Šantl et al_2015_Hydropower Suitability Analysis on a Large Scale

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Water Resources Management
An International Journal - Published
for the European Water Resources
Association (EWRA)
ISSN 0920-4741
Water Resour Manage
DOI 10.1007/s11269-014-0830-9
Hydropower Suitability Analysis on a Large
Scale Level: Inclusion of a Calibration
Phase to Support Determination of Model
Parameters
Sašo Šantl & Franci Steinman

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Hydropower Suitability Analysis on a Large Scale Level:
Inclusion of a Calibration Phase to Support Determination
of Model Parameters
Sašo Šantl & Franci Steinman
Received: 14 November 2013 /Accepted: 13 October 2014
# Springer Science+Business Media Dordrecht 2014
Abstract The paper presents an approach to the modelling of watercourses or their sections
according to and in order to determine their suitability for hydropower water use on a large
scale. The method is based on a multi-criteria analysis approach which in addition to existing
guidelines defines and describes in detail the main stages for model establishment and
hydropower suitability analysis. Since hydropower planning stands in direct conflict with
other ecological water-related objectives, evaluation of suitability is based on two main
criteria, which are supported with the belonging criteria. The first main criterion is based on
evaluation of watercourses by their attractiveness for hydropower water use; the second one on
evaluation of watercourses according to their ecological state or value. To support proper
determination of unknown model parameters (e.g. weights of selected criteria) the paper also
presents an upgrade of general multi-criteria analysis process with a calibration stage, which
can efficiently upgrade in cases when calibration data is available. The proposed method was
tested and discussed on a real case study with three dislocated Slovenian Alpine watercourses,
where weights of preselected criteria and some thresholds of performance functions were
selected as model variables and calibrated.
Keywords Hydropower. Ecology. Suitability. Multiple criteria analysis . Calibration . Genetic
algorithms
1 Introduction
Within the objective of increasing the share of RES (European Commission 2009) the
hydropower (HP) electricity production is still considered as very relevant, since it has the
highest electricity production share among RES (66.4 % in 2008 in EU-27; EEA 2008). On the
other side, HP implementation causes hydrological alterations and disruption of longitudinal
and lateral connectivity of the affected watercourses (Schinegger et al. 2012) and consequently
has direct impact on water ecology related objectives (EC 2000; 2003). At the EU level, this
Water Resour Manage
DOI 10.1007/s11269-014-0830-9
S. Šantl (*) :F. Steinman
Chair of Fluid Mechanics, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Hajdrihova
28, 1000 Ljubljana, Slovenia
e-mail: saso.santl@izvrs.si
Author's personal copy

problem of cross objectives has been recognized, therefore, additional guidelines have been
elaborated to provide an efficient methodological approach to support decision making on
regional and strategic levels (Swiss Confederation 2011; Alpine Convention 2011). To inte-
grate ecological and HP exploitation objectives within sustainable HP, these guidelines
determine two main evaluation criteria (hereafter: “Ecological value” and “HP attractiveness”),
classification scheme for determination of potential appropriateness (hereafter: HP suitability)
of the watercourses concerned and provide sets of possible criteria to support evaluation.
However, a concrete and more detailed method (hereafter: the method) on the evaluation of
watercourses or their parts is still missing.
Since multiple objectives are involved, it is appropriate that the method for HP suitability
analysis is based on multiple criteria analysis (MCA) process, which adds structure, auditabil-
ity, transparency and rigour to decisions (Dunning et al. 2000) and has been found as an
effective and widely applied tool in the field of water management (Hajkowicz and Collins
2007). Therefore, the method should provide a technique for scoring the watercourses or their
parts (hereafter: the options) as well as guidance to obtain utility functions and for efficient
weights assignment of selected criteria (Hajkowicz and Collins 2007).
In the method as the scoring technique, weighted summation technique is proposed and
described, which is arguably the simplest and the most widely applied technique of MCA
(Howard 1991), also in water resources management (e.g. Tsakiris and Spiliotis 2011; Barlow
and Tanyimboh 2014). Determination of utility functions should be sourced from expert
judgement or other available environmental, technical and economic models (Hajkowicz and
Collins 2007). The most subjective stage of the MCA is weight assignment, where also
decision makers usually actively participate (Ribas 2014). In the current practice, the weight
assignment is, at first, based on expert and pragmatic judgement (e.g. Supriyasilp et al. 2009).
To support decision making in the process of weight assignment, a common practice to test
how changes in model parameters (weights, scoring methods, utility functions etc.) affect final
outcomes is the application of a sensitivity analysis (Steele et al. 2009). Many researchers test
the sensitivity of a decision to the particular values of criteria weightings selected whether via
the analytic hierarchy process (Gallego-Ayala and Juízo 2014a) or by some other existing
methods (e.g. aggregation methods; Gonzalez-Pachon and Romero 2001). Nevertheless, the
weight assignment can still be the greatest source of controversy and uncertainty (Chen et al.
2009), even more so when numerous criteria are considered. The weight assignment can also
require a tremendous amount of time and effort invested in resolving the conflicts in cases
where multiple decision makers with different and conflicting objectives are involved (Cai
et al. 2004). For this reason, the goal of this research was to examine a possible upgrade of
MCA, which would efficiently support the stage of weight assignment; and also determination
of other unknown model parameters. As a possible improvement, an inclusion of a calibration
phase was examined.
When HP suitability analysis is planned for a larger scale area (e.g. catchment,
region) it can be expected that some belonging watercourse sections are jointly agreed
to be suitable or not for HP implementation. Hypothesis of this study in such cases is
that an inclusion of a calibration phase, which is based on agreed watercourses sections, in the
MCA process can support determination of undefined model parameters (hereafter: model
variables).
In this paper we propose the method with the focus on the calibration phase. The
method is applied on a real case study area, i.e. three Alpine watercourses in
Slovenia, where unknown parameters are weights and thresholds of some performance
functions. Calibration and confirmation were performed on the basis of four and two water-
course sections respectively.
S. Šantl, F. Steinman

