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Indices are used for representing complex phenomena; however, concerns usually arise regarding their objectivity and reliability, particularly dealing with their uncertainties during the development process. The current overarching objective is to reveal the significance of employing different weighting techniques in the application of the Standardized Drought Vulnerability Index (SDVI) and demarcate any pertinent implications that may emerge in drought decision making. Greece, as it is very often facing the catastrophic effects of droughts, presents an almost ideal case for the SDVI testing. SDVI outcomes were tested utilizing five weighting techniques deriving from four weighting methods. The analyses indicated that the use of complex weighting models may not be necessary in all cases and that the simple equal weighting method seems more effective to estimate drought vulnerability. It also seems more important to address the search for valid, reliable and relevant individual indicators forming the complex index as well as appropriate index development processes that would measure performance of water bodies, systems and schemes, monitor the process of equitable sharing, and provide mechanisms for monitoring the state and changes in interdependent water systems.
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1 23
Stochastic Environmental Research
and Risk Assessment
ISSN 1436-3240
Stoch Environ Res Risk Assess
DOI 10.1007/s00477-019-01648-4
Assessing structural uncertainty caused
by different weighting methods on the
Standardized Drought Vulnerability Index
Demetrios E.Tsesmelis, Panagiotis
D.Oikonomou, Constantina
G.Vasilakou, Nikolaos A.Skondras,
Vassilia Fassouli, et al.
1 23
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Assessing structural uncertainty caused by different weighting
methods on the Standardized Drought Vulnerability Index (SDVI)
Demetrios E. Tsesmelis
·Panagiotis D. Oikonomou
·Constantina G. Vasilakou
·Nikolaos A. Skondras
Vassilia Fassouli
·Stavros G. Alexandris
·Neil S. Grigg
·Christos A. Karavitis
©Springer-Verlag GmbH Germany, part of Springer Nature 2019
Indices are used for representing complex phenomena; however, concerns usually arise regarding their objectivity and
reliability, particularly dealing with their uncertainties during the development process. The current overarching objective
is to reveal the significance of employing different weighting techniques in the application of the Standardized Drought
Vulnerability Index (SDVI) and demarcate any pertinent implications that may emerge in drought decision making.
Greece, as it is very often facing the catastrophic effects of droughts, presents an almost ideal case for the SDVI testing.
SDVI outcomes were tested utilizing five weighting techniques deriving from four weighting methods. The analyses
indicated that the use of complex weighting models may not be necessary in all cases and that the simple equal weighting
method seems more effective to estimate drought vulnerability. It also seems more important to address the search for
valid, reliable and relevant individual indicators forming the complex index as well as appropriate index development
processes that would measure performance of water bodies, systems and schemes, monitor the process of equitable sharing,
and provide mechanisms for monitoring the state and changes in interdependent water systems.
Keywords Drought · Drought vulnerability · Drought Vulnerability Index · Indices · Indicators · Structural uncertainty ·
Water resources management
1 Introduction
Decision making may generally be defined as the art and
science behind the identification and selection of the
actions that best fulfill the overall objectives of a stated
issue (Corvala
´n et al. 2000; Bianco 2006; Adair 2010).
However, in many cases, such a definition may seem both
simple and elusive. Part of that resides in systems com-
plexity and the resulting difficulty of the emerging man-
agement problems (Daellenbach 1994; Qudrat-Ullah et al.
2007). The various sets of indicators and indices are usu-
ally employed for the simplification, quantification and
provision of pertinent information concerning complex
events and phenomena (Breier et al. 2012; Dahl 2012;
Becker et al. 2017). They usually derive from an appro-
priate selection and elaboration of primary or secondary
data (Segnestam 2002). On a greater scale, they may also
be used to evaluate and rank the national/regional perfor-
mance of various countries towards achieving specified
goals (Bandura 2005,2008; Rogge 2012; Landerretche
et al. 2017). In the pertinent literature, their overall
objective is to facilitate the communication among the
respective authorities and foster decision and policy mak-
ing (European Environment Agency 1999,2005; Rogge
2012). As tools, their useful value increases in cases where
the lack of direct knowledge sets them as the only means
&Demetrios E. Tsesmelis
Department of Natural Resources Development and
Agricultural Engineering, Agricultural University of Athens,
75 Iera Odos, 11855 Athens, Greece
Colorado Water Institute, Colorado State University, 1033
Campus Delivery, Fort Collins, CO 80523-1033, USA
Department of Civil and Environmental Engineering,
Colorado State University, 1372 Campus Delivery,
Fort Collins, CO 80523-1372, USA
Department of Civil and Environmental Engineering, Faculty
Affiliate, Colorado State University, 1372 Campus Delivery,
Fort Collins, CO 80523-1372, USA
Stochastic Environmental Research and Risk Assessment
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towards obtaining a fracture of the required information
(Haines-Young et al. 2012). Climatic, social, and ecologi-
cal indicators and indices such as Human poverty index,
population density, crop type, rainfall etc. play an essential
role in water resources management adapted to the com-
plexity of such systems mostly through social and envi-
ronmental learning.
Such systems’ complexity leads also to an extremely
important obstacle of the decision making environment:
uncertainty. Decision makers are often forced to deal with
several uncertainty generating sources including the vari-
ous factors embodied in the decision making process itself.
Such sources are mainly convoluted around the quality and
validity of the input data and the outcomes of the selected
decisions (French 1995; Briggs and Peat 2000; Burgman
2005; Norton 2005; Sage 2007; Williams 2011). Further-
more, lack of pertinent data and information are crucial
issues that may also constitute another impediment in the
whole process (Morc¸ӧl2006; Adam 2008). However,
indices have caused many debates regarding their objec-
tivity and reliability towards dealing with uncertainty
(Nardo et al. 2005; Saisana et al. 2005; Singh et al. 2009;
Rogge 2012). In this essence, Hilborn (1987) and Peterson
et al. (1997) identify three types of uncertainty that pertains
in composite indicators:
Statistical Uncertainty—referring to the uncertainty on
the state of each variable (the components of the
composite indicators).
Model Uncertainty—signifying the relation among the
variables, in other words, the structure of the composite
Fundamental Uncertainty—oncerning the novel situa-
tions which cannot be described by the existing
models/composite indicators.
Moreover, the greatest number of the disputes and criti-
cisms mainly focuses on the development process of such
indicators. Especially, when a particular process is obscure
and not characterized by clarity (OECD 2008). To some
extent, such debates may be justified since the development
process involves some steps in which the subjectivity may
be great and often unavoidable (Cherchye et al. 2007;
OECD 2008). In those steps, due to the lack of a univer-
sally accepted formulation procedure, the respective
developer is usually compelled to mostly arbitrarily select
or develop the various components in order to carry out the
index formulation. Consequently, these steps “bear the
signature” of the respective development procedures and
they are characterized by their reliability. Similarly to
many other customized tools and practices, the transfer-
ability of a successfully applied index structure may not be
enabled without specific considerations (Welte et al. 2004;
Peterson et al. 2007). All in all, the phases that mainly
concentrate the majority of the emerged criticism concern
the weighting, normalization, and aggregation processes
(OECD and European Commission 2008; Dobbie and Dail
2013; Gan et al. 2017). Each of those phases may be
achieved by a variety of methods and tools, but the
employment of simple approaches is mostly facilitating
both comprehension and calculation of an index (Booysen
Decision making in the field of water resources man-
agement, most of the times, includes thorny issues that are
part of a complex environment. It usually incorporates
conflicting natural and anthropogenic interests and objec-
tives, multiple water uses, sensitive social and ecological
equilibria, recurring natural hazards. Frameworks like
integrated water resources management and sustainable
development are implement within the environmental
decision making process in order to mitigate adverse
effects and achieve consensus among different water user
groups (Margerum and Born 1995; Grigg 1996; Loucks
et al. 2005; Grigg 2008; Karavitis et al. 2015b). Thus,
regarding the implementation of any comprehensive
drought management scheme, the crucial obstacles to be
surmounted are having as epicenters drought’s onset, areal
extent, and severity. In order to overcome them drought
index methods are coming to an aid. Such an effort por-
trays drought characteristics based on a specific index,
which index may serve at the same time as a drought alert
mechanism (Oikonomou et al. 2018). This research need
was early on addressed in the literature (Palmer 1965;
National Research Council 1986; Dracup 1991; Karavitis
1992; Grigg 1996; Karavitis 1999; Wilhite et al. 2000).
Thus, a drought index, particularly a drought vulnerability
related one, should support decision making and convey
necessary information in demarcating drought’s beginning,
severity fluctuations, and termination.
One of the attempts to fill this gap was the development
of the Standardized Drought Vulnerability Index (SDVI)
that was initially designed to use Equal Weighting (Kar-
avitis et al. 2012b,2013,2014). Thus, in order to test if its
applicability is amplified in a weighted version, the current
approach was launched. The comparison between statisti-
cal techniques for weighting indicators has already been
extensively studied in the scientific literature (Saisana et al.
2005; OECD and European Commission 2008; Zhou et al.
2010). In the present attempt, SDVI is examined under five
weighting techniques that derive from four different
weighting methods. The SDVI was initially developed
using Equal Weighting and thus, three additional weighting
methods are tested, namely; the Principal Component
Analysis, the Multiple Correspondence Analysis, and the
Analytical Hierarchy Process. Their resulting values are
evaluated through statistical analyses. More specifically,
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the statistical significance of their differences in terms of
produced values is examined through correlations, struc-
tural uncertainty deriving from weights and input values.
