Potential near future runoff changes in Croatia
Ivan Güttler, dipl.ing., dr.sc. Dušan Trninić, Tomislava Bošnjak, ing., mr.sc. Lidija Srnec,
mr.sc. Mirta Patarčić, dr.sc. Čedo Branković
Državni hidrometeorološki zavod, Grič 3, Zagreb, 10000, Hrvatska, guettler@cirus.dhz.hr,
trninic@cirus.dhz.hr, bosnjak@cirus.dhz.hr, srnec@cirus.dhz.hr, patarcic@cirus.dhz.hr,
brankovic@cirus.dhz.hr
Abstract
The Regional Climate Model (RegCM3) is used to dynamically downscale three realisations
of present (1961-1990) and future climates (2011-2040) as defined by EH5OM global climate
model. For future climate the IPCC A2 high forcing scenario is used. RegCM3 domain covers
a large part of Europe and the Mediterranean basin with 35 km horizontal spatial step. Focus
of analysis is simulated surface runoff over Croatia and its neighbourhood in present and
future climates. Derived surface runoff from measurements of water level in the hydrological
network of Meteorological and Hydrological Service of Croatia (DHMZ) is also analyzed for
the present climate. The near-future changes in runoff can be associated with the changes in
total precipitation and temperature. Seasonal and spatial variability in magnitude and sign of
future changes in runoff are identified.
Keywords: regional climate model, surface runoff, climate change
Moguće promjene otjecanja u bliskoj budućnosti u Hrvatskoj
Sažetak
Tri člana ansambla simulacija sadašnje klime (1961-1990) i klime u bliskoj budućnosti (20112040) globalnog klimatskog modela EH5OM korištena su za dinamičku prilagodbu
regionalnim klimatskim modelom RegCM3 s prostornim korakom od 35 km iznad širokog
dijela Europe i Mediterana. Za buduću klimu korišten je IPCC klimatski scenarij A2 koji
uključuje visoke koncentracije stakleničkih plinova. Fokus analize je simulirano površinsko
otjecanje na području Hrvatske i okolnih zemalja u sadašnjoj i budućoj klimi. Također je
analizirano površinsko otjecanje izvedeno iz mjerenja vodostaja u hidrološkoj mreži
Državnog hidrometeoroškog zavoda (DHMZ). Promjene u otjecanju u bliskoj budućnosti
moguće je povezati s promjenama u ukupnoj količini oborine i temperaturi. Prisutna je
sezonska i prostorna varijabilnost u iznosu i predznaku promjena otjecanja.
.
Ključne riječi: regionalni klimatski model, površinsko otjecanje, klimatske promjene
1
1. Introduction
Regional or local climate is a result of interplay between parts of the Earth climate
system on the time-scale of few decades. One way to describe, simulate and predict climate
on regional spatial scales is by using regional climate models (RCMs). RCMs are being used
in the last two decades and have many features in common with limited area numerical
weather prediction models. They enable to dynamically downscale the results of global
climate models (GCMs) and provide climate information at higher spatial resolution than
normally found in GCMs. In general, current GCMs have spatial resolution of approximately
150-250 km and RCMs of about 20-50 km.
When validating a RCM one compares a variable in dynamically consistent simulation
with corresponding observation. The three “types” of variables may be considered: (1) state
variables (e.g. near-surface temperature and precipitation), (2) process variables (e.g. heat
flux) and (3) integrative variables (e.g. runoff). In a recent review, Rummukainen (2010)
emphasised the need to evaluate models more in terms of integrative metrics, such as river
runoff, than state variables. This can be expanded to include surface runoff as in this study. In
practice, however, additional postproccessing of the modelled data might be needed in order
to have comparable variables in simulated and observed datasets.
There are examples of surface runoff analysis in RCM framework in the last decade.