2 The Method
The consecutive stages of the method are:
1. Decision on scoring technique,
2. Determination of Decision Options, Pre-selection of Evaluation Criteria and Pre-selection
of Utility functions,
3. Selection of Calibration and Confirmation Data, Calibration of selected Model Parame-
ters, Confirmation Stage,
4. Scoring of Options,
5. Decision.
In comparison to the general MCA process (Howard 1991), instead of the stage of weight
assignment, the stage of calibration process is foreseen, which starts with the selection of
calibration data. To confirm the predictive ability of the calibrated model, a stage of confirmation
is included (Oreskes et al. 1994). The confirmation is based on the confirmation data. Also the
stage of sensitivity analysis can be skipped if other model parameters (performance functions,
scoring technique as well as the length of the options in this case) which are not calibrated are
determined. If the model results are not confirmed (and also agreed by decision makers) the
process can return to the previous stages. The stages are described in the following sub chapters.
2.1 Scoring Technique
The guidelines end with the classification scheme with two main criteria and proposal of
supporting criteria. According to the first main criterion, options are evaluated by their
attractiveness for HP exploitation (“HP attractiveness”) and by their ecological state (“Eco-
logical value”). It is proposed that the HP suitability, as overall utility score (S) of the options,
is evaluated on the basis of the summation of both main criteria utility scores. The Si of the i-th
option is calculated as a sum of the main utility score of the criterion “HP attractiveness” (Ai)
and the reverse value of the main utility score of the criterion “Ecological value” (Bi) (Fig. 1d).
The Si of the i-th option is then expressed as:
S i ¼ Ai þ 1−Bið Þ; ð1Þ
The range of S values is from 0 to 2. Lower S values indicate lower HP suitability and
higher S values higher HP suitability (Fig. 2). The Ai and Bi are valued by expression:
A i ¼
X n
j¼1
Aij Ã wj

; ð2Þ
where Aij is the criterion score and wj is the weight of the j-th criterion and n is the total
number of the selected criteria for the main criterion “HP attractiveness” (Fig. 1c). The
calculation of Bi is expressed similarly. Normalization of weights assures the range of utility
scores from 0 to 1.
Aij is expressed as:
Aij ¼ f j xij
À Á

; ð3Þ
where normalized fj is the utility function (UF) of the j-th criterion and xij is the raw
performance value (RPV; also performance measure) of the i-th option for the j-th criterion
(Fig. 1a). Bik is expressed similarly. Since the MCA method contains RPVs of selected criteria
Hydropower suitability analysis on a large scale

in different units (e.g. energy production, distance, classes etc.) normalization of UFs places
the utility scores onto the commensurate scale from 0 to 1 (Fig. 1b).
2.2 Decision Options
Each watercourse or its segments in the study area represent one option. To ensure more
comparative scoring among the options, the watercourses of the study area should be divided
Example of continuous increasing utility
function to calculate the score for criterion
HP Potential for i-th option (AiHP)
AiHP = || f j (xiHP)||
Example of a set of selected criteria with
weighting for main criterion HP
Attractiveness
=
Criteria for HP
attractiveness (j = 1 to 6)
Weight
Normalized
weigth ||wj||
HP Potential 4.0 0.0615
HPS potential 14.0 0.2154
Road access 14.0 0.2154
Grid access 1.0 0.0154
Barriers 24.0 0.3692
Exisisting HPS 8.0 0.1231
For all selected criteria the criterion scores
for i-th option are calculated. To calculate the
main criterion score (Ai) of the i-th option for
HP Attractiveness they are multipied by
assigned normalized weights and summed
up.
In the same way also main criterion score for
Ecological value (Bi) for the i-th option is
calculated.
1
0
1
HPattractiveness
Ecological value
i-th optionAi
Bi
Areas with high
and low HP
suitability are
indicated by green
and red colour
respectively.
For example if for the i-th option Ai = 0.63
and Bi = 0.30 then Si=0.63+(1-0.30)=1.33.
This option (watercourse segment) is
assessed with higher HP suitability.
(a) (b)
(c) (d)
Fig. 1 Illustrative example of the scoring technique on the basis of evaluation of one option
0 1
1
0 1
1
-DA
-DB
+DA
+DB
(a) (b)
Fig. 2 Two examples of the results of the analysed options based on differently selected sets of values of model
variables with marked ideal (green) and anti-ideal (red) COs and distances between maximums and minimums