The Sterea Hellas region in central Greece was selected as
an area of interest/application of the pertinent procedure
being inflicted very seriously by the 1989–1990, 1992–
1993, 2000 and 2007–2008 droughts (Tsesmelis 2010;
Karavitis et al. 2011a). The central objective of the present
effort is to reveal the significance of the differences among
the examined weighting techniques and pinpoint any
potential implications that may emerge in decision making
from the application of the SDVI. Furthermore, a potential
contribution may be a framework of spatially assessing the
effects of different weighting methods in developing indi-
ces. The different models are compared employing three
different testing approaches. The first part of the effort
analyzes the occurred drought event of August 2000. The
produced vulnerability maps are representing only the 2000
drought event. As the scope of the work was to test
structural uncertainty during the calculation of the SDVI,
the 2000 drought also serves as a case study event at the
region. In the second part, a higher spatial variability of
drought vulnerability is simulated, where the contributing
characteristics to the SDVI were randomly generated. The
scope of such an effort is to escape the local characteristics
and test the structural uncertainty of the SDVI in a hypo-
thetical region with greater variability. Finally, different
scenarios are employed for a specific location in the region
to also focus on the local effects of the different random
generated SDVI values.
2 Study area
Greece is located in south-eastern Europe and presents the
typical climatic characteristics of the northern Mediter-
ranean climate. The Sterea Hellas region (Fig. 1) located
almost at the center of Greece, occupies an area of 23,818.3
and constitutes of Attica (sector GR 06) and Eastern
Sterea Hellas (sector GR 07), two of the 14 water sectors in
the country. Sterea Hellas’ climate is generally temperate
along the coastline and colder in the interior, while its
average annual precipitation ranges from about 340 to
1050 mm/year (Fig. 1and Table 1). Table 1shows the 24
rain stations used with available rainfall data records from
1981 to 2010. It has to be pointed out that the stations
present significant variations due to exposure and altitude.
Overall, Greece has an average annual rainfall ranging
from 250 to 2150 mm/year (decreasing from northwest to
southeast), with a country average of 760 mm/year
exhibiting at the same time, a very strong seasonal distri-
bution with cool rainy winters and prolonged hot dry
summers (Karavitis et al. 2011a,2014; Tsesmelis et al.
2019). Sterea Hellas region exhibits also the same trends
(Fig. 1). Hence, the precipitation’s extreme seasonal dis-
tribution makes water storage systems an absolute neces-
sity, a fact even more pronounced as Sterea Hellas is the
epicenter of such issues, since its total population surpasses
4.8 million inhabitants, making it the most populous region
of the country. Attica sector in particularly, where also the
capital city of Athens is located, displays the greatest
population density of the whole country with 987.9
(HSA 2011). Attica’s water supply comes
from the dams and reservoirs of Marathon, Mornos, and
Euenos as well as from the natural lake of Hylice. The
water bodies of Marathon, Mornos, and Hylice, are within
the study area. Mornos reservoir and Hylice Lake lay west
of Attica in the prefectures of Phocis and Boeotia respec-
tively, supplying Attica including Metropolitan Athens,
with 200 km long aqueducts yielding an average water
discharge of 530910
/year. Thus the area, as well as
the whole of Greece, is heavily dependent on the annual
precipitation, since water supply of the major cities as well
as the large irrigation schemes to the center of it are relying
on such reservoir storage operated mostly on an annual
basis (Karavitis et al. 2011a,b). During periods of drought,
the reservoir stored water is usually reduced and hence,
increasing the vulnerability to pertinent drought impacts
affecting almost half of the whole country’s population.
All in all, any precipitation deficiency and moreover a
drought are producing significant impacts, as the droughts
of 1989–1990, 1992–1993, 2000 and 2007–2008 have
clearly demonstrated (Karavitis 1998; Tsesmelis 2010;
Karavitis et al. 2012a). In this context, Greece and partic-
ularly Sterea Hellas are presenting almost an ideal envi-
ronment for the SDVI testing in an effort to assess its
structural uncertainty caused by different weighting
Additionally, in order to test SDVI from a regional to a
local scale, scenarios for a specific local were applied at the
Aliartos area. Aliartos area is a rural area in Sterea Hellas
(Fig. 1) comprises for its most part from the Boeotikos
Cephissus—Lake Hykice River basin. The hydrologic
complex of Boeoticos Cephissus and Lakes Hylice and
Paralimne is one of the most important hydrological sys-
tems of Sterea Hellas, supplying as pointed out, industrial,
agricultural and urban water. Morphologically, the region
in its biggest part is an extensive plain, composed of loose
sediments with slopes of less than 5%. The main part of the
plain is at an altitude between 100 and 200 m asl. Hence,
for such testing, the presented in the following methodol-
ogy was applied on the whole Sterea Hellas for regional
and in Aliartos area for local scale.
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3 Methods and tools
3.1 The standardized Drought Vulnerability
The SDVI is an index developed in the context of the
Drought Management Centre (DMCSEE) Project by the
Agricultural University of Athens (AUA) research team
(Karavitis et al. 2011b,2012b,2014). One of the parame-
ters needed to calculate SDVI is the Standardized Precip-
itation Index (SPI). The principal reason that SPI was
chosen in relation to other indices is that it can be easily
calculated from precipitation data time series. Such an
advantage may be proven valuable especially in cases
where there is lack of other climatic data. Furthermore,
according to Guttman (1998) and Hayes et al. (1999), the
SPI central benefit is that through the different time scales
of precipitation irregularities, it can describe short term and
long term drought events and impacts. Additionally, it is
statistically consistent and has a great performance in
drought risk analysis (Guttman 1999).
SDVI aims at integrating the various manifestations of
drought (Meteorological, Agricultural, Hydrological, and
Socioeconomic) and the concept of vulnerability into a
single value. It comprises of six components cSPI-6 (re-
classified SPI-6), cSPI-12, Supply, Demand, Impacts, and
Infrastructure (Karavitis et al. 2014; Oikonomou et al.
2019). These six components are classified into four vul-
nerability categories (0–3 scale) according to their perfor-
mance as presented in Table 2. To derive such a
categorization extensive pertinent literature review and
expert knowledge was applied for the percentages scaling
of the various components (Karavitis et al. 2012b,2014;
Tsesmelis 2017). The vulnerability value is calculated by
the average scaled value of the components following
Eq. (1). In this regard, the SDVI adopts the mechanics of
the Environmental Vulnerability Index that was developed
by the South Pacific Geoscience Commission (Kaly et al.
2004; Skondras et al. 2011).
Scaled Values of the Components ð1Þ
The SDVI application procedure is describe in detail by
Karavitis et al. (2014), and briefly in the following:
1. Precipitation patterns usually affect the surface waters
and the recharge of the aquifers. SDVI employs the
SPI that is an index based on precipitation time series.
For the calculation of the index, the first step is to
identify the probability density function, which better
describes the distribution of the time series over the
different time scales (6 and 12 months) and then the
current process is operated for every month of the
precipitation time series. Urban water supply and
Fig. 1 Sterea Hellas elevation (left) and annual average precipitation (1981–2010) (right). The black line portrays the counties boundaries and the
black dot on the upper right map depicts the location of Aliartos area
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irrigation are usually rely significantly on annual
reservoir and/or aquifer storage. Hence, the 12-month
SPI depicting long-term anomalies becomes pivotal for
the SDVI estimation, since it may exemplify the
overall water availability (irrigation, hydropower,
industry domestic and tourism). SPI-6 may denote
the non-irrigation water availability for rain-fed crops,
as it may effectively show the rainfall patterns in
different seasons, pointing out medium-term trends. It
is has presented that their mutual incorporation
improves interconnections, operability and may lead
to more sound results (Karavitis et al. 2014). The
values of cSPI-6 and cSPI-12 are calculated on local
(meteorological station) scale. Overall, SPI does not
impose any restrictions, except that it does not have
application in the deserts or at the earth poles.
2. Supply and Demand, which describe the deficits in the
corresponding capacities (including network losses),
based on pertinent data. They depend on the available
water quantity, existing water consumption, reported
uses, and demographic, social and technical develop-
ment patterns.
3. Impacts that express the losses in monetary units.
Usually, impacts mitigation measures on the demand
side of the supply-minus-demand economic drought
equation, have the basic objective to trim the water use
Table 1 The basic statistics of precipitation in Sterea Hellas
Station UTM X UTM Y Average annual precipitation (1981–
Elevation in m
Minimum Maximum
Agia Triada 405136 4244800 986.56 400 220.27 534.3 1382.7
Amfissa 358886 4265827 681.94 180 161.66 328.4 1025.7
Gravia 363497 4280548 822.31 450 143.23 431.3 1048.9
Davlia 389166 4248703 770.83 380 153.20 463.5 1062.1
Distomo 383406 4253888 605.78 450 121.66 329.4 821.7
Eptalofos 367725 4273077 972.64 830 221.66 612.1 1420.1
Zilefto 349557 4310404 394.19 120 166.89 96.9 835.7
Thisvi 409381 4233654 458.00 174 149.83 185.6 874.9
Itea 363056 4254654 377.45 20 119.48 187.7 660.9
Kalithea 451708 4238840 468.85 333 157.18 321.3 753.6
Kaloskopi 354830 4282551 879.01 1000 235.07 568.0 1740.1
K.Tirothea 388071 4274616 655.60 170 129.32 388.8 991.9
Lamia 361050 4306493 538.38 144 115.08 328.0 870.5
Livadia 400880 4254100 737.21 200 156.38 414.1 1037.8
Lilea 369237 4276752 848.55 330 335.74 465.2 1272.4
Pavlos 421355 4264972 479.07 200 160.67 82.5 741.5
Trilofo 345367 4317887 598.79 580 126.42 373.0 826.5
Tymfristos 319174 4309189 1022.99 850 382.17 141.0 1713.3
Elliniko 475537 4194336 363.63 15 96.39 155.4 546.6
Asteroskopio 475089 4202597 397.06 110 138.72 150.6 896.0
Elefsina 440916 4212390 344.17 31 91.06 117.4 468.3
Tatoi 480783 4218209 435.09 235 153.46 169.6 812.7
Filladelphia 477479 4210345 452.17 138 177.52 180.5 1015.5
Ypati 346524 4303061 790.74 286 293.18 327.6 1431.0
Table 2 SDVI components
vulnerability scale classification Vulnerability level Scales categorization based on SPI classes and experts’ knowledge
cSPI-6 and cSPI-12 Supply, demand, impact and infrastructure
Non vulnerable 0 Wet 1.50 0 No deficits
Vulnerable 1 Quite wet 0–1.49 1 15% Deficits
Highly vulnerable 2 quite dry 0 to 1.49 2 16–50% Deficits
Extremely Vulnerable 3 Dry 1.50 3 [50% Deficits
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of the least unit impact, provided that legal consent
permits it. Then, the resulting impacts are mainly
focused on economic costs and production losses.
