Enabling Climate Information Services for Europe

Enabling Climate Information Services for Europe
Report
Climate change effect on hydro-meteorologic variables
related to water budget and precipitation extremes for the
region of Crete
Activity:
Activity number:
WATER
5, T5.4
Deliverable:
Report on climate change effect for
Crete
5.5
Deliverable number:
Author:
Ioannis K. Tsanis, Aristeidis G. Koutroulis,
Manolis G. Grillakis, (TUC)
The work leading to this publication has received funding from
the European Union's Seventh Framework Programme
(FP7/2007-2013) under grant agreement n° 265240.
1
Contents
Contents .................................................................................................................................................. 2
1. Executive Summary............................................................................................................................. 3
2. Introduction and study area ................................................................................................................. 4
3. Datasets............................................................................................................................................... 6
3.1 Local measurements ......................................................................................................................... 6
3.2 Long term transient experiments ....................................................................................................... 7
3.3 Decadal prediction experiments ........................................................................................................ 8
3.4 Non-hydrostatic high-resolution RCMs experiments ......................................................................... 8
4. Methods ............................................................................................................................................... 8
4.1 Case study framework ....................................................................................................................... 8
4.2. Bias adjustment ................................................................................................................................ 9
4.3 Hydrologic modelling ....................................................................................................................... 10
4.4 Annual Exceedance Probability ....................................................................................................... 10
5. Results ............................................................................................................................................... 11
5.1 Projections of water availability ....................................................................................................... 11
5.1.1 Recent past .................................................................................................................................. 11
5.1.2 Long term projections ................................................................................................................... 11
5.1.3 Scenarios of Water demand and technical infrastructure ............................................................ 13
Level of water demand: ......................................................................................................................... 13
Level of water supply: ............................................................................................................................ 13
5.2 Decadal projections ......................................................................................................................... 16
5.3 Precipitation extremes: Annual exceedance probability of maximum daily precipitation and higher
percentiles ............................................................................................................................................. 21
5.4 Non-hydrostatic high-resolution RCMs experiments ....................................................................... 24
5.4.1 November 17, 2006 – the Almirida basin flash flood event. ......................................................... 25
5.4.2 January 13, 1994 – the Giofiros basin flash flood event .............................................................. 29
5.4.3 November 22, 2008 – the Ierapetra flash flood event .................................................................. 32
6. Conclusions ....................................................................................................................................... 36
6.1 Long term projections of water availability ...................................................................................... 36
6.2 Decadal projections ......................................................................................................................... 36
6.3 Extremes and non-hydrostatic high-resolution RCMs experiments ................................................ 37
References ............................................................................................................................................ 38
APPENDIX............................................................................................................................................. 42
Page 2 of 43
1. Executive Summary
The objective of this report is to present the technical details of climate information processing and
modelling for the extraction of climate information in its most useful and manageable form, meeting the
needs of the corresponding users. These needs are related to climate modelling outputs ranging from
event scale to decadal and centennial experiments, at temporal scales ranging from hourly to monthly,
and at spatial scales from very high resolution regional climate models (2 km) to typical GCMs.
Centennial scale experiments were used in order to assess climate change impacts on water
resources availability combined with estimates of the potential future water demand of the island of
Crete. A Grand Ensemble of specialized hydrological information was structured from seven CMIP5
simulations (under RCPs 2.6, 4.5 and 8.5), from three GCMs (under A2 and B1 emission scenarios)
and from 10 RCMs (ENSEMBLES FP6, under A1B emission scenario), bias adjusted against local
observations. The range of projected information among the different emission scenarios, different
climate model setups and scale represent a wide range of the uncertainty assessed in the projected
hydro-climatology of the region. The continuous rainfall-runoff model SAC-SMA was used to estimate
the hydrologic impact during the 21st century for the study area. Water availability for the whole island
was then estimated for a range of different scenarios of projected hydro-climatological regime,
demand and supply potential. Regarding the shorter term water management planning according to
Water Framework Directive (WFD), the ability of decadal GCM prediction experiments to reproduce
basic hydro-meteorological variables like precipitation and temperature, was also examined.
In terms of potential future changes in extreme events related to precipitation the recurrence intervals
of specific magnitude was studied using the Annual Exceedance Probability (AEP) approach.
Additionally, three special cases were framed, in collaboration with the WP2 modeling group, for
demonstrating high resolution climate modeling applications of extreme flood events over the case
study area and the evolution of the corresponding events at a +2oC warmer climate. This application
had an ambiguous purpose firstly to convince the user for the capabilities of climate modeling against
scaling uncertainty and secondly to test the high resolution modeling capability of realistic
representation of such events.
The main findings can be summarized as follows:
•
A robust signal of temperature increase and precipitation decrease is projected for all the
pathways and scenarios resulting to a severe decrease of water availability.
•
A significant increase of the 2% Annual Exceedance Probability in maximum daily precipitation
is projected for all future periods over the island of Crete.
•
Non-hydrostatic high-resolution RCMs proved to be sufficiently efficient in realistic capturing
storm events and thus valuable in impact modelling.
•
Historical storm events over Crete, could produce significantly higher precipitation
accumulations and intensities in a warmer climate.
The final product of the communication with the end user and the research instigated by this
interaction, was future climate statistics and time series on meteorologic variables and rainfall-runoff
simulations supporting water availability decision-making, as well as statistics of projected precipitation
extremes in terms of recurrence intervals delivered to the Regional Authority for Water Resources
Management of Crete.
2. Introduction and study area
The Mediterranean can be considered as one of the “hot spot” areas for recent and projected climate
change (Giorgi, 2006), most vulnerable to climatic and anthropogenic changes (Milano et al., 2013).
The Mediterranean is getting drier and warmer with an observed increased frequency and intensity of
summer temperature extremes (Frank et al., 2013). Model projections reveal a continual and gradual
warming trend while exceptional hot summers during the control period may become “typical” by the
end of the 21st century (Kostopoulou et al., 2013).
Decreasing annual rainfall trends and rainfall events of shorter periods but of higher intensities are
present in most projections from both global and regional climate change models and are consistent
across emission scenarios, concentration pathways and future time periods (Ludwig et al., 2011).
Currently, the southern and eastern rims are experiencing high to severe water stress induced by
human and climatic drivers. Future scenarios for water resources in the Mediterranean region suggest
a progressive decline in the average stream flow (García-Ruiz et al., 2011; Murray et al., 2012). By the
2050s, this stress could increase over the whole Mediterranean basin, notably because of a 30–50%
decline in freshwater resources as a result of climate change (Milano et al., 2013).
The current financial stress and the continuously reduced national investment programmes call for low
cost, short and long term water management strategies in order to tackle the climate induced changes
in water resources (Koutroulis et al., 2013). All the above indicates the necessity to improve and
update local water management planning and adaptation strategies in order to attain future water
security, and thus specialized climate services supporting these actions is of crucial importance.
The Regional Authority for Water Resources Management applies Hydrological Balance models in
order to manage water distribution and availability for domestic and irrigation use. Planning involves
domestic and irrigation use infrastructure and policy development. Currently the Regional Authority
owns a large number of hydro-meteorological stations over the island that allow for and adequate
modelling of the current conditions. The availability of future climate data, during the 21st century, is
crucial for the development of water management scenarios and risk assessment for future water
availability.
Page 4 of 43
The Crete region has suffered from numerous sever flood and flash flood event in the past decades.
They have been a result of a combination of factors ranging from extreme climatic conditions to
negligence and poor flood mitigation measures. Upgrading facilities for extreme events readiness is a
major effort. At the same time, an assessment of the severity and frequency of future precipitation
extremes is also important in order to develop Annual Exceedance Probability (AEP) curves and
design infrastructure.
The island of Crete occupies the southern part of the country of Greece (Figure 1) and is divided into
four prefectures from East to West: Lassithi, Iraklion, Rethymno, and Chania. With an area of
8,265km2, Crete covers almost 6.3% of the area of Greece (Tsanis et al., 2013). The mean elevation
is 482m and the average slope 228m/km with the topography fracturing into small catchments with
ephemeral streams and karst geology. Crete has a typical Mediterranean island environment with
about 40% of the annual precipitation occurring in the winter months while there is negligible rainfall
during the summer. The average precipitation ranges from 440mm/year on the Plain of Ierapetra
(Lassithi) to more than 2,000 mm/year on the Askifou upland (Chania), where orographic effects tend
to increase both frequency and intensity of winter precipitation (Naoum and Tsanis, 2004; Koutroulis
and Tsanis, 2010).