into the segments with equal length. The selection of the length of the options is usually
predetermined by spatial accuracy of the input data as well as by the size of the study area
since the decision to analyse very short segments can lead to numerous options.
2.3 Evaluation Criteria
When comparing different watercourses according to their HP suitability, the spatial scale is
larger and the analysis is based on a large amount of input data. To improve the efficiency, the
data for the selected supporting criteria should be available or elaborated by accepted and
available methods. To reduce the total number of criteria, redundant criteria and criteria which
can be represented by other criteria can be skipped. For example, if annual energy production
is selected as a criterion, the criteria head, flow, gradient/flow ratio can be skipped since it is
determined on the basis of the latter criteria.
The guidelines propose the sets of criteria to support the evaluation of both main criteria.
Also additional documentation is available where sets of criteria are given (e.g.
Lebensministerium 2012).
2.4 Utility Functions
Which type of UF to apply, first depends on the type of the RPV for a certain criterion. If for
the criterion concerned the RPVs are continuous, a continuous UF can be applied. For
example, HP annual electricity production can be evaluated in this way. On the other hand,
if the options are valued by discrete values or classes (for example by finite number of classes
of hydro-morphological state) a discrete UF should be applied. When modelling on a large
spatial scale, uniform transformation (increase or decrease) of both types of UF can be
adequate. If more detailed previous modelling and analyses (environmental, economic etc.)
provided more accurate correlations, other shapes of UFs can be applied. When continuous
functions for UFs are applied, thresholds should be defined too (Fig. 3a and b).
0
0.25
0.5
0.75
1
0 2000 4000 6000 8000 10000
HPSPS potential, L =L = 2000 m [MWh[MWh/yeaear]
0
0.25
0.5
0.75
1
1 2 3 4 5
Class ofof morphpholoologigicacal modificacationon
0
0.25
0.5
0.75
1
NO YES
Sectction isis within NATUATURA20A2000
Continuous increasing UF Continuous decreasing UF
Discrete decreasing UF with 5
scores
Discrete UF with 2 scores
(0 and 1)
Threshold
Threshold
(a) (b)
(c) (d)
Fig. 3 Examples of applied types of UFs

2.5 Calibration Method
When selecting the calibration data it must be taken into account that the calibration data must
provide at least two different RPVs for each criterion selected; the calibration data must be
representative. If all the options used as calibration data have the same RPV for a certain
criterion, this criterion will be recognized as redundant.
Besides selection of the calibration data, the calibration process requires determination of an
objective, how the S of the calibration data should be distributed (scored) in the two-
dimensional solution space. Figure 1 presents marked areas of high and low HP suitability
where ideal and anti-ideal options (Hajkowicz and Collins 2007) should be situated respec-
tively. It can be concluded that the values of model variables should be selected in a way that
the S of the calibration options (COs) with recognized high and low HP suitability is closer to
ideal (S = 2) and anti-ideal value (S = 0) respectively. We proposed that the calibration problem
is formulated to maximize the distance between the ideal CO with the minimum A among
ideal COs and anti-ideal CO with the maximum A among anti-ideal COs; and the distance
between the ideal CO with the maximum B among ideal COs and anti-ideal CO with the
minimum B among anti-ideal COs. If this approach to the calibration is selected, both ideal
(HP suitable) and anti-ideal (HP non-suitable) COs must be selected. The objective function
can be expressed as:
max E ¼ Δ A þ Δ B ¼ mink¼1 to N Ak−maxl¼1 to M Alð Þ
þ minl¼1 to M Bl− maxk¼1 to N Bkð Þ ð4Þ
where Ak and Bk are the main utility scores of the ideal COs in total number N; and Al and
Bl are the main utility scores of the anti-ideal COs in total number M. The range of score E is
from −2 to 2. Figure 2 shows two illustrative examples of the results which are based on two
different sets of values of model variables. The example (a) where COs distribution results in
negative score E and the example (b) where COs distribution results in positive (and closer to
the objective) score E.
3 Case Study
The method was applied to three dislocated Alpine watercourses in Slovenia:
Watercourse 1: Catchment area 224 km2, Mean annual discharge 5.3 m3/s, Max. elevation
2,551 m, Min. elevation 348 m, analysed length 36.1 km, determined number of options
679,
1,515 m, Min. elevation 277 m, analysed length 29.3 km, determined number of options 567,
1,345 m, Min. elevation 267 m, analysed length 13.3 km, determined number of options 265.
The analysed watercourses demonstrate variability in different aspects, for example in
catchment size, hydrology, spatial orientation, presence of HP water use and geological
composition (Šantl et al. 2010; Šantl et al. 2012a). Recently, positive and negative decisions
on the granting of water rights were issued for some sections of the analysed watercourses, and
more detailed analyses for HP suitability were carried out. This provided calibration and
confirmation data.

