48
Fig. 56: Mean annual runoff time series (mean annual Q ‐ median Q of the entire period) for the
simulations with control run climate model data for Aladin‐Arpege (left) and RegCM3 (right). ......... 48
Fig. 57: Skewness measure g for the distributions of run lengths in simulated runoff for the periods of
1952‐1999, 2002‐2049 and 2043‐2090 (Aladin‐Arpege: violet, RegCM3: orange, both A1B) .............. 49
Fig. 58: Autocorrelation plots for the Enns; simulations for 1952‐1999 driven by observations (top),
driven by REMO‐UBA A1B data for 2002‐2049 (middle left) and for 2043‐2090 (middle right), and
driven by REMO‐UBA A2 data for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right) ........... 50
Fig. 59: Autocorrelation plots for the Drau; simulations for 1952‐1999 driven by observations (top),
driven by REMO‐UBA A1B data for 2002‐2049 (middle left) and for 2043‐2090 (middle right), and
driven by REMO‐UBA A2 data for 2002‐2049 (bottom left) and for 2043‐2090 (bottem right) ........... 51
Fig. 60: Autocorrelation plots for the Drau with Aladin‐Arpege; simulations for 1952‐1999 (top), for
2002‐2049 (bottom left) and for 2043‐2090 (bottem right) ................................................................. 51
Fig. 61: Autocorrelation plots for the Drau with RegCM3; simulations for 1952‐1999 (top), for 2002‐
2049 (bottom left) and for 2043‐2090 (bottem right) .......................................................................... 52
Fig. 62: Development of precipitation and runoff in the 21st century, compared to 1961‐1990
(Gepatsch) ............................................................................................................................................. 53
Fig. 63: Seasonal course of the snowfall portion of total precipitation, for 1961‐90 (left) and 2061‐90
(right) ..................................................................................................................................................... 53
Fig. 64: Seasonal course of snow water equivalent, accumulated snowfall, snow melt and snow cover
for 1961‐90 and 2061‐90 (Gepatsch) .................................................................................................... 54
Fig. 65: Seasonal course of total runoff and snow melt and glacier melt portions, for the glacier
simulation with ice DDF values like snow DDF values (left) and the simulation with doubled ice DDF
values (right) .......................................................................................................................................... 55
Fig. 66: Annual sums of total runoff and snow melt and glacier melt contributions, for the glacier
simulation with ice DDF values like snow DDF values (left) and the simulation with doubled ice DDF
values (right) .......................................................................................................................................... 55
Fig. 67: Floating 30‐year means of ice melt contributions to runoff in the Pitze catchment for different
climate change scenarios ...................................................................................................................... 56
Fig. 68: Floating 30‐year means of ice melt contributions to runoff in the Pitze catchment simulated
with climate data of different models and different emission scenarios ............................................. 57
Fig. 69: Development of simulated ice water equivalent in the cachment areas of the Gepatsch
reservoir in the 21st century, with ice DDF values like snow DDF values (left) and doubled ice DDF
values (right) .......................................................................................................................................... 57
82
Fig. 70: Simulated development of glaciers in western Tyrol, with low ice DDFs (left) and high ice
DDFs (right, for 2011 only the results with low ice DDFs are displayed, as differences are negligible) 58
Fig. 71: Development of precipitation and runoff in the 21st century, compared to 1961‐1990
(Sellrain‐Silz) .......................................................................................................................................... 59
Fig. 72: Seasonal course of snow water equivalent, accumulated snowfall, snow melt and snow cover
for 1961‐90 and 2061‐90 (Sellrain‐Silz) ................................................................................................. 59
Fig. 73: Development of precipitation and runoff in the 21st century, compared to 1961‐1990
(Kaprun‐Uttendorf) ............................................................................................................................... 60
Fig. 74: Seasonal course of total runoff and snow melt and glacier melt portions (Kaprun‐Uttendorf)
............................................................................................................................................................... 60
Fig. 75: Trends in water temperature resulting from water REMO‐UBA A1B climate model data, for
the Mur (at Mureck/Spielfeld) and the Ager (at Schalchham) .............................................................. 61
Fig. 76: Change in simulated mean annual groundwater recharge 2061‐2090 relative to 1961‐1990,
results with REMO‐UBA A1B input data ................................................................................................ 62
Fig. 77: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for
Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative
changes of the three models for the entire year and the period of March to June( MAMJ, bottom
left); results for the downstream Mur basin ......................................................................................... 63
Fig. 78: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for
Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative
changes of the three models for the entire year and the period of March to June( MAMJ, bottom
left); results for the Raab basin ............................................................................................................. 63
Fig. 79: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for
Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative
changes of the three models for the entire year and the period of March to June( MAMJ, bottom
left); results for the Rabnitz basin ......................................................................................................... 64
Fig. 80: Mean monthly simulated groundwater recharge for 1961‐1990 and 2061‐2090 for
Aladin‐Arpege (top left), RegCM3 (top right) and REMO‐UBA (bottom left) and comparison of relative
changes of the three models for the entire year and the period of March to June( MAMJ, bottom
left); results for the Leitha basin ........................................................................................................... 64
Fig. 81: Relative change of mean runoff (compared to 1961‐90) for the 2020s (top) and the 2070s
(bottom), and the ECHAM4 (left) and HadCM3 (right) models. Source: Lehner et al. 2005 ................ 66
Fig. 82: Change in the month of maximum runoff by 2050, compared to 1961‐90. Source: Arnell 1999
............................................................................................................................................................... 68
Fig. 83: Change in seasonal runoff to the Baltic Sea, 2071‐2100 compared to 1961‐90, in 2 B2
scenarios (left) and 3 A2 scenarios (right). Source: Graham et al. 2007 ............................................... 69
Fig. 84: Month of minimal runoff (left) and trend of runoff in the respective month (right), from
1962‐2004 runoff observations. Source: Stahl et al. 2010 .................................................................... 70
Fig. 85: Change in the flow exceeded 90% of the time (Q90) by 2050, compared to 1961‐90. Source:
Arnell 1999 ............................................................................................................................................ 71
83
Fig. 86: Change in recurrence of today’s 100‐year droughts (1961‐90) by the 2070s.
Source: Lehner et al. 2006 .................................................................................................................... 72
84
8. List of Acronyms
BOKUMET Institute of Meteorology, University of Natural Resources and Life Sciences
COSERO Continuous semi‐distributed rainfall‐runoff model
DDF Day degree factor
EEG Energy Economics Group, Vienna University of Technology
GCM General Circulation Model
IWHW Institute of Hydrology, Water Management and Hydraulic Engineering, University of
Natural Resources and Life Sciences
m a.s.l. Meters above sea level
MAM7 Mean annual 7‐day minimum flow
MAMJ period of March, April, May and June
Q10, Q90, Q95 Runoff exceeded in 10%, 90%, 95% of the time
Qmin Lowest monthly runoff of a year
RCM Regional Climate Model
US United States of America
WP Work package
85
9. Annex – Comments on the delivered runoff simulation time
series
9.1. Delivered runoff simulation time series
Runoff time series with following specifications are delivered as Excel‐files:
Regional climate models
1. RegCM (REG)
2. Aladin‐Arpege (ARP)
3. REMO‐UBA (REMO)
Time periods
1. Reference: 1961 ‐ 1990
2. Scenario: 2051 – 2080
Runoff time series
For every regional climate model a seperate Excel‐file is provided, which includes times series of
runoff simulations of the water balance model and runoff time series of selected danube river power
plants.
Model output of water balance model:
Time series of 188 catchments (Figure 1)
Runoff data of the catchments illustrated in Figure 1 are deliverd. The numbers of the catchment
shown in the map are the same as the headers of the columns in the Excel‐files.
Danube river power plants (Figure 2):
Aschach
Abwinden‐Asten
Wallsee‐Mitterkirchen
Ybbs‐Persenbeug
Altenwörth
Freudenau
86
Figure 1: Catchments of the water balance model. The 16 river basins are represented in different colours
918
11131012
802
1108
1306
1209
1617
512 806
606
1018
1303
1114
309
203
503
1409
809
409
401 505801
1403
308
708
405
703
1406403
914
504
915
511
404103
805
510 507
1511
807
101
913
1304
513
1408
706
1616
1112
1405
209
1004
410
502
506
1407
803
1508
808
1205
1612
605
1608
1305408
514
1009
702
910
1613
1302
916
1510
804
1201
1214
705
1515
902
602
707
1507
108
1106
3041609
1404
912
1019
1207
906
306
1110
701
1203
1514
1606
1206
603
106
1109
1211
907
1007
1016
1501
302 1615
917
1513
407
1213
1506
1005
1204
1003
1610
1402
905
1504
1008
904
1611
1605
202
1103
1601
402
1001
1014
704 1104
1301
903
104
1107
909
1013
1002
1502
1512
1212
1010
207
901
204
406
1604
205
303
501
208
508 1401
307
102
1006
10111101
908
604
1603
509
1607
206
1202
1102
1503
1614
1017
911
1208
1015
305
107
1602
1111
301
201
1618
1210
105
601
919919
1105
15091505
411
919
1213
1213411411
1017
411
0 50 10025Kilometer
±
87
Figure 2: Storage and river power stations (source: BMLFUW, 2005). Delivered Danube river power plants are shown separately.