Figure 1: Location of the island of Crete, Greece and areas equipped with irrigation systems (source:
FAO-Aquastat, 2007).
Annual air temperature in Crete increases 1.5oC from West to East and 1oC from North to South.
Potential evapotranspiration varies from 1370 to 1570mm/year, representing 75% to 85% of the mean
annual precipitation in low elevation areas (less than 300m a.sl.) and 50% to 70% in high elevation
areas. The annual water balance breaks down to 68-76% evapotranspiration, 14-17% infiltration and
10-15% runoff. The precipitation and ensuing runoff are greater in the north than in the south because
Page 5 of 43
the prevailing wind direction is northwesterly. Generally, the western part of the island receives more
precipitation than the eastern part (Naoum and Tsanis, 2004).
Total water uses in the region in 2000 amounted to 420 million m 3, approximately 5.5% of the
precipitation of a normal year (Fasoulas, 2002). Of this, 16% is used for domestic, tourist, and
industrial uses, 3% for livestock and a vast 81% for irrigated agriculture on less than 30% of the total
cultivated land, using mainly ground water in drip irrigation methods. Irrigation and tourism create peak
demands resulting in a marked seasonal pattern in water demand with an annual volume of water
abstracted exceeding 50% of the average annual runoff and 35% of the groundwater potential. The
intense development of Crete in the last 40 years has exerted strong pressures on many sectors in the
region. Urbanization and growth of tourism and agriculture had an adverse impact on the water
resources of the Island. Despite the financial crisis of the last five years, water use in Crete continues
to escalate at alarming proportions. Regarding the future water demands of the island, recent
estimates forecast total uses for the year 2015 in the order of 550 m3/year, which represents 7% of the
mean annual precipitation. It is therefore considered essential to encounter the increasingly severe
water problems faced in the Island only by strategic policies using integrated water management
systems (Tsanis and Koutroulis, 2011).
3. Datasets
3.1 Local measurements
The observational dataset for the assessment of the water resources availability is structured from
records in the period 1973 – 2005. It consists of daily precipitation at 53 precipitation stations, runoff at
22 stations (Figure 2), 15 temperature stations and 18 pan Evaporation stations.
Figure 2: Location of the island of Crete, Greece and spatial distribution of hydro-meteorological
gauging stations.
Regarding the demonstration cases of non-hydrostatic high-resolution RCMs runs the following
observations were used to support their evaluation:
•
November 17, 2006 – the Almirida basin flash flood event.
Page 6 of 43
50 stations of daily accumulated precipitation and 3 stations of 10 min time step recording. A
well-documented flash flood event with available rainfall runoff modeling data
•
January 13, 1994 – the Giofiros basin flash flood event
50 stations of daily accumulated precipitation. A well-documented flash flood event with
available rainfall runoff modeling data
•
November 22, 2008 – the Ierapetra flash flood event
44 stations of daily accumulated precipitation and 13 stations 10 min time step recording.
A detailed description of the events is included in Sobolowski et al., (2014).
3.2 Long term transient experiments
Ten (10) different RCMs runs of the Ensembles FP6 project (Van der Linden and Mitchell, 2009)
focusing on the Island of Crete over the European continent were used, at a horizontal resolution of
about 25 km. The RCMs’ lateral boundary conditions were provided by 8 GCMs for the period 19512100 (Table 1). All simulations were forced using observed GHG greenhouse gas and aerosol
concentrations until 2000 and SRES A1B concentrations scenario until 2100.
No
Table 1: List of Ensembles FP6 Regional Climate models (RCMs).
Institute
RCM
Driving GCM
References
1
2
3
4
5
6
7
8
9
10
ETH
ICTP
KNMI
METOHC
METOHC
METOHC
C4I
MPI
SMHI
DMI
CLM
RegCM
RACMO2
HadRM3Q0
HadRM3Q3
HadRM3Q16
RCA3
REMO
RCA
HIRHAM
HadCM
ECHAM5-r3
ECHAM5-r3
HadCM3Q0
HadCM3Q3
HadCM3Q16
HadCM3Q16
ECHAM5-r3
BCM
ARPEGE
Jaeger et al. (2008)
Giorgi and Mearns (1999)
van Meijgaard et al. (2008)
Collins et al. (2010)
Collins et al. (2010)
Collins et al. (2010)
Kjellström et al. (2005)
Jacob (2001)
Kjellström et al. (2005)
Christensen et al. (2006)
Seven Earth System Models (ESMs) outputs analysed within COMBINE FP7 framework (Table 2),
under three Representative Concentration Pathways (RCPs), a total of 45 realizations, and used in
the ELCISE project as climate information of the new CMIP5 modelling experiment.
Table 2: CMIP5 RCPs simulations
Realizations
Horiz. Resol.
References
CMCC-CESM
MPI-ESM
EC-Earth
1
3
3
3
RCP8.5
RCP4.5
RCP2.6
Model
1
3.75ox3.75o
1
1.875ox1.875o
3
o
1.125 x1.125
Hazeleger et al., 2011
Collins et al., 2011
Dufresne et al., 2013
HadGEM
4
4
4
1.875ox1.25o
IPSL-CM5
4
4
4
3.75ox1.875o
Page 7 of 43
Fogli et al., 2009
Roeckner et al., 2006
o
CNRM-CM5
1
1
1
1.4ox1.4o
Voldoire et al., 2012
2.5ox1.875o
Bensten et al., 2012
NorESM
1
1
1
Total
14
16
15
Six additional datasets available for the period 2001-2100 based on 3 different GCMs (Echam5, IPSL,
and CNRM) and two emission scenarios (A2 and B1), at a spatial resolution of 0.5o, were used for
analysis. These datasets were developed in the context of WATCH FP6 project.
3.3 Decadal prediction experiments
Decadal prediction experiments within the CMIP5 protocol were also analyzed in order to examine the
ability of realistic reproduction of decadal trends, number of wet and dry years, and general
information useful for short term resources planning. A large number of different initializations and
realizations (totally 439) from five contributing models (Table 3) were analyzed.
Table 3: Decadal prediction datasets from CMIP5 archive
Realizations
Model
1970
1975
1980
1985
1990
1995
CMCC-CM
3
3
3
3
3
3
CNRM-CM5
10
10
10
10
10
10
EC-Earth-KNMI
11
11
11
11
11
HadCM3
20
20
20
20
20
MPI-ESM-LR
10
10
10
10
Total
54
54
54
54
2000
2005
2010
3
0
10
10
0
11
11
11
0
20
20
20
0
10
10
10
10
10
54
54
51
54
10
3.4 Non-hydrostatic high-resolution RCMs experiments
High-resolution non-hydrostatic regional climate modelling experiments (NHRM) were conducted for
the selected cases over the island of Crete (Sobolowski et al., 2014). Three models (i) REMO by CSC
(Jacob and Podzun, 1997, Jacob, 2001), (ii) HARMONIE by SMHI (Lindstedt et al., 2014) and (iii)
WRF by UNI (Hong and Lim, 2006), were set up and run for a model domain covering the island of
Crete at a past climate and a warmer climate (+2oC) mode.
4. Methods
4.1 Case study framework
A wide range of available climate information were communicated to the user (Directorate of Water).
An established assessment framework was used (Tsanis and Koutroulis, 2011; Tsanis et al., 2013)
which was based on previous studies but also on new interactions during the ECLISE project. A
detailed quantification of trends in water needs for Crete were implemented regarding future
Page 8 of 43
perspectives for policy, management and hydrological (climate) regime, resulting in a simple and
practical framework for assessing future water resources status for the island of Crete (Figure 3).
These future scenarios includes the augmentation of agricultural practices, improvement and
extension of the already established irrigation network to tourism, permanent population and demand
trends (Koutroulis et al., 2013). The projected reference periods were split to 2000-2050 and 20502100. The present report includes the assessment of water practices of the local water managing
authority as well as the current status and the future plans for water use (demand and supply).
Figure 3: Water resources assessment framework for Crete study site.