Since the data on the terrain elevation model were available with a raster cell size of 12.5×
12.5 m, it was decided to split the watercourses into segments with length L=50 m to define
the options. Selecting this length offers 1,511 options in total.
Selection of the criteria is based on the guidelines and references as well as on the data
availability. A short description of the selected criteria is given below.
3.1 Criteria for the Main Criterion “HP Attractiveness”
The most relevant and representative criterion for evaluation of HP attractiveness is the
criterion “HP potential”. Some studies actually apply only this decisive criterion (e.g.
Geisler and Wellacher 2012). This criterion is evaluated by calculating annual electricity
production. The RPV of annual electricity production for this criterion of the i-th option
is calculated as:
xi HPð Þ ¼ E i; Lð Þ ¼ η0 ˙9; 81 ˙Hni Lð Þ˙ Q ið Þ˙t˙ Cut ð5Þ
where:
E(i, L) is annual electricity production [MWh/year] of the i-th option with the selected
options’ length L=50 m,
Hni [m] is net head, which is calculated on the basis of the selected L and the required
hydraulic parameters where the maximum allowed water velocity in pipes is selected as
vmax =1.8 m/s, and roughness coefficient of pipe by Strickler is selected as ks =90 s/m3,
η0 is efficiency considering losses in penstock, turbo generator efficiency, transmission
losses and is assigned the value η0=80 %,
Q(i) [m3/s] is available annual average discharge at the location of the i-th option,
t [h] is duration of electricity production in 1 year and is assigned the value t=8,760 h,
Cut [%] defines average percentage of (total) operation over 1 year and is assigned the
value Cut=95 %.
To determine the available annual average discharge, environmental flow (EF) according to
the Slovenian legislation was subtracted from the annual average discharge (Smolar-Žvanut
et al. 2008). To provide information on the total HP potential, the existing water uses were not
included.
Since the selected options’ length (L=50 m) is short, which causes rapid changes in HP
potential between the neighbouring options, and HP schemes are usually longer, additional
criterion “HPS potential” (HP scheme potential) was included. The RPVs of this criterion are
calculated by Eq. (5) too. The length of HPS (LHPS) was set to 2,000 m. The i-th option can be
situated in the location of water release, in the location of water intake and in all other locations
between water release and intake. With the selected length of HPS and the selected step=50 m
(the same as the options’ length), the total number of HPS schemes checked for each option is
40. The RPV of the i-th option is then determined as the maximum value of the calculated
annual electricity production of the checked HP schemes, of which the i-th option can be a part.
To calculate the RPVs for the abovementioned criteria, a GIS-based tool, VapIdroAste,
which was developed for large-scale analysis, was applied (Šantl et al. 2012).
Since road and grid access (CEDSC 2003) can significantly influence the investment costs
and the data are usually available, this decisive criterion should be taken into account. So the
criteria “Grid access” and “Road access” were included. The RPVs are defined by a linear
distance of the options to the nearest existing power grid and to the nearest public road.