Hagemann et al. (2009) analyzed runoff changes from three IPCC scenarios (Nakićenović et
al. 2000) in GCM EH5OM and RCM REMO and found a significant reduction in summer
runoff for the Danube catchment in the period 2071-2100 relative to 1961-1990, primarily as
a consequence of the decrease in total precipitation. Rauscher et al. (2008) compared the
timing of a specific runoff event in RCM RegCM3 (Pal et al. 2007) and observations, and
analyzed climate change between the periods 1961-1989 and 2071-2099. In their study, the
increase in total precipitation and snow melt (due to a general increase of surface temperature
in the A2 scenario) contributed to an increase of surface runoff in mountainous region over
western U.S. In this study, we focus to a future period much closer than those in the above
studies, when care is needed in the interpretation of results since the signal of climate change
can be similar in magnitude to internal variability of climate system.
On the other hand, river discharge and surface runoff can by analyzed in context of
available measurements. At DHMZ there are examples of this type of analysis done for
meteorological and hydrological stations in Croatia. Pandžić and Trninić (1998) documented
a strong dependence of river discharge to temperature and precipitation for stations in the
river Sava catchment (partly included in the domain in this study). Gajić-Čapka and Cesarec
(2010) analyzed trends and variability of river discharge and climatological parameters for
stations in the river Drava catchment. Pandžić et al. (2009) estimated water balance
components for several meteorological stations in Croatia using Palmer (1965) method.
Palmer method was also applied in detailed analysis of water balance components for stations
in coastal Croatia in e.g. Pandžić (1985), Pandžić and Trninić (2004), Zaninović and GajićČapka (2005) and Gajić-Čapka and Zaninović (2006) and for stations in continental Croatia in
e.g. Zaninović and Gajić-Čapka (2000), Gajić-Čapka and Zaninović (2004) and Pandžić and
Trninić (2004). Additionally, Pandžić et al. (2009) used hydrological measurements of river
discharge at two locations on river Sava. In their study, surface runoff estimated using Palmer
method for Zagreb-Grič meteorological station was shown to be significantly correlated to the
real river discharge measurements on annual time-scale at the Zagreb-Sava hydrological
station (their Fig. 16). This partially forms a basis for this study where simulated surface
runoff (similar in definition to surface runoff in Palmer method) is analyzed together with
derived surface runoff from the water level measurements in the DHMZ hydrological
network.
2
In general, higher level of comparability in the RCM framework between modelled
and observed hydrological quantities can be obtained by direct comparison of modelled and
observed river discharge when coupling RCM with hydrological model as in e.g. Samuels et
al. (2010) and Andersson et al. (2011). However, open issues in the process of linking RCM
and other downscaling techniques to hydrological models are presented in the review paper
by Fowler et al. (2007). Our aim here was to explore what are possibilities in validating
surface runoff in RegCM3 simulations in the context of available observations and
observation-derived quantities at DHMZ.
The structure of this study is following: we first compare seasonal means of surface
runoff and the structure of annual cycle of surface runoff and total precipitation in simulations
and observations; after that, climate change in runoff, total precipitation and upper layer soil
water between the current and future climate periods is analyzed.
2. Method
Simulations of EH5OM GCM were dynamically downscaled by using RegCM3 for
the two periods: the near-present period P0, 1961-1990, and the near-future period P1, 20112040. Three ensemble members were downscaled in order to take into account internal
variability of climate system. During P0, EH5OM used observed aerosol and greenhouse
gasses (GHG) concentrations, while for P1 the IPCC A2 scenario was applied. Although A2
is a high forcing scenario, during the first decades of the 21st century all IPCC scenarios have
similar concentrations of GHG, and dominant source of uncertainty are initial conditions
(Hawkins and Sutton, 2009). For the discussion of EH5OM simulations the reader is referred
to e.g. Branković et al. (2010) and for climatological analysis of RegCM3 simulations to
Branković et al. (2011). Surface runoff in RegCM3 is computed within the BATS landsurface scheme (Dickinson et al., 1993) and it is a function of precipitation rate and soil water
content relative to saturation. It is calculated for each time step and there is no communication
between the adjacent cells over land. Mean monthly surface runoff for P0 period derived from
observed water level at DHMZ hydrological stations is defined as a layer of water that flows
over catchment area in specified time interval (e.g. Jevđević, 1956).