Environmental impacts are analyzed only if they can
have a direct monetary quantification. Drought social
impacts assessments are very rare and usually present
qualitative criteria and social stress indicators with the
latter ones being very difficult to quantify. The current
effort has not endeavor in such an estimation. It is
pointed out that the developed Drought Vulnerability
Index is portraying above all the magnitude of the
societal exposure and disruption to drought and its
output is also a societal Index of drought pressures. All
in all, it may be suggested that for the last two
categories of impacts more specialized analyses should
be applied in order to quantify them in an another
pertinent research approach. Such approach may use
the SDVI as an initial indication for more specialized
research tools.
4. Infrastructure that imports to the index the status of the
current operational water infrastructure in terms of its
deficiency magnitude. The components of infrastruc-
ture and supply should be cautionary treated, as on one
hand they may easily spark some misperceptions (e.g.
a 15% of an infrastructure deficiency may in some
cases, because a proportional supply deficit). On the
other hand, an infrastructure may be in excellent
operational condition, and a supply deficit still to be
induced e.g. in agriculture as it may be decided to
divert irrigation water during a drought in order to
fully supply an urban population. Hence, the infras-
tructure component incorporates its condition and
degree of efficiency in conveying water timely and
In this regard, the SDVI is calculated on a monthly step. In
a given area (basin/county) the total supply data and
demand deficits, as well as the resulting impacts are rep-
resented as ratios (proportions) over the highest value of
the corresponding parameters, while the infrastructure sub
index is portrayed by a weighted average value (ratio) of
the infrastructure performance (Karavitis et al. 2014). The
SDVI may be expanded to other regions with similar or
different climates since the SDVI index is calculated using
average values and deviations for the supply, demand and
impact sub-indices (Karavitis et al. 2015a; Oikonomou
2017; Oikonomou and Waskom 2018; Oikonomou et al.
2019; Tsesmelis 2017). The SDVI results are classified into
six vulnerability categories ranging from 0 to 3 as shown in
Table 3(Karavitis et al. 2014). Finally, all the obtained
data as well as the SDVI results, were visualized on a GIS
environment using geostatistical techniques (Karavitis
et al. 2012a,2014).
3.2 Weighting methods and structural
uncertainty analysis
Weighting methods are a step during the development of an
index that usually attracts criticism because different
weighting methods could have different index values. In
this context, the equal weights technique is used mainly
because in most cases there is no objective way to deter-
mine the relative importance of each component (Garriga
and Foguet 2010). In similar approaches incorporating
social, economic, and environmental aspects in composite
indicator development for e.g. land exposure to natural
events as the: Human Development Index and the Envi-
ronmentally Sensitive Areas (ESA) index, the equal
weights usage achieves effective results (Sagar and Najam
1998; Kosmas et al. 2006; Becker et al. 2017). Thus, the
equal weights approach by giving the same importance on
every index component, and especially for vulnerability
related indices is the most common technique employed
(Eakin and Bojo
´rquez-Tapia 2008). Other methods used in
developing indices may be the Principal Component
Analysis, the Multiple Correspondence Analysis, the
Analytical Hierarchy Process, the Conjoint Analysis, and
the Benefit of the Doubt (OECD and European Commis-
sion 2008; Rogge 2012; Kosmas et al. 2014; Li et al. 2015).
Each of those simpler methods is characterized by its
respective advantages and disadvantages, while providing
different sets of weights and, consequently, different sets of
index values. Table 4explicitly portrays such advantages
and disadvantages stating relevant comparisons among the
pertinent methods as well as corresponding references.
Table 3 SDVI scaled values (Karavitis et al. 2014)
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Table 4 Advantages and disadvantages of various weighting methods
Advantages Disadvantages Source
Equal weighting
Removes the overweighting
and underweighting
Easier to construct
May deviate from reality
Disguises the absence of a statistical or
an empirical basis
OECD and European Commission (2008)
analysis (PCA)
Sustains the maximum
variance within the data
Greater weights are assigned
to variables with greater
Depends on the data
Sensitive to outliers
Requires correlation among the
Minimization of the variables
contribution with different behavior
OECD and European Commission (2008)
analysis (MCA)
Can be applied with both
quantitative and nominal
Delicate and quite difficult to apply if
necessary data are not available
Abdi and Valentin (2007)
process (AHP)
Decomposes a decision
problem into its
Captures both subjective and
objective evaluation
Controls inconsistencies
Supports group decision-
Develops scales where
measures ordinarily do not
Ranking irregularities can occur
Compensation between good scores on
some criteria and bad scores on other
criteria can occur
The number of pairwise comparisons to
be made, may become very large
Difficult to distinguish among the
applied scales
Kasperczyk and Knickel (1996), Zahir (1999),
Ramanathan (2001), Millet and Wedley
(2002), Macharis et al. (2004)
Conjoint analysis
Breaks the task into a series
of choices
The results may be used for
model development
Calculates utilities at the
individual level
Straightforward experimental
Easily used in hybrid
Designing conjoint studies can be
Poorly designed studies may over-value
preference variables and undervalue
concrete variables
Desarbo et al. (1995), Orme (2009)
Benefit of the
doubt (BD)
Useful in policy making
Helps to define trade-offs
The weights are determined
by the observed
The indices are on a linear
combination of observed
best performances
In some cases the optimal set of
weights may be undetermined
Higher weights are assigned to higher
In some cases, the best performer will
not see its progress reflected in the
Cherchye et al. (2004), OECD (2008), Rogge
model (UCM)
Decomposes the problem at
Provides powerful models
Easily interpreted results
Data dependent
Sufficient data dependent
The indicators should not be correlated
Kaufmann et al. (1999,2003), Fomby (2008),
OECD and European Commission (2008)
Budget allocation
process (BAP)
Relatively straightforward
Short duration
Requires well-defined framework
Optimal for a maximum of 10–12
The selected experts should be
specialists on the given sub-index
Munda (2005,2007), OECD and European
Commission (2008)
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Additionally, according to the OECD and European
Commission (2008), in the case of similar results with the
various weighting methods the simplest method should be
chosen. Furthermore, other complex weighting methods
have been emerged that apply fuzzy logic (Sodhi and
Prabhakar 2012) or even combine spatial and temporal
weighting (Wang et al. 2005; Huang et al. 2010; Harris
et al. 2010). In such a setting, the unavoidable cus-
tomization increases the inherent subjectivity of the pro-
cess (Singh et al. 2009), while, at the same time, the
uncertainty towards the formulation of decisions may reach
critical levels. The latter methods have not been considered
and thus they are not further examined. In this rationale,
simpler waiting methods have been chosen in this effort,
despite the nascent complexity of any vulnerability illus-
tration, following the premise of accepting a reasonable
approximation of such illustration, which may be very
valuable for applicable drought management decision-
In the current effort, the original weighting method
(equal weighting-EW) of computing the SDVI index is
compared to four weighting techniques that derive from
three different weighting methods employed. Apart from
the EW method, the next examined one, Principal Com-
ponent Analysis (PCA), is a multivariate statistical proce-
dure that uses orthogonal transformation to identify the
principal components that explain most of the variance in
the original data set. The index components are weighted
with the proportion of variance in the original set of vari-
ables and it could be argued that it is a relatively more
objective option for weight selection (Booysen 2002).
Multiple Correspondence Analysis (MCA) is also a sta-
tistical method which identifies the relationship patterns
between categorical variables and it could be seen as
analogous to Principal Component Analysis (Abdi and
Valentin 2007). Last, the Analytic Hierarchy Process
(AHP) is a multi-criterion decision making method which
through expert opinion and pairwise comparison the
weights of the SDVI models are calculated (Saaty
1980,1988; Sadiq and Tesfamariam 2009). Thus, the main
objective of comparing the SDVI results deriving from
different weighting methods is to help gain a better
understanding of their impacts on the SDVI results. In
other words, it is a means to test the robustness of the
In order to assess the structural uncertainty caused by
the selected four weighting methods, five techniques
(PCA1, PCA2, EW, AHP, and MCA) were applied. Then,
three distinct approaches in the area were used to supply
different data sets to the models for the structural uncer-
tainty analysis of SDVI. The first approach comprises of
the existing data for the recorded conditions. The vulner-
ability to drought values derived by the five techniques
applied in Sterea Hellas and they were spatially represented
in GIS environment. Index values of each produced map
were extracted in order to create a sample of data (N =
23,429, the number of raster tiles) according to the existing
conditions of the study area and to examine the similarity
of the results. The second approach explores the situation
where the study area exhibits large variability in the SDVI
components, as the existing natural and anthropogenic
conditions in Sterea Hellas are rather homogeneous. In
order to achieve this, ten thousand random component class
values (0–3), from each component, were generated from a
uniform distribution using R (R Core Team 2016), so as to
simulate an area that could have greater variability.
Finally, the third approach was to test local effects of the
different weighting techniques. The Aliartos area serves as
case study. Four scenarios (Α, B, C and D; presented in
detail on Tables 5and 11) were generated, each one rep-
resenting a feasible and plausible set of SDVI components’
state. For the scenarios creation, SDVI is divided in three
fixed components and three varied ones. The fixed com-
ponents are cSPI-6, cSPI-12, and infrastructure, whereas
varying ones are considered to be supply, demand and
impacts. Thus, scenario A depicts the real recorded con-
ditions for the selected area. Scenarios B, C, and D reflect
future plausible combinations of conditions in all the
varying components (increasing/declining demand,
increasing/declining supply, etc.).