Existing water infrastructure (dams, weirs, sills) shows positive effects on financial feasi-
bility (Swiss Confederation 2011). A criterion called “Existing barriers” was included. If the
goal in the decision making process is that HP suitability is higher in watercourses where HP
water use is already present, it is appropriate to include additional criterion “Existing HPS”.
For the last two criteria, the RPVs are defined by a river station distance to the nearest existing
barrier and to the nearest existing HPS.
3.2 Criteria for the Main Criterion “Ecological Value”
To select the criteria for the main criterion “Ecological value”, it is proposed that the
requirements of the WFD are followed. Its implementation provides data on a set of repre-
sentative criteria on hydro-morphology, biological variables, chemical and ecological status,
stresses etc. It also considers other national or local water-related objectives in the field of
nature preservation and protection of certain areas. The most representative and relevant
criterion to evaluate this main criterion would be the criterion “Ecological status” (Irvine
2004). The main weakness of this criterion is that it is not available for smaller rivers where
biological monitoring is often missing. This was the reason for this criterion not being used in
this study case is that the Ecological status is determined only for Watercourse 1. Because this
criterion is not available, hydro-morphology state and length of the watercourse without
barriers can be selected as the most representative criteria (Walder and Litschauer 2010).
The criterion “Hydro-morphology” comprises many aspects which support evaluation of
ecological status, such as changes in water regime, morphology and longitudinal and lateral
connectivity of watercourses (Tavzes and Urbanič 2009). The RPVs of the options are defined
by five classes, from natural stream (1) to heavily modified stream (5).
The next criterion to be applied is the criterion “Barrier free”, by which the RPVs are
calculated according to the distance between the first upstream and the first downstream barrier
from the certain option.
The criterion “Spawning site” is included since fish is considered as one of the important
biological criteria. Spawning sites are very sensitive to influences of HP implementation,
especially when rapid changes in flow and water depth occur during changes in HPS operation
(Tuhtan and Noack 2012). The RPVs are calculated according to their river station distance to
the nearest spawning site.
The criterion “Natura 2000 areas” is included since it follows relevant water-related species
and ecosystems. The RPVs are determined by their position in (1) or out (0) of the Natura 2000
areas determined for water-related species and ecosystems.
Prior to the implementation of the European directives in Slovenia, protection of good
ecological status, endangered habitats and endangered species was ensured by the adoption of
the areas of significant nature value and the areas of ecological importance. This is the reason
the available criteria “Nature value” and “Ecological importance” are included. The RPVs are
determined by their position in (1) or out (0) of the areas.
For each selected criterion UF is defined. Figure 3 shows examples of applied UFs in this
study case. Also Table 1 (columns 1 and 2) shows the selected criteria and the belonging type
of UFs which are all defined uniformly.
3.3 Calibration
Calibration of the selected model variables is based on the adopted decisions or on more
detailed examination for decision making in the study area. The data were available for six
such sections after 2008, four of which were used for calibration and two for confirmation. The

Table1SelectionofthetypeofUF,predefinedthresholdsandweightsandresultsofthecalibrationprocess
1234567
CriterionUFThreshold(predefined)Threshold
(calibrated)
highest
Weight[%]
(calibrated)
highest
Threshold
(calibrated)
average
Weight[%]
(calibrated)
average
HPattractivenessHPpotentialContinuousincreasing330MWh/yDetermined0.0Determined1.2
HPSpotentialContinuousincreasing7300MWh/yDetermined23.0Determined20.3
RoadaccessContinuousdecreasing500m100m23.0131m23.8
GridaccessContinuousdecreasing500m500m1.6685m5.0
ExistingbarriersContinuousdecreasing2000m1000m39.3892m32.8
ExistingHPSContinuousdecreasing2000m500m13.1800m16.8
EcologicalvalueHydro-morphologyDiscretedecreasing(5classes)NANA3.8NA9.6
BarrierfreeContinuousincreasing4000m4000m45.33923m38.2
SpawningsitesContinuousdecreasing5000m11200m45.310708m39.1
Natura2000Discrete(1/0)NANA0.0NA3.1
ProtectedareasDiscrete(1/0)NANA5.7NA10.0
EcologicalimportanceDiscrete(1/0)NANA0.0NA0.0
E0.4110.352

calibration data provide at least two different RPVs for each criterion selected, except for the
criterion “Ecological importance”. Although all three analysed watercourses are entirely within
the areas of ecological importance and this criterion can be already recognized as redundant, it
was kept to evaluate the performance of the calibration. By selecting the length of the options
L=50 m, the total number of the ideal COs is 52 and anti-ideal COs is 92.
The model variables are the weights and selected thresholds. The thresholds for the criteria
“HP potential” and “HPS potential” are defined with maximum RPV calculated in the study
area and are not subject to calibration. Also the thresholds for the criteria with discrete UF are
not applicable. Table 1 (column 3) gives predefined values for the thresholds, which are
applied to provide results comparison in the discussion section.
The solution space is large (18 model variables; 12 weights and 6 thresholds), therefore,
genetic algorithms were applied for searching of maxE. Genetic algorithms and other evolu-
tionary methods have been found to be very effective in searching of the global or near global
optimum solution and are well introduced and evaluated, also in the field of water management
(e.g. Ahmad et al. 2014; Barlow and Tanyimboh 2014).
In the calibration process, the solution with the highest E which was found (maxE =0.411) is
given in columns 4 and 5. Average values for the model variables in the calibration process (repeated
10 times) are given in columns 6 and 7. Figure 4 presents distribution of utility scores of the options
in two-dimensional space on the basis of the highest E found in the calibration process, where COs
are additionally marked, and an example of calculating the utility score for selected option.
For the main criterion “Ecological value” two criteria are evaluated by continuous UFs.
Although the first criterion “Barrier free” is based on continuous UF, the scores are equal for
all options situated between two same barriers. Thus, if the options for the second criterion
with continuous UF “Spawning site” are all scored with zero, certain options can be scored
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
HPHPattracactiveneness
Ecolological valueue
Watercourse 1 opƟons
Ideal COs - Watercourse 1
AnƟ-ideal COs - Water course 1
Ideal COs - Water course 3
AnƟ-ideal COs - Water course 3
E = 0.411
RPV Utility score
Hydro-morph. 3.8 1 0.038
Barrier free 45.3 4600 m 0.453
Spawn. sites 45.3 350 m 0.439
Natura 2000 0.0 NO 0.000
Protect. areas 5.7 NO 0.000
Eco. Importan. 0.0 YES 0.000
HP potential 0.0 190 MWh/y 0.000
HPS potential 23.0 5578 MWh/y 0.175
Road access 23.0 50 m 0.110
Grid access 1.6 24 m 0.020
Exist. barriers 39.3 50 m 0.374
Exist. HPS 13.1 350 m 0.040
A 0.719
S = A + (1-B) 0.789
Option on Watercourse 1
River station: 5350 m
B 0.930
Criteria
Weight
[%]
Fig. 4 Presentation of the options’ main criteria utility scores in two-dimensional space on the basis of the
highest E found in the calibration process with additionally marked COs and with example of calculating the
utility score for selected option