RegCM3 grid cells and topography over Croatia and the location of DHMZ
hydrological stations are shown in Fig. 1. Clearly, model topography on this spatial resolution
resembles only roughly to true topography. Due to relatively crude land surface scheme and
inadequate resolution of topography (RCM domain is divided into 35 x 35 km grid cells), it is
not expected to reach the full realism in simulated surface runoff. Estimates of runoff from
local observations and RCM simulations in this study cannot be compared directly because
surface runoff in RegCM represents average conditions in a grid cell 35 x 35 km, while
surface runoff derived from local observations of water level represents mean conditions on
river catchment from the river source up to the location of hydrological station. Nevertheless,
this type of analysis can provide an insight into how surface runoff is related to e.g.
precipitation features in both simulation and reality.
3. Results
3.1. Seasonal sums of surface runoff
Seasons in following discussion are defined as follows: DJF is December-JanuaryFebruary, MAM is March-April-May, JJA is June-July-August and SON is SeptemberOctober-November. Ensemble-mean seasonal sums of surface runoff simulated by RegCM3
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are shown in Fig. 2. Seasonal sums of runoff in the model can reach up to 400 mm during
DJF in the mountainous region near the coast and even higher values in the Alpine region
during MAM. DHMZ estimates of seasonal runoff sums are shown in Fig. 3 and are around
100-200 mm on average in the continental Croatia, but can reach more than 1000 mm for
stations in the mountainous region. The two typical regions are seen in Fig. 2 and Fig. 3: (1)
the continental part of Croatia with a rather homogenous simulated and derived runoff; (2) the
coastal and mountainous part of Croatia where model and observations show high spatial
variability. In general, the lowest amount of surface runoff is found in JJA in both model and
observations. In the region shown in Fig 1., one can also detect two specific regions: (1)
south-eastern part of the domain with high runoff values during DJF and SON, associated
with precipitation maxima - this is a climatological feature in both observations (Zaninović et
al. 2008) and this experiment (Branković et al. 2011); (2) north-western part of the domain
with high runoff values during MAM, associated with snow melt. Differences between two
datasets in amount of surface runoff are discussed in the following subsection.
Figure 1: Topography in RegCM3 subdomain and DHMZ hydrological stations. Yellow circles and
red numbers denote DHMZ stations, where numbers in red are stations for which monthly data are
analyzed: 1 Sava Zagreb, 2 Sava Davor, 3 Sava Županja, 4 Una Kostajnica, 5 Drava Botovo, 6 Drava
Donji Miholjac, 7 Kupa Rečica II, 8 Mirna Portonski most, 9 Cetina Vinalić, 10 Zrmanja Jankovića
Buk
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Figure 2: Mean seasonal sums of surface runoff simulated by RegCM3 for present climate (19611990).
Figure 3: Mean seasonal sums of derived surface runoff from DHMZ measurements for present
climate (1961-1990).
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3.2. Monthly amounts of surface runoff and precipitation
For selected locations, representing various regions in Croatia, derived surface runoff
is shown together with RegCM3 grid-cell values on monthly time scale (Fig. 4). In general,
model simulated phases of surface runoff annual cycle are similar to those in derived surface
runoff. For DHMZ estimate (open circles) the two types of mean annual cycle can be seen:
(1) minimum runoff is in (late) summer, between high runoff in spring due to snow melt and
high runoff in autumn due to the maximum in total precipitation (for all stations except for
those at the river Drava), (2) the minimum runoff during winter (only for the Drava stations).
The river Drava discharge is strongly influenced by snow melt in the Alpine region and this
partially causes a distinct shape in annual cycle of surface runoff. RegCM3 is, however,
unable to correctly reproduce this feature. For the first type of annual cycle, secondary
minimum appears in winter. In RegCM3 simulations, all grid cells show runoff features as in
the first type and are similar to surface runoff given by Palmer method as in Gajić-Čapka and
Zaninović (2003) and Zaninović and Gajić-Čapka (2005).
Model simulated annual cycle of total precipitation is compared to measurements at
meteorological stations in DHMZ network that are near corresponding hydrological stations.