4.2. Bias adjustment
In order to optimally remove the systematic bias of the modeled precipitation, a new methodology that
was developed in the frame of European funded projects, including ECLISE, and applied to climate
model outputs. The methodology belongs to the widely used family of quantile mapping correction
methods. The method uses different instances of gamma function that are fitted on multiple discrete
segments on the precipitation CDF, instead of the common quantile-quantile approach that uses one
theoretical distribution to fit the entire CDF. This imposes to the method the ability to better transfer the
observed precipitation statistics to the raw GCM data. The selection of the segment number is
performed by an information criterion to poise between complexity and efficiency of the transfer
function (Grillakis et al., 2013). The methodology was tested on CMIP3 global climate models resulting
to a very good performance in reducing the systematic biases of the climate model data. Details of the
methodology and the validation are presented in Grillakis et al. (2013).
Page 9 of 43
4.3 Hydrologic modelling
Hydrologic modelling was performed with the use of the continuous rainfall-runoff model SAC-SMA
(Sacramento) model (Tsanis and Apostolaki 2009). Precipitation and potential evapotranspiration are
used as sole model inputs to generate an estimated flow which can be used to calibrate the model
with a genetic algorithm optimization process (Wang, 1998). The objective function of the optimization
is the Nash-Sutcliffe model accuracy statistic. An optimization method based on genetic algorithms
was used in 17 gauged basins (Figure 4) using a monthly time step. In general, the calibration yielded
satisfactory results. For 15 out of the 17 basins the R2 of the calibration was between 0.52 and 0.90
while the Nash–Sutcliffe criterion was between 0.51 and 0.90. The comparison of the observed and
simulated flow for all calibrated basins yields a total R2 of 0.81 and a Nash–Sutcliffe criterion yielding
the same value. The remaining 2 basins failed to be calibrated to a satisfactory degree. The averaging
for all basins parameters was implemented using a naïve” parameter to make estimations for all 110
basins.
Figure 4: The 17 gauged basin of the island of Crete.
4.4 Annual Exceedance Probability
Changes in the recurrence intervals of specific magnitude events are studied using the Annual
Exceedance Probability (AEP) approach (Eash et al., 2013). Theoretical parametric distributions are
fitted on the annual maximum values of precipitation (the maximum of each year). Following the rule
that a theoretical curve can be extended to as far as the double size of the fitted sample, it is safe to
extrapolate the distributions to as far as 1% AEP. Nonetheless, the agreement between the observed
and the bias corrected historical data is also an indicator of the a) quality of the bias correction, b) the
uncertainty induced to the extrapolation, which should be taken into consideration.
Page 10 of 43
5. Results
5.1 Projections of water availability
5.1.1 Recent past
The hydrological water balance of the control period (1970-2000) corresponds to a typical east
Mediterranean island state. The majority of the precipitation fraction evapotranspirates (72%) while 1417% infiltrates and the rest (10-15%) goes towards surface runoff. The monthly distribution of
estimated actual ET, runoff and infiltration is shown in Figure 5. As expected, the estimated runoff and
infiltration is high during the winter and the actual ET during spring. According to the above simulation,
the approximately 934mm or 7,697Mm3 (Table 4) of long term annual precipitation that falls on the
island of Crete following the standardization with the respective basin areas, is distributed into 70%
Evapotranspiration, 17% Infiltration and 13% Runoff. These values are produced for the median
Sacramento model parameters.
(a)
(b)
(c)
Figure 5: Monthly estimated actual ET, Runoff and Infiltration for the control period.
Table 4: Water balance components for the island of Crete for the period 1970-2000
Precipitation
Runoff
ET Actual
Infiltration
934mm
7,697Mm
3
93-140mm
635-710mm
131-159mm
770-1,555Mm3
5,234-5,850Mm3
1,078-1,308Mm3
10-15%
68-76%
14-17%
5.1.2 Long term projections
The climate models output analysis was based on one 30-year and two 50-year discrete periods over
Crete in the COMBINE domain WATCH and Ensembles domain. The control period (1970-2000) was
used for weighting of ENSEMBLES RCMs, bias correction of COMBINE, WATCH and ENSEMBLES
model results and interpretation for the two future (2000-2050 and 2050-2100) periods. The projected
hydrologic regime was also distinguished in two time periods (2000-2050 and 2050-2100) for the three
pathways (RCPs 2.6, 4.5 and 8.5) and three emission scenarios (B1, A2 and A1B) based on the
Page 11 of 43
hydrologic simulation of the COMBINE, WATCH and ENSEMBLES climate model input data through
continuous rainfall-runoff modelling. Detailed results for WATCH FP6 and COMBINE FP7 modelling is
included in Tsanis and Koutroulis, (2011); Koutroulis et al., (2013) and Tsanis et al., 2013. Figures A1
and A2 in Appendix summarizes the projected temperature and precipitation change over Crete for the
above set of experiments. Hydrological modeling results of these trends are presented in the following
Figures A3 and A4 of the Appendix showing the continuously decrease water availability. Table 5
summarizes the projected hydrological changes for all pathways/scenarios and multi-model ensemble
for the two reference periods according to the water resources assessment framework for Crete.
Period
Table 5: Summary of projected hydrological changes.
Results
Average annual precipitation is expected to be slightly decreased (RCP2.6 1.7%) with a parallel increase of temperature by 1.8 oC on average that
could lead to a decrease of about 6.5% in availability
Precipitation is projected to slightly decrease (-3.4%) and combined
with the slightly less (compared to RCP2.6) temperature increase 1.7
RCP4.5 oC, could result to an average decrease in availability by 2.1%.
Scenario
2000-2050
RCP8.5
B1
A2
A1B
RCP2.6
2050-2100
RCP4.5
RCP8.5
B1
A2
A1B
Projections show an average decrease of 4.7% in average annual
rainfall and an average temperature increase of 2.1 oC, leading to a
water availability decrease by 13.5%.
Projected average annual precipitation is expected to be similar to that
of the control period (1970-2000). Average annual temperature (16.3 oC
of 1970-2000 period) could increase by an average of 1.5oC. We could
expect an average increase of water availability by 8%, varying from
4% to 11%.
Annual precipitation could slightly decrease (2%) to an average of
919mm. Average annual temperature could increase by an average of
1.4oC and average water availability conditions remain similar to past
climate.
According to the Ensembles results from 10 RCMs, future projections
show an average decrease of 14% in average annual rainfall and an
average temperature increase of 1.7 oC, leading to a water availability
decrease by 28%.
Average annual precipitation is expected to be slightly decreased (-1%)
with a parallel increase of temperature by 2.5 oC on average that could
lead to a decrease of about 21% in availability
Annual precipitation could decrease (-8.7%), annual temperature could
increase by an average of 3.2oC and average water availability could
decrease by 17% compared to past climate.
Projections show an average decrease of average annual rainfall by
18.6% and an average temperature increase of 5.5 oC, leading to a
water availability decrease of -33%.
Projected average annual precipitation is expected to decrease slightly
(4%, 989mm). Average annual temperature could increase by an
average of 3.1oC. During this period we could expect on average 10%
less available water resources.
Projected average annual precipitation is expected to drop to 735mm,
21% less in comparison to the period 1970-2000. Average annual
temperature could increase by an average of 4.5oC. Average water
availability could decrease by 40%.
Projections show an average decrease of average annual rainfall by
26%, reaching 688mm, and an average temperature increase of 4.6 oC,
leading to a water availability decrease of 48%.
Page 12 of 43
5.1.3 Scenarios of Water demand and technical infrastructure
The adopted scenarios of water demand and supply potential are described in detail in Tsanis and
Koutroulis, (2011); Koutroulis et al., (2013). The developed scenarios were based on the water
practices of the local water management authority (Papagrigoriou et al., 2001) and have three basic
components: (a) the level of demand of water for the various uses and (b) the projected water supply
potential derived from the combination of the technical infrastructure and the projected water
availability (dams, barrages, groundwater abstractions, etc.). Each scenario is composed by a
concrete condition of the above two components, for a climate model projection under various
emission scenarios, during each of the analysis periods (2000-2050, 2050-2100). Summarizing:
Level of water demand:
D1: Existing situation of water demand: it includes the current level of demand (2000), based on
elements that were assembled from irrigation, water supply authorities and other water users over the
whole extent of the Island estimated at 535.7 Mm3/year (458.4 Mm3 in irrigation and 77.3 Mm3 in
general water supply).