with equal utility score for the main criterion “Ecological value”. This can be observed in this
study case on Watercourse 3 where belonging options situated between two same barriers have
the same score for Ecological value (Fig. 4).
To overview the results and confirm the right predictability of the model which is based on
the calibrated model variables, all utility scores of the options are presented along the analysed
watercourses where locations of COs and confirmation options are marked (Fig. 5).
It can be observed that the confirmation options are scored in accordance to their agreed
suitability and non-suitability for HP implementation.
4 Discussion
On the basis of the results it can be observed that some criteria are irrelevant or of low
importance. As predicted, the criterion “Ecological importance” was recognized as redundant
since the RPVs for all the COs for this criterion are the same. The criterion “Natura 2000” is
found as less important. This is mainly due to the fact that also a part of ideal COs is situated
within Natura 2000. Higher importance of this criterion would be reached if differentiation of
the areas according to their importance was applied. For example, the options within Natura
2000 defined to protect species, which exist only in a water type habitat (fish, frogs etc.),
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000
Score
Watercourse station from mouth [m]
Water course 3 HP suitability Main criterion Ecological value Main criterion HP aƩracƟveness
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 5000 10000 15000 20000 25000 30000
Score
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 4000 8000 12000 16000 20000 24000 28000 32000 36000
Score
Ideal COs
Anti-ideal COs
Ideal confirmation options
Anti-ideal confirmation options
Fig. 5 Comparison of the utility scores along analysed watercourses

should be scored higher than the options within Natura 2000 defined to protect species, the
feeding grounds of which include also riparian areas (bats, terrestrial snails, etc.). When the
mentioned was tested even higher maxE was found (maxE =0.469; weight for the criterion
“Natura 2000” found was w=21.2 %).
For the criterion “HP potential” it is recognized that it varies significantly among COs, in fact
the CO recognized with highest RPV is a part of non-ideal COs. Because of that this criterion
prevents to reach higher E and its impact is reduced by assignment of low weight in the calibration
process. For the same reason, the criterion “Grid access” is also assigned low weight; two of the
non-ideal COs of Watercourse 1 are closer to the existing electric grid than all the ideal COs.
From this brief comparison analysis of RPVs of COs for these criteria it can be assumed
that a certain criterion is found irrelevant or of low importance if RPVs of the COs vary
significantly among COs or/and if some of anti-ideal COs are scored higher than any of ideal
COs or/and if the difference between average RPVs of ideal and non-ideal COs is lower. These
assumptions should be argued and confirmed in future applicative researches.
If a larger number of COs is used, it can also be expected that lower maxE will be found in
the calibration process. In this case study this is confirmed when the confirmation data is
additionally used as calibration data. The highest E is found with value maxE =0.282. When
comparing the weight assignments of the selected criteria and utility scores along analysed
watercourses (Fig. 6) of both calibration cases similarity is observed. However, in the second
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000
Score
Water course 3 HP suitability 1 HP suitability 2 HP suitability 3
HP suitability 1 - first calibration process
HP suitability 2 - calibration process based on larger number of COs
HP suitability 3 - on the basis of predefined values of the model variables
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 4000 8000 12000 16000 20000 24000 28000 32000 36000
Score
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 5000 10000 15000 20000 25000 30000
Score
Fig. 6 Comparison of utility scores along analysed watercourses calculated on the basis of discussed determined
values of the model variables