For most stations model displays dry bias during summer. This can be partially attributed to
model’s low resolution, which is unable to resolve details of topography at local sites, and
partially to the impact of parameterizations of unresolved processes, such as convection. This
type of limitation is common to many RCM verification studies (e.g. Moberg and Jones
2004). The comparison between simulated RegCM3 precipitation and CRU gridded
observations (New et al. 2002) also reveals negative precipitation biases for central and
coastal Croatia (Branković et al. 2011, their Fig 4.). However, the summer drying problem is
not limited to RegCM3 but is also found in many other RCMs for the southeastern Europe
(Hagemann et al., 2004). On the other hand, for the Pazin station the model is able to simulate
observed total precipitation fairly well in terms of both the amplitude and the shape of annual
cycle. The problem of dry bias can influence ability of land surface scheme to correctly
simulate surface runoff. This is however not the main source of differences in surface runoff
in the two datasets. The differences in simulated and derived surface runoff lie primarily in
the area average they represent as was emphasized in section 2.
Figure 4: Ensemble-mean monthly sums of simulated surface runoff (blue) and derived surface runoff
from water level measurements (green) for the period 1961-1990. There are y-axes for each dataset.
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Figure 5: Ensemble-mean monthly simulated precipitation (blue) and observed precipitation (green)
for the period 1961-1990. Model values are for grid points as in Fig. 4. DHMZ meteorological stations
are those relative close to hydrological stations in Fig. 4. Climatology for station Hrvatska Kostajnica
is available for the period 1964-1987 and for station Nova Gradiška for the period 1963-1990.
3.3. Climate change
Climate change in all seasons between the two periods, P0 and P1, is shown for
surface runoff, total precipitation and upper soil water content in Fig. 6. Although the model
underestimates precipitation amount in present climate, similar to Räisänen (2007) the delta
change method is applied. It is based on the assumption that biases in present and future
climates tend to cancel each other when the difference between the two climates is analyzed.
During DJF, a weak increase in runoff over much of Croatia is seen, but with some
reduction in the central and nortwestern parts. To the west, in the eastern Alpine region,
precipitation is not significantly changed, but climate change is dominated by an increase in
near-surface temperature of about 0.5°C (Branković et al. 2011). This might imply that the
partitioning between solid (snow) and liquid (rain) precipitation is shifted towards rain, thus
effecting an increase in runoff and soil moisture. In the eastern part of the domain, an increase
in precipitation acts directly as to increase runoff. However, the amount of soil water is not
much changed or it is even slightly reduced, probably because the soil, in this predominatly
flat terrain, is nearly saturated. Of course, the changes in climate over Croatia are connected
with broader, regional-scale features.
During MAM and JJA an increase in precipitation and runoff is simulated over
continental Croatia. For the coastal part no clear picture emerges due to high spatial
variability of changes in both fields.
During SON, the sign of the surface runoff change is again the same as the sign of
precipitation change: an increase in the eastern part of the domain and a decrease in the
coastal region. The change of the same sign is also detected in the upper-layer soil water. A
reduction of precipitation in the south and an increase in the north could be indicative of
changes in circulation pattern that would be more pronounced during the late 21st century in
climate scenarios including increase of greenhouse gases concentrations (e.g. Giorgi and
Coppola 2007).
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Figure 6: Climatic change in P1 relative to P0 for surface runoff (left column), total precipitation
(middle column) and upper layer soil water (right column) for all seasons. P0 represents the period
1961-1990, and P1 is the period 2011-2040.
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4. Conclusions
The results from the RegCM3 climate model integrations were used to study
climatological statistics of surface runoff on monthly and seasonal timescales over a regional
spatial domain. The typical annual cycle of surface runoff with the minimum during the warm
part of the year is simulated in this experiment and closely resembles to the one found in the
studies that applied Palmer method to observational data.
Climate change in surface runoff is strongly influenced by the snow and precipitation
changes. In the context of previous climatological analyses of the same experiment, the
following picture emerges: changes in snow are strongly affected by an increase in the nearsurface temperature, and changes in precipitation are linked to changes in the upper-air
circulation.
Future research might include RCM simulations with e.g. higher spatial resolution,
different land surface schemes or an offline coupling with a river-resolving hydrological
model. The latter, with directly simulated river discharge, would enable a closer comparison
of simulation with the available observational data from the DHMZ hydrological network.
Acknowledgments
The work of Ivan Güttler was supported by the Croatian Ministry of science, education
and sports project 004-1193086-3035. Authors want to thank Marjana Gajić-Čapka for
comments that improved the draft version of this paper.
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