All in all, it is believed that the above methodology
enhances the understanding of the examined weighting
techniques’ effects on SDVI values in various conditions
(real and hypothetical). This multi-approach analysis may
contribute in drought vulnerability assessment, by trying to
quantify the uncertainties in the various index weighing
3.3 Data sets for the SDVI
In the present attempt, the SDVI is applied to picture the
vulnerability conditions that occurred in Sterea Hellas on
August 2000 a dry hydrological year (Karavitis et al.
2012a,b). The gathered data sets reflect the conditions
occurred in August 2000 and are presented in Fig. 2.
More specifically:
Table 5 Possible four scenarios in Aliartos area
Scenario Fixed Varied
cSPI-6 cSPI-12 Infr. Supply Demand Impact
B33112 2
C33121 1
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Precipitation data from 1981 to 2010 for 24 stations in
the area (Table 1), were collected from the National
Meteorological Service of Greece (HNMS 2016), the
Special Secretariat for Water (SSW 2016) and the
Public Power Corporation (PPC S.A. 2016). Data gaps
in all 24 stations were minimal and raw data seem more
appropriate to be inserted so as to represent the natural
drought conditions (extreme minimum values), hence,
no attempt made to apply “corrective” homogeneity in
the data sets (Karavitis et al. 2011a). The implicit sta-
tionary assumptions of the SPI were tested with all
stations exhibiting stationary process (Karavitis et al.
2015b). The SPI then was calculated on monthly basis
both with the 6 month (SPI6) and 12 month (SPI12)
The Demand component was calculated by taking into
account urban, industrial and agricultural water demand
on county scale. Attica region has the greatest industrial
concentration of the whole Greece with an industrial
water consumption of more than 50910
(Karavitis et al. 2001; Barraque
´et al. 2008). The urban
water supply of Attica region, including Metropolitan
Athens, has an average water consumption of 365 9
/year. The remaining urban water consumption
in the area of Sterea Hellas reaches 70 910
(SSW 2013). In this case.
i. For the calculation of agricultural demand, both
crop demands and water losses were estimated
using the crop water requirements as a function
of Crop evapotranspiration and Actual
evapotranspiration (Suat et al, 2003). In other
words, Crop Water requirement equals Crop
evapotranspiration minus Actual evapotranspira-
tion (Doorenbos, J. & Pruitt, W.O., 1977; Allen
et al, 1999). Actual evapotranspiration Et
calculated on a plot scale, however pertinent data
are not available on the Case Study Area (Allen
et al, 1999; Jensen & Allen, 2016). It is also
pointed out that during a drought (without
irrigation), actual evapotransipation (Et
) reaches
asymptotically to zero (as by definition refers to
the condition of existing water supply), since
drought progresses and water supply diminishes.
Hence, Et
cannot alone represent the water
demand (or Crop Water requirements). Crop
evapotranspiration ET
may be estimated by the
equation: ET
, where Kc is the crop
coefficient according to the growth rate and ET
is the reference evapotranspiration (Allen et al,
1999; Jensen & Allen, 2016). Then, the equation
used for sub-index agricultural water demand (in
other words crop water requirement) was:
SubindexWater Demand Aug 2000
¼ETcAug 2000
ETcAug 1
Which becomes by substituting from the previ-
ous equation:
Fig. 2 Collected datasets for Sterea Hellas region
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SubindexWater Demand Aug2000
EToAug 1
Therefore, it is only needed to estimate ET
ii. ET
can be estimated by several methods which
include models based on pan evaporation, radi-
ation, temperature, and mass transfer (Alexandris
et al. 2006; Ambas and Baltas 2012; Nikam et al.
2014; Chatzithomas and Alexandris 2015). For
the calculation of SDVI the temperature based
method of Hargreaves-Samani (1985) was chosen
(Eq. 2). The method was selected as it is
recommended in cases where reliable data are
lacking (Droogers and Allen 2002; Allen et al.,
1999); furthermore, it usually performs better
than other temperature based estimation methods
(Valipour 2014; Valipour and Eslamian 2014).
Tmax Tmin
0:5Tmax þTmin
where Tmax and Tmin and are maximum and
minimum temperature respectively (°C) and Rais
the extraterrestrial radiation (mm/day).
iii. The data used for the calculation of urban and
industrial water demand were retrieved from the
River Basins Management Plans of Greece (SSW
Thus, it would seem that the water demand
estimation strengthens the index testing process
by applying it to the complete spectrum of water
uses (agricultural, domestic and industrial
The Supply component was calculated on a county
scale, the average size of the counties is approximately
3200 km
, according to the registered delivered flow
(including losses) in the major water supply networks.
The counties boundary lines are presented in Fig. 1. The
corresponding data were collected from the pertinent
authorities (SSW 2013).
For the calculation of the economic and environmental
impacts component (as analyzed in Sect. 3.1) the
reduction of mainly the agricultural production was
used on county scale. More specifically, for that
particular component, the difference (%) in agricultural
production between August of 2000 and the average
agricultural production of the same month for previous
5 years was calculated. The corresponding data were
collected from the Hellenic Statistical Agency (HSA
The calculation of the Infrastructure component was
performed according to the difference between the
designed and the actual capacity of the reservoirs
(dams) and other pertinent infrastructure. The corre-
sponding data were collected on county scale from the
River Basins Management Plans by the Special Secre-
tariat for Water (SSW 2013).
The w1 to w6 symbols in Fig. 2represent the weights
assigned to each component for the calculation of the SDVI
4 Application, results and discussion
Based on the gathered data and the previously presented
methodology, the SDVI has been calculated with each of
the following weighting techniques:
The equal weighting technique (EW) The initial form of
the SDVI employs that technique which is described by
Eq. (1).
The principal component analysis (PCA) In order to
have a large database for a complete as possible
provision of principal components, the application of
the PCA was performed on the raw data gathered from
the seven of the initial DMCSEE countries (Karavitis
et al. 2013) namely Slovenia, Hungary, Serbia, Mon-
tenegro, Bulgaria, FYROM and Greece, but of course
excluding data sets of Sterea Hellas, the case study area.
The region encloses 224 precipitation stations (more
than 700,000 values) and provided six principal
Table 6 The weights from the
five weighting techniques Methods cSPI-6 cSPI-12 Supply Demand Infrastructure Impacts
EW 0.166 0.166 0.166 0.166 0.166 0.166
PCA1 0.100 0.050 0.320 0.320 0.150 0.060
PCA2 0.170 0.170 0.200 0.200 0.100 0.160
AHP 0.201 0.193 0.154 0.154 0.125 0.173
MCA 0.230 0.250 0.100 0.100 0.130 0.190
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components. However, only the first two components
have been selected for the calculation of the SDVI
weights. Two different approaches were tested. In the
first (PCA1), the indicators’ weights have been deter-
mined by the squared loadings of the first principal
component (Explained variance: 46.40%). In the second
approach (PCA2), the squared standardized values of
both the first and second principal components (that
were selected out of six and explain 74.12% of the data
variance) were multiplied to their respective standard-
ized percentage of explained variance and were added
to each other. The result of the latter process was a
weighted averaged set of weights.
The analytic hierarchy process (AHP) The AHP
application was built on the weighting preference of
15 water resources management experts (University
Professors, Researchers from Greece, Slovenia, Serbia
and Portugal, and Greek Ministries Employees). It is
worth stating that all the experts provided zero valued
consistency ratios, meaning that their responses were
not inconsistent due to confusion or other related
reasons (Saaty 1980). That was quite expected consid-
ering the small number of components and the expe-
rience of the correspondents. The final weights are
calculated as the mean value derived by the experts’
weighting preference. In this technique, the SDVI
weights were calculated on individual level (per expert)
and the final weights derived from the mean value of
the 15 sets of weight.
The multiple correspondence analysis (MCA) Com-
pared to PCA—which was used on the raw collected
data—the MCA was used on the scaled (ordinal) values
of the SDVI components. The weights of this technique
derived from the first six dimensions (Explained
variance: 73.57%).
The weights which derived from the two PCA, AHP
and MCA approaches, as well as, from the EW process
were inserted into five linearly aggregated models
(Table 6). All the six index sub-indices have been visual-
ized in a GIS environment and are displayed in Figs. 3and
Fig. 3 SPI 6 and 12 monthly step and classified SPI 6 and 12 monthly step (cSPI6 and cSPI12) visualization for August 2000
Stochastic Environmental Research and Risk Assessment
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4. August 2000 was chosen for the SPI visualization, as
Greece was severely stricken by the 2000 drought. Con-
secutively, the SDVI calculations were made for August
2000. According to Fig. 3and based on SPI-6 (upper left
quarter), the area of interest displays “severe” to “extreme”
drought conditions in a uniform pattern. Conversely,
according to SPI-12 (lower left quarter), the area presents
almost “normal” conditions with some areas being classi-
fied as “mildly” to “very wet” while others classified as
being “mildly” to “severely dry”. However, the data
transformation from SPI to cSPI involved a decreased
number of classes and produced maps which display quite
uniform drought vulnerability conditions. More specifi-
cally, cSPI-6 presents uniform conditions of extreme vul-
nerability across the area of interest, except on three areas
with “highly vulnerable” classification. The cSPI-12 the
uniformity of high drought vulnerability is mildly disrupted
by some areas classified as “less vulnerable” and “vulner-
able” or even “extremely vulnerable”.