calibration case the criterion “Hydro morphology” is much more relevant (31,8 %). Selection
of different calibration data could additionally support final decision on determination of
values of the model variables.
The calibration ensures wider distribution of utility scores, which improves distinction in
HP suitability. For example, if the options are evaluated on the basis of the predefined values
for thresholds (column 3 in Table 2) and predefined equal weighting of the selected criteria, the
utility scores show much lower distinction in HP suitability along the analysed watercourses
(Fig. 6). Also E is much lower (maxE=−0.07). Nevertheless, similar trends in HP suitability
can be observed for all three cases.
In the criteria selection phase, the discussion was to use the criterion “Flow Duration Curve”,
but the data were not available. The analysed watercourses show HP suitability decrease when
approaching water spring sections where periods without water flow can occur (Fig. 5). This
criterion, which would require acquiring a lot of data, is well represented by the applied criteria.
The results show some variability on a small scale, however on a large scale, more moderate
changes in HP suitability along the watercourses are observed. In this case variations on a small
scale result mainly from the criterion “Road access”, since the existing road network globally
follows watercourses with occasional approaching or bridging. Smoothing methods can be applied
to prevent variability on a small scale, but, precisely in the case of road access, this can be questioned
since road accessibility can influence the viability of HP implementation (CEDSC 2003).
According to the guidelines, exclusion areas (e.g. reference network; EC 2000) are
considered, too. This condition can be simply included by conditional multiplication of S of
the options by their position in (0) or out (1) of the exclusion areas concerned.
5 Conclusions
The novelty of the paper is a introduction of a detailed objective MCA method to support HP
suitability analysis on a large scale level. Important addition to the method is a calibration phase
which supports determination of model variables, especially recognition of certain criterion
relevance and consequently the possibility of reducing the total number of applied criteria. With
the inclusion of the calibration phase the participation and focus of decision makers is actually
shifted from the process of exact criteria selection and weight assignment to the process of joint
determination of some suitable and not suitable watercourse sections for HP implementation.
Since modelling is based on a calibration data, it is necessary for the calibration data to be
correct and consistent with the considered objectives and criteria. To properly evaluate the
importance of a certain criterion, calibration data must provide at least two differently scored
options for this criterion. The calibration also provides wider distribution of HP suitability
scores in the solution space. Wider distribution of HP suitability scores is also ensured with
less calibration data. The method is applied and tested on the selected study area, i.e. the area
of smaller Alpine watercourses. When, for example, an entire basin or a wider area is analysed,
the analysis would probably reveal that modelling of hydropower suitability of the area should
be split into areas with similar characteristics to reach higher objective score; for example, into
eco regions (Illies 1978), by river hydro morphology types (Rosgen 1994) or some other
recognized criterion. But this is part of future applicative research.
Acknowledgments The work presented herein is also based on the data, informatics tool development support
and cooperation provided by various competent authorities within two projects co-financed by EU programmes,
SHARE (www.share-alpinerivers.eu/) and SEE Hydropower (www.seehydropower.eu/). We are grateful to all
participating partners and stakeholders.