D2: Scenario of future water demand a realistic future scenario of demand which results from the local
irrigation facilities construction programs, the future irrigation extend plans, the estimate of future
population trends and an increase of tourism activities, resulting to a total of 775.8 Mm3/year (670.8
Mm3 for irrigation and 105 Mm3 for general water supply).
Level of water supply:
S1: Business as usual (existing infrastructure): it includes all the existing works of exploitation of water
resources. A total of 302 Mm3 are supplied for irrigation and 69.7 Mm3 for general water supply. An
additional 42.8 Mm3, originating from groundwater abstractions, are supplied to the system, shaping
the total supply to 414.6 Mm3 (normal hydrological conditions).
S2: Future technical infrastructure: it includes the future technical infrastructure based on the proposed
technical work of exploitation of water resources that has been evaluated as mature enough for
construction. A total of 398.3 Mm3 are supplied for irrigation (59.4% of the demand), 100.4 Mm3 for
general water supply, and an additional 35.4 Mm3 from groundwater abstractions are supplied to the
system, shaping the total supply to 534.2 Mm3 (normal hydrological conditions).
A direct connection of the decrease (%) of projected water availability to the supply formulated from
the two technical infrastructure scenarios (S1 and S2) depicts the estimation of the impact of climate
change to the projected supply potential. The combination of the above components produces a large
number of different scenarios and offers ample flexibility in their configuration. The estimated excess
or deficit of the water balance system from the combination of all components is included in Tables 6.1
and 6.2 for the first half and the second half of the 21st century, respectively, including comparison with
the current status (Scenario1 – BAU) of the deficit of 122 Mm3 (23% less from demand).
Page 13 of 43
For the first time slice (2000-2050) a range of 24 different futures of projected hydro-climatological
regime, demand and supply potential are examined. Only six of these scenarios result to excess in
water resources and only for the cases of D1 (existing demand) and S2 (future infrastructure). For the
second period (2050-2100) a robust signal of water scarcity is projected for all the combinations of
pathways/emissions, demand and infrastructure scenarios, with the estimated deficit ranging from
10% to 74%. The only case that an improvement of the current condition (BAU) could be expected is
under the assumption of B1 emission scenario and RCPs 2.6 or 4.5 and the combination of current
demand (D1) and future infrastructure (S2).
Page 14 of 43
Demand
Infrastructure
Est. Demand
(Mm3)
Est. Supply
(Mm3)
Est. Deficit
(Mm3)
Est. Excess
or Deficit (%)
Difference from
BAU (Mm3)
D2
S1
776
388
-388
-50%
-266
D2
S1
776
328
-448
-58%
-326
D2
S2
776
499
-277
-36%
-155
D2
S2
776
422
-354
-46%
-232
D1
S1
536
406
-130
-24%
-8
D1
S1
536
344
-192
-36%
-70
D1
S2
536
523
-13
-2%
109
D1
S2
536
443
-93
-17%
29
D2
S1
776
406
-370
-48%
-248
D2
S1
776
344
-432
-56%
-310
D2
S2
776
523
-253
-33%
-131
D2
S2
776
443
-333
-43%
-211
D1
S1
536
359
-177
-33%
-55
D1
S1
536
278
-258
-48%
-136
D1
S2
536
462
-74
-14%
48
D1
S2
536
358
-178
-33%
-56
D2
S1
776
359
-417
-54%
-295
D2
S1
776
278
-498
-64%
-376
D2
S2
776
462
-314
-40%
-192
D2
S2
776
358
-418
-54%
-296
D1
S1
536
449
-87
-16%
35
D1
S1
536
374
-162
-30%
-40
D1
S2
536
579
43
8%
165
D1
S2
536
482
-54
-10%
68
D2
S1
776
449
-327
-42%
-205
D2
S1
776
374
-402
-52%
-280
16
D2
S2
776
579
-197
-25%
-75
D2
S2
776
482
-294
-38%
-172
17
D1
S1
536
410
-126
-24%
-4
D1
S1
536
251
-285
-53%
-163
D1
S2
536
529
-7
-1%
115
D1
S2
536
323
-213
-40%
-91
D2
S1
776
410
-366
-47%
-244
D2
S1
776
251
-525
-68%
-403
20
D2
S2
776
529
-247
-32%
-125
D2
S2
776
323
-453
-58%
-331
21
D1
S1
536
297
-239
-45%
-117
D1
S1
536
200
-336
-63%
-214
D1
S2
536
382
-154
-29%
-32
D1
S2
536
257
-279
-52%
-157
D2
S1
776
297
-479
-62%
-357
D2
S1
776
200
-576
-74%
-454
D2
S2
776
382
-394
-51%
-272
D2
S2
776
257
-519
-67%
-397
19
22
23
24
-122
-23%
0
COMBINE
COMBINE
WATCH
RCP2.6
RCP4.5
RCP8.5
414
WATCH
18
536
ENSEMBLE
S
15
B1
14
A2
13
A1B
12
2000-2050
11
S1
2051-2100
8
10
Difference from
BAU (Mm3)
-86
-21%
9
Est. Excess
or Deficit (%)
-39%
-114
8
Est. Deficit
(Mm3)
-208
422
7
Est. Supply
(Mm3)
328
536
6
Est. Demand
(Mm3)
536
S2
5
Infrastructure
S1
D1
4
Demand
D1
85
3
Hydrologic
regime
-26
-7%
2
D1
COMBINE
-28%
-37
Emission
scenario
-148
Period
388
499
Past
536
536
SCENARIO
S1
S2
1 BAU
D1
D1
1
Normal
Table 6: Estimated excess- deficit of the water balance from the combination of all components for the 2000-2050 and 2051-2100 periods.
5.2 Decadal projections
The Directorate of Water (user) as the general managing authority of water resources in the Prefecture
of Crete is responsible for the implementation of the Water Framework Directive (WFD) for the
corresponding hydrological compartment (GR13). The implementation of the WFD is based on 6 years
term management plans. The first draft management plan was developed during the course of the
ECLISE project and information regarding short term experiments such as decadal projections were
considered as very useful and appropriate for this application. In light of this climate information need,
the ability of decadal prediction experiments to reproduce temperature and precipitation parameters
over Crete was examined based on a set of 439 realizations from ESMs available in the frame of
COMBINE FP7 project. Figure 6 compares the models skill of the experiments in simulating annual
average temperature over Crete region starting from 1970, initializing every 5 years. EC-Earth and
CMCC-CM models resulted to better representation of average annual temperature. Each modelling
group provided a different number of realization in CMIP5 archive as referred in section 3.3.
Figure 6: Decadal predictions experiments from 5 ESMs over Crete. For all the plots the black solid
lines shows the observed average annual temperature. Black dots and lines shows initialization year
and realizations average.
Similarly, Figure 7 compares the skill of the decadal experiments in simulating average annual
precipitation over Crete.
Figure 7: Decadal predictions experiments from 5 ESMs over Crete. For all the plots the black solid
lines shows the observed total annual precipitation. Black dots and lines shows initialization year and
realizations average.
A summary of simple performance metrics like mean, standard deviation and coefficient of variation of
monthly precipitation and temperature values are included in Table 7. It is obvious that decadal
predictions cannot describe the precipitation regime over the study area with a bias ranging from -55%
to -70%. These biases could be caused by imperfections in sub-grid scale climate model
Page 17 of 43
conceptualizations and the still course spatial resolution. This pronounced underestimation makes
decadal experiment results of low usefulness in their native form.
MPI-ESM
0.166
1.041
CV
1.235
0.867
1.132
1.087
1.233
83.4
STDEV
29.6
32.1
36.1
35.3
32.7
80.1
Mean
23.9
37.1
31.9
32.5
26.5
Bias
-56.2
-44.7
-48.4
-54.3
-55.2
OBS
Precipitation
Temperature
CV-DEV
MPI-ESM
HadCM3
-0.025
HadCM3
EC-EARTH
0.047
EC-EARTH
CNRM-CM5
-0.204
CNRM-CM
0.125
CMCC-CM
CV-DEV
OBS
CMCC-CM
Table 7: Summary of the skill of decadal experiments for precipitation and temperature.