Figure 4presents the scaled values of the remaining
SDVI components calculated on county scale, based on the
classification of Table 2. In the case of Demand compo-
nent, the area of interest displays almost uniform condi-
tions of “no vulnerability” with scattered areas classified as
“vulnerable” to “extremely vulnerable”. Those areas are
either highly urbanized locations or areas with significant
agricultural production. Continuing, the Supply sub-index
in Fig. 4reflects conditions of high to extreme vulnera-
bility. The produced result was expected since flows pre-
dominantly for agriculture, in the major water supply
networks were dramatically reduced during August of
2000. The Impacts component pinpoints the loss of agri-
cultural production reflecting “vulnerable” to “extreme
vulnerable” conditions. The result may seem rough enough
but data on finer spatial resolution were not available.
Finally, the Infrastructure component reflects the difference
between the designed and the actual capacity of water
supply infrastructure. It has to be mentioned that apart from
two areas that display conditions of “less vulnerability”, the
Fig. 4 Visualization of four sub-indices: demand, supply, impacts and infrastructure for August 2000
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remaining area of interest is classified as being “vulnera-
ble” to “highly vulnerable” to drought. On one hand, by
referring also in combination with Fig. 1, the northwestern
part that displays lesser vulnerability is a highly
mountainous area with minimal anthropogenic activities
and, consequently, it cannot be affected by the examined
infrastructure deficiencies. On the other hand, the high
vulnerability region in Fig. 4coincides with the Athens
Fig. 5 August 2000 SDVI results for the five weighting techniques
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Metropolitan and industrial area, where all the water is
supplied by the relevant infrastructure and if there are
deficiencies are directly associated to high impacts.
According to the gathered data and the produced results,
the summer season of 2000 (August in particular) was a
difficult period for Sterea Hellas (and the country in gen-
eral) in terms of drought conditions. During that period
quite a portion of the population was affected in terms of
limited water supply and high agricultural losses. Drawing
on the produced material (Fig. 5), the whole region of
Central Greece may be classified as being “Highly Vul-
nerable” by four out of the five (EW, AHP, and PCA2 and
MCA) weighting approaches and “Medium Vulnerable” by
the remaining one (PCA1). Looking at Table 5, PCA1 and
2 prioritize the overall supply and demand and MCA
boosts the meteorological component through SPI and by it
predominantly the natural water supply. Such facts are
reflected in Fig. 5, which displays the SDVI results derived
from the five weighting techniques that were tested.
Regarding PCA, Fig. 5correctly displays with lower
Table 7 Descriptive statistics of
the five weighting techniques in
Sterea Hellas based on real
N 23,429 23,429 23,429 23,429 23,429
Mean 1.541 1.443 1.584 1.599 1.632
Std. error of mean 0.002 0.002 0.002 0.002 0.002
Median 1.494 1.430 1.530 1.548 1.570
Std. deviation 0.311 0.359 0.322 0.315 0.314
Variance 0.097 0.129 0.103 0.099 0.099
Range 1.826 1.850 1.900 1.875 1.860
Minimum 0.664 0.800 0.730 0.682 0.620
Maximum 2.490 2.650 2.630 2.557 2.480
Table 8 Correlation matrix
between the five weighting
techniques based on real
conditions in Sterea Hellas
SDVI EW 1 0.879 0.985 0.988 0.507
SDVI PCA1 1 0.912 0.849 0.419
SDVI PCA2 1 0.989 0.501
SDVI AHP 1 0.509
Table 9 Descriptive statistics
for Sterea Hellas results of the
five weighting techniques with
random input values
N 10,000 10,000 10,000 10,000 10,000
Mean 1.498 1.498 1.499 1.499 1.500
Std. error of mean 0.005 0.006 0.005 0.005 0.005
Median 1.500 1.510 1.500 1.502 1.500
Std. deviation 0.461 0.556 0.471 0.468 0.491
Variance 0.213 0.309 0.222 0.219 0.241
Range 2.830 2.950 2.830 2.850 2.900
Minimum \0.001 \0.001 \0.001 \0.001 \0.001
Maximum 2.830 2.950 2.830 2.850 2.900
Table 10 Correlation matrix of
the five weighting techniques
with random input values
SDVI EW 1 0.830 0.981 0.988 0.942
SDVI PCA1 1 0.868 0.775 0.614
SDVI PCA2 1 0.982 0.915
SDVI AHP 1 0.973
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vulnerability the Athens metropolitan area, where infras-
tructure conveys the urban water supply, than the central
agricultural areas where irrigation water supply was inter-
rupted in favor of the cities. Additionally, it should be
mentioned that in the PCA1 approach the produced weight
for the impact component is rather low resulting in
underestimating drought vulnerability, when there are
significant impacts. It should be also mentioned that the
increase in ET
caused an increase in the demand in the
irrigated areas, which was not supplied due to a manage-
ment decision to divert water to the cities, despite the fact
that the infrastructure operational condition was at the
normal levels, and thus in these areas impacts were more
The results of the weighting techniques uncertainty
analysis are described below with the order mentioned in
Sect. 3. Table 7shows the descriptive statistics of the
techniques based on the real conditions (Fig. 5) in the study
area. It should be underlined that the area has a relative
small variability. Table 8displays the similarity metric
(correlation) between the techniques results (real condi-
tions). All the techniques exhibit a high correlation apart
from the MCA method which has the lowest correlation
with all the other ones. Commenting on the MCA, as it
prioritizes the meteorological dimension, natural water
deficiency dictates a high vulnerability value, which was
true for the central agricultural areas, but not true for the
Athens area, as Fig. 5portrays, where the water deficit is
consistently and efficiently supplied by the greatest water
infrastructure works in the country. However, since the
supplied water comes from surface water reservoirs in the
western part of Sterea Hellas, a meteorological drought in
the reservoirs’ watersheds plays an extremely important
role for the future years’ water supply and thus the index
results should initiate drought contingency measures.
The following tables (Tables 9,10) display the
descriptive statistics and correlations produced by random
components class values for the five weighing techniques
and the corresponding statistics were derived. It is only
noted that the pertinent values of median, mean and stan-
dard deviation fall within the expected limits of the
generation approach. The values produced with the random
mode (Table 8) have a greater standard deviation than the
real condition’s one (Table 7), in an effort to overcome the
region’s homogeneity and thus to be able to test conditions
with higher variability of drought vulnerability. The tech-
niques with random values are correlated rather well and
MCA is strongly correlated with all the other ones, with the
PCA1 technique being the one having the lowest correla-
tion with the rest of the techniques (Table 10), which can
be attributed to the low weight assigned to the impact
component, in other words as explained it prioritizes sup-
ply and demand.
The results of the two previous approaches on examin-
ing the similarity of the techniques with real values and
second with randomly generated ones for cases that there is
a larger variability in an area are based on spatial data and
the overall performance was calculated. For the third
approach to test local effects of the different weighting
techniques the SDVI components’ selected for the sce-
narios are shown in Table 11. The results point out that the
SDVI values for the Aliartos area with different weighting
techniques can have significant differences among them-
selves, depending on the scenario, resulting in different
drought vulnerability classification. Especially, when the
PCA1 and MCA techniques are used for when impacts are
significant, the SDVI values tend to be classified one level
lower or higher, respectively, than with another weighting
approach. This is attributed to the fact that the PCA1 pri-
oritize Supply and Demand whereas MCA boosts the
meteorological dimension as it has been also noted in the
previous approaches.
5 Conclusions
Decision makers usually count on a great number of tools,
methods and techniques as well as on simplified heuristics,
rules of thumb and frames, which are usually intertwined to
provide a representation of the complex reality so as to lead
to appropriate courses of action. In this context, indices are
used as tools to represent complex systems and they are
Table 11 Possible scenarios in Aliartos area and the computed SDVI values for each weighting technique
SCENARIO Fixed Varied Weighting techniques results
A 3 3 1 0 3 3 2.16 1.74 2.20 2.29 2.44
B 3 3 1 1 2 2 1.99 1.68 2.04 2.12 2.25
C 3 3 1 2 1 1 1.83 1.62 1.88 1.94 2.06
D 3 3 1 1 1 2 1.83 1.36 1.84 1.96 2.15
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built around such elements. Subjectivity and uncertainty
are constantly present in the various index development
phases and affect their reliability and effectiveness towards
informed decision making. Hence, testing of weighting
methods for indices becomes crucial in any effort to
examine drought vulnerability.
The scope of this effort was to investigate the effect of
various weighting methods and techniques upon the values
of such an index, as the Standardized Drought Vulnera-
bility Index (SDVI), and to pinpoint any potential inter-
connection and implication towards decision making
efforts. Four weighting methods were examined, namely;
the Principal Component Analysis (PCA), the Analytic
Hierarchy Process (AHP), the Multiple Correspondence
Analysis (MCA) and the Equal Weighting (EW). In the
case of PCA, two different component based techniques
were tested. The comparison of the examined techniques
was achieved via a framework that incorporates three dis-
tinct testing approaches. Firstly, the SDVI outcomes from
the five weighting techniques were correlated under a real
drought case in the region of Sterea Hellas, Greece. Then,
random SDVI values were generated to test the weighting
effects in the case that a region may exhibit higher spatial
component variability. Finally, it was examined if there
were any significant differences among the examined
weighting techniques on a local scale, by developing sce-
narios for an agricultural area (Aliartos), a part of Sterea
Hellas region.
The analysis of the applied techniques with the various
tested approaches (actual values, random values and sce-
narios) indicated that in all cases the Equal Weighting
Technique (EW), the Principal Component Analysis
(PCA2) and the Analytic Hierarchy Process (AHP) might
equally serve as tools for the decision makers, since they
all perform in a similar manner for the vulnerability
characterization. At the same time, PCA1, presented high
correlations with the other methods, but during extreme
drought events falls short to depict the state of the system
as it is capturing lower system drought impacts by design.
Contrariwise, the MCA method does not always follow the
other ones, especially in cases where the sample depends
on specific conditions as the applied scenarios depicted.