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Aschach
Freudenau
Altenwörth
Abwinden-AstenYbbs-Persenbeug
Wallsee-Mitterkirchen
0 50 10025Kilometer
±
! Storage power plants
! River power plants
# Delivered Danube river power plants
88
9.2. Estimation of the Swiss and German Inn and the German Danube
The water balance model only covers Austrian catchments. Therefore, seperate calculations had to
be performed to estimate the inflow of the Inn river from Switzerland, the runoff contributions of the
German areas to the Inn and the Danube from Germany.
Observations of runoff for the gauging station Martina at the border between Switzerland and
Austria were provided by the Swiss Federal Office for the Environment (Bundesamt für Umwelt
BAFU). Mean monthly correlation‐values between these obervations and simulations of subbasin
0309 of the Inn basin (Figure 1) were determined. These values were also used for the estimation of
inflow from Switzerland in the future scenarios.
For the estimation of the German Inn, the runoff values of the low lying catchments of the Austrian
Inn (subbasin 0601, 0602, 0603, 0604, 0605, 0606, 0411, 0511 ‐ Figure 1) were extrapolated, taking
into account the ratio between the areas of the Austrian and German Inn. The overall Inn was then
calculated as the sum of the German contribution and the runoff contributions of the Austrian
catchments, including the Swiss share. This method is also applied for the future scenarios.
Similar to the estimation of the Swiss Inn, mean monthly correlation‐values were determined
between the overall Inn and German Danube based on values provided by Kling et al. (2012a –
RegCM and Aladin; 2012b – REMO‐UBA) for the reference period 1961 ‐ 1990. These values were
used to calculate runoff in the German Danube in the future, with the overall Inn as a basis. Changes
in seasonality – which show different characteristics for the German Danube than for the more alpine
Inn – are considered by incorporating the future runoff simulations of Kling et al. (2012a, 2012b).
Kling et al. (2012a, 2012b) use the same climate models as applied in PRESENCE, but the period of
2051‐2080 is not analysed in their study. From their results for 2021‐2050 and 2071‐2100, mean
monthly runoff for 2051‐2080 was derived by linear interpolation. These results were then used to
correct the seasonal course of runoff in the German Danube. This is regarded as reasonable, as
seasonal changes are triggered strongly by temperature change, which shows a constant rate of
increase in the climate models. The mean runoff is not corrected, but used from the water balance
model simulations for the Inn (Figure 3), which incorporate the actual long term fluctuation of
precipitation in the climate model data for the period of 2051‐2080.
Figure 3: Mean monthly runoff conditions of the German Danube (RegCM3 2051 – 2080) with and without consideration of a seasonal shift.
0
250
500
750
1000
1 2 3 4 5 6 7 8 9 10 11 12
m³/s
Month
Danube Kling 2065 Danube from Inn (2051‐2080) ‐ without seasonal shift
Danube from Inn (2051‐2080) ‐ including seasonal shift
89
9.3. Estimation of a typical hydrological regime shift for German
hydropower production
For the estimation of hydropower production in Germany in subsequent modelling, information
about hydrological changes under climate change conditions are needed. Projections for the Rhine by
Middelkoop et al. 2001, simulated with an earlier generation of climate models, are shown for an
alpine gauge and the entire Rhine basin in Figure 4.
Figure 4: Monthly average discharge of the Rhine at Rheinfelden (alpine Rhine) and Rees (entire basin); source: Middelkoop et al. 2001
Figure 5 shows PRESENCE results for the Danube. The pattern of seasonal shift is similar to the
patterns for the Rhine. Results with RegCM3 show a future seasonality somehow inbetween the
alpine and the entire Rhine in the results of Middelkoop et al. (2001). The change projected by
Aladin‐Arpege shows a much more pronounced seasonality. This is also the case for the REMO‐UBA‐
simulation. While the strong decrease in summer is also simulated by Middelkoop et al. (2001) for
the alpine Rhine, the marked increase in spring is not present in their simulations.
As the results in Figure 5 are generated with the PRESENCE climate models, they are in general
accordance with the projections for Austria used in subsequent modelling. It is therefore
recommended to apply the changes shown in Figure 5 as typical hydrological changes for German
hydropower production. If only one future scenario is applied, the RegCM3 or REMO‐UBA projections
projections are recommended, as they are in better agreement with the results of Middelkoop et al.
(2001).
Figure 5: Monthly average discharge of the Danube upstream of the inflow of the Inn
0
250
500
750
1000
1250
1500
1 2 3 4 5 6 7 8 9 10 11 12
m³/s
Month
German Danube 1961‐1990 RegCM3 2051‐2080
Aladin‐Arpege 2051‐2080 REMO‐UBA 2051 ‐ 2080
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