-0.146
-0.066
-0.156
-0.149
-0.162
0.214
0.295
0.204
0.211
0.198
3.6
5.5
3.4
3.9
3.7
0.350
CV
5.6
STDEV
16.1
Mean
16.8
18.8
16.8
18.4
18.8
Bias
0.5
2.4
0.4
2.1
2.5
In order to be comparable with local observations, results were adjusted for their mean value and
variability against observations and tested for the ability to capture decadal trends, number of “wet”
and “dry” years and number of “hot” and “cold” years (Table 8). Years with total annual precipitation
below the 20th percentile (<780mm) of the control period 1970-2000 were defined as “Dry”, while years
over the 80th percentile (>1100mm) were defined as “Wet”. Similarly, years with average annual
temperature below the 20th percentile (<15.9oC) of the control period 1970-2000 were defined as
“Cold”, while years over the 80th percentile (>16.7oC) were defined as “Hot”. Figure 8 presents the
annual temperature and precipitation over Crete from decadal prediction experiments, after bias and
variability adjustment from a number of realizations (initializations) per model. It is clear that no robust
signal, neither for temperature nor for precipitation is detected. The identification of similar slope
trends, number of Wet/Dry and Hot/Cold years for a few decades and models but not in a systematic
pattern seems to be due to randomness. The low resolution of the models when compared to the size
of the study area could be the main reason of the presence of these biases. Decadal experiments with
a horizontal resolution of the order of one or more decimal degree cannot capture and resolve local
scale processes. Therefore, every effort of downscaling, even with a high resolution dynamical nonhydrostatic application for the case study region, could probably be redundant and without any
practical outcome.
Page 18 of 43
Temperature
Precipitation
Table 8: Decadal experiments: slope, correlation coefficient, number of “wet” and “dry” years and
number of “hot” and “cold” years.
1970 1975
1980
1985
1990
1995
OBS
Slope (mm/y)
-11.0
-45.0
-11.4
17.1
19.3
WET(#y)
0
2
3
4
1
DRY(#y)
0
2
2
3
1
CMCC-CM
Slope (mm/y)
-14.0
-30.0
-16.7
-24.6
-14.3
WET(#y)
1
3
2
2
3
DRY(#y)
0
1
2
1
1
Correl
0.41
0.28
-0.11
0.17
0.04
CNRM-CM5
Slope (mm/y)
4.4
12.1
-24.0
-18.7
48.9
WET(#y)
1
2
3
2
3
DRY(#y)
0
1
2
2
1
Correl
-0.19
-0.03
-0.17
0.11
0.37
EC-Earth-KNMI Slope (mm/y)
21.3
41.2
49.3
54.0
66.6
WET(#y)
1
2
3
2
1
DRY(#y)
0
2
2
2
1
Correl
-0.31
-0.44
-0.01
0.27
0.24
HadCM3
Slope (mm/y)
-2.6
-4.8
14.9
13.5
-6.2
WET(#y)
1
2
2
3
2
DRY(#y)
0
1
2
2
1
Correl
0.04
-0.06
-0.08
-0.06
-0.14
MPI-ESM-LR
Slope (mm/y)
10.9
0.9
-29.3
-39.0
-4.5
WET(#y)
2
1
2
2
3
DRY(#y)
0
1
1
2
1
Correl
-0.18
-0.12
0.64
-0.21
0.69
OBS
Slope (oC/y)
-0.02
0.04
0.06
0.03
0.13
0.06
COLD(#y)
0
0
1
1
3
2
HOT(#y)
1
2
2
2
3
2
CMCC-CM
Slope (oC/y)
-0.09 -0.10
-0.07
-0.09
-0.14
-0.12
COLD(#y)
1
1
1
2
3
1
HOT(#y)
1
1
0
1
4
2
Correl
0.18 -0.39
-0.23
-0.06
-0.37
-0.86
CNRM-CM5
Slope (oC/y)
-0.10 -0.10
-0.09
-0.12
-0.09
-0.11
COLD(#y)
1
1
1
2
1
2
HOT(#y)
1
1
2
3
2
4
Correl
0.28 -0.23
-0.35
-0.15
-0.21
-0.61
EC-Earth-KNMI
Slope (oC/y)
-0.03 -0.03
0.09
-0.05
0.14
0.13
COLD(#y)
1
2
1
3
2
2
HOT(#y)
1
1
1
1
3
3
Correl
0.52
0.31
0.47
-0.14
0.22
0.42
HadCM3
Slope (oC/y)
-0.07 -0.09
0.02
0.06
-0.03
0.07
COLD(#y)
1
0
1
2
1
2
HOT(#y)
0
2
3
2
4
2
Correl
0.20 -0.07
0.41
0.20
-0.10
0.47
MPI-ESM-LR
Slope (oC/y)
-0.01
0.06
0.09
-0.02
0.10
0.09
COLD(#y)
0
1
2
1
4
1
HOT(#y)
2
1
1
2
3
1
Correl
-0.26
0.57
0.58
0.44
0.24
0.20
Page 19 of 43
Figure 8: Annual temperature and precipitation over Crete from decadal prediction experiments, after
bias and variability adjustment. Solid black line corresponds to observations and color lines to average
model results from a number of realizations (initializations).
Page 20 of 43
5.3 Precipitation extremes: Annual exceedance probability of maximum daily precipitation and
higher percentiles
Projected changes in precipitation extremes were communicated in terms of changes in Annual
Exceedance Probability (AEP) based on annual maximum precipitation values. Annual Exceedance
from 10% Probability (10 years return period) up to 1% (100 years return period events) were
estimated at station level based on daily precipitation records at 52 locations over Crete
(observations), control period RCM runs (1970-2000) and three projection periods (2010-2040, 20412070, 2071-2100). Based on 30 years long time-series it is safe to extrapolate up to 2% AEP (50 years
return period) and results presented below are based on these estimates. Generalized Extreme Value
(GEV) distribution was used to model the annual rainfall maxima.
Projections were obtained from the most complete in terms of models number and more detailed in
terms of spatial resolution simulations of ENSEMBLES FP6 project (Van der Linden et al., 2009),
forced using observed GHG greenhouse gas and aerosol concentrations until 2000 and SRES A1B
concentrations scenario until 2100. Simulations from 17 RCMs were retrieved and adjusted for biases
at station level against daily precipitation records using a multi-segment bias correction method that
performs better especially for extremes values, compared to methods using one theoretical distribution
to fit the entire CDF or distribution free methods. The coarse density of the station network over the
western part of Crete results to biased results based on a few stations.
The effectiveness of the bias correction method to realistically adjust climate modelled precipitation
maxima close to observations is presented in Figure 9 (three upper panels). The average bias of 50
years return period extreme values (2% AEP) based on raw RCMs outputs compared to the ones
estimated from observations was underestimated by more than 50% (-87mm results not shown here
and ranging from -19mm to -221mm among the 52 stations) and with the application of the adjustment
the underestimation dropped to 5% (remaining average bias of 8-mm ranging from -04mm to +25mm
among the 52 stations).
A significant increase of the 2% AEP is projected for all future periods. An average increase of over
40% (over 65mm) is projected form the average of the 17 RCMs under A1B scenario ranging from
9mm to 175mm among the 52 stations (Figure 9 lower panels). A similar but less increase in average
2% AEP is projected for the subsequent periods. Extreme daily precipitation of 2% AEP could increase
over 30% (50mm) for the 2041-2070 period (ranging from 18mm to 230mm among the 52 stations)
and over 25% (40mm) during the 2071-2100 period (ranging from 36mm to 236mm among the 52
stations). Although average 2% AEP is decreasing moving from 2010-2040 to 2041-2070 and 20712100, the 2% AEP variability among the 52 station locations is continuously increasing.
Page 21 of 43
Figure 9: Spatially interpolated (IDW) 2% Annual Exceedance Probability using GEV based on
precipitation records of 52 stations (OBS), and average AEP estimated from 17 bias adjusted RCMs
for the control (1970-2000) and three projection periods (2010-2040, 2041-2070, 2071-2100).
Precipitation maxima were also expressed in terms of extreme percentiles and more specifically the
99.9-ile%. This percentile for a 30 years long time series of daily precipitation corresponds to a
threshold of the 11 maximum values. The value of this percentile based on daily precipitation records
of the 52 stations is described by an average of 89mm ranging from 59mm over central south Crete to
184mm over Western Crete. The application of the bias adjustment had similar results to the 2% AEP.