The most important finding of the present effort may refer
to the statistically significant differences that were detected
among the actual drought case, the random SDVI values,
both on regional scale, and the scenario-based analysis on
local level. However, such differences may not be easily
detectible in real conditions applications and the presence
of these differences dictates that decision making efforts
may be at risk in cases where the various indices are treated
as fully reliable tools or without further reservations. In this
context, the index classification pattern/scale, which is
usually less flexible compared to the respective index
values, becomes more important to decision makers and it
may affect their decision.
All in all, the use of complex weighting techniques
seems not always necessary and the simple equal weighting
method may be effective in representing drought vulnera-
bility. However, the inherent complexity and uncertainty in
drought vulnerability assessment propagates in using sim-
ple weighting methods and does not necessarily mean that
the simple equal weighting method is more effective than
other complex ones. SDVI performed well in all approa-
ches and characterized well enough the drought vulnera-
bility conditions to support drought management options.
Nonetheless, research using a greater number of indicators,
various regions, alternative index structural steps, different
map scales, decision making simulations and above all,
reliable data is required for a more comprehensive appli-
cation. It is important to address the search for valid,
reliable and relevant indicators that would measure per-
formance of water bodies and environmental conditions,
monitor the process of equitable water sharing, and provide
mechanisms for monitoring state and changes in interde-
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... In each survey, experts are asked to conduct a weighting process according to the susceptibility of each element to drought impacts [42]. There is also another type of weighting technique based on vulnerability index [43] which uses the Standardized Drought Vulnerability Index (SDVI). ...
... Exposure is one of the most important concepts in disaster risk analysis. Based on the exact definition of UNDRR terminology, exposure to some natural hazards may be described as "being in the wrong place at the wrong time" [43]. In the case of drought, exposure usually focuses on life damages and losses [43], which is determined by several factors, such as population and livestock density, utilization of land for agriculture (percentage of irrigated farms), as well as water extraction, especially for the industrial sector [44]. ...
... Based on the exact definition of UNDRR terminology, exposure to some natural hazards may be described as "being in the wrong place at the wrong time" [43]. In the case of drought, exposure usually focuses on life damages and losses [43], which is determined by several factors, such as population and livestock density, utilization of land for agriculture (percentage of irrigated farms), as well as water extraction, especially for the industrial sector [44]. Exposure mapping sometimes implies the estimation of population and the number of infrastructures which are under the impact of disasters consequences. ...
Full-text available
A drought risk map has been developed at the national scale by using remote-sensing satellite data over Iran by combining output layers resulting from three main components of a risk-evaluation procedure including Hazard Quantification (HQ), Vulnerability Assessment (VA) and Identification of Elements at Risk (IER) in a GIS environment. In this respect, Drought Severity (DS) was calculated by using the monthly Normalized Difference Vegetation Index (NDVI) (over 31 years from 1986–2016). Iran landcover classification and a slope map, population density maps, and irrigated farm percentages at the provincial scale were utilized within the drought risk evaluation (DRE) process. The final risk map reveals that the northwest of the country, with a climate similar to the central European weather conditions, is exposed to the maximum drought risk. In contrast, the areas with an arid climate, mainly located in the middle of Iran, exhibits minimum risk against drought. Based on the risk map, the southern part of the Caspian Sea shows very low drought risk due to the moderate and subtropical climate in this region. The outputs of this research will provide advice and warnings to help decision makers reduce drought risk consequences after prioritizing risk areas at the administrative scale.
... The latter makes the country highly dependent on the annual rainfall and temperature patterns, meaning that any water shortage or any unexpected temperature variation may initiate, major impacts on environment (forests, species, etc.) and society. Greece is characterised as drought prone, given that severe droughts have occurred in consequent time periods (e.g., 1989-90, 1993, 2000, 2003 and 2007) (Karavitis, 1998(Karavitis, , 1999Karavitis et al., 2014;Loukas et al., 2007;Livada and Assimakopoulos, 2007;Tsakiris and Vangelis, 2004;Tsesmelis et al., 2019;Vasiliades et al., 2009) affecting all kind of life (humans, animals, plants). Unfortunately, it is still not clear whether the impacts of these extreme events are intensified due to the extreme water deficiency or due to the lack of local or country level contingency planning and drought management (Karavitis 1992(Karavitis , 1998(Karavitis , 1999Karavitis et al., 2012;Tsesmelis et al., 2019). ...
... Greece is characterised as drought prone, given that severe droughts have occurred in consequent time periods (e.g., 1989-90, 1993, 2000, 2003 and 2007) (Karavitis, 1998(Karavitis, , 1999Karavitis et al., 2014;Loukas et al., 2007;Livada and Assimakopoulos, 2007;Tsakiris and Vangelis, 2004;Tsesmelis et al., 2019;Vasiliades et al., 2009) affecting all kind of life (humans, animals, plants). Unfortunately, it is still not clear whether the impacts of these extreme events are intensified due to the extreme water deficiency or due to the lack of local or country level contingency planning and drought management (Karavitis 1992(Karavitis , 1998(Karavitis , 1999Karavitis et al., 2012;Tsesmelis et al., 2019). ...
The forest policy in Greece and the current regulatory framework is not efficient in supporting the implementation of sustainability at a satisfactory level. The main scope of this study is to review and present constrains and practices across the sectors of forest and water resources management, flora and fauna biodiversity. The hypothesis is that common practices in the forest field combined with inefficient and obsolete legislation are responsible for delays in the implementation of a national forest policy, which will promote sustainability. A systematic reviewing methodology was applied so to ensure a rigorous and repeatable method of sustainability constraints identification and evaluation. The identification of the constraints can promote the improvement of legislation, the revision of common practices concerning the forest sector and finally can help the forest managers to better understand how to work effectively within legal, regulatory and operational environments deriving from forest policy.
... Initially, primitive composite models such as Multivariate Standardized Drought Index (MSDI 1 ) were developed, utilizing simplified methods that can be seen as advanced versions of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) indices. Furthermore, MSDI 1 represents an improvement over P-ET ref input models, which tend to overestimate drought events in desert and semi-desert climates (Fassouli et al. 2021;Tsesmelis et al. 2019). ...
Full-text available
In order to improve the reliable monitoring and prediction of drought behavior, it is crucial to comprehensively consider composite indices. Initially, four univariate drought indices were evaluated: the Standardized Precipitation Index, the Standardized Soil Moisture Index of the top two layers (SSI1 and SSI2), and the Standardized Precipitation Evapotranspiration Index. Subsequently, this paper introduces five composite drought indices: the Multivariate Standardized Drought Index, the modified Aggregate Drought Index, the Joint Drought Deficit Index, the Machine Learning (ML-based) Drought Index (SVMs), and the Artificial Intelligence (AI-based) Drought Index (ANF-PSOs) models based on precipitation (P), soil moisture at two layers (SM1 and SM2), and reference evapotranspiration as input variables. These drought indices are formulated using P-ETref inputs, P-SM1 inputs, and P-SM2 inputs. The study covers 30 main basins classified into six climate zones, including coastal wet, mountain, semi mountain, semi desert, desert, and coastal desert across Iran from 1979 to 2021. The performance of the studied models was evaluated using the correlation coefficient and root mean square error in comparison with SPI at the same time scales as the target model. Drought characteristics, including the number of drought events, duration, frequency, and intensity, were determined for each model at monthly, seasonal, and yearly time scales. The results revealed that the recommended P-SM1 inputs for SVMs and ANF-PSOs models significantly outperformed the MSDIs, modified ADIs, and JDIs models. The results indicated that the introduced composite models effectively captured the comprehensive SM situation without being heavily influenced by individual parameters. The behavioral patterns of these indices remained consistent, except for the specific performance of ETref, which caused some inconsistencies. Moreover, a comparison among different climates revealed that ETref played a prominent role in the discrepancies observed in the output of the composite models, resulting in a strong relationship between P and ETref. Consequently, when constructing composite indices, the information conveyed by ETref was more readily disregarded by JDI and ADI but retained in ANF-PSO. This research sheds light on the mechanisms of these ML-based and AI-based composite approaches in integrating different drought features. It offers valuable insights into the performance of composite drought models and provides benchmarks, particularly in dry climates, to enhance drought monitoring methods.
... Multivariate drought index (MDI) can simultaneously reflect the multiple characteristics of meteorological, hydrological, and agricultural droughts and accurately captures more drought events . Considering atmospheric water deficit, river basin water shortage and crop irrigation deficiency (Um et al., 2022), SPEI, standardized runoff index (SRI) and standardized soil moisture index (SSMI) were selected (representing meteorology, hydrology, and agricultural drought, respectively) to construct MDI. Weighting method (Tsesmelis et al., 2019), fuzzy comprehensive method and other methods are effective methods to construct MDI, but the subjectivity is too strong. Projection pursuit model (PPM) is a statistical method for processing and analyzing high-level data , which can comprehensively, objectively, and quantitatively evaluate drought risk when it is used to construct MDI. ...
... The expert knowledge weighting methods (Ex-sp and Ex-ov) require a highly complex and time-consuming procedure to obtain feedback [86] and might have led to a greater degree of subjectivity to the DV evaluation [27]. The random weighting method is more time-consuming than the equal weighting technique, which has been used in many DV studies [48,84,87,88] due to its simplicity and ease of reproducibility. However, the factors unlikely have the same influence. ...