References
Ahmad A, El-Shafie A, Razali SFM, Mohamad ZS (2014) Reservoir optimization in water resources: a review.
Water Resour Manag 28(11):3391–3405
Alpine Convention (2011) Common guidelines for the use of small hydropower in the Alpine region. Permanent
Secretariat of the Alpine Convention. Innsbruck: Permanent Secretariat of the Alpine Convention
Barlow E, Tanyimboh TT (2014) Multiobjective memetic algorithm applied to the optimisation of water
distribution systems. Water Resour Manag 28(8):2229–2242
Cai X, Lasdon L, Michelsen AM (2004) Group decision making in water resources planning using multiple
objective analysis. J Water Resour Plan Manag 130(1):4–14
Chen Y, Yu J, Shahbaz K, Xevi E (2009) A GIS-Based Sensitivity Analysis of Multi-Criteria Weights. 18th
World IMACS / MODSIM Congress, Cairns, Australia 13–17 July 2009: 3137–3143
Clean Energy Decision Support Centre (2003) Retscreen® Engineering cases textbook – small hydro project
analysis. minister of natural resources Canada 2001–2004
Dunning DJ, Ross QE, Merkhofer MW (2000) Multiattribute utility analysis for addressing section 316(b) of the
clean water Act. Environ Sci and Policy 3:7–14
European Commission (2000) Directive 2000/60/ EC of the European parliament and the council of 23 October
2000 establishing a framework for community action in the field of water policy. OJEC L 327:1–73
European Commission (2003) Council directive 92/43/EEC of 21 May 1992 on the conservation of natural
habitats and of wild fauna and flora. Official Journal of the European Union L 236 33 23.9.2003. Brussels.
European Commission 1992/95/2003
European Commission (2009) Directive 2009/28/EC of the European parliament and of the council of 23 April
2009 on the promotion of the use of energy from renewable sources and amending and subsequently
repealing directives 2001/77/EC and 2003/30/EC. OJ L 140:16–62
European Environment Agency (2008) Energy and environment report 2008. Available from: http://www.eea.
europa.eu//publications/eea_report_2008_6 [Accessed 10 August 2014]
Gallego-Ayala J, Juízo D (2014) Integrating Stakeholders’ preferences into water resources management
planning in the incomati river basin. Water Resour Manag 28(2):527–540
Geisler T, Wellacher J (2012) Potenzialstudie Wasserkraft Steiermark (Hydropower potential study in
Steiermark). 12. Symposium Energieinnovation, 15.-17.2.2012, Graz/Austria
Gonzalez-Pachon J, Romero C (2001) Aggregation of partial ordinal rankings: an interval goal programming
approach. Comput Oper Res 28:827–834
Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning and
management. Water Resour Manag 21(9):1553–1566
Howard AF (1991) A critical look at multiple criteria decision making techniques with reference to forestry
applications. Can J For Res 21:1649–1659
Illies J (1978) Limnofauna Europaea, 2nd edn. Gustav Fischer Verlag, Stuttgart
Irvine K (2004) Classifying ecological status under the European water framework directive: the need for
monitoring to account for natural variability. Aquat Conserv Mar Freshwat Ecosyst 14:107–112
Lebensministerium (2012) Österreichischer Wasserkatalog Wasser schützen – Wasser nutzen Kriterien zur
Beurteilung einer nachhaltigen Wasserkraftnutzung. Available from: http://www.bmlfuw.gv.at/wasser/
wasser-oesterreich/plan_gewaesser_ngp/wasserwirtsch_planung/wasserkatalog.html [Accessed 10 August]
Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation, and confirmation of numerical models
in the earth sciences. Science. New Ser 263(5147):641–646
Ribas JR, (2014) An assessment of conflicting intentions in the use of multipurpose water reservoirs. water
resources management [Accepted 25 Jun 2014]
Rosgen DL (1994) A classification of natural rivers. Catena 22:169–199
Šantl S, Kozelj D, Cimperman A, Bajcar T (2010) WP7.2 – Kokra River Pilot Case Study Monograph. The
Alpine Space Programme The European Regional Development Fund. Available from: http://share-
alpinerivers.eu/tools-and-resources/pilot-case-studies/SHARE_WP4%204_Kokra_MCA%20tree-
alternatives_170810_.pdf/view [Accessed 10 August 2014]
Šantl S, Kozelj D, Bajcar T, Kozelj K, Steinman F (2012a) Report on pilot case study area Drava river sub-basin
with tributaries of Oplotnica and Lobnica streams –Deliverable 6. Available from: http://www.
seehydropower.eu/download_deliverables/ [Accessed 10 August 2014]
Šantl S, Alterach J, Kozelj D (2012) Development of an informatics tool for efficient hydropower potential
determination. Acta hydrotech 25(42):17–28
Schinegger R, Trautwein C, Melcher A, Schmutz S (2012) Multiple human pressures and their spatial patterns in
European running waters. Water and Environ J 26(2):261–273
Smolar-Žvanut N, Maddock I, Vrhovšek D (2008) Evaluation and application of environmental flows for running
waters in Slovenia. Water Resour Dev 24(4):609–619

Steele K, Carmel Y, Cross J, Wilcox C (2009) Uses and misuses of multicriteria decision analysis (MCDA) in
environmental decision making. Risk Anal 29:26–33
Supriyasilp T, Pongput K, Boonyasirikul T (2009) Hydropower development priority using MCDM method.
Energy Policy 37:1866–1875
Swiss Confederation (2011) Recommendations for developing cantonal conservation and exploitation strategies
for small hydropower plants Available from: http://www.environment-switzerland.ch/ud-1037-e [Accessed
10 August 2014]
Tavzes B, Urbanič G (2009) New indices for assessment of hydromorphological alteration of rivers and their
evaluation with benthic invertebrate communities: Alpine case study. Rev of hydrobiol 2:133–161
Tsakiris G, Spiliotis M (2011) Planning against long TermWater scarcity: a fuzzy multicriteria approach. Water
Resour Manag 25(4):1103–1129
Tuhtan JA, Noack M, Wieprecht S (2012) Estimating stranding risk due to hydropeaking for juvenile European
grayling considering river morphology. KSCE J Civ Eng 16(2):197–206
Walder C, Litschauer C (2010) Ökomasterplan - Stufe II - Schutz für Österreichs Flussjuwele (Eco Master Plan –
Phase II – Protection of Austrian River Juweliers). WWF Österreich Available from: http://www.
oekomasterplan.at/ [Accessed 10 August 2014]

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