Page 22 of 43
The remaining bias of the average 99.9-ile% over the 52 stations dropped to about 1% from an over
55% average bias (-50mm). Projections indicate a similar increase of the 99.9-ile% values of the order
of 9%, 8% and 7% for the 2010-2040, 2041-2070 and 2071-2100 periods, respectively. The values
range from a decrease of 2.5mm to an increase of 27mm among the stations.
Figure 10: Spatially interpolated (IDW) 99.9-ile% based on precipitation records of 52 stations (OBS),
and average 99.9-ile% estimated from 17 bias adjusted RCMs for the control (1970-2000) and three
projection periods (2010-2040, 2041-2070, 2071-2100).
Page 23 of 43
5.4 Non-hydrostatic high-resolution RCMs experiments
Simulations for three storm events triggering flash flooding in different parts of Crete were performed
by the modelling groups. The events were selected in terms of severity, data availability and location
(Figure 11). A short overview of the events is presented in D2.4 (Sobolowski et al., 2014).
Figure 11: Three high resolution test cases over Crete. The 99.5-ile% classified map derived from the
interpolation (IDW) of daily precipitation values from 50 stations (with more than 30 gauging years).
Black dots indicate precipitation stations with daily accumulated precipitation lower that the 99.5-ile%.
Blue and green dots correspond to stations daily accumulated precipitation over the 99.5-ile%, for two
classes of less than 100mm and more than 100mm, respectively.
Page 24 of 43
5.4.1 November 17, 2006 – the Almirida basin flash flood event.
The Almirida watershed is located in the northwest part of the island of Crete. The watershed covers
an area of 25 km2 and has a mean slope of 11.9 % with elevation ranging from 5 to 527 m above sea
level. The mean annual precipitation is 648 mm. On 17 October, 2006, a frontal depression in the
central Mediterranean moved eastward and crossed the island of Crete mid-day on 17 October 2006
(Tsanis et al., 2008). This depression caused a high intensity short duration heavy rainfall event
resulting in a flash flood. Weather radar located less than 25 km from the watershed provided sparse
reflectivity data during the event due to power outages in the area (Daliakopoulos and Tsanis, 2011).
The neighboring rain gauge of Souda Bay (16 km) recorded a maximum hourly precipitation of 25.2
mm and a daily gauge (Kalives) located just 3 km from the watershed recorded 220 mm, far exceeding
the 1% AEP (100yrs return period) of the particular station (Koutroulis et al., 2010). Hydrologic
analysis showed that this flash flood event produced an estimated peak flow on the order of 120 m3/s
(Tsanis et al., 2013), which corresponds to a specific peak discharge of 5 m3/s/km2 (Gaume et al.,
2009). This extreme event led to the loss of one life and over € 1M in damages in Almirida alone and a
total damage toll of approximately € 3M.
This storm event was simulated by the HCLIM, HARMONIE and WRF NHRMs. The location of the
storm over Western Crete was adequately captured by all three models. Observed accumulated
precipitation (up to 330mm) for the three days 16-18 October 2006 (Figure 12, upper) was simulated
more realistically by WRF model in terms of rainfall height. HCLIM and HARMONIE resulted in less
rainfall compared to observations. The lower panels of the Figure 9 shows the difference in the +2oC
warming scenario as compared to the reference simulations. Especially for the stations in the wider
are of the flood impacted region, simulations result to higher precipitation totals ranging from 7% to
40%. It is also interesting that all models agree to increased precipitation over the south central part of
the island.
The temporal evolution of the precipitation was captured by all models but more realistically in terms of
rainfall amount by WRF, for the flood nearby station of Souda airport (Figure 13). WRF model
overestimated precipitation for all high temporal resolution gauging stations in terms of total
accumulations and intensity (Table 9).
Simulations of +2oC resulted to increased precipitation totals for the Souda-airport and Chania-Halepa
stations that are located relatively close to the most impacted flood area. The relative increase for
Souda station ranges from 12% to 58% for total precipitation and from 18% to 210% for hourly
precipitation intensity depending on the model (Table 9).
Page 25 of 43
Figure 12: Storm event accumulated precipitation for the three days 16-18 October 2006 based on local
observations at 62 stations and as simulated by the HCLIM, HARMONIE and WRF NHRMs interpolated
at station locations (upper panels). Difference in the +2oC warming scenario as compared to the
reference simulations (lower panels).
Page 26 of 43
Figure 13: Temporal evolution of precipitation at station scale based on records and simulations of the
HCLIM, HARMONIE and WRF NHRMs. Simulations of +2oC are plotted in dashed lines.
Table 9: Information of accumulated and maximum hourly precipitation at station level for reference
and +2oC simulations.
77.5
18.8
38.5
21.3
77.8
54.2
13.0
36.9
9.7
131.9
Chania - Halepa
Knossos
Palaiochora
Heraclio Airport
Souda Airport
32
29
3
29
144
7.4
13.6
14.4
8.2
25.2
11.1
4.9
25.0
5.9
16.7
4.7
2.9
7.7
2.4
13.2
Page 27 of 43
4%
5%
35%
17%
43%
15%
-4%
-4%
0%
12%
119%
44%
95%
-12%
58%
82%
-26%
43%
11%
18%
21%
6%
-10%
13%
22%
287%
105%
126%
16%
211%
WRF
WRF
HARM
Accumulated precipitation
205.1
80.6
62.2 449.1
140.2
19.7
12.5 201.3
66.4
52.1
35.5 129.5
196.2
25.0
9.7 172.4
246.4
111.4 147.7 389.9
Maximum hourly precipitation
30.4
20.3
5.7 117.8
23.8
3.6
3.1
48.6
13.9
35.7
7.0
31.5
46.9
6.6
2.8
54.2
31.1
19.6
16.1
96.7
HCLIM
81.8
73.2
39.0
55.2
154.4
Relative change (%)
HARM
32
29
3
29
144
WRF
HCLIM
HARM
OBS
Elev.(m)
Station
Chania - Halepa
Knossos
Palaiochora
Heraclio Airport
Souda Airport
HCLIM
+2oC
Reference simulations
Figure 14 illustrates total event precipitation for the 16-18 October 2006 storm based on simulations by
the HCLIM, HARMONIE and WRF models for the reference and warmer climate experiments, as well
as, their difference. The table on the right includes spatially aggregated information of accumulated
precipitation for Almirida watershed and for the total land area of Crete. Information on maximum and
minimum point (NHRMs node) rainfall is also included. WRF simulation resulted to total precipitation of
more than double in comparison to the HCLIM and HARMONIE models. HARMONIE model simulates
25% more total precipitation at basin level for a warmer climate of 2oC, while HCLIM results to 17%
and WRF to 1% higher precipitation that would probably result to more severe flooding conditions.
2006
WRF
HCLIM
HARMONIE
Alimirida
Crete
Mean Simulation
123.3
47.5
Max Simulation
140.2
255.9
Min Simulation
116.6
0.0
Mean +2C
154.7
56.5
Max +2C
166.8
334.6
Min +2C
142.7
0.1
Diff Mean
31.3
9.0
Diff Max
44.2
113.9
Diff Min
16.2
-19.4
Mean Simulation
150.5
72.1
Max Simulation
171.1
484.6
Min Simulation
129.8
0.3
Mean +2C
175.7
76.5
Max +2C
199.7
546.3
Min +2C
145.9
0.4
Diff Mean
25.2
4.4
Diff Max
33.9
61.8
Diff Min
16.0
-16.8
Mean Simulation
354.6
149.9
Max Simulation
370.8
559.2
Min Simulation
308.1
0.3
Mean +2C
359.9
189.8
Max +2C
404.2
660.7
Min +2C
295.6
28.5
5.3
39.9
Diff Max
65.9
312.6
Diff Min
-57.2
-172.3
Diff Mean
Figure 14: Storm event accumulated precipitation for the three days 16-18 October 2006 based on
simulations by the HCLIM, HARMONIE and WRF NHRMs and difference in the +2oC warming scenario
as compared to the reference simulations. Table includes spatial aggregated information of mean, max
and min precipitation and corresponding differences at basin and whole island level.