Current approaches to identify vulnerability to drought often lack ground-truthing and transparent methodology; they are also often biased by expert subjectivity. Furthermore, most drought vulnerability assessments are seldom system-specific but of general social relevance. Consequently, resultant vulnerability maps are typically a general snapshot of case-specific decisions, lacking teeth in their statistical soundness and system-specific relevance. Thus, this study presents an impact-based method for a transparent selection of effective vulnerability factors for six vulnerable systems (social, economic, agricultural, ecological, water resources, and energy-industry) The study is based on a backward multivariate linear regression (MLR) model, which links vulnerability factors to historical drought impacts for the case of Iran. The drought vulnerability index (DVI) and the comprehensive drought vulnerability index (CDVI) are calculated by aggregating the vulnerability of the six systems. Four different weighting methods are then applied to combine driving vulnerability factors to assess the vulnerability of each system for three timesteps (2005, 2010, and 2015). The vulnerability values are presented as ranges by combining the results of four weighting methods. Furthermore, identified driving vulnerability factors are classified by their manageability. A range of 154 vulnerability factors were tested as possible drivers, from which 44 were identified as effective factors. Overall, the results especially highlight that the social and economic systems are vulnerable to drought. From a systemic perspective, socioeconomic factors, such as infrastructure development, health conditions, and industrialization, might play a crucial role in reducing vulnerability. Thus, they should be prioritized in vulnerability reduction programs.
... Nevertheless, a full assessment of drought in the region requires the characterisation of drought, which permits activities such as early drought alarm and drought risk mitigation; this assessment would improve the preparation and catastrophe planning (Tsesmelis et al. 2019;Garca In principle, the paradigm for assessing vulnerability to disasters described in this study may be employed to update drought vulnerability information with real-time data for adjustments to drought mitigation techniques. ...
Full-text available
Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.
... The standardized precipitation evapotranspiration index (SPEI) takes PET into account, but in fact in arid and semi-arid areas where potential evapotranspiration is greater than precipitation, the monthly total PET is actually the amount of water that is not available and therefore cannot be evaporated and transpired. Its use may lead to inaccurate estimates of drought events, such as overestimating droughts [30][31][32][33]. The standardized precipitation index (SPI) is a standardized value that expresses the actual precipitation as a deviation from the probability distribution function of precipitation. ...
Full-text available
In northern China, precipitation fluctuates greatly and drought occurs frequently, which mark some of the important threats to agricultural and animal husbandry production. Understanding the meteorological dry-wet change and the evolution law of drought events in northern China has guiding significance for regional disaster prevention and mitigation. Based on the standardized precipitation index (SPI), this paper explored the spatio-temporal evolution of meteorological dry-wet in northern China. Our results showed that arid area (AA) and semi-arid area (SAA) in the west showed a trend of wetting at inter-annual and seasonal scales, while humid area (HA) and semi-humid area (SHA) in the east showed a different dry-wet changing trend at different seasons under the background of inter-annual drying. AA and HA showed obvious “reverse fluctuation” characteristics in summer. The drought frequency (DF) and drought intensity (DI) were high in the east and low in the west, and there was no significant difference in drought duration (DD) and drought severity (DS) between east and west. The DD, DS and DI of AA and SAA showed a decreasing trend, while the DD and DS of HA and SHA showed a slight increasing trend, and the DS decreased. In summer and autumn, the main influencing factors of drying in the east and wetting in the west were PNA, WP, PDO and TP1, and the fluctuations of NAO-SOI, NAO-AMO and PNA-NINO3.4 jointly determined the characteristics of SPI3 reverse fluctuations of HA and AA in summer.
... The comparison of SPI and SPEI is made to assess the impact of potential evapotranspiration which is a metric of the atmospheric evaporative demand (AED) to determine the drought in the study areas as well as the uncertainty in the results obtained using the SPI. SPI or SPEI values of 6 and 12 months are proposed as more appropriate for denoting droughts in arid and semi-arid regions, applied in several studies (e.g., [76,[80][81][82][83]). Accordingly, the SPI-6, SPEI-6, SPI-12, and SPEI-12 are selected for the drought characterization in Greece. ...
Full-text available
Future changes in drought characteristics in Greece were investigated using dynamically downscaled high-resolution simulations of 5 km. The Weather Research and Forecasting model simulations were driven by EC-EARTH output for historical and future periods, under Representative Concentration Pathways 4.5 and 8.5. For the drought analysis, the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI) were calculated. This work contributed to achieve an improved characterization of the expected high-resolution changes of drought in Greece. Overall, the results indicate that Greece will face severe drought conditions in the upcoming years, particularly under RCP8.5, up to 8/5 y of severity change signal. The results of 6-month timescale indices suggest that more severe and prolonged drought events are expected with an increase of 4 months/5 y, particularly in areas of central and eastern part of the country in near future, and areas of the western parts in far future. The indices obtained in a 12-month timescale for the period 2075–2099 and under RCP8.5 have shown an increase in the mean duration of drought events along the entire country. Drought conditions will be more severe in lowland areas of agricultural interest (e.g., Thessaly and Crete).
... Due to the differences in environmental and terrain factors in each region, different weighting methods may obtain different results. However, there is currently no fixed standard for the weight of each factor in the model (Tsesmelis et al. 2019). In recent years, AHP (Mokarram et al. 2021) and PCA (Alonso et al. 2019;Aryal and Zhu 2021) have been widely and successfully applied in drought research. ...
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Droughts in winter and spring are one of the most prominent natural disasters in the Yunnan Province in China. They occur frequently, with long durations and have a wide range of damage, which has a serious impact on social and economic development, as well as agricultural production and, therefore, strongly impacts the lives of the people living in the region. The traditional drought monitoring model does not take terrain into consideration, thereby affecting the comparative nature of results, as baseline conditions are not the same. Therefore, this study proposed a comprehensive drought monitoring model considering the influence of terrain factors to improve the evaluation effect. Firstly, based on NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measurement Mission (TRMM 3B43) data, vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (TRCI), and three terrain factors ground elevation (DEM), slope (SLOPE), aspect (ASPECT) were selected as model parameters. Then, a comprehensive drought monitoring model without considering terrain factors (Model A) and a comprehensive drought monitoring model of considering terrain factors (Model B) were constructed by using multiple linear regression models. Finally, the effects of the two models were evaluated by using standardized precipitation evapotranspiration index (SPEI) in southwest Yunnan Province, China, and model B was used to analyze the drought in winter and spring in the study area from 2008 to 2019. The results showed that (1) the correlation coefficient of model B was higher than that of model A in winter and spring and the standard error of model B was lower than that of model A. (2) The grade consistency rate of Model A and SPEI was 0.92 in winter and 0.33 in spring; the grade consistency between model B and SPEI was 0.83 in winter and 0.75 in spring, and therefore the monitoring effect of model B was more stable. (3) There were periodic droughts during the study period, and the degree of drought in spring was less than in winter. Medium and severe droughts were observed in winter. Thus, this study concluded that the effect of terrain has an important influence on the evaluation of droughts. The comprehensive drought monitoring model which considers topographic factors can effectively identify the occurrence of drought, and therefore provide significant input with regards to disaster prevention and mitigation policies in southwest Yunnan.
... In 13.1% and 4.7% respectively 1.00, 1.80 and 2.00). The annual rainfall map based on daily data (1991-2014) and the co-kriging (rainfall and Digital Elevation Model) interpolation method transformed the point data to spatial values. ...
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Natural resources degradation poses multiple challenges, particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.
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The degradation of natural resources at an intense rate creates serious problems in the environmental systems particularly with the compounding effects of climatic vagaries and changes. On the one hand, desertification is a crucial universal, mostly an anthropogenic environmental issue affecting soils all over the world. On the other hand, drought is a natural phenomenon in direct association with reduced rainfall in various spatial and temporal frames. Vulnerabilities to drought and desertification are complex processes caused by environmental, ecological, social, economic and anthropogenic factors. Particularly for the Mediterranean semi-arid conditions, where the physical and structural systems are more vulnerable, the abuse and overuse of the natural resources lead to their degradation and ultimately, if the current trends continue, to their marginalization. The scope of the current effort is trying to find any common drivers for the pressures of both processes. Thus, the vulnerabilities to drought and desertification are comparing by using the Standardized Drought Vulnerability Index (SDVI) and the Environmentally Sensitive Areas Index (ESAI). The indices are calculated from October 1983 to September 1996 in Greece. Greece is prone to desertification and it is often experiencing intense droughts, thus it presents an almost ideal case study area. The results may indicate that the most important factor for such procedures is the deficits in water resources, either due to lower than usually expected rainfall or to higher societal water demand.
Conference Paper
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Any attempt for the application of integrated drought management, requires identifying and characterizing the event per se. The questions of scale, boundary, and of geographic areal extend are of central concern for any efforts of drought assessment, impacts identification, and thus of drought mitigation implementation mechanisms. The use of drought indices, such as Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), has often lead to pragmatic realization of drought duration, magnitude and spatial extend. The current effort presents the implementation of SPI and SPEI on a Pan-European scale and it is evaluated using existing precipitation and temperature data. The E-OBS gridded dataset for precipitation, minimum temperature, and maximum temperature used covered the period 1969 – 2018. The two indices estimated for time steps of 6, and 12 months. The results for the application period of recurrent droughts indicate the potential that both indices offer for an improvement on drought critical areas identification, threshold definitions and comparability, towards contingency planning leading to better mitigation efforts.
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Η παρούσα διδακτορική εργασία έχει ως τομέα μελέτης την Ολοκληρωμένη Διαχείριση των Υδατικών Πόρων (ΟΔΥΠ), βάσει των αρχών της οποίας δημιουργήθηκε ένα εργαλείο (δείκτης) για την αναγνώριση των ξηρασιών και των τρωτών περιοχών, καθώς και τη σύνδεση της τρωτότητας στηv ξηρασία και την ερημοποίηση. Η Ελλάδα παρουσιάζει συχνά φαινόμενα ξηρασίας, τυχαία και περιοδικά. Αυτό οφείλεται στη χωροχρονική κατανομή των κατακρημνισμάτων, δηλαδή στη διαδοχή των υγρών (Οκτώβριος-Μάρτιος) και των ξηρών περιόδων (Απρίλιος-Σεπτέμβριος). Αν δεν σημειωθούν βροχοπτώσεις κατά τη διάρκεια των υγρών περιόδων τότε ενδέχεται να δημιουργηθούν προβλήματα στη διαθεσιμότητα των υδατικών πόρων κατά τη διάρκεια των ξηρών περιόδων. Για την αντιμετώπιση αυτού του προβλήματος απαιτείται τακτική παρατήρηση και επεξεργασία των μετεωρολογικών δεδομένων, αλλά και ταυτόχρονη εφαρμογή του προληπτικού σχεδιασμού. Ένας τρόπος πρόληψης είναι ο υπολογισμός απλών και σύνθετων δεικτών ξηρασίας μιας περιοχής ή μιας χώρας για την έγκαιρη αναγνώριση του σχετικού προβλήματος. Σε αυτό το πλαίσιο, παρουσιάζεται η ανάπτυξη και η δημιουργία ενός σύνθετου δείκτη που αποτελείται από έξι υπο-δείκτες και συγκεκριμένα από τους υπο-δείκτες κατηγοριοποιημένα SPI6 (cSPI6 και cSPI12), Ζήτηση (Demand), Εφοδιασμός (Supply), Επιπτώσεις (Impacts) και Υποδομές στους Υδατικούς πόρους (Infrastructure), έτσι οι υπο-δείκτες του Standardized Drought Vulnerability Index (SDVI) σχετίζονται με όλες τις πτυχές μιας ξηρασίας (μετεωρολογική, αγροτική, υδρολογική και κοινωνικοοικονομική). Συνυπολογίζοντας όλα τα παραπάνω, εκτιμάται η τρωτότητα σε ένα σημείο ή μια περιοχή. Έπειτα, εξετάστηκε η σχέση των υπο-δεικτών και αναπτύχθηκαν συντελεστές βαρύτητας με στατιστικές και εμπειρικές μεθόδους. Ο σύνθετος δείκτης εφαρμόστηκες σε διάφορες συνθήκες (σενάρια, πραγματικές και υποθετικές τιμές) και διαφορετικές περιοχές (Ελλάδα και Νοτιοανατολική Ευρώπη με τη χρήση δεδομένων από 324 μετεωρολογικούς σταθμούς και περίοδο τουλάχιστον 30 ετών). Η αρχική υπόθεση του SDVI με ίδια βάρη και η μέθοδος των κύριων συνιστωσών δείχνουν ότι προσεγγίζουν καλύτερα την τρωτότητα. Στην συνέχεια, εξετάζεται εάν υπάρχει σύνδεση των δύο διαδικασιών τρωτότητας στην ξηρασία και τρωτότητας στην ερημοποίηση. Για τη συσχέτιση αυτή χρειάστηκε να γίνει όμοια κατηγοριοποίηση και των σύνθετων δεικτών σε 3 κλάσεις (χαμηλή, μέτρια και υψηλή). Με βάση αυτή την παραδοχή, εμφανίστηκε μία σχέση μεταξύ τους, της οποίας η εφαρμογή θα ήταν δόκιμο να εξεταστεί και σε άλλες περιοχές. Τέλος, αναλύθηκαν οι δείκτες και των δύο διαδικασιών και δημιουργήθηκε ένας σύνθετος δείκτης που εμφανίζει συνδυαστικά την υδατική και εδαφική υποβάθμιση μιας περιοχής έχοντας, επίσης, τη δυνατότητα να τις εμφανίσει και ξεχωριστά. Οι δείκτες που περιλαμβάνονται στην τελική εξίσωση είναι αυτοί της Ξηρότητας (Aridity), της Ζήτησης (Drought Demand), των Επιπτώσεων από την ξηρασία (Drought Impacts), της Αντοχής στην ξηρασία (Drought Resistance), των Υποδομών στους υδατικούς πόρους (Infrastructure), της Έντασης Χρήσης Γης (Land use intensity), του Μητρικού Υλικού (Parent Material), της Φυτοκάλυψης (Plant Cover), των Βροχοπτώσεων (Rainfall), της Κλίσης (Slope) και της Σύστασης του Εδάφους (Soil Texture). ENG The present work refers to the implementation of the integrated water resources management methodology for the development of a tool in the form of an index for the recognition of droughts and pertinent vulnerable areas. Such an attempt is further expanded in an effort to produce the connection of vulnerability to drought with that to desertification. Greece is sensitive to drought phenomena (random and periodical). This is a consequence of predominant spatial and temporal distribution of precipitation in the area, expressed with the existing climatic succession of wet (October-March) and dry periods (April-September). Specifically, lower precipitation values during the wet periods could cause serious issues in the water resources availability. Regular observation and processing of meteorological data as well as concurrent application of contingency planning are necessary in order to deal with such issues. Early recognition and possible prevention could be achieved through the estimation of simple and composite drought indices of an area. In this context, the development of a composite index was presented, that included six indicators, namely categorized SPI (cSPI6 and cSPI12), Demand, Supply, Impacts and Infrastructures. The Standardized Drought Vulnerability Index is attempting to relate with all the aspects of drought (meteorological, agricultural, hydrological and socio-economic) and provide an estimation procedure. Thereafter, the relation between the indicators was examined and weights with statistical and empirical methods were developed. The composite index was implemented in various conditions (existing, real and artificial generated data for scenarios testing) and in different areas (Greece and South Eastern Europe with data from 324 meteorological stations for a period of more than 30 years). The initial hypothesis of equal weighting and the method of principal components expressed the vulnerability estimation more effectively. Furthermore, it was analyzed whether there was a correlation between drought and desertification vulnerability. To associate these attempts, a similar classification of these composite indices into three classes (low, medium and high) was necessary. Based on this assumption a relationship between drought and desertification vulnerability was surfaced. Finally, the indicators of both procedures were analyzed and a composite index was created, which shows the water resources and soil degradation of a region. The indicators that have occurred in the final equation are Αridity, Water Demand, Drought Impacts, Drought Resistance, Water Resources Infrastructure, Land Use Intensity, Parent Material, Rainfall, Slope and Soil Texture. All in all, it would be appropriate for further research to implement this methodology in other regions worldwide.
Drought is a complex natural hazard with its adverse multifaceted impacts cascading in every physical and human system. The vulnerability magnitude of various areas to drought mostly depends on their exposure to water deficiency, the existing water management policy framework and its implementation. The Standardized Drought Vulnerability Index (SDVI) is an integrated attempt towards characterizing drought vulnerability based on a comparative classification system, incorporating precipitation patterns, the supply and demand trends, and the socioeconomic background as the most crucial contributors to drought vulnerability. This work attempts to evolve the SDVI by presenting a more rigorous method of index parameters estimation and argues that the combination of in-situ and satellite data improve the index results in an effort to further minimize the paucity of drought related information. At the same time, it helps to surpass previous limitations in temporal and spatial propagation of the vulnerability concept. The new framework is applied in the South Platte Basin, within Colorado, on the 2012 summer drought (July-September). The proposed index modification may convey drought information in a more holistic manner to decision makers. SDVI could aid in advancing the understanding of each component contribution through in situ and remote sensing data integration and in avoiding existing practices of broken linkages and fragmentation of the reported impacts. Thus, it is believed that the SDVI could serve as an additional tool to guide decisions and target mitigation and adaptation actions, allowing for a more integrated management approach.
Sustainability indices (SIs) have become increasingly important to sustainability research and practice. However, while the validity of SIs is heavily dependent on how their components are weighted and aggregated, the typology and applicability of the existing weighting and aggregation methods remain poorly understood. To close the knowledge gap regarding when to use which weighting and aggregation methods for constructing SIs, we review the most commonly used methods for weighting and aggregating SIs, discuss their benefits and drawbacks, and suggest a process-oriented approach for choosing appropriate weighting and aggregation methods depending on research objectives. Our review synthesis was based on peer-reviewed journal articles, books, and reports by international organizations, governmental agencies, and research institutions. After carefully examining their principles, characteristics, and applications, we selected and classified the frequently used methods for indicator weighting and aggregation. We systematically discuss the benefits and drawbacks of nine weighting methods and three aggregation methods. We propose a four-step process for choosing the most suitable weighting and aggregation methods based on: research purposes, spatial and temporal scales, and sustainability perspectives. In this research, we chose the most commonly used methods for weighting and aggregating SIs and analyzed the characteristics, strengths and weaknesses of each method. We found that choosing appropriate weighting and aggregation methods for a specific sustainability assessment project is an extremely important and challenging task. To meet this challenge, we propose a process-oriented approach for properly selecting methods according to the purpose, scale and sustainability concept. This approach can facilitate the proper selection of these methods in sustainability research and practice.
Sponsored by the Committee on Evapotranspiration in Irrigation and Hydrology of the Irrigation and Drainage Council of the Environmental and Water Resources Institute of ASCE Evaporation, Evapotranspiration, and Irrigation Water Requirements is a comprehensive reference to estimating the water quantities needed for irrigation of crops based upon the physics of evaporation and evapotranspiration (ET). This new edition of MOP 70, which updates and expands the 1990 original, provides improved and standardized methods to estimate evaporation and ET and to apply and evaluate calculations. The methods, which have both a physical and practical basis, improve clarity, accuracy, and consistency for systems design and management. They are also useful in legal applications involving water agreements, water disputes, and water rights. As competition for water increases and water resources are depleted, this book provides the critical tools to accurately quantify amounts of water consumed by irrigated agriculture. The first part covers basic concepts and physical principles, such as evaporation and ET processes, soil-water-plant systems, energy balance, surface energy-air mass interactions, and evaporation from water surfaces. The second part on ET from land surfaces explains components of measurement and details on estimation; reference crop ET; evaporation from soil; the crop coefficient method; the Penman-Monteith and energy-balance equations; and regional estimates. A third part discusses estimation of irrigation water requirements (IWR) and streamflow depletions. Thirteen appendixes provide tables of mean and basal crop coefficients and background information on concepts and equations used throughout the manual. MOP 70 is intended for use by consulting engineers and scientists working on water issues and by instructors of agricultural and civil engineering, hydrology, and environmental and agricultural sciences.