Page 28 of 43
5.4.2 January 13, 1994 – the Giofiros basin flash flood event
The Giofiros basin is located in North-Central Crete and occupies an area of 186 km2. The Giofiros
basin has a seasonal flow during the wet winter period (December to March), when the most of the
mean annual rainfall of 827 mm occurs. On January, 1994, a frontal depression in the south-eastern
Mediterranean moved eastward then north crossing the island of Crete (Koutroulis and Tsanis, 2010).
On the 13th January 1994 around 1500 UTC, the intensity of the light precipitation that saturated
Giofiros soils during the previous days increased. The warm air mass with high dew point, southwest–
northeast flow (as determined from an analysis at 500 hPa) and highly positive relative vorticity was
primarily blocked by the Psiloritis Mountains’ steep topography. The air mass was forced to higher
elevations, cooled and produced high rates of precipitation of up to 37 mm/h at Ag. Varvara station
(uphill). A maximum 5 h accumulated precipitation of 123 mm was recorded at 2100 UTC. This
extreme rainfall eventually stopped at 2400 UTC and lasted almost 9 h, triggering a flash flood. The
resulting flash flood had catastrophic impacts on the Giofiros basin. Houses located near the coast
were flooded leaving 49 people homeless. The single most adverse effect of the flood was the
damage caused to the city’s wastewater treatment plant, which was still under construction at the time
of the flood. Many of the concrete tanks were rendered inoperable or were completely destroyed by
the force of the flood wave.
The location of the storm over central Crete was fairly captured by all three models. Observed
accumulated precipitation (up to 200mm) for the three days 12-14 January 1994 (Figure 15, upper)
was underestimated by all models. HCLIM and HARMONIE resulted in less rainfall compared to
observations as the case of 2006. The lower panels of the Figure 14 shows the difference in the +2oC
warming scenario as compared to the reference simulations. Especially for the uphill stations of the
flood impacted watershed, simulations result to higher precipitation totals ranging from 5% to 55%
depending on station and model.
WRF proved more efficient in capturing the temporal evolution of the precipitation and but more
realistically in terms of rainfall amount, for the flood nearby station called Ag. Varvara (Figure 16).
Figure 17 shows total event precipitation for the 12-14 January 1994 storm based on
NHRMs
simulations for the reference and warmer climate (+2oC) experiments, as well as, their difference. The
table on the right includes spatially aggregated information of accumulated precipitation for Giofiros
watershed and for the total land area of Crete. Information on maximum and minimum point (NHRMs
node) rainfall is also included. All simulations resulted to similar total basin precipitation estimations of
the order of 60mm to 70mm. HARMONIE model simulates 18% more total precipitation at basin level
for a warmer climate of 2oC, while HCLIM results to 64% and WRF to 62% higher precipitation.
Page 29 of 43
Figure 15: Storm event accumulated precipitation for the three days 12-14 January 1994 based on local
observations at 51 stations and as simulated by the HCLIM, HARMONIE and WRF NHRMs interpolated
at station locations (upper panels). Difference in the +2oC warming scenario as compared to the
reference simulations (lower panels).
Page 30 of 43
Figure 16: Temporal evolution of precipitation at Ag. Varvara station based on hourly records and
simulations of the three NHRMs. Simulations of +2oC are plotted in dashed lines.
1994
HCLIM
HARMONIE
Giofiros
Mean Simulation
61.0
76.4
Max Simulation
85.8
240.0
Min Simulation
38.1
6.2
Mean +2C
72.1
79.0
Max +2C
96.2
293.0
Min +2C
45.6
1.2
Diff Mean
11.2
2.6
Diff Max
22.7
142.2
Diff Min
0.0
-58.7
Mean Simulation
69.2
94.1
Max Simulation
93.0
352.9
Min Simulation
47.0
14.2
Mean +2C
113.7
119.2
Max +2C
145.9
382.1
Min +2C
81.3
14.2
Diff Mean
44.5
25.1
Diff Max
71.5
81.3
Diff Min
WRF
Crete
5.8
-14.9
Mean Simulation
63.8
62.4
Max Simulation
97.3
217.1
Min Simulation
44.2
5.4
Mean +2C
103.3
70.6
Max +2C
158.5
190.4
Min +2C
70.3
5.5
Diff Mean
39.5
8.2
Diff Max
65.8
65.8
Diff Min
19.7
-90.5
Figure 17: Storm event accumulated precipitation for the three days 12-14 January 1994 based on
simulations by the HCLIM, HARMONIE and WRF NHRMs and difference in the +2oC warming scenario
as compared to the reference simulations. Table includes spatial aggregated information of mean, max
and min precipitation and corresponding differences at basin and whole island level.
Page 31 of 43
5.4.3 November 22, 2008 – the Ierapetra flash flood event
The Ierapetra region, located in the Southeast of Crete is an area of high agricultural and tourism
activity. The average annual precipitation (440 mm) is significantly lower compared to the western part
of the island. The late autumn event of the 22th of November 2008 with an overall duration of 17 hours
affected mainly the eastern and central part of the island of Crete, causing extensive damage to
property and infrastructure (Koutroulis et al., 2012). The closest synoptic scale atmospheric system
was a strong, closed, intense depression of 988 hPa centered over southern Serbia at 0600 UTC 22
November (time of flood event) with an extended effective radius of 5.28 degrees and a depth of 6.4
hPa/degree (Iordanidou et al., 2013). This closed system moved over northern Greece at 1200 UTC
22 November with the same intensity and dissipated within the next few hours. The accumulated
rainfall from a nearby station was 73.8 mm and the maximum 10 minutes recorded precipitation was
12.4mm. Exceptional lightning activity was also recorded during the evolution of the storm.
The location of the storm over eastern Crete was not captured the models. Rainfall occurrence was
better captured over the western and central part of Crete. Observed accumulated precipitation (up to
210mm) for the three days 21-23 November 2008 (Figure 18, upper) was largely underestimated by all
models. The lower panels of the Figure 17 shows the difference in the +2oC warming scenario as
compared to the reference simulations. WRF proved efficient in capturing the temporal evolution of the
precipitation at the Ierapetra station (Figure 19) located near the flood location.
Figure 20 shows total event precipitation for the 21-23 November 2008 storm based on NHRMs
simulations for the reference and warmer climate (+2oC) experiments, as well as, their difference. The
table on the right includes spatially aggregated information of accumulated precipitation for Giofiros
watershed and for the total land area of Crete. Information on maximum and minimum point (NHRMs
node) rainfall is also included. All simulations except WRF resulted to increase in total precipitation at
watershed level estimations due to a warmer climate.
Page 32 of 43
Figure 18: Storm event accumulated precipitation for the three days 21-23 November 2008 based on
local observations at 51 stations and as simulated by the HCLIM, HARMONIE and WRF NHRMs
interpolated at station locations (upper panels). Difference in the +2oC warming scenario as compared
to the reference simulations (lower panels).
Page 33 of 43
Figure 19: Temporal evolution of precipitation at station scale based on records and simulations of the
HCLIM, HARMONIE and WRF NHRMs. Simulations of +2oC are plotted in dashed lines.
9.0
3.6
16.4
1.4
1.8
17.8
10.0
6.0
1.2
6.6
5.4
8.4
2.8
0.8
0.2
0.4
1.4
0.0
1.8
0.8
0.2
1.2
1.5
0.5
0.7
0.1
0.5
0.1
1.2
0.0
0.0
0.0
1.0
1.7
0.0
7.6
2.5
0.6
0.2
WRF
801
137
240
115
10
5
418
3
39
1250
405
820
58
HCLIM
Anogia
Chania
FragmaP
Knossos
Heraklio (Port)
Ierapetra
Metaxochori
Paleochora
Rethimno
Samaria
Spili
Tzermiado
Vrisses
HARM
4.4
0.3
10.6
0.0
0.0
0.0
9.3
18.0
0.1
170.1
36.1
6.0
1.2
WRF
2.6
0.4
2.2
5.1
0.2
9.2
4.4
0.8
7.1
29.0
3.8
3.2
0.1
HCLIM
34.8
5.8
52.2
5.0
4.4
74.0
47.6
24.6
2.6
70.8
30.2
32.6
5.6
HARM
HCLIM
801
137
240
115
10
5
418
3
39
1250
405
820
58
WRF
HARM
Anogia
Chania
Fragma P.
Knossos
Heraklio (Port)
Ierapetra
Metaxochori
Paleochora
Rethimno
Samaria
Spili
Tzermiado
Vrisses
Elev.
(m)
OBS
Station
Table 10: Information of accumulated and max hourly precipitation at station level for reference and
+2oC simulations.
Reference simulation
+2oC
Relative change (%)
47%
94%
52%
263%
604%
37%
-9%
13%
-5%
2%
110%
17%
6%
-29%
204%
12%
54%
54%
316%
-5%
6%
455%
6%
-7%
21%
-40%
160%
-87%
118%
1682%
5489%
-31%
8%
4%
-49%
12%
-3%
33%
-39%
95%
119%
81%
443%
1630%
29%
-76%
0%
-1%
11%
170%
63%
0%
-57%
150%
-12%
-21%
-21%
133%
5%
2%
347%
-3%
-12%
12%
-2%
477%
-89%
363%
1840%
3686%
-30%
4%
-37%
-41%
15%
35%
105%
15%
Accumulated precipitation
8.3
3.8
3.1
21.6
2.9
0.7
0.8
0.4
12.0
3.3
12.0
26.1
0.4
18.5
0.0
6.7
0.0
1.2
0.0
1.9
14.3
12.6
0.1
9.8
26.2
4.0
8.8
28.3
4.5
0.9
19.0
4.6
1.0
6.8
0.6
0.5
132.7
29.6 180.3 148.9
26.0
7.9
33.6
25.2
8.5
3.8
7.3
11.3
6.3
0.1
0.7
3.9
Maximum 30min precipitation
1.4
1.6
0.2
8.4
0.8
0.5
0.1
0.1
1.3
0.8
1.1
6.0
0.2
7.8
0.0
4.6
0.0
0.7
0.0
1.0
2.6
2.4
0.0
1.8
3.4
0.2
1.1
3.5
1.0
0.2
1.7
0.6
0.4
1.2
0.2
0.3
9.0
1.7
7.3
10.3
3.6
1.3
2.2
4.9
1.4
1.1
0.7
2.9
1.6
0.1
0.2
1.8
Page 34 of 43
2008
Ierapetra
WRF
HCLIM
HARMONIE
Mean Simulation
Crete
5.0
19.9
Max Simulation
19.8
244.5
Min Simulation
1.4
0.0
Mean +2C
7.0
24.0
Max +2C
24.0
306.3
Min +2C
2.5
0.0
Diff Mean
2.1
4.1
Diff Max
5.6
77.8
Diff Min
0.6
-17.3
Mean Simulation
7.0
43.9
Max Simulation
67.5
420.6
Min Simulation
0.0
0.0
Mean +2C
12.1
46.3
Max +2C
69.4
435.5
Min +2C
0.1
0.0
Diff Mean
0.5
2.4
Diff Max
2.7
29.2
Diff Min
-0.1
-22.2
Mean Simulation
23.4
32.1
Max Simulation
47.0
253.8
Min Simulation
15.2
0.3
Mean +2C
21.1
38.2
Max +2C
46.0
301.2
Min +2C
10.8
0.2
Diff Mean
-2.3
6.1
Diff Max
5.0
73.8
Diff Min
-8.1
-28.8
Figure 20: Storm event accumulated precipitation for the three days 21-23 November 2008 based on
simulations by the HCLIM, HARMONIE and WRF NHRMs and difference in the +2oC warming scenario
as compared to the reference simulations. Table includes spatial aggregated information of mean, max
and min precipitation and corresponding differences at wider impacted area and whole island level.
Page 35 of 43
6. Conclusions
Despite limitations and uncertainties, this study presents a wide range of draft estimates and results,
providing water resources management community with a glimpse into a very plausible future where
the quantitative impact of climate change on water availability and water extremes can be substantial,
especially in a Mediterranean island like Crete.
6.1 Long term projections of water availability
Basin scale climate information was delivered to the user, useful in prioritizing certain water resources
related infrastructure development. A robust signal of water scarcity is projected for all the
combinations of emission, demand and infrastructure scenarios. According to the Water Directorate,
the overwhelming majority of small works (barrages, small dams) are projects of purely local character
regarding and most of these infrastructures do not have a significant effect on a municipal or greater
level. Therefore, they should be evaluated only based on technical, economic and social criteria of the
local region in which they are located and which they will serve. Regarding the large scale water
resources engineering, some of them are presented as additional supply for covering the existing
demand, while other are of course surplus in the present situation and consequently should be
connected with further cultivation. The planned development policy of new water resources
infrastructures is very closely connected with the growth of new irrigated areas. This leads to a great
increase of irrigation demand level, as this has been determined in the present study, precluding the
practicability of this scenario. The conclusion is that an alternative policy of development of new
infrastructure should be adapted. This policy should not only give priority to the increase of irrigated
areas, but should also assist in the practice of the existing irrigated areas with parallel evaluation of
plans for newly irrigated areas. It emerges that the inability to control large outflow quantities that
runoff into the sea, despite recent improvements, remains a problem despite the growth of an
abundance of infrastructure aiming to store winter spring and stream flows.
6.2 Decadal projections
The coarse resolution of ESMs compared to the extent of the study area, cannot describe the local
precipitation regime probably due to failure in capturing small scale processes. The bias is ranging
from -55% to -70% and could be caused by imperfections in sub-grid scale climate model
conceptualizations. This pronounced underestimation makes decadal experiment results of low
usefulness in their native form. Dynamical downscaling of these experiments is proposed to be
performed and evaluated for their applicability on short term management planning.
Page 36 of 43
6.3 Extremes and non-hydrostatic high-resolution RCMs experiments
A robust increasing signal of daily precipitation maxima under A1B emission scenarios is projected
from a set of 17 RCMs. Projected changes in precipitation extremes were communicated in terms of
changes in Annual Exceedance Probability (AEP) based on annual maximum precipitation values and
in terms of extreme percentiles and more specifically the 99.9-ile%. A significant increase of the 2%
AEP (50 years return period daily precipitation maxima) is projected for all future periods by an
average of almost 40% (over 65mm) ranging from 9mm to 236mm among the 52 stations analysed.
Projections indicate a similar increase of the 99.9-ile% values of the order of 8%.
Three present day extreme events over the island of Crete were simulating with the use of Nonhydrostatic high-resolution RCMs by the modelling groups of SMHI, UNI and KNMI. These events
were also simulated under conditions of a warmer climate (+2oC). Simulations proved to be sufficiently
efficient in realistic capturing historical storm events and thus valuable in impact modelling. Historical
storm events over Crete, could produce significantly higher precipitation accumulations and intensities
in a warmer climate. Further detailed hydrological and hydraulic impact assessment is planned to be
performed and communicated to the end user. Hydrological modelling using HBV and HEC-HMS
models will follow, examining peak discharge estimations and propagation of flood waves. Hydraulic
modelling will be conducted in order to validate maximum water levels of reference simulations and
projected maximum water level and flood extend under warmer climate conditions. Results will be
presented to relevant local end users, the Directorate of Water and Civil Protection Service of the
decentralized administration of Crete. Expanded finding from hydrological and hydraulic modelling
combined with storms analysis will result to one joint manuscript.
Page 37 of 43
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APPENDIX
Figure A1. Normal PDFs representing the average bias adjusted WATCH models and weighted bias-adjusted results
of all ENSEMBLES members per time slice for (a) annual precipitation (left panels) and (b) average annual
temperature (right panels).
Figure A2: Projections of annual average temperature and precipitation over Crete from a statistically downscaled bias
adjusted multi-model ensemble for the three RCPs for different reference periods (a) 2006-2100, (b) 2006-2050 and
(c) 2051-2100.
Figure A3. Black lines depict the historical simulated annual availability based on observations. Red dotted lines
correspond to historical and projected multi-model mean annual availability. Trend lines represent the average annual
availability slopes for the observed period (in black ) and for the ensemble projections (in red) under (a) B1, (b) A2 and
(c) A1B emission scenarios. Grey areas indicate the amplitude of historical and projected availability of multi-model
RCM and GCM ensembles and hydrological modeling.
Figure A4: Projections of annual average runoff (availability) over Crete from a multi-model ensemble for the three
RCPs for different reference periods (a) 2006-2100, (b) 2006-2050 and (c) 2051-2